Nutrient-Wide Associations with the Cardiometabolic Index in Older Adults: Insights from NHANES 2007–2016

Nutrient-Wide Associations with the Cardiometabolic Index in Older Adults: Insights from NHANES 2007–2016 | 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 Nutrient-Wide Associations with the Cardiometabolic Index in Older Adults: Insights from NHANES 2007–2016 Wen Li, Siqi Liu, Xiaoxia Meng, Huaman Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5690596/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: The cardiometabolic index (CMI) is an innovative composite marker integrating adiposity and lipid metabolism, serving as a surrogate endpoint for chronic disease and mortality risks. This study employed a nutrient-wide association study (NWAS) approach to explore the associations between dietary nutrients and CMI in older U.S. adults. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007–2016 were analyzed, including 2,673 participants aged ≥ 65 years. Multivariable linear regression adjusted for energy intake and traditional confounders was used to evaluate 56 dietary nutrients. Restricted cubic spline analyses assessed nonlinear dose-response relationships. Results: Carbohydrate and total sugars were positively associated with CMI (Carbohydrate: Coefficient = 0.001, Adjusted P = 0.016; Total sugars: Coefficient = 0.001, Adjusted P = 0.021). In contrast, vitamin E and MFA 20:1 (eicosenoic acid) exhibited inverse associations with CMI (Vitamin E: Coefficient = -0.007, Adjusted P = 0.021; MFA 20:1: Coefficient = -0.129, Adjusted P = 0.035). Restricted cubic spline analyses revealed significant nonlinear relationships for vitamin E ( P -nonlinearity = 0.043) and MFA 20:1 ( P -nonlinearity = 0.028), identifying intake thresholds for protective and detrimental effects. Conclusion: These findings underscore the complex interplay between diet and cardiometabolic risk, highlighting the potential of targeted nutritional interventions to reduce disease burden and improve health outcomes in aging populations. Further research is warranted to validate these findings and inform dietary guidelines. Nutrition & Dietetics Geriatrics & Gerontology Biostatistics Food Chemistry Cardiometabolic index Nutrient-wide association National Health and Nutrition Examination Survey Figures Figure 1 Figure 2 Introduction The cardiometabolic index (CMI) is an emerging composite indicator that integrates waist-to-height ratio (WHtR) and triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio to assess cardiometabolic risk. Unlike traditional risk factors that evaluate individual components of metabolic health, CMI captures both adiposity and lipid metabolism abnormalities, which are central to cardiometabolic disease progression [ 1 – 6 ]. Additionally, CMI has been proposed as a surrogate endpoint for aging-related frailty and mortality in older populations, making it a valuable marker for evaluating interventions aimed at reducing these risks. As the prevalence of cardiometabolic diseases continues to rise among older adults in the United States, the need for accurate, population-specific tools like CMI has grown increasingly evident [ 7 ]. Diet and nutrition are fundamental to cardiometabolic health. Numerous studies have established that dietary patterns and specific nutrients influence metabolic pathways, including lipid metabolism, insulin sensitivity, and systemic inflammation [ 8 , 9 ]. While prior research has identified associations between isolated dietary components, such as omega-3 fatty acids or dietary fiber, and metabolic outcomes, the role of individual nutrients in shaping the CMI remains underexplored. A nutrient-wide association study (NWAS) approach offers a systematic and hypothesis-free framework to investigate the relationship between a wide range of nutrients and CMI, potentially uncovering novel dietary determinants of cardiometabolic health [ 10 ]. Older adults represent a particularly critical population for this line of inquiry. Aging is associated with profound physiological changes that impact nutrient metabolism, adiposity distribution, and cardiometabolic regulation [ 11 , 12 ]. Furthermore, the dietary behaviors of older adults often differ from those of younger populations, with factors such as nutrient deficiencies, dietary restrictions, and polypharmacy playing significant roles [ 13 , 14 ]. Given these unique characteristics, elucidating the nutrient-CMI relationship in older adults may yield insights that inform targeted dietary interventions to mitigate cardiometabolic risk. Identifying protective or detrimental nutrients associated with CMI could provide actionable targets for dietary modification, ultimately aiding in the prevention of frailty and mortality in this population. In the United States, large-scale epidemiological cohorts, such as the National Health and Nutrition Examination Survey (NHANES), provide a unique opportunity to investigate these associations. NHANES offers comprehensive data on nutrient intake from dietary recalls, biomarker assessments, and detailed health measures, including lipid profiles and anthropometrics [ 15 ]. This dataset is particularly well-suited to NWAS, allowing for the evaluation of multiple nutrients in relation to CMI while controlling for confounding variables such as age, sex, socioeconomic status, and comorbid conditions. This study aims to fill a critical gap in the literature by systematically evaluating the associations between nutrient intake and CMI among older U.S. adults. Specifically, we hypothesize that certain nutrients, such as dietary fiber, polyunsaturated fatty acids, and vitamins with antioxidant properties, are inversely associated with CMI, while other nutrients, such as added sugars and trans fats, are positively associated. Using a NWAS framework, we aim to identify both well-established and novel nutrient predictors of CMI, providing evidence to guide dietary recommendations for older adults. By leveraging the robust and nationally representative NHANES dataset, this study seeks to advance our understanding of the dietary determinants of cardiometabolic health in aging populations. Furthermore, the findings have the potential to inform precision nutrition strategies aimed at reducing the burden of cardiometabolic diseases and improving quality of life among older adults. Methods Study design and population The present study is a cross-sectional analysis utilizing data from the NHANES. NHANES is a comprehensive survey designed to collect data on the health and nutritional status of the United States population. By employing a stratified multistage random sampling methodology, NHANES ensures a nationally representative sample of the population [ 16 ]. Ethical approval for NHANES was granted by the ethical review board of the National Center for Health Statistics, and all participants provided informed consent via signed agreements [ 17 ]. The datasets, accompanied by thorough documentation and protocols, are publicly accessible on the NHANES website and align with the laboratory technologists and anthropometric procedures used in previous studies [ 18 , 19 ]. For this prospective cohort study, data spanning five two-year cycles from 2007 to 2016 were screened and analyzed. To ensure the integrity and reliability of the results, specific exclusion criteria were applied, including: (1) individuals under 65 years of age (n = 43,575); (2) individuals without a calculated CMI value (n = 4,204); (3) individuals without total nutrient intake measurements (n = 136). After applying these criteria, a total of 2,673 participants were included in the final analysis from the years 2007 to 2016 (Fig. 1 ). Assessment of dietary nutrient intakes The primary variable of interest in this study is nutrient intake, measured through a 24-hour dietary recall methodology. This process captured detailed information on both nutrient and energy consumption for further analysis ( Table S1 ). Participants in NHANES provided dietary data through two separate 24-hour recalls. The initial session was conducted in person at a mobile examination center, while the second was completed via telephone within a timeframe of 3 to 10 days. Nutrient intake data were processed using the Food and Nutrient Database for Dietary Studies (FNDDS) developed by the United States Department of Agriculture (USDA). Assessment of CMI The CMI was calculated by dividing the triglyceride level, measured in millimoles per liter (mmol/L), by the HDL-C level, also measured in mmol/L. This ratio was then multiplied by the waist circumference, measured in centimeters (cm), divided by height, also measured in centimeters (cm) [ 20 ]. This formula integrates lipid metabolism and central adiposity into a single composite index. In this study, CMI was utilized as a continuous exposure variable. For further analyses, all participants were stratified into tertiles based on their CMI values to explore its relationship with other variables. Covariates The following covariates were collected for this study: age, sex, race, education level, family poverty-to-income ratio (PIR), body mass index (BMI), smoking status, drinking status, hypertension, diabetes, and cardiovascular disease (CVD). These covariates were chosen based on their potential to confound the association between dietary nutrient intake and CMI. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m 2 ) and categorized as underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), or obesity (BMI ≥ 30). Smoking status was categorized into two groups (yes/no) based on responses to the questionnaire items "Smoked at least 100 cigarettes in life?" (SMQ020) and "Do you now smoke cigarettes?" (SMQ040). Drinking status was determined using the questionnaire item "Had at least 12 alcohol drinks/1 year?" (ALQ101) and categorized into two groups depending on whether participants reported consuming at least 12 alcoholic drinks per year. One unit of alcohol was defined as 12 ounces of beer, 5 ounces of wine, or 1.5 ounces of liquor. Self-reported questionnaires were also used to diagnose the following conditions: hypertension (BPD035), diabetes mellitus (DM, DIQ010), heart failure (MCQ160b), coronary heart disease (CHD, MCQ160c), angina (MCQ160d), heart attack (MCQ160e), and stroke (MCQ160f). In this study, CVD was considered as having heart failure, coronary heart disease, angina, heart attack, or stroke. Statistical analysis All statistical analyses were conducted using SAS software, version 9.4 (Cary, North Carolina, USA) and R software (version 4.3.1; https://www.R-project.org ). Categorical variables were expressed as frequency and percentage, while continuous variables were represented as mean and standard deviation (SD). A two-sided P -value of less than 0.05 was considered to indicate statistical significance. First, we used CMI as a continuous dependent variable and included 11 covariates (age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD) in a multivariable linear regression model to identify significant confounders associated with CMI [ 21 ]. Second, adjusting for these covariates and total energy intake, we sequentially included 56 individual nutrients in the regression models to screen for nutrients significantly associated with CMI. P-values were corrected using the false discovery rate (FDR) method with the Benjamini-Hochberg procedure [ 22 ]. Finally, we employed restricted cubic spline (RCS) analyses to investigate dose-response relationships between the identified nutrients and CMI across different thresholds ( P 85 , P 90 , P 95 ). