Pathogenesis of type 2 diabetes mellitus (T2DM) is combined from initial insulin resistance (IR) and subsequent β-cell dysfunction. Insulin therapy can replace β-cell function in advanced stages. However excessive insulin therapy increases IR and may expose the patients to risk of cardiovascular disease. We aim to assess β-cell function and IR in patients with type 2 diabetes on insulin therapy by fasting C-peptide to glucose ratio (FCPGR), and triglyceride glucose (TyG) index respectively to support treatment plans.

A cross-sectional study was conducted at the Galiawa Diabetes and Endocrinology Teaching Center in Erbil City, Iraq, from June 2023 to January 2024. A convenient sample of 100 patients with T2DM on insulin-based therapy were included after obtaining informed written consent and excluding conditions such as acute illness, uncertain type of diabetes, etc. Each patient was evaluated for anthropometric parameters and current treatment details. Biochemical tests were then carried out to calculate metabolic syndrome (MetS) index score, FCPGR, and TyG index. Finally, patients were divided into four subgroups according to their FCPGR and TyG index and the data were analyzed statistically.

The data showed those patients with sufficient β-cell function were 60 (60%), and patients with high TyG index were 95 (95%). There was a significant negative correlation between FCPGR and hemoglobin A1c (HbA1c) (p-value=0.001), while there was a positive correlation between TyG index and HbA1C (p-value=0.001). None of these markers were correlated with BMI (p-value=0.297, and 0.976), duration of T2DM (p-value=0.258, and 0.458), and dose of insulin therapy (p-value=0.901, and 0.477). Patients with sufficient β-cell function and high TyG index had the lowest HbA1C.

The study provides valuable insights into the utility of FCPGR and TyG index as biomarkers for β-cell function and insulin resistance in T2DM patients on insulin therapy. The significant correlation with HbA1C underscores their potential in clinical practice. However, the lack of correlation with BMI, disease duration, and insulin dose suggests that further investigation is needed to fully understand these biomarkers' implications across diverse patient profiles.

Hypertension is the leading preventable risk factor for cardiovascular disease, chronic kidney disease and cognitive impairment, and mortality and disability worldwide. Since prevention, early detection, and treatment of blood pressure improve public health, the aim of present study was to determine the best obesity indices and estimate the optimal cut-off point for each one to predict the risk of elevated/stage 1 and undiagnosed hypertension in the population of center of Iran based on American ACC/AHA 2020 guidelines.

This cross-sectional study was performed on 9715 people who enrolled in 2018 in Persian Adult Cohort in Shahedieh area of Yazd, Iran in 2018. The anthropometric indices including body mass index (BMI) and waist circumference (WC), wrist circumference, hip circumference, waist-to-hip ratio, and waist-to height ratio of individuals, were extracted. The receiver operating characteristic curve was utilized to determine the optimum cut-off point of each anthropometric index to predict hypertension stages and compare their predictive power by age-sex categories. Statistical analysis was done using SPSS version 23.0.

The results showed that BMI has the best predictive power to recognize the risk of elevated/stage 1 hypertension for female (area under the curve [AUC] = 0.72 and optimal cut-off = 30.10 kg/m2) and WC for male (AUC = 0.66 and optimal cut-off = 93.5 cm) in 35-45 age group. BMI had the best predictive power for the risk of undiagnosed hypertension for 35-45 years old male (AUC = 0.73 and optimal cut-off = 28.90 kg/m2) and female (AUC = 0.75 and optimal cut-off = 5.10 kg/m2), and hip circumference revealed similar predictive power for female as well (AUC = 0.75 and optimal cut-off = 112 cm).

Based on our findings, BMI and WC, which are simple, inexpensive, and noninvasive means, are the best markers to predict the risk of elevated/stage 1 and undiagnosed hypertension in young Iranians. It shows that the approach of reducing hypertension prevalence through primary prevention, early detection, and enhancing its treatment is achievable.

