Causal associations of waist circumference and waist-to-hip ratio with type II diabetes mellitus: new evidence from Mendelian randomization

Li, Kexin; Feng, Tianyu; Wang, Limin; Chen, Yang; Zheng, Pingping; Pan, Pan; Wang, Min; Binnay, Isaac T S; Wang, Yingshuang; Chai, Ruiyu; Liu, Siyu; Li, Bo; Yao, Yan

doi:10.1007/s00438-020-01752-z

Cited by 16 publications

(10 citation statements)

References 32 publications

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“…Investigations of the association between metabolic measures and neuroimaging variables have typically relied on regression analyses which assume that the predictor variables represent independent observations with uniform linear associations across the entire sample. However, as we have shown here, measures of insulin-dependent-glucose uptake and adiposity are highly correlated (Supplementary Table S2) and prior literature has shown that their association is complex and interactive: obesity engenders insulin resistance [ 50 , 51 ], but not all overweight or obese individuals are insulin resistant [ 52 ]. Therefore, the internal milieu of each individual person is likely to reflect the joint influences of these metabolic pathways.…”

Section: Discussionsupporting

confidence: 57%

Lower functional hippocampal connectivity in healthy adults is jointly associated with higher levels of leptin and insulin resistance

et al. 2022

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Background Metabolic dysregulation is currently considered a major risk factor for hippocampal pathology. The aim of the present study was to characterize the influence of key metabolic drivers on functional connectivity of the hippocampus in healthy adults. Methods Insulin resistance was directly quantified by measuring steady-state plasma glucose (SSPG) concentration during the insulin suppression test and fasting levels of insulin, glucose, leptin, and cortisol, and measurements of body mass index and waist circumference were obtained in a sample of healthy cognitively intact adults (n = 104). Resting-state neuroimaging data were also acquired for the quantification of hippocampal functional cohesiveness and integration with the major resting-state networks (RSNs). Data-driven analysis using unsupervised machine learning (k-means clustering) was then employed to identify clusters of individuals based on their metabolic and functional connectivity profiles. Results K-means clustering identified two clusters of increasing metabolic deviance evidenced by cluster differences in the plasma levels of leptin (40.36 (29.97) vs. 27.59 (25.58) μg/L) and the degree of insulin resistance (SSPG concentration: 161.63 (65.27) vs. 125.72 (66.81) mg/dL). Individuals in the cluster with higher metabolic deviance showed lower functional cohesiveness within each hippocampus and lower integration of posterior and anterior components of the left and right hippocampus with the major RSNs. The two clusters did not differ in general intellectual ability or episodic memory. Conclusions We identified two clusters of individuals differentiated by abnormalities in insulin resistance, leptin levels, and hippocampal connectivity, with one of the clusters showing greater deviance. These findings support the link between metabolic dysregulation and hippocampal function even in nonclinical samples.

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Section: Discussionsupporting

confidence: 57%

Lower functional hippocampal connectivity in healthy adults is jointly associated with higher levels of leptin and insulin resistance

et al. 2022

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“…Some studies stated that high WC can increase the risk of T2DM. 14,33 We agree in terms of WC is a risk factor for diabetes. To the best of our knowledge, this was the first study to evaluate the relationship between demispan and T2DM.…”

Section: Discussionmentioning

confidence: 60%

Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements

Saberi‐Karimian

Mansoori

Bajgiran

et al. 2022

Clinical Laboratory Analysis

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Background The aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches. Methods A prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35–65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid‐arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist‐to‐height Ratio (WHtR), and Waist‐to‐hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate the DT's accuracy, sensitivity, and specificity using R software. Results Traditionally, 1461 women and 875 men with T2DM (T2DM group). According to the LR, in males, WC and BIA (p‐value < 0.001) and in females, demispan and WC (p‐value < 0.001) had the highest correlation with T2DM development risk. The DT indicated that WC has the most crucial effect on T2DM development risk, followed by HC, and BAI. Conclusions Our results showed that in both men and women, WC was the most important anthropometric factor to predict T2DM.

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“…As the development of new-onset diabetes involves multiple factors which may play independent (direct or indirect) and/or synergistic roles, the directed arc of BN can also account for collinearity and interactions among multiple factors and provide a full network for the included factors. Our BN model identified three key factors of new-onset diabetes, of which higher systolic blood pressure and WC have also been identified as likely causes of diabetes in previous MR studies ( 22 ), and the other factor of IFG can be considered as pre-diabetes. Notably, we found that WC in all participants (and in women), rather than BMI, was directly associated with new-onset diabetes, which is consistent with a meta-analysis showing that WC was superior to BMI in predicting diabetes in women aged over 60 years ( 23 ).…”

Section: Discussionmentioning

confidence: 72%

A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study

et al. 2022

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BackgroundExisting diabetes risk prediction models based on regression were limited in dealing with collinearity and complex interactions. Bayesian network (BN) model that considers interactions may provide additional information to predict risk and infer causation.MethodsBN model was constructed for new-onset diabetes using prospective data of 15,934 participants without diabetes at baseline [73% women; mean (standard deviation) age = 61.0 (6.9) years]. Participants were randomly assigned to a training (n = 12,748) set and a validation (n = 3,186) set. Model performances were assessed using area under the receiver operating characteristic curve (AUC).ResultsDuring an average follow-up of 4.1 (interquartile range = 3.3–4.5) years, 1,302 (8.17%) participants developed diabetes. The constructed BN model showed the associations (direct, indirect, or no) among 24 risk factors, and only hypertension, impaired fasting glucose (IFG; fasting glucose of 5.6–6.9 mmol/L), and greater waist circumference (WC) were directly associated with new-onset diabetes. The risk prediction model showed that the post-test probability of developing diabetes in participants with hypertension, IFG, and greater WC was 27.5%, with AUC of 0.746 [95% confidence interval CI) = 0.732–0.760], sensitivity of 0.727 (95% CI = 0.703–0.752), and specificity of 0.660 (95% CI = 0.652–0.667). This prediction model appeared to perform better than a logistic regression model using the same three predictors (AUC = 0.734, 95% CI = 0.703–0.764, sensitivity = 0.604, and specificity = 0.745).ConclusionsWe have first reported a BN model in predicting new-onset diabetes with the smallest number of factors among existing models in the literature. BN yielded a more comprehensive figure showing graphically the inter-relations for multiple factors with diabetes than existing regression models.

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Causal associations of waist circumference and waist-to-hip ratio with type II diabetes mellitus: new evidence from Mendelian randomization

Cited by 16 publications

References 32 publications

Lower functional hippocampal connectivity in healthy adults is jointly associated with higher levels of leptin and insulin resistance

Lower functional hippocampal connectivity in healthy adults is jointly associated with higher levels of leptin and insulin resistance

Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements

A Bayesian network model of new-onset diabetes in older Chinese: The Guangzhou biobank cohort study

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