Diet Quality, Adiposity, and Hypertension Among Malaysian Adults: A Cross-Sectional Analysis from the May Measurement Month 2025 Participants

Diet Quality, Adiposity, and Hypertension Among Malaysian Adults: A Cross-Sectional Analysis from the May Measurement Month 2025 Participants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diet Quality, Adiposity, and Hypertension Among Malaysian Adults: A Cross-Sectional Analysis from the May Measurement Month 2025 Participants Yee-How Say, Hooi Chin Beh, Karleen Chong, Maong Hui Cheng, Jazlan Jamaluddin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8962091/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Hypertension is a leading modifiable risk factor for cardiovascular disease. Excess adiposity and poor diet quality contribute to elevated blood pressure (BP), yet few studies have examined both factors simultaneously using standardized diet quality measures in Southeast Asian populations. This study investigated the associations between diet quality, body composition, hypertension presence, and BP control among Malaysian adults. Methods: In this cross-sectional study, 998 adults were assessed. Hypertension was defined as systolic BP (SBP) ≥140 mmHg and/or diastolic BP (DBP) ≥90 mmHg, or self-reported prior physician diagnosis. Diet quality was evaluated using the Diet Quality Questionnaire (DQQ) of the Global Diet Quality Project, generating Dietary Diversity Score (DDS), NCD Protect, NCD Risk, ALL5, and Global Dietary Recommendation (GDR) indicators. Anthropometric and body composition measures included body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), total body fat (TBF), visceral fat level (VFL), and skeletal muscle mass (SM). Multivariable models adjusted for sociodemographic and lifestyle factors. Results: Hypertension prevalence was 35.3%. Hypertensive participants had significantly higher WC, BMI, TBF, and VFL and lower SM than normotensive participants (all p <0.001). High VFL (OR 2.22), high TBF (OR 2.10), overweight (OR 1.86), and obesity (OR 2.04) were independently associated with hypertension, whereas high SM was inversely associated (OR 0.57). Among hypertensive individuals, uncontrolled BP was strongly associated with central adiposity. Diet quality differences between normotensive and hypertensive participants were modest; however, hypertensives with uncontrolled BP were associated with lower DDS and NCD Protect scores. Conclusions: Central and visceral adiposity were significantly associated with hypertension and poor BP control. Diet quality, assessed using the DQQ, was more closely associated with BP control among hypertensives than with hypertension presence. hypertension diet quality visceral fat body composition Malaysia Introduction Hypertension is one of the most important modifiable risk factors for cardiovascular disease (CVD), chronic kidney disease, and premature mortality worldwide. The World Health Organization (WHO) estimates that more than 1.2 billion adults globally are living with hypertension, with nearly half unaware of their condition [1]. Elevated blood pressure (BP) is responsible for a substantial proportion of global CVD deaths, contributing significantly to stroke, ischemic heart disease, and heart failure [2]. Despite the availability of effective pharmacological and lifestyle interventions, hypertension detection, treatment, and control rates remain suboptimal in many low- and middle-income countries. In Malaysia, hypertension continues to represent a major public health concern. The National Health and Morbidity Survey 2023 indicates that approximately one in three Malaysian adults has hypertension, yet a considerable proportion remain undiagnosed or inadequately controlled [3]. Rapid urbanization, sedentary lifestyles, increased consumption of energy-dense and ultra-processed foods, and rising obesity prevalence have contributed to the growing cardiometabolic burden in the country. Improving early detection and awareness is therefore critical to reducing long-term complications and healthcare costs. Excess adiposity is strongly associated with hypertension through multiple physiological mechanisms. Central and visceral adiposity in particular contribute to sympathetic nervous system activation, renin–angiotensin–aldosterone system dysregulation, sodium retention, endothelial dysfunction, and chronic inflammation [4]. Measures of abdominal obesity, such as waist circumference (WC), waist-height ratio (WHtR), and waist-hip ratio (WHR), have been shown to better predict cardiometabolic risk than body mass index (BMI) alone [5]. Furthermore, emerging evidence suggests that body composition components, including skeletal muscle (SM) mass, may influence metabolic and vascular health, potentially modifying hypertension risk [6]. Dietary patterns are another critical determinant of BP. Randomized controlled trials have demonstrated that dietary approaches emphasizing fruits, vegetables, whole grains, legumes, and reduced sodium intake—such as the Dietary Approaches to Stop Hypertension (DASH) diet—significantly lower BP [7, 8]. Conversely, high consumption of ultra-processed foods and sodium is associated with increased hypertension risk [9]. While many studies focus on individual nutrients, a broader assessment of overall diet quality may better capture real-world eating patterns and their relationship to cardiometabolic outcomes [10]. The Diet Quality Questionnaire (DQQ), developed under the Global Diet Quality Project, is a standardized, rapid, food group–based tool designed for population-level monitoring of diet quality and non-communicable disease (NCD)–related dietary risk [11]. The DQQ generates indicators such as the Dietary Diversity Score (DDS), NCD Protect and NCD Risk scores, and the Global Dietary Recommendation (GDR) score, enabling simultaneous evaluation of protective and risk-related dietary components without detailed nutrient quantification. Incorporating DQQ-based assessment into large-scale screening initiatives such as the May Measurement Month (MMM) [12] offers a pragmatic approach to understanding how diet quality relates to hypertension detection and control in community populations. Although obesity and diet are well-established contributors to hypertension, limited data exist examining the combined influence of standardized diet quality indicators and detailed body composition measures within the context of a national screening campaign in Malaysia. Moreover, little is known about how diet quality and body composition relate not only to hypertension presence, but also to BP control among individuals identified through community screening. Therefore, this study aimed to examine the associations between diet quality—assessed using the DQQ of the Global Diet Quality Project—anthropometric and body composition measures, and (1) hypertension presence and (2) BP control among hypertensive Malaysian adults participating in May Measurement Month 2025 screening activities in greater Klang Valley, Malaysia. By integrating dietary assessment with detailed measures of adiposity and lean mass in a large-scale community screening context, this study seeks to provide evidence to inform targeted prevention and management strategies for hypertension in Malaysia. Materials and Methods Study Design and Setting This study was a cross-sectional analysis conducted as part of the May Measurement Month (MMM) 2025 BP screening campaign in the greater Klang Valley, Malaysia. MMM is an annual global initiative coordinated by the International Society of Hypertension to improve awareness, detection, and management of hypertension [12]. Screening activities were conducted throughout April – October 2025 across multiple community-based sites in Malaysia, including public spaces, workplaces, educational institutions, and healthcare facilities. These included Universiti Malaya Medical Centre (UMMC) outpatient clinic (Kuala Lumpur), Universiti Putra Malaysia (UPM; Serdang), Sunway University (Subang Jaya), AEON Mall Nilai Health Campaign (Nilai), and Shangri-la Hotel (Kuala Lumpur). In addition to the standard protocol of basic sociodemographic, lifestyle, dietary habits, and BP screening as outlined in MMM2025 [13], all sites incorporated extended health assessments, including dietary evaluation and body composition measurements, to investigate cardiometabolic risk factors associated with elevated BP. The primary objective was to examine the associations between diet quality—assessed using the Diet Quality Questionnaire (DQQ) of the Global Diet Quality Project—anthropometric and body composition measures, and both hypertension presence and BP control among hypertensives. Study Population Adults aged 18 years and above were eligible to participate in the study. Participants were recruited from community settings through outreach activities and voluntary participation. Individuals were excluded if they had incomplete data on gender, ethnicity, BP, and anthropometric/body composition measurements. Only participants with complete datasets were included in the final analysis, resulting in a total sample size of 998 people (or 96.5%) out of the 1034 people who participated in the study. Hypertension status was determined based on a single visit on-site measured BP and/or a self-reported prior physician diagnosis, as reported in the MMM2025 questionnaire question “Have you ever been diagnosed with high BP by a health professional (except in pregnancy)?” [13]. Participants were classified as hypertensive if they had a systolic BP (SBP) ≥ 140 mmHg and/or diastolic BP (DBP) ≥ 90 mmHg, or if they reported a previous diagnosis of hypertension. Normotensive participants were those with measured SBP < 140 mmHg and/or DBP < 90 mmHg, or without a prior diagnosis. Among hypertensive participants, awareness of hypertensive condition was determined by an affirmative answer to the question on self-reported prior physician diagnosis, while BP control status was determined using measured values at the time of assessment: controlled BP — SBP < 140 mmHg and/or DBP < 90 mmHg; uncontrolled BP — SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg. Ethical approval for the study was obtained from the relevant institutional research ethics committee before data collection (UMMC: MREC: 202535-14824, UPM: JKEUPM-2024-277, Sunway University: 2025/REC0104). All procedures were conducted in accordance with the principles outlined in the Declaration of Helsinki. Participants were informed about the purpose of the study, the procedures involved, potential risks and benefits, and their right to withdraw at any time without penalty. Written informed consent was obtained from all participants before enrolment. All data were anonymized and stored securely to ensure confidentiality. Data Collection Procedures Sociodemographic and Lifestyle Assessment Participants completed a self-administered structured questionnaire, with trained personnel on standby to assist with any queries on the questionnaire. Information collected included age, sex, ethnicity, years of education, smoking status, vaping behaviour, alcohol consumption, caffeine intake, and engagement in vigorous physical activity. These variables were considered potential confounders and were included in multivariable analyses. BP Measurement BP was measured using a calibrated automated blood pressure monitor (Omron HEM-7120, Omron HEM-7121, or Rossmax MJ701f) following standardized procedures by trained personnel. Participants were seated comfortably with back support and feet flat on the floor for at least five minutes before measurement. An appropriately sized cuff was placed on the upper arm at heart level. At least duplicate readings were taken, and the average value was used for analysis to improve measurement reliability. Dietary Assessment Dietary intake was assessed using the Diet Quality Questionnaire (DQQ) developed under the Global Diet Quality Project. The DQQ is a standardized, food group–based tool designed for rapid population-level monitoring of diet quality and NCD–related dietary risk [11]. It captures consumption of key food groups relevant to nutrient adequacy and chronic disease prevention without requiring detailed nutrient quantification. From the DQQ responses, several diet quality indicators were derived. The DDS reflects the variety of food groups consumed. It is a semi-continuous score (0-10), expressed as the average score out of 10 for the population. The NCD Protect score (range 0-9) represented consumption of foods considered protective against NCDs, such as fruits, vegetables, legumes, and nuts. The NCD Risk score (range 0-9) captured consumption of foods associated with increased NCD risk, including ultra-processed foods and processed meats. The ALL5 indicator identified whether participants consumed all five recommended core food groups: fruits, vegetables, pulses, nuts, or seeds; animal-source foods, and starchy staples. A score of less than 5 indicates that not all five recommended food groups were consumed (binary score: 1/0). The GDR score (range 0-18) reflected overall adherence to recommended dietary patterns. Both validated English and Malay versions of the DQQ, as provided by the Global Diet Quality Project, were used. The scoring rubrics and ranges are as described by the original authors of DDQ [11]. These indicators were analysed as both categorical and continuous variables. A higher score for the continuous variables indicates better adherence. Anthropometric and Body Composition Measurements Anthropometric and body composition measurements were conducted by trained personnel using standardized protocols. Height was measured using a portable stadiometer (Seca 213). Waist and hip circumferences were measured using a stretch-resistant tape that provided a constant 100 g tension, at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest, and around the widest portion of the buttocks, respectively [14]. WHR and WHtR were calculated by dividing WC by hip circumference and height, respectively. A bioimpedance analysis (BIA) body composition scale (Omron HBF-375) was used to determine weight, BMI (kg/m 2 ), total body fat (TBF; %), visceral fat level (VFL; %), subcutaneous fat (SF; %), skeletal muscle percentage (SM; %) and resting metabolism rate (RM; kcal). Whole body and regional segments (trunk, arms, and legs) of SF and SM were also recorded. The cutoff points for overweight, obesity, high TBF, high VFL, high SM, high WC, high WHR and high WHtR were ≥23 kg/m 2 [15]; ≥27.5 kg/m 2 [15]; 20 % (men) or 30 % (women) [16]; 10 % [16]; 35.8 % (men) or 28 % (women) [16]; 90 cm (men) or 80 cm (women) (WHO/IOTF/ IASO, 2000); 0.90 (men) or 0.85 (women) (WHO, 2011); and 0.50 [17], respectively. Statistical Analysis All statistical analyses were conducted using IBM SPSS Statistics for Windows 26.0 (IBM Corp., Armonk, NY, USA). The conformity of the numerical variables to normal distribution was determined by the Kolmogorov-Smirnov test, where p < 0.05 indicates non-normally distributed data. Variables that were not normally distributed were summarized as medians with interquartile ranges, whereas normally distributed variables were summarized as means with 95% confidence intervals where appropriate. Differences between normotensive and hypertensive participants were assessed using the Mann–Whitney U test for non-normally distributed continuous variables and Pearson’s chi-square test for categorical variables. Similar comparisons were conducted between hypertensive participants with controlled and uncontrolled BP. Binary logistic regression analyses were performed to examine the independent associations between diet quality indicators, anthropometric/body composition classes, and hypertension presence. Separate logistic regression models were conducted to assess associations with uncontrolled BP among hypertensive participants. Results were presented as regression coefficients, odds ratios (ORs), 95% confidence intervals (CIs), and corresponding p -values. Generalized linear models were also used to evaluate associations between hypertension status or BP control and continuous diet quality and anthropometric/body composition measures. Depending on the distribution of the outcome variables, either linear or Poisson log-linear models were applied. All multivariable models were adjusted for potential confounders, including age, sex, ethnicity, years of education, smoking status, vaping, alcohol consumption, caffeine intake, and vigorous physical activity. All statistical tests were two-tailed, and a p < 0.05 was considered statistically significant. Results Participant Characteristics A total of 998 participants were included in the analysis, consisting of all Malaysian nationals, comprising 479 Malays (48.0%), 370 Chinese (37.1%), 124 Indians (12.4%), and 25 other ethnicities (2.5%), of whom 645 (64.6%) were normotensive, and 353 (35.3%) were hypertensive (Table 1). Among hypertensive participants, 207 (59.5%) were aware of their diagnosis, and 118 (33.9%) had controlled BP. Hypertensive participants were significantly older than normotensive individuals, with a median age of 53 years compared to 36 years ( p < 0.001; Table 1). Median SBP and DBP were also significantly higher among hypertensive participants (141 mmHg vs . 116 mmHg and 80 mmHg vs . 72 mmHg, respectively; both p < 0.001). Within the hypertensive group, those with uncontrolled BP had significantly higher SBP and DBP compared with those whose BP was controlled (both p < 0.001; Table 1). There was a significant difference in sex distribution between normotensive and hypertensive participants ( p = 0.004; Table 1), with a higher proportion of males among hypertensive individuals (38.0%) compared with normotensive participants (29.1%). Among hypertensive participants, sex distribution differed significantly by awareness status ( p = 0.044; Table 1), with a higher proportion of males in the unaware group (44.0%) compared with the aware group (33.3%). However, there was no statistically significant difference in sex distribution between those with controlled and uncontrolled BP ( p = 0.083; Table 1). Dietary Intake and Diet Quality About dietary intake, whole grain consumption was significantly more common among hypertensive participants than normotensive participants (46.2% vs . 39.5%, p = 0.042; Table 1). In contrast, normotensive individuals were significantly more likely to consume unprocessed red meat (44.8% vs . 37.4%, p = 0.023), processed meat (24.8% vs . 13.9%, p < 0.001), meat, poultry and fish (82.2% vs . 76.2%, p = 0.024), fast foods and instant noodles (38.0% vs . 26.9%, p < 0.001), and packaged ultra-processed foods (43.9% vs . 31.4%, p < 0.001) than hypertensives. No significant differences were observed for pulses, vegetables, fruits, dairy, or overall animal-source food consumption. Among hypertensive participants, those with uncontrolled BP were significantly less likely to consume pulses, nuts, and seeds ( p = 0.008), nuts and seeds alone ( p = 0.001), and dairy ( p = 0.041). They were significantly more likely to consume fast foods and instant noodles ( p = 0.020) and packaged ultra-processed foods ( p = 0.014) compared with those with controlled BP (Table 1). Hypertension was not associated with ALL5 Score Category but was negatively associated with the failure to meet the minimum GDR - a lower prevalence of those not meeting the recommendation was found among hypertensive than normotensive participants (11.9% vs . 