Optimal cut-points of different anthropometric indices and their joint effect in prediction of type 2 diabetes: results of a cohort study

Assessing and predicting type 2 diabetes risk with triglyceride glucose-body mass index in the Chinese nondiabetic population-Data from long-term follow-up of Da Qing IGT and Diabetes Study

AIMS

We intended to characterize the superiority of triglyceride glucose-body mass index (TyG-BMI) in predicting type 2 diabetes mellitus (T2DM) compared with triglyceride glucose (TyG) and homeostatic model assessment for insulin resistance (HOMA-IR).

METHODS

A total of 699 nondiabetic participants in the Da Qing IGT and Diabetes Study were involved in the present analysis and classified according to the median of baseline TyG-BMI, namely the G1 (low TyG-BMI) and G2 (high TyG-BMI) groups. Information on developing diabetes was assessed from 1986 to 2020.

RESULTS

During the 34-year follow-up, after adjustment for confounders, the G2 group had a higher risk of developing type 2 diabetes than the G1 group (hazard ratio [HR]: 1.92, 95% confidence interval [CI]: 1.51-2.45, p < 0.0001). Restricted cubic spline analyses showed that increased TyG-BMI was linearly related to higher risks of type 2 diabetes (p for non-linearity>0.05). Time-dependent receiver operator characteristics curves suggested that TyG-BMI exhibited higher predictive ability than TyG (6-year: area under the curve [AUC]TyG-BMI vs. AUCTyG, 0.78 vs. 0.70, p = 0.03; 34-year: AUCTyG-BMI vs. AUCTyG, 0.79 vs. 0.73, p = 0.04) and HOMA-IR (6-year: AUCTyG-BMI vs. AUCHOMA-IR, 0.78 vs. 0.70, p = 0.07; 34-year: AUCTyG-BMI vs. AUCHOMA-IR, 0.79 vs. 0.71, p = 0.04) in both short and long terms, and the thresholds of TyG-BMI to predict type 2 diabetes were relatively stable (195.24-208.41) over the 34-year follow-up.

CONCLUSIONS

In this post hoc study, higher TyG-BMI was associated with an increased risk of type 2 diabetes and demonstrated better predictability than TyG and HOMA-IR, favoring the application of TyG-BMI as a potential tool for evaluating the risk of type 2 diabetes in clinical practice.

J-shaped association of the triglyceride glucose-body mass index with new-onset diabetes

The triglyceride glucose-body mass index (TyG-BMI) is a convenient and clinically significant indicator of insulin resistance. This study aims to investigate the correlation between TyG-BMI and the onset of new-onset diabetes and determine an optimal reflection point for TyG-BMI. An analysis was conducted on 1917 participants from the risk evaluation of cancers in Chinese diabetic individuals: a lONgitudinal (REACTION) study. Participants were categorized based on their TyG-BMI, and the relationship between TyG-BMI and the incidence of new-onset diabetes was explored through logistic regression models, smoothed curve fitting with restricted cubic spline, and a two-piecewise logistic regression model. The mean age of the participants was 57.60 ± 8.89 years, with 66.5% being females. The mean TyG-BMI was 223.3 ± 32.8. Ultimately, 137 individuals (7.1%) progressed to diabetes after three years. After adjusting for covariates, TyG-BMI exhibited a positive correlation with new-onset diabetes (odd ratios (OR) for each standard deviation increase = 1.330, 95% CI 1.110-1.595). The relationship between TyG-BMI and new-onset diabetes was non-linear, with a inflcetion point at 202.9. This study reveals a positive non-linear relationship between TyG-BMI and the risk of new-onset diabetes in Chinese middle-aged and elderly individuals. When TyG-BMI exceeds 202.9, there is a significantly heightened risk of new-onset diabetes. These findings offer valuable insights for preventing new-onset diabetes.

Novel anthropometric indices for predicting type 2 diabetes mellitus

BACKGROUND

This study aimed to compare anthropometric indices to predict type 2 diabetes mellitus (T2DM) among first-degree relatives of diabetic patients in the Iranian community.