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the strength and direction of these associations [ 23 ]. Results Study population characteristics Table 1 shows that the study population included 2,673 participants with a mean age of 73.1 years (SD: 5.3) and an average PIR of 2.5 (SD: 1.5). Women made up 51.1% of the cohort, and 56.5% had a high school education or less. The racial distribution was predominantly Non-Hispanic White (57.5%), followed by Non-Hispanic Black (15.4%) and Mexican American (10.7%). Alcohol consumption was reported by 36.7%, and 9.4% were current smokers. Regarding BMI, 26.1% were underweight, 37.4% were overweight, and 35.2% were obese. Hypertension affected 63.7% of participants, while diabetes and cardiovascular disease were present in 18.4% and 16.7%, respectively. Table 1 – The characteristics of study population in the NHANES (2007–2016) Characteristics Study population (n = 2,673) a Age, mean (SD) 73.1 (5.3) PIR, mean (SD) 2.5 (1.5) Sex, n (%) Women 1,366 (51.1) Men 1,307 (48.9) Education, n (%) High school or less 1,510 (56.5) College or above 1,163 (43.5) Race, n (%) Mexican American 287 (10.7) Other Hispanic 271 (10.1) Non-Hispanic White 1,538 (57.5) Non-Hispanic Black 411 (15.4) Other Race 166 (6.2) Drinking, n (%) No 1,691 (63.3) Yes 982 (36.7) Smoking Status, n (%) No 2,422 (90.6) Yes 251 (9.4) Body Mass Index, n (%) Underweight 697 (26.1) Normal weight 35 (1.3) Overweight 999 (37.4) Obesity 942 (35.2) Hypertension, n (%) No 971 (36.3) Yes 1,702 (63.7) Diabetes No 2,181 (81.6) Yes 492 (18.4) Cardiovascular disease, n (%) b No 2,227 (83.3) Yes 446 (16.7) a) Data shown are either frequency (%) or mean (SD). b) Including heart failure, coronary heart disease, angina, heart attack, or stroke. PIR, family poverty-to-income ratio. Associations between traditional risk factors and CMI In the multivariable regression analysis, higher CMI was significantly associated with male sex, lower PIR, current smoking, alcohol consumption, hypertension, diabetes, and cardiovascular disease (Table 2 ). Among BMI categories, overweight and obesity demonstrated the strongest positive associations with CMI, while underweight was not significantly related. Non-Hispanic Black participants exhibited significantly lower CMI compared to Mexican Americans, with no significant differences observed for other racial/ethnic groups. Table 2 – The associations between traditional risk factors and CMI in the NHANES (2007–2016) Characteristics Estimate Std. Error t -value P -value a Intercept 0.769 0.194 3.960 < 0.001 Age 0.005 0.003 1.924 0.059 PIR -0.035 0.009 -3.678 < 0.001 Sex Women Ref. Ref. Ref. Ref. Men 0.084 0.035 2.392 0.020 Education High school or less Ref. Ref. Ref. Ref. College or above -0.057 0.033 -1.689 0.096 Race Mexican American Ref. Ref. Ref. Ref. Other Hispanic -0.005 0.048 -0.111 0.912 Non-Hispanic White 0.041 0.043 0.947 0.347 Non-Hispanic Black -0.326 0.043 -7.645 < 0.001 Other Race 0.054 0.056 0.962 0.340 Drinking No Ref. Ref. Ref. Ref. Yes 0.059 0.029 2.038 0.046 Smoking Status No Ref. Ref. Ref. Ref. Yes 0.151 0.061 2.479 0.016 Body Mass Index Underweight 0.092 0.082 1.123 0.266 Normal weight Ref. Ref. Ref. Ref. Overweight 0.239 0.029 8.295 < 0.001 Obesity 0.461 0.039 11.871 < 0.001 Hypertension No Ref. Ref. Ref. Ref. Yes 0.101 0.034 3.009 0.004 Diabetes No Ref. Ref. Ref. Ref. Yes 0.187 0.069 2.700 0.009 Cardiovascular disease b No Ref. Ref. Ref. Ref. Yes 0.130 0.050 2.620 0.011 a) Multivariable linear regression model adjusted for age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD. b) Including heart failure, coronary heart disease, angina, heart attack, or stroke. CMI, cardiometabolic index; PIR, family poverty-to-income ratio. Identification of nutrients influencing CMI Table 3 shows that among the 56 nutrients analyzed, only four were significantly associated with the CMI after FDR correction. Carbohydrate intake and total sugars were positively associated with CMI, indicating that higher intake of these nutrients corresponds to elevated CMI levels (Carbohydrate: Coefficient = 0.001, Adjusted P = 0.016; Total sugars: Coefficient = 0.001, Adjusted P = 0.021). In contrast, vitamin E and MFA 20:1 (eicosenoic acid) were negatively associated with CMI, suggesting that higher intake of these nutrients is linked to lower CMI levels (Vitamin E: Coefficient = -0.007, Adjusted P = 0.021; MFA 20:1: Coefficient = -0.129, Adjusted P = 0.035). Table 3 – The associations between dietary nutrient intakes and CMI in the NHANES (2007–2016) Characteristics Coefficient Std. Error P -value a Adjusted P -value b Carbohydrate 0.001 0.000 0.000 0.016 Vitamin E -0.007 0.002 0.001 0.021 Total sugars 0.001 0.000 0.001 0.021 MFA 20:1 (Eicosenoic) -0.129 0.041 0.002 0.035 PFA 20:5 (Eicosapentaenoic) -0.251 0.087 0.006 0.062 Niacin -0.003 0.001 0.017 0.159 MFA 22:1 (Docosenoic) -0.092 0.039 0.021 0.167 Total monounsaturated fatty acids -0.004 0.002 0.028 0.182 Total fat -0.002 0.001 0.033 0.182 Selenium -0.001 0.000 0.037 0.182 PFA 22:6 (Docosahexaenoic) -0.111 0.053 0.040 0.182 PFA 18:4 (Octadecatetraenoic) -0.623 0.297 0.040 0.182 SFA 16:0 (Hexadecanoic) -0.008 0.004 0.042 0.182 SFA 12:0 (Dodecanoic) -0.020 0.010 0.046 0.183 MFA 18:1 (Octadecenoic) -0.004 0.002 0.055 0.193 MFA 16:1 (Hexadecenoic) -0.040 0.020 0.055 0.193 PFA 22:5 (Docosapentaenoic) -0.716 0.382 0.066 0.216 Protein -0.001 0.001 0.075 0.234 Total saturated fatty acids -0.003 0.002 0.140 0.411 Lycopene 1.96E-06 0.000 0.154 0.431 PFA 20:4 (Eicosatetraenoic) -0.211 0.155 0.177 0.472 SFA 8:0 (Octanoic) -0.076 0.058 0.196 0.483 Vitamin K -8.59E-05 0.000 0.198 0.483 Total choline 0.000 0.000 0.232 0.485 Vitamin C 0.000 0.000 0.241 0.485 Magnesium 0.000 0.000 0.242 0.485 PFA 18:2 (Octadecadienoic) -0.003 0.003 0.251 0.485 Total polyunsaturated fatty acids -0.002 0.002 0.252 0.485 SFA 10:0 (Decanoic) -0.049 0.043 0.252 0.485 Iron 0.002 0.002 0.266 0.485 Phosphorus -5.49E-05 0.000 0.270 0.485 Thiamin (Vitamin B 1 ) -0.035 0.032 0.277 0.485 Vitamin A, RAE 2.21E-05 0.000 0.313 0.518 Vitamin D (D 2 + D 3 ) -0.002 0.002 0.315 0.518 Lutein + zeaxanthin -3.17E-06 0.000 0.346 0.553 Folic acid 0.000 0.000 0.408 0.633 Dietary fiber 0.002 0.002 0.418 0.633 SFA 14:0 (Tetradecanoic) -0.010 0.013 0.435 0.641 Beta-carotene 2.58E-06 0.000 0.468 0.672 PFA 18:3 (Octadecatrienoic) -0.007 0.011 0.502 0.686 Total folate 7.62E-05 0.000 0.503 0.686 Copper 0.012 0.020 0.554 0.735 Cholesterol -5.14E-05 0.000 0.574 0.735 Retinol 1.63E-05 0.000 0.577 0.735 Alpha-carotene 6.46E-06 0.000 0.613 0.763 Riboflavin (Vitamin B 2 ) -0.006 0.016 0.701 0.837 Calcium -1.28E-05 0.000 0.708 0.837 Zinc -0.001 0.003 0.717 0.837 SFA 6:0 (Hexanoic) -0.019 0.059 0.749 0.856 SFA 18:0 (Octadecanoic) -0.002 0.007 0.785 0.879 Vitamin B 12 0.000 0.003 0.873 0.958 SFA 4:0 (Butanoic) -0.005 0.035 0.890 0.958 Vitamin B 6 -0.001 0.013 0.921 0.958 Sodium -1.43E-06 0.000 0.924 0.958 Beta-cryptoxanthin -3.01E-06 0.000 0.952 0.970 Potassium -5.71E-07 0.000 0.974 0.974 a) Multivariable linear regression model adjusted for energy, age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD. b) False discovery rate (FDR) correction was performed using the Benjamini-Hochberg (BH) method. RAE, retinol activity equivalents; SFA, saturated fatty acids; MFA, monounsaturated fatty acids; PFA, polyunsaturated fatty acids. Dose-response relationships between identified nutrients and CMI The restricted cubic spline (RCS) model was applied to explore potential nonlinear relationships between nutrient intake and CMI (Fig. 2 ), adjusting for energy intake, age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD. The RCS analysis revealed that while the nonlinear relationships between carbohydrate (Fig. 2 A) and total sugars (Fig. 2 D) intake with CMI > 85 were not significant ( P > 0.05), significant nonlinear associations were observed for vitamin E (Fig. 2 G; P -nonlinearity = 0.043) and MFA 20:1 (eicosenoic acid) (Fig. 2 J; P -nonlinearity = 0.028). Furthermore, results stratified by CMI thresholds (CMI > 85, CMI > 90, and CMI > 95) demonstrated an upward trend in the cutoff values for all four nutrients as the CMI threshold increased. However, the results for CMI > 85 were more robust compared to higher thresholds. Specifically, carbohydrate intake > 0.25 kg/day and total sugars intake > 0.05 kg/day were associated with a significantly increased risk of elevated CMI. In contrast, vitamin E intake > 5.4 mg/day and MFA 20:1 intake > 0.16 g/day were associated with a significantly reduced risk of elevated CMI. Discussion This study provides valuable insights into the relationships between nutrient intake and the CMI in a representative U.S. population of older adults. CMI, as a surrogate endpoint for chronic diseases and mortality, offers a comprehensive marker for assessing cardiometabolic health. Four key nutrients were identified as significantly associated with CMI: carbohydrate, total sugars, vitamin E, and MFA 20:1 (eicosenoic acid). While carbohydrate and total sugars were positively associated with CMI, vitamin E and MFA 20:1 demonstrated protective associations. These findings underscore the multifaceted influences of dietary components on cardiometabolic health and highlight the potential for targeted dietary interventions to mitigate disease burden, reduce healthcare costs, and improve quality of life in this population of older adults. Four key nutrients-increasingly prevalent [ 24 , 25 ]. The predominance of these comorbidities underscores the relevance of CMI as a comprehensive marker for assessing cardiometabolic risk in this population. Our analysis revealed significant associations between traditional risk factors and CMI. Higher CMI was observed in men, current smokers, and individuals with lower poverty-to-income ratios, hypertension, diabetes, or cardiovascular disease. Overweight and obesity demonstrated the strongest positive associations with CMI, consistent with the central role of adiposity in cardiometabolic dysfunction [ 24 , 26 ]. The inverse association between Non-Hispanic Black ethnicity and CMI, compared to Mexican Americans, warrants further investigation, as it may reflect underlying differences in metabolic health or environmental exposures. The analysis identified four nutrients significantly associated with CMI. To enhance the robustness of these findings, multivariable regression models were used, adjusting not only for traditional covariates but also for energy intake. This comprehensive adjustment strengthens the reliability