The study aimed to identify accurate cut-off points for waist circumference (WC), body fat percentage (BF%), body mass index (BMI), fat mass index (FMI), and fat-free mass index (FFMI), and to determine their effective accuracy to predict cardiovascular risk factors (CVRFs) among Mexican young adults. A cross-sectional study was conducted among 1730 Mexican young adults. Adiposity measures and CVRFs were assessed under fasting conditions. The optimal cut-off points were assessed using the receiver operating characteristic curve (ROC). Age-adjusted odds ratios (OR) were used to assess the associations between anthropometric measurements and CVRFs. The cut-off values found, in females and males, respectively, for high WC (≥72.3 and ≥84.9), high BF% (≥30 and ≥22.6), high BMI (≥23.7 and ≥24.4), high FMI (≥7.1 and ≥5.5), and low FFMI (≤16 and ≤18.9) differ from those set by current guidelines. High BMI in women, and high FMI in men, assessed by the 50th percentile, had the best discriminatory power in detecting CVRFs, especially high triglycerides (OR: 3.07, CI: 2.21-4.27 and OR: 3.05, CI: 2.28-4.08, respectively). Therefore, these results suggest that BMI and FMI measures should be used to improve the screening of CVRFs in Mexican young adults.

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders, affecting 5%-10% of women of reproductive age. The importance of this syndrome lies in the magnitude of associated comorbidities: infertility, metabolic dysfunction, cardiovascular disease (CVD), plus psychological and oncological complications. Insulin resistance (IR) is a prominent feature of PCOS with a prevalence of 35%-80%. Without adequate management, IR with compensatory hyperinsulinemia contributes directly to reproductive dysfunction in women with PCOS. Furthermore, epidemiological data shows compelling evidence that PCOS is associated with an increased risk of impaired glucose tolerance, gestational diabetes mellitus and type 2 diabetes. In addition, metabolic dysfunction leads to a risk for CVD that increases with aging in women with PCOS. Indeed, the severity of IR in women with PCOS is associated with the amount of abdominal obesity, even in lean women with PCOS. Given these drastic implications, it is important to diagnose and treat insulin resistance as early as possible. Many markers have been proposed. However, quantitative assessment of IR in clinical practice remains a major challenge. The gold standard method for assessing insulin sensitivity is the hyperinsulinemic euglycemic glucose clamp. However, it is not used routinely because of the complexity of its procedure. Consequently, there has been an urgent need for surrogate markers of IR that are more applicable in large population-based epidemiological investigations. Despite this, many of them are either difficult to apply in routine clinical practice or useless for women with PCOS. Considering this difficulty, there is still a need for an accurate marker for easy, early detection and assessment of IR in women with PCOS. This review highlights markers of IR already used in women with PCOS, including new markers recently reported in literature, and it establishes a new classification for these markers.

Whether anthropometric or body composition indices are better indicators of metabolic risk remains unclear. The objectives of this study were to compare the association of metabolic risk factors with anthropometric and body composition indices and to identify the better indicators for risk factors in a large-scale Korean population. In this cross-sectional study, the associations of body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) as anthropometric indices and trunk fat mass (TFM), percent trunk fat mass (%TFM), whole-body total fat mass (WBTFM), and percent whole-body total fat mass (%WBTFM) as body composition indices with metabolic risk factors were compared by complex-samples multiple logistic regression models based on complex-sample survey data. In men, WHtR, BMI, and TFM were similarly associated with hypertension. Diabetes, hyperlipidemia, and hypo-high-density lipoprotein (HDL) cholesterolemia tended to be more strongly associated with WHtR and WC than body composition indices. Hypertriglyceridemia and hypercholesterolemia were more strongly associated with WHtR and %TFM than other indices. In women, hypertension tended to be more strongly associated with WHtR than other indices. TFM, %TFM, and WHtR were similarly associated with hyperlipidemia. Diabetes and hypo-HDL cholesterolemia were more strongly associated with WHtR and WC than body composition indices. Hypertriglyceridemia and hypercholesterolemia were more strongly associated with WHtR and %TFM than other indices. Among six metabolic risk factors, the validity and utility of the anthropometric indices in identifying risk factors tended to be similar to or better than those of the body composition indices, except for hypertension and hypercholesterolemia in men and hyperlipidemia and hypercholesterolemia in women.