METHODS

In this study, information on 3483 first-degree relatives (FDRs) of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences. Overall, 2082 FDRs were included in the analyses. A logistic regression model was used to evaluate the association between anthropometric indices and the odds of having diabetes. Furthermore, a receiver operating characteristic (ROC) curve was applied to estimate the optimal cutoff point based on the sensitivity and specificity of each index. In addition, the indices were compared based on the area under the curve (AUC).

RESULTS

The overall prevalence of diabetes was 15.3%. The optimal cutoff points for anthropometric measures among men were 25.09 for body mass index (BMI) (AUC = 0.573), 0.52 for waist-to-height ratio (WHtR) (AUC = 0.648), 0.91 for waist-to-hip ratio (WHR) (AUC = 0.654), 0.08 for a body shape index (ABSI) (AUC = 0.599), 3.92 for body roundness index (BRI) (AUC = 0.648), 27.27 for body adiposity index (BAI) (AUC = 0.590), and 8 for visceral adiposity index (VAI) (AUC = 0.596). The optimal cutoff points for anthropometric indices were 28.75 for BMI (AUC = 0.610), 0.55 for the WHtR (AUC = 0.685), 0.80 for the WHR (AUC = 0.687), 0.07 for the ABSI (AUC = 0.669), 4.34 for the BRI (AUC = 0.685), 39.95 for the BAI (AUC = 0.583), and 6.15 for the VAI (AUC = 0.658). The WHR, WHTR, and BRI were revealed to have fair AUC values and were relatively greater than the other indices for both men and women. Furthermore, in women, the ABSI and VAI also had fair AUCs. However, BMI and the BAI had the lowest AUC values among the indices in both sexes.

CONCLUSION

The WHtR, BRI, VAI, and WHR outperformed other anthropometric indices in predicting T2DM in first-degree relatives (FDRs) of diabetic patients. However, further investigations in different populations may need to be implemented to justify their widespread adoption in clinical practice.

Associations of waist-to-height ratio with the incidence of type 2 diabetes and mediation analysis: Two independent cohort studies

AIM

To assess the relationship between waist-to-height ratio (WHtR) and the incidence of type 2 diabetes (T2D)/impaired fasting glucose (IFG) and to explore to what extent these associations are mediated by blood pressure, lipids and other indicators related to liver and kidney metabolism.

MATERIALS AND METHODS

This study was based on a functional community cohort included 6109 participants which were divided into two sub-cohorts. One sub-cohort included participants with normal fasting glucose (n = 5563), another included IFG individuals at baseline (n = 546). Cox regression models were used to evaluate the relationships of WHtR with T2D/IFG. Four-year time-dependent receiver operating characteristic (ROC) curve and area under curve (AUC) were calculated to estimate the discriminatory power of WhtR and other anthropometric indices on T2D. Mediation analysis was performed to estimate which risk factors mediate the association between WHtR and T2D.

RESULTS

Significant positive associations were found between WHtR and the incidence of T2D/IFG in both sub-cohort. WhtR was a useful predictor of T2D (P < 0.05). Mediation analysis showed that HOMA-IR (0.45 %), SBP (5.10 %), triglycerides (11.02 %), creatinine (9.36 %) and combined kidney indicators (17.48 %) partly mediated the effect of WHtR on T2D in men. For women, this association was partly mediated by SBP (13.86 %), HDL (24.54 %), ALT (6.29 %), UA (22.58 %) and combined kidney indicators (39.51 %).

CONCLUSIONS

WHtR was an independent risk factor for the development of T2D and IFG. This association was partly mediated by HOMA-IR, SBP, lipids and other liver and kidney metabolism indicators.

Performance of Stepwise Screening Methods in Identifying Individuals at High Risk of Type 2 Diabetes in an Iranian Population

BACKGROUND

Recent evidence recommended stepwise screening methods for identifying individuals at high risk of type 2 diabetes to be recruited in the lifestyle intervention programs for the prevention of the disease. This study aims to assess the performance of different stepwise screening methods that combine non-invasive measurements with lab-based measurements for identifying those with 5-years incident type 2 diabetes.

