Optimal cut-off points of anthropometric and body roundness indices associated with diabetes: Persian (Shahedieh) cohort study
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SUBMITTER: Ghomi F
PROVIDER: S-EPMC11345168 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundTo determine the anthropometric indices that would predict type 2 diabetes (T2D) and delineate their optimal cut-points.MethodsIn a cohort study, 7017 Iranian adults, aged 20-60 years, free of T2D at baseline were investigated. Using Cox proportional hazard models, hazard ratios (HRs) for incident T2D per 1 SD change in body mass index (BMI), waist circumference (WC), waist to height ratio (WHtR), waist to hip ratio (WHR), and hip circumference (HC) were calculated. The area under the receiver operating characteristics (ROC) curves (AUC) was calculated to compare the discriminative power of anthropometric variables for incident T2D. Cut-points of each index were estimated by the maximum value of Youden's index and fixing the sensitivity at 75%. Using the derived cut-points, joint effects of BMI and other obesity indices on T2D hazard were assessed.ResultsDuring a median follow-up of 12 years, 354 men, and 490 women developed T2D. In both sexes, 1 SD increase in anthropometric variables showed significant association with incident T2D, except for HC in multivariate adjusted model in men. In both sexes, WHtR had the highest discriminatory power while HC had the lowest. The derived cut-points for BMI, WC, WHtR, WHR, and HC were 25.56 kg/m2, 89 cm, 0.52, 0.91, and 96 cm in men and 27.12 kg/m2, 87 cm, 0.56, 0.83, and 103 cm in women, respectively. Assessing joint effects of BMI and each of the obesity measures in the prediction of incident T2D showed that among both sexes, combined high values of obesity indices increase the specificity for the price of reduced sensitivity and positive predictive value.ConclusionsOur derived cut-points differ between both sexes and are different from other ethnicities.
| S-EPMC5987476 | biostudies-literature
Project description:ObjectiveIdentifying metabolic disorders at the earliest phase of their development allows for an early intervention and the prevention of serious consequences of diseases. However, it is difficult to determine which of the anthropometric indices of obesity is the best tool for diagnosing metabolic disorders. The aims of this study were to evaluate the usefulness of selected anthropometric indices and to determine optimal cut-off points for the identification of single metabolic disorders that are components of metabolic syndrome (MetS).DesignCross-sectional study.ParticipantsWe analyzed the data of 12,328 participants aged 55.7±5.4 years. All participants were of European descent.Primary outcome measureFour MetS components were included: high glucose concentration, high blood triglyceride concentration, low high-density lipoprotein cholesterol concentration, and elevated blood pressure. The following obesity indices were considered: waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body fat percentage (%BF), Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE), body roundness index (BRI), and a body shape index (ABSI).ResultsThe following indices had the highest discriminatory power for the identification of at least one MetS component: CUN-BAE, BMI, and WC in men (AUC = 0.734, 0.728, and 0.728, respectively) and WHtR, CUN-BAE, and WC in women (AUC = 0.715, 0.714, and 0.712, respectively) (p<0.001 for all). The other indices were similarly useful, except for the ABSI.ConclusionsFor the BMI, the optimal cut-off point for the identification of metabolic abnormalities was 27.2 kg/m2 for both sexes. For the WC, the optimal cut-off point was of 94 cm for men and 87 cm for women. Prospective studies are needed to identify those indices in which changes in value predict the occurrence of metabolic disorders best.
| S-EPMC7307766 | biostudies-literature
Project description:BackgroundDetecting the early onset of metabolic syndrome (MetS) allows for quick intervention which may slow progression to a variety of health consequences, hence, determining the best measurement to detect MetS is essential.AimThis research aimed at examining the MetS predictive power of anthropometric indices, such as body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), body shape index (ABSI), body roundness index (BRI), percentage body fat (%BF), conicity index (CI), and Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE) to determine the cut-off points to identify male South African taxi drivers with MetS.MethodA cross-sectional study was conducted among 185 male taxi drivers. Their weight, height, WC, blood lipid profile were measured. International Diabetes Federation (IDF) definition was used to define MetS. Receiver Operating Characteristic (ROC) curves were used to compare the predictive ability of Anthropometric indices to detect MetS.ResultsThe mean age of the participants was 39.84 years. Overall, 41.6% (N = 77) of the participants presented with MetS. The mean values for BMI, WC, WHtR, %BF, BRI, CUN-BAE, ABSI and CI were 28.60 ± 6.20 kg/m2, 99.13 ± 17.59 cm, 0.58 ± 0.10, 27.28 ± 8.28%, 5.09 ± 2.33, 27.78 ± 8.34, 0.08 ± 0.01 and 1.70 ± 0.19, respectively. The mean values for these indices were significantly (p < 0.001) higher in participants with MetS. The highest area under the curve (AUC) outcomes for screening MetS were for the %BF and CUN-BAE, followed by the BMI and WHtR, and lastly the BRI. All these anthropometric indices had outstanding discriminatory powers for predicting MetS with AUCs and sensitivity values above 80%. The BMI, WHtR, %BF, BRI, and CUN-BAE, had cut-off points for detection of metS in South African men at 28.25 kg/m2, 0.55, 25.29%, 4.55, and 27.10, respectively. Based on the logistic regression models abnormal BMI, WHtR, %BF, BRI, CUN-BAE, TG, FBG, systolic BP, diastolic BP and WC showed increased risk of MetS.ConclusionWhile the %BF, CUN-BAE, BMI, WC, WHtR, BRI, CI and CUN-BAE could predict MetS among South African male taxi drivers, these indices were less effective in predicting the individual MetS risk factors such as TG, BP, and FBG.
| S-EPMC9406286 | biostudies-literature
Project description:Recent decades have seen a dramatic rise in the prevalence of obesity. While genetic factors can influence obesity, environmental factors and lifestyle may play important roles as well. Sleep can be regarded as one of these factors. This study aimed to examine sleep duration, as a potential risk factor for obesity in an Iranian population. In this cross-sectional study, the Fasa PERSIAN cohort study data was used and 10,136 subjects aged 35-70 were entered. Anthropometrics indices have been measured and the total body fat percentage (BFP) was obtained by Bio-Impedance Analysis. Also, physical activity and dietary intake have been recorded. Sleep duration was obtained and individuals categorized into two groups of "< 8" and "≥ 8" h of sleep. The mean age and sleep duration of the participants were 48.63 ± 9.57 years and 6.92 ± 1.62 h in the total population, respectively. All of the anthropometric indices were significantly higher in the "< 8 h of sleep" group than in the "≥ 8 h of sleep" group. Regarding BFP and fat mass index (FMI) the same results was seen (p-value < 0.05). Body mass index (BMI), Waist and hip circumferences (WC, HC), and waist-to-height ratio (WHtR) were in a significant negative association with night time sleep (p-value < 0.001), while these associations with daytime napping were positive (p-value < 0.001). After multi-variable adjusting, BMI, WC, HC, WHtR, and wrist circumference showed significant negative associations with 24-h sleep duration (p-value < 0.05). This study established the association between nocturnal, daytime napping, 24-h sleep duration and obesity parameters. Daytime napping was positively associated with obesity parameters and short 24-h sleep duration was associated with higher risk of overweight/obesity. These results indicate that insufficient sleep can be a screening indicator for an unhealthy lifestyle and poor health outcomes.
| S-EPMC8355308 | biostudies-literature
Project description:ObjectiveThis study compares the relationships between five anthropometric indices, a body shape index (ABSI), body roundness index (BRI), waist circumference (WC), body mass index (BMI) and waist-to-height ratio (WHtR), and hypertension, assessing their predictive capacities. The aim is to determine the specific numerical changes in hypertension incidence, systolic blood pressure (SBP) and diastolic blood pressure (DBP) for each increase in standard deviation of these indices, and to identify the optimal predictive indicators for different populations, including the calculation of cutoff values.MethodsThis study used data from the NHANES datasets spanning 2007 to 2018. Logistic regression analysis was used to quantify the associations between these anthropometric indices and hypertension, calculating β coefficients and odds ratios (ORs). Receiver operating characteristic (ROC) analysis was used to evaluate the predictive ability of each index for hypertension.ResultsFor each increase in standard deviation in WC, BMI, WHtR, ABSI and BRI, the prevalence of hypertension increased by 33% (95% CI: 27%-40%), 32% (95% CI: 26%-38%), 35% (95% CI: 28%-42%), 9% (95% CI: 4%-16%) and 32% (95% CI: 26%-38%), respectively. The SBP correspondingly increased by 2.36 mmHg (95% CI: 2.16-2.56), 2.41 mmHg (95% CI: 2.21-2.60), 2.48 mmHg (95% CI: 2.28-2.68), 0.42 mmHg (95% CI: 0.19-0.66) and 2.46 mmHg (95% CI: 2.26-2.66), respectively. Similarly, DBP increased by 1.83 mmHg (95% CI: 1.68-1.98), 1.72 mmHg (95% CI: 1.58-1.87), 1.72 mmHg (95% CI: 1.57-1.88), 0.44 mmHg (95% CI: 0.27-0.62) and 1.64 mmHg (95% CI: 1.48-1.79). In the youth and middle-aged groups, WC had the best predictive ability, with AUCs of 0.749 and 0.603, respectively. Among the elderly group, the AUCs for all five indices ranged between 0.5 and 0.52.ConclusionIncreases in WC, BMI, WHtR and BRI are significantly associated with higher incidences of hypertension and increases in SBP and DBP, while the impact of ABSI on blood pressure is relatively weak. Stratified analysis indicates significant age-related differences in the predictive value of these indices, with the strongest associations observed in the youth group, followed by the middle age group, and the weakest in the elderly. WC demonstrates excellent predictive ability across youth populations.
| S-EPMC11669579 | biostudies-literature
Project description:Introduction: Non-alcoholic fatty liver disease (NAFLD) is an obesity-associated health problem that causes other liver diseases for the patient. Four anthropometric indices: body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were analyzed as NAFLD predictors in the present study. Methods: From the total number of individuals who referred to the PERSIAN Guilan Cohort study (PGCS) located in the north of Iran during the period of study, a total of 960 people were enrolled in the present study. NAFLD was diagnosed using through an abdominal ultrasound exam. Height, weight, WC, BMI, WHR and WHtR were later calculated. Chi-square, ANOVA and logistic regression analyses were used to analyze the risk factors. Results: Out of the 960 individuals who were enrolled in the study, 597 (62.2%) were male and 363 (37.8%) were female (with an average age of 47.21 ± 7.29 years). There was a significant relationship between weight and NAFLD (P<0.001). There was also a significant relationship between BMI (OR= 8.41; 95% CI = 5.59-12.75), WC (OR= 2.67; 95% CI = 2.05-3.48), WHR (OR= 3.84; 95% CI = 2.26-6.52), WHtR (OR= 28.53; 95% CI = 6.94-117.31) and NAFLD (P<0.001). The results of the logistic regression analysis showed that WHtR, BMI and WC were effective predictors for the risk of NAFLD while WHtR played a more important role in the prediction of NAFLD. Conclusion: Anthropometric indices, especially WHtR, as a simple screening tool, seem to be an important criterion for the detection of NAFLD.
| S-EPMC6197514 | biostudies-other
Project description:Anthropometric measurements are simple and useful methods for predicting metabolic syndrome (MetS) because obesity is a predominant feature of MetS. Although carotid intima-medial thickness (IMT) is generally used to evaluate subclinical atherosclerosis, the relationship between the optimal cut-off anthropometric values for predicting MetS and carotid IMT has not been analyzed in a Korean population.Anthropometric measurements including waist circumference (WC), waist hip ratio (WHR), waist height ratio (WHtR), body mass index (BMI), and carotid IMT were assessed in 2560 Korean subjects without previous history of cardiovascular disease, cerebrovascular disease, neurological abnormalities, or malignancy who participated in baseline health examinations in a self-referral setting in the Seoul area between April 2010 and November 2012. MetS was defined using the National Cholesterol Education Program-Adult Treatment Panel III criteria.In both men and women, the levels of all anthropometric indices were significantly higher in subjects with MetS than in those without MetS. According to the receiver operating characteristic curve, the values of 80.8 cm for WC, 0.87 for WHR, 0.52 for WHtR, and 24.6 kg/m for BMI were the optimal cut-offs for predicting MetS in women. The values of 89.3 cm for WC, 0.90 for WHR, 0.52 for WHtR, and 25.1 kg/m for BMI were the optimal cut-offs for predicting MetS in men. After adjusting for confounding factors, the WC optimal cut-off values for predicting MetS were independently associated with carotid IMT in both women and men (women: β = 0.016, P = .008; men: β = 0.033, P = .009). The optimal BMI cut-off value was independently associated with carotid IMT in men only (β = 0.027, P = .032).Among anthropometric indices including WC, WHR, WHtR, and BMI, the WC optimal cut-off values for MetS were independently associated with an increased carotid IMT in both women and men in a Korean population.
| S-EPMC6824692 | biostudies-literature
Project description:The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestational weeks, we estimated optimal gestational-age (GA) cut points for dichotomizing fetuses into 'young' and 'old' groups based on global network features. We computed the small-world index, normalized clustering and path length, global and local efficiency, and modularity from connectivity matrices comprised 200 regions and their corresponding pairwise connectivity. We modeled the effect of GA at scan on each metric using separate repeated-measures generalized estimating equations. Our modeling strategy involved stratifying fetuses into 'young' and 'old' based on the scan occurring before or after a selected GA (i.e., 28 to 33). We then used the quasi-likelihood independence criterion statistic to compare model fit between 'old' and 'young' cohorts and determine optimal cut points for each graph metric. Trends for all metrics, except for global efficiency, decreased with increasing gestational age. Optimal cut points fell within 30-31 weeks for all metrics coinciding with developmental events that include a shift from endogenous neuronal activity to sensory-driven cortical patterns.
