The Double Burden of Normal Weight Obesity (NWO) and Normal Weight Central Obesity (NWCO) on Hypertension Risk: A Cross-sectional Study

The Double Burden of Normal Weight Obesity (NWO) and Normal Weight Central Obesity (NWCO) on Hypertension Risk: A Cross-sectional Study | 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 The Double Burden of Normal Weight Obesity (NWO) and Normal Weight Central Obesity (NWCO) on Hypertension Risk: A Cross-sectional Study Yogesh M, Naresh Makwana, Nirmalkumar Shaileshbhai Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3875558/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Normal weight obesity (NWO) and normal weight central obesity (NWCO) have emerged as risk factors for cardiovascular disease. However, the association between the prevalence of these two conditions and hypertension risk has not been well studied, especially in Asian populations. Methods In this community-based cross-sectional study, we assessed body composition and blood pressure in 1000 adults aged > 20–65 years. NWO was defined as a normal body mass index (BMI 18.5–22.9 kg/m2) plus a high body fat percentage (> 20.6% in men, > 33.4% in women). NWCO was defined on the basis of a normal BMI plus high waist circumference (≥ 90 cm in men, ≥ 80 cm in women). Hypertension and prehypertension were defined as per the JNC 7 guidelines. Results The prevalence of NWO was 38%, affecting 228 of 600 normal-weight individuals. The prevalence of normal weight central obesity was 32% (192 of 600 participants with a normal BMI NWO and NWCO had drastically elevated hypertension odds of 3.2 (95% CI 2.1–4.7) and 3.5 (95% CI 2.3–5.2) times versus metabolically healthy normal weight people, respectively, independent of confounders). The odds for prehypertension were also greater for NWO (OR 1.7, 95% CI 1.1–2.6) and NWCO (OR 2.0, 95% CI 1.3–3.2). Conclusion A high proportion of normal-weight adults in this population have elevated cardiovascular risk based on excess body fat and abdominal adiposity. Assessing body composition would better identify at-risk individuals missed by BMI categorization alone. Reducing the double burden of NWO and NWCO may help lower the prevalence of hypertension in the population. Normal weight obesity Normal weight central obesity Hypertension Prehypertension Body composition Asian population Introduction Obesity is a major public health concern globally. It is an established risk factor for various cardiometabolic disorders, such as hypertension, type 2 diabetes, dyslipidemia, and cardiovascular disease ( 1 ). The global prevalence of obesity nearly tripled between 1975 and 2016, with 650 million obese adults worldwide in 2016 ( 2 ). However, the reliance on body mass index (BMI) for defining and categorizing obesity has certain limitations. Although a higher BMI is correlated with greater adiposity and metabolic risk, it does not distinguish between lean mass and fat mass. A subset of individuals with a normal BMI may have an excess body fat percentage that has yet to be identified. This phenotype, called normal weight obesity (NWO), is defined as a normal BMI (18.5–24.9 kg/m2) plus a high body fat percentage (> 20% in men, > 30% in women) and has emerged as an underrecognized risk factor for cardiometabolic abnormalities ( 3 ). Recent data show that 20–30% of normal-weight adults, as per BMI criteria, have excess adiposity qualifying as NWO ( 4 ). Visceral and ectopic fat deposition rather than total body fat strongly predicts insulin resistance, inflammation, hypertension, and cardiovascular events ( 5 ). Central obesity characterized by increased waist circumference despite a normal BMI, termed normal-weight central obesity (NWCO), may constitute a high risk factor. Studies have shown that, compared with their metabolically healthy peers, NWO individuals have impaired insulin sensitivity, atherogenic dyslipidaemia, and vascular inflammation ( 6 ). Moreover, WO confers increased odds of prediabetes/diabetes, hypertension, and cardiovascular mortality, independent of BMI ( 7 ). However, most evidence on NWO is from Western populations, and data from Asia are scarce. Studying NWO in Asian Indians holds significance considering the excess metabolic risk associated with lower BMIs ( 8 ). In this context, the present cross-sectional community-based study aimed to determine the prevalence of normal weight obesity (NWO) and normal weight central obesity (NWCO) in an urban Asian Indian population and the odds of prehypertension and hypertension associated with NWO and NWCO compared to those associated with individuals with a metabolically healthy normal weight. The study's significance lies in its potential to reveal the hidden cardiovascular risks among normal-weight individuals and highlight the inadequacy of relying solely on BMI for risk assessment. By identifying individuals with NWO and NWCO, which may be missed by traditional obesity categorization, this study aimed to contribute valuable insights for risk stratification and preventive strategies tailored to the unique characteristics of the Asian population. Reducing the double burden of NWO and NWCO has the potential to enhance public health interventions targeting hypertension and, consequently, cardiovascular disease in this population. Methodology Study Design and Setting This was a community-based cross-sectional study conducted in urban areas of Gujarat among adults aged 18–65 years between Jan 2023 and Oct 2023. Sample Size Calculation The sample size was calculated using a multiple logistic regression model with G*Power 3.1. ( 9 ) With an alpha of 0.05, 80% power, a 20% expected prevalence of hypertension in the unexposed group, and an odds ratio of 2 for the main exposure (normal weight obesity), the required sample size was 292 subjects. After accounting for 7 predictor variables (normal weight obesity, normal weight central obesity, age, sex, smoking status, alcohol use, and physical activity) and using an odds ratio of 1.8, the required sample size was 395 subjects. With an anticipated 10% nonresponse rate, the final minimum sample size needed was 435 subjects. Based on the logistic regression power analysis, the minimum required sample size was calculated to be 435 subjects. With a multistage cluster sampling design, a design effect needs to be applied to account for intraclass correlations within the clusters. Using a conservative design effect of 2, the revised minimum sample size required is: 435 x 2 = 870 The final minimum sample size needed with a design effect of 2 was 870 subjects. Sampling technique A multistage cluster sampling technique was used because this is a cost-effective method for community-based surveys. The city was divided into 5 zones based on administrative boundaries. From each zone, 4 colonies/neighbourhoods were randomly selected by drawing lots, for a total of 20 clusters. This approach ensured geographic representation across the city. In each selected cluster, systematic sampling was used to enroll participants by visiting every 5th household until 50 participants were recruited. If a house was locked/refused, the adjacent house was approached. The eligibility criterion for participation in this study encompassed a target population of adults aged 18 to 65 years who were residents of specific clusters. Prospective participants were required to willingly provide informed consent to be included in the study, indicating their understanding and agreement to participate in the research. On the other hand, certain exclusion criteria were established to ensure the study's focus and the safety of participants. Individuals who were pregnant or lactating were excluded, as were those with documented cases of hypertension undergoing