Insulin resistance (IR) is the main complication found in 35%-80% of women with polycystic ovary syndrome (PCOS). However, there is no definite consensus regarding which marker to use for its assessment in PCOS women. Research has shown that hyperinsulinemia is correlated with increased bone mass. Given that most women with PCOS are insulin resistant, which is independent from body fat and characterized by hyperinsulinemia, it could be hypothesized that there would be an increased bone mass in the patient as a result. Subsequently, increased bone mass could be measured using the wrist circumference method.

To assess the wrist circumference as an easy-to-detect marker of IR in Congolese women with PCOS.

Seventy-two Congolese women with PCOS and seventy-one controls from the same ethnic group, were enrolled in the study (mean age 24.33 ± 5.36 years). Fasting biochemical parameters, and the Homeostasis Model Assessment of insulin resistance (HOMA-IR) and body composition were evaluated. The non-dominant wrist circumference was measured manually, as was the waist circumference (WC), hip circumference, height and weight. Calculated measures included evaluation of body mass index (BMI), Waist-to-Height (WHtR) and Waist-to-hip ratio (WHR). In addition, body composition was assessed by Bioelectrical Impedance Analysis using a body fat analyzer.

The non-dominant wrist circumference was more closely correlated with HOMA-IR (r = 0.346; P = 0.003) and was the best anthropometrical marker correlated with IR (P = 0.011 ) compared with other anthropometrical markers in women with PCOS: Dominant Wrist Circumference (r = 0.315; P = 0.007), Waist Circumference (WC) (r = 0.259; P = 0.028), BMI (r = 0.285; P = 0.016), WHR (r = 0.216; P = 0,068) and WHtR (r = 0.263; P = 0.027). The diagnostic accuracy of the non-dominant wrist circumference for the presence or absence of IR using Receiver-operating characteristic (ROC) curve analysis showed that the area under the ROC curve was 0.72. A cutoff value for the non-dominant wrist circumference of 16.3 cm was found to be the best predictor of IR in Congolese women with PCOS.

Non-dominant wrist circumference is, to date, the best anthropometrical marker of IR in Sub-Saharan African women with PCOS. It could be suggested as an easy-to-detect marker for assessing IR.

This study aimed to evaluate and compare the abilities of waist circumference (WC), body mass index (BMI), hip circumference (HC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) to predict recently and previously diagnosed diabetes and hypertension and assess their appropriate cut-off values among Jordanian adults.

Data from the 2017 cardiovascular risk factors survey were analyzed to achieve the study objective. The survey collected extensive data from a national population-based sample of Jordanian residents. A structured questionnaire was used to collect sociodemographic variables and clinical data. Blood samples were taken for biochemical measurements. Anthropometric characteristics were measured by the same team of trained field researchers.

This study included a total of 1193 men and 2863 women. Their age ranged from 18 to 90 year with a mean (SD) of 43.8 (14.2) year. WHtR performed better than other anthropometric measures and had a good ability (AUC > 0.80) among women and fair ability among men to predict newly diagnosed diabetes and previously diagnosed diabetes and hypertension. The appropriate cut-off points for anthropometric measures among women were 92 cm form WC, 104 cm for HC, 30 Kg/m2 for BMI, 0.85 for WHR, and 0.60 for WHtR. For men, the appropriate cut-off points were 100 cm for WC, 104 cm for HC, 27 Kg/m2 for BMI, 0.93 for WHR, and 0.57 for WHtR.

WHtR performed better than other anthropometric measures in predicting diabetes and hypertension among adult population in Jordan. We recommend WHtR as a measure of choice with a cut-off value of 0.6 for women and 0.57 for men to predict diabetes and hypertension among Jordanians.

Obesity has become a serious epidemic health problem in both developing and developed countries. There is much evidence that obesity among adolescents contributed significantly to the development of type 2 diabetes and coronary heart disease in adulthood. Very limited information exists on the prevalence of overweight, obesity, and associated metabolic risk factors among Syrian adolescents. Therefore, the purpose of this study was to determine the relationship between obesity determined by body mass index and the major metabolic risk factors among Syrian adolescents.

