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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).

Nonalcoholic fatty liver disease (NAFLD) is expanding as a global health problem with approximately 25% of the world\'s population affected by it. Dietary modification is one of the most important strategies for preventing NAFLD. The association between nutrient density and the Healthy Eating Index 2015 (HEI2015) with NAFLD demonstrates that nutrient density is an independent predictor of NAFLD in Iranian adults [fully adjusted model: OR (95% CI)tertile3vs.1: 0.68 (0.54-0.85), P for trend = 0.001]. However, a favorable association between NAFDL and diet quality (HEI 2015) is more pronounced in participants with abdominal obesity [fully adjusted model: OR (95% CI)tertile3vs.1: 0.63 (0.41-0.98), P for trend = 0.03]. Based on the gender-stratified path analysis, diet quality indirectly through Waist-to-Height Ratio (WHtR), C-reactive protein (CRP), and metabolic syndrome in women, and men through WHtR, hemoglobin A1c (HBA1c), CRP, and metabolic syndrome affects NAFLD. Nutrient density directly and indirectly in women through WHtR, CRP, and metabolic syndrome, and in men indirectly through WHtR, hemoglobin A1c, and metabolic syndrome negatively affect NAFLD. Hence, in these subjects; we can provide early dietary intervention and education to prevent progression to NAFLD.

BACKGROUND: Metabolically associated fatty liver disease (MAFLD) insidiously affects people\'s health, and many models have been proposed for the evaluation of liver fibrosis. However, there is still a lack of noninvasive and sensitive models to screen MAFLD in high-risk populations.
OBJECTIVE: The purpose of this study was to explore a new method for early screening of the public and establish a home-based tool for regular self-assessment and monitoring of MAFLD.
METHODS: In this cross-sectional study, there were 1758 eligible participants in the training set and 200 eligible participants in the testing set. Routine blood, blood biochemistry, and FibroScan tests were performed, and body composition was analyzed using a body composition instrument. Additionally, we recorded multiple factors including disease-related risk factors, the Forns index score, the hepatic steatosis index (HSI), the triglyceride glucose index, total body water (TBW), body fat mass (BFM), visceral fat area, waist-height ratio (WHtR), and basal metabolic rate. Binary logistic regression analysis was performed to explore the potential anthropometric indicators that have a predictive ability to screen for MAFLD. A new model, named the MAFLD Screening Index (MFSI), was established using binary logistic regression analysis, and BFM, WHtR, and TBW were included. A simple rating table, named the MAFLD Rating Table (MRT), was also established using these indicators.
RESULTS: The performance of the HSI (area under the curve [AUC]=0.873, specificity=76.8%, sensitivity=81.4%), WHtR (AUC=0.866, specificity=79.8%, sensitivity=80.8%), and BFM (AUC=0.842, specificity=76.9%, sensitivity=76.2%) in discriminating between the MAFLD group and non-fatty liver group was evaluated (P<.001). The AUC of the combined model including WHtR, HSI, and BFM values was 0.900 (specificity=81.8%, sensitivity=85.6%; P<.001). The MFSI was established based on better performance at screening MAFLD patients in the training set (AUC=0.896, specificity=83.8%, sensitivity=82.1%) and was confirmed in the testing set (AUC=0.917, specificity=89.8%, sensitivity=84.4%; P<.001).
CONCLUSIONS: The novel MFSI model was built using WHtR, BFM, and TBW to screen for early MAFLD. These body parameters can be easily obtained using a body fat scale at home, and the mobile device software can record specific values and perform calculations. MFSI had better performance than other models for early MAFLD screening. The new model showed strong power and stability and shows promise in the area of MAFLD detection and self-assessment. The MRT was a practical tool to assess disease alterations in real time.

This study aimed to evaluate the superiority of anthropometric indices compared to others for predicting ischemic heart disease (IHD) or cardiometabolic risk factors. This study was a cross-sectional analysis of the Pars Cohort Study data. In total, 9229 Valashahr inhabitants aged 40-75 were included in the analysis. The area under the receiver operating characteristic curve (AUC) analyses was used to compare the predictive accuracy of four anthropometric measures, including body mass index, waist to height ratio (WHtR), waist to hip ratio (WHR), and waist circumference (WC). IHD prevalence was 10.4% in our sample. The prevalence of diabetes mellitus (DM), hypertension, dyslipidemia, and metabolic syndrome was 12.7%, 29.2%, 58.4%, and 22.3%, respectively. All anthropometric indices had poor to good accuracy in predicting IHD risk factors, with AUCs ranging between 0.580 and 0.818. WHR was the most accurate measure for predicting IHD in both genders. All indexes had a better accuracy for predicting DM, dyslipidemia, and metabolic syndrome (MetS) in males than in females. To conclude, anthropometric measures, especially WC and WHtR, are recommended for predicting metabolic syndrome in primary prevention settings. These simple indices could help physicians find those who need further evaluation for MetS.

