BMC Endocrine Disorders最新文献

Purpose: Currently, there is a lack of early biomarkers to identify the risk of dysglycemia in patients with concurrent hypertension and obstructive sleep apnea (OSA). The aim of our study is to evaluate the efficacy of the recently proposed Body Mass Index (BMI)-Glucose (ByG) index in identifying the risk of dysglycemia in patients with hypertension and OSA.

Methods: A retrospective cohort study of 1,759 adults with hypertension and OSA from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH) was conducted. Cox proportional hazards models were used to assess the associations between the ByG index and new-onset dysglycemia, diabetes, and prediabetes. Time-dependent receiver operating characteristic (ROC) curves were constructed to compare the efficacy of the ByG index with traditional insulin resistance indicators.

Results: During a median follow-up of 7.25 years, 212 cases of dysglycemia (157 diabetes, 55 prediabetes) were identified. Participants in the highest ByG tertile had a significantly increased risk of dysglycemia (HR 3.20; 95% CI: 2.13-4.80), diabetes (HR 3.38; 95% CI: 2.02-5.66), and prediabetes (HR 2.95; 95% CI: 1.44-6.06) compared to the lowest tertile, after full adjustment. Time-dependent ROC showed the ByG index was more discriminative in predicting dysglycemia (including diabetes and prediabetes) events at 3, 5 and 7 years compared to BMI, TyG, TyG-BMI indices, waist circumference, and waist-to-height ratio.

Conclusion: The ByG index serves as an independent predictor of dysglycemia, encompassing diabetes and prediabetes, among patients with hypertension and OSA. As a simple, accessible, and reliable tool for early risk stratification, it demonstrates application potential but requires validation through prospective studies.

Trial registration: Not applicable.

Background: Hyperinsulinemia and insulin resistance (IR) are early indicators of metabolic dysfunction that often precede the development of Type 2 Diabetes (T2D). Identifying these conditions in apparently healthy youth-who may not yet exhibit clinical symptoms-is essential for early intervention. Therefore, we conducted this community-based study to determine the prevalence of hyperinsulinemia, IR, and their associations with measures of adiposity and body composition in 16-25-year-old adolescents and young adults in Mumbai, India.

Methods: Participants (n = 1313, M_age 19.6 (2.1) years, 65.2% females) reported socio-demography, medical history, and physical activity levels. Hyperinsulinemia was classified as fasting insulin ≥ 15mIU/ml and/or stimulated insulin ≥ 80 mIU/ml and IR as HOMA-IR > 2.5. Anthropometry and blood pressure were measured. Sex-adjusted logistic regression analyses were performed to examine the association of adiposity measures with hyperglycemia and hyperinsulinemia. The odds ratios (ORs) and their 95% confidence intervals (CI) were calculated, significant at p ≤ 0.05.

Results: The prevalence of hyperinsulinemia, IR, overweight, and obesity was 39.7%, 17.3%, 15.0%, and 20.5% respectively. Of those with prediabetes (4.5%), more than half (58.1%) were overweight and almost one-third (29.1%) had abdominal obesity. The prevalence of abdominal obesity was 15.1% in participants with normal weight and 36.4% in those with overweight or obesity. Additionally, we observed that 30.1% of participants who were normal or underweight had hyperinsulinemia, and one-third of those with IR were neither overweight nor obese. The odds of hyperglycemia increased from 2.41 times in individuals with overweight (95% CI 1.91, 6.71, p = 0.03) to 4.67 times in those with obesity (95% CI 1.33, 6.37, p = 0.01). Hyperinsulinemia showed significant associations with body fat percent (OR 1.13 (95% CI 1.02, 1.29) p = 0.006), waist-to-height ratio (OR 1.66 (95% CI,1.15, 3.30), p = 0.02), and blood pressure (OR 1.08, 95% CI 1,03, 1.20, p = 0.04).

Conclusions: The high prevalence of hyperinsulinemia and IR in adolescents and young adults and their associations with obesity and elevated blood pressure highlight a clustering of cardiometabolic risk factors. The findings related to the prevalence of hyperinsulinemia and abdominal obesity in participants with normal weight suggest a need for comprehensive screening and further research to develop targeted interventions.

Clinical trial number: Not applicable.

Background: Non-traditional lipid indices are diabetes risk factors, but their combined effects with inflammatory markers remain unclear. The present study systematically investigated and compared the associations between eight non-traditional lipid-inflammatory parameters and new-onset diabetes.

Methods: Data were obtained from the 2011-2020 China Health and Retirement Longitudinal Study. The lipid-inflammatory parameters were created by integrating non-traditional lipid indices and high-sensitivity C-reactive protein (hsCRP). Cox regression and restricted cubic spline models examined the associations of baseline and cumulative lipid-inflammatory parameters with diabetes risk. Time-dependent receiver operating characteristic analyses evaluated the predictive performance of these parameters. Mediation analyses explored the reciprocal links between non-traditional lipid indices, hsCRP, and diabetes risk.

Results: The study enrolled 7,356 participants, with 1,173 (15.95%) developing diabetes over a median follow-up period of 9 years. In the multivariable-adjusted model, individuals with higher baseline and cumulative non-traditional lipid-inflammatory parameter levels had an elevated risk of developing diabetes, especially for lipoprotein combined index (LCI)-hsCRP (hazard ratio [HR] = 2.03, 95% confidence interval [CI]: 1.70-2.41). All lipid-inflammatory parameters were nonlinearly associated with diabetes risk. The area under the curve (AUC) values for the eight lipid-inflammatory parameters ranged from 0.595 to 0.619, with LCI-hsCRP exhibiting the highest predictive value (AUC = 0.619, 95% CI: 0.597-0.640). The results were consistent across sensitivity and subgroup analyses. Mediation analyses showed significant bidirectional mediation between non-traditional lipid indices and hsCRP for diabetes.

Conclusions: Elevated baseline and cumulative non-traditional lipid-inflammatory parameter levels are linked to a higher risk of incident diabetes, with LCI-hsCRP showing the highest predictive value.

Clinical trial number: Not applicable.