Association of waist and hip circumference and waist-hip ratio with type 2 diabetes risk in first-degree relatives

Prevalence of Obesity and Cardiovascular Risk Factors Among Type 2 Diabetes Mellitus Patients in Al-Khobar, Saudi Arabia

BACKGROUND

The prevalence of obesity has grown significantly worldwide. It is considered a major cardiovascular risk factor among type II diabetes mellitus (T2DM) patients.

OBJECTIVES

The main objective of this study is to determine the prevalence of obesity in patients with T2DM at King Fahd University Hospital (KFUH), Al-Khobar, and to assess the relationship between T2DM and cardiovascular risk factors with body mass index (BMI) and waist to hip ratio (WHR).

METHODS

A retrospective, cross-sectional study, included T2DM patients from the Internal Medicine department at KFHU. The investigators recorded patient demographics (age and gender), weight (kg), height (cm), body mass index (Kg/m²), waist and hip circumference (cm), smoking status, physical activity, blood pressure measurements (mmHg) and laboratory results of fasting blood glucose (FBG), glycated haemoglobin (HbA1c) and lipid profile.

RESULTS

Among 346 patients, the prevalence of obesity and overweight was 62.4% and 27.2%, respectively. The relationship between BMI and demographic data including age and gender was statistically significant (P<0.05). The correlation between the BMI with cardiovascular risk factors including smoking, physical activity and WHR found to be statistically significant (P<0.05).

CONCLUSION

Our study showed that obesity and overweight affect 89.6% of patients with T2DM. Therefore, it is important to take into consideration weight control strategies to effectively manage diabetic patients.

Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards

Background

Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation. Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.

Methods

In this study, we analyzed data from 44,709 nondiabetic UK Biobank participants aged 40-69, predicting the risk of T2D onset within a selected time frame (mean of 7.3 years with an SD of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one nonlaboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes subcohorts, and compared the results to the results of the general cohort. We established the nonlaboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip ratio, and body mass index. For the laboratory model, we used age and sex together with four common blood tests: high-density lipoprotein (HDL), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.

Results

The nonlaboratory scorecard model achieved an area under the receiver operating curve (auROC) of 0.81 (95% confidence interval [CI] 0.77-0.84) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (95% CI 5-66). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood test model based on age, sex, and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (95% CI 0.85-0.90) and a deciles' OR of ×48 (95% CI 12-109). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4-7%); 3% (2-4%); 10% (8-12%); and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (95% CI 0.74-0.75). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the anthropometry models.

Conclusions

The four blood test and anthropometric models outperformed the commonly used nonlaboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.

Funding

The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Application of Traditional and Emerging Methods for the Joint Analysis of Repeated Measurements With Time-to-Event Outcomes in Rheumatology

OBJECTIVE

The goal of this paper is to describe approaches for the joint analysis of repeatedly measured data with time-to-event end points, first separately and then in the framework of a single comprehensive model, emphasizing the efficiency of the latter approach. Data from the Johnston County Osteoarthritis (JoCo OA) Project will be used as an example to investigate the relationship between the change in repeatedly measured body mass index (BMI) and the time-to-event end point of incident worsening of radiographic knee OA that was defined as an increased Kellgren/Lawrence grade in at least 1 knee over time.

METHODS

First, we provide an overview of the methods for analyzing repeated measurements and time-to-event end points separately. Then, we describe traditional (Cox proportional hazards model [CoxPH]) and emerging (joint model [JM]) approaches, both of which allow combined analysis of repeated measures with a time-to-event end point in the framework of a single statistical model. Finally, we apply the models to JoCo OA data and interpret and compare the results from the different approaches.

RESULTS

Applications of the JM (but not the CoxPH) showed that the risk of worsening radiographic OA is higher when BMI is higher or increasing, thus illustrating the advantages of the JM for analyzing such dynamic measures in a longitudinal study.

CONCLUSION

Joint models are preferable for simultaneous analyses of repeated measurement and time-to-event outcomes, particularly in the context of chronic disease, where dependency between the time-to-event end point and the longitudinal trajectory of repeated measurements is inherent.