A cross-sectional study of a randomly selected sample of 2064 apparently healthy Syrian adolescents aged 18 to 19 years from Damascus city, in Syria, was performed. Body mass index and blood pressure were measured. Serum concentrations of glucose, triglycerides, total cholesterol, high-density lipoprotein-cholesterol, and low-density lipoprotein-cholesterol were determined. Metabolic syndrome was defined using the national criteria for each determined metabolic risk factor. Individuals with a body mass index 25 to 29.9 were classified as overweight, whereas individuals with a body mass index ≥30 were classified as obese. A receiver operating characteristics curve was drawn to determine appropriate cut-off points of the body mass index for defining overweight and obesity, and to indicate the performance of body mass index as a predictor of risk factors.

The obtained data showed that blood pressure and the overall mean concentrations of fasting blood sugar, triglycerides, cholesterol, low-density lipoprotein-cholesterol, and triglycerides/high-density lipoprotein-cholesterol were significantly higher in overweight and obese adolescent groups (p <0.0001) in comparison with the normal group. Based on receiver operating characteristics calculation for body mass index and some metabolic risks, the data suggest the best body mass index cut-offs ranged between 23.25 and 24.35 kg/m².

A strong association between overweight and obesity as determined by body mass index and high concentrations of metabolic syndrome components has been demonstrated. Although body mass index values were lower than the international cut-offs, these values were good predictors of some metabolic abnormalities in Syrian adolescents; body mass index is a good predictor of these abnormalities in this population.

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns.

Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures.

In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression.

Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

This study examined the relation between waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR) and triceps skin-fold thickness and percentage body fat in young Syrian men. The aim was to develop equations that can use these anthropometric characteristics to estimate percentage body fat in this group of subjects. A total of 2470 healthy Syrian men aged 18-19 years were enrolled in the study in 2010-2011. The anthropometric characteristics of all subjects were measured. The percentage body fat of 213 of the subjects was determined using the deuterium dilution (DD) technique as a reference method. The validity of using WC, HC, WHpR and WHtR to calculate percentage body fat, in comparison with the reference method (DD technique), was assessed by calculating biases and limits of agreement. The estimates of percentage body fat using 'WC' and 'WC with triceps skin-fold' measurements ranged from 13.00±5.56% to 14.55±8.63%, and were lower than those determined using the reference method (21.32±6.42%). A better prediction equation is proposed for young adults, based on a multiple linear regression model using WC, HC and WHtR.

The present study was performed to test the predictive value of different cut-off points of anthropometric parameters for the presence of type 2 diabetes (T2DM) or glucose tolerance abnormalities in north-east Chinese adults.

Multistage random cluster sampling method in a cross-sectional study.

Height, body weight, maximal body weight in the past, waist and hip circumferences, blood pressure, 2 h post-load glucose and other lifestyle factors were measured.

We used data from 1058 adults aged 20 years or over, selected in the city of Mudanjiang, in 2005.

BMI, maximal BMI in the past (MAXBMI), waist:hip ratio (WHR), waist:height ratio (WHtR) and waist circumference (WC) were significantly correlated with each other. Partial correlation coefficients between WHtR and WC, and between MAXBMI and BMI, were higher than those between the other indices. The association of anthropometric indices with T2DM or glucose tolerance abnormalities was significantly highest for the collaboration cut-off points of MAXBMI (≥ 23.0 kg/m(2) for T2DM, ≥ 22.0 kg/m(2) for glucose tolerance abnormalities) with WHtR (≥ 0.52). Areas under the receiver-operating characteristic curves also showed that WHtR was a better anthropometric index that discriminated between the presence and absence of T2DM and an excellent indicator with high Youden's index.

MAXBMI combined with WHtR was a better anthropometric index associated with T2DM or glucose tolerance abnormalities. The combined use of these two measures is a good choice for T2DM prevention and screening.