METHODS

3037 participants aged ≥30 years without diabetes at baseline in the Tehran Lipid and Glucose Study (TLGS) were followed. Thirty-two stepwise screening methods were developed by combining a non-invasive measurement (an anthropometric measurement (waist-to-height ratio, WtHR) or a score based on a non-invasive risk score [Australian Type 2 Diabetes Risk Assessment Tool, AUSDRISK]) with a lab-based measurement (different cut-offs of fasting plasma glucose [FPG] or predicted risk based on three lab-based prediction models [Saint Antonio, SA; Framingham Offspring Study, FOS; and the Atherosclerosis Risk in Communities, ARIC]). The validation, calibration, and usefulness of lab-based prediction models were assessed before developing the stepwise screening methods. Cut-offs were derived either based on previous studies or decision-curve analyses.

RESULTS

203 participants developed diabetes in 5 years. Lab-based risk prediction models had good discrimination power (area under the curves [AUCs]: 0.80-0.83), achieved acceptable calibration and net benefits after recalibration for population's characteristics and were useful in a wide range of risk thresholds (5%-21%). Different stepwise methods had sensitivity ranged 20%-68%, specificity 70%-98%, and positive predictive value (PPV) 14%-46%; they identified 3%-33% of the screened population eligible for preventive interventions.

CONCLUSION

Stepwise methods have acceptable performance in identifying those at high risk of incident type 2 diabetes.

Predictive Effect of Triglyceride Glucose-Related Parameters, Obesity Indices, and Lipid Ratios for Diabetes in a Chinese Population: A Prospective Cohort Study

Objective

The purpose of this study was to evaluate the association between triglyceride glucose (TyG) index and new-onset diabetes under different glycemic states and to compare the predictive value of TyG-related parameters, obesity indices, and lipid ratios for new-onset diabetes.

Methods

Data were collected from the China Health and Retirement Longitudinal Study (CHARLS), consisting of 6,258 participants aged ≥45 years. Participants were grouped according to their glycemic states. Cox proportional hazards models and restricted cubic spline regression were used to explore the association between TyG index and diabetes. Cox proportional hazard models were applied to confirm the predictive value of the optimal marker. Receiver operating characteristic (ROC) curves were used to compare the predictive value.

Results

TyG index was positively correlated with the risk of diabetes (hazard ratio (HR), 1.75; 95% confidence interval (CI), 1.56-1.97), and the linear association existed (p < 0.001). The highest correlation with diabetes was visceral adiposity index (VAI) (HR, 2.04; 95% CI, 1.44-2.90) in normal fasting glucose (NFG) group and TyG-body mass index (TyG-BMI) (HR, 2.53; 95% CI, 1.97-3.26) in impaired fasting glucose (IFG) group. The largest area under curve (AUC) was observed in TyG-waist-to-height ratio (TyG-WHtR) in the NFG group (AUC, 0.613; 95% CI, 0.527-0.700), and TyG-BMI had the highest AUC in the IFG group (AUC, 0.643; 95% CI, 0.601-0.685).

Conclusion

The association between TyG index and new-onset diabetes was positive and linear. TyG-WHtR was a clinically effective marker for identifying the risks of diabetes in the NFG group and TyG-BMI was an effective marker to predict diabetes in the IFG group.

Accuracy of obesity indices alone or in combination for prediction of diabetes: A novel risk score by linear combination of general and abdominal measures of obesity

Background

The predictive power of obesity measures varies according to the presence of coexistent measures. The present study aimed to determine the predictive power of combinations of obesity measures for diabetes by calculation of a linear risk score.

Methods

Data from a population-based cross-sectional study of 994 representative samples of Iranian adults in Babol, Iran were analyzed. Measures of obesity including waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), and waist to hip ratio (WHR) were calculated, and diabetes was diagnosed by fasting blood sugar >126 mg/dl or taking antidiabetic medication. Multiple logistic regression model was used to develop a logit risk score based on BMI, WC, WHtR, and WHR. The ROC analysis was applied to determine the priority of every single index and combined logit score for the prediction of diabetes.