24.8%, p < 0.001; Table 1). Indeed, when controlled for confounding sociodemographic and lifestyle factors, hypertensive participants still had a significantly lower odds of not meeting the minimum GDR (OR 0.61; 95% CI 0.40–0.92; p = 0.018; Table 2). Diet quality scores derived from generalized linear models are presented in Table 3. There were no significant differences between normotensive and hypertensive participants in the DDS or ALL5 score. However, hypertensive participants had significantly lower NCD Risk scores (B = −0.328, p < 0.001) and significantly higher GDR scores (B = 0.060, p = 0.004) compared with normotensive participants (Table 3). When hypertensive participants were stratified by BP control status, those with uncontrolled BP had significantly poorer diet quality. BP control status was significantly associated with DDS (Wald χ² = 7.039, p = 0.008), NCD Protect (Wald χ² = 5.934, p = 0.015), and NCD Risk (Wald χ² = 16.03, p < 0.001). Participants with controlled BP had a significantly higher DDS (B = 0.133, 95% CI: 0.035–0.231), higher NCD Protect scores (B = 0.130, 95% CI: 0.025–0.234), and higher NCD Risk scores (B = 0.313, 95% CI: 0.160–0.466) compared with those with uncontrolled BP (Table 3). Anthropometric/Body Composition Classifications Anthropometric and body composition differences between normotensive and hypertensive participants are presented in Tables 1 and 3. Categorical analyses in Table 1 showed that hypertensive participants were significantly more likely to have high WC ( p = 0.003), high WHtR ( p < 0.001), high WHR ( p = 0.017), high TBF ( p < 0.001), high VFL ( p < 0.001), and to be classified as overweight or obese ( p < 0.001). Among hypertensive individuals, those with uncontrolled BP were significantly more likely to have high WC, high WHtR, high WHR, and obesity compared with those with controlled BP (all p ≤ 0.007; Table 1). When controlled for confounding sociodemographic and lifestyle factors, hypertension presence was independently associated with high WC (OR 1.54; 95% CI 1.11–2.13; p = 0.009), high WHtR (OR 1.43; p = 0.032), high TBF (OR 2.10; p < 0.001), high VFL (OR 2.22; p < 0.001), overweight status (OR 1.86; p < 0.001), and obesity (OR 2.04; p < 0.001). High SM was associated with lower odds of hypertension (OR 0.57; p = 0.009) (Table 2). Indeed, adjusted generalized linear model results for continuous anthropometric/body composition variables showed that hypertensive participants had significantly greater WC (87.89 cm vs . 83.96 cm, p < 0.001), higher BMI (26.69 kg/m² vs . 24.57 kg/m², p < 0.001), higher TBF (31.36% vs . 28.85%, p < 0.001), and higher VFL (10.19 vs . 7.19, p < 0.001) compared with normotensive individuals (Table 3). They also had significantly higher SF measures and lower SM across whole body and regional segments (all p < 0.001; Table 3). Discussion This study examined the associations between diet quality—assessed using the DQQ of the Global Diet Quality Project—food group consumption, body composition, and both hypertension presence and BP control among Malaysian adults. Several important findings emerged. First, central and visceral adiposity were the strongest and most consistent correlates of hypertension and poor BP control. Second, overall diet quality differences between normotensive and hypertensive individuals were modest, and in some cases suggested post-diagnosis dietary modification. Third, among individuals with established hypertension, poorer diet quality was significantly associated with uncontrolled BP. Interpretation of Adiposity Findings The dominant role of central adiposity observed in this study aligns with the current understanding of hypertension pathophysiology. Visceral adipose tissue is metabolically active and contributes to increased sympathetic nervous system activation, renin–angiotensin–aldosterone system upregulation, sodium retention, endothelial dysfunction, and chronic low-grade inflammation [4, 18]. These mechanisms collectively promote elevated vascular resistance and impaired BP regulation. Our findings showed that high WC, elevated WHtR, high TBF, and especially high VFL were independently associated with hypertension presence. The magnitude of association was strongest for VFL, which more than doubled the odds of hypertension. This is consistent with evidence that central adiposity is more strongly associated with cardiometabolic risk than BMI alone [5, 19, 20]. BMI does not distinguish between fat and lean mass or capture fat distribution; therefore, reliance on BMI alone may underestimate risk in individuals with normal weight but high visceral adiposity. Among hypertensive participants, uncontrolled BP was significantly associated with markers of central adiposity, including WC, WHtR, and WHR. These findings suggest that excess abdominal fat not only contributes to hypertension development [21], but may also impair effective BP control [22, 23]. SM was found to be inversely associated with hypertension presence. Reduced SM mass is increasingly recognized as an important factor influencing metabolic and vascular health, with potential implications for hypertension risk. Because SM is the primary site of insulin-mediated glucose uptake, lower muscle mass is associated with insulin resistance and adverse cardiometabolic profiles [24, 25]. In addition, SM functions as an endocrine organ, and reductions in muscle-derived myokines may contribute to systemic inflammation and endothelial dysfunction [26]. Recent population-based studies have shown that low SM mass and sarcopenic obesity are associated with higher cardiometabolic risk and greater prevalence of hypertension [6, 27]. Together, these findings suggest that reduced SM mass may contribute to hypertension risk both directly through metabolic dysregulation and indirectly through its interaction with excess adiposity. These findings also reinforce the importance of resistance training and preservation of lean mass in hypertension prevention and management strategies [28, 29]. Diet Quality Assessed Using the DQQ Diet quality in this study was assessed using the DQQ of the Global Diet Quality Project, which captures consumption of key food groups and generates indicators including DDS, NCD Protect, NCD Risk, ALL5, and GDR scores [11]. The DQQ is designed as a standardized, low-burden tool for population-level monitoring of diet quality across countries. Hypertensive participants demonstrated lower NCD Risk scores and higher GDR scores compared with normotensive participants. This finding likely reflects dietary modification after hypertension diagnosis. Individuals aware of their hypertensive status may reduce intake of ultra-processed foods, processed meats, and energy-dense products in response to medical advice. Similar behavioural shifts following diagnosis have been observed in other populations [30–32]. However, diet quality indicators were not independently associated with hypertension presence in multivariable logistic regression. Several explanations are possible. First, reverse causation is likely: individuals with hypertension may have already improved their diets, attenuating cross-sectional differences. Second, cumulative long-term dietary exposure—rather than current dietary intake—may be more relevant to hypertension development. Third, adiposity may mediate much of the diet–hypertension relationship, thereby attenuating independent associations in fully adjusted models. In contrast, among hypertensive participants, uncontrolled BP was significantly associated with poorer diet quality. Individuals with uncontrolled BP had lower DDS and lower NCD Protect scores (reflecting lower intake of protective foods such as fruits, vegetables, legumes, and nuts). These findings are consistent with clinical trial evidence demonstrating that adherence to dietary patterns rich in plant foods and low in sodium and processed products improves BP control [7, 8, 33] and all-cause and CV mortality in hypertensive individuals [34, 35]. Although BP control is determined objectively through measured BP values, DQQ-based indicators provide complementary insight into modifiable dietary factors among hypertensive individuals. In this study, participants with uncontrolled hypertension had poorer diet quality scores, suggesting that suboptimal dietary patterns may contribute to inadequate control in some individuals. However, not all participants with uncontrolled BP had poor diet scores, indicating that other factors—such as medication adherence, comorbidities (e.g., chronic kidney disease), or resistant hypertension—may also play a role. Therefore, the DQQ should not be viewed as a tool to determine control status, but rather as a practical screening instrument to identify individuals with both uncontrolled hypertension and poor diet quality who may benefit from targeted dietary counselling or referral to a dietitian. While adiposity may be the primary driver of hypertension onset, sustained diet quality may be critical for effective BP management. Clinical Implications The findings have several important implications for clinical practice and public health. First, routine assessment of central adiposity should be integrated into hypertension screening and management. WC and WHtR are simple, inexpensive measures that provide meaningful cardiometabolic risk stratification. Given the strong association between visceral adiposity and both hypertension presence and control, clinicians should consider central fat reduction as part of the overall hypertension management. Second, hypertension management programs should emphasize combined lifestyle interventions that address both fat mass reduction and lean mass preservation. Aerobic exercise reduces BP and VFL, while resistance training increases SM and improves metabolic health [36, 37]. The observed protective association of SM reinforces the importance of resistance-based interventions. Third, although cross-sectional associations between overall diet quality and hypertension presence were modest, diet quality was clearly associated with BP control among hypertensives. This underscores the need for sustained dietary counselling after diagnosis, rather than reliance solely on pharmacotherapy and cursory dietary advice. Tools such as the DQQ may be particularly useful in primary care and community settings for rapid diet quality assessment and monitoring. Finally, at the population level, the findings support public health strategies aimed at reducing ultra-processed food consumption [38] and promoting dietary diversity rich in fruits, vegetables, pulses, nuts, and whole grains [39]. Such approaches may not only prevent obesity and hypertension but also improve hypertension control among those who already have hypertension. Strengths and Limitations This study has several strengths. It included a relatively large adult sample and comprehensively assessed diet quality using the standardized DQQ framework from the Global Diet Quality Project. The DQQ enables harmonized monitoring across settings and captures both protective and risk-related dietary components. Additionally, detailed body composition measures—including VFL and regional SM—provided greater precision than BMI alone. The use of multivariable models adjusted for sociodemographic and lifestyle covariates further strengthens the validity of the findings. However, several limitations must be considered. The cross-sectional design precludes causal inference, and reverse causation—particularly dietary modification following hypertension diagnosis—is highly plausible. Dietary intake was assessed using self-reported information from the previous 24 hours and is therefore subject to recall bias and potential social desirability bias. The DQQ, while validated for population monitoring, does not provide quantitative nutrient intake estimates, and sodium intake was not objectively measured through 24-hour urinary excretion. Residual confounders, such as detailed sodium intake, medication adherence (which may influence BP control), duration and severity of hypertension, genetic predisposition, stress levels, and healthcare access, cannot be excluded, as there may be unmeasured or imperfectly measured variables that influence both exposure and outcome. Body composition was measured using BIA, which is suitable for field settings but has inherent limitations. BIA estimates can be affected by hydration status, recent physical activity, and recent food or fluid intake. In addition, device-specific prediction equations may introduce variability. Therefore, body composition findings should be interpreted with caution. Furthermore, because the study was conducted among Malaysian adults participating in a community screening campaign, the findings may not be generalizable to populations with different demographic, cultural, or healthcare characteristics. Conclusion In summary, central and visceral adiposity were strongly and independently associated with hypertension presence and poor BP control among Malaysian adults. Diet quality, as assessed using the DQQ of the Global Diet Quality Project, showed modest associations with hypertension presence but was significantly associated with BP control among hypertensive individuals. These findings emphasize the dominant role of central adiposity in hypertension pathophysiology while highlighting the importance of sustained, high-quality dietary patterns for optimal BP management. Integrated strategies targeting both body composition and diet quality are likely necessary to reduce hypertension burden in this population. List of Abbreviations ALL5 – Consumption of all five recommended food groups indicator BIA – Bioelectrical impedance analysis BMI – Body mass index BP – Blood pressure CI – Confidence interval CVD – Cardiovascular disease DBP – Diastolic blood pressure DDS – Dietary Diversity Score DQQ – Diet Quality Questionnaire GDR – Global Dietary Recommendation score IQR – Interquartile range MMM – May Measurement Month NCD – Non-communicable disease OR – Odds ratio SBP – Systolic blood pressure SM – Skeletal muscle TBF – Total body fat VFL – Visceral fat level WC – Waist circumference WHR – Waist-to-hip ratio WHtR – Waist-to-height ratio Declarations Ethics approval and consent to participate Ethical approval was obtained from the relevant institutional research ethics committee before data collection (UMMC: MREC: 202535-14824, UPM: JKEUPM-2024-277, Sunway University: 2025/REC0104). Written informed consent was obtained from all participants before enrolment. Consent for publication Not applicable Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable requests. Competing interests All authors declare that they have no competing interests. Funding The Malaysian Society of Hypertension (MSH) provided funding support for the screening campaigns, while the Malaysian Society for World Action on Salt, Sugar and Health (MyWASSH) contributed materials such as posters as part of their corporate social responsibility initiatives. The funders did not play a role in the design of this study nor was involved in the writing of this manuscript. Authors' contributions Conceptualization: YCC; Methodology: YHS and YCC; Data collection: YHS, HCB, KC, MHC, JJ, and SMC; Formal analysis: YHS and YCC; Writing - original draft preparation: YHS and YCC; Writing - review and editing: YHS, HCB, KC, MHC, JJ, SMC, and YCC; Supervision: YCC. All authors have read and agreed to the published version of the manuscript. Acknowledgements The authors would also like to extend their gratitude to all participants who participated in this study. We would also like to thank all student helpers who helped with the recruitment of participants and measurements. References World Health Organisation (WHO). Global report on hypertension: the race against a silent killer. 2023. Hay SI, Ong KL, Santomauro DF, A B, Aalipour MA, Aalruz H, et al. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023. The Lancet. 2025;406:1873–922. https://doi.org/10.1016/S0140-6736(25)01637-X. Institute for Public Health. National Health and Morbidity Survey (NHMS) 2023 Non-Communicable Diseases and Healthcare Demand: Key Findings. 2024. Hall JE, Do Carmo JM, Da Silva AA, Wang Z, Hall ME. Obesity-Induced Hypertension: Interaction of Neurohumoral and Renal Mechanisms. Circulation Research. 2015;116:991–1006. https://doi.org/10.1161/CIRCRESAHA.116.305697. Ashwell M, Gibson S. A proposal for a primary screening tool: `Keep your waist circumference to less than half your height’. BMC Med. 2014;12:207. https://doi.org/10.1186/s12916-014-0207-1. Viken AF, Garcia-Aymerich J, Janson C, Schlünssen V, Thorarinsdottir EH, Gómez Real F, et al. Is more muscle mass linked to less hypertension? Exploring sex-specific effects and the role of body composition in older European adults. J Public Health (Berl). 2025. https://doi.org/10.1007/s10389-025-02644-5. Appel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM, et al. A Clinical Trial of the Effects of Dietary Patterns on Blood Pressure. N Engl J Med. 1997;336:1117–24. https://doi.org/10.1056/NEJM199704173361601. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, et al. Effects on Blood Pressure of Reduced Dietary Sodium and the Dietary Approaches to Stop Hypertension (DASH) Diet. N Engl J Med. 2001;344:3–10. https://doi.org/10.1056/NEJM200101043440101. Grillo A, Salvi L, Coruzzi P, Salvi P, Parati G. Sodium Intake and Hypertension. Nutrients. 2019;11:1970. https://doi.org/10.3390/nu11091970. Lim GH, Neelakantan N, Lee YQ, Park SH, Kor ZH, Van Dam RM, et al. Dietary Patterns and Cardiovascular Diseases in Asia: A Systematic Review and Meta-Analysis. Advances in Nutrition. 2024;15:100249. https://doi.org/10.1016/j.advnut.2024.100249. Herforth AW, Wiesmann D, Martínez-Steele E, Andrade G, Monteiro CA. Introducing a Suite of Low-Burden Diet Quality Indicators That Reflect Healthy Diet Patterns at Population Level. Current Developments in Nutrition. 2020;4:nzaa168. https://doi.org/10.1093/cdn/nzaa168. Beaney T, Burrell LM, Castillo RR, Charchar FJ, Cro S, Damasceno A, et al. May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension. European Heart Journal. 2019;40:2006–17. https://doi.org/10.1093/eurheartj/ehz300. May Measurement Month. May Measurement Month 2025 (MMM2025) Clinical Study Protocol. 2025. World Health Organisation (WHO). Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation. 2011. WHO/IOTF/IASO. The Asia-Pacific perspective: Redefining obesity and its treatment. 2000. Omron. Instruction Manual—Body Composition Monitor Model HBF-375 KaradaScan (TM). Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. International Journal of Food Sciences and Nutrition. 2005;56:303–7. https://doi.org/10.1080/09637480500195066. Schütten MTJ, Houben AJHM, De Leeuw PW, Stehouwer CDA. The Link Between Adipose Tissue Renin-Angiotensin-Aldosterone System Signaling and Obesity-Associated Hypertension. Physiology. 2017;32:197–209. https://doi.org/10.1152/physiol.00037.2016. NCD Risk Factor Collaboration (NCD-RisC). General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants. Lancet. 2024;404:851–63. https://doi.org/10.1016/S0140-6736(24)01405-3. Qin X, Chen C, Wang J, Cai A, Feng X, Jiang X, et al. Association of adiposity indices with cardiometabolic multimorbidity among 101,973 chinese adults: a cross-sectional study. BMC Cardiovasc Disord. 2023;23:514. https://doi.org/10.1186/s12872-023-03543-x. Sun J-Y, Su Z, Shen H, Hua Y, Sun W, Kong X-Q. Abdominal fat accumulation increases the risk of high blood pressure: evidence of 47,037 participants from Chinese and US national population surveys. Nutr J. 2024;23:153. https://doi.org/10.1186/s12937-024-01058-5. El Meouchy P, Wahoud M, Allam S, Chedid R, Karam W, Karam S. Hypertension Related to Obesity: Pathogenesis, Characteristics and Factors for Control. Int J Mol Sci. 2022;23:12305. https://doi.org/10.3390/ijms232012305. Shariq OA, McKenzie TJ. Obesity-related hypertension: a review of pathophysiology, management, and the role of metabolic surgery. Gland Surg. 2020;9:80–93. https://doi.org/10.21037/gs.2019.12.03. Farmer RE, Mathur R, Schmidt AF, Bhaskaran K, Fatemifar G, Eastwood SV, et al. Associations Between Measures of Sarcopenic Obesity and Risk of Cardiovascular Disease and Mortality: A Cohort Study and Mendelian Randomization Analysis Using the UK Biobank. JAHA. 2019;8:e011638. https://doi.org/10.1161/JAHA.118.011638. Knowles R, Carter J, Jebb SA, Bennett D, Lewington S, Piernas C. Associations of Skeletal Muscle Mass and Fat Mass With Incident Cardiovascular Disease and All‐Cause Mortality: A Prospective Cohort Study of UK Biobank Participants. JAHA. 2021;10:e019337. https://doi.org/10.1161/JAHA.120.019337. Hoffmann C, Weigert C. Skeletal Muscle as an Endocrine Organ: The Role of Myokines in Exercise Adaptations. Cold Spring Harb Perspect Med. 2017;7:a029793. https://doi.org/10.1101/cshperspect.a029793. Yu B, Jia S, Sun T, Liu J, Jin J, Zhang S, et al. Sarcopenic obesity is associated with cardiometabolic multimorbidity in Chinese middle-aged and older adults: a cross-sectional and longitudinal study. J Nutr Health Aging. 