Anthropometric Indexes for Predicting High Blood Pressure in Vietnamese Adults: A Cross-Sectional Study

Comparison of waist circumference and waist-to-height ratio as predictors of clustering of cardiovascular risk factors among middle-aged people in rural Khanh Hoa, Vietnam

OBJECTIVE

Given the population-level variation in stature, a universal cut-off for waist circumference (WC) may not be appropriate for some populations. We compared the performance of WC and waist-to-height ratio (WHtR) to detect the clustering of cardiovascular disease (CVD) risk factors in rural Vietnam.

METHODS

We obtained data from a baseline survey of the Khanh Hoa Cardiovascular Study comprising 2942 middle-aged residents (40-60 years). We used areas under the receiver operating characteristics curve (AUROC), net reclassification index (NRI), and integrated discrimination improvement (IDI) to compare the performance of WC and WHtR in predicting CVD risk clustering (≥2 of the following risk factors: hypertension, diabetes, dyslipidemia, and elevated C-reactive protein).

RESULTS

The optimal cut-off values for WC were 81.8 and 80.7 cm for men and women, respectively. Regarding the clustering of CVD risk factors, the AUROC (95% CI) of WC and WHtR were 0.707 (0.676 to 0.739) and 0.719 (0.689 to 0.749) in men, and 0.682 (0.654 to 0.709) and 0.690 (0.663 to 0.717) in women, respectively. Compared with WC, WHtR had a better NRI (0.229; 0.102-0.344) and IDI (0.012; 0.004-0.020) in men and a better NRI (0.154; 0.050-0.257) in women.

CONCLUSIONS

The optimal WC cut-off for Vietnamese men was approximately 10 cm below the recommended Asian cut-off. WHtR might perform slightly better in predicting the clustering of CVD risk factors among the rural population in Vietnam.

Predictive Models of Muscle Strength in Older People with Type 2 Diabetes Mellitus

Purpose

To propose predictive models for absolute muscle strength (AMS) of elderly people with type 2 Diabetes Mellitus (DM2) in primary health care.

Patients and Methods

The cross-sectional study was conducted with 138 elderly diabetics. The AMS was measured by a JAMAR® hydraulic handgrip dynamometer, determined by the sum of both hands. The following indices were evaluated: waist-to-height ratio (WHtR), body mass index (BMI), Lipid Accumulation Product (LAP), Triglyceride/High Density Lipoprotein (TG/HDL) ratio and platelet/lymphocyte ratio (PLR). Multiple linear regression was used in the statistical analysis.

Results

The final regression model indicated 66.4% (R²=0.66) of the variation in AMS. WHtR decreased AMS by 41.1% (β = -0.19; t = -3.70; p < 0.001), while PLR by 11.3% (β = -0.12; t = -2.36; p = 0.020). Male sex increased AMS by 10.6% (β = 0.32; t = 4.16; p < 0.001), and lean mass (LM) by 0.89% (β = 0.46; t = 6.03; p < 0.001).

Conclusion

WHtR and PLR predicted a decrease, while male sex and LM predicted an increase in AMS. It is suggested that these markers be used as screening measures for variation in AMS in older adults with DM2. These results have relevant practical application in primary health care since the markers are easy to use.

Cut-off points for anthropometric indices to screen for hypertension among Iranian adults of the Bandare-Kong cohort: a cross-sectional study

Background

Obesity is one of the major determinants of blood pressure. This study aimed to determine the optimal sex- and age-specific cut-off points of anthropometric indices, including body mass index (BMI), waist circumference (WC), hip circumference (HC), wrist circumference (WrC), waist-hip ratio (WHR), and waist-height ratio (WHtR), to screen for hypertension (HTN) in a cohort of Iranian adults aged 35 to 70 years, and to compare the predictive performance of the indices based on receiver operating characteristic (ROC) curves.

Methods

This population-based study was carried out on the participants aged 35 to 70 years of the Bandare-Kong Non-Communicable Diseases (BKNCD) Cohort Study, a part of the Prospective Epidemiological Research Studies in IrAN (PERSIAN). Using the area under the receiver operating characteristic curve (AUC) and Youden's J index, optimal sex- and age-specific cut-off points of the anthropometric indices in association with HTN were calculated.

Results

This study included a total of 2256 females and 1722 males. HTN was diagnosed in 736 females (32.6%) and 544 males (31.6%). The optimal cut-off of WC for HTN was 90 cm in males and 95 cm in females, with an area under the ROC curve (AUROC) of 0.60 and 0.64, respectively. For HC, the optimal cut-off was 95 cm for males and 108 cm for females (AUROC = 0.54 for both). Moreover, WrC optimal cut-offs were 17 cm for males (AUROC = 0.56) and 15 cm for females (AUROC = 0.57). As for BMI, the optimal cut-off was 25 kg/m² in males and 27 kg/m² in females (AUROC of 0.59 and 0.60, respectively). Also, a cut-off of 0.92 was optimal for WHR in males (AUROC = 0.64) and 0.96 in females (AUROC = 0.67). On the other hand, WHtR optimal cut-offs were 0.52 for males and 0.60 for females (AUROC of 0.63 and 0.65, respectively).