Results

All four measures of general and abdominal obesity were predictors of diabetes individually in both sexes (P=0.0001). Calculation of risk score for a combination of all measures use full model improved predictive power. Adjustment for age resulted in further improvement in diagnostic power and combined novel risk score differentiated individuals with and without diabetes with an accuracy of 0.747 (95%CI: 0.690-0.808) in men and 0.789 (95%CI: 0.740, 0.837) in women.

Conclusion

These findings indicate that the simultaneous calculation of age-adjusted risk score for all measures provides stronger diagnostic accuracy in both sexes. This issue suggests the calculation of combined risk scores for all obesity indices especially in a population at borderline risk.

Association of anthropometric indices with cardiovascular disease risk factors among adults: a study in Iran

AIMS

Cardiovascular diseases (CVDs) are the leading cause of death in the world. Many modifiable risk factors have been reported to synergistically act in the development of CVDs. We aimed to compare the predictive power of anthropometric indices, as well as to provide the best cut-off point for these indicators in a large population of Iranian people for the prediction of CVDs and CVD risk factors.

METHODS AND RESULTS

All the data used in the present study were obtained from Khuzestan comprehensive health study (KCHS). Anthropometric indices, including BMI (body mass index), WC (waist circumference), HC (hip circumference), WHR (waist-to-hip ratio), WHtR (waist-to-height ratio), ABSI (a body shape index), as well as CVD risk factors [dyslipidaemia, abnormal blood pressure (BP), and hyperglycaemia] were recorded among 30 429 participants. WHtR had the highest adjusted odds ratios amongst anthropometric indices for all the risk factors and CVDs. WC had the highest predictive power for dyslipidaemia and hyperglycaemia [area under the curve (AUC) = 0.622, 0.563; specificity 61%, 59%; sensitivity 69%, 60%; cut-off point 87.95, 92.95 cm, respectively], while WHtR had the highest discriminatory power for abnormal BP (AUC = 0.585; specificity 60%; sensitivity 65%; cut-off point 0.575) and WHR tended to be the best predictor of CVDs (AUC = 0.527; specificity 58%; sensitivity 64%; cut-off point 0.915).

CONCLUSION

In this study, we depicted a picture of the Iranian population in terms of anthropometric measurement and its association with CVD risk factors and CVDs. Different anthropometric indices showed different predictive power for CVD risk factors in the Iranian population.

Obesity markers for the prediction of incident type 2 diabetes mellitus in resource-poor settings: The CRONICAS Cohort Study

AIMS

To determine the predictive performance of well-known obesity markers: body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), waist-height ratio (WHtR), and total body fat percentage (TBF%), to identify incident cases of type 2 diabetes mellitus T2DM.

METHODS

Secondary data analysis of the CRONICAS Cohort Study, conducted in 3 regions of Peru. Participants without T2DM at baseline were selected for analyses. The obesity markers were evaluated at the beginning of the study, and the development of T2DM was determined at 30 months of follow-up. The predictive performance of the markers was calculated using areas under the curve (AUC), and sensitivity and specificity of the best cutoff points were estimated.

RESULTS

A total of 2510 participants with no diabetes at baseline, median age 54.1 years (inter-quartile range: 44.6 to 63.5), were included in the analysis. The cumulative incidence of T2DM at 30 months of follow-up was 4.7%. All the AUC studied for obesity markers and TBF% were poor.

CONCLUSIONS

We found that obesity markers had a poor predictive performance (AUC) for the incidence of T2DM when used alone. The BMI, WC and WHtR had better performance for the incidence of T2DM relative to the WHR among women, and no differences in performance between obesity markers were found among men.