treatment. Additionally, participants with a history of weight loss surgery were ineligible, as this could introduce confounding variables into the study. Furthermore, individuals experiencing physical disabilities that impeded accurate assessment of body composition were excluded, ensuring that the research could effectively and ethically measure the intended outcomes. These criteria collectively served to refine the study population, enhancing the precision and reliability of the research findings. Data collection Sociodemographic details, medical history, and lifestyle factor information were collected via interviews. Height, weight, waist circumference, and body fat percentage (bioelectrical impedance analysis) were measured using standard protocols. Blood pressure was recorded while the participants were in a sitting position after 5 minutes of rest using an automated digital device. Definitions Normal weight obesity: BMI 18.5–22.9 kg/m2 plus e (≥ 20.6% in men and ≥ 33.4% in women), as defined by previously published criteria ( 10 ). - Normal weight central obesity: BMI 18.5–22.9 kg/m2 plus waist circumference ≥ 90 cm in men and ≥ 80 cm in women ( 11 , 12 ). - Hypertension and prehypertension were defined using the JNC 7 guidelines ( 13 ). Statistical analysis Categorical variables are expressed as proportions and were compared using the chi-square test. Continuous variables are expressed as the mean ± SD and were compared using an independent t test. Univariate and multivariate logistic regression analyses were also conducted to determine odds ratios (ORs). The multivariate analysis could be adjusted according to the following criteria: age (continuous), sex (male/female), smoking status (packs per day), alcohol use (drinks per week), and physical activity (metabolic equivalent hours per week based on questionnaire). Diet (vegetarian vs. nonvegetarian diet or scores based on the food frequency questionnaire) Controlling for these covariates in the regression model provides a more accurate estimate of the independent association between maternal exposure (normal weight obesity, central obesity, etc.) and hypertension/prehypertension outcomes. A two-tailed p value < 0.05 was considered to indicate statistical significance. The statistical analysis was performed using SPSS version 21. Ethical considerations Ethical approval was obtained from the Institutional Ethics Committee. Informed consent was obtained from all participants. Confidentiality was maintained using unique identifiers. Results Table 1 Characteristics of the study participants stratified by sex Characteristic Total (N = 1000) Men (n = 500) Women (n = 500) p value Age in years, mean (SD) 41.5 (11.8) 41.2 (11.5) 41.9 (12.1) 0.32 BMI in kg/m2, mean (SD) 23.8 (4.2) 24.1 (4.0) 23.5 (4.4) 0.04 Normal weight, n (%) 600 (60%) 290 (58%) 310 (62%) 0.13 Overweight, n (%) 200 (20%) 96 (19%) 104 (21%) 0.14 Obese, n (%) 200 (20%) 120 (12%) 80 (8%) 0.24 Smokers, n (%) 140 (14%) 120 (24%) 20 (4%) <0.001 Alcohol use, n (%) 200 (20%) 160 (32%) 40 (8%) <0.001 Physical activity in MET-hours/week, mean (SD) 19.1 (6.2) 20.5 (6.6) 17.6 (5.4) <0.001 Vegetarian diet, n (%) 550 (55%) 250 (50%) 300 (60%) 0.003 Systolic BP in mmHg, mean (SD) 127.8 (17.4) 129.2 (17.1) 126.3 (17.6) 0.02 Diastolic BP in mmHg, mean (SD) 81.7 (11.8) 83.5 (12.1) 79.9 (11.3) <0.001 Hypertension, n (%) 274 (27.4%) 167 (33%) 107 (21%) <0.001 Prehypertension, n (%) 163 (16.3%) 110 (22%) 53 (11%) 0.002 p < 0.05* indicates significance, and p < 0.001** indicates high significance; p values were calculated from chi-square tests for categorical variables and t tests for continuous variables. Table 2 Prevalence of normal weight obesity and normal weight central obesity BMI category Normal weight (n = 600) Overweight (n = 200) Obese (n = 200) High BF% 228 (38%) 36 (18%) 28 (14%) High WC 192 (32%) 26 (13%) 18 (9%) WC-waist circumference, BFP-Body Fat % Table 3 Prevalence of hypertension and prehypertension by weight status Group n Hypertension, n (%) Prehypertension, n (%) Metabolically healthy normal weight 180 18 (10%) 20 (11%) Normal weight with high BF% 228 80 (35%) 35 (15%) Normal weight with high WC 192 70 (36%) 40 (21%) Overweight without high BF% 164 40 (24%) 25 (15%) Overweight with high BF% 36 16 (44%) 8 (22%) Obese without high BF% 172 32 (19%) 20 (12%) Obese with high BF% 28 18 (64%) 5 (18%) Overweight without high WC 174 30 (17%) 20 (11%) Overweight with high WC 26 10 (38%) 4 (15%) Obese without high WC 182 25 (14%) 15 (8%) Obese with high WC 18 7 (39%) 3 (17%) WC-waist circumference, BFP-Body Fat % Table 4 Bivariate analysis for prehypertension and hypertension risk Variable Unadjusted Odds Ratio for Hypertension, (95% CI) Unadjusted Odds Ratio for Prehypertension, (95% CI) Age 1.05 (1.03–1.07) * 1.02 (1.00-1.04) * Male gender 1.9 (1.4–2.6) * 2.3 (1.6–3.4) ** Vegetarian 0.8 (0.6-1.0) 0.9 (0.6–1.2) Smoking 2.0 (1.3-3.0) ** 1.2 (0.7–1.9) * Alcohol use 1.4 (0.9-2.0) 0.9 (0.6–1.5) Physical activity 0.97 (0.95–0.99) ** 0.98 (0.96-1.0) ** BMI 1.09 (1.05–1.13) * 1.07 (1.03–1.10) * Normal weight obesity 3.9 (2.8–5.5) ** 2.0 (1.4–2.9) ** Normal weight central obesity 4.2 (3.0–6.0) ** 2.7 (1.8-4.0) ** p < 0.05*-significant, p < 0.001**-highly significant Table 5 Multivariate analysis for risk of hypertension and prehypertension Variable Adjusted Odds Ratio for Hypertension (95% CI) Adjusted Odds Ratio for Prehypertension (95% CI) Age 1.04 (1.02–1.06) * 1.01 (0.99–1.03) Male gender 1.7 (1.2–2.4) ** 2.0 (1.3-3.0) ** Vegetarian 0.9 (0.6–1.2) 1.0 (0.7–1.5) Smoking 1.8 (1.1–2.8) * 1.0 (0.6–1.7) Alcohol use 1.2 (0.8–1.8) 0.8 (0.5–1.3) Physical activity 0.98 (0.96–0.99) * 0.99 (0.97-1.00) BMI 1.07 (1.03–1.11) ** 1.05 (1.02–1.09) ** Normal weight obesity 3.2 (2.1–4.7) *** 1.9 (1.1–2.6) * Normal weight central obesity 3.5 (2.3–5.2) *** 2.0 (1.3–3.2) ** |p < 0.05*-significant, p < 0.001**-highly significant Table 1 shows the characteristics of the 1,000 study participants stratified by sex. Overall, the mean age was 41.5 years, and the mean BMI was 23.8 kg/m2. 600 (60%) had a normal weight, 200 (20%) were overweight, and 200 (20%) were obese. Significant differences were detected between men and women, with men having a higher BMI (24.1 vs 23.5, p = 0.04), smoking more (120/500 (24%) vs 20/500 (4%), p < 0.001) and alcohol use more (160/500 (32%) vs 40/500 (8%), p < 0.001), while women had higher rates of vegetarian diet consumption (300/500 (60%) vs 250/500 (50%), p = 0.003). Moreover, men had higher blood pressure and rates of hypertension (167/500 (33%) vs 107/500 (21%), p < 0.001) and prehypertension (110/500 (22%) vs 53/500 (11%), p = 0.002). Table 2 shows the prevalence of normal-weight obesity (high body fat percentage) and normal-weight central obesity (high waist circumference). Among the 600 individuals with a normal BMI, 228 (38%) had high body fat, and 192 (32%) had high waist circumference. The prevalence of overweight decreased in 200 participants (36/200 (18%); high BF, 26/200 (13%); high WC) and 200 obese participants (28/200 (14%); high BF, 18/200 [9%]; high WC]). Approximately 51 (8.5%) had both high WC and high BF%. Table 3 shows the prevalence of hypertension and prehypertension among the metabolically healthy and unhealthy subgroups within each weight status. Normal-weight individuals with high body fat 80 (35%) or central obesity 70 (36%) had a 3–4 times greater prevalence of hypertension than did their metabolically healthy counterparts 18 (10%). Similar trends were observed for prehypertension. Table 4 presents the results of the bivariate logistic regression analysis. Older age, male sex, smoking status, higher BMI, and the presence of normal weight obesity or central obesity were significantly associated with higher odds of hypertension. The