of the identified associations. Carbohydrate and total sugars were positively associated with higher CMI, highlighting their potential contributions to adverse cardiometabolic outcomes. In contrast, vitamin E and MFA 20:1 intake were inversely associated with CMI, suggesting protective effects. These findings align with existing literature on the complex roles of macronutrients and micronutrients in modulating cardiometabolic health [ 8 ]. RCS analysis revealed nuanced dose-response relationships for these key nutrients. While carbohydrate and total sugars intake exhibited linear trends, significant nonlinear associations were observed for vitamin E and MFA 20:1. These findings emphasize the importance of identifying intake thresholds to guide dietary recommendations. For example, carbohydrate intake > 0.25 kg/day and total sugars intake > 0.05 kg/day were associated with increased CMI, while vitamin E intake > 5.4 mg/day and MFA 20:1 intake > 0.16 g/day were associated with reduced CMI. These thresholds provide actionable targets for dietary modifications to optimize cardiometabolic health. The positive association between carbohydrate intake and CMI observed in this study aligns with previous findings that excessive carbohydrate consumption, particularly refined carbohydrates, contributes to dyslipidemia and insulin resistance [ 27 , 28 ]. High glycemic load diets have been shown to exacerbate postprandial hyperglycemia and hyperinsulinemia, promoting metabolic dysfunction through pathways such as increased de novo lipogenesis and hepatic triglyceride accumulation [ 29 ]. However, complex carbohydrates, such as those from whole grains, have demonstrated protective effects in some studies, suggesting that the quality of carbohydrate intake may modulate its impact on CMI [ 30 ]. The lack of differentiation between carbohydrate sources in our study may partially explain the observed associations, warranting further investigation. High glycemic load diets have been shown to exacerbate postprandial hyperglycemia and hyperinsulinemia, promoting metabolic dysfunction. However, complex carbohydrates, such as those from whole grains, have demonstrated protective effects in some studies, suggesting that the quality of carbohydrate intake may modulate its impact on CMI [ 30 ]. The lack of differentiation between carbohydrate sources in our study may partially explain the observed associations, warranting further investigation. The observed positive relationship between total sugars intake and CMI corroborates evidence linking high sugar consumption to adverse cardiometabolic outcomes [ 31 ]. Sugars, particularly added sugars, contribute to increased triglyceride levels and reduced HDL cholesterol, key components of CMI. This effect is partly mediated by increased hepatic lipogenesis and fat deposition, leading to ectopic fat accumulation [ 32 ]. Furthermore, excessive sugar intake is associated with increased visceral adiposity, which is strongly linked to cardiometabolic risk through pro-inflammatory cytokine release and insulin resistance [ 33 ]. The findings highlight the need to reduce added sugar consumption as part of dietary interventions aimed at improving CMI and overall metabolic health. Sugars, particularly added sugars, contribute to increased triglyceride levels and reduced HDL cholesterol, key components of CMI. Furthermore, excessive sugar intake is associated with increased visceral adiposity, which is strongly linked to cardiometabolic risk. The findings highlight the need to reduce added sugar consumption as part of dietary interventions aimed at improving CMI and overall metabolic health. Vitamin E’s inverse association with CMI suggests its potential role as a protective nutrient against cardiometabolic dysfunction. As a potent antioxidant, vitamin E mitigates oxidative stress and inflammation, key drivers of cardiometabolic diseases [ 34 ]. Mechanistically, vitamin E has been shown to inhibit the oxidation of low-density lipoprotein (LDL), thereby reducing atherosclerosis progression and improving endothelial function [ 35 ]. Furthermore, vitamin E modulates gene expression related to inflammatory pathways, such as NF-κB signaling, contributing to its anti-inflammatory effects [ 36 ]. However, the effectiveness of vitamin E may depend on baseline oxidative stress levels and individual variations in nutrient absorption and metabolism, which should be considered in future research. As a potent antioxidant, vitamin E mitigates oxidative stress and inflammation, key drivers of cardiometabolic diseases [ 34 ]. Studies have demonstrated that vitamin E supplementation improves lipid profiles and reduces markers of oxidative damage. However, the effectiveness of vitamin E may depend on baseline oxidative stress levels and individual variations in nutrient absorption and metabolism, which should be considered in future research. The protective association between MFA 20:1 (eicosenoic acid) and CMI aligns with emerging evidence on the beneficial effects of monounsaturated fatty acids (MUFAs) on cardiometabolic health [ 37 ]. MUFAs improve lipid metabolism, enhance insulin sensitivity, and reduce inflammation, all of which contribute to lower CMI. Eicosenoic acid specifically has been implicated in modulating hepatic lipid metabolism, including reducing triglyceride synthesis and promoting fatty acid oxidation [ 38 ]. Additionally, its anti-inflammatory effects may stem from downregulation of pro-inflammatory cytokines, such as TNF-α and IL-6, further supporting its cardiometabolic benefits [ 39 ]. Further studies are needed to elucidate the specific mechanisms underlying its effects on CMI. MUFAs improve lipid metabolism, enhance insulin sensitivity, and reduce inflammation, all of which contribute to lower CMI. Eicosenoic acid, found in certain plant oils and seafood, may exert additional benefits through its unique metabolic properties. Further studies are needed to elucidate the specific mechanisms underlying its effects on CMI. One notable strength of this study is that it is the first NWAS to explore the relationship between dietary nutrients and CMI. This innovative approach allowed for a comprehensive assessment of multiple nutrients, providing novel insights into their potential roles in cardiometabolic health. This study benefits from the use of a nationally representative sample, robust statistical methods, and comprehensive adjustment for confounders. The integration of RCS modeling allowed for the exploration of nonlinear relationships, providing nuanced insights into nutrient-CMI associations. However, the cross-sectional design limits causal inference, and the reliance on self-reported dietary data may introduce recall bias. Additionally, the lack of differentiation between nutrient sources (e.g., refined vs. complex carbohydrates) restricts the granularity of our findings. Future longitudinal studies and biomarker-based assessments are warranted to validate and expand upon these results. Conclusion This study identifies key dietary nutrients significantly associated with CMI in older U.S. adults, highlighting both protective and risk-related roles. As a surrogate endpoint for chronic diseases and mortality outcomes, CMI provides actionable insights for assessing cardiometabolic risk. Carbohydrate and total sugars intake were positively associated with higher CMI, while vitamin E and MFA 20:1 demonstrated protective associations. These findings underscore the importance of dietary quality in modulating cardiometabolic risk and provide actionable targets for nutritional interventions. By reducing disease burden and healthcare costs through improved dietary recommendations, this research offers a pathway to enhance health care and quality of life for aging populations. Further research is needed to elucidate the mechanisms underlying these associations and to develop tailored dietary guidelines, highlighting both protective and risk-related roles. Carbohydrate and total sugars intake were positively associated with higher CMI, while vitamin E and MFA 20:1 demonstrated protective associations. These findings underscore the importance of dietary quality in modulating cardiometabolic risk and provide actionable targets for nutritional interventions. Further research is needed to elucidate the mechanisms underlying these associations and to develop tailored dietary guidelines to improve cardiometabolic health in aging populations. Declarations Funding information The research received financial support from the Panzhihua Central Hospital. The findings and conclusions expressed in this article are those of the authors and do not necessarily represent the official position of the CDC or the U.S. Department of Health and Human Services. No private sponsors were involved in the decision to design the study, collect data, analyze or interpret data, write reports, or submit manuscripts. Author contributions The authors’ responsibilities were as follows—Wen Li contributed to conceptualization, data curation, formal analysis, writing – original draft, and project administration; Xiaoxia Meng was responsible for conceptualization, visualization and methodology. Huaman Liu was responsible for funding acquisition and investigation. Siqi Liu was responsible for supervision, validation and writing – review & editing. All authors declare that they have read and approved the final version of the manuscript. Acknowledgments We thank the leaders of the Panzhihua Central Hospital for their full support during the implementation of the project. Data availability The data, codebook, and analytic code will not be made available as the data used in this study are from the publicly accessible NHANES database, available to researchers worldwide. The database can be accessed at https://www.cdc.gov/nchs/nhanes/index.htm . Ethics approval and consent to participate Not applicable. Conflict of interest The authors declare no competing interest. Consent for publication Not applicable. References Yan L, Hu X, Wu S, Cui C, Zhao S (2024) Association between the cardiometabolic index and NAFLD and fibrosis. Sci Rep 14:13194 Song J, Li Y, Zhu J, Liang J, Xue S, Zhu Z (2024) Non-linear associations of cardiometabolic index with insulin resistance, impaired fasting glucose, and type 2 diabetes among US adults: a cross-sectional study. Front Endocrinol (Lausanne) 15:1341828 Guo Q, Wang Y, Liu Y, Wang Y, Deng L, Liao L et al (2024) Association between the cardiometabolic index and chronic kidney disease: a cross-sectional study. Int Urol Nephrol 56:1733–1741 Valenzuela PL, Carrera-Bastos P, Castillo-García A, Lieberman DE, Santos-Lozano A, Lucia A (2023) Obesity and the risk of cardiometabolic diseases. Nat Rev Cardiol 20:475–494 Sun Q, Ren Q, Du L, Chen S, Wu S, Zhang B et al (2023) Cardiometabolic Index (CMI), Lipid Accumulation Products (LAP), Waist Triglyceride Index (WTI) and the risk of acute pancreatitis: a prospective study in adults of North China. Lipids Health Dis 22:190 Zou J, Xiong H, Zhang H, Hu C, Lu S, Zou Y (2022) Association between the cardiometabolic index and non-alcoholic fatty liver disease: insights from a general population. BMC Gastroenterol 22:20 Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA et al (2018) /ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139:e1082-e143 Mozaffarian D (2016) Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review. Circulation 133:187–225 Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D (2017) Association Between Dietary Factors and Mortality From Heart Disease, Stroke, and Type 2 Diabetes in the United States. JAMA 317:912–924 Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S et al (2019) Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 393:447–492 Fulgoni VL 3rd, Keast DR, Drewnowski A (2009) Development and validation of the nutrient-rich foods index: a tool to measure nutritional quality of foods. J Nutr 139:1549–1554 Mathers JC (2015) Impact of nutrition on the ageing process. Br J Nutr. ;113 Suppl:S18-22. Ahmed T, Haboubi N (2010) Assessment and management of nutrition in older people and its importance to health. Clin Interv Aging 5:207–216 Volkert D, Beck AM, Cederholm T, Cruz-Jentoft A, Goisser S, Hooper L et al (2019) ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin Nutr 38:10–47 Rehm CD, Peñalvo JL, Afshin A, Mozaffarian D (2016) Dietary Intake Among US Adults, 1999–2012. JAMA 315:2542–2553 Curtin LR, Mohadjer LK, Dohrmann SM, Kruszon-Moran D, Mirel LB, Carroll MD et al (2013) National Health and Nutrition Examination Survey: sample design, 2007–2010. Vital Health Stat 2:1–23 Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA et al (2021) Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 326:1614–1621 Yan Y, Zhou L, La R, Jiang M, Jiang D, Huang L et al (2023) The association between triglyceride glucose index and arthritis: a population-based study. Lipids Health Dis 22:132 Yan Y, La R, Jiang M, Xu W, Jiang D, Wang S et al (2024) The association between remnant cholesterol and rheumatoid arthritis: insights from a large population study. Lipids Health Dis 23:38 Wakabayashi I, Daimon T (2015) The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta 438:274–278 Núñez E, Steyerberg EW, Núñez J (2011) [Regression modeling strategies]. Rev Esp Cardiol 64:501–507 Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300 Desquilbet L, Mariotti F (2010) Dose-response analyses using restricted cubic spline functions in public health research. Stat Med 29:1037–1057 Rakhmat II, Putra ICS, Wibowo A, Henrina J, Nugraha GI, Ghozali M et al (2022) Cardiometabolic risk factors in adults with normal weight obesity: A systematic review and meta-analysis. Clin Obes 12:e12523 Aggarwal R, Ostrominski JW, Vaduganathan M (2024) Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011–2020. JAMA 331:1858–1860 Rajjo T, Almasri J, Al Nofal A, Farah W, Alsawas M, Ahmed AT et al (2017) The Association of Weight Loss and Cardiometabolic Outcomes in Obese Children: Systematic Review and Meta-regression. J Clin Endocrinol Metab 102:758–762 Sievenpiper JL (2020) Low-carbohydrate diets and cardiometabolic health: the importance of carbohydrate quality over quantity. Nutr Rev 78:69–77 Atzeni A, Nishi SK, Babio N, Belzer C, Konstanti P, Vioque J et al (2023) Carbohydrate quality, fecal microbiota and cardiometabolic health in older adults: a cohort study. Gut Microbes 15:2246185 Parks EJ, Krauss RM, Christiansen MP, Neese RA, Hellerstein MK (1999) Effects of a low-fat, high-carbohydrate diet on VLDL-triglyceride assembly, production, and clearance. J Clin Invest 104:1087–1096 Sawicki CM, Jacques PF, Lichtenstein AH, Rogers GT, Ma J, Saltzman E et al (2021) Whole- and Refined-Grain Consumption and Longitudinal Changes in Cardiometabolic Risk Factors in the Framingham Offspring Cohort. J Nutr 151:2790–2799 Te Morenga L, Mallard S, Mann J (2012) Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 346:e7492 Stanhope KL (2016) Sugar consumption, metabolic disease and obesity: The state of the controversy. Crit Rev Clin Lab Sci 53:52–67 Gallagher C, Moschonis G, Lambert KA, Karaglani E, Mavrogianni C, Gavrili S et al (2021) Sugar-sweetened beverage consumption is associated with visceral fat in children. Br J Nutr 125:819–827 Traber MG, Stevens JF (2011) Vitamins C and E: beneficial effects from a mechanistic perspective. Free Radic Biol Med 51:1000–1013 Ashor AW, Siervo M, Lara J, Oggioni C, Afshar S, Mathers JC (2015) Effect of vitamin C and vitamin E supplementation on endothelial function: a systematic review and meta-analysis of randomised controlled trials. Br J Nutr 113:1182–1194 Meydani SN, Barklund MP, Liu S, Meydani M, Miller RA, Cannon JG et al (1990) Vitamin E supplementation enhances cell-mediated immunity in healthy elderly subjects. Am J Clin Nutr 52:557–563 Schwingshackl L, Hoffmann G (2014) Monounsaturated fatty acids, olive oil and health status: a systematic review and meta-analysis of cohort studies. Lipids Health Dis 13:154 Hodson L, Fielding BA (2013) Stearoyl-CoA desaturase: rogue or innocent bystander? Prog Lipid Res 52:15–42 Yang L, Yang C, Chu C, Wan M, Xu D, Pan D et al (2022) Beneficial effects of monounsaturated fatty acid-rich blended oils with an appropriate polyunsaturated/saturated fatty acid ratio and a low n-6/n-3 fatty acid ratio on the health of rats. J Sci Food Agric 102:7172–7185 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterial.docx 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5690596","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":393214836,"identity":"089aadc1-50c4-4e5a-8503-2de205ce988d","order_by":0,"name":"Wen Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACA2bGBiB1ILGfmfnwA9K0zGxnSzMgTguEOpC44TyPggRxWtiZ2yQ+7jiQuPkwD1B/jU00MQ5rk5x55kDutsO8Bx4wHEvLbSBGy23eNpAWvgQDxobDRGr5C9SyuZnHQIJ4LYxtB+o3MJOgpf1nb9uB4hmHgYGcQIxf7PuPPzb42fYvsb//8OEHH2psCGtBBQmkKR8Fo2AUjIJRgAsAAOUoRFfdfj7hAAAAAElFTkSuQmCC","orcid":"","institution":"Panzhihua Municipal Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wen","middleName":"","lastName":"Li","suffix":""},{"id":393214837,"identity":"c8237dcb-c8bc-477f-8655-6822d82f4653","order_by":1,"name":"Siqi Liu","email":"","orcid":"","institution":"Panzhihua Municipal Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Liu","suffix":""},{"id":393214838,"identity":"5685b60e-2642-4ac9-9b7b-b02a359ae0df","order_by":2,"name":"Xiaoxia Meng","email":"","orcid":"","institution":"Panzhihua Municipal Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Meng","suffix":""},{"id":393214839,"identity":"bc6217fa-75ea-444f-8c35-bdf1c4f1dbe8","order_by":3,"name":"Huaman Liu","email":"","orcid":"","institution":"Panzhihua Municipal Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huaman","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-12-21 16:19:53","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5690596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5690596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72277802,"identity":"7f11f01f-eee0-4e54-b845-86d3bcea56a9","added_by":"auto","created_at":"2024-12-24 15:29:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1048486,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study participants selection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5690596/v1/1e74f9fc27aa53b0bb0ef076.png"},{"id":72278421,"identity":"84194ebe-5c0e-457b-8236-b5be2fe3f07e","added_by":"auto","created_at":"2024-12-24 15:37:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2541534,"visible":true,"origin":"","legend":"\u003cp\u003eRestrictive cubic spline analysis. All models were adjusted for energy, age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD. The dose-response relationships of (ABC) daily carbohydrate intake (kg/day), (DEF) total sugars (kg/day), (GHI) vitamin E (mg/day), and (JKL) monounsaturated fatty acid 20:1 (eicosenoic acid, g/day) with different CMI strata: CMI \u0026gt; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e85\u003c/sub\u003e, CMI \u0026gt; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e90\u003c/sub\u003e, and CMI \u0026gt; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e95\u003c/sub\u003e. OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5690596/v1/b2e2f794d18acdd26a152aad.png"},{"id":72279241,"identity":"9213fb1b-219f-4f65-a5b8-cc2f147fe272","added_by":"auto","created_at":"2024-12-24 15:45:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4804477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5690596/v1/8fda71fe-04b2-4c4b-b11f-8acc2d47b869.pdf"},{"id":72277801,"identity":"2785ca69-4cea-458c-8a12-74da885f0211","added_by":"auto","created_at":"2024-12-24 15:29:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20994,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5690596/v1/28bea1790fd2ccd82162f814.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eNutrient-Wide Associations with the Cardiometabolic Index in Older Adults: Insights from NHANES 2007–2016\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe cardiometabolic index (CMI) is an emerging composite indicator that integrates waist-to-height ratio (WHtR) and triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio to assess cardiometabolic risk. Unlike traditional risk factors that evaluate individual components of metabolic health, CMI captures both adiposity and lipid metabolism abnormalities, which are central to cardiometabolic disease progression [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, CMI has been proposed as a surrogate endpoint for aging-related frailty and mortality in older populations, making it a valuable marker for evaluating interventions aimed at reducing these risks. As the prevalence of cardiometabolic diseases continues to rise among older adults in the United States, the need for accurate, population-specific tools like CMI has grown increasingly evident [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiet and nutrition are fundamental to cardiometabolic health. Numerous studies have established that dietary patterns and specific nutrients influence metabolic pathways, including lipid metabolism, insulin sensitivity, and systemic inflammation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While prior research has identified associations between isolated dietary components, such as omega-3 fatty acids or dietary fiber, and metabolic outcomes, the role of individual nutrients in shaping the CMI remains underexplored. A nutrient-wide association study (NWAS) approach offers a systematic and hypothesis-free framework to investigate the relationship between a wide range of nutrients and CMI, potentially uncovering novel dietary determinants of cardiometabolic health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOlder adults represent a particularly critical population for this line of inquiry. Aging is associated with profound physiological changes that impact nutrient metabolism, adiposity distribution, and cardiometabolic regulation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, the dietary behaviors of older adults often differ from those of younger populations, with factors such as nutrient deficiencies, dietary restrictions, and polypharmacy playing significant roles [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Given these unique characteristics, elucidating the nutrient-CMI relationship in older adults may yield insights that inform targeted dietary interventions to mitigate cardiometabolic risk. Identifying protective or detrimental nutrients associated with CMI could provide actionable targets for dietary modification, ultimately aiding in the prevention of frailty and mortality in this population.