Waist-to-height ratio, waist circumference, body mass index, waist divided by height0.5 and the risk of cardiometabolic multimorbidity: A national longitudinal cohort study

Lu, Yanqiang; Liu, Siyuan; Qiao, Yanan; Li, Guochen; Wu, Ying; Ke, Chaofu

doi:10.1016/j.numecd.2021.05.026

Cited by 35 publications

(31 citation statements)

References 41 publications

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“…Such features, found to carry the highest importance, are related to chronic diseases and multimorbidity. Sex and age are commonly reported as risk factors for multimorbidity, BMI, BP, and waist-hip ratio are also pointed out as a risk factor, especially for cardio-metabolic multimorbidities, and sleep disturbances and multimorbidity have been related previously, in particular associated with neuropsychiatric and musculoskeletal conditions [8][9][10][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study

Paula

Aguiar²,

Marques³

et al. 2022

PLoS ONE

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Background Multimorbidity is a worldwide concern related to greater disability, worse quality of life, and mortality. The early prediction is crucial for preventive strategies design and integrative medical practice. However, knowledge about how to predict multimorbidity is limited, possibly due to the complexity involved in predicting multiple chronic diseases. Methods In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared the performance of seven multilabel classifiers (multivariate random forest, and classifier chain, binary relevance and binary dependence, with random forest and support vector machine as base classifiers), using a sample of 15105 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We developed a web application for the building and use of prediction models. Results Classifier chain with random forest as base classifier performed better (accuracy = 0.34, subset accuracy = 0.15, and Hamming Loss = 0.16). For different feature sets, random forest based classifiers outperformed those based on support vector machine. BMI, blood pressure, sex, and age were the features most relevant to multimorbidity prediction. Conclusions Our results support the choice of random forest based classifiers for multimorbidity prediction.

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Section: Discussionmentioning

confidence: 99%

Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study

Paula

Aguiar²,

Marques³

et al. 2022

PLoS ONE

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“…Unlike the WHtR, the WHt.5R is a relatively new obesity metric that has weak effects on height and has a significant association with adiposity [ 6 , 29 ]. Regarding the association between eGFRs and the WHt.5R, WC, WHtR, and the WHt.5R were good predictors of the outcomes during metabolic studies of middle-aged and elderly Chinese populations [ 16 ]. The WHt.5R had the best predictive ability when identifying visceral adipose tissue, which could be observed with polycystic ovary syndrome [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, previous studies that used the WHtR have focused on its association with cardiometabolic diseases. The waist-to-height 0.5 ratio (WHt.5R) is a relatively newly established WC-related obesity metric [ 5 , 6 , 14 , 15 , 16 ]; however, its association with renal function has not yet been tested.…”

Section: Introductionmentioning

confidence: 99%

Association between Three Waist Circumference-Related Obesity Metrics and Estimated Glomerular Filtration Rates

Lee

Kim

et al. 2022

JCM

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Studies that have assessed the associations between obesity and the estimated glomerular filtration rate (eGFR) have reported inconsistent results. This cross-sectional study was performed to investigate the associations between three waist circumference (WC)-related obesity metrics (waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and waist-to-height0.5 ratio (WHt.5R)) and eGFRs. This study included 2133 men and 3443 women who were older than 40 years with eGFRs ≥ 60 mL/min/1.73 m² from the Korean Multi-Rural Communities Cohort. We calculated the residual body mass index (BMI) to reduce multicollinearity among the obesity metrics and performed multiple linear regression. For both sexes, among the adjusted models, most of the general obesity metrics were significantly associated with eGFRs. Particularly for women, the WC-related and general obesity metrics had a stronger effect on eGFRs in the quartile models that included the BMI and the residual BMI, respectively. When WC-related obesity metrics had a stronger effect than the general obesity metric, for both sexes, WHtR showed a significant impact than WHt.5R and WHR on eGFRs. Reducing multicollinearity had an important role in assessing the obesity metrics’ association with eGFRs. Overall, applying the residual method in further studies might help with evaluating the obesity paradox on renal function.

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“…Student’s t-test for the comparison of continuous data. Three models were adapted for the regression analysis used in previous studies 3 , 4 , 13 . Model 1 † was unadjusted.…”

Section: Methodsmentioning

confidence: 99%

Comparison of adiposity anthropometric indices and their associations with visceral fat levels determined by bioelectrical impedance analysis among diabetic patients

Tuglo

2022

Sci Rep

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Visceral fat (VF) and its effect on metabolic disorders have been extensively studied; nevertheless, there is a need for a simple and reliable index to equally assess VF in low-resource settings. This multihospital-based study was designed to compare the five adiposity anthropometric indices and their associations with VF levels determined by bioelectrical impedance analysis as the reference standard among diabetic patients. A pretested questionnaire was used to collect anthropometric, biochemical and hemodynamic data from 473 diabetic patients. Regression analysis was performed to determine the associations between the five adiposity anthropometric indices and VF levels. Receiver operating characteristic (ROC) curves were used to confirm the predictive capacities of the five adiposity anthropometric indices with VF levels. The waist-to-height ratio WHtR showed the greatest ROC value [area under the curve (AUC) = 0.745, p ˂0.001] in identifying diabetic patients with high VF levels compared to body mass index BMI [AUC = 0.584, p = 0.047], waist circumference WC [AUC = 0.723, p ˂0.001], hip circumference HC [AUC = 0.647, p ˂0.001] and waist-to-hip ratio WHR [AUC = 0.711, p ˂0.001]. Likewise, the regression analysis of WHtR and VF levels revealed the strongest association [unadjusted odds ratio (UOR) = 21.49, p < 0.001] compared to BMI [UOR = 6.77, p = 0.008], WC [UOR = 6.37, p < 0.001], HC [UOR = 5.93, p = 0.002] and WHR [UOR = 13.17, p < 0.001]. The optimal cut-off values to identify diabetic patients with high VF levels were > 0.5 for WHtR, > 25.7 kg/m2 for BMI, > 80.5 cm for WC, > 95.5 cm for HC and > 0.82 for WHR. WHtR was shown to have overpowered BMI, HC, WC and WHR in identifying diabetic patients with high VF levels. Therefore, the Ghana Health Service could recommend WHtR as a better diagnostic index for assessing VF levels due to its high predictive capacity.

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Waist-to-height ratio, waist circumference, body mass index, waist divided by height0.5 and the risk of cardiometabolic multimorbidity: A national longitudinal cohort study

Cited by 35 publications

References 41 publications

Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study

Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study

Association between Three Waist Circumference-Related Obesity Metrics and Estimated Glomerular Filtration Rates

Comparison of adiposity anthropometric indices and their associations with visceral fat levels determined by bioelectrical impedance analysis among diabetic patients

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