significant factors for prehypertension were similar, but the association between smoking and alcohol use was not significant. Table 5 shows multivariate-adjusted odds ratios. Age, male sex, physical activity, BMI, and normal weight obesity/central obesity remained significant independent predictors of hypertension and prehypertension risk after adjustment for other covariates. Individuals with normal-weight obesity and central obesity had 2–3 times greater odds of having hypertension and prehypertension than did individuals with a metabolically healthy normal weight. Discussion This study revealed a high prevalence of normal-weight obesity (38%) and normal-weight central obesity (32%) among normal-weight adults. These rates are higher than those of several previous studies reporting 20–30% NWO rates ( 14 – 18 ). Our study revealed an even greater prevalence, probably due to sampling differences. The substantially greater odds of hypertension and prehypertension observed in our analysis for NWO (3.2 and 1.9 times greater, respectively) and NWCO (3.5 and 2.0 times greater, respectively) align with existing evidence. A meta-analysis reported that NWO is associated with 2.7 times greater cardiovascular mortality and 2.1 times greater cardiovascular events ( 19 ). Another systematic review showed that NWO was associated with a 3.4-fold greater prevalence of hypertension than was a normal BMI and a normal fat percentage ( 20 ). The key mechanisms linking normal-weight obesity to hypertension are believed to be insulin resistance, abnormal lipid profiles, increased systemic inflammation and impaired adiponectin production ( 22 ). Excess visceral fat deposition specifically accompanies sympathetic overactivity and increased production of aldosterone, promoting sodium retention and vasoconstriction and ultimately elevating blood pressure levels ( 23 ). Our study additionally highlighted the role of normal weight central obesity in the odds of hypertension, which is comparable to that of normal weight obesity. Earlier studies have also demonstrated independent effects of visceral adiposity, indicated by high waist circumference, on incident hypertension after controlling for BMI ( 24 ). This underscores the need to measure central obesity as well as general obesity. The distinct risks associated with normal weight obesity beyond BMI highlight that a metabolically healthy normal weight should be the goal rather than just a normal BMI. Lifestyle management addressing diet quality, physical activity, and stress is assumed to be even more important among at-risk individuals with a normal weight. Our study expands the current literature by quantifying both NWO and NWCO burdens and associated hypertension risk concurrently in an Asian population. Our study provides novel data on the concurrent burden of both general and abdominal adiposity among Asian Indians across BMI categories. A key strength of this analysis was the adjustment for major confounders, including lifestyle factors such as diet, smoking status, and physical activity, in the multivariate models. The cluster sampling methodology also enhanced representation across diverse socioeconomic neighborhoods. However, several limitations must be noted. The cross-sectional design restricts causal interpretations of the direction of observed associations. Diet was broadly categorized as vegetarian or nonvegetarian and lacked granularity in quality and composition. Other unmeasured confounders, such as stress and medications, may have influenced the results. We could not distinguish between visceral and subcutaneous adiposity contributions within the normal weight central obesity phenotype. As visceral fat has greater metabolic activity and drainage into the liver, differential quantification of deep abdominal fat compartments via CT/MRI would involve enrichment analysis ( 5 ). Finally, the use of an urban sample may limit the generalizability of the findings to rural populations. Our study echoes global calls for adopting ethnicity-specific BMI cut-offs, as current WHO categories likely underestimate adiposity-related disease burdens in South Asians ( 8 ). Integrating simple body composition metrics such as waist circumference and skinfold thickness into screening protocols can enhance risk identification among normal-weight individuals missed by BMI alone ( 21 ). As normalization of overweight/obesity continues across the socioeconomic spectrum, targeted management of high-risk normal weight phenotypes through lifestyle changes may mitigate future cardiometabolic eruptions in this population. At the policy level, our data advocate for systematic surveillance and health programming focused on dual burdens of abdominal and general adiposity. Conclusion Normal weight and general obesity appear to be highly prevalent among adults with a normal BMI in urban India, disproportionately escalating the odds of hypertension. Integrating body composition for screening beyond just BMI could better unmask “at-risk” normal-weight individuals who may benefit from early risk factor optimization. Abbreviations BMI – Body mass index BIA – bioimpedance analysis NWO­- normal weight obesity NWCO- normal weight central obesity Declarations Conflict of interest – The authors have no conflicts of interest associated with the material presented in this paper. Ethics approval and consent to participate Good clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008. All the participants were given clear instructions about the study before the start of the study. Written informed consent was obtained from the patients in their vernacular language for study participation, and no identifying information or images were included in the original article, which was submitted for publication in an online open-access publication. The entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India. An ethical approval was obtained from the institute (Shri M P Shah Government Medical College, Jamnagar, Gujarat, India) before the start of the study. (REF No:40/01/23) Consent for publication Not Applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding The MDRU unit of the institution was the source of support for the provision of the bioimpedance analysis machine Omron Body Composition Monitor, Model HBF-702T, for this study. Authors' contributions YM contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, NM, and NP contributed to conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, NM, and NP contributed to the methodology, resources, supervision, validation, and writing (review and editing). YM, NM, and NP contributed to the formal analysis, investigation, writing (original draft), and writing (review and editing). All the authors read and approved the final manuscript. 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Assessment and definition of lean body mass deficiency in elderly individuals. Eur J Clin Nutr. 2014 Nov;68(11):1220-7. doi: 10.1038/ejcn.2014.169. Epub 2014 Aug 20. PMID: 25139559. Lee, S. H., Kim, Y. H., Han, K., Yang, H. K., Kim, M. K., Yoon, K. H., Nam, J. H., Park, Y. M., & Park, Y. W. Identifying metabolically obese but normal-weight (MONW) individuals in a nondiabetic Korean population: the Chungju Metabolic Disease Cohort (CMC) study. Clinical endocrinology. 2014 ;81(3), 475–482. https://doi.org/10.1111/cen.12507 Zhang, M., Zheng, L., Zhou, P., Wang, C., & Li, M. Association of obesity and cardiovascular risk factors with carotid intima-media thickness and leptin in metabolically healthy obese subjects: a cross-sectional study. Lipids in health and disease. 