It is well known that body fat distribution and obesity are important risk factors for type 2 diabetes. Prediction of type 2 diabetes using a combination of anthropometric measures remains a controversial issue. This study aims to predict the fasting plasma glucose (FPG) status that is used in the diagnosis of type 2 diabetes by a combination of various measures among Korean adults. A total of 4870 subjects (2955 females and 1915 males) participated in this study. Based on 37 anthropometric measures, we compared predictions of FPG status using individual versus combined measures using two machine-learning algorithms. The values of the area under the receiver operating characteristic curve in the predictions by logistic regression and naive Bayes classifier based on the combination of measures were 0.741 and 0.739 in females, respectively, and were 0.687 and 0.686 in males, respectively. Our results indicate that prediction of FPG status using a combination of anthropometric measures was superior to individual measures alone in both females and males. We show that using balanced data of normal and high FPG groups can improve the prediction and reduce the intrinsic bias of the model toward the majority class.

Obesity is a risk factor for type 2 diabetes mellitus which may be addressed by application of intensive lifestyle interventions. Thus, establishing normative values of anthropometric indices in our environment is crucial. This study aimed to determine normative values of anthropometric indices of nutrition among residents of Calabar.

This cross sectional observational study recruited residents of Calabar aged between 15-79 years using a multistage sampling method. Trained research assistants collected socio-demographic data and did anthropometric measurements.

There were 645 (56.5%) males and 489 (43.1%) females. Males had significantly lower general adiposity and hip circumference (HC) than females while females had significantly lower waist circumference (WC) and waist hip ratio (WHR) than males. The WHR increased with age particularly among males. Body mass index (BMI) also increased with age in both males and females with a peak in the middle age bracket, followed by a decline among the elderly. The mean (SD) BMI was 27.7 (5.0) kg/m(2). Males had a mean (SD) BMI of 27.0 (4.4) kg/m(2), while females had a mean (SD) BMI of 28.5 (5.5) kg/m(2) respectively. WC correlated positively and significantly with BMI and WHR in males and females. WHR correlated positively and significantly with BMI in males and females.

There are positive linear inter relationships between the indices of nutrition which is strongest between WC and BMI. In view of the strong independent association of DM with indices of nutrition, it is appropriate to derive normal cut-off values for WC, WHR and BMI nationally.

It is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men.

For 12789 subjects 21-85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques.

The best indicator for predicting hypertension was rib circumference in both women (p = <0.0001; OR = 1.813; AUC = 0.669) and men (p = <0.0001; OR = 1.601; AUC = 0.627); for hypotension, the strongest predictor was chest circumference in women (p = <0.0001; OR = 0.541; AUC = 0.657) and neck circumference in men (p = <0.0001; OR = 0.522; AUC = 0.672). In experiments using combined indices, the areas under the receiver operating characteristic curves (AUC) for the prediction of hypertension risk in women and men were 0.721 and 0.652, respectively, according to the logistic regression with wrapper-based variable selection; for hypotension, the corresponding values were 0.675 in women and 0.737 in men, according to the naïve Bayes with wrapper-based variable selection.

The best indicators of the risk of hypertension and the risk of hypotension may differ. The use of combined indices seems to slightly improve the predictive power for both hypertension and hypotension.

The identification of increased cardiometabolic risk among asymptomatic individuals remains a huge challenge. The aim of this meta-analysis was to compare the association of body mass index (BMI), which is an index of general obesity, and waist-to-height ratio (WHtR), an index of abdominal obesity, with cardiometabolic risk in cross-sectional and prospective studies.

PubMed and Embase databases were searched for cross-sectional or prospective studies that evaluated the association of both BMI and WHtR with several cardiometabolic outcomes. The strength of relative risk (RR) with 95% confidence interval (CI) was calculated using the optimal cutoffs of BMI and WHtR in cross-sectional studies, while any available cutoff was used in prospective studies. The pooled estimate of the ratio of RRs (rRR [=RRBMI/RRWHtR]) with 95% CIs was used to compare the association of WHtR and BMI with cardiometabolic risk. Meta-regression was used to identify possible sources of heterogeneity between the studies.