Beyond Asian-Specific Cutoffs: Gender Effects on the Predictability of Body Mass Index, Waist Circumference, and Waist Circumference to Height Ratio on Hemoglobin A1c

OBJECTIVES

This study examines the gender effect on the associations between body mass index (BMI), waist circumference (WC), and waist circumference to height ratio (WHtR) with hemoglobin A1c (HbA1c) when Asian-specific cutoffs are applied among Asians living in the USA.

DESIGN

This study used the pooled 2013-2014 and 2015-2016 National Health and Nutrition Examination Survey (NHANES) data to produce a sample of 900 Asians who were non-pregnant and non-Hispanic aged 20-65. Bivariate and general linear regression analyses were conducted based on gender and age group.

RESULTS

The group variations of BMI, WC, and WHtR all exhibited different patterns between males and females. Among the bivariate correlations with HbA1c, WHtR was the strongest in males and WC was the strongest in females. All three measures performed better in predicting HbA1c among younger Asians. WC predicted more of the variance in HbA1c among females, whereas WHtR predicted more of the variance in HbA1c among males.

CONCLUSIONS

WC and WHtR are two anthropometric measures that serve as appropriate proxy of HbA1c for gauging the risk of developing type 2 diabetes among Asians living in the USA. They can be easily performed at non-clinical settings and should be used by individuals to monitor their health and be a part of disease prevention.

Assessing different anthropometric indices and their optimal cutoffs for prediction of type 2 diabetes and impaired fasting glucose in Asians: The Jinchang Cohort Study

BACKGROUND

To study the association between anthropometric measurements and the risk of diabetes and impaired fasting glucose (IFG) and compare body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) to determine the best indicator and its optimal cutoffs for predicting type 2 diabetes and IFG.

METHODS

A Chinese prospective (2011-2019) cohort named the Jingchang cohort that included 48 001 participants was studied. Using Cox proportional hazard models, hazard ratios (HRs) for incident type 2 diabetes or IFG per 1 SD change in BMI, WC, and WHtR were calculated. Area under the curve (AUC) was compared to identify the best anthropometric variable and its optimal cutoff for predicting diabetes.

RESULTS

The association of BMI, WC, and WHtR with type 2 diabetes or IFG risk was positive in the univariate and multivariable-adjusted Cox proportional hazard models. Of all three indexes, the AUC of BMI was largest and that of WC was smallest. The derived cutoff values for BMI, WC, and WHtR were 24.6 kg/m² , 89.5 cm, and 0.52 in men and 23.4 kg/m² , 76.5 cm, and 0.47 in women for predicting diabetes, respectively. The derived cutoff values for BMI, WC, and WHtR were 23.4 kg/m² , 87.5 cm, and 0.50 in men and 22.5 kg/m² , 76.5 cm, and 0.47 in women for predicting IFG, respectively. [Correction added on 14 April 2020, after first online publication: '0' has been deleted from 'WC,0' in the first sentence.].

CONCLUSIONS

Our derived cutoff points were lower than the values specified in the most current Asian diabetes guidelines. We recommend a cutoff point for BMI in Asians of 23 kg/m² and for WC a cutoff point of 89 cm in men and 77 cm in women to define high-risk groups for type 2 diabetes; screening should be considered for these populations.

Effectiveness of Anthropometric Measurements for Identifying Diabetes and Prediabetes among Civil Servants in a Regional City of Northern Ethiopia: A Cross-Sectional Study

Methods

The study involved a cross-sectional survey carried out from October 2015 to February 2016 among 1504 subjects aged from 18 to 75 years of age. Receiver operating characteristic (ROC) was used to select the most effective anthropometric cut-off point among waist circumference, waist-to-hip ratio, waist-to-height ratio, and BMI for identifying prediabetic and diabetes. Statistical significance was declared at p value of ≤0.05.

Results

Waist circumference was found better for identifying diabetes (AUC = 0.69) and prediabetes (AUC = 0.63) in women, respectively. Waist-to-hip ratio was better identifying diabetes (AUC = 0.67) while waist circumference-to-height ratio was better identifying prediabetes (AUC = 0.63) in men compared to body mass index. The optimal cut-off point with maximum sensitivity and specificity of waist circumference for identifying diabetes and prediabetes was 83.5 cm and 82.9 cm in women, respectively. The optimal ut-off point with maximum sensitivity and specificity of waist-to-hip ratio for identifying diabetes and prediabetes was 0.97 and 0.82 in men, respectively.