Conclusions

WHR and WHtR, as anthropometric indices of obesity, were demonstrated to be significant predictors of HTN. Further, we suggest using WHR (cut-off point of 0.92 for males and 0.96 for females) and WHtR (cut-off point of 0.52 for males and 0.60 for females) as measures of preference to predict HTN among the southern Iranian population. Further multicenter longitudinal studies are recommended for a more accurate prediction of HTN.

Utility of Obesity Indicators for Predicting Hypertension among Older Persons in Limpopo Province, South Africa

In view of the epidemic proportions of obesity in South Africa and its relationship to cardiometabolic diseases, such as hypertension, a cross sectional study was conducted to investigate the utility of obesity indicators for predicting hypertension among older persons (≥60 years, n = 350) in the Limpopo Province of South Africa. The WHO STEPwise approach was used to collect data on demographic and lifestyle factors. Anthropometrics and blood pressure were measured according to the standard procedures. Receiver operating characteristic curves (ROC) were used to investigate and compare the ability of obesity indicators to predict overall hypertension and either increased systolic (SBP) or increased diastolic (DBP) blood pressure. The area under the ROC curve (AUC) was used to assess a certain indicator’s potential to predict overall hypertension and either increased SBP or increased DBP. Multivariate logistic regression analysis was used to determine the relationship of hypertension with obesity indicators. The mean age of the participants was 69 years (±SD = 7), and hypertension (46%), general obesity (36%) and abdominal obesity (57%) were prevalent among older persons. The obesity indicator body mass index (BMI) (AUC = 0.603 (0.52; 0.69)) was the best predictor of hypertension in older men. Waist circumference (WC) (AUC = 0.640 (0.56; 0.72)) and waist-to-height ratio (WHtR) (AUC = 0.605 (0.52; 0.69)) were better predictors of hypertension than BMI and waist-to-hip ratio (WHR) in older women. After adjustment for risk factors, only WC (AOR = 1.22 (1.16; 1.79)) was significantly associated with hypertension in older women, proposing WC as a screening tool for the prediction of hypertension in South African older women.

Body Roundness Index and Waist–Hip Ratio Result in Better Cardiovascular Disease Risk Stratification: Results From a Large Chinese Cross-Sectional Study

Background

The appropriate optimal anthropometric indices and their thresholds within each BMI category for predicting those at a high risk of cardiovascular disease risk factors (CVDRFs) among the Chinese are still under dispute.

Objectives

We aimed to identify the best indicators of CVDRFs and the optimal threshold within each BMI category among the Chinese.

Methods

Between 2012 and 2020, a total of 500,090 participants were surveyed in Hunan, China. Six anthropometric indices including waist circumference (WC), a body shape index (ABSI), body roundness index (BRI), waist–hip ratio (WHR), hip circumference (HC), and waist–height ratio (WHtR) were evaluated in the present study. Considered CVDRFs included dyslipidaemia, hypertension, diabetes mellitus (DM), and chronic kidney disease (CKD). The associations of anthropometrics with CVDRFs within each BMI category were evaluated through logistic regression models. The area under the receiver operating characteristic curve (AUROC) was used to assess the predictive abilities.

Results

For the presence of at least one CVDRFs, the WHR had the highest AUROC in overweight [0.641 (95%CI:0.638, 0.644)] and obese [0.616 (95%CI:0.609, 0.623)] men. BRI had the highest AUROC in underweight [0.649 (95%CI:0.629, 0.670)] and normal weight [0.686 (95%CI:0.683, 0.690)] men. However, the BRI had the highest discrimination ability among women in all the BMI categories, with AUROC ranging from 0.641 to 0.727. In most cases, the discriminatory ability of WHtR was similar to BRI and was easier to calculate; therefore, thresholds of BRI, WHR, and WHtR for CVDRFs identification were all calculated. In men, BRI thresholds of 1.8, 3.0, 3.9, and 5.0, WHtR thresholds of 0.41, 0.48, 0.53, and 0.58, and WHR thresholds of 0.81, 0.88, 0.92, and 0.95 were identified as optimal thresholds across underweight, normal weight, overweight, and obese populations, respectively. The corresponding BRI values in women were 1.9, 2.9, 4.0, and 5.2, respectively, and WHtR were 0.41, 0.48, 0.54, and 0.59, while the WHR values were 0.77, 0.83, 0.88, and 0.90. The recommended BRI, WHtR, or WHR cut-offs could not statistically differentiate high-risk CKD or hypercholesterolemia populations.

Conclusions

We found that BRI and WHR were superior to other indices for predicting CVD risk factors, except CKD or hypercholesterolemia, among the Chinese.

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F-score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes' Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes' Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.