Clustering for a better prediction of type 2 diabetes mellitus

Retinol binding protein 4 and type 2 diabetes: from insulin resistance to pancreatic β-cell function

BACKGROUND AND AIM

Retinol binding protein 4 (RBP4) is an adipokine that has been explored as a key biomarker of type 2 diabetes mellitus (T2DM) in recent years. Researchers have conducted a series of experiments to understand the interplay between RBP4 and T2DM, including its role in insulin resistance and pancreatic β-cell function. The results of these studies indicate that RBP4 has a significant influence on T2DM and is considered a potential biomarker of T2DM. However, there have also been some controversies about the relationship between RBP4 levels and T2DM. In this review, we update and summarize recent studies focused on the relationship between RBP4 and T2DM and its role in insulin resistance and pancreatic β-cell function to clarify the existing controversy and provide evidence for future studies. We also assessed the potential therapeutic applications of RBP4 in treating T2DM.

METHODS

A narrative review.

RESULTS

Overall, there were significant associations between RBP4 levels, insulin resistance, pancreatic β-cell function, and T2DM.

CONCLUSIONS

More mechanistic studies are needed to determine the role of RBP4 in the onset of T2DM, especially in terms of pancreatic β-cell function. In addition, further studies are required to evaluate the effects of drug intervention, lifestyle intervention, and bariatric surgery on RBP4 levels to control T2DM and the role of reducing RBP4 levels in improving insulin sensitivity and pancreatic β-cell function.

Clinical phenotypes of comorbidities in end-stage knee osteoarthritis: a cluster analysis

OBJECTIVES

Comorbidities, as components of these heterogeneous features, often coexist with knee osteoarthritis, and are particularly prevalent in end-stage knee osteoarthritis. Here, we attempted to identify the different clinical phenotypes of comorbidities in patients with end-stage knee osteoarthritis by cluster analysis.

METHODS

A total of 421 inpatients diagnosed with end-stage knee osteoarthritis who underwent inpatient surgery were included in this cross-sectional study. 23 demographic, comorbidity, inflammatory immune and evaluation scale variables were collected. Systematic clustering after factor analysis and separate two-step cluster analysis were performed for individual comorbidity variables and all variables, respectively, to objectively identify the different clinical phenotypes of the study patients.

RESULTS

Four clusters were finally identified. Cluster 1 had the largest proportion of obese patients (93.8%) and hypertension was common (71.2%). Almost all patients in cluster 2 were depressed (95.8%) and anxiety disorders (94.7%). Cluster 3 combined patients with isolated end-stage knee osteoarthritis and a few comorbidities. Cluster 4 had the highest proportion of patients with rheumatoid arthritis (58.8%).

CONCLUSIONS

Patients with end-stage knee osteoarthritis may be classified into four different clinical phenotypes: "isolated end-stage knee osteoarthritis"; "obesity + hypertension"; "depression + anxiety"; and "rheumatoid arthritis", which may help guide individualized patient care and treatment strategies.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Helical-Like Assembly of Nateglinide as Coating for Oral Delivery of Insulin and Their Synergistic Prevention of Diabetes Mellitus

Oral delivery of antidiabetic active components promises to free millions of people from daily suffering who require routine injections. However, oral insulin (Ins) and other short-acting compounds such as nateglinide (NG) in harsh gastrointestinal tract still face great challenging, including low bioavailability, and rapid elimination. In this study, inspired by the self-assembly of phenylalanine-based peptides in nature, it is showed that NG a small phenylalanine derivative, assembles into left-handed helical nanofibers in the presence of Ca2+ . These helical NG nanofibers functioned as a coating layer on the surface of Ca2+ -linked alginate (Alg) microgels for the effective encapsulation of Ins. As expected, the sustained release and prolonged circulation of Ins and NG from the Ins-loading Alg/NG microgels (Ins@Alg/NG) in the intestinal tract synergistically maintain a relatively normal blood glucose level in streptozotocin-induced diabetic mice after oral administration of Ins@Alg/NG. This further confirms that Ins@Alg/NG ameliorated Ins resistance mainly through activating Insreceptor substrate 1 (IRS1), protein kinase B (AKT), and AMP-activated protein kinase (AMPK), as well as by repressing glycogen synthase kinase-3β (GSK-3β). The strategy of using the assembly of NG as a coating achieves the oral delivery of insulin and showcases a potential for the treatment of diabetes.

Monogenic diabetes

Monogenic diabetes includes several clinical conditions generally characterized by early-onset diabetes, such as neonatal diabetes, maturity-onset diabetes of the young (MODY) and various diabetes-associated syndromes. However, patients with apparent type 2 diabetes mellitus may actually have monogenic diabetes. Indeed, the same monogenic diabetes gene can contribute to different forms of diabetes with early or late onset, depending on the functional impact of the variant, and the same pathogenic variant can produce variable diabetes phenotypes, even in the same family. Monogenic diabetes is mostly caused by impaired function or development of pancreatic islets, with defective insulin secretion in the absence of obesity. The most prevalