Association of heart rate and diabetes among 0.5 million adults in the China Kadoorie biobank: Results from observational and Mendelian randomization analyses

Development and validation of a machine learning-based model to predict isolated post-challenge hyperglycemia in middle-aged and elder adults: Analysis from a multicentric study

INTRODUCTION

Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post-challenge hyperglycemia (IPH), that is., patients with normal fasting plasma glucose (<7.0 mmoL/L) and abnormal 2-h postprandial blood glucose (≥11.1 mmoL/L). We aimed to develop a model to differentiate individuals with IPH from the normal population.

METHODS

Data from 54301 eligible participants were obtained from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a longitudinal (REACTION) study in China. Data from 37740 participants were used to develop the diagnostic system. External validation was performed among 16561 participants. Three machine learning algorithms were used to create the predictive models, which were further evaluated by various classification algorithms to establish the best predictive model.

RESULTS

Ten features were selected to develop an IPH diagnosis system (IPHDS) based on an artificial neural network. In external validation, the AUC of the IPHDS was 0.823 (95% CI 0.811-0.836), which was significantly higher than the AUC of the Taiwan model [0.799 (0.786-0.813)] and that of the Chinese Diabetes Risk Score model [0.648 (0.635-0.662)]. The IPHDS model had a sensitivity of 75.6% and a specificity of 74.6%. This model outperformed the Taiwan and CDRS models in subgroup analyses. An online site with instant predictions was deployed at https://app-iphds-e1fc405c8a69.herokuapp.com/.

CONCLUSIONS

The proposed IPHDS could be a convenient and user-friendly screening tool for diabetes during health examinations in a large general population.

Resting heart rate and the risk of incident type 2 diabetes mellitus among non-diabetic and prediabetic Iranian adults: Tehran lipid and glucose study

BACKGROUND

Resting heart rate (RHR) has been found to be a potential risk factor for developing type 2 diabetes mellitus (T2DM), with a highly significant heterogeneity among previous studies. Therefore, we examined the association of RHR and risk of incident T2DM among non-diabetic and prediabetic adults.

METHODS

The study population included 2431 men and 2910 women aged ≥ 20 years without T2DM at baseline (2001-2005). Participants were followed for incident T2DM by about 3-year intervals up to April 2018. The multivariable Cox proportional models were applied to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). The models were adjusted for age, body mass index, waist circumference, educational level, physical activity, smoking, hypertension, family history of diabetes, triglycerides/ high-density lipoprotein cholesterol ratio, and fasting plasma glucose.

RESULTS

During a median follow-up of 12.2 years, 313 men and 375 women developed T2DM. Interestingly, a significant sex-difference was found (all P-values for sex interaction < 0.025). Among men, compared to the first quintile (< 68 bpm: beats per minute), those who had RHR of over 84 bpm were at higher T2DM risk with a HR (95%CI) of 1.69 (1.16-2.47). Furthermore, considering RHR as a continuous variable, an increase of 10 bpm caused 17% significantly higher risk among men with a HR of 1.17 (1.05-1.30). However, among women, there was no significant association between incident T2DM and RHR. Moreover, among prediabetic participants at baseline, the association of RHR and risk of T2DM progression was generally similar to the general population, which means higher RHR increased the risk of T2DM development only among men with a HR of 1.26 (1.09-1.46) for 10 bpm increase.

CONCLUSIONS

Among men, being either non-diabetic or prediabetic at baseline, higher RHR can be associated with incident T2DM; however, women didn't show a significant association. Further studies are needed to determine the added value of RHR as a potential modifiable risk factor in screening and risk prediction of incident T2DM.

Genotyping and population characteristics of the China Kadoorie Biobank

The China Kadoorie Biobank (CKB) is a population-based prospective cohort of >512,000 adults recruited from 2004 to 2008 from 10 geographically diverse regions across China. Detailed data from questionnaires and physical measurements were collected at baseline, with additional measurements at three resurveys involving ∼5% of surviving participants. Analyses of genome-wide genotyping, for >100,000 participants using custom-designed Axiom arrays, reveal extensive relatedness, recent consanguinity, and signatures reflecting large-scale population movements from recent Chinese history. Systematic genome-wide association studies of incident disease, captured through electronic linkage to death and disease registries and to the national health insurance system, replicate established disease loci and identify 14 novel disease associations. Together with studies of candidate drug targets and disease risk factors and contributions to international genetics consortia, these demonstrate the breadth, depth, and quality of the CKB data. Ongoing high-throughput omics assays of collected biosamples and planned whole-genome sequencing will further enhance the scientific value of this biobank.

Observational and Genetic Associations of Modifiable Risk Factors with Aortic Valve Stenosis: A Prospective Cohort Study of 0.5 Million Participants

Background: Observational studies have shown that modifiable risk factors are associated with aortic valve stenosis (AVS). However, the causality behind these associations remains largely unknown. Objectives: To explore the associations of modifiable risk factors, including metabolic factors, biochemical measures, education, and lifestyles with AVS and their potential causal associations. Methods: We enrolled 361,930 British white people with genetic data in the UK biobank. Cox proportional risk regression models were used to estimate the hazard ratios between 28 modifiable risk factors and AVS. We used genetic instruments for modifiable risk factors to determine the potential causal relationships using a one-sample Mendelian randomization (MR) approach. Results: A total of 1602 participants developed AVS during an 8.4-year follow-up. Observational analyses showed higher adiposity, blood pressure, heart rate, low-density lipoprotein, urate, C-reactive protein, creatinine, albumin, and glycated hemoglobin, but lower serum vitamin D, and education, unhealthy lifestyle, and poor sleep quality were related to a higher risk of AVS after adjusting for the Bonferroni correction (p < 0.0013). Genetically predicted 1-SD higher levels of body mass index [HR: 1.09, 95% CI: 1.03 to 1.16], body fat percentage (1.17, 1.03 to 1.33), triglyceride (TG) [1.08, 1.00 to 1.16], low-density lipoprotein (LDL) (1.15, 1.08 to 1.21) and serum total cholesterol (TC) (1.13, 1.02 to 1.25) were associated with a higher risk of AVS, respectively. Genetically determined per category higher insomnia (1.32, 1.13 to 1.55) was also associated with AVS. The abovementioned genetic associations with the incident AVS showed an increasing relationship pattern. Conclusions: This study provides strong evidence for the potential causal roles of cardiometabolic factors in developing AVS, highlighting that an idea of metabolic status through a healthy lifestyle may help prevent AVS.