Twenty-four cross-sectional studies and ten prospective studies with a total number of 512,809 participants were identified as suitable for the purpose of this meta-analysis. WHtR was found to have a stronger association than BMI with diabetes mellitus (rRR: 0.71, 95% CI: 0.59-0.84) and metabolic syndrome (rRR: 0.92, 95% CI: 0.89-0.96) in cross-sectional studies. Also in prospective studies, WHtR appears to be superior to BMI in detecting several outcomes, including incident cardiovascular disease, cardiovascular disease mortality, and all-cause mortality. The usefulness of WHtR appears to be better in Asian than in non-Asian populations. BMI was not superior to WHtR in any of the outcomes that were evaluated. However, the results of the utilized approach should be interpreted cautiously because of a substantial heterogeneity between the results of the studies. Meta-regression analysis was performed to explain this heterogeneity, but none of the evaluated factors, ie, sex, origin (Asians, non-Asians), and optimal BMI or WHtR cutoffs were significantly related with rRR.

The results of this meta-analysis support the use of WHtR in identifying adults at increased cardiometabolic risk. However, further evidence is warranted because of a substantial heterogeneity between the studies.

Our aim was to differentiate the screening potential of waist-to-height ratio (WHtR) and waist circumference (WC) for adult cardiometabolic risk in people of different nationalities and to compare both with body mass index (BMI). We undertook a systematic review and meta-analysis of studies that used receiver operating characteristics (ROC) curves for assessing the discriminatory power of anthropometric indices in distinguishing adults with hypertension, type-2 diabetes, dyslipidaemia, metabolic syndrome and general cardiovascular outcomes (CVD). Thirty one papers met the inclusion criteria. Using data on all outcomes, averaged within study group, WHtR had significantly greater discriminatory power compared with BMI. Compared with BMI, WC improved discrimination of adverse outcomes by 3% (P < 0.05) and WHtR improved discrimination by 4-5% over BMI (P < 0.01). Most importantly, statistical analysis of the within-study difference in AUC showed WHtR to be significantly better than WC for diabetes, hypertension, CVD and all outcomes (P < 0.005) in men and women. For the first time, robust statistical evidence from studies involving more than 300 000 adults in several ethnic groups, shows the superiority of WHtR over WC and BMI for detecting cardiometabolic risk factors in both sexes. Waist-to-height ratio should therefore be considered as a screening tool.

To set references and evaluate the associations between the predictive powers of the anthropometric indices of obesity, particularly central obesity, including body mass index (BMI), waist circumference (WC), waist to hip ratio (WHpR) and waist to height ratio (WHtR), and the risk factor accumulations of ≥ 2 of the components of the metabolic syndrome (MS) in a group of Jordanian men and women.

Five hundreds subjects were randomly selected from among the visitors attending several family clinics in Amman. Obesity was assessed using BMI, WC, WHpR and WHtR anthropometric indices. MS risk factors as defined by the International Diabetes Federation were determined. Receiver operating characteristic curve (ROC) analysis was used to determine the predictive powers and the cut off points of each index associated with increased MS risk.

There were 212 men and 288 women with age ranged 20-85 years. Optimal cut off points of BMI, WC, WHpR for MS diagnosis in men were 28.4 kg/m(2), 97.8 cm and 0.89, respectively. In women, these were 30.3 kg/m(2), 95.6 cm and 0.84, respectively. WHtR was 0.61 in both genders. Area under the curve (AUC) of ROC analysis for identifying of MS (≥ 2 risk factors) was the highest for WHpR (AUC=0.71), followed by WHtR (AUC=0.67), WC (AUC=0.64) and BMI (AUC=0.59) in men; whereas in women WHpR, WHtR and WC were almost equal (AUC=0.76, 0.75 and 0.74, respectively), followed by BMI (AUC=0.67). Correlation coefficients (r) between WHpR and MS risk factors were the strongest among the other obesity indices, followed by WC and WHtR. WHpR correlated significantly with FBG (r=0.27, p<0.01), systolic blood pressure (r=0.20, p<0.01), TGs (r=0.24, p<0.01) and HDL-C (r=-0.39, p<0.01). The respective r-values between WC and WHtR and each MS risk factors were: FBG (r=0.15, p<0.001 or r=0.13, p<0.01), systolic blood pressure (r=0.16, p<0.01 or r=0.11, p<0.05), TGs (r=0.20, p<0.01 or r=0.14, p<0.01) and HDL-C (r=-0.25, p<0.01 or r=-0.11, p<0.01).

