OBJECTIVE: To track body size and proportions, arm dimensions and grip strength in children, adolescents, and adults resident in an indigenous community in Oaxaca who were measured on two or three occasions across surveys in 1968, 1978, and 2000.
METHODS: The three cross-sectional surveys included measures of height, weight, sitting height, arm circumference, triceps skinfold, and grip strength in surveys of schoolchildren in 1968 and of schoolchildren, adolescents and adults in 1978 and 2000. Cross-checks of surnames, forenames and ages/dates of birth of participants in the three surveys identified three samples of individuals measured on two occasions (1968-1978, two age groups in 1978-2000) and a subsample of individuals measured in the three surveys. Partial correlations controlling for age at each observation were calculated for each variable in the three sex-specific samples measured on two occasions, and for the subsamples of males and females measured on three occasions.
RESULTS: Allowing for variation in age among subsamples, inter-age correlations were moderate to high for stature, moderate for sitting height and estimated leg length, and low to moderate for weight, BMI, arm and estimated arm muscle circumference, triceps skinfold, and grip strength.
CONCLUSIONS: Allowing for the relatively broad chronological age intervals, the inter-age correlations for height, weight and BMI were at the low end, while those for grip strength and for strength per unit body weight for males (though not females) were generally in the range of correlations noted in studies of European samples. Likely associated with improved health, nutritional, and sanitation conditions, obesity and overweight were emerging among adults by 2000. Obesity and overweight in adults paralleled the introduction of mechanized agriculture that reduced routine physical work. Among children, the association of obesity and overweight is likely with increased nutritional availability, but poor choices in diet.

BACKGROUND: The fat-to-muscle mass ratio (FMR), integrating the antagonistic effects of fat and muscle mass, has been suggested as a valuable indicator to assess cardiometabolic health independent of overall adiposity. However, the specific associations of total and regional FMR with cardiometabolic risk are poorly understood. We aimed to examine sex-specific associations of total and regional FMR with single and clustered cardiometabolic risk factors (CRFs).
METHODS: 13,505 participants aged 20 years and above were included in the cross-sectional study. Fat mass and muscle mass were assessed using a bioelectrical impedance analysis device. FMR was estimated as fat mass divided by muscle mass in corresponding body parts (whole body, arm, leg, and trunk). Clustered CRFs was defined as the presence of two or more risk factors, including hypertension, elevated blood glucose, dyslipidemia, insulin resistance (IR), and hyperuricemia. IR was assessed by the triglyceride glucose (TyG) index. Multivariable logistic regression models were applied to explore the associations of FMR in the whole body and body parts with single and clustered CRFs.
RESULTS: The odds ratios (ORs) increased significantly for all single and clustered CRFs with the per quartile increase of total and regional FMR in both sexes (P for trend < 0.001), following adjustment for confounders. Among the regional parts, FMRs of the legs presented the strongest associations for clustered CRFs in both men and women, with adjusted OR of 8.54 (95% confidence interval (CI): 7.12-10.24) and 4.92 (95% CI: 4.24-5.71), respectively. Significant interactions (P for interaction < 0.05) were identified between age and FMRs across different body parts, as well as between BMI status and FMRs in different regions for clustered CRFs. Restricted cubic splines revealed significant non-linear relationships between FMRs of different body parts and clustered CRFs in both sexes (P for nonlinear < 0.05).
CONCLUSIONS: FMRs in the whole body and different regions were significantly associated with single and clustered CRFs in the general Chinese population. The association between FMR and clustered CRFs was more pronounced in youngers than in the elderly.