2017 ;16(1), 131. https://doi.org/10.1186/s12944-017-0527-5 Sahakyan, K. R., Somers, V. K., Rodriguez-Escudero, J. P., Hodge, D. O., Carter, R. E., Sochor, O., Coutinho, T., Jensen, M. D., Roger, V. L., Singh, P., & Lopez-Jimenez, F. Normal-Weight Central Obesity: Implications for Total and Cardiovascular Mortality. Annals of Internal Medicine. 2015; 163(11), 827–835. https://doi.org/10.7326/M14-2525 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3875558","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268049588,"identity":"e098b83f-db87-44ee-b774-c0279d0090dc","order_by":0,"name":"Yogesh M","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYLACxoYDYOrhhwogxczcQLQWZmOJMyCKkXgtbBK8bRAuXtUGx9svPvy5406+wfHDByQk59VG87cDtfyo2IZby5kzxca8Z55ZbjiTlmBQuO147ozDjA2MPWdu49ZyIydNmrHtsIHBDR6DBMltx3IbgFqYGdvwakn/+ROshf/DAd45x3LnE9aSfoyBF2ILYwNvQ03uBkJaJM+cYZbmbXtmIHkmzZhZ4tiB3I1ALQfx+YXvePvDjz/b7hjwHT/8/OeHmrrceecPH3zwowK3FoUDPAbI/MNg8gBO9UAg38D+AJlfh0/xKBgFo2AUjFAAAPfrZ/MXRfvAAAAAAElFTkSuQmCC","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yogesh","middleName":"","lastName":"M","suffix":""},{"id":268049589,"identity":"2a7ee420-bf6b-4a7a-88e8-d64b88ba1eee","order_by":1,"name":"Naresh Makwana","email":"","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Naresh","middleName":"","lastName":"Makwana","suffix":""},{"id":268049590,"identity":"9a336338-3712-4da1-9915-03b91efd8397","order_by":2,"name":"Nirmalkumar Shaileshbhai Patel","email":"","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Nirmalkumar","middleName":"Shaileshbhai","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2024-01-18 10:59:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3875558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3875558/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81156083,"identity":"deba374d-ba24-4a16-b9dd-da10bc0273d7","added_by":"auto","created_at":"2025-04-22 23:31:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":788642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3875558/v1/a275aa7a-ee55-4094-938d-63d5bb02b16d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Double Burden of Normal Weight Obesity (NWO) and Normal Weight Central Obesity (NWCO) on Hypertension Risk: A Cross-sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a major public health concern globally. It is an established risk factor for various cardiometabolic disorders, such as hypertension, type 2 diabetes, dyslipidemia, and cardiovascular disease (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e). The global prevalence of obesity nearly tripled between 1975 and 2016, with 650\u0026nbsp;million obese adults worldwide in 2016 (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e). However, the reliance on body mass index (BMI) for defining and categorizing obesity has certain limitations. Although a higher BMI is correlated with greater adiposity and metabolic risk, it does not distinguish between lean mass and fat mass. A subset of individuals with a normal BMI may have an excess body fat percentage that has yet to be identified.\u003c/p\u003e\n\u003cp\u003eThis phenotype, called normal weight obesity (NWO), is defined as a normal BMI (18.5–24.9 kg/m2) plus a high body fat percentage (\u0026gt; 20% in men, \u0026gt; 30% in women) and has emerged as an underrecognized risk factor for cardiometabolic abnormalities (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e). Recent data show that 20–30% of normal-weight adults, as per BMI criteria, have excess adiposity qualifying as NWO (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e). Visceral and ectopic fat deposition rather than total body fat strongly predicts insulin resistance, inflammation, hypertension, and cardiovascular events (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e). Central obesity characterized by increased waist circumference despite a normal BMI, termed normal-weight central obesity (NWCO), may constitute a high risk factor.\u003c/p\u003e\n\u003cp\u003eStudies have shown that, compared with their metabolically healthy peers, NWO individuals have impaired insulin sensitivity, atherogenic dyslipidaemia, and vascular inflammation (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e). Moreover, WO confers increased odds of prediabetes/diabetes, hypertension, and cardiovascular mortality, independent of BMI (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e). However, most evidence on NWO is from Western populations, and data from Asia are scarce. Studying NWO in Asian Indians holds significance considering the excess metabolic risk associated with lower BMIs (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). In this context, the present cross-sectional community-based study aimed to determine the prevalence of normal weight obesity (NWO) and normal weight central obesity (NWCO) in an urban Asian Indian population and the odds of prehypertension and hypertension associated with NWO and NWCO compared to those associated with individuals with a metabolically healthy normal weight.\u003c/p\u003e\n\u003cp\u003eThe study's significance lies in its potential to reveal the hidden cardiovascular risks among normal-weight individuals and highlight the inadequacy of relying solely on BMI for risk assessment. By identifying individuals with NWO and NWCO, which may be missed by traditional obesity categorization, this study aimed to contribute valuable insights for risk stratification and preventive strategies tailored to the unique characteristics of the Asian population. Reducing the double burden of NWO and NWCO has the potential to enhance public health interventions targeting hypertension and, consequently, cardiovascular disease in this population.\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003cp\u003e\u003c/p\u003e\n\n\n\n\n"},{"header":"Methodology","content":"\u003cp\u003eStudy Design and Setting\u003c/p\u003e\u003cp\u003eThis was a community-based cross-sectional study conducted in urban areas of Gujarat among adults aged 18–65 years between Jan 2023 and Oct 2023.\u003c/p\u003e\u003cp\u003eSample Size Calculation\u003c/p\u003e\u003cp\u003eThe sample size was calculated using a multiple logistic regression model with G*Power 3.1. (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e) With an alpha of 0.05, 80% power, a 20% expected prevalence of hypertension in the unexposed group, and an odds ratio of 2 for the main exposure (normal weight obesity), the required sample size was 292 subjects.\u003c/p\u003e\u003cp\u003eAfter accounting for 7 predictor variables (normal weight obesity, normal weight central obesity, age, sex, smoking status, alcohol use, and physical activity) and using an odds ratio of 1.8, the required sample size was 395 subjects.\u003c/p\u003e\u003cp\u003eWith an anticipated 10% nonresponse rate, the final minimum sample size needed was 435 subjects. Based on the logistic regression power analysis, the minimum required sample size was calculated to be 435 subjects.\u003c/p\u003e\u003cp\u003eWith a multistage cluster sampling design, a design effect needs to be applied to account for intraclass correlations within the clusters.\u003c/p\u003e\u003cp\u003eUsing a conservative design effect of 2, the revised minimum sample size required is:\u003c/p\u003e\u003cp\u003e435 x 2 = 870\u003c/p\u003e\u003cp\u003eThe final minimum sample size needed with a design effect of 2 was 870 subjects.\u003c/p\u003e\u003cp\u003eSampling technique\u003c/p\u003e\u003cp\u003eA multistage cluster sampling technique was used because this is a cost-effective method for community-based surveys. The city was divided into 5 zones based on administrative boundaries. From each zone, 4 colonies/neighbourhoods were randomly selected by drawing lots, for a total of 20 clusters. This approach ensured geographic representation across the city. In each selected cluster, systematic sampling was used to enroll participants by visiting every 5th household until 50 participants were recruited. If a house was locked/refused, the adjacent house was approached.