\u003c/p\u003e \u003cp\u003eIn the United States, large-scale epidemiological cohorts, such as the National Health and Nutrition Examination Survey (NHANES), provide a unique opportunity to investigate these associations. NHANES offers comprehensive data on nutrient intake from dietary recalls, biomarker assessments, and detailed health measures, including lipid profiles and anthropometrics [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This dataset is particularly well-suited to NWAS, allowing for the evaluation of multiple nutrients in relation to CMI while controlling for confounding variables such as age, sex, socioeconomic status, and comorbid conditions.\u003c/p\u003e \u003cp\u003eThis study aims to fill a critical gap in the literature by systematically evaluating the associations between nutrient intake and CMI among older U.S. adults. Specifically, we hypothesize that certain nutrients, such as dietary fiber, polyunsaturated fatty acids, and vitamins with antioxidant properties, are inversely associated with CMI, while other nutrients, such as added sugars and trans fats, are positively associated. Using a NWAS framework, we aim to identify both well-established and novel nutrient predictors of CMI, providing evidence to guide dietary recommendations for older adults.\u003c/p\u003e \u003cp\u003eBy leveraging the robust and nationally representative NHANES dataset, this study seeks to advance our understanding of the dietary determinants of cardiometabolic health in aging populations. Furthermore, the findings have the potential to inform precision nutrition strategies aimed at reducing the burden of cardiometabolic diseases and improving quality of life among older adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe present study is a cross-sectional analysis utilizing data from the NHANES. NHANES is a comprehensive survey designed to collect data on the health and nutritional status of the United States population. By employing a stratified multistage random sampling methodology, NHANES ensures a nationally representative sample of the population [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ethical approval for NHANES was granted by the ethical review board of the National Center for Health Statistics, and all participants provided informed consent via signed agreements [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The datasets, accompanied by thorough documentation and protocols, are publicly accessible on the NHANES website and align with the laboratory technologists and anthropometric procedures used in previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor this prospective cohort study, data spanning five two-year cycles from 2007 to 2016 were screened and analyzed. To ensure the integrity and reliability of the results, specific exclusion criteria were applied, including: (1) individuals under 65 years of age (n\u0026thinsp;=\u0026thinsp;43,575); (2) individuals without a calculated CMI value (n\u0026thinsp;=\u0026thinsp;4,204); (3) individuals without total nutrient intake measurements (n\u0026thinsp;=\u0026thinsp;136). After applying these criteria, a total of 2,673 participants were included in the final analysis from the years 2007 to 2016 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of dietary nutrient intakes\u003c/h3\u003e\n\u003cp\u003eThe primary variable of interest in this study is nutrient intake, measured through a 24-hour dietary recall methodology. This process captured detailed information on both nutrient and energy consumption for further analysis (\u003cb\u003eTable S1\u003c/b\u003e). Participants in NHANES provided dietary data through two separate 24-hour recalls. The initial session was conducted in person at a mobile examination center, while the second was completed via telephone within a timeframe of 3 to 10 days. Nutrient intake data were processed using the Food and Nutrient Database for Dietary Studies (FNDDS) developed by the United States Department of Agriculture (USDA).\u003c/p\u003e\n\u003ch3\u003eAssessment of CMI\u003c/h3\u003e\n\u003cp\u003eThe CMI was calculated by dividing the triglyceride level, measured in millimoles per liter (mmol/L), by the HDL-C level, also measured in mmol/L. This ratio was then multiplied by the waist circumference, measured in centimeters (cm), divided by height, also measured in centimeters (cm) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This formula integrates lipid metabolism and central adiposity into a single composite index.\u003c/p\u003e \u003cp\u003eIn this study, CMI was utilized as a continuous exposure variable. For further analyses, all participants were stratified into tertiles based on their CMI values to explore its relationship with other variables.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe following covariates were collected for this study: age, sex, race, education level, family poverty-to-income ratio (PIR), body mass index (BMI), smoking status, drinking status, hypertension, diabetes, and cardiovascular disease (CVD). These covariates were chosen based on their potential to confound the association between dietary nutrient intake and CMI.\u003c/p\u003e \u003cp\u003eBMI was calculated as weight in kilograms divided by the square of height in meters (kg/m\u003csup\u003e2\u003c/sup\u003e) and categorized as underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal weight (18.5 \u0026le; BMI\u0026thinsp;\u0026lt;\u0026thinsp;25), overweight (25 \u0026le; BMI\u0026thinsp;\u0026lt;\u0026thinsp;30), or obesity (BMI \u0026ge; 30). Smoking status was categorized into two groups (yes/no) based on responses to the questionnaire items \"Smoked at least 100 cigarettes in life?\" (SMQ020) and \"Do you now smoke cigarettes?\" (SMQ040). Drinking status was determined using the questionnaire item \"Had at least 12 alcohol drinks/1 year?\" (ALQ101) and categorized into two groups depending on whether participants reported consuming at least 12 alcoholic drinks per year. One unit of alcohol was defined as 12 ounces of beer, 5 ounces of wine, or 1.5 ounces of liquor. Self-reported questionnaires were also used to diagnose the following conditions: hypertension (BPD035), diabetes mellitus (DM, DIQ010), heart failure (MCQ160b), coronary heart disease (CHD, MCQ160c), angina (MCQ160d), heart attack (MCQ160e), and stroke (MCQ160f). In this study, CVD was considered as having heart failure, coronary heart disease, angina, heart attack, or stroke.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using SAS software, version 9.4 (Cary, North Carolina, USA) and R software (version 4.3.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Categorical variables were expressed as frequency and percentage, while continuous variables were represented as mean and standard deviation (SD). A two-sided \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003cp\u003eFirst, we used CMI as a continuous dependent variable and included 11 covariates (age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD) in a multivariable linear regression model to identify significant confounders associated with CMI [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Second, adjusting for these covariates and total energy intake, we sequentially included 56 individual nutrients in the regression models to screen for nutrients significantly associated with CMI. P-values were corrected using the false discovery rate (FDR) method with the Benjamini-Hochberg procedure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Finally, we employed restricted cubic spline (RCS) analyses to investigate dose-response relationships between the identified nutrients and CMI across different thresholds (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e85\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e90\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e95\u003c/sub\u003e). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the strength and direction of these associations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy population characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the study population included 2,673 participants with a mean age of 73.1 years (SD: 5.3) and an average PIR of 2.5 (SD: 1.5). Women made up 51.1% of the cohort, and 56.5% had a high school education or less. The racial distribution was predominantly Non-Hispanic White (57.5%), followed by Non-Hispanic Black (15.4%) and Mexican American (10.7%). Alcohol consumption was reported by 36.7%, and 9.4% were current smokers. Regarding BMI, 26.1% were underweight, 37.4% were overweight, and 35.2% were obese. Hypertension affected 63.7% of participants, while diabetes and cardiovascular disease were present in 18.4% and 16.7%, respectively.\u003c/p\u003e \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\u003e\u0026ndash; The characteristics of study population in the NHANES (2007\u0026ndash;2016)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy population (n\u0026thinsp;=\u0026thinsp;2,673) \u003csup\u003ea\u003c/sup\u003e\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, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.1 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR, mean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,366 (51.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,307 (48.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,510 (56.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,163 (43.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287 (10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271 (10.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,538 (57.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e411 (15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,691 (63.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e982 (36.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,422 (90.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e251 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Mass Index, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e697 (26.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e999 (37.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e942 (35.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e971 (36.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,702 (63.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,181 (81.