This study showed that BMI tended to be the weakest index for identifying MS risk factors in both sexes. WHpR exhibited the best predictive index for MS, particularly in men. Almost similar predictive powers of WHtR, WHpR and WC for identifying MS risk factors were seen in women. WHtR had the highest sensitivity for MS diagnosis among obesity indices in men and its boundary value was the same for both men and women. These cut off values of obesity particularly waist circumference should be advocated and used in Arab Jordanians until larger cross sectional studies shows different results.

Due to the lack of compelling evidence for waist circumference (WC) as a cardiovascular disease (CVD) risk factor in many ethnic groups, the need for local research has been expressed by international authorities. This study was undertaken to determine the optimal cut-off points of WC for predicting incident CVD and metabolic syndrome in an Iranian population.

A total of 6,504 participants from three areas in central Iran were followed over 7 years. Receiver operating characteristics (ROC) and area under the curve (AUC) were used to identify the maximum value of sensitivity and specificity combinations corresponding to the appropriate cut-off points of WC for the detection of the metabolic syndrome and CVD events. The optimal cut-off values were defined as the point at which the value of "sensitivity+specificity-1" reached the maximum value. Finally, Cox proportional hazard modeling was used to determine which cut-off point was better fit in the CVD risk prediction model.

After 394,418 person-years of follow-up, 427 incident primary CVD events (233 men) were identified. Considering CVD, the optimum cut-off points were 99/103.5 cm (men/women) but these had a low sensitivity (AUC: 0.59, 95%CI 0.55-0.63 in both men and women). The second highest values for discriminating CVD were 93/97 cm that resulted in acceptable sensitivity. Regarding the metabolic syndrome, 92.6/97.8 cm were identified as optimum (AUC: 0.67, 95%CI 0.65-0.69 in men and 0.65, 95%CI 0.63-0.67 in women). The best cut-off values that fit in the Cox regression model were 90/97 cm.

International recommended WC cut-off values for the Middle East are not appropriate compared to the locally defined cut-off values in Iran.

We aimed to explore an optimal anthropometric indicator and optimal cut-off points for incident diabetes in Chinese adults.

61,703 subjects were followed for a median duration of 2 years. Body mass index, waist circumference, waist-to-hip ratio and waist-to-height ratio were collected base on a standard protocol. Receiver Operating Characteristic curve analyses were used to compare the predictive power of baseline BMI, WC, WHpR and WHtR for development of type 2 diabetes.

There were 2991 new cases of type 2 diabetes during follow-up. ROC curve analyses indicated that WHtR was the best predictor of type 2 diabetes for male (AUC = 0.633). For female, WHtR and WC had similar predictive ability (AUC = 0.701 and 0.695 respectively) and were superior to BMI. WHpR was the weakest predictor in both genders. The optimal WHtR cut-off values for incidence of type 2 diabetes were similar in both genders (0.53 vs. 0.52). BMI was higher in men (26 kg/m(2)) than women (24 kg/m(2)); and so did WC (91 cm in men vs. 85 cm in women).

WHtR, and to some degree WC, are the best predictors of type 2 diabetes, followed by BMI then WHpR which is the weakest predictor in the tested adults.

To determine cutoff points of anthropometric variables for predicting incident cardiovascular disease (CVD) in Iranian adults.

It is a population-based longitudinal study.

A total of 1614 men and 2006 women, aged > or =40 years, free of CVD at baseline were included in the study.

Body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and cardiovascular risks were assessed. Incident CVD was ascertained over a median of 7.6 years follow-up. The adjusted hazard ratios (HRs) for CVD were calculated for 1 s.d. change in all obesity variables using Cox proportional hazards regression analysis. Receiver operator characteristic (ROC) curve analysis was used as the method of defining the points of the maximum sum of sensitivity and specificity (MAXss) of each variable as a predictor of CVD.