| S-EPMC8304646 | biostudies-literature
Project description:BackgroundPatients with comorbidity of hypertension and diabetes are associated with higher morbidity and mortality of cardiovascular disease than those with hypertension or diabetes alone. The present study aimed to identify anthropometric risk factors for diabetes among hypertensive patients who were included in a retrospective cohort study.MethodsHypertensive adults without diabetes were recruited in China. Demographic, clinical, biochemical, and anthropometric indices were collected at baseline and during the follow-up. Anthropometric measures included BMI, waist circumference, waist-to-height ratio (WHtR), and waist-to-hip ratio, and several novel indices. To estimate the effect of baseline and dynamic changes of each anthropometric index on risk of new-onset diabetes (defined as self-reported physician-diagnosed diabetes and/or use of hypoglycemic medication, or new-onset FPG≥7.0 mmol/L during follow-up), Cox regression models were used.ResultsA total of 3852 hypertensive patients were studied, of whom 1167 developed diabetes during follow-up. Multivariate Cox regression analyses showed that there was a graded increased risk of incident diabetes with successively increasing anthropometric indices mentioned above (all P<0.05). Regardless of the baseline general obesity status, elevated WHtR was both related to higher risk of diabetes; the HRs (95%CI) of baseline BMI<24 kg/m2 & WHtR≥0.5 group and BMI≥24 kg/m2 & WHtR≥0.5 group were 1.34 (1.05, 1.72), 1.85 (1.48, 2.31), respectively. Moreover, the dynamic changes of WHtR could sensitively reflect diabetes risk. Diabetes risk significantly increased when patients with baseline WHtR<0.5 progressed to WHtR≥0.5 during the follow-up (HR=1.63; 95%CI, 1.11, 2.40). There was also a decreasing trend towards the risk of incident diabetes when baseline abnormal WHtR reversed to normal at follow-up (HR=1.93; 95%CI, 1.36, 2.72) compared with those whose WHtR remained abnormal at follow-up (HR=2.04; 95%CI, 1.54, 2.71).ConclusionsCentral obesity is an independent and modifiable risk factor for the development of diabetes among hypertensive patients. Measuring indices of central obesity in addition to BMI in clinics could provide incremental benefits in the discrimination of diabetes among Chinese hypertensive patients. Dynamic changes of WHtR could sensitively reflect changes in the risk of diabetes. Therefore, long-term monitoring of hypertensive patients using non-invasive anthropometric measures and timely lifestyle intervention could effectively reduce the development of diabetes.
| S-EPMC8525507 | biostudies-literature
Project description:BackgroundDespite compelling links between excess body weight and cancer, body mass index (BMI) cut-points, or thresholds above which cancer incidence increased, have not been identified. The objective of this study was to determine if BMI cut-points exist for 14 obesity-related cancers.Subjects/methodsIn this retrospective cohort study, patients 18-75 years old were included if they had ≥2 clinical encounters with BMI measurements in the electronic health record (EHR) at a single academic medical center from 2008 to 2018. Patients who were pregnant, had a history of cancer, or had undergone bariatric surgery were excluded. Adjusted logistic regression was performed to identify cancers that were associated with increasing BMI. For those cancers, BMI cut-points were calculated using adjusted quantile regression for cancer incidence at 80% sensitivity. Logistic and quantile regression models were adjusted for age, sex, race/ethnicity, and smoking status.ResultsA total of 7079 cancer patients (mean age 58.5 years, mean BMI 30.5 kg/m2) and 270,441 non-cancer patients (mean age 43.8 years, mean BMI 28.8 kg/m2) were included in the study. In adjusted logistic regression analyses, statistically significant associations were identified between increasing BMI and the incidence of kidney, thyroid, and uterine cancer. BMI cut-points were identified for kidney (26.3 kg/m2) and uterine (26.9 kg/m2) cancer.ConclusionsBMI cut-points that accurately predicted development kidney and uterine cancer occurred in the overweight category. Analysis of multi-institutional EHR data may help determine if these relationships are generalizable to other health care settings. If they are, incorporation of BMI into the screening algorithms for these cancers may be warranted.
| S-EPMC9615027 | biostudies-literature