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e492 (18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease, n (%)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,227 (83.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e446 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003ea)\u003c/b\u003e Data shown are either frequency (%) or mean (SD). \u003cb\u003eb)\u003c/b\u003e Including heart failure, coronary heart disease, angina, heart attack, or stroke. PIR, family poverty-to-income ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between traditional risk factors and CMI\u003c/h3\u003e\n\u003cp\u003eIn the multivariable regression analysis, higher CMI was significantly associated with male sex, lower PIR, current smoking, alcohol consumption, hypertension, diabetes, and cardiovascular disease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among BMI categories, overweight and obesity demonstrated the strongest positive associations with CMI, while underweight was not significantly related. Non-Hispanic Black participants exhibited significantly lower CMI compared to Mexican Americans, with no significant differences observed for other racial/ethnic groups.\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\u003e\u0026ndash; The associations between traditional risk factors and CMI in the NHANES (2007\u0026ndash;2016)\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value \u003csup\u003ea\u003c/sup\u003e\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\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Mass Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular disease\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003ea)\u003c/b\u003e Multivariable linear regression model adjusted for age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD. \u003cb\u003eb)\u003c/b\u003e Including heart failure, coronary heart disease, angina, heart attack, or stroke. CMI, cardiometabolic index; PIR, family poverty-to-income ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of nutrients influencing CMI\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that among the 56 nutrients analyzed, only four were significantly associated with the CMI after FDR correction. Carbohydrate intake and total sugars were positively associated with CMI, indicating that higher intake of these nutrients corresponds to elevated CMI levels (Carbohydrate: Coefficient\u0026thinsp;=\u0026thinsp;0.001, Adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016; Total sugars: Coefficient\u0026thinsp;=\u0026thinsp;0.001, Adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). In contrast, vitamin E and MFA 20:1 (eicosenoic acid) were negatively associated with CMI, suggesting that higher intake of these nutrients is linked to lower CMI levels (Vitamin E: Coefficient = -0.007, Adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021; MFA 20:1: Coefficient = -0.129, Adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035).\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\u003e\u0026ndash; The associations between dietary nutrient intakes and CMI in the NHANES (2007\u0026ndash;2016)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted \u003cem\u003eP\u003c/em\u003e-value \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFA 20:1 (Eicosenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFA 20:5 (Eicosapentaenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFA 22:1 (Docosenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal monounsaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFA 22:6 (Docosahexaenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFA 18:4 (Octadecatetraenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 16:0 (Hexadecanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 12:0 (Dodecanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFA 18:1 (Octadecenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFA 16:1 (Hexadecenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFA 22:5 (Docosapentaenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal saturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLycopene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFA 20:4 (Eicosatetraenoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 8:0 (Octanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.59E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal choline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFA 18:2 (Octadecadienoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal polyunsaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 10:0 (Decanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.49E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamin (Vitamin B\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A, RAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D (D\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;D\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLutein\u0026thinsp;+\u0026thinsp;zeaxanthin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.17E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 14:0 (Tetradecanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.58E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFA 18:3 (Octadecatrienoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal folate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.62E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.14E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha-carotene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.46E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiboflavin (Vitamin B\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.28E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 6:0 (Hexanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 18:0 (Octadecanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B\u003csub\u003e12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA 4:0 (Butanoic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.43E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-cryptoxanthin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.01E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.71E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003ea)\u003c/b\u003e Multivariable linear regression model adjusted for energy, age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD. \u003cb\u003eb)\u003c/b\u003e False discovery rate (FDR) correction was performed using the Benjamini-Hochberg (BH) method. RAE, retinol activity equivalents; SFA, saturated fatty acids; MFA, monounsaturated fatty acids; PFA, polyunsaturated fatty acids.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDose-response relationships between identified nutrients and CMI\u003c/h2\u003e \u003cp\u003eThe restricted cubic spline (RCS) model was applied to explore potential nonlinear relationships between nutrient intake and CMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), adjusting for energy intake, age, sex, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, and CVD. The RCS analysis revealed that while the nonlinear relationships between carbohydrate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and total sugars (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) intake with CMI\u0026thinsp;\u0026gt;\u0026thinsp;85 were not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), significant nonlinear associations were observed for vitamin E (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG; \u003cem\u003eP\u003c/em\u003e-nonlinearity\u0026thinsp;=\u0026thinsp;0.043) and MFA 20:1 (eicosenoic acid) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ; \u003cem\u003eP\u003c/em\u003e-nonlinearity\u0026thinsp;=\u0026thinsp;0.028). Furthermore, results stratified by CMI thresholds (CMI\u0026thinsp;\u0026gt;\u0026thinsp;85, CMI\u0026thinsp;\u0026gt;\u0026thinsp;90, and CMI\u0026thinsp;\u0026gt;\u0026thinsp;95) demonstrated an upward trend in the cutoff values for all four nutrients as the CMI threshold increased. However, the results for CMI\u0026thinsp;\u0026gt;\u0026thinsp;85 were more robust compared to higher thresholds. Specifically, carbohydrate intake\u0026thinsp;\u0026gt;\u0026thinsp;0.25 kg/day and total sugars intake\u0026thinsp;\u0026gt;\u0026thinsp;0.05 kg/day were associated with a significantly increased risk of elevated CMI. In contrast, vitamin E intake\u0026thinsp;\u0026gt;\u0026thinsp;5.4 mg/day and MFA 20:1 intake\u0026thinsp;\u0026gt;\u0026thinsp;0.16 g/day were associated with a significantly reduced risk of elevated CMI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides valuable insights into the relationships between nutrient intake and the CMI in a representative U.S. population of older adults. CMI, as a surrogate endpoint for chronic diseases and mortality, offers a comprehensive marker for assessing cardiometabolic health. Four key nutrients were identified as significantly associated with CMI: carbohydrate, total sugars, vitamin E, and MFA 20:1 (eicosenoic acid). While carbohydrate and total sugars were positively associated with CMI, vitamin E and MFA 20:1 demonstrated protective associations. These findings underscore the multifaceted influences of dietary components on cardiometabolic health and highlight the potential for targeted dietary interventions to mitigate disease burden, reduce healthcare costs, and improve quality of life in this population of older adults. Four key nutrients-increasingly prevalent [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The predominance of these comorbidities underscores the relevance of CMI as a comprehensive marker for assessing cardiometabolic risk in this population.\u003c/p\u003e \u003cp\u003eOur analysis revealed significant associations between traditional risk factors and CMI. Higher CMI was observed in men, current smokers, and individuals with lower poverty-to-income ratios, hypertension, diabetes, or cardiovascular disease. Overweight and obesity demonstrated the strongest positive associations with CMI, consistent with the central role of adiposity in cardiometabolic dysfunction [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The inverse association between Non-Hispanic Black ethnicity and CMI, compared to Mexican Americans, warrants further investigation, as it may reflect underlying differences in metabolic health or environmental exposures.