Factors associated with progression to pre-diabetes: a recurrent events analysis

AIMS

Pre-diabetes is a strong risk factor for type 2diabetes (T2D). The aim of this study was to explore factors associated with normal glucose maintenance and pre-diabetes prevention or delay.

METHODS

Data of 1016 first-degree relatives of T2D patients were retrieved from the Isfahan Diabetes Prevention Study (IDPS). Association of various variables including nutrients, serum tests and physical activity with the risk of pre-diabetes was assessed using recurrent events approach.

RESULTS

Cumulative incidence of diabetes was 8.17, 9.44, and 4.91% for total sample and individuals with and without pre-diabetes experience in the follow-up. Risk of progression to pre-diabetes was higher in women and older people (p < 0.01). Additionally, BMI and blood pressure had significant association with the risk (p < 0.01) and individuals with higher intake of fat were at higher risk (HR = 2.26; 95% CI 1.66-3.07 for high-intake and HR = 1.52; 95% CI 1.27-1.83 for medium-intake compared to low-intake group). Carbohydrates and protein intake were positively associated with the risk of pre-diabetes with HR = 8.63 per 49 g extra carbohydrates per day and HR = 1.32 per 6 g extra protein per day (p < 0.01). The association was also significant for triglyceride (TG) with 7% risk increase per 1 SD = 1.14 increase in TG level.

CONCLUSION

Despite frequent studies on lifestyle modification for pre-diabetes prevention, less information is available about the role of nutritional components. We observed direct effects for intake of macronutrients including fat, carbohydrates, and protein in first-degree relatives. Further research is warranted to assess these associations in general populations.

LEVEL OF EVIDENCE

Level III: Evidence obtained from a single-center cohort study.

Association of Lipid Profile with Type 2 Diabetes in First-Degree Relatives: A 14-Year Follow-Up Study in Iran

PURPOSE

Dyslipidemia is claimed to be associated with an increased risk of type 2 diabetes mellitus (T2DM). However, first-degree relatives (FDRs) of patients with T2DM are reported to be at higher risk. The aim of this study was to evaluate the association between serum lipid profile and T2DM incidence in FDRs.

PATIENTS AND METHODS

Information on 1222 T2DM FDRs during 14 years of follow-up was retrieved. All individuals were examined for diabetes status and dyslipidemia once a year. We used a Bayesian joint longitudinal-survival model to assess the association.

RESULTS

Our data showed that a 10 mg/dL increase in triglycerides (TG), very-low-density lipoprotein (VLDL), and non-high-density lipoprotein (non-HDL) cholesterol levels during the follow-up period was associated with an increased risk of diabetes by 5%, 29%, and 6.6%, respectively. Moreover, for every one-unit increase in the TG to HDL ratio, the T2DM incidence increased by 35%. Subgroup analysis also showed that the increased risk of diabetes was significant only in female FDRs, so that a 10 mg/dL increase in TG and VLDL cholesterol level and a one-unit increase in TG to HDL ratio in female FDRs resulted in an increased risk of diabetes by 7.8%, 46%, and 64%, respectively. However, analysis of HDL, low-density lipoprotein (LDL), total cholesterol (TC), TC to HDL, and LDL to HDL cholesterol levels/ratios did not find any statistically significant associations.

CONCLUSION

Increases in TG, VLDL, non-HDL cholesterol level, and TG to HDL ratio are associated with an increased risk of T2DM in FDRs, especially in female FDRs.

Cause-Specific Risk Factors of Death in Individuals with Diabetes: A Competing Risks Modeling

BACKGROUND

Diabetes is on the rise worldwide.

OBJECTIVES

This study aimed to evaluate the risk factors of various causes of death in people with type 2 diabetes (T2D).

METHODS

In this cohort study on 2638 people with T2D, we applied cause-specific and sub-distribution hazards models to assess the impact of various factors on the risk of death. Moreover, we plotted a cumulative incidence curve to summarize cumulative failure rates over time.