\u003c/p\u003e\u003cp\u003eThe eligibility criterion for participation in this study encompassed a target population of adults aged 18 to 65 years who were residents of specific clusters. Prospective participants were required to willingly provide informed consent to be included in the study, indicating their understanding and agreement to participate in the research.\u003c/p\u003e\u003cp\u003eOn the other hand, certain exclusion criteria were established to ensure the study's focus and the safety of participants. Individuals who were pregnant or lactating were excluded, as were those with documented cases of hypertension undergoing treatment. Additionally, participants with a history of weight loss surgery were ineligible, as this could introduce confounding variables into the study. Furthermore, individuals experiencing physical disabilities that impeded accurate assessment of body composition were excluded, ensuring that the research could effectively and ethically measure the intended outcomes. These criteria collectively served to refine the study population, enhancing the precision and reliability of the research findings.\u003c/p\u003e\u003cp\u003eData collection\u003c/p\u003e\u003cp\u003eSociodemographic details, medical history, and lifestyle factor information were collected via interviews. Height, weight, waist circumference, and body fat percentage (bioelectrical impedance analysis) were measured using standard protocols. Blood pressure was recorded while the participants were in a sitting position after 5 minutes of rest using an automated digital device.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDefinitions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNormal weight obesity: BMI 18.5–22.9 kg/m2 plus e (≥ 20.6% in men and ≥ 33.4% in women), as defined by previously published criteria (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e- Normal weight central obesity: BMI 18.5–22.9 kg/m2 plus waist circumference ≥ 90 cm in men and ≥ 80 cm in women (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e- Hypertension and prehypertension were defined using the JNC 7 guidelines (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables are expressed as proportions and were compared using the chi-square test. Continuous variables are expressed as the mean ± SD and were compared using an independent t test. Univariate and multivariate logistic regression analyses were also conducted to determine odds ratios (ORs). The multivariate analysis could be adjusted according to the following criteria: age (continuous), sex (male/female), smoking status (packs per day), alcohol use (drinks per week), and physical activity (metabolic equivalent hours per week based on questionnaire). Diet (vegetarian vs. nonvegetarian diet or scores based on the food frequency questionnaire)\u003c/p\u003e\u003cp\u003eControlling for these covariates in the regression model provides a more accurate estimate of the independent association between maternal exposure (normal weight obesity, central obesity, etc.) and hypertension/prehypertension outcomes. A two-tailed p value \u0026lt; 0.05 was considered to indicate statistical significance. The statistical analysis was performed using SPSS version 21.\u003c/p\u003e\u003cp\u003eEthical considerations\u003c/p\u003e\u003cp\u003eEthical approval was obtained from the Institutional Ethics Committee. Informed consent was obtained from all participants. Confidentiality was maintained using unique identifiers.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study participants stratified by sex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;1000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.5 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.9 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI in kg/m2, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.8 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmokers, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity in MET-hours/week, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.1 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.6 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetarian diet, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP in mmHg, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.8 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.2 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.3 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP in mmHg, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.7 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.5 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.9 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrehypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05* indicates significance, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001**\u003c/b\u003e indicates high significance; p values were calculated from chi-square tests for categorical variables and t tests for continuous variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of normal weight obesity and normal weight central obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight (n\u0026thinsp;=\u0026thinsp;600)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObese (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eWC-waist circumference, BFP-Body Fat %\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of hypertension and prehypertension by weight status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrehypertension, n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolically healthy normal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight with high BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight with high WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight without high BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight with high BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese without high BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese with high BF%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight without high WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight with high WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (15%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese without high WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese with high WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eWC-waist circumference, BFP-Body Fat %\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate analysis for prehypertension and hypertension risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted Odds Ratio for Hypertension, (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnadjusted Odds Ratio for Prehypertension, (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.03\u0026ndash;1.07) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (1.00-1.04) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.9 (1.4\u0026ndash;2.6) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (1.6\u0026ndash;3.4) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVegetarian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.6-1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.3-3.0) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.7\u0026ndash;1.9) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.9-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.95\u0026ndash;0.99) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.96-1.0) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.05\u0026ndash;1.13) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.10) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal weight obesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9 (2.8\u0026ndash;5.5) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.4\u0026ndash;2.9) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal weight central obesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.2 (3.0\u0026ndash;6.0) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 (1.8-4.0) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05*-significant, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001**-highly significant\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis for risk of hypertension and prehypertension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted Odds Ratio for Hypertension (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted Odds Ratio for Prehypertension (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.02\u0026ndash;1.06) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.7 (1.2\u0026ndash;2.4) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.3-3.0) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVegetarian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.6\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.7\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.1\u0026ndash;2.8) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.6\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.8\u0026ndash;1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.5\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.96\u0026ndash;0.99) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.97-1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.11) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05 (1.02\u0026ndash;1.09) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal weight obesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.2 (2.1\u0026ndash;4.7) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9 (1.1\u0026ndash;2.6) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal weight central obesity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5 (2.3\u0026ndash;5.2) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.3\u0026ndash;3.2) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e|p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*-significant, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001**-highly significant\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characteristics of the 1,000 study participants stratified by sex. Overall, the mean age was 41.5 years, and the mean BMI was 23.8 kg/m2. 600 (60%) had a normal weight, 200 (20%) were overweight, and 200 (20%) were obese. Significant differences were detected between men and women, with men having a higher BMI (24.1 vs 23.5, p\u0026thinsp;=\u0026thinsp;0.04), smoking more (120/500 (24%) vs 20/500 (4%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and alcohol use more (160/500 (32%) vs 40/500 (8%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while women had higher rates of vegetarian diet consumption (300/500 (60%) vs 250/500 (50%), p\u0026thinsp;=\u0026thinsp;0.003). Moreover, men had higher blood pressure and rates of hypertension (167/500 (33%) vs 107/500 (21%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and prehypertension (110/500 (22%) vs 53/500 (11%), p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the prevalence of normal-weight obesity (high body fat percentage) and normal-weight central obesity (high waist circumference). Among the 600 individuals with a normal BMI, 228 (38%) had high body fat, and 192 (32%) had high waist circumference. The prevalence of overweight decreased in 200 participants (36/200 (18%); high BF, 26/200 (13%); high WC) and 200 obese participants (28/200 (14%); high BF, 18/200 [9%]; high WC]). Approximately 51 (8.5%) had both high WC and high BF%.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the prevalence of hypertension and prehypertension among the metabolically healthy and unhealthy subgroups within each weight status. Normal-weight individuals with high body fat 80 (35%) or central obesity 70 (36%) had a 3\u0026ndash;4 times greater prevalence of hypertension than did their metabolically healthy counterparts 18 (10%). Similar trends were observed for prehypertension.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of the bivariate logistic regression analysis. Older age, male sex, smoking status, higher BMI, and the presence of normal weight obesity or central obesity were significantly associated with higher odds of hypertension. The significant factors for prehypertension were similar, but the association between smoking and alcohol use was not significant.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows multivariate-adjusted odds ratios. Age, male sex, physical activity, BMI, and normal weight obesity/central obesity remained significant independent predictors of hypertension and prehypertension risk after adjustment for other covariates. Individuals with normal-weight obesity and central obesity had 2\u0026ndash;3 times greater odds of having hypertension and prehypertension than did individuals with a metabolically healthy normal weight.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study revealed a high prevalence of normal-weight obesity (38%) and normal-weight central obesity (32%) among normal-weight adults. These rates are higher than those of several previous studies reporting 20\u0026ndash;30% NWO rates (\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Our study revealed an even greater prevalence, probably due to sampling differences.\u003c/p\u003e \u003cp\u003eThe substantially greater odds of hypertension and prehypertension observed in our analysis for NWO (3.2 and 1.9 times greater, respectively) and NWCO (3.5 and 2.0 times greater, respectively) align with existing evidence. A meta-analysis reported that NWO is associated with 2.7 times greater cardiovascular mortality and 2.1 times greater cardiovascular events (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Another systematic review showed that NWO was associated with a 3.4-fold greater prevalence of hypertension than was a normal BMI and a normal fat percentage (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The key mechanisms linking normal-weight obesity to hypertension are believed to be insulin resistance, abnormal lipid profiles, increased systemic inflammation and impaired adiponectin production (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Excess visceral fat deposition specifically accompanies sympathetic overactivity and increased production of aldosterone, promoting sodium retention and vasoconstriction and ultimately elevating blood pressure levels (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study additionally highlighted the role of normal weight central obesity in the odds of hypertension, which is comparable to that of normal weight obesity. Earlier studies have also demonstrated independent effects of visceral adiposity, indicated by high waist circumference, on incident hypertension after controlling for BMI (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This underscores the need to measure central obesity as well as general obesity.\u003c/p\u003e \u003cp\u003eThe distinct risks associated with normal weight obesity beyond BMI highlight that a metabolically healthy normal weight should be the goal rather than just a normal BMI. Lifestyle management addressing diet quality, physical activity, and stress is assumed to be even more important among at-risk individuals with a normal weight.\u003c/p\u003e \u003cp\u003eOur study expands the current literature by quantifying both NWO and NWCO burdens and associated hypertension risk concurrently in an Asian population. Our study provides novel data on the concurrent burden of both general and abdominal adiposity among Asian Indians across BMI categories.