\u003c/p\u003e \u003cp\u003eThe analysis identified four nutrients significantly associated with CMI. To enhance the robustness of these findings, multivariable regression models were used, adjusting not only for traditional covariates but also for energy intake. This comprehensive adjustment strengthens the reliability of the identified associations. Carbohydrate and total sugars were positively associated with higher CMI, highlighting their potential contributions to adverse cardiometabolic outcomes. In contrast, vitamin E and MFA 20:1 intake were inversely associated with CMI, suggesting protective effects. These findings align with existing literature on the complex roles of macronutrients and micronutrients in modulating cardiometabolic health [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRCS analysis revealed nuanced dose-response relationships for these key nutrients. While carbohydrate and total sugars intake exhibited linear trends, significant nonlinear associations were observed for vitamin E and MFA 20:1. These findings emphasize the importance of identifying intake thresholds to guide dietary recommendations. For example, carbohydrate intake\u0026thinsp;\u0026gt;\u0026thinsp;0.25 kg/day and total sugars intake\u0026thinsp;\u0026gt;\u0026thinsp;0.05 kg/day were associated with increased CMI, while vitamin E intake\u0026thinsp;\u0026gt;\u0026thinsp;5.4 mg/day and MFA 20:1 intake\u0026thinsp;\u0026gt;\u0026thinsp;0.16 g/day were associated with reduced CMI. These thresholds provide actionable targets for dietary modifications to optimize cardiometabolic health.\u003c/p\u003e \u003cp\u003eThe positive association between carbohydrate intake and CMI observed in this study aligns with previous findings that excessive carbohydrate consumption, particularly refined carbohydrates, contributes to dyslipidemia and insulin resistance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. High glycemic load diets have been shown to exacerbate postprandial hyperglycemia and hyperinsulinemia, promoting metabolic dysfunction through pathways such as increased de novo lipogenesis and hepatic triglyceride accumulation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, complex carbohydrates, such as those from whole grains, have demonstrated protective effects in some studies, suggesting that the quality of carbohydrate intake may modulate its impact on CMI [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The lack of differentiation between carbohydrate sources in our study may partially explain the observed associations, warranting further investigation. High glycemic load diets have been shown to exacerbate postprandial hyperglycemia and hyperinsulinemia, promoting metabolic dysfunction. However, complex carbohydrates, such as those from whole grains, have demonstrated protective effects in some studies, suggesting that the quality of carbohydrate intake may modulate its impact on CMI [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The lack of differentiation between carbohydrate sources in our study may partially explain the observed associations, warranting further investigation.\u003c/p\u003e \u003cp\u003eThe observed positive relationship between total sugars intake and CMI corroborates evidence linking high sugar consumption to adverse cardiometabolic outcomes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Sugars, particularly added sugars, contribute to increased triglyceride levels and reduced HDL cholesterol, key components of CMI. This effect is partly mediated by increased hepatic lipogenesis and fat deposition, leading to ectopic fat accumulation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, excessive sugar intake is associated with increased visceral adiposity, which is strongly linked to cardiometabolic risk through pro-inflammatory cytokine release and insulin resistance [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The findings highlight the need to reduce added sugar consumption as part of dietary interventions aimed at improving CMI and overall metabolic health. Sugars, particularly added sugars, contribute to increased triglyceride levels and reduced HDL cholesterol, key components of CMI. Furthermore, excessive sugar intake is associated with increased visceral adiposity, which is strongly linked to cardiometabolic risk. The findings highlight the need to reduce added sugar consumption as part of dietary interventions aimed at improving CMI and overall metabolic health.\u003c/p\u003e \u003cp\u003eVitamin E\u0026rsquo;s inverse association with CMI suggests its potential role as a protective nutrient against cardiometabolic dysfunction. As a potent antioxidant, vitamin E mitigates oxidative stress and inflammation, key drivers of cardiometabolic diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Mechanistically, vitamin E has been shown to inhibit the oxidation of low-density lipoprotein (LDL), thereby reducing atherosclerosis progression and improving endothelial function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, vitamin E modulates gene expression related to inflammatory pathways, such as NF-κB signaling, contributing to its anti-inflammatory effects [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, the effectiveness of vitamin E may depend on baseline oxidative stress levels and individual variations in nutrient absorption and metabolism, which should be considered in future research. As a potent antioxidant, vitamin E mitigates oxidative stress and inflammation, key drivers of cardiometabolic diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Studies have demonstrated that vitamin E supplementation improves lipid profiles and reduces markers of oxidative damage. However, the effectiveness of vitamin E may depend on baseline oxidative stress levels and individual variations in nutrient absorption and metabolism, which should be considered in future research.\u003c/p\u003e \u003cp\u003eThe protective association between MFA 20:1 (eicosenoic acid) and CMI aligns with emerging evidence on the beneficial effects of monounsaturated fatty acids (MUFAs) on cardiometabolic health [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. MUFAs improve lipid metabolism, enhance insulin sensitivity, and reduce inflammation, all of which contribute to lower CMI. Eicosenoic acid specifically has been implicated in modulating hepatic lipid metabolism, including reducing triglyceride synthesis and promoting fatty acid oxidation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, its anti-inflammatory effects may stem from downregulation of pro-inflammatory cytokines, such as TNF-α and IL-6, further supporting its cardiometabolic benefits [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Further studies are needed to elucidate the specific mechanisms underlying its effects on CMI. MUFAs improve lipid metabolism, enhance insulin sensitivity, and reduce inflammation, all of which contribute to lower CMI. Eicosenoic acid, found in certain plant oils and seafood, may exert additional benefits through its unique metabolic properties. Further studies are needed to elucidate the specific mechanisms underlying its effects on CMI.\u003c/p\u003e \u003cp\u003eOne notable strength of this study is that it is the first NWAS to explore the relationship between dietary nutrients and CMI. This innovative approach allowed for a comprehensive assessment of multiple nutrients, providing novel insights into their potential roles in cardiometabolic health. This study benefits from the use of a nationally representative sample, robust statistical methods, and comprehensive adjustment for confounders. The integration of RCS modeling allowed for the exploration of nonlinear relationships, providing nuanced insights into nutrient-CMI associations. However, the cross-sectional design limits causal inference, and the reliance on self-reported dietary data may introduce recall bias. Additionally, the lack of differentiation between nutrient sources (e.g., refined vs. complex carbohydrates) restricts the granularity of our findings. Future longitudinal studies and biomarker-based assessments are warranted to validate and expand upon these results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies key dietary nutrients significantly associated with CMI in older U.S. adults, highlighting both protective and risk-related roles. As a surrogate endpoint for chronic diseases and mortality outcomes, CMI provides actionable insights for assessing cardiometabolic risk. Carbohydrate and total sugars intake were positively associated with higher CMI, while vitamin E and MFA 20:1 demonstrated protective associations. These findings underscore the importance of dietary quality in modulating cardiometabolic risk and provide actionable targets for nutritional interventions. By reducing disease burden and healthcare costs through improved dietary recommendations, this research offers a pathway to enhance health care and quality of life for aging populations. Further research is needed to elucidate the mechanisms underlying these associations and to develop tailored dietary guidelines, highlighting both protective and risk-related roles. Carbohydrate and total sugars intake were positively associated with higher CMI, while vitamin E and MFA 20:1 demonstrated protective associations. These findings underscore the importance of dietary quality in modulating cardiometabolic risk and provide actionable targets for nutritional interventions. Further research is needed to elucidate the mechanisms underlying these associations and to develop tailored dietary guidelines to improve cardiometabolic health in aging populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eThe research received financial support from the Panzhihua Central Hospital. The findings and conclusions expressed in this article are those of the authors and do not necessarily represent the official position of the CDC or the U.S. Department of Health and Human Services. No private sponsors were involved in the decision to design the study, collect data, analyze or interpret data, write reports, or submit manuscripts.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eThe authors\u0026rsquo; responsibilities were as follows\u0026mdash;Wen Li contributed to conceptualization, data curation, formal analysis, writing \u0026ndash; original draft, and project administration; Xiaoxia Meng was responsible for conceptualization, visualization and methodology. Huaman Liu was responsible for funding acquisition and investigation. Siqi Liu was responsible for supervision, validation and writing \u0026ndash; review \u0026amp; editing. All authors declare that they have read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank the leaders of the Panzhihua Central Hospital for their full support during the implementation of the project.