2024;28:100353. https://doi.org/10.1016/j.jnha.2024.100353. Correia RR, Veras ASC, Tebar WR, Rufino JC, Batista VRG, Teixeira GR. Strength training for arterial hypertension treatment: a systematic review and meta-analysis of randomized clinical trials. Sci Rep. 2023;13:201. https://doi.org/10.1038/s41598-022-26583-3. Wang X, Wang Q, Zhao W, Wang J, Chen L, Wang L. The efficacy of resistance training for the management of hypertension: a systematic review and meta-analysis. Postepy Kardiol Interwencyjnej. 2025;21:163–70. https://doi.org/10.5114/aic.2025.151598. Aburto TC, Gordon-Larsen P, Poti JM, Howard AG, Adair LS, Avery CL, et al. Is a Hypertension Diagnosis Associated With Improved Dietary Outcomes Within 2 to 4 Years? A Fixed-Effects Analysis From the China Health and Nutrition Survey. J Am Heart Assoc. 2019;8:e012703. https://doi.org/10.1161/JAHA.119.012703. Alanazi Z, Alanazi R, Alanazi H, Alanazi J. A systematic review of adherence to lifestyle modifications by hypertensive patients. East Mediterr Health J. 2025;31:590–6. https://doi.org/10.26719/2025.31.10.590. Zhang Z, Xuan Z, Fu Y, Zhao H, Zhan P, Yang C, et al. Knowledge, attitudes, and practices regarding hypertension diet among patients with hypertension. Sci Rep. 2025;15:11915. https://doi.org/10.1038/s41598-025-97016-0. Tomé-Carneiro J, Visioli F. Plant-Based Diets Reduce Blood Pressure: A Systematic Review of Recent Evidence. Curr Hypertens Rep. 2023;25:127–50. https://doi.org/10.1007/s11906-023-01243-7. Chen F, Lin H, Shen Y, Fang L, Chen X, Zheng D, et al. Associations of dietary indices with risk of all-cause and cardiovascular mortality in hypertensive adults. Ann Med. 2025;57:2584427. https://doi.org/10.1080/07853890.2025.2584427. Morze J, Danielewicz A, Hoffmann G, Schwingshackl L. Diet Quality as Assessed by the Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension Score, and Health Outcomes: A Second Update of a Systematic Review and Meta-Analysis of Cohort Studies. Journal of the Academy of Nutrition and Dietetics. 2020;120:1998-2031.e15. https://doi.org/10.1016/j.jand.2020.08.076. Cornelissen VA, Smart NA. Exercise training for blood pressure: a systematic review and meta-analysis. J Am Heart Assoc. 2013;2:e004473. https://doi.org/10.1161/JAHA.112.004473. Edwards JJ, Deenmamode AHP, Griffiths M, Arnold O, Cooper NJ, Wiles JD, et al. Exercise training and resting blood pressure: a large-scale pairwise and network meta-analysis of randomised controlled trials. Br J Sports Med. 2023;57:1317–26. https://doi.org/10.1136/bjsports-2022-106503. Scaranni P de O da S, Cardoso L de O, Chor D, Melo ECP, Matos SMA, Giatti L, et al. Ultra-processed foods, changes in blood pressure and incidence of hypertension: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2021;24:3352–60. https://doi.org/10.1017/S136898002100094X. Carey RM, Muntner P, Bosworth HB, Whelton PK. Prevention and Control of Hypertension: JACC Health Promotion Series. J Am Coll Cardiol. 2018;72:1278–93. https://doi.org/10.1016/j.jacc.2018.07.008. Tables Table 1 . Dietary intake, diet quality, and anthropometric/body composition status among normotensive and hypertensive participants. Total, N (%) Normotensive, n (%) Hypertensive, n (%) Hypertensive Aware, n (%) Hypertensive Unaware, n (%) Hypertensive Controlled BP, n (%) Hypertensive Uncontrolled BP, n (%) N 998 645 (64.6) 353 (35.3) 207 (59.5) 141 (40.5) 118 (33.9) 230 (66.1) Median age ± Interquartile Range 40 ± 35 36 ± 28 53 ± 32 55 ± 31 50 ± 30 46 ± 32 56 ± 29 p <0.001** 0.108 0.002** Median SBP ± Interquartile Range 123 ± 27 116 ± 19 141 ± 23 137 ± 25 145 ± 15 127 ± 15 149 ± 17 p <0.001** <0.001** <0.001** Median DBP ± Interquartile Range 73 ± 14 72 ± 11 80 ± 19 75 ± 17 88 ± 16 70 ± 12 85 ± 16 p <0.001** <0.001** <0.001** Sex Male 322 (32.3) 188 (29.1) 134 (38.0) 69 (33.3) 62 (44.0) 37 (31.4) 94 (40.9) Female 676 (67.7) 457 (70.9) 219 (62.0) 138 (66.7) 79 (56.0) 81 (68.6) 136 (59.1) p 8.108; 0.004** 4.044; 0.044* 3.007; 0.083 Grains Yes 959 (96.1) 622 (96.4) 337 (95.5) 199 (96.1) 133 (94.3) 113 (95.8) 219 (95.2) No 39 (3.9) 23 (3.6) 16 (4.5) 8 (3.9) 8 (5.7) 5 (4.2) 11 (4.8) χ 2 ; p 0.568; 0.451 0.626; 0.429 0.053; 0.818 Whole grains Yes 418 (41.9) 255 (39.5) 163 (46.2) 103 (49.8) 56 (39.7) 56 (47.5) 103 (44.8) No 580 (58.1) 390 (60.5) 190 (53.8) 104 (50.2) 85 (60.3) 62 (52.5) 127 (55.2) χ 2 ; p 4.133; 0.042* 3.408; 0.065 0.225; 0.635 Pulses Yes 410 (41.1) 278 (43.1) 132 (37.4) 83 (40.1) 46 (32.6) 50 (42.4) 79 (34.3) No 588 (58.9) 367 (56.9) 221 (62.6) 124 (59.9) 95 (67.4) 68 (57.6) 151 (65.7) χ 2 ; p 3.070; 0.080 2.008; 0.157 2.153; 0.142 Green leafy vegetables Yes 585 (58.6) 379 (58.8) 206 (58.4) 122 (58.9) 81 (57.4) 72 (61.0) 131 (57.0) No 413 (41.4) 266 (41.2) 147 (41.6) 85 (41.1) 60 (42.6) 46 (39.0) 99 (43.0) χ 2 ; p 0.015; 0.902 0.077; 0.782 0.529; 0.467 All vegetables Yes 802 (80.4) 518 (80.3) 284 (80.5) 172 (83.1) 108 (76.6) 101 (85.6) 179 (77.8) No 196 (19.6) 127 (19.7) 69 (19.5) 35 (16.9) 33 (23.4) 17 (14.4) 51 (22.2) χ 2 ; p 0.003; 0.957 2.251; 0.134 2.993; 0.084 Vitamin A-rich fruits and vegetables Yes 540 (54.1) 348 (54.0) 192 (54.4) 113 (54.6) 75 (53.2) 72 (61.0) 116 (50.4) No 458 (45.9) 297 (46.0) 161 (45.6) 94 (45.4) 66 (46.8) 46 (39.0) 114 (49.6) χ 2 ; p 0.018; 0.895 0.066; 0.797 3.561; 0.061 All fruits Yes 633 (63.4) 397 (61.6) 236 (66.9) 139 (67.1) 93 (66.0) 83 (70.3) 149 (64.8) No 365 (36.6) 248 (38.4) 117 (33.1) 68 (32.9) 48 (34.0) 35 (29.7) 81 (35.2) χ 2 ; p 2.768; 0.096 0.054; 0.817 1.083; 0.298 Fruits and vegetables Yes 868 (87.0) 553 (85.7) 315 (89.2) 187 (90.3) 123 (87.2) 108 (91.5) 202 (87.8) No 130 (13.0) 92 (14.3) 38 (10.8) 20 (9.7) 18 (12.8) 10 (8.5) 28 (12.2) χ 2 ; p 2.465; 0.116 0.831; 0.362 1.097; 0.295 Unprocessed red meat Yes 421 (42.2) 289 (44.8) 132 (37.4) 74 (35.7) 55 (39.0) 50 (42.4) 79 (34.3) No 577 (57.8) 356 (55.2) 221 (62.6) 133 (64.3) 86 (61.0) 68 (57.6) 151 (65.7) χ 2 ; p 5.140; 0.023* 0.382; 0.537 2.513; 0.142 Processed meat Yes 209 (20.9) 160 (24.8) 49 (13.9) 28 (13.5) 21 (14.9) 22 (18.6) 27 (11.7) No 789 (79.1) 485 (75.2) 304 (86.1) 179 (86.5) 120 (85.1) 96 (81.4) 203 (88.3) χ 2 ; p 16.447; <0.001** 0.130; 0.719 3.074; 0.08 Meat, poultry and fish Yes 799 (80.1) 530 (82.2) 269 (76.2) 159 (76.8) 105 (74.5) 93 (78.8) 171 (74.3) No 199 (19.9) 115 (17.8) 84 (23.8) 48 (23.2) 36 (25.5) 25 (21.2) 59 (25.7) χ 2 ; p 5.088; 0.024* 0.252; 0.616 0.849; 0.357 Pulses, nuts and seeds Yes 495(49.6) 331 (51.3) 164 (46.5) 102 (49.3) 58 (41.1) 66 (55.9) 94 (40.9) No 503 (50.4) 314 (48.7) 189 (53.5) 105 (50.7) 83 (58.9) 52 (44.1) 136 (59.1) χ 2 ; p 2.155; 0.142 2.238; 0.135 7.124; 0.008** Nuts and seeds Yes 314 (31.5) 210 (32.6) 104 (29.5) 63 (30.4) 38 (27.0) 47 (39.8) 54 (23.5) No 684 (68.5) 435 (67.4) 249 (70.5) 144 (69.6) 103 (73.0) 71 (60.2) 176 (76.5) χ 2 ; p 1.014; 0.314 0.494; 0.482 10.123; 0.001** Dairy Yes 517 (51.8) 344 (53.3) 173 (49.0) 105 (50.7) 66 (46.8) 67 (56.8) 104 (45.2) No 481 (48.2) 301 (46.7) 180 (51.0) 102 (49.3) 75 (53.2) 51 (43.2) 126 (54.8) χ 2 ; p 1.709; 0.1191 0.515; 0.473 4.172; 0.041* Animal source foods Yes 886 (88.8) 576 (89.3) 310 (87.8) 185 (89.4) 120 (85.1) 106 (89.8) 199 (86.5) No 112 (11.2) 69 (10.7) 43 (12.2) 22 (10.6) 21 (14.9) 12 (10.2) 31 (13.5) χ 2 ; p 0.504; 0.478 1.409; 0.235 0.788; 0.375 Fast foods and instant noodles Yes 340 (34.1) 245 (38.0) 95 (26.9) 56 (27.1) 38 (27.0) 41 (34.7) 53 (23.0) No 658 (65.9) 400 (62.0) 258 (73.1) 151 (72.9) 103 (73.0) 77 (65.3) 177 (77.0) χ 2 ; p 12.452; <0.001** 0; 0.983 5.417; 0.02* Packaged ultra-processed foods Yes 394 (39.5) 283 (43.9) 111 (31.4) 64 (30.9) 45 (31.9) 47 (39.8) 62 (27.0) No 604 (60.5) 362 (56.1) 242 (68.6) 143 (69.1) 96 (68.1) 71 (60.2) 168 (73.0) χ 2 ; p 14.756; <0.001** 0.039; 0.844 6.009; 0.014* ALL5 Score Category <5 609 (61.0) 390 (60.5) 219 (62.0) 124 (59.9) 93 (66.0) 65 (55.1) 152 (66.1) 5 389 (39.0) 255 (39.5) 134 (38.0) 83 (40.1) 48 (34.0) 53 (44.9) 78 (33.9) χ 2 ; p 0.238; 0.626 1.310; 0.252 4.022; 0.045* GDR Score Meets Recommendation Yes 796 (79.8) 485 (75.2) 311 (88.1) 182 (87.9) 124 (87.9) 97 (82.2) 209 (90.9) No 202 (20.2) 160 (24.8) 42 (11.9) 25 (12.1) 17 (12.1) 21 (17.8) 21 (9.1) χ 2 ; p 23.547; <0.001** 0; 0.995 5.519; 0.019* WC Class Normal 451 (45.2) 314 (48.7) 137 (38.8) 82 (39.6) 54 (38.3) 60 (50.8) 76 (33.0) High 547 (54.8) 331 (51.3) 216 (61.2) 125 (60.4) 87 (61.7) 58 (49.2) 154 (67.0) χ 2 ; p 8.977; 0.003** 0.061; 0.805 10.384; 0.001** WHtR Class Normal 390 (39.1) 278 (43.1) 112 (31.7) 67 (32.4) 44 (31.2) 53 (44.9) 58 (25.2) High 608 (60.9) 367 (56.9) 241 (68.3) 140 (67.6) 97 (68.8) 65 (55.1) 172 (74.8) χ 2 ; p 12.394; <0.001** 0.052; 0.819 13.930; <0.001** WHR Class Normal 540 (54.1) 367 (56.9) 173 (49.0) 107 (51.7) 64 (45.4) 71 (60.2) 100 (43.5) High 458 (45.9) 278 (43.1) 180 (51.0) 100 (48.3) 77 (54.6) 47 (39.8) 130 (56.5) χ 2 ; p 5.721; 0.017* 1.332; 0.248 8.694; 0.003** TBF Class Normal 302 (31.4) 233 (37.2) 69 (20.6) 40 (20.4) 28 (20.9) 25 (22.5) 43 (19.6) High 659 (68.6) 393 (62.8) 266 (79.4) 156 (79.6) 106 (79.1) 86 (77.5) 176 (80.4) χ 2 ; p 27.983; <0.001** 0.012; 0.914 0.376; 0.540 VFL Class Normal 627 (63.8) 453 (71.3) 174 (50.1) 104 (50.7) 68 (49.6) 63 (53.4) 109 (48.7) High 355 (36.2) 182 (28.7) 173 (49.9) 101 (49.3) 69 (50.4) 55 (46.6) 115 (51.3) χ 2 ; p 43.668; <0.001** 0.040; 0.842 0.691; 0.406 BMI Class Normal 369 (38.2) 278 (44.2) 91 (27.0) 60 (30.3) 29 (21.6) 42 (37.5) 47 (21.4) Overweight 303 (31.4) 191 (30.4) 112 (33.2) 59 (29.8) 51 (38.1) 31 (27.7) 79 (35.9) Obese 294 (30.4) 160 (25.4) 134 (39.8) 79 (39.9) 54 (40.3) 39 (34.8) 94 (42.7) χ 2 ; p 32.355; <0.001** 3.886; 0.143 9.884; 0.007** SM Class Normal 758 (78.5) 474 (75.6) 284 (84.0) 164 (82.8) 115 (85.2) 95 (84.8) 184 (83.3) High 207 (21.5) 153 (24.4) 54 (16.0) 34 (17.2) 20 (14.8) 17 (15.2) 37 (16.7) χ 2 ; p 9.253; 0.002** 0.328; 0.567 0.134; 0.715 ALL5: Consumed all five recommended food groups – starchy staples, vegetables, fruits, pulses, nuts and seeds, animal-source foods; GDR: Global Dietary Recommendations; WC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle. Continuous variables analyzed by Mann-Whitney U test; Categorical variables analyzed by Pearson’s Chi-square test; * p -value significant at < 0.05; ** p -value significant at < 0.01 Table 2 . Binary logistic regression for the association of hypertension presence and BP control with diet quality and anthropometric/body composition classes. Classes B S.E. Wald p -value Odds Ratio 95.0% C.I. Lower Upper Hypertension Presence ALL5Score = 5 -0.027 0.158 0.030 0.863 0.973 0.714 1.326 GDR Does Not Meet Minimum Recommendation -0.502 0.213 5.573 0.018* 0.605 0.399 0.918 High WC 0.432 0.166 6.789 0.009** 1.541 1.113 2.133 High WHtR Class 0.355 0.166 4.601 0.032* 1.427 1.031 1.974 High WHR Class 0.279 0.158 3.105 0.078 1.322 0.969 1.803 High TBF Class 0.743 0.189 15.475 <0.001** 2.103 1.452 3.045 High VFL Class 0.799 0.165 23.456 <0.001** 2.223 1.609 3.072 BMI Overweight 0.620 0.175 12.593 <0.001** 1.858 1.320 2.617 BMI Obese 0.712 0.170 17.513 <0.001** 2.038 1.460 2.845 High SM Class -0.556 0.212 6.841 0.009** 0.574 0.378 0.870 Hypertensives with BP Uncontrolled ALL5Score = 5 -0.402 0.272 2.179 0.140 0.669 0.392 1.141 GDR Does Not Meet Minimum Recommendation -0.525 0.396 1.759 0.185 0.591 0.272 1.285 High WC 1.178 0.300 15.373 <0.001** 3.247 1.802 5.850 High WHtR Class 1.081 0.292 13.744 <0.001** 2.948 1.665 5.221 High WHR Class 0.792 0.274 8.369 0.004** 2.207 1.291 3.773 High TBF Class 0.099 0.343 0.083 0.773 1.104 0.564 2.162 High VFL Class -0.040 0.265 0.023 0.880 0.961 0.572 1.615 BMI Overweight 0.828 0.301 7.592 0.006** 2.290 1.270 4.127 BMI Obese 0.455 0.281 2.630 0.105 1.577 0.909 2.734 High SM Class 0.377 0.414 0.830 0.362 1.458 0.648 3.280 ALL5: Consumed all five recommended food groups – starchy staples, vegetables, fruits, pulses, nuts and seeds, animal-source foods; GDR: Global Dietary Recommendations; WC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle. Continuous variables analyzed by Mann-Whitney U test; Categorical variables analyzed by Pearson’s Chi-square test; * p -value significant at < 0.05; ** p -value significant at < 0.01 Table 3 . Diet quality scores among normotensive and hypertensive participants. Normotensive ( n = 645) Hypertensive ( n = 353) Hypertensive Controlled BP ( n = 118) Hypertensive Uncontrolled BP ( n = 230) DDS Mean (95% CI) 6.26 (6.06, 6.46) 5.99 (5.71, 6.27) 6.49 (5.99, 7.00) 5.69 (5.35, 6.02) Wald Chi-square 2.248 7.039 B (95% CI); p -0.044(-0.100, 0.013); 0.134 0.133 (0.035, 0.231); 0.008** ALL5 Mean (95% CI) 3.84 (3.68, 4.00) 3.79 (3.57, 4.01) 4.03 (3.62, 4.43) 3.65 (3.38, 3.92) Wald Chi-square 0.116 2.45 B (95% CI); p -0.012 (-0.084, 0.059); 0.734 0.099 (-0.025, 0.223); 0.117 NCD Protect Mean (95% CI) 5.50 (5.31, 5.69) 5.28 (5.02, 5.55) 5.71 (5.24, 6.19) 5.02 (4.70, 5.33) Wald Chi-square 1.635 5.934 B (95% CI); p -0.040 (-0.100, 0.021); 0.201 0.130 (0.025, 0.234); 0.015* NCD Risk Mean (95% CI) 3.26 (3.11, 3.40) 2.35 (2.17, 2.52) 2.86 (2.52, 3.19) 2.09 (1.88, 2.29) Wald Chi-square 54.464 16.03 B (95% CI); p -0.328 (-0.416, -0.241); <0.001** 0.313 (0.160, 0.466); <0.001** GDR Mean (95% CI) 11.24 (10.97, 11.51) 11.94 (11.54, 12.33) 11.86 (11.17, 12.54) 11.93 (11.44, 12.42) Wald Chi-square 8.312 0.028 B (95% CI); p 0.060 (0.019, 0.101); 0.004** -0.006 (-0.077, 0.065); 0.868 WC Mean (95% CI) 83.96 (83.88, 84.04) 87.89 (87.78, 88.01) 82.58 (82.39, 82.78) 90.55 (90.41, 90.70) Wald Chi-square 3026.05 4093.065 B (95% CI); p 3.937 (3.797, 4.077); <0.001** -7.970 (-8.214, -7.725); <0.001** WHtR Mean (95% CI) 0.52 (0.44, 0.60) 0.54 (0.42, 0.65) 0.50 (0.30, 0.70) 0.55 (0.41, 0.69) Wald Chi-square 0.065 0.156 B (95% CI); p 0.018 (-0.122, 0.159); 0.799 -0.049 (-0.293, 0.195); 0.693 WHR Mean (95% CI) 0.86 (0.77, 0.94) 0.87 (0.76, 0.99) 0.84 (0.64, 1.04) 0.89 (0.75, 1.03) Wald Chi-square 0.036 0.190 B (95% CI); p 0.014 (-0.127, 0.154); 0.850 -0.054 (-0.298, 0.190); 0.663 TBF Mean (95% CI) 28.85 (28.77, 28.93) 31.36 (31.25, 31.48) 31.77 (31.57, 31.98) 31.15 (31.01, 31.30) Wald Chi-square 1180.741 23.511 B (95% CI); p 2.517 (2.373, 2.661); <0.001** 0.623 (0.371, 0.874); <0.001** VFL Mean (95% CI) 7.19 (7.11, 7.27) 10.19 (10.07, 10.31) 9.41 (9.20, 9.61) 10.58 (10.43, 10.72) Wald Chi-square 16.77715 83.109 B (95% CI); p 3.001 (2.857, 3.145); <0.001** -1.170 (-1.422, -0.919); <0.001** RM Mean (95% CI) 1380.52 (1380.44, 1380.61) 1472.26 (1472.14, 1472.38) 1387.35 (1387.15, 1387.56) 1515.62 (1515.47, 1515.76) Wald Chi-square 1559228.283 996281.02 B (95% CI); p 91.734 (91.590, 91.878); <0.001** -128.267 (-128.519, -128.015); <0.001** BMI Mean (95% CI) 24.57 (24.49, 24.65) 26.69 (26.57, 26.80) 25.68 (25.48, 25.89) 27.24 (27.10, 27.39) Wald Chi-square 840.747 149.409 B (95% CI); p 2.118 (1.975, 2.261); <0.001 -1.562 (-1.813, -1.312); <0.001** Body age Mean (95% CI) 41.05 (40.96, 41.13) 54.18 (54.06, 54.30) 53.74 (53.53, 53.95) 54.20 (54.06, 54.35) Wald Chi-square 31962.081 12.903 B (95% CI); p 13.131 (12.987, 13.275) -0.462 (-0.716, -0.211); <0.001** SF Whole Body Mean (95% CI) 23.54 (23.46, 23.62) 25.12 (25.00, 25.24) 25.95 (25.74, 26.15) 24.74 (24.59, 24.89) Wald Chi-square 458.431 87.936 B (95% CI); p 1.581 (1.436, 1.726); <0.001 1.208 (0.956, 1.461); <0.001** SF Trunk Mean (95% CI) 20.50 (20.42, 20.59) 22.53 (22.41, 22.65) 23.05 (22.85, 23.26) 22.31 (22.16, 22.45) Wald Chi-square 762.12 33.419 B (95% CI); p 2.028 (1.884, 2.172); <0.004 0.742 (0.491, 0.994); <0.001** SF Arms Mean (95% CI) 35.50 (35.42, 35.58) 35.36 (35.24, 35.48) 36.78 (36.57, 36.98) 34.75 (34.61, 34.90) Wald Chi-square 3.694 246.308 B (95% CI); p -0.141 (-0.286, 0.003); 0.055 2.026 (1.773, 2.279); <0.001** SF Legs Mean (95% CI) 32.85 (32.77, 39.93) 32.99 (32.87, 33.11) 33.47 (33.26, 33.67) 32.81 (32.66, 32.95) Wald Chi-square 3.865 26.315 B (95% CI); p 0.144 (0, 0.289); 0.049* 0.660 (0.408, 0.912); <0.001** SM Whole Body Mean (95% CI) 27.79 (27.71, 27.87) 26.39 (26.28, 26.51) 25.56(25.35, 25.76) 26.83 (26.68, 26.97) Wald Chi-square 367.134 98.519 B (95% CI); p -1.399 (-1.542, -1.256); <0.001** -1.267 (-1.518, -1.017); <0.001** SM Trunk Mean (95% CI) 21.74 (21.66, 21.83) 20.38 (20.27, 20.50) 19.68 (19.48, 19.89) 20.77 (20.62, 20.91) Wald Chi-square 344.080 71.440 B (95% CI); p -1.359 (-1.503, -1.216); <0.001** -1.085 (-1.337, -0.834); <0.001** SM Arms Mean (95% CI) 31.03 (30.95, 31.11) 29.46 (29.34, 29.58) 28.54 (28.33, 28.74) 29.88 (29.74, 30.03) Wald Chi-square 463.185 110.454 B (95% CI); p -1.573 (-1.716, -1.430); <0.001** -1.347 (-1.598, -1.096); <0.001** SM Legs Mean (95% CI) 41.67 (41.59, 41.75) 40.64 (40.52, 40.76) 39.79 (39.58, 39.99) 41.05 (40.91, 41.20) Wald Chi-square 199.320 98.459 B (95% CI); p -1.031 (-1.174, -0.888); <0.001** -1.267 (-1.517, -1.017); <0.001** DDS: Dietary Diversity Score; ALL5: Consumed all five recommended food groups – starchy staples, vegetables, fruits, pulses, nuts and seeds, animal-source foods; NCD Protect: Dietary factors protective against non-communicable disease score; NCD Risk: Dietary factors for non-communicable disease score; GDR: Global Dietary Recommendations; WC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle. Means are estimated marginal means, Wald Chi-square, B, 95% CI, and p -values are by Poisson loglinear or linear generalized linear model, with continuous variables as dependents and controlling for categorical variables (years of education, ethnicity, gender, tobacco smoking, vaping, alcohol drinking, caffeine intake, and vigorous exercise) as “factors” and age as “covariate”. Normotensives and Uncontrolled BP are the reference groups. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Feb, 2026 Editor assigned by journal 25 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8962091","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597177467,"identity":"efe0438e-1997-490d-8bd5-d0a06e7cf580","order_by":0,"name":"Yee-How Say","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACNhAhh+AfIFKLMelaEhuI1mJwvPnYg49tdenzZ+Qek/jZxiDHdyOBdTMPPi1njqUbzmw7nLvhRl6aZG8bg7HkjQS223i13Mgxk+bddiB3g0SO2Q3eNobEDQS13H//DailLl1+Ro7Zzb9tDPWEtdzgYQNqYU5gAFp3G2hLggEhLZJn0swkZ/47bLjhzBvz3zLnJAxnnnnYdnMOHi18xw8/k/hwpk5evj3H2PBNmY083/HkYzfe4NGicACZx8gmASIbmPA5TL4BhfsHqvUHHi2jYBSMglEw4gAAMFFUfZhSuBsAAAAASUVORK5CYII=","orcid":"","institution":"Sunway University","correspondingAuthor":true,"prefix":"","firstName":"Yee-How","middleName":"","lastName":"Say","suffix":""},{"id":597177468,"identity":"ee5b5cdb-42b3-446c-851d-f46f0979ccd0","order_by":1,"name":"Hooi Chin Beh","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Hooi","middleName":"Chin","lastName":"Beh","suffix":""},{"id":597177469,"identity":"2879b14d-125c-4e56-853a-590ccf30084e","order_by":2,"name":"Karleen Chong","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Karleen","middleName":"","lastName":"Chong","suffix":""},{"id":597177470,"identity":"19f0407a-08d8-4777-a908-b12787b16927","order_by":3,"name":"Maong Hui Cheng","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Maong","middleName":"Hui","lastName":"Cheng","suffix":""},{"id":597177471,"identity":"471b0c81-180a-4202-8c96-dba38dea9b2f","order_by":4,"name":"Jazlan Jamaluddin","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Jazlan","middleName":"","lastName":"Jamaluddin","suffix":""},{"id":597177472,"identity":"8b5b0a92-a3b9-4c78-96f4-a3705dddeca6","order_by":5,"name":"Siew Mooi Ching","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Siew","middleName":"Mooi","lastName":"Ching","suffix":""},{"id":597177476,"identity":"0b02d0b7-ae99-4d05-903a-ecb6ebaeed34","order_by":6,"name":"Yook Chin Chia","email":"","orcid":"","institution":"Sunway University","correspondingAuthor":false,"prefix":"","firstName":"Yook","middleName":"Chin","lastName":"Chia","suffix":""}],"badges":[],"createdAt":"2026-02-25 02:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8962091/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8962091/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103475923,"identity":"5ed2f378-67d0-4881-ad93-c17a1e450581","added_by":"auto","created_at":"2026-02-26 06:57:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1917465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8962091/v1/36056457-9685-4ee4-8918-911bbdf74d77.