\u003c/p\u003e \u003cp\u003eA key strength of this analysis was the adjustment for major confounders, including lifestyle factors such as diet, smoking status, and physical activity, in the multivariate models. The cluster sampling methodology also enhanced representation across diverse socioeconomic neighborhoods.\u003c/p\u003e \u003cp\u003eHowever, several limitations must be noted. The cross-sectional design restricts causal interpretations of the direction of observed associations. Diet was broadly categorized as vegetarian or nonvegetarian and lacked granularity in quality and composition. Other unmeasured confounders, such as stress and medications, may have influenced the results.\u003c/p\u003e \u003cp\u003eWe could not distinguish between visceral and subcutaneous adiposity contributions within the normal weight central obesity phenotype. As visceral fat has greater metabolic activity and drainage into the liver, differential quantification of deep abdominal fat compartments via CT/MRI would involve enrichment analysis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Finally, the use of an urban sample may limit the generalizability of the findings to rural populations.\u003c/p\u003e \u003cp\u003eOur study echoes global calls for adopting ethnicity-specific BMI cut-offs, as current WHO categories likely underestimate adiposity-related disease burdens in South Asians (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Integrating simple body composition metrics such as waist circumference and skinfold thickness into screening protocols can enhance risk identification among normal-weight individuals missed by BMI alone (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs normalization of overweight/obesity continues across the socioeconomic spectrum, targeted management of high-risk normal weight phenotypes through lifestyle changes may mitigate future cardiometabolic eruptions in this population. At the policy level, our data advocate for systematic surveillance and health programming focused on dual burdens of abdominal and general adiposity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNormal weight and general obesity appear to be highly prevalent among adults with a normal BMI in urban India, disproportionately escalating the odds of hypertension. Integrating body composition for screening beyond just BMI could better unmask \u0026ldquo;at-risk\u0026rdquo; normal-weight individuals who may benefit from early risk factor optimization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003col\u003e\n \u003cli\u003eBMI \u0026ndash; Body mass index\u003c/li\u003e\n \u003cli\u003eBIA \u0026ndash; bioimpedance analysis\u003c/li\u003e\n \u003cli\u003eNWO\u0026shy;- normal weight obesity\u003c/li\u003e\n \u003cli\u003eNWCO- normal weight central obesity\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einterest\u003c/strong\u003e \u0026ndash;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors have no conflicts of interest associated with the material presented in this paper.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eGood clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008.\u003c/li\u003e\n \u003cli\u003eAll the participants were given clear instructions about the study before the start of the study.\u003c/li\u003e\n \u003cli\u003eWritten informed consent was obtained from the patients in their vernacular language for study participation, and no identifying information or images were included in the original article, which was submitted for publication in an online open-access publication.\u003c/li\u003e\n \u003cli\u003eThe entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;An ethical approval was obtained from the institute (Shri M P Shah Government Medical College, Jamnagar, Gujarat, India) before the start of the study. (REF No:40/01/23)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe MDRU unit of the institution was the source of support\u0026nbsp;for\u0026nbsp;the provision of the\u0026nbsp;bioimpedance analysis machine\u0026nbsp;Omron Body\u0026nbsp;Composition Monitor, Model HBF-702T,\u0026nbsp;for this study.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYM contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, NM, and NP contributed to conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, NM, and NP contributed to the methodology, resources, supervision, validation, and writing (review and editing). YM, NM, and NP contributed to the formal analysis, investigation, writing (original draft), and writing (review and editing). All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe acknowledge and are grateful to all the patients who contributed to the collection of data for this study. We are also thankful to Dr. Nandini Desai (Dean and Chairperson of MDRU), Dr. Dipesh Parmar (Professor and Head, of the Department of Community Medicine), Shri M P Shah Government Medical College, Jamnagar, India.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLavie CJ, De Schutter A, Milani RV. Healthy obese versus unhealthy lean: the obesity paradox. Nat Rev Endocrinol. 2015 Jan;11(1):55-62. doi: 10.1038/nrendo.2014.165. Epub 2014 Sep 30. PMID: 25265977.\u003c/li\u003e\n\u003cli\u003eGBD 2015 Obesity Collaborators; Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, Marczak L, et al., Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017 Jul 6;377(1):13-27. doi: 10.1056/NEJMoa1614362. Epub 2017 Jun 12. PMID: 28604169; PMCID: PMC5477817.\u003c/li\u003e\n\u003cli\u003eOliveros E, Somers VK, Sochor O, Goel K, Lopez-Jimenez F. The concept of normal weight obesity. Prog Cardiovasc Dis. 2014 Jan-Feb;56(4):426-33. doi: 10.1016/j.pcad.2013.10.003. Epub 2013 Oct 5. PMID: 24438734..\u003c/li\u003e\n\u003cli\u003eKarastergiou K, Smith SR, Greenberg AS, Fried SK. Sex differences in human adipose tissues - the biology of pear shape. Biol Sex Differ. 2012 May 31;3(1):13. doi: 10.1186/2042-6410-3-13. PMID: 22651247; PMCID: PMC3411490.\u003c/li\u003e\n\u003cli\u003eNeeland IJ, Turer AT, Ayers CR, Powell-Wiley TM, Vega GL, Farzaneh-Far R, Grundy SM, Khera A, McGuire DK, de Lemos JA. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA. 2012 Sep 19;308(11):1150-9. doi: 10.1001/2012.jama.11132. PMID: 22990274; PMCID: PMC3556508.\u003c/li\u003e\n\u003cli\u003eDe Lorenzo A, Martinoli R, Vaia F, Di Renzo L. Normal weight obese (NWO) women: an evaluation of a candidate new syndrome. Nutr Metab Cardiovasc Dis. 2006 Dec;16(8):513-23. doi: 10.1016/j.numecd.2005.10.010. Epub 2006 Mar 3. PMID: 17126766.\u003c/li\u003e\n\u003cli\u003eLee IT, Chiu YF, Hwu CM, He CT, Chiang FT, Lin YC, Assimes T, Curb JD, Sheu WH. Central obesity is an important but not essential component of the metabolic syndrome for predicting diabetes mellitus in a hypertensive family-based cohort. The results from the Stanford Asia-Pacific program for hypertension and insulin resistance (SAPPHIRe) Taiwan follow-up study. Cardiovasc Diabetol. 2012 Apr 26;11:43. doi: 10.1186/1475-2840-11-43. PMID: 22537054; PMCID: PMC3476431.\u003c/li\u003e\n\u003cli\u003eMisra A, Khurana L, Isharwal S, Bhardwaj S. South Asian diets and insulin resistance. Br J Nutr. 2009 Feb;101(4):465-73. doi: 10.1017/S0007114508073649. Epub 2008 Oct 9. PMID: 18842159.\u003c/li\u003e\n\u003cli\u003eFaul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009 Nov;41(4):1149-60. doi: 10.3758/BRM.41.4.1149. PMID: 19897823.\u003c/li\u003e\n\u003cli\u003eLana P. Franco, Carla C. Morais, Cristiane Cominetti, Normal-weight obesity syndrome: diagnosis, prevalence, and clinical implications, Nutrition Reviews, Volume 74, Issue 9, September 2016, Pages 558\u0026ndash;570, https://doi.org/10.1093/nutrit/nuw019.