Relationships of pancreatic beta-cell function with microalbuminuria and glomerular filtration rate in middle-aged and elderly population without type 2 diabetes mellitus: a Chinese community-based analysis

Clinical characteristics and complication risks in data-driven clusters among Chinese community diabetes populations

BACKGROUND

Novel diabetes phenotypes were proposed by the Europeans through cluster analysis, but Chinese community diabetes populations might exhibit different characteristics. This study aims to explore the clinical characteristics of novel diabetes subgroups under data-driven analysis in Chinese community diabetes populations.

METHODS

We used K-means cluster analysis in 6369 newly diagnosed diabetic patients from eight centers of the REACTION (Risk Evaluation of cAncers in Chinese diabeTic Individuals) study. The cluster analysis was performed based on age, body mass index, glycosylated hemoglobin, homeostatic modeled insulin resistance index, and homeostatic modeled pancreatic β-cell functionality index. The clinical features were evaluated with the analysis of variance (ANOVA) and chi-square test. Logistic regression analysis was done to compare chronic kidney disease and cardiovascular disease risks between subgroups.

RESULTS

Overall, 2063 (32.39%), 658 (10.33%), 1769 (27.78%), and 1879 (29.50%) populations were assigned to severe obesity-related and insulin-resistant diabetes (SOIRD), severe insulin-deficient diabetes (SIDD), mild age-associated diabetes mellitus (MARD), and mild insulin-deficient diabetes (MIDD) subgroups, respectively. Individuals in the MIDD subgroup had a low risk burden equivalent to prediabetes, but with reduced insulin secretion. Individuals in the SOIRD subgroup were obese, had insulin resistance, and a high prevalence of fatty liver, tumors, family history of diabetes, and tumors. Individuals in the SIDD subgroup had severe insulin deficiency, the poorest glycemic control, and the highest prevalence of dyslipidemia and diabetic nephropathy. Individuals in MARD subgroup were the oldest, had moderate metabolic dysregulation and the highest risk of cardiovascular disease.

CONCLUSION

The data-driven approach to differentiating the status of new-onset diabetes in the Chinese community was feasible. Patients in different clusters presented different characteristics and risks of complications.

Association of serum high-density lipoprotein cholesterol with microalbuminuria in type 2 diabetes patients

BACKGROUND

The association of serum high-density lipoprotein cholesterol (HDL-C) with microalbuminuria in type 2 diabetes mellitus (T2DM) remains controversial. Therefore, a cross-sectional study was conducted on patients with T2DM to investigate the relationship of HDL-C with microalbuminuria.

METHODS

A total of 524 participants with T2DM were recruited in this cross-sectional study. The patients were divided into four groups according to serum HDL-C quartile. A nonparametric test was employed to assess the relationships across quartiles with clinical parameters and demographics. Multivariate logistic regression analysis was further performed.

RESULTS

Of the 524 patients, 138 (26.3%) were found to have microalbuminuria by urinary albumin excretion rate determination. Serum HDL-C levels in microalbuminuria group were significantly lower than those in non-microalbuminuria group (1.04 (0.90-1.21) vs. 1.10 (0.94-1.31) mmol/L, P = 0.002). The nonparametric test for trend showed that the prevalence of microalbuminuria was significantly reduced for subjects of the fourth quartile of HDL-C compared to the first to third quartile (13.5% vs. 33.1%, 28.6%, 29.4%, P = 0.001). Multivariate logistic regression showed that subjects within the highest quartile of HDL-C had lower odds of microalbuminuria than those within the lowest quartile of HDL-C (OR = 0.17, 95% CI 0.15-0.52, P = 0.004).

CONCLUSIONS

Higher levels of serum HDL-C were associated with decreased rates of microalbuminuria in T2DM patients.

Biomarkers in Cardiorenal Syndromes

There is a consensus that cardiorenal syndromes (CRS) are defined as the disorders of heart and kidney where acute or chronic dysfunction in one organ may induce acute or chronic dysfunction in another. Patients with CRS have increased hospitalization and mortality rates, and thus their identification is of great implication. Biomarkers are not only predictive in heart failure or renal diseases, but also useful in identifying cardiac dysfunction in renal diseases and renal injury in heart failure. Thus, they may be applied in order to identify patients with CRS and even assess prognosis and guide therapy in these patients. However, studies on biomarkers have just begun in CRS. Future studies are essential to observe current biomarkers and find novel biomarkers in CRS so as to improve diagnosis, therapy, and prognosis of CRS.