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data, codebook, and analytic code will not be made available as the data used in this study are from the publicly accessible NHANES database, available to researchers worldwide. The database can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYan L, Hu X, Wu S, Cui C, Zhao S (2024) Association between the cardiometabolic index and NAFLD and fibrosis. Sci Rep 14:13194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong J, Li Y, Zhu J, Liang J, Xue S, Zhu Z (2024) Non-linear associations of cardiometabolic index with insulin resistance, impaired fasting glucose, and type 2 diabetes among US adults: a cross-sectional study. Front Endocrinol (Lausanne) 15:1341828\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Q, Wang Y, Liu Y, Wang Y, Deng L, Liao L et al (2024) Association between the cardiometabolic index and chronic kidney disease: a cross-sectional study. Int Urol Nephrol 56:1733\u0026ndash;1741\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValenzuela PL, Carrera-Bastos P, Castillo-Garc\u0026iacute;a A, Lieberman DE, Santos-Lozano A, Lucia A (2023) Obesity and the risk of cardiometabolic diseases. Nat Rev Cardiol 20:475\u0026ndash;494\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Q, Ren Q, Du L, Chen S, Wu S, Zhang B et al (2023) Cardiometabolic Index (CMI), Lipid Accumulation Products (LAP), Waist Triglyceride Index (WTI) and the risk of acute pancreatitis: a prospective study in adults of North China. Lipids Health Dis 22:190\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou J, Xiong H, Zhang H, Hu C, Lu S, Zou Y (2022) Association between the cardiometabolic index and non-alcoholic fatty liver disease: insights from a general population. BMC Gastroenterol 22:20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA et al (2018) /ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139:e1082-e143\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMozaffarian D (2016) Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review. Circulation 133:187\u0026ndash;225\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMicha R, Pe\u0026ntilde;alvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D (2017) Association Between Dietary Factors and Mortality From Heart Disease, Stroke, and Type 2 Diabetes in the United States. JAMA 317:912\u0026ndash;924\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillett W, Rockstr\u0026ouml;m J, Loken B, Springmann M, Lang T, Vermeulen S et al (2019) Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 393:447\u0026ndash;492\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFulgoni VL 3rd, Keast DR, Drewnowski A (2009) Development and validation of the nutrient-rich foods index: a tool to measure nutritional quality of foods. J Nutr 139:1549\u0026ndash;1554\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathers JC (2015) Impact of nutrition on the ageing process. Br J Nutr. ;113 Suppl:S18-22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed T, Haboubi N (2010) Assessment and management of nutrition in older people and its importance to health. Clin Interv Aging 5:207\u0026ndash;216\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkert D, Beck AM, Cederholm T, Cruz-Jentoft A, Goisser S, Hooper L et al (2019) ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin Nutr 38:10\u0026ndash;47\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehm CD, Pe\u0026ntilde;alvo JL, Afshin A, Mozaffarian D (2016) Dietary Intake Among US Adults, 1999\u0026ndash;2012. JAMA 315:2542\u0026ndash;2553\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurtin LR, Mohadjer LK, Dohrmann SM, Kruszon-Moran D, Mirel LB, Carroll MD et al (2013) National Health and Nutrition Examination Survey: sample design, 2007\u0026ndash;2010. Vital Health Stat 2:1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA et al (2021) Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 326:1614\u0026ndash;1621\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Zhou L, La R, Jiang M, Jiang D, Huang L et al (2023) The association between triglyceride glucose index and arthritis: a population-based study. Lipids Health Dis 22:132\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, La R, Jiang M, Xu W, Jiang D, Wang S et al (2024) The association between remnant cholesterol and rheumatoid arthritis: insights from a large population study. Lipids Health Dis 23:38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakabayashi I, Daimon T (2015) The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta 438:274\u0026ndash;278\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026uacute;\u0026ntilde;ez E, Steyerberg EW, N\u0026uacute;\u0026ntilde;ez J (2011) [Regression modeling strategies]. Rev Esp Cardiol 64:501\u0026ndash;507\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289\u0026ndash;300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesquilbet L, Mariotti F (2010) Dose-response analyses using restricted cubic spline functions in public health research. Stat Med 29:1037\u0026ndash;1057\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakhmat II, Putra ICS, Wibowo A, Henrina J, Nugraha GI, Ghozali M et al (2022) Cardiometabolic risk factors in adults with normal weight obesity: A systematic review and meta-analysis. Clin Obes 12:e12523\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAggarwal R, Ostrominski JW, Vaduganathan M (2024) Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011\u0026ndash;2020. JAMA 331:1858\u0026ndash;1860\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajjo T, Almasri J, Al Nofal A, Farah W, Alsawas M, Ahmed AT et al (2017) The Association of Weight Loss and Cardiometabolic Outcomes in Obese Children: Systematic Review and Meta-regression. J Clin Endocrinol Metab 102:758\u0026ndash;762\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSievenpiper JL (2020) Low-carbohydrate diets and cardiometabolic health: the importance of carbohydrate quality over quantity. Nutr Rev 78:69\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtzeni A, Nishi SK, Babio N, Belzer C, Konstanti P, Vioque J et al (2023) Carbohydrate quality, fecal microbiota and cardiometabolic health in older adults: a cohort study. Gut Microbes 15:2246185\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParks EJ, Krauss RM, Christiansen MP, Neese RA, Hellerstein MK (1999) Effects of a low-fat, high-carbohydrate diet on VLDL-triglyceride assembly, production, and clearance. J Clin Invest 104:1087\u0026ndash;1096\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSawicki CM, Jacques PF, Lichtenstein AH, Rogers GT, Ma J, Saltzman E et al (2021) Whole- and Refined-Grain Consumption and Longitudinal Changes in Cardiometabolic Risk Factors in the Framingham Offspring Cohort. J Nutr 151:2790\u0026ndash;2799\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTe Morenga L, Mallard S, Mann J (2012) Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 346:e7492\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanhope KL (2016) Sugar consumption, metabolic disease and obesity: The state of the controversy. Crit Rev Clin Lab Sci 53:52\u0026ndash;67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallagher C, Moschonis G, Lambert KA, Karaglani E, Mavrogianni C, Gavrili S et al (2021) Sugar-sweetened beverage consumption is associated with visceral fat in children. Br J Nutr 125:819\u0026ndash;827\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTraber MG, Stevens JF (2011) Vitamins C and E: beneficial effects from a mechanistic perspective. Free Radic Biol Med 51:1000\u0026ndash;1013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshor AW, Siervo M, Lara J, Oggioni C, Afshar S, Mathers JC (2015) Effect of vitamin C and vitamin E supplementation on endothelial function: a systematic review and meta-analysis of randomised controlled trials. Br J Nutr 113:1182\u0026ndash;1194\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeydani SN, Barklund MP, Liu S, Meydani M, Miller RA, Cannon JG et al (1990) Vitamin E supplementation enhances cell-mediated immunity in healthy elderly subjects. Am J Clin Nutr 52:557\u0026ndash;563\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwingshackl L, Hoffmann G (2014) Monounsaturated fatty acids, olive oil and health status: a systematic review and meta-analysis of cohort studies. Lipids Health Dis 13:154\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodson L, Fielding BA (2013) Stearoyl-CoA desaturase: rogue or innocent bystander? Prog Lipid Res 52:15\u0026ndash;42\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Yang C, Chu C, Wan M, Xu D, Pan D et al (2022) Beneficial effects of monounsaturated fatty acid-rich blended oils with an appropriate polyunsaturated/saturated fatty acid ratio and a low n-6/n-3 fatty acid ratio on the health of rats. J Sci Food Agric 102:7172\u0026ndash;7185\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Panzhihua Municipal Central Hospital","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":"Cardiometabolic index, Nutrient-wide association, National Health and Nutrition Examination Survey","lastPublishedDoi":"10.21203/rs.3.rs-5690596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5690596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The cardiometabolic index (CMI) is an innovative composite marker integrating adiposity and lipid metabolism, serving as a surrogate endpoint for chronic disease and mortality risks. This study employed a nutrient-wide association study (NWAS) approach to explore the associations between dietary nutrients and CMI in older U.S. adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007–2016 were analyzed, including 2,673 participants aged ≥ 65 years. Multivariable linear regression adjusted for energy intake and traditional confounders was used to evaluate 56 dietary nutrients. Restricted cubic spline analyses assessed nonlinear dose-response relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Carbohydrate and total sugars were positively associated with CMI (Carbohydrate: Coefficient = 0.001, Adjusted \u003cem\u003eP\u003c/em\u003e= 0.016; Total sugars: Coefficient = 0.001, Adjusted \u003cem\u003eP\u003c/em\u003e = 0.021). In contrast, vitamin E and MFA 20:1 (eicosenoic acid) exhibited inverse associations with CMI (Vitamin E: Coefficient = -0.007, Adjusted \u003cem\u003eP\u003c/em\u003e = 0.021; MFA 20:1: Coefficient = -0.129, Adjusted \u003cem\u003eP\u003c/em\u003e = 0.035). Restricted cubic spline analyses revealed significant nonlinear relationships for vitamin E (\u003cem\u003eP\u003c/em\u003e-nonlinearity = 0.043) and MFA 20:1 (\u003cem\u003eP\u003c/em\u003e-nonlinearity = 0.028), identifying intake thresholds for protective and detrimental effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These findings underscore the complex interplay between diet and cardiometabolic risk, highlighting the potential of targeted nutritional interventions to reduce disease burden and improve health outcomes in aging populations. Further research is warranted to validate these findings and inform dietary guidelines.\u003c/p\u003e","manuscriptTitle":"Nutrient-Wide Associations with the Cardiometabolic Index in Older Adults: Insights from NHANES 2007–2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 15:29:37","doi":"10.21203/rs.3.rs-5690596/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":"ab67f6d3-cd6d-4ca8-ab34-2f88d5410b3e","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41919974,"name":"Nutrition \u0026 Dietetics"},{"id":41919975,"name":"Geriatrics \u0026 Gerontology"},{"id":41919976,"name":"Biostatistics"},{"id":41919977,"name":"Food Chemistry"}],"tags":[],"updatedAt":"2024-12-24T15:29:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-24 15:29:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5690596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5690596","identity":"rs-5690596","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}