\u003c/li\u003e\n\u003cli\u003eRomero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, Allison TG, Batsis JA, Sert-Kuniyoshi FH, Lopez-Jimenez F. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond). 2008 Jun;32(6):959-66. doi: 10.1038/ijo.2008.11. Epub 2008 Feb 19. PMID: 18283284; PMCID: PMC2877506.\u003c/li\u003e\n\u003cli\u003eMisra A. Ethnic-Specific Criteria for Classification of Body Mass Index: A Perspective for Asian Indians and American Diabetes Association Position Statement. Diabetes Technol Ther. 2015 Sep;17(9):667-71. doi: 10.1089/dia.2015.0007. Epub 2015 Apr 22. PMID: 25902357; PMCID: PMC4555479.\u003c/li\u003e\n\u003cli\u003eChobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, Jones DW, Materson BJ, Oparil S, Wright JT Jr, Roccella EJ; National Heart, Lung, and Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National High Blood Pressure Education Program Coordinating Committee. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003 May 21;289(19):2560-72. doi: 10.1001/jama.289.19.2560. Epub 2003 May 14. Erratum in: JAMA. 2003 Jul 9;290(2):197. PMID: 12748199.\u003c/li\u003e\n\u003cli\u003eKapoor N, Lotfaliany M, Sathish T, Thankappan KR, Thomas N, Furler J, Oldenburg B, Tapp RJ. Prevalence of normal weight obesity and its associated cardio-metabolic risk factors - Results from the baseline data of the Kerala Diabetes Prevention Program (KDPP). PLoS One. 2020 Aug 25;15(8):e0237974. doi: 10.1371/journal.pone.0237974. PMID: 32841271; PMCID: PMC7446975.\u003c/li\u003e\n\u003cli\u003eMadeira FB, Silva AA, Veloso HF, Goldani MZ, Kac G, Cardoso VC, Bettiol H, Barbieri MA. Normal weight obesity is associated with metabolic syndrome and insulin resistance in young adults from a middle-income country. PLoS One. 2013;8(3):e60673. doi: 10.1371/journal.pone.0060673. Epub 2013 Mar 28. PMID: 23556000; PMCID: PMC3610876\u003c/li\u003e\n\u003cli\u003ePhillips, C. M. (2013). Nutrigenetics and metabolic disease: current status and implications for personalized nutrition. Nutrients, 5(1), 32\u0026ndash;57. https://doi.org/10.3390/nu5010032\u003c/li\u003e\n\u003cli\u003eVelho, S., Paccaud, F., Waeber, G., Vollenweider, P., \u0026amp; Marques-Vidal, P. (2010). Metabolically healthy obesity: different prevalences using different criteria. European Journal of Clinical Nutrition, 64(10), 1043\u0026ndash;1051. https://doi.org/10.1038/ejcn.2010.114\u003c/li\u003e\n\u003cli\u003eBell, J. A., Hamer, M., Batty, G. D., Singh-Manoux, A., Sabia, S., \u0026amp; Kivimaki, M. (2015). The combined effect of physical activity and leisure time sitting on long-term risk of incident obesity and metabolic risk factor clustering. Diabetologia, 59(8), 2048-2056. https://doi.org/10.1007/s00125-016-4089-8.\u003c/li\u003e\n\u003cli\u003eLiu PJ, Ma F, Lou HP, Zhu YN. Normal-weight central obesity is associated with metabolic disorders in Chinese postmenopausal women. Asia Pacific Journal of Clinical Nutrition. 2017;26(4):692-697. DOI: 10.6133/apjcn.052016.08. PMID: 28582821.\u003c/li\u003e\n\u003cli\u003eSeo MH, Lee WY, Kim SS, Kang JH, Kang JH, Kim KK, et al., Committee of Clinical Practice Guidelines, Korean Society for the Study of Obesity (KSSO). 2018 Korean Society for the Study of Obesity Guideline for the Management of Obesity in Korea. J Obes Metab Syndr. 2019 Mar;28(1):40-45. doi: 10.7570/jomes.2019.28.1.40. Epub 2019 Mar 30. Erratum in: J Obes Metab Syndr. 2019 Jun;28(2):143. PMID: 31089578; PMCID: PMC6484940.\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller MJ, Geisler C, Pourhassan M, Gl\u0026uuml;er CC, Bosy-Westphal A. Assessment and definition of lean body mass deficiency in elderly individuals. Eur J Clin Nutr. 2014 Nov;68(11):1220-7. doi: 10.1038/ejcn.2014.169. Epub 2014 Aug 20. PMID: 25139559.\u003c/li\u003e\n\u003cli\u003eLee, S. H., Kim, Y. H., Han, K., Yang, H. K., Kim, M. K., Yoon, K. H., Nam, J. H., Park, Y. M., \u0026amp; Park, Y. W. Identifying metabolically obese but normal-weight (MONW) individuals in a nondiabetic Korean population: the Chungju Metabolic Disease Cohort (CMC) study. Clinical endocrinology. 2014 ;81(3), 475\u0026ndash;482. https://doi.org/10.1111/cen.12507\u003c/li\u003e\n\u003cli\u003eZhang, M., Zheng, L., Zhou, P., Wang, C., \u0026amp; Li, M. Association of obesity and cardiovascular risk factors with carotid intima-media thickness and leptin in metabolically healthy obese subjects: a cross-sectional study. Lipids in health and disease. 2017 ;16(1), 131. https://doi.org/10.1186/s12944-017-0527-5\u003c/li\u003e\n\u003cli\u003eSahakyan, K. R., Somers, V. K., Rodriguez-Escudero, J. P., Hodge, D. O., Carter, R. E., Sochor, O., Coutinho, T., Jensen, M. D., Roger, V. L., Singh, P., \u0026amp; Lopez-Jimenez, F. Normal-Weight Central Obesity: Implications for Total and Cardiovascular Mortality. Annals of Internal Medicine. 2015; 163(11), 827\u0026ndash;835. https://doi.org/10.7326/M14-2525\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Normal weight obesity, Normal weight central obesity, Hypertension, Prehypertension, Body composition, Asian population","lastPublishedDoi":"10.21203/rs.3.rs-3875558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3875558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNormal weight obesity (NWO) and normal weight central obesity (NWCO) have emerged as risk factors for cardiovascular disease. However, the association between the prevalence of these two conditions and hypertension risk has not been well studied, especially in Asian populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this community-based cross-sectional study, we assessed body composition and blood pressure in 1000 adults aged\u0026thinsp;\u0026gt;\u0026thinsp;20\u0026ndash;65 years. NWO was defined as a normal body mass index (BMI 18.5\u0026ndash;22.9 kg/m2) plus a high body fat percentage (\u0026gt;\u0026thinsp;20.6% in men, \u0026gt;\u0026thinsp;33.4% in women). NWCO was defined on the basis of a normal BMI plus high waist circumference (\u0026ge;\u0026thinsp;90 cm in men, \u0026ge;\u0026thinsp;80 cm in women). Hypertension and prehypertension were defined as per the JNC 7 guidelines.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of NWO was 38%, affecting 228 of 600 normal-weight individuals. The prevalence of normal weight central obesity was 32% (192 of 600 participants with a normal BMI NWO and NWCO had drastically elevated hypertension odds of 3.2 (95% CI 2.1\u0026ndash;4.7) and 3.5 (95% CI 2.3\u0026ndash;5.2) times versus metabolically healthy normal weight people, respectively, independent of confounders). The odds for prehypertension were also greater for NWO (OR 1.7, 95% CI 1.1\u0026ndash;2.6) and NWCO (OR 2.0, 95% CI 1.3\u0026ndash;3.2).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA high proportion of normal-weight adults in this population have elevated cardiovascular risk based on excess body fat and abdominal adiposity. Assessing body composition would better identify at-risk individuals missed by BMI categorization alone. Reducing the double burden of NWO and NWCO may help lower the prevalence of hypertension in the population.\u003c/p\u003e","manuscriptTitle":"The Double Burden of Normal Weight Obesity (NWO) and Normal Weight Central Obesity (NWCO) on Hypertension Risk: A Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 09:58:49","doi":"10.21203/rs.3.rs-3875558/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41d3f9cd-3f2b-41d2-8e2c-71f3201bb6b2","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-22T23:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 09:58:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3875558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3875558","identity":"rs-3875558","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}