Diabetes duration and types of diabetes treatment in data-driven clusters of patients with diabetes

Clinical application of cluster analysis in patients with newly diagnosed type 2 diabetes

AIMS

Early prevention and treatment of type 2 diabetes mellitus (T2DM) is still a huge challenge for patients and clinicians. Recently, a novel cluster-based diabetes classification was proposed which may offer the possibility to solve this problem. In this study, we report our performance of cluster analysis of individuals newly diagnosed with T2DM, our exploration of each subtype's clinical characteristics and medication treatment, and the comparison carried out concerning the risk for diabetes complications and comorbidities among subtypes by adjusting for influencing factors. We hope to promote the further application of cluster analysis in individuals with early-stage T2DM.

METHODS

In this study, a k-means cluster algorithm was applied based on five indicators, namely, age, body mass index (BMI), glycosylated hemoglobin (HbA1c), homeostasis model assessment-2 insulin resistance (HOMA2-IR), and homeostasis model assessment-2 β-cell function (HOMA2-β), in order to perform the cluster analysis among 567 newly diagnosed participants with T2DM. The clinical characteristics and medication of each subtype were analyzed. The risk for diabetes complications and comorbidities in each subtype was compared by logistic regression analysis.

RESULTS

The 567 patients were clustered into four subtypes, as follows: severe insulin-deficient diabetes (SIDD, 24.46%), age-related diabetes (MARD, 30.86%), mild obesity-related diabetes (MOD, 25.57%), and severe insulin-resistant diabetes (SIRD, 20.11%). According to the results of the oral glucose tolerance test (OGTT) and biochemical indices, fasting blood glucose (FBG), 2-hour postprandial blood glucose (2hBG), HbA1c, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and triglyceride-glucose index (TyG) were higher in SIDD and SIRD than in MARD and MOD. MOD had the highest fasting C-peptide (FCP), 2-hour postprandial C-peptide (2hCP), fasting insulin (FINS), 2-hour postprandial insulin (2hINS), serum creatinine (SCr), and uric acid (UA), while SIRD had the highest triglycerides (TGs) and TyG-BMI. Albumin transaminase (ALT) and albumin transaminase (AST) were higher in MOD and SIRD. As concerms medications, compared to the other subtypes, SIDD had a lower rate of metformin use (39.1%) and a higher rate of α-glucosidase inhibitor (AGI, 61.7%) and insulin (74.4%) use. SIRD showed the highest frequency of use of sodium-glucose cotransporter-2 inhibitors (SGLT-2i, 36.0%) and glucagon-like peptide-1 receptor agonists (GLP-1RA, 19.3%). Concerning diabetic complications and comorbidities, the prevalence of diabetic kidney disease (DKD), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), dyslipidemia, and hypertension differed significantly among subtypes. Employing logistic regression analysis, after adjusting for unmodifiable (sex and age) and modifiable related influences (e.g., BMI, HbA1c, and smoking), it was found that SIRD had the highest risk of developing DKD (odds ratio, OR = 2.001, 95% confidence interval (CI): 1.125-3.559) and dyslipidemia (OR = 3.550, 95% CI: 1.534-8.215). MOD was more likely to suffer from NAFLD (OR = 3.301, 95%CI: 1.586-6.870).

CONCLUSIONS

Patients with newly diagnosed T2DM can be successfully clustered into four subtypes with different clinical characteristics, medication treatment, and risks for diabetes-related complications and comorbidities, the cluster-based diabetes classification possibly being beneficial both for prevention of secondary diabetes and for establishment of a theoretical basis for precision medicine.

The differences of metabolic profiles, socioeconomic status and diabetic retinopathy in U.S. working-age and elderly adults with diabetes: results from NHANES 1999-2018

AIMS

Controlled metabolic factors and socioeconomic status (SES) was crucial for prevention of diabetic retinopathy (DR). The study aims to assess the metabolic factors control and SES among working-age adults (18-64 years) with diabetes compared to older adults (65 years and older).

METHODS

Totals of 6738 participants with self-reported diagnosed diabetes from National Health and Nutrition Examination Survey were included, of whom 3482 were working-age and 3256 were elderly. The prevalence of DR, metabolic factors control, and the impact of SES and diabetic duration on DR was estimated. Subgroup analysis among working-age adults was employed across different diabetic duration and SES level.

RESULTS

The prevalence of DR was 20.8% among working-age adults and 20.6% in elderly adults. Further, working-age adults possessed suboptimal control on glycemia (median HbA1c: 7.0% vs. 6.8%, p < 0.001) and lipids (Low-density lipoprotein < 100 mg/dL: 46.4% vs. 63.5%, p < 0.001), but better blood pressure control (< 130/80 mmHg: 53.5% vs. 37.5%, p < 0.001) compared to the elderly, judging based on age-specific control targets. Prolonged diabetic duration didn't improve glycemic and composite factors control. SES like education and income impacted metabolic factors control and adults with higher SES were more likely to control well. Diabetic duration was a significant risk factor (OR = 4.006, 95%CI= (2.752,5.832), p < 0.001) while higher income (OR = 0.590, 95%CI= (0.421,0.826), p = 0.002) and educational level (OR = 0.637, 95%CI= (0.457,0.889), p = 0.008) were protective against DR.

