Lipid risk factors among elderly with normal fasting glucose, impaired fasting glucose and type 2 diabetes mellitus. The Italian longitudinal study on aging

HDL levels modulate the impact of type 2 diabetes susceptibility alleles in older adults

BACKGROUND

Type 2 Diabetes (T2D) is influenced by genetic, environmental, and ageing factors. Ageing pathways exacerbate metabolic diseases. This study aimed to examine both clinical and genetic factors of T2D in older adults.

METHODS

A total of 2,909 genotyped patients were enrolled in this study. Genome Wide Association Study was conducted, comparing T2D patients to non-diabetic older adults aged ≥ 60, ≥ 65, or ≥ 70 years, respectively. Binomial logistic regressions were applied to examine the association between T2D and various risk factors. Stepwise logistic regression was conducted to explore the impact of low HDL (HDL < 40 mg/dl) on the relationship between the genetic variants and T2D. A further validation step using data from the UK Biobank with 53,779 subjects was performed.

RESULTS

The association of T2D with both low HDL and family history of T2D increased with the age of control groups. T2D susceptibility variants (rs7756992, rs4712523 and rs10946403) were associated with T2D, more significantly with increased age of the control group. These variants had stronger effects on T2D risk when combined with low HDL cholesterol levels, especially in older control groups.

CONCLUSIONS

The findings highlight a critical role of age, genetic predisposition, and HDL levels in T2D risk. The findings suggest that individuals over 70 years who have high HDL levels without the T2D susceptibility alleles may be at the lowest risk of developing T2D. These insights can inform tailored preventive strategies for older adults, enhancing personalized T2D risk assessments and interventions.

Prediction model for the onset risk of impaired fasting glucose: a 10-year longitudinal retrospective cohort health check-up study

BACKGROUND

Impaired fasting glucose (IFG) is a prediabetic condition. Considering that the clinical symptoms of IFG are inconspicuous, these tend to be easily ignored by individuals, leading to conversion to diabetes mellitus (DM). In this study, we established a prediction model for the onset risk of IFG in the Chongqing health check-up population to provide a reference for prevention in a health check-up cohort.

METHODS

We conducted a retrospective longitudinal cohort study in Chongqing, China from January 2009 to December 2019. The qualified subjects were more than 20 years old and had more than two health check-ups. After following the inclusion and exclusion criteria, the cohort population was randomly divided into a training set and a test set at a ratio of 7:3. We first selected the predictor variables through the univariate generalized estimation equation (GEE), and then the training set was used to establish the IFG risk model based on multivariate GEE. Finally, the sensitivity, specificity, and receiver operating characteristic curves were used to verify the performance of the model.

RESULTS

A total of 4,926 subjects were included in this study, with an average of 3.87 check-up records, including 2,634 males and 2,292 females. There were 442 IFG cases during the follow-up period, including 286 men and 156 women. The incidence density was 26.88/1000 person-years for men and 18.53/1000 person-years for women (P<0.001). The predictor variables of our prediction model include male (relative risk (RR) =1.422, 95 % confidence interval (CI): 0.923-2.193, P=0.3849), age (RR=1.030, 95 %CI: 1.016-1.044, P<0.0001), waist circumference (RR=1.005, 95 %CI: 0.999-1.012, P=0.0975), systolic blood pressure (RR=1.004, 95 %CI: 0.993-1.016, P=0.4712), diastolic blood pressure (RR=1.023, 95 %CI: 1.005-1.041, P=0.0106), obesity (RR=1.797, 95 %CI: 1.126-2.867, P=0.0140), triglycerides (RR=1.107, 95 %CI: 0.943-1.299, P=0.2127), high-density lipoprotein cholesterol (RR=0.992, 95 %CI: 0.476-2.063, P=0.9818), low-density lipoprotein cholesterol (RR=1.793, 95 %CI: 1.085-2.963, P=0.0228), blood urea (RR=1.142, 95 %CI: 1.022-1.276, P=0.0192), serum uric acid (RR=1.004, 95 %CI: 1.002-1.005, P=0.0003), total cholesterol (RR=0.674, 95 %CI: 0.403-1.128, P=0.1331), and serum creatinine levels (RR=0.960, 95 %CI: 0.945-0.976, P<0.0001). The area under the receiver operating characteristic curve (AUC) in the training set was 0.740 (95 %CI: 0.712-0.768), and the AUC in the test set was 0.751 (95 %CI: 0.714-0.817).

CONCLUSIONS

The prediction model for the onset risk of IFG had good predictive ability in the health check-up cohort.