Ethnic differences between Asians and non-Asians in clustering-based phenotype classification of adult-onset diabetes mellitus: A systematic narrative review

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.

Using Cluster Analysis to Identify Metabolic Syndrome Components and Physical Fitness in Patients with Metabolic Syndrome

Background: Metabolic syndrome (MetS) comprises a cluster of cardiovascular risk factors. Physical inactivity and reduced physical fitness are associated with one or more components of MetS. However, MetS has many components, and the unclear relationship between the components and physical fitness parameters can provide a plain and straightforward understanding of the clustering method. Aim: To identify the relationship between physical fitness parameters, physical activity levels, and components of MetS using hierarchical cluster analysis. Methods: One hundred twenty-one patients (mean age = 51.4 ± 7.1/years, F:90, M:31) who were diagnosed as having MetS according to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria were included in the study. Fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were analyzed. Systolic and diastolic blood pressures, (SBP and DBP), were evaluated. Body composition (waist and hip circumference, (WC and HC), waist-to-hip ratio (WHR), body mass index (BMI), percent body fat, and visceral fat), upper and lower extremity muscle strength (dynamometer), and functional exercise capacity [6-minute walk test (6MWT)] were assessed as physical fitness parameters. Physical activity levels were assessed using a pedometer and number of steps (NS) was determined. Results: Of the patients, 45.5% were diagnosed as having MetS based on four components. The dendrogram consisted of two main clusters and four subclusters. The main cluster I composed of BMI, HC, WC, visceral fat, HDL-C, percent fat, SBP, DBP, and percent quadriceps. The main cluster II comprised FPG, TG, WHR, handgrip strength, 6MWT, and NS. Conclusion: MetS components clustered with different physical fitness parameters. The clusters in the dendrogram can provide substantial implications for heterogeneous MetS components and physical fitness parameters. Future studies are needed to elucidate the effectiveness of dendrogram-derived exercise programs in MetS.

Diabetes in South Asians: Uncovering Novel Risk Factors With Longitudinal Epidemiologic Data: Kelly West Award Lecture 2023

South Asian populations have a higher prevalence and earlier age of onset of type 2 diabetes and atherosclerotic cardiovascular diseases than other race and ethnic groups. To better understand the pathophysiology and multilevel risk factors for diabetes and cardiovascular disease, we established the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study in 2010. The original MASALA study cohort (n = 1,164) included 83% Asian Indian immigrants, with an ongoing expansion of the study to include individuals of Bangladeshi and Pakistani origin. We have found that South Asian Americans in the MASALA study had higher type 2 diabetes prevalence, lower insulin secretion, more insulin resistance, and an adverse body composition with higher liver and intermuscular fat and lower lean muscle mass compared with four other U.S. race and ethnic groups. MASALA study participants with diabetes were more likely to have the severe hyperglycemia subtype, characterized by β-cell dysfunction and lower body weight, and this subtype was associated with a higher incidence of subclinical atherosclerosis. We have found several modifiable factors for cardiometabolic disease among South Asians including diet and physical activity that can be influenced using specific social network members and with cultural adaptations to the U.S. context. Longitudinal data with repeat cardiometabolic measures that are supplemented with qualitative and mixed-method approaches enable a deeper understanding of disease risk and resilience factors. Studying and contrasting Asian American subgroups can uncover the causes for cardiometabolic disease heterogeneity and reveal novel methods for prevention and treatment.

Progression of insulin resistance in individuals with type 1 diabetes: A retrospective longitudinal study on individuals from Thailand

AIMS

To investigate temporal changes in glycaemic control and weight contributing to insulin resistance (IR), in Thai individuals with type 1 diabetes (T1D).

METHODS

Longitudinal data of 69 individuals with T1D were retrospectively collected over a median follow-up of 7.2 years. The estimated glucose disposal rate (eGDR), a marker of IR, was calculated using an established formula. Individuals were assigned as insulin-sensitive T1D (the latest eGDR≥8 mg/kg/min), or insulin-resistant T1D/double diabetes (the latest eGDR<8 mg/kg/min). Generalised linear mixed model was employed to compare the temporal patterns of HbA1c, BMI, and eGDR between the two groups.