CONCLUSIONS

Working-age adults with diabetes demonstrate suboptimal metabolic profile control, especially glycemia and lipids. Additional efforts are needed to improve metabolic factor control and reduce DR risk, particularly for those with longer diabetes duration, less education, and lower incomes.

Twenty-four-hour physical behaviour profiles across type 2 diabetes mellitus subtypes

AIM

To investigate how 24-h physical behaviours differ across type 2 diabetes (T2DM) subtypes.

MATERIALS AND METHODS

We included participants living with T2DM, enrolled as part of an ongoing observational study. Participants wore an accelerometer for 7 days to quantify physical behaviours across 24 h. We used routinely collected clinical data (age at onset of diabetes, glycated haemoglobin level, homeostatic model assessment index of beta-cell function, homeostatic model assessment index of insulin resistance, body mass index) to replicate four previously identified subtypes (insulin-deficient diabetes [INS-D], insulin-resistant diabetes [INS-R], obesity-related diabetes [OB] and age-related diabetes [AGE]), via k-means clustering. Differences in physical behaviours across the diabetes subtypes were assessed using generalized linear models, with the AGE cluster as the reference.

RESULTS

A total of 564 participants were included in this analysis (mean age 63.6 ± 8.4 years, 37.6% female, mean age at diagnosis 53.1 ± 10.0 years). The proportions in each cluster were as follows: INS-D: n = 35, 6.2%; INS-R: n = 88, 15.6%; OB: n = 166, 29.4%; and AGE: n = 275, 48.8%. Compared to the AGE cluster, the OB cluster had a shorter sleep duration (-0.3 h; 95% confidence interval [CI] -0.5, -0.1), lower sleep efficiency (-2%; 95% CI -3, -1), lower total physical activity (-2.9 mg; 95% CI -4.3, -1.6) and less time in moderate-to-vigorous physical activity (-6.6 min; 95% CI -11.4, -1.7), alongside greater sleep variability (17.9 min; 95% CI 8.2, 27.7) and longer sedentary time (31.9 min; 95% CI 10.5, 53.2). Movement intensity during the most active continuous 10 and 30 min of the day was also lower in the OB cluster.

CONCLUSIONS

In individuals living with T2DM, the OB subtype had the lowest levels of physical activity and least favourable sleep profiles. Such behaviours may be suitable targets for personalized therapeutic lifestyle interventions.

Disease severity-based subgrouping of type 2 diabetes does not parallel differences in quality of life: the Maastricht Study

AIMS/HYPOTHESIS

Type 2 diabetes is a highly heterogeneous disease for which new subgroups ('clusters') have been proposed based on disease severity: moderate age-related diabetes (MARD), moderate obesity-related diabetes (MOD), severe insulin-deficient diabetes (SIDD) and severe insulin-resistant diabetes (SIRD). It is unknown how disease severity is reflected in terms of quality of life in these clusters. Therefore, we aimed to investigate the cluster characteristics and cluster-wise evolution of quality of life in the previously defined clusters of type 2 diabetes.

METHODS

We included individuals with type 2 diabetes from the Maastricht Study, who were allocated to clusters based on a nearest centroid approach. We used logistic regression to evaluate the cluster-wise association with diabetes-related complications. We plotted the evolution of HbA1c levels over time and used Kaplan-Meier curves and Cox regression to evaluate the cluster-wise time to reach adequate glycaemic control. Quality of life based on the Short Form 36 (SF-36) was also plotted over time and adjusted for age and sex using generalised estimating equations. The follow-up time was 7 years. Analyses were performed separately for people with newly diagnosed and already diagnosed type 2 diabetes.

RESULTS

We included 127 newly diagnosed and 585 already diagnosed individuals. Already diagnosed people in the SIDD cluster were less likely to reach glycaemic control than people in the other clusters, with an HR compared with MARD of 0.31 (95% CI 0.22, 0.43). There were few differences in the mental component score of the SF-36 in both newly and already diagnosed individuals. In both groups, the MARD cluster had a higher physical component score of the SF-36 than the other clusters, and the MOD cluster scored similarly to the SIDD and SIRD clusters.

CONCLUSIONS/INTERPRETATION

Disease severity suggested by the clusters of type 2 diabetes is not entirely reflected in quality of life. In particular, the MOD cluster does not appear to be moderate in terms of quality of life. Use of the suggested cluster names in practice should be carefully considered, as the non-neutral nomenclature may affect disease perception in individuals with type 2 diabetes and their healthcare providers.

The Role of Inflammation and Therapeutic Concepts in Diabetic Retinopathy-A Short Review

Diabetic retinopathy (DR) as a microangiopathy is the most common complication in patients with diabetes mellitus (DM) and remains the leading cause of blindness among adult population. DM in its complicated pathomechanism relates to chronic hyperglycemia, hypoinsulinemia, dyslipidemia and hypertension-all these components in molecular pathways maintain oxidative stress, formation of advanced glycation end-products, microvascular changes, inflammation, and retinal neurodegeneration as one of the key players in diabetes-associated retinal perturbations. In this current review, we discuss the natural history of DR with special emphasis on ongoing inflammation and the key role of vascular endothelial growth factor (VEGF). Additionally, we provide an overview of the principles of diabetic retinopathy treatments, i.e., in laser therapy, anti-VEGF and steroid options.