We found 333 CVD events during follow-up. The risk-factor-adjusted HRs were significant for all anthropometric variables in males and WHR in females and were 1.19, 1.24, 1.21 and 1.24 for BMI, WC, WHR and WHtR in males and 1.27 for WHR in females, respectively (all P<0.05). ROC analysis showed the highest area under curve (AUC) for WHR, WHtR and WC, followed by BMI in males and both genders aged< or =60 years. In females, WHR and WHtR had the highest AUC, followed by WC and BMI. Among those >60 years old, all the anthropometric variables showed same CVD predicting power. The cutoff values (MAXss) for CVD prediction in males and females were BMIs 26.95 and 29.19 kg m(-2),WCs 94.5 and 94.5 cm, WHRs 0.95 and 0.90, and WHtR 0.55 and 0.62, respectively.

There was no difference between central obesity variables in predicting CVD in males, whereas in females WHR and WHtR were more appropriate. The cutoff values of anthropometric variables were higher in the Iranian than in other Asian populations.

This systematic review collated seventy-eight studies exploring waist-to-height ratio (WHtR) and waist circumference (WC) or BMI as predictors of diabetes and CVD, published in English between 1950 and 2008. Twenty-two prospective analyses showed that WHtR and WC were significant predictors of these cardiometabolic outcomes more often than BMI, with similar OR, sometimes being significant predictors after adjustment for BMI. Observations from cross-sectional analyses, forty-four in adults, thirteen in children, supported these predictions. Receiver operator characteristic (ROC) analysis revealed mean area under ROC (AUROC) values of 0·704, 0·693 and 0·671 for WHtR, WC and BMI, respectively. Mean boundary values for WHtR, covering all cardiometabolic outcomes, from studies in fourteen different countries and including Caucasian, Asian and Central American subjects, were 0·50 for men and 0·50 for women. WHtR and WC are therefore similar predictors of diabetes and CVD, both being stronger than, and independent of, BMI. To make firmer statistical comparison, a meta-analysis is required. The AUROC analyses indicate that WHtR may be a more useful global clinical screening tool than WC, with a weighted mean boundary value of 0·5, supporting the simple public health message 'keep your waist circumference to less than half your height'.

In total, 17 prospective and 35 cross-sectional studies in adults aged 18-74 years, with the aim of comparing betweenbody mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) in their relation to the incidence and prevalence of type II diabetes, were reviewed. Among these studies, only a few have used C-statistic, paired homogeneity test or log-likelihood ratio test for formally comparing the differences. Five prospective studies, in which formal statistic tests have been made, came out with inconsistent findings: two results were in favour of WC in Mexicans African Americanss, respectively, one result was in favour of BMI in Pima Indians, and no difference was found in the other 2 studies. Among the 11 cross-sectional studies that have formally tested the differences, most found a higher odds ratio or slightly larger area under the ROC curve (AUC) for WC than for BMI. A meta-analysis based on the individual data of the Asian cohorts using a paired homogeneity test showed, however, that there was no difference in odds ratio between BMI and WC in Chinese, Japanese, Indian, Mongolian and Filipino men. In conclusion, all studies included in this review showed that either BMI or WC (WHR) predicted or was associated with type II diabetes independently, regardless of the controversial findings on which of these obesity indicators is better.

To analyse data from the nationally representative National Diet and Nutrition Survey (NDNS) collected in 2000/2001 and to investigate how the BMI and two proxy indicators of central fat distribution, namely the waist circumference and the waist to height ratio (WHtR), are associated with each other and with cardiovascular disease (CVD) risk factors.

Screening health risk by BMI alone would 'miss' 35% of men and 14% of women who are within the normal BMI range (18.5-25 kg/m(2)) but have central fat distribution, defined by WHtR > 0.5. In the total population this equates to 17% of all men and 6% of all women who would be inadequately screened by BMI alone. Compared to BMI, WHtR was more closely associated with CVD risk factors among both men and women. Furthermore, in a combined analysis of men and women, central fat distribution with a normal BMI was associated with higher levels of CVD risk factors than being overweight without central fat distribution.

WHtR is a simple and effective, non-invasive screening tool for CVD risk factors. Our proposed boundary value of 0.5 translates into a simple public health message: 'Keep your waist circumference to less than half your height'.