To date, the best obesity-related indicators (ORIs) for predicting hypertension, dyslipidaemia, Type 2 diabetes mellitus (T2DM) and multimorbidity are still controversial. This study assessed the ability of 17 ORIs [body mass index (BMI), body fat percentage (BF%), c-index, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), waist-hip ratio (WHR), waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), triglyceride glucose index (TYG), lipid accumulation product (LAP), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), waist triglyceride index (WTI) and cardiometabolic index (CMI)] to predict hypertension, dyslipidemia, T2DM, and multimorbidity in populations aged 40-69 years. From November 2017 to December 2022, 10,432 compliant residents participated in this study. Receiver operating characteristic curves were used to assess the ability of ORIs to predict target diseases across the whole population and genders. The DeLong test was used to analyse the heterogeneity of area under curves (AUCs). Multivariable logistic regression was used to analyse the association of ORIs with hypertension, dyslipidaemia, T2DM, and multimorbidity. The prevalence of hypertension, dyslipidaemia, T2DM, and multimorbidity was 67.46%, 39.36%, 12.54% and 63.58%, respectively. After excluding ORIs associated with the target disease components, in the whole population, CVAI (AUC = 0.656), BMI (AUC = 0.655, not significantly different from WC and AVI), CVAI (AUC = 0.645, not significantly different from LAP, CMI, WHR, and WTI), and TYG (AUC = 0.740) were the best predictor of hypertension, dyslipidemia, T2DM, and multimorbidity, respectively (all P < 0.05). In the male population, BF% (AUC = 0.677), BMI (AUC = 0.698), CMI (AUC = 0.648, not significantly different from LAP and CVAI), and TYG (AUC = 0.741) were the best predictors (all P < 0.05). In the female population, CVAI (AUC = 0.677), CUN-BAE (AUC = 0.623, not significantly different from BF%, WC, WHR, WHtR, BRI and BMI), CVAI (AUC = 0.657, not significantly different from WHR), TYG (AUC = 0.740) were the best predictors (all P < 0.05). After adjusting for all covariates, all ORIs were significantly associated with hypertension, dyslipidaemia, T2DM, and multimorbidity (all P < 0.05), except for ABSI and hypertension and BAI and T2DM, which were insignificant. Ultimately, after considering the heterogeneity of prediction of ORIs among different populations, for hypertension, BF% was the best indicator for men and CVAI for the rest of the population. The best predictors of dyslipidaemia, T2DM, and multimorbidity were BMI, CVAI and TYG, respectively. Screening for common chronic diseases in combination with these factors may help to improve the effectiveness.

Increased cardiometabolic risk among children is increasingly becoming a concern, with evidence indicating that obesity, diet, and serum 25-hydroxyvitamin D (25(OH)D) are associated with cardiometabolic risk. However, such studies among Malaysian children are scarce. Thus, this study explores the associations between adiposity, dietary quality, and 25(OH)D, with cardiometabolic risk factors among Malaysian children aged 4-12 years. Data of 479 children (mean age: 8.2 ± 2.3 years old, 52% females) from the South East Asian Nutrition Surveys (SEANUTS II) Malaysia, were included in this analysis. Adiposity (percentage of body fat) was assessed with bioelectrical impedance technique. Dietary quality was assessed using 24 h dietary recall and calculated as mean adequacy ratio. Vitamin D was assessed based on serum 25-hydroxyvitamin D (25(OH)D). Measurements of cardiometabolic risk factors included waist circumference (WC), mean arterial pressure (MAP), fasting blood glucose (FBG), high-density lipoprotein (HDL), triglyceride, and high-sensitivity C-reactive protein, and cardiometabolic risk cluster score (siMS) was calculated. Overall, higher adiposity was positively associated with all cardiometabolic risk factors (WC, ß = 0.907; 95% CI = 0.865, 0.948; MAP, ß = 0.225; 95% CI = 0.158, 0.292; HDL, ß = -0.011; 95% CI = -0.014, -0.009; Triglyceride, ß = 0.012; 95% CI = 0.009, 0.016; FBG, ß = 0.006; 95% CI = 0.002, 0.011) and siMS score (ß = 0.033; 95% CI = 0.029, 0.037). Serum 25(OH)D was inversely associated with siMS score (ß = -0.002; 95% CI = -0.004, -0.000008) and positively associated with HDL (ß = 0.002; 95% CI = 0.0001, 0.003). Our findings suggest that adiposity is a key determinant of adverse cardiometabolic risk factors in children, while serum 25(OH)D may be associated with overall cardiometabolic health. Interventions to reduce obesity are needed to mitigate the deleterious consequences of cardiometabolic dysregulation in children.