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diet Quality, Adiposity, and Hypertension Among Malaysian Adults: A Cross-Sectional Analysis from the May Measurement Month 2025 Participants","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is one of the most important modifiable risk factors for cardiovascular disease (CVD), chronic kidney disease, and premature mortality worldwide. The World Health Organization (WHO) estimates that more than 1.2 billion adults globally are living with hypertension, with nearly half unaware of their condition [1]. Elevated blood pressure (BP) is responsible for a substantial proportion of global CVD deaths, contributing significantly to stroke, ischemic heart disease, and heart failure [2]. Despite the availability of effective pharmacological and lifestyle interventions, hypertension detection, treatment, and control rates remain suboptimal in many low- and middle-income countries. In Malaysia, hypertension continues to represent a major public health concern. The National Health and Morbidity Survey 2023 indicates that approximately one in three Malaysian adults has hypertension, yet a considerable proportion remain undiagnosed or inadequately controlled [3]. Rapid urbanization, sedentary lifestyles, increased consumption of energy-dense and ultra-processed foods, and rising obesity prevalence have contributed to the growing cardiometabolic burden in the country. Improving early detection and awareness is therefore critical to reducing long-term complications and healthcare costs.\u003c/p\u003e\n\u003cp\u003eExcess adiposity is strongly associated with hypertension through multiple physiological mechanisms. Central and visceral adiposity in particular contribute to sympathetic nervous system activation, renin\u0026ndash;angiotensin\u0026ndash;aldosterone system dysregulation, sodium retention, endothelial dysfunction, and chronic inflammation [4]. Measures of abdominal obesity, such as waist circumference (WC), waist-height ratio (WHtR), and waist-hip ratio (WHR), have been shown to better predict cardiometabolic risk than body mass index (BMI) alone [5]. Furthermore, emerging evidence suggests that body composition components, including skeletal muscle (SM) mass, may influence metabolic and vascular health, potentially modifying hypertension risk [6].\u003c/p\u003e\n\u003cp\u003eDietary patterns are another critical determinant of BP. Randomized controlled trials have demonstrated that dietary approaches emphasizing fruits, vegetables, whole grains, legumes, and reduced sodium intake\u0026mdash;such as the Dietary Approaches to Stop Hypertension (DASH) diet\u0026mdash;significantly lower BP [7, 8]. Conversely, high consumption of ultra-processed foods and sodium is associated with increased hypertension risk [9]. While many studies focus on individual nutrients, a broader assessment of overall diet quality may better capture real-world eating patterns and their relationship to cardiometabolic outcomes [10].\u003c/p\u003e\n\u003cp\u003eThe Diet Quality Questionnaire (DQQ), developed under the Global Diet Quality Project, is a standardized, rapid, food group\u0026ndash;based tool designed for population-level monitoring of diet quality and non-communicable disease (NCD)\u0026ndash;related dietary risk [11]. The DQQ generates indicators such as the Dietary Diversity Score (DDS), NCD Protect and NCD Risk scores, and the Global Dietary Recommendation (GDR) score, enabling simultaneous evaluation of protective and risk-related dietary components without detailed nutrient quantification. Incorporating DQQ-based assessment into large-scale screening initiatives such as the May Measurement Month (MMM) [12] offers a pragmatic approach to understanding how diet quality relates to hypertension detection and control in community populations.\u003c/p\u003e\n\u003cp\u003eAlthough obesity and diet are well-established contributors to hypertension, limited data exist examining the combined influence of standardized diet quality indicators and detailed body composition measures within the context of a national screening campaign in Malaysia. Moreover, little is known about how diet quality and body composition relate not only to hypertension presence, but also to BP control among individuals identified through community screening.\u003c/p\u003e\n\u003cp\u003eTherefore, this study aimed to examine the associations between diet quality\u0026mdash;assessed using the DQQ of the Global Diet Quality Project\u0026mdash;anthropometric and body composition measures, and (1) hypertension presence and (2) BP control among hypertensive Malaysian adults participating in May Measurement Month 2025 screening activities in greater Klang Valley, Malaysia. By integrating dietary assessment with detailed measures of adiposity and lean mass in a large-scale community screening context, this study seeks to provide evidence to inform targeted prevention and management strategies for hypertension in Malaysia.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a cross-sectional analysis conducted as part of the May Measurement Month (MMM) 2025 BP screening campaign in the greater Klang Valley, Malaysia. MMM is an annual global initiative coordinated by the International Society of Hypertension to improve awareness, detection, and management of hypertension [12]. Screening activities were conducted throughout April – October 2025 across multiple community-based sites in Malaysia, including public spaces, workplaces, educational institutions, and healthcare facilities. These included Universiti Malaya Medical Centre (UMMC) outpatient clinic (Kuala Lumpur), Universiti Putra Malaysia (UPM; Serdang), Sunway University (Subang Jaya), AEON Mall Nilai Health Campaign (Nilai), and Shangri-la Hotel (Kuala Lumpur).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to the standard protocol of basic sociodemographic, lifestyle, dietary habits, and BP screening as outlined in MMM2025 [13], all sites incorporated extended health assessments, including dietary evaluation and body composition measurements, to investigate cardiometabolic risk factors associated with elevated BP. The primary objective was to examine the associations between diet quality—assessed using the Diet Quality Questionnaire (DQQ) of the Global Diet Quality Project—anthropometric and body composition measures, and both hypertension presence and BP control among hypertensives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdults aged 18 years and above were eligible to participate in the study. Participants were recruited from community settings through outreach activities and voluntary participation. Individuals were excluded if they had incomplete data on gender, ethnicity, BP, and anthropometric/body composition measurements. Only participants with complete datasets were included in the final analysis, resulting in a total sample size of 998 people (or 96.5%) out of the 1034 people who participated in the study.\u003c/p\u003e\n\u003cp\u003eHypertension status was determined based on a single visit on-site measured BP and/or a self-reported prior physician diagnosis, as reported in the MMM2025 questionnaire question “Have you ever been diagnosed with high BP by a health professional (except in pregnancy)?” [13]. Participants were classified as hypertensive if they had a systolic BP (SBP) ≥ 140 mmHg and/or diastolic BP (DBP) ≥ 90 mmHg, or if they reported a previous diagnosis of hypertension. Normotensive participants were those with measured SBP \u0026lt; 140 mmHg and/or DBP \u0026lt; 90 mmHg, or without a prior diagnosis. Among hypertensive participants, awareness of hypertensive condition was determined by an affirmative answer to the question on self-reported prior physician diagnosis, while BP control status was determined using measured values at the time of assessment: controlled BP — SBP \u0026lt; 140 mmHg and/or DBP \u0026lt; 90 mmHg; uncontrolled BP — SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg.\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the relevant institutional research ethics committee before data collection (UMMC: MREC: 202535-14824, UPM: JKEUPM-2024-277, Sunway University: 2025/REC0104). All procedures were conducted in accordance with the principles outlined in the Declaration of Helsinki. Participants were informed about the purpose of the study, the procedures involved, potential risks and benefits, and their right to withdraw at any time without penalty. Written informed consent was obtained from all participants before enrolment. All data were anonymized and stored securely to ensure confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociodemographic and Lifestyle Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants completed a self-administered structured questionnaire, with trained personnel on standby to assist with any queries on the questionnaire. Information collected included age, sex, ethnicity, years of education, smoking status, vaping behaviour, alcohol consumption, caffeine intake, and engagement in vigorous physical activity. These variables were considered potential confounders and were included in multivariable analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBP Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBP was measured using a calibrated automated blood pressure monitor (Omron HEM-7120, Omron HEM-7121, or Rossmax MJ701f) following standardized procedures by trained personnel. Participants were seated comfortably with back support and feet flat on the floor for at least five minutes before measurement. An appropriately sized cuff was placed on the upper arm at heart level. At least duplicate readings were taken, and the average value was used for analysis to improve measurement reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDietary intake was assessed using the Diet Quality Questionnaire (DQQ) developed under the Global Diet Quality Project. The DQQ is a standardized, food group–based tool designed for rapid population-level monitoring of diet quality and NCD–related dietary risk [11]. It captures consumption of key food groups relevant to nutrient adequacy and chronic disease prevention without requiring detailed nutrient quantification. From the DQQ responses, several diet quality indicators were derived. The DDS reflects the variety of food groups consumed. It is a semi-continuous score (0-10), expressed as the average score out of 10 for the population. The NCD Protect score (range 0-9) represented consumption of foods considered protective against NCDs, such as fruits, vegetables, legumes, and nuts. The NCD Risk score (range 0-9) captured consumption of foods associated with increased NCD risk, including ultra-processed foods and processed meats. The ALL5 indicator identified whether participants consumed all five recommended core food groups: fruits, vegetables, pulses, nuts, or seeds; animal-source foods, and starchy staples. A score of less than 5 indicates that not all five recommended food groups were consumed (binary score: 1/0). The GDR score (range 0-18) reflected overall adherence to recommended dietary patterns. Both validated English and Malay versions of the DQQ, as provided by the Global Diet Quality Project, were used. The scoring rubrics and ranges are as described by the original authors of DDQ\u0026nbsp;[11]. These indicators were analysed as both categorical and continuous variables. A higher score for the continuous variables indicates better adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric and Body Composition Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthropometric and body composition measurements were conducted by trained personnel using standardized protocols. Height was measured using a portable stadiometer (Seca 213). Waist and hip circumferences were measured using a stretch-resistant tape that provided a constant 100 g tension, at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest, and around the widest portion of the buttocks, respectively [14]. WHR and WHtR were calculated by dividing WC by hip circumference and height, respectively. A bioimpedance analysis (BIA) body composition scale (Omron HBF-375) was used to determine weight, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), total body fat (TBF; %), visceral fat level (VFL; %), subcutaneous fat (SF; %), skeletal muscle percentage (SM; %) and resting metabolism rate (RM; kcal). Whole body and regional segments (trunk, arms, and legs) of SF and SM were also recorded. The cutoff points for overweight, obesity, high TBF, high VFL, high SM, high WC, high WHR and high WHtR were ≥23 kg/m\u003csup\u003e2\u003c/sup\u003e [15]; ≥27.5 kg/m\u003csup\u003e2\u003c/sup\u003e [15]; 20 % (men) or 30 % (women) [16]; 10 % [16]; 35.8 % (men) or 28 % (women) [16]; 90 cm (men) or 80 cm (women) (WHO/IOTF/ IASO, 2000); 0.90 (men) or 0.85 (women) (WHO, 2011); and 0.50 [17], respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics for Windows 26.0 (IBM Corp., Armonk, NY, USA). The conformity of the numerical variables to normal distribution was determined by the Kolmogorov-Smirnov test, where \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 indicates non-normally distributed data. Variables that were not normally distributed were summarized as medians with interquartile ranges, whereas normally distributed variables were summarized as means with 95% confidence intervals where appropriate. Differences between normotensive and hypertensive participants were assessed using the Mann–Whitney \u003cem\u003eU\u003c/em\u003e test for non-normally distributed continuous variables and Pearson’s chi-square test for categorical variables. Similar comparisons were conducted between hypertensive participants with controlled and uncontrolled BP. Binary logistic regression analyses were performed to examine the independent associations between diet quality indicators, anthropometric/body composition classes, and hypertension presence. Separate logistic regression models were conducted to assess associations with uncontrolled BP among hypertensive participants. Results were presented as regression coefficients, odds ratios (ORs), 95% confidence intervals (CIs), and corresponding \u003cem\u003ep\u003c/em\u003e-values. Generalized linear models were also used to evaluate associations between hypertension status or BP control and continuous diet quality and anthropometric/body composition measures. Depending on the distribution of the outcome variables, either linear or Poisson log-linear models were applied. All multivariable models were adjusted for potential confounders, including age, sex, ethnicity, years of education, smoking status, vaping, alcohol consumption, caffeine intake, and vigorous physical activity. All statistical tests were two-tailed, and a \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 998 participants were included in the analysis, consisting of all Malaysian nationals, comprising 479 Malays (48.0%), 370 Chinese (37.1%), 124 Indians (12.4%), and 25 other ethnicities (2.5%), of whom 645 (64.6%) were normotensive, and 353 (35.3%) were hypertensive (Table 1). Among hypertensive participants, 207 (59.5%) were aware of their diagnosis, and 118 (33.9%) had controlled BP. Hypertensive participants were significantly older than normotensive individuals, with a median age of 53 years compared to 36 years (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Table 1). Median SBP and DBP were also significantly higher among hypertensive participants (141 mmHg \u003cem\u003evs\u003c/em\u003e. 116 mmHg and 80 mmHg \u003cem\u003evs\u003c/em\u003e. 72 mmHg, respectively; both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Within the hypertensive group, those with uncontrolled BP had significantly higher SBP and DBP compared with those whose BP was controlled (both \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Table 1). There was a significant difference in sex distribution between normotensive and hypertensive participants (\u003cem\u003ep\u003c/em\u003e = 0.004; Table 1), with a higher proportion of males among hypertensive individuals (38.0%) compared with normotensive participants (29.1%). Among hypertensive participants, sex distribution differed significantly by awareness status (\u003cem\u003ep\u003c/em\u003e = 0.044; Table 1), with a higher proportion of males in the unaware group (44.0%) compared with the aware group (33.3%). However, there was no statistically significant difference in sex distribution between those with controlled and uncontrolled BP (\u003cem\u003ep\u003c/em\u003e = 0.083; Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary Intake and Diet Quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout dietary intake, whole grain consumption was significantly more common among hypertensive participants than normotensive participants (46.2% \u003cem\u003evs\u003c/em\u003e. 39.5%, \u003cem\u003ep\u003c/em\u003e = 0.042; Table 1). In contrast, normotensive individuals were significantly more likely to consume unprocessed red meat (44.8% \u003cem\u003evs\u003c/em\u003e. 37.4%, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.023), processed meat (24.8% \u003cem\u003evs\u003c/em\u003e. 13.9%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), meat, poultry and fish (82.2% \u003cem\u003evs\u003c/em\u003e. 76.2%, \u003cem\u003ep\u003c/em\u003e = 0.024), fast foods and instant noodles (38.0% \u003cem\u003evs\u003c/em\u003e. 26.9%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and packaged ultra-processed foods (43.9% \u003cem\u003evs\u003c/em\u003e. 31.4%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) than hypertensives. No significant differences were observed for pulses, vegetables, fruits, dairy, or overall animal-source food consumption.\u003c/p\u003e\n\u003cp\u003eAmong hypertensive participants, those with uncontrolled BP were significantly less likely to consume pulses, nuts, and seeds (\u003cem\u003ep\u003c/em\u003e = 0.008), nuts and seeds alone (\u003cem\u003ep\u003c/em\u003e = 0.001), and dairy (\u003cem\u003ep\u003c/em\u003e = 0.041). They were significantly more likely to consume fast foods and instant noodles (\u003cem\u003ep\u003c/em\u003e = 0.020) and packaged ultra-processed foods (\u003cem\u003ep\u003c/em\u003e = 0.014) compared with those with controlled BP (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypertension was not associated with ALL5 Score Category but was negatively associated with the failure to meet the minimum GDR - a lower prevalence of those not meeting the recommendation was found among hypertensive than normotensive participants (11.9% \u003cem\u003evs\u003c/em\u003e. 24.8%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Table 1). Indeed,\u0026nbsp;when controlled for confounding sociodemographic and lifestyle factors, hypertensive participants still had a significantly lower odds of not meeting the minimum GDR (OR 0.61; 95% CI 0.40–0.92; \u003cem\u003ep\u003c/em\u003e = 0.018; Table 2).