RESULTS

About 75% of individuals with T2D died from cardiovascular disease (CVD) and cerebrovascular accidents (CVA). Death from CVD was associated with the increased risk of hypertension (hazard ratio (HR) = 1.83, 95% CI: 1.37 - 2.46), hypercholesterolemia (HR = 1.58, 95% CI: 1.17 - 2.14), and diabetes duration. The risk of death from CVA was related to hypertension (HR = 2.76, 95% CI: 1.67 - 4.55) and hyperglycemia (HR = 4.34, 95% CI: 1.75 - 10.79). The CVA risk in patients with diabetes duration of 10 - 20 years was higher than the risk in patients with diabetes duration > 20 years (diabetes duration of ≤ 10 years as the reference category). Diabetes duration of longer than 20 years was associated with a higher risk of death from cancer (HR = 2.65, 95% CI: 1.05 - 6.68). The risk of death from foot infection and diabetic nephropathy increased in patients with longer diabetes duration after adjustment for sex, age, and body mass index.

CONCLUSIONS

Regardless of the cause, death rates in people with T2D increase over time and risk factors have different impacts on death from each cause. This should be acknowledged in risk management in individuals with T2D.

Risk modeling in prospective diabetes studies: Association and predictive value of anthropometrics

AIMS

This study aimed to introduce and apply modern statistical techniques for assessing association and predictive value of risk factors in first-degree relatives (FDR) of patients with diabetes from repeatedly measured diabetes data.

METHODS

We used data from 1319 FDR's of patients with diabetes followed for 8 years. Association and predictive performance of weight (Wt), body mass index (BMI), waist and hip circumferences (WC and HC) and their ratio (WHR), waist-height ratio (WHtR) and a body shape index (ABSI) in relation to future diabetes were evaluated by using Cox regression and joint longitudinal-survival modeling.

RESULTS

According to Cox regression, in total sample, WC, HC, Wt, WHtR and BMI had significant direct association with diabetes (all p < 0.01) with the best predictive ability for WHtR (concordance probability estimate = 0.575). Joint modeling suggested direct associations between diabetes and WC, WHR, Wt, WHtR and BMI in total sample (all p < 0.05). According to LPML criterion, WHtR was the best predictor in both total sample and females with LPML of -2666.27 and -2185.67, respectively. However, according to AUC criteria, BMI had the best predictive performance with AUC-JM = 0.7629 and dAUC-JM = 0.5883 in total sample. In females, both AUC criteria indicated that WC was the best predictor followed by WHtR.

CONCLUSION

WC, WHR, Wt, WHtR and BMI are among candidate anthropometric measures to be monitored in diabetes prevention programs. Larger multi-ethnic and multivariate research are warranted to assess interactions and identify the best predictors in subgroups.

High-intensity interval training versus continuous training on physiological and metabolic variables in prediabetes and type 2 diabetes: A meta-analysis

AIMS

To compare the effects of high-intensity interval training (HIIT) versus moderate-intensity continuous training (MICT) on functional capacity and cardiometabolic markers in individuals prediabetes and type 2 diabetes (T2D).

METHODS

The search was performed in PubMed (MEDLINE), EMBASE, PEDro, CENTRAL, Scopus, LILACS database, and Clinical Trials from the inception to July 2017, included randomized clinical trials that compared the use of HIIT and MICT in prediabetes and T2D adults. The risk of bias was defined by Cochrane Handbook and quality of evidence by GRADE.

RESULTS

From 818 relevant records, seven studies were included in systematic review (64 prediabetes and 120 T2D patients) and five with T2D were meta-analyzed. HIIT promoted significantly increased of 3.02 mL/kg/min (CI95% 1.42-4.61) of VO₂max, measured for functional capacity, compared to MICT. No differences were found between two modalities of exercises considering the outcomes HbA1c, systolic and diastolic blood pressure, total cholesterol, HDL and LDL cholesterol, triglycerides, BMI, and waist-to-hip ratio. Most of the studies presented unclear risk of bias, and low and very low quality of evidence.

CONCLUSION

HIIT induces cardiometabolic adaptations similar to those of MICT in prediabetes and T2D, and provides greater benefits to functional capacity in patients with T2D.

PROSPERO

CRD42016047151.