Conclusion

Waist circumference and waist-to-hip ratio exhibited better discriminate performance than BMI for identifying prediabetes and diabetes in women and men, respectively.

Use of a prediction method for early pregnancy status utilizing receiver operating characteristic curve analysis of peripheral blood leukocyte interferon-stimulated genes in Japanese-Black cattle

A prediction method for early pregnancy status (pregnant or non-pregnant) in cattle that can be used within 3 weeks after insemination is desired. Interferon-stimulated genes (ISGs) in peripheral blood leukocytes (PBLs) have been examined as prediction molecules for determination of pregnancy status. Relative abundances of ISG15 and MX2 gene transcripts in PBLs were suitable biomarkers for the prediction of pregnancy status when there were assessments of Holstein cattle. In the present study, it was determined whether ISG biomarkers are applicable for predicting gestation in Japanese-Black (JB) cattle and evaluation of the applicability of receiver operating characteristic (ROC) analysis procedures for this purpose. There was assessment of the reliability of using average ISG values in PBLs collected during the estrous cycle (AVE) as a cutoff compared to the Youden index cutoff values. Application of AVE to assessment of pregnancy status in JB cattle indicated there was reliable predictions for pregnancy status when using ISG15 and MX2 values on day 21 after insemination, which coincided with the time of assessment in the previous study with Holstein cattle. The area under the curve values of the ROC curves confirmed the reliability of using ISGs to predict pregnancy from days 18 to 21 after insemination. Comparing AVE with Youden index values, there was confirmation of the accuracy of AVE for predicting gestation. The average mRNA transcript abundance values of ISG15 and MX2 may serve as excellent pregnancy biomarkers for cattle within 3 weeks of insemination.

Evaluation of Several Anthropometric and Metabolic Indices as Correlates of Hyperglycemia in Overweight/Obese Adults

AIM

Rapid and growing rise in obesity and diabetes mellitus, as serious human health-threatening issues, is alarming. The aim of the present study was assessing the accuracy of several obesity indices to predict hyperglycemia in overweight and obese Iranian populations and determining the value of such indices in comparison to the conventional parameters. We also evaluated new latent combined scores in this matter.

PATIENTS AND METHODS

Overall, there were 2088 patients recruited from the weight loss clinic of Sina Hospital, an educational hospital of Tehran University of Medical Sciences for this cross-sectional study. Demographic information, anthropometric indices and biochemical measurements were collected and calculated. The multivariable regression modeling as well as area under the receiver-operating characteristic (ROC) analysis was used. To detect the existence of new combined scores, we used SEM (structural equation modeling) analysis through SmartPLS.

RESULTS

Combined latent scores and WHtR (waist-to-height ratio) gave us a higher area under the curve in predicting hyperglycemia associated with WC (waist circumference) in women, whereas FFMI (fat-free mass index) gave low values. Additionally, BRI (body roundness index) and latent scores had slightly higher AUC values in predicting hyperglycemia in men. According to the age-adjusted odds ratio (OR) in the presence of hyperglycemia, OR was the highest for WHR (waist to hip ratio) in women (OR, 7.74; 95% confidence interval [CI], 1.71-15.13). The association of WHR and hyperglycemia remained significant by adjusting for BMI (body mass index), WC and menopausal status.

CONCLUSION

WHR had the strongest association with hyperglycemia in women with only sufficient discrimination ability. However, neither BSI (body shape index) and BAI (body adiposity index) nor FMI (fat mass index) and FFMI were superior to BMI (body mass index), WC or WHtR in predicting hyperglycemia. It was revealed that BRI and combined scores had a more predictive power compared to the BSI, BAI, FMI and FFMI, simplifying hyperglycemia evaluation.