\u003c/p\u003e\n\u003cp\u003eDiet quality scores derived from generalized linear models are presented in Table 3. There were no significant differences between normotensive and hypertensive participants in the DDS or ALL5 score. However, hypertensive participants had significantly lower NCD Risk scores (B = −0.328, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and significantly higher GDR scores (B = 0.060, \u003cem\u003ep\u003c/em\u003e = 0.004) compared with normotensive participants (Table 3). When hypertensive participants were stratified by BP control status, those with uncontrolled BP had significantly poorer diet quality. BP control status was significantly associated with DDS (Wald χ² = 7.039, \u003cem\u003ep\u003c/em\u003e = 0.008), NCD Protect (Wald χ² = 5.934, \u003cem\u003ep\u003c/em\u003e = 0.015), and NCD Risk (Wald χ² = 16.03, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Participants with controlled BP had a significantly higher DDS (B = 0.133, 95% CI: 0.035–0.231), higher NCD Protect scores (B = 0.130, 95% CI: 0.025–0.234), and higher NCD Risk scores (B = 0.313, 95% CI: 0.160–0.466) compared with those with uncontrolled BP (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric/Body Composition Classifications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthropometric and body composition differences between normotensive and hypertensive participants are presented in Tables 1 and 3. Categorical analyses in Table 1 showed that hypertensive participants were significantly more likely to have high WC (\u003cem\u003ep\u003c/em\u003e = 0.003), high WHtR (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), high WHR (\u003cem\u003ep\u003c/em\u003e = 0.017), high TBF (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), high VFL (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and to be classified as overweight or obese (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Among hypertensive individuals, those with uncontrolled BP were significantly more likely to have high WC, high WHtR, high WHR, and obesity compared with those with controlled BP (all \u003cem\u003ep\u003c/em\u003e ≤ 0.007; Table 1).\u003c/p\u003e\n\u003cp\u003eWhen controlled for confounding sociodemographic and lifestyle factors, hypertension presence was independently associated with high WC (OR 1.54; 95% CI 1.11–2.13; \u003cem\u003ep\u003c/em\u003e = 0.009), high WHtR (OR 1.43; \u003cem\u003ep\u003c/em\u003e = 0.032), high TBF (OR 2.10; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), high VFL (OR 2.22; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), overweight status (OR 1.86; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), and obesity (OR 2.04; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). High SM was associated with lower odds of hypertension (OR 0.57; \u003cem\u003ep\u003c/em\u003e = 0.009) (Table 2). Indeed, adjusted generalized linear model results for continuous anthropometric/body composition variables showed that hypertensive participants had significantly greater WC (87.89 cm \u003cem\u003evs\u003c/em\u003e. 83.96 cm, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), higher BMI (26.69 kg/m² \u003cem\u003evs\u003c/em\u003e. 24.57 kg/m², \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), higher TBF (31.36% \u003cem\u003evs\u003c/em\u003e. 28.85%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and higher VFL (10.19 \u003cem\u003evs\u003c/em\u003e. 7.19, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) compared with normotensive individuals (Table 3). They also had significantly higher SF measures and lower SM across whole body and regional segments (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the associations between diet quality—assessed using the DQQ of the Global Diet Quality Project—food group consumption, body composition, and both hypertension presence and BP control among Malaysian adults. Several important findings emerged. First, central and visceral adiposity were the strongest and most consistent correlates of hypertension and poor BP control. Second, overall diet quality differences between normotensive and hypertensive individuals were modest, and in some cases suggested post-diagnosis dietary modification. Third, among individuals with established hypertension, poorer diet quality was significantly associated with uncontrolled BP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Adiposity Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dominant role of central adiposity observed in this study aligns with the current understanding of hypertension pathophysiology. Visceral adipose tissue is metabolically active and contributes to increased sympathetic nervous system activation, renin–angiotensin–aldosterone system upregulation, sodium retention, endothelial dysfunction, and chronic low-grade inflammation [4, 18]. These mechanisms collectively promote elevated vascular resistance and impaired BP regulation. Our findings showed that high WC, elevated WHtR, high TBF, and especially high VFL were independently associated with hypertension presence. The magnitude of association was strongest for VFL, which more than doubled the odds of hypertension. This is consistent with evidence that central adiposity is more strongly associated with cardiometabolic risk than BMI alone [5, 19, 20]. BMI does not distinguish between fat and lean mass or capture fat distribution; therefore, reliance on BMI alone may underestimate risk in individuals with normal weight but high visceral adiposity. Among hypertensive participants, uncontrolled BP was significantly associated with markers of central adiposity, including WC, WHtR, and WHR. These findings suggest that excess abdominal fat not only contributes to hypertension development [21], but may also impair effective BP control [22, 23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSM was found to be inversely associated with hypertension presence. Reduced SM mass is increasingly recognized as an important factor influencing metabolic and vascular health, with potential implications for hypertension risk. Because SM is the primary site of insulin-mediated glucose uptake, lower muscle mass is associated with insulin resistance and adverse cardiometabolic profiles [24, 25]. In addition, SM functions as an endocrine organ, and reductions in muscle-derived myokines may contribute to systemic inflammation and endothelial dysfunction [26]. Recent population-based studies have shown that low SM mass and sarcopenic obesity are associated with higher cardiometabolic risk and greater prevalence of hypertension [6, 27]. Together, these findings suggest that reduced SM mass may contribute to hypertension risk both directly through metabolic dysregulation and indirectly through its interaction with excess adiposity. These findings also reinforce the importance of resistance training and preservation of lean mass in hypertension prevention and management strategies [28, 29].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiet Quality Assessed Using the DQQ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiet quality in this study was assessed using the DQQ of the Global Diet Quality Project, which captures consumption of key food groups and generates indicators including DDS, NCD Protect, NCD Risk, ALL5, and GDR scores [11]. The DQQ is designed as a standardized, low-burden tool for population-level monitoring of diet quality across countries. Hypertensive participants demonstrated lower NCD Risk scores and higher GDR scores compared with normotensive participants. This finding likely reflects dietary modification after hypertension diagnosis. Individuals aware of their hypertensive status may reduce intake of ultra-processed foods, processed meats, and energy-dense products in response to medical advice. Similar behavioural shifts following diagnosis have been observed in other populations [30–32].\u003c/p\u003e\n\u003cp\u003eHowever, diet quality indicators were not independently associated with hypertension presence in multivariable logistic regression. Several explanations are possible. First, reverse causation is likely: individuals with hypertension may have already improved their diets, attenuating cross-sectional differences. Second, cumulative long-term dietary exposure—rather than current dietary intake—may be more relevant to hypertension development. Third, adiposity may mediate much of the diet–hypertension relationship, thereby attenuating independent associations in fully adjusted models.\u003c/p\u003e\n\u003cp\u003eIn contrast, among hypertensive participants, uncontrolled BP was significantly associated with poorer diet quality. Individuals with uncontrolled BP had lower DDS and lower NCD Protect scores (reflecting lower intake of protective foods such as fruits, vegetables, legumes, and nuts). These findings are consistent with clinical trial evidence demonstrating that adherence to dietary patterns rich in plant foods and low in sodium and processed products improves BP control [7, 8, 33] and all-cause and CV mortality in hypertensive individuals [34, 35]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough BP control is determined objectively through measured BP values, DQQ-based indicators provide complementary insight into modifiable dietary factors among hypertensive individuals. In this study, participants with uncontrolled hypertension had poorer diet quality scores, suggesting that suboptimal dietary patterns may contribute to inadequate control in some individuals. However, not all participants with uncontrolled BP had poor diet scores, indicating that other factors—such as medication adherence, comorbidities (e.g., chronic kidney disease), or resistant hypertension—may also play a role. Therefore, the DQQ should not be viewed as a tool to determine control status, but rather as a practical screening instrument to identify individuals with both uncontrolled hypertension and poor diet quality who may benefit from targeted dietary counselling or referral to a dietitian. While adiposity may be the primary driver of hypertension onset, sustained diet quality may be critical for effective BP management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings have several important implications for clinical practice and public health. First, routine assessment of central adiposity should be integrated into hypertension screening and management. WC and WHtR are simple, inexpensive measures that provide meaningful cardiometabolic risk stratification. Given the strong association between visceral adiposity and both hypertension presence and control, clinicians should consider central fat reduction as part of the overall hypertension management.\u003c/p\u003e\n\u003cp\u003eSecond, hypertension management programs should emphasize combined lifestyle interventions that address both fat mass reduction and lean mass preservation. Aerobic exercise reduces BP and VFL, while resistance training increases SM and improves metabolic health [36, 37]. The observed protective association of SM reinforces the importance of resistance-based interventions.\u003c/p\u003e\n\u003cp\u003eThird, although cross-sectional associations between overall diet quality and hypertension presence were modest, diet quality was clearly associated with BP control among hypertensives. This underscores the need for sustained dietary counselling after diagnosis, rather than reliance solely on pharmacotherapy and cursory dietary advice. Tools such as the DQQ may be particularly useful in primary care and community settings for rapid diet quality assessment and monitoring.\u003c/p\u003e\n\u003cp\u003eFinally, at the population level, the findings support public health strategies aimed at reducing ultra-processed food consumption [38] and promoting dietary diversity rich in fruits, vegetables, pulses, nuts, and whole grains [39]. Such approaches may not only prevent obesity and hypertension but also improve hypertension control among those who already have hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. It included a relatively large adult sample and comprehensively assessed diet quality using the standardized DQQ framework from the Global Diet Quality Project. The DQQ enables harmonized monitoring across settings and captures both protective and risk-related dietary components. Additionally, detailed body composition measures—including VFL and regional SM—provided greater precision than BMI alone. The use of multivariable models adjusted for sociodemographic and lifestyle covariates further strengthens the validity of the findings.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations must be considered. The cross-sectional design precludes causal inference, and reverse causation—particularly dietary modification following hypertension diagnosis—is highly plausible. Dietary intake was assessed using self-reported information from the previous 24 hours and is therefore subject to recall bias and potential social desirability bias. The DQQ, while validated for population monitoring, does not provide quantitative nutrient intake estimates, and sodium intake was not objectively measured through 24-hour urinary excretion. Residual confounders, such as detailed sodium intake, medication adherence (which may influence BP control), duration and severity of hypertension, genetic predisposition, stress levels, and healthcare access, cannot be excluded, as there may be unmeasured or imperfectly measured variables that influence both exposure and outcome. Body composition was measured using BIA, which is suitable for field settings but has inherent limitations. BIA estimates can be affected by hydration status, recent physical activity, and recent food or fluid intake. In addition, device-specific prediction equations may introduce variability. Therefore, body composition findings should be interpreted with caution. Furthermore, because the study was conducted among Malaysian adults participating in a community screening campaign, the findings may not be generalizable to populations with different demographic, cultural, or healthcare characteristics.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, central and visceral adiposity were strongly and independently associated with hypertension presence and poor BP control among Malaysian adults. Diet quality, as assessed using the DQQ of the Global Diet Quality Project, showed modest associations with hypertension presence but was significantly associated with BP control among hypertensive individuals. These findings emphasize the dominant role of central adiposity in hypertension pathophysiology while highlighting the importance of sustained, high-quality dietary patterns for optimal BP management. Integrated strategies targeting both body composition and diet quality are likely necessary to reduce hypertension burden in this population.\u003c/p\u003e"},{"header":"List of Abbreviations","content":"\u003cp\u003eALL5 \u0026ndash; Consumption of all five recommended food groups indicator\u003c/p\u003e\n\u003cp\u003eBIA \u0026ndash; Bioelectrical impedance analysis\u003c/p\u003e\n\u003cp\u003eBMI \u0026ndash; Body mass index\u003c/p\u003e\n\u003cp\u003eBP \u0026ndash; Blood pressure\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence interval\u003c/p\u003e\n\u003cp\u003eCVD \u0026ndash; Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eDBP \u0026ndash; Diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eDDS \u0026ndash; Dietary Diversity Score\u003c/p\u003e\n\u003cp\u003eDQQ \u0026ndash; Diet Quality Questionnaire\u003c/p\u003e\n\u003cp\u003eGDR \u0026ndash; Global Dietary Recommendation score\u003c/p\u003e\n\u003cp\u003eIQR \u0026ndash; Interquartile range\u003c/p\u003e\n\u003cp\u003eMMM \u0026ndash; May Measurement Month\u003c/p\u003e\n\u003cp\u003eNCD \u0026ndash; Non-communicable disease\u003c/p\u003e\n\u003cp\u003eOR \u0026ndash; Odds ratio\u003c/p\u003e\n\u003cp\u003eSBP \u0026ndash; Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eSM \u0026ndash; Skeletal muscle\u003c/p\u003e\n\u003cp\u003eTBF \u0026ndash; Total body fat\u003c/p\u003e\n\u003cp\u003eVFL \u0026ndash; Visceral fat level\u003c/p\u003e\n\u003cp\u003eWC \u0026ndash; Waist circumference\u003c/p\u003e\n\u003cp\u003eWHR \u0026ndash; Waist-to-hip ratio\u003c/p\u003e\n\u003cp\u003eWHtR \u0026ndash; Waist-to-height ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the relevant institutional research ethics committee before data collection (UMMC: MREC: 202535-14824, UPM: JKEUPM-2024-277, Sunway University: 2025/REC0104). Written informed consent was obtained from all participants before enrolment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable requests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Malaysian Society of Hypertension (MSH) provided funding support for the screening campaigns, while the Malaysian Society for World Action on Salt, Sugar and Health (MyWASSH) contributed materials such as posters as part of their corporate social responsibility initiatives. The funders did not play a role in the design of this study nor was involved in the writing of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: YCC; Methodology: YHS and YCC; Data collection: YHS, HCB, KC, MHC, JJ, and SMC; Formal analysis: YHS and YCC; Writing - original draft preparation: YHS and YCC; Writing - review and editing: YHS, HCB, KC, MHC, JJ, SMC, and YCC; Supervision: YCC. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would also like to extend their gratitude to all participants who participated in this study. We would also like to thank all student helpers who helped with the recruitment of participants and measurements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organisation (WHO). Global report on hypertension: the race against a silent killer. 2023.\u003c/li\u003e\n\u003cli\u003eHay SI, Ong KL, Santomauro DF, A B, Aalipour MA, Aalruz H, et al. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990\u0026ndash;2023: a systematic analysis for the Global Burden of Disease Study 2023. The Lancet. 2025;406:1873\u0026ndash;922. https://doi.org/10.1016/S0140-6736(25)01637-X.\u003c/li\u003e\n\u003cli\u003eInstitute for Public Health. National Health and Morbidity Survey (NHMS) 2023 Non-Communicable Diseases and Healthcare Demand: Key Findings. 2024.\u003c/li\u003e\n\u003cli\u003eHall JE, Do Carmo JM, Da Silva AA, Wang Z, Hall ME. Obesity-Induced Hypertension: Interaction of Neurohumoral and Renal Mechanisms. Circulation Research. 2015;116:991\u0026ndash;1006. https://doi.org/10.1161/CIRCRESAHA.116.305697.\u003c/li\u003e\n\u003cli\u003eAshwell M, Gibson S. A proposal for a primary screening tool: `Keep your waist circumference to less than half your height\u0026rsquo;. BMC Med. 2014;12:207. https://doi.org/10.1186/s12916-014-0207-1.\u003c/li\u003e\n\u003cli\u003eViken AF, Garcia-Aymerich J, Janson C, Schl\u0026uuml;nssen V, Thorarinsdottir EH, G\u0026oacute;mez Real F, et al. Is more muscle mass linked to less hypertension? Exploring sex-specific effects and the role of body composition in older European adults. J Public Health (Berl). 2025. https://doi.org/10.1007/s10389-025-02644-5.\u003c/li\u003e\n\u003cli\u003eAppel LJ, Moore TJ, Obarzanek E, Vollmer WM, Svetkey LP, Sacks FM, et al. A Clinical Trial of the Effects of Dietary Patterns on Blood Pressure. N Engl J Med. 1997;336:1117\u0026ndash;24. https://doi.org/10.1056/NEJM199704173361601.\u003c/li\u003e\n\u003cli\u003eSacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, et al. Effects on Blood Pressure of Reduced Dietary Sodium and the Dietary Approaches to Stop Hypertension (DASH) Diet. N Engl J Med. 2001;344:3\u0026ndash;10. https://doi.org/10.1056/NEJM200101043440101.