UNASSIGNED: Anthropometric measurement provides a simple, noninvasive approach to evaluate obesity in pregnant women. We aimed to develop a predictive model utilizing anthropometric index for gestational diabetes mellitus (GDM), the most common obesity-related complications during pregnancy.
UNASSIGNED: A prospective cohort of 4709 women was enrolled in Qingdao, China. Logistic regression model was constructed to determine the association of body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) in the first trimester (<14 weeks\' gestation) with GDM. The discrimination ability for GDM was assessed using areas under the receiver operating characteristic (ROC) curve (AUC). Delong tests were performed to compare AUC values between different measures.
UNASSIGNED: The GDM incidence was 19.50%. GDM risk increased with VAT during early pregnancy, and the risk increased by 117% (OR = 2.17, 95% CI: 1.23-2.83) to 326% (OR = 4.26, 95% CI: 2.29-7.91) in pregnant women with the second quartile or above after adjusting for confounders (all p<.05). Combined index using VAT and BMI demonstrated superior predictive power for GDM compared with BMI alone (p<.05), but didn\'t differ from VAT (p>.05). Overall, VAT was positively correlated with GDM occurrence, outperforming BMI, WHR, WHtR and SAT in the predicative model. A first-trimester VAT cutoff of 27.05 mm might be promising for GDM risk stratification.
UNASSIGNED: First-trimester routine ultrasound screening may facilitate earlier identification and intervention of GDM. Pregnant women with VAT above the optimal threshold (27.05 mm) might benefit from targeted GDM monitoring.

BACKGROUND: Although triglyceride-glucose (TyG) index is a reliable indicator of insulin resistance and cardiometabolic disease, its effectiveness in predicting mortality risk has not been adequately validated. We aimed to investigate the association between the TyG-related indices and all-cause and cause-specific mortality in the general population.
METHODS: A total of 27,642 individuals were included from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018. Three indicators were constructed, including the TyG index, TyG combined with waist-to-height ratio (TyG-WHtR), and TyG combined with waist circumference (TyG-WC). Mortality data was acquired through the linkage of NHANES data with National Death Index records. Weighted Cox proportional hazards models were used to estimate the independent association between the TyG-related indices and mortality. Nonlinear associations were explored using restricted cubic splines.
RESULTS: Multivariable adjusted models showed a progressive increase in all-cause and cause-specific mortality across quartiles of the TyG-related indices. Compared with the lowest quartile of the TyG index, the highest quartile had adjusted hazard ratios of 1.26 (95% CI 1.04-1.52) for all-cause mortality, 1.38 (1.04-1.74) for cardiovascular mortality, and 1.23 (1.01-1.50) for non-cardiovascular mortality, respectively. For the TyG-WHtR index, the corresponding hazard ratios were 1.60 (1.25-2.05), 1.86 (1.26-2.50), and 1.48 (1.10-1.99), respectively. For the TyG-WC index, the corresponding hazard ratios were 1.42 (1.11-1.75), 1.48 (1.04-1.96), and 1.38 (1.05-1.72), respectively. The associations between the three TyG-related indices and all-cause, cardiovascular and non-cardiovascular mortality were J-shaped. Interaction tests revealed significant effect modification by age, low-density lipoprotein cholesterol (LDL-C) level, and statin use (all P values < 0.05).
CONCLUSIONS: The TyG-related indices were independent predictors of all-cause and cause-specific mortality in the general population. Young individuals should be particularly vigilant, whereas low LDL-C levels and statin use are potentially protective.

The current study was planned to compare serum levels of secreted frizzled related protein-4, insulin resistance and waist-to-height ratio in individuals with and without a diabetic background, and to assess the correlation of these markers with family history of diabetes. The cross-sectional comparative study comprised 80 subjects with confirmed normal glucose tolerance values. Parameters assessed included secreted frizzled related protein-4, fasting glucose, random glucose, fasting insulin, homeostasis model of assessment of insulin resistance and waist-toheight ratio values. Those without a diabetic background had significantly higher frizzled related protein-4 levels (p=0.02). Although subjects with family history of diabetes showed higher mean fasting glucose, waist circumference and waist-to-height ratio, these differences were not statistically significant (p>0.05). However, there was a strong positive correlation with waist circumference, waistto- height ratio, fasting insulin and homeostasis model of assessment of insulin resistance (p=0.0001). There was no significant correlation of diabetic background with frizzled related protein-4 SFRP-4, homeostasis model of assessment of insulin resistance and waist-to-height ratio (p>0.05).

Limited studies have investigated the correlation between fat distribution and the risk of diabetic retinopathy (DR) in the general population with diabetes. The relationship between obesity and DR remains inconclusive, possibly due to using simple anthropometric measures to define obesity. This study investigates the relationships between the android-to-gynoid fat ratio (A/G ratio, measured using dual-energy X-ray absorptiometry) and DR within the US population with diabetes.
The study used a population-based, cross-sectional approach based on the 2003-2006 and 2011-2018 data of the National Health and Nutrition Examination Survey (NHANES). Multivariable logistic regression analyses were performed on participants with diabetes to evaluate the contribution of body mass index (BMI), waist-to-height ratio (WHtR), and A/G ratio to the prevalence of DR.
The prevalence of DR was 22.2, 21.2, and 17.6% among participants with A/G ratios <1.0, 1.0-1.2, and ≥1.2, respectively. After adjusting sex, age, ethnicity, diabetes duration, hemoglobin A1c level, blood pressure level, and non-high-density lipoprotein cholesterol level, a higher A/G ratio (≥1.2) was independently associated with decreased odds of DR (odds ratio [OR], 0.565; 95% CI: 0.372-0.858) compared with the A/G ratio of 1.0-1.2. Associations between a higher A/G ratio and DR remained statistically significant after adjusting for BMI (OR, 0.567; 95% CI: 0.373-0.861) and WHtR (OR, 0.586; 95% CI: 0.379-0.907). Moreover, these associations remained statistically significant in analyses using the ethnic-specific tertiles for the A/G ratio. In sex-stratified models, these correlations remained in males. There was a significant inverse association between the A/G ratio and diabetes duration in males, which persisted after multivariable adjustments (p < 0.05).
A novel finding indicates that a higher A/G ratio is associated with a reduced likelihood of DR in males with diabetes. The results from NHANES underscore the importance of considering imaging-based fat distribution as a critical indicator in clinical practice.