OBJECTIVE: The impact of life-course different adiposity indices on diabetes mellitus (DM) is poorly understood. We aimed to do trajectory analysis with repeated measurements of adiposity indices in the development of DM among women across the lifespan.
METHODS: This study prospectively investigated the 1,681 population of Tehran Lipid and Glucose Study. At baseline, all individuals were free of diabetes. Trajectory analysis was used to identify homogeneous distinct clusters of adiposity indices trajectories and assign individuals to unique clusters.
RESULTS: Of the 1681 healthy women, 320 progressed to the DM. Three distinct body mass index (BMI) trajectories and 2 distinct trajectories of other adiposity indices (waist circumstance (WC), conicity index (C-index), and body roundness index (BRI)) were chosen as the best fitting of the latent class growth mixture model. In the adjusted model, total participants [HR (CI 95%): 2.83 (2.05, 3.91); p < 0.001], and menopause [1.35 (1.10, 2.11); p = 0.001] and reproductive-age women [2.93 (1.80, 4.78); p = 0.003] of the high BMI trajectory compared to the ones in the low trajectory of BMI were more likely to develop DM. The high BRI in menopause had a significantly higher risk of DM compared to the low trajectory. In menopause (1.80 (1.26, 2.58)) and reproductive-age women (4.32 (2.49, 7.47)) with high trajectory of C-index, the DM risk decreased after adjustment.
CONCLUSIONS: The risk of DM was greater for high BMI, WC, C-index, and BRI trajectories than for lower trajectories. Hence, the development of general, abdominal, and visceral obesity trajectories in the prevention of DM should be considered by clinicians.

BACKGROUND: Evidence suggests that exposure to per- and polyfluoroalkyl substances (PFAS) increases the risk of developing cardiometabolic disease risk factors. Limited research has evaluated associations between PFAS, assessed during pregnancy, a sensitive window for maternal endocrine effects, and long-term maternal adiposity.
OBJECTIVE: Estimate associations of early pregnancy measures of individual PFAS, and PFAS mixtures, with maternal adiposity in midlife.
METHODS: We studied 547 Project Viva participants with measures of early pregnancy (mean gestation 10.0 weeks; mean age 32.5 years) plasma concentrations of 6 PFAS and midlife adiposity outcomes (mean follow-up 17.7 years; mean age 50.7 years), including weight, waist circumference (WC), trunk fat mass (TFM), and total body fat mass (TBFM). We used linear regression and Bayesian Kernel Machine Regression (BKMR).
RESULTS: Linear regression estimated higher midlife weight per doubling of perfluorooctane sulfonate (PFOS) (3.8 kg [95% CI: 1.6, 5.9]) and 2-(N-ethyl-perfluorooctane sulfonamido) acetate (2.3 kg [95% CI: 0.9, 3.7]). BKMR analyses of single PFAS plasma concentrations (comparing the 25th percentile concentration to the 75th percentile) showed a positive association between PFOS and midlife adiposity (weight: 7.7 kg [95% CI: 4.0, 11.5]; TFM: 1.2 kg [95% CI: 0.0, 2.3]; TBFM: 3.0 kg [95% CI: 0.8, 5.2]), but inverse associations with perfluorononanoate (weight: -6.0 kg [95% CI: -8.5, -3.5]; WC: -1.8 cm [95% CI: -3.2, -0.3]; TFM: -0.8 kg [95% CI: -1.5, -0.1]; TBFM: -1.4 kg [95% CI: -2.7, -0.3]) and perfluorohexane sulfonate (TFM: -0.8 kg [95% CI: -1.5, -0.1]; TBFM: -1.4 kg [95% CI: -2.6, -0.2]). No associations were observed with the overall PFAS mixture.
CONCLUSIONS: Select PFAS, assessed in pregnancy, may differentially affect maternal midlife adiposity, influencing later-life maternal cardiometabolic health.

BACKGROUND: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).
METHODS: EPIDIAB is a post hoc analysis from the AngioSafe T2D study, which is a multicentric study aimed at determining the safety of antihyperglycemic drugs on retina and including patients with T2D screened for diabetic retinopathy (DR) (n = 7200) and deeply phenotyped for MVC. Patients included who had undergone cardiac CT for CAC (Coronary Artery Calcium) scoring after inclusion (n = 1253) were tested with a validated deep learning segmentation pipeline for EAT volume quantification.
RESULTS: Median age of the study population was 61 [54;67], with a majority of men (57%) a median duration of the disease 11 years [5;18] and a mean HbA1c of7.8 ± 1.4%. EAT was significantly associated with all traditional CV risk factors. EAT volume significantly increased with chronic kidney disease (CKD vs no CKD: 87.8 [63.5;118.6] vs 82.7 mL [58.8;110.8], p = 0.008), coronary artery disease (CAD vs no CAD: 112.2 [82.7;133.3] vs 83.8 mL [59.4;112.1], p = 0.0004, peripheral arterial disease (PAD vs no PAD: 107 [76.2;141] vs 84.6 mL[59.2; 114], p = 0.0005 and elevated CAC score (> 100 vs  < 100 AU: 96.8 mL [69.1;130] vs 77.9 mL [53.8;107.7], p < 0.0001). By contrast, EAT volume was neither associated with DR, nor with peripheral neuropathy. We further evidenced a subgroup of patients with high EAT volume and a null CAC score. Interestingly, this group were more likely to be composed of young women with a high BMI, a lower duration of T2D, a lower prevalence of microvascular complications, and a higher inflammatory profile.
CONCLUSIONS: Fully-automated EAT volume quantification could provide useful information about the risk of both renal and macrovascular complications in T2D patients.

BACKGROUND:  Waist-related measures are commonly used to classify central adiposity and related comorbidities. This classification may be essential among children, as it may identify the risk of future non-communicable diseases.
METHODS:  A cross-sectional study was conducted in the Eastern Cape province, South Africa, among 459 primary school learners aged 9-14 years. Height, weight and waist circumference (WC) were measured using standardised techniques recommended by World Health Organization (WHO). The anthropometric measurements, including body mass index (BMI), WC, waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were computed and evaluated.
RESULTS:  Most participants were girls (57.70%) with an average age of 11.20 ± 1.60 years. The average weight was 38.81 kg ± 10.49 kg with an average height of 144.16 (standard deviation [s.d.] = 10.37) cm. The sample had a BMI of 18.41 kg/m2 (s.d. = 3.19). The results showed, on average, WC of 62.10 cm ± 8.12 cm, WHR of 0.82 ± 0.15 and WHtR of 0.44 ± 0.05. Girls reported significantly higher BMI, WC and WHtR. Based on WHtR, the results showed the acceptable ability to classify children according to abdominal obesity, thus identifying their risk for comorbidities.
CONCLUSIONS:  Overall body fat indicated by BMI and central obesity shown by waist-related anthropometric measures can play a significant role in classifying children in terms of their risk of comorbidities.Contribution: To prevent the risks of metabolic diseases in childhood, it is necessary to detect abdominal obesity early using WC-based anthropometric measurements, especially WHtR, to identify those at risk.

BACKGROUND: Adiposity can be measured using BMI (which is based on weight and height) as well as indices of abdominal adiposity. We examined the association between BMI and waist-to-height ratio (WHtR) within and across populations of different world regions and quantified how well these two metrics discriminate between people with and without hypertension.
METHODS: We used data from studies carried out from 1990 to 2023 on BMI, WHtR and hypertension in people aged 20-64 years in representative samples of the general population in eight world regions. We graphically compared the regional distributions of BMI and WHtR, and calculated Pearson\'s correlation coefficients between BMI and WHtR within each region. We used mixed-effects linear regression to estimate the extent to which WHtR varies across regions at the same BMI. We graphically examined the prevalence of hypertension and the distribution of people who have hypertension both in relation to BMI and WHtR, and we assessed how closely BMI and WHtR discriminate between participants with and without hypertension using C-statistic and net reclassification improvement (NRI).
RESULTS: The correlation between BMI and WHtR ranged from 0·76 to 0·89 within different regions. After adjusting for age and BMI, mean WHtR was highest in south Asia for both sexes, followed by Latin America and the Caribbean and the region of central Asia, Middle East and north Africa. Mean WHtR was lowest in central and eastern Europe for both sexes, in the high-income western region for women, and in Oceania for men. Conversely, to achieve an equivalent WHtR, the BMI of the population of south Asia would need to be, on average, 2·79 kg/m2 (95% CI 2·31-3·28) lower for women and 1·28 kg/m2 (1·02-1·54) lower for men than in the high-income western region. In every region, hypertension prevalence increased with both BMI and WHtR. Models with either of these two adiposity metrics had virtually identical C-statistics and NRIs for every region and sex, with C-statistics ranging from 0·72 to 0·81 and NRIs ranging from 0·34 to 0·57 in different region and sex combinations. When both BMI and WHtR were used, performance improved only slightly compared with using either adiposity measure alone.
CONCLUSIONS: BMI can distinguish young and middle-aged adults with higher versus lower amounts of abdominal adiposity with moderate-to-high accuracy, and both BMI and WHtR distinguish people with or without hypertension. However, at the same BMI level, people in south Asia, Latin America and the Caribbean, and the region of central Asia, Middle East and north Africa, have higher WHtR than in the other regions.
BACKGROUND: UK Medical Research Council and UK Research and Innovation (Innovate UK).

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