\u003c/li\u003e\n\u003cli\u003eGrillo A, Salvi L, Coruzzi P, Salvi P, Parati G. Sodium Intake and Hypertension. Nutrients. 2019;11:1970. https://doi.org/10.3390/nu11091970.\u003c/li\u003e\n\u003cli\u003eLim GH, Neelakantan N, Lee YQ, Park SH, Kor ZH, Van Dam RM, et al. Dietary Patterns and Cardiovascular Diseases in Asia: A Systematic Review and Meta-Analysis. Advances in Nutrition. 2024;15:100249. https://doi.org/10.1016/j.advnut.2024.100249.\u003c/li\u003e\n\u003cli\u003eHerforth AW, Wiesmann D, Mart\u0026iacute;nez-Steele E, Andrade G, Monteiro CA. Introducing a Suite of Low-Burden Diet Quality Indicators That Reflect Healthy Diet Patterns at Population Level. Current Developments in Nutrition. 2020;4:nzaa168. https://doi.org/10.1093/cdn/nzaa168.\u003c/li\u003e\n\u003cli\u003eBeaney T, Burrell LM, Castillo RR, Charchar FJ, Cro S, Damasceno A, et al. May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension. European Heart Journal. 2019;40:2006\u0026ndash;17. https://doi.org/10.1093/eurheartj/ehz300.\u003c/li\u003e\n\u003cli\u003eMay Measurement Month. May Measurement Month 2025 (MMM2025) Clinical Study Protocol. 2025.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation (WHO). Waist Circumference and Waist\u0026ndash;Hip Ratio: Report of a WHO Expert Consultation. 2011.\u003c/li\u003e\n\u003cli\u003eWHO/IOTF/IASO. The Asia-Pacific perspective: Redefining obesity and its treatment. 2000.\u003c/li\u003e\n\u003cli\u003eOmron. Instruction Manual\u0026mdash;Body Composition Monitor Model HBF-375 KaradaScan (TM).\u003c/li\u003e\n\u003cli\u003eAshwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. International Journal of Food Sciences and Nutrition. 2005;56:303\u0026ndash;7. https://doi.org/10.1080/09637480500195066.\u003c/li\u003e\n\u003cli\u003eSch\u0026uuml;tten MTJ, Houben AJHM, De Leeuw PW, Stehouwer CDA. The Link Between Adipose Tissue Renin-Angiotensin-Aldosterone System Signaling and Obesity-Associated Hypertension. Physiology. 2017;32:197\u0026ndash;209. https://doi.org/10.1152/physiol.00037.2016.\u003c/li\u003e\n\u003cli\u003eNCD Risk Factor Collaboration (NCD-RisC). General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7\u0026middot;5 million participants. Lancet. 2024;404:851\u0026ndash;63. https://doi.org/10.1016/S0140-6736(24)01405-3.\u003c/li\u003e\n\u003cli\u003eQin X, Chen C, Wang J, Cai A, Feng X, Jiang X, et al. Association of adiposity indices with cardiometabolic multimorbidity among 101,973 chinese adults: a cross-sectional study. BMC Cardiovasc Disord. 2023;23:514. https://doi.org/10.1186/s12872-023-03543-x.\u003c/li\u003e\n\u003cli\u003eSun J-Y, Su Z, Shen H, Hua Y, Sun W, Kong X-Q. Abdominal fat accumulation increases the risk of high blood pressure: evidence of 47,037 participants from Chinese and US national population surveys. Nutr J. 2024;23:153. https://doi.org/10.1186/s12937-024-01058-5.\u003c/li\u003e\n\u003cli\u003eEl Meouchy P, Wahoud M, Allam S, Chedid R, Karam W, Karam S. Hypertension Related to Obesity: Pathogenesis, Characteristics and Factors for Control. Int J Mol Sci. 2022;23:12305. https://doi.org/10.3390/ijms232012305.\u003c/li\u003e\n\u003cli\u003eShariq OA, McKenzie TJ. Obesity-related hypertension: a review of pathophysiology, management, and the role of metabolic surgery. Gland Surg. 2020;9:80\u0026ndash;93. https://doi.org/10.21037/gs.2019.12.03.\u003c/li\u003e\n\u003cli\u003eFarmer RE, Mathur R, Schmidt AF, Bhaskaran K, Fatemifar G, Eastwood SV, et al. Associations Between Measures of Sarcopenic Obesity and Risk of Cardiovascular Disease and Mortality: A Cohort Study and Mendelian Randomization Analysis Using the UK Biobank. JAHA. 2019;8:e011638. https://doi.org/10.1161/JAHA.118.011638.\u003c/li\u003e\n\u003cli\u003eKnowles R, Carter J, Jebb SA, Bennett D, Lewington S, Piernas C. Associations of Skeletal Muscle Mass and Fat Mass With Incident Cardiovascular Disease and All‐Cause Mortality: A Prospective Cohort Study of UK Biobank Participants. JAHA. 2021;10:e019337. https://doi.org/10.1161/JAHA.120.019337.\u003c/li\u003e\n\u003cli\u003eHoffmann C, Weigert C. Skeletal Muscle as an Endocrine Organ: The Role of Myokines in Exercise Adaptations. Cold Spring Harb Perspect Med. 2017;7:a029793. https://doi.org/10.1101/cshperspect.a029793.\u003c/li\u003e\n\u003cli\u003eYu B, Jia S, Sun T, Liu J, Jin J, Zhang S, et al. Sarcopenic obesity is associated with cardiometabolic multimorbidity in Chinese middle-aged and older adults: a cross-sectional and longitudinal study. J Nutr Health Aging. 2024;28:100353. https://doi.org/10.1016/j.jnha.2024.100353.\u003c/li\u003e\n\u003cli\u003eCorreia RR, Veras ASC, Tebar WR, Rufino JC, Batista VRG, Teixeira GR. Strength training for arterial hypertension treatment: a systematic review and meta-analysis of randomized clinical trials. Sci Rep. 2023;13:201. https://doi.org/10.1038/s41598-022-26583-3.\u003c/li\u003e\n\u003cli\u003eWang X, Wang Q, Zhao W, Wang J, Chen L, Wang L. The efficacy of resistance training for the management of hypertension: a systematic review and meta-analysis. Postepy Kardiol Interwencyjnej. 2025;21:163\u0026ndash;70. https://doi.org/10.5114/aic.2025.151598.\u003c/li\u003e\n\u003cli\u003eAburto TC, Gordon-Larsen P, Poti JM, Howard AG, Adair LS, Avery CL, et al. Is a Hypertension Diagnosis Associated With Improved Dietary Outcomes Within 2 to 4 Years? A Fixed-Effects Analysis From the China Health and Nutrition Survey. J Am Heart Assoc. 2019;8:e012703. https://doi.org/10.1161/JAHA.119.012703.\u003c/li\u003e\n\u003cli\u003eAlanazi Z, Alanazi R, Alanazi H, Alanazi J. A systematic review of adherence to lifestyle modifications by hypertensive patients. East Mediterr Health J. 2025;31:590\u0026ndash;6. https://doi.org/10.26719/2025.31.10.590.\u003c/li\u003e\n\u003cli\u003eZhang Z, Xuan Z, Fu Y, Zhao H, Zhan P, Yang C, et al. Knowledge, attitudes, and practices regarding hypertension diet among patients with hypertension. Sci Rep. 2025;15:11915. https://doi.org/10.1038/s41598-025-97016-0.\u003c/li\u003e\n\u003cli\u003eTom\u0026eacute;-Carneiro J, Visioli F. Plant-Based Diets Reduce Blood Pressure: A Systematic Review of Recent Evidence. Curr Hypertens Rep. 2023;25:127\u0026ndash;50. https://doi.org/10.1007/s11906-023-01243-7.\u003c/li\u003e\n\u003cli\u003eChen F, Lin H, Shen Y, Fang L, Chen X, Zheng D, et al. Associations of dietary indices with risk of all-cause and cardiovascular mortality in hypertensive adults. Ann Med. 2025;57:2584427. https://doi.org/10.1080/07853890.2025.2584427.\u003c/li\u003e\n\u003cli\u003eMorze J, Danielewicz A, Hoffmann G, Schwingshackl L. Diet Quality as Assessed by the Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension Score, and Health Outcomes: A Second Update of a Systematic Review and Meta-Analysis of Cohort Studies. Journal of the Academy of Nutrition and Dietetics. 2020;120:1998-2031.e15. https://doi.org/10.1016/j.jand.2020.08.076.\u003c/li\u003e\n\u003cli\u003eCornelissen VA, Smart NA. Exercise training for blood pressure: a systematic review and meta-analysis. J Am Heart Assoc. 2013;2:e004473. https://doi.org/10.1161/JAHA.112.004473.\u003c/li\u003e\n\u003cli\u003eEdwards JJ, Deenmamode AHP, Griffiths M, Arnold O, Cooper NJ, Wiles JD, et al. Exercise training and resting blood pressure: a large-scale pairwise and network meta-analysis of randomised controlled trials. Br J Sports Med. 2023;57:1317\u0026ndash;26. https://doi.org/10.1136/bjsports-2022-106503.\u003c/li\u003e\n\u003cli\u003eScaranni P de O da S, Cardoso L de O, Chor D, Melo ECP, Matos SMA, Giatti L, et al. Ultra-processed foods, changes in blood pressure and incidence of hypertension: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2021;24:3352\u0026ndash;60. https://doi.org/10.1017/S136898002100094X.\u003c/li\u003e\n\u003cli\u003eCarey RM, Muntner P, Bosworth HB, Whelton PK. Prevention and Control of Hypertension: JACC Health Promotion Series. J Am Coll Cardiol. 2018;72:1278\u0026ndash;93. https://doi.org/10.1016/j.jacc.2018.07.008.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Dietary intake, diet quality, and anthropometric/body composition status among normotensive and hypertensive participants.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"942\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal, \u003cem\u003eN\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormotensive,\u003cem\u003e\u0026nbsp;n\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive Aware, \u003cem\u003en\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive Unaware, \u003cem\u003en\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive Controlled BP,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive Uncontrolled BP, \u003cem\u003en\u003c/em\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e645 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e353 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e207 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e141 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e118 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e230 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eMedian age \u0026plusmn; Interquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e40 \u0026plusmn; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e36 \u0026plusmn; 28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e53 \u0026plusmn; 32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e55 \u0026plusmn; 31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e50 \u0026plusmn; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e46 \u0026plusmn; 32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e56 \u0026plusmn; 29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eMedian SBP \u0026plusmn; Interquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e123 \u0026plusmn; 27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e116 \u0026plusmn; 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e141 \u0026plusmn; 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e137 \u0026plusmn; 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e145 \u0026plusmn; 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e127 \u0026plusmn; 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e149 \u0026plusmn; 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eMedian DBP \u0026plusmn; Interquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e73 \u0026plusmn; 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e72 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e80 \u0026plusmn; 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e75 \u0026plusmn; 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e88 \u0026plusmn; 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e70 \u0026plusmn; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e85 \u0026plusmn; 16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e322 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e188 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e134 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e69 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e62 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e37 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e94 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e676 (67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e457 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e219 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e138 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e79 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e81 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e136 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e8.108; 0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e4.044; 0.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e3.007; 0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrains\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e959 (96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e622 (96.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e337 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e199 (96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e133 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e113 (95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e219 (95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e39 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e23 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e16 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e8 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e8 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e11 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.568; 0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.626; 0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.053; 0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhole grains\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e418 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e255 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e163 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e103 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e56 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e56 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e103 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e580 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e390 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e190 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e104 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e85 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e62 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e127 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e4.133; 0.042*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e3.408; 0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.225; 0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e410 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e278 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e132 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e83 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e46 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e50 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e79 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e588 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e367 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e221 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e124 (59.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e95 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e68 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e151 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e3.070; 0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e2.008; 0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e2.153; 0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGreen leafy vegetables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e585 (58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e379 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e206 (58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e122 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e81 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e72 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e131 (57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e413 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e266 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e147 (41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e85 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e60 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e46 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e99 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.015; 0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.077; 0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.529; 0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll vegetables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e802 (80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e518 (80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e284 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e172 (83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e108 (76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e101 (85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e179 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e196 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e127 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e69 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e35 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e33 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e17 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e51 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.003; 0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e2.251; 0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e2.993; 0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitamin A-rich fruits and vegetables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e540 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e348 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e192 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e113 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e75 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e72 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e116 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e458 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e297 (46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e161 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e94 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e66 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e46 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e114 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.018; 0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.066; 0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e3.561; 0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll fruits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e633 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e397 (61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e236 (66.