RESULTS

26 insulin-resistant T1D had a gradual decline in eGDR, corresponding with increased weight and HbA1c. In contrast, 43 insulin-sensitive T1D had stable insulin sensitivity with an improvement in HbA1c over time, associated with a modest weight gain. Fluctuations of glucose levels were observed during the early diabetes course leading to unstable eGDR, thus limiting the use of eGDR to classify insulin-resistant T1D.

CONCLUSION

T1D individuals who eventually develop IR are likely to experience early increasing IR over time. In contrast, those who ultimately do not have IR, maintain their insulin sensitivity throughout their course at least in the medium term.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.

Disparities in diabetes prevalence and management by race and ethnicity in the USA: defining a path forward

Type 2 diabetes disparities in the USA persist in both the prevalence of disease and diabetes-related complications. We conducted a literature review related to diabetes prevention, management, and complications across racial and ethnic groups in the USA. The objective of this review is to summarise the current understanding of diabetes disparities by examining differences between and within racial and ethnic groups and among young people (aged <18 years). We also examine the pathophysiology of diabetes as it relates to race and ethnic differences. We use a conceptual framework built on the socioecological model to categorise the causes of diabetes disparities across the lifespan looking at factors in five domains of health behaviours and social norms, public awareness, structural racism, economic development, and access to high-quality care. The range of disparities in diabetes prevalence and management in the USA calls for a community-engaged and multidisciplinary approach that must involve community partners, researchers, practitioners, health system administrators, and policy makers. We offer recommendations for each of these groups to help to promote equity in diabetes prevention and care in the USA.

A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda

Indian people are at high risk for type 2 diabetes (T2DM) even at younger ages and lower body weights. Already 74 million people in India have the disease, and the proportion of those with T2DM is increasing across all strata of society. Unique aspects, related to lower insulin secretion or function, and higher hepatic fat deposition, accompanied by the rise in overweight (related to lifestyle changes) may all be responsible for this unrelenting epidemic of T2DM. Yet, research to understand the causes, pathophysiology, phenotypes, prevention, treatment, and healthcare delivery of T2DM in India seriously lags behind. There are major opportunities for scientific discovery and technological innovation, which if tapped can generate solutions for T2DM relevant to the country's context and make leading contributions to global science. We analyze the situation of T2DM in India, and present a four-pillar (etiology, precision medicine, implementation research, and health policy) strategic research framework to tackle the challenge. We offer key research questions for each pillar, and identify infrastructure needs. India offers a fertile environment for shifting the paradigm from imprecise late-stage diabetes treatment toward early-stage precision prevention and care. Investing in and leveraging academic and technological infrastructures, across the disciplines of science, engineering, and medicine, can accelerate progress toward a diabetes-free nation.

Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables

Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clusters, of which four correspond to type 2 diabetes (T2DM). Here, we classified nondiabetic individuals to identify risk clusters for incident T2DM to facilitate the refinement of prevention strategies. Of the 1167 participants in the population-based Iwaki Health Promotion Project in 2014 (baseline), 868 nondiabetic individuals who attended at least once during 2015-2019 were included in a prospective study. A hierarchical cluster analysis was performed using four variables (BMI, HbA1c, and HOMA2 indices). Of the four clusters identified, cluster 1 (n = 103), labeled as "obese insulin resistant with sufficient compensatory insulin secretion", and cluster 2 (n = 136), labeled as "low insulin secretion", were found to be at risk of diabetes during the 5-year follow-up period: the multiple factor-adjusted HRs for clusters 1 and 2 were 14.7 and 53.1, respectively. Further, individuals in clusters 1and 2 could be accurately identified: the area under the ROC curves for clusters 1and 2 were 0.997 and 0.983, respectively. The risk of diabetes could be better assessed on the basis of the cluster that an individual belongs to.