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e139 (67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e93 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e83 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e149 (64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e365 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e248 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e117 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e68 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e48 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e35 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e81 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e2.768; 0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.054; 0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e1.083; 0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFruits and vegetables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e868 (87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e553 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e315 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e187 (90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e123 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e108 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e202 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e130 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e92 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e38 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e20 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e18 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e10 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e28 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e2.465; 0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.831; 0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e1.097; 0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnprocessed red meat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e421 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e289 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e132 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e74 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e55 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e50 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e79 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e577 (57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e356 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e221 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e133 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e86 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e68 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e151 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e5.140; 0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.382; 0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e2.513; 0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcessed meat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e209 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e160 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e49 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e28 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e21 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e22 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e27 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e789 (79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e485 (75.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e304 (86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e179 (86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e120 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e96 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e203 (88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e16.447; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.130; 0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e3.074; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeat, poultry and fish\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e799 (80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e530 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e269 (76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e159 (76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e105 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e93 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e171 (74.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e199 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e115 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e84 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e48 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e36 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e25 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e59 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e5.088; 0.024*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.252; 0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.849; 0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulses, nuts and seeds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e495(49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e331 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e164 (46.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e102 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e58 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e66 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e94 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e503 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e314 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e189 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e105 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e83 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e52 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e136 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e2.155; 0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e2.238; 0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e7.124; 0.008**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNuts and seeds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e314 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e210 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e104 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e63 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e38 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e47 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e54 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e684 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e435 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e249 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e144 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e103 (73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e71 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e176 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e1.014; 0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.494; 0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e10.123; 0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDairy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e517 (51.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e344 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e173 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e105 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e66 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e67 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e104 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e481 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e301 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e180 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e102 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e75 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e51 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e126 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e1.709; 0.1191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.515; 0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e4.172; 0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnimal source foods\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e886 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e576 (89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e310 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e185 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e120 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e106 (89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e199 (86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e112 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e69 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e43 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e22 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e21 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e12 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e31 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.504; 0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e1.409; 0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.788; 0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFast foods and instant noodles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e340 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e245 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e95 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e56 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e38 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e41 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e53 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e658 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e400 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e258 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e151 (72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e103 (73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e77 (65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e177 (77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e12.452; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0; 0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e5.417; 0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePackaged ultra-processed foods\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e394 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e283 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e111 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e64 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e45 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e47 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e62 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e604 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e362 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e242 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e143 (69.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e96 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e71 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e168 (73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e14.756; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.039; 0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e6.009; 0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALL5 Score Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e609 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e390 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e219 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e124 (59.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e93 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e65 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e152 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e389 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e255 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e134 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e83 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e48 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e53 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e78 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.238; 0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e1.310; 0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e4.022; 0.045*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDR Score Meets Recommendation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e796 (79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e485 (75.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e311 (88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e182 (87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e124 (87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e97 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e209 (90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e202 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e160 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e42 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e25 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e17 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e21 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e21 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e23.547; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0; 0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e5.519; 0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e451 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e314 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e137 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e82 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e54 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e60 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e76 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e547 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e331 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e216 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e125 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e87 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e58 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e154 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e8.977; 0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.061; 0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e10.384; 0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHtR Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e390 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e278 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e112 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e67 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e44 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e53 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e58 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e608 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e367 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e241 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e140 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e97 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e65 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e172 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e12.394; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.052; 0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e13.930; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHR Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e540 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e367 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e173 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e107 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e64 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e71 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e100 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e458 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e278 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e180 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e100 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e77 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e47 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e130 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e5.721; 0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e1.332; 0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e8.694; 0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBF Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e302 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e233 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e69 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e40 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e28 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e25 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e43 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e659 (68.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e393 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e266 (79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e156 (79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e106 (79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e86 (77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e176 (80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e27.983; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.012; 0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.376; 0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVFL Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e627 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e453 (71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e174 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e104 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e68 (49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e63 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e109 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e355 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e182 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e173 (49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e101 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e69 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e55 (46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e115 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e43.668; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.040; 0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.691; 0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e369 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e278 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e91 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e60 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e29 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e42 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e47 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e303 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e191 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e112 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e59 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e51 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e31 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e79 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e294 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e160 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e134 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e79 (39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e54 (40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e39 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e94 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e32.355; \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e3.886; 0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e9.884; 0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSM Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e758 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e474 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e284 (84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e164 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e115 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e95 (84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e184 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e207 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e153 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e54 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e34 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e20 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e17 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e37 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e9.253; 0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e0.328; 0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.134; 0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eALL5: Consumed all five recommended food groups \u0026ndash; starchy staples, vegetables, fruits, pulses, nuts and seeds, animal-source foods; GDR: Global Dietary Recommendations; WC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContinuous variables analyzed by Mann-Whitney U test; Categorical variables analyzed by Pearson\u0026rsquo;s Chi-square test; *\u003cem\u003ep\u003c/em\u003e-value significant at \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e-value significant at \u0026lt; 0.01\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Binary logistic regression for the association of hypertension presence and BP control with diet quality and anthropometric/body composition classes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClasses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.E.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.0% C.I.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension Presence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eALL5Score = 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eGDR Does Not Meet Minimum Recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh WC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e6.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh WHtR Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh WHR Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh TBF Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh VFL Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e23.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBMI Overweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e12.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBMI Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e17.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh SM Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e6.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.009**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensives with BP Uncontrolled\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eALL5Score = 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eGDR Does Not Meet Minimum Recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh WC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e15.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh WHtR Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e13.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh WHR Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e8.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh TBF Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh VFL Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBMI Overweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eBMI Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eHigh SM Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eALL5: Consumed all five recommended food groups \u0026ndash; starchy staples, vegetables, fruits, pulses, nuts and seeds, animal-source foods; GDR: Global Dietary Recommendations; WC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContinuous variables analyzed by Mann-Whitney U test; Categorical variables analyzed by Pearson\u0026rsquo;s Chi-square test; *\u003cem\u003ep\u003c/em\u003e-value significant at \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e-value significant at \u0026lt; 0.01\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Diet quality scores among normotensive and hypertensive participants.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormotensive (\u003cem\u003en\u003c/em\u003e = 645)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive (\u003cem\u003en =\u0026nbsp;\u003c/em\u003e353)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive Controlled BP (\u003cem\u003en\u003c/em\u003e = 118)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertensive Uncontrolled BP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 230)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6.26 (6.06, 6.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e5.99 (5.71, 6.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e6.49 (5.99, 7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.69 (5.35, 6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e7.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-0.044(-0.100, 0.013); 0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.133 (0.035, 0.231); 0.008**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.84 (3.68, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e3.79 (3.57, 4.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e4.03 (3.62, 4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.65 (3.38, 3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-0.012 (-0.084, 0.059); 0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.099 (-0.025, 0.223); 0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCD Protect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e5.50 (5.31, 5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e5.28 (5.02, 5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e5.71 (5.24, 6.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.02 (4.70, 5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e5.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-0.040 (-0.100, 0.021); 0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.130 (0.025, 0.234); 0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCD Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3.26 (3.11, 3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e2.35 (2.17, 2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e2.86 (2.52, 3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2.09 (1.88, 2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e54.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e16.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-0.328 (-0.416, -0.241); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.313 (0.160, 0.466); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e11.24 (10.97, 11.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e11.94 (11.54, 12.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e11.86 (11.17, 12.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e11.93 (11.44, 12.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e8.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.060 (0.019, 0.101); 0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-0.006 (-0.077, 0.065); 0.868\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e83.96 (83.88, 84.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e87.89 (87.78, 88.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e82.58 (82.39, 82.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e90.55 (90.41, 90.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3026.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e4093.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.937 (3.797, 4.077); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-7.970 (-8.214, -7.725); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHtR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.52 (0.44, 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.54 (0.42, 0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.50 (0.30, 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.55 (0.41, 0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.018 (-0.122, 0.159); 0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-0.049 (-0.293, 0.195); 0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.86 (0.77, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.87 (0.76, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.84 (0.64, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.89 (0.75, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.014 (-0.127, 0.154); 0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-0.054 (-0.298, 0.190); 0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e28.85 (28.77, 28.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e31.36 (31.25, 31.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e31.77 (31.57, 31.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e31.15 (31.01, 31.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1180.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e23.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.517 (2.373, 2.661); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.623 (0.371, 0.874); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVFL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e7.19 (7.11, 7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e10.19 (10.07, 10.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e9.41 (9.20, 9.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e10.58 (10.43, 10.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e16.77715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e83.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.001 (2.857, 3.145); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-1.170 (-1.422, -0.919); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1380.52 (1380.44, 1380.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1472.26 (1472.14, 1472.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1387.35 (1387.15, 1387.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1515.62 (1515.47, 1515.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1559228.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e996281.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e91.734 (91.590, 91.878); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-128.267 (-128.519, -128.015); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e24.57 (24.49, 24.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e26.69 (26.57, 26.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e25.68 (25.48, 25.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e27.24 (27.10, 27.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e840.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e149.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.118 (1.975, 2.261); \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-1.562 (-1.813, -1.312); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e41.05 (40.96, 41.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e54.18 (54.06, 54.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e53.74 (53.53, 53.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e54.20 (54.06, 54.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e31962.081\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e12.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e13.131 (12.987, 13.275)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-0.462 (-0.716, -0.211); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF Whole Body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e23.54 (23.46, 23.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e25.12 (25.00, 25.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e25.95 (25.74, 26.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e24.74 (24.59, 24.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e458.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e87.936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e1.581 (1.436, 1.726); \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e1.208 (0.956, 1.461); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF Trunk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e20.50 (20.42, 20.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e22.53 (22.41, 22.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e23.05 (22.85, 23.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e22.31 (22.16, 22.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e762.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e33.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e2.028 (1.884, 2.172); \u0026lt;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.742 (0.491, 0.994); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF Arms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e35.50 (35.42, 35.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e35.36 (35.24, 35.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e36.78 (36.57, 36.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e34.75 (34.61, 34.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e246.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-0.141 (-0.286, 0.003); 0.055\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e2.026 (1.773, 2.279); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF Legs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e32.85 (32.77, 39.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e32.99 (32.87, 33.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e33.47 (33.26, 33.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e32.81 (32.66, 32.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e3.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e26.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e0.144 (0, 0.289); 0.049*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e0.660 (0.408, 0.912); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSM Whole Body\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e27.79 (27.71, 27.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e26.39 (26.28, 26.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e25.56(25.35, 25.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e26.83 (26.68, 26.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e367.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e98.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-1.399 (-1.542, -1.256); \u0026lt;0.001**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-1.267 (-1.518, -1.017); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSM Trunk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e21.74 (21.66, 21.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e20.38 (20.27, 20.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e19.68 (19.48, 19.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e20.77 (20.62, 20.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e344.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e71.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-1.359 (-1.503, -1.216); \u0026lt;0.001**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-1.085 (-1.337, -0.834); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSM Arms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e31.03 (30.95, 31.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e29.46 (29.34, 29.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e28.54 (28.33, 28.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e29.88 (29.74, 30.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e463.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e110.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-1.573 (-1.716, -1.430); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-1.347 (-1.598, -1.096); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSM Legs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMean (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e41.67 (41.59, 41.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e40.64 (40.52, 40.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e39.79 (39.58, 39.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e41.05 (40.91, 41.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWald Chi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e199.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e98.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB (95% CI); \u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 252px;\"\u003e\n \u003cp\u003e-1.031 (-1.174, -0.888); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e-1.267 (-1.517, -1.017); \u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 604px;\"\u003e\n \u003cp\u003eDDS: Dietary Diversity Score; ALL5: Consumed all five recommended food groups \u0026ndash; starchy staples, vegetables, fruits, pulses, nuts and seeds, animal-source foods; NCD Protect: Dietary factors protective against non-communicable disease score; NCD Risk: Dietary factors for non-communicable disease score; GDR: Global Dietary Recommendations; WC: Waist circumference; WHtR: Waist-Height Ratio; WHR: Waist-Hip Ratio; TBF: Total Body Fat; VFL: Visceral Fat Level; BMI: Body Mass Index; SM: Skeletal Muscle.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMeans are estimated marginal means, Wald Chi-square, B, 95% CI, and \u003cem\u003ep\u003c/em\u003e-values are by Poisson loglinear or linear generalized linear model, with continuous variables as dependents and controlling for categorical variables (years of education, ethnicity, gender, tobacco smoking, vaping, alcohol drinking, caffeine intake, and vigorous exercise) as \u0026ldquo;factors\u0026rdquo; and age as \u0026ldquo;covariate\u0026rdquo;. Normotensives and Uncontrolled BP are the reference groups.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hypertension, diet quality, visceral fat, body composition, Malaysia","lastPublishedDoi":"10.21203/rs.3.rs-8962091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8962091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Hypertension is a leading modifiable risk factor for cardiovascular disease. Excess adiposity and poor diet quality contribute to elevated blood pressure (BP), yet few studies have examined both factors simultaneously using standardized diet quality measures in Southeast Asian populations. This study investigated the associations between diet quality, body composition, hypertension presence, and BP control among Malaysian adults.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: In this cross-sectional study, 998 adults were assessed. Hypertension was defined as systolic BP (SBP) ≥140 mmHg and/or diastolic BP (DBP) ≥90 mmHg, or self-reported prior physician diagnosis. Diet quality was evaluated using the Diet Quality Questionnaire (DQQ) of the Global Diet Quality Project, generating Dietary Diversity Score (DDS), NCD Protect, NCD Risk, ALL5, and Global Dietary Recommendation (GDR) indicators. Anthropometric and body composition measures included body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), total body fat (TBF), visceral fat level (VFL), and skeletal muscle mass (SM). Multivariable models adjusted for sociodemographic and lifestyle factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Hypertension prevalence was 35.3%. Hypertensive participants had significantly higher WC, BMI, TBF, and VFL and lower SM than normotensive participants (all p \u0026lt;0.001). High VFL (OR 2.22), high TBF (OR 2.10), overweight (OR 1.86), and obesity (OR 2.04) were independently associated with hypertension, whereas high SM was inversely associated (OR 0.57). Among hypertensive individuals, uncontrolled BP was strongly associated with central adiposity. Diet quality differences between normotensive and hypertensive participants were modest; however, hypertensives with uncontrolled BP were associated with lower DDS and NCD Protect scores.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: Central and visceral adiposity were significantly associated with hypertension and poor BP control. Diet quality, assessed using the DQQ, was more closely associated with BP control among hypertensives than with hypertension presence.\u003c/p\u003e","manuscriptTitle":"Diet Quality, Adiposity, and Hypertension Among Malaysian Adults: A Cross-Sectional Analysis from the May Measurement Month 2025 Participants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 06:55:08","doi":"10.21203/rs.3.rs-8962091/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-26T05:18:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T22:19:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T22:17:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2026-02-25T01:51:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d28107b-81e0-46a1-b6ef-ed6d421b4b20","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T12:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 06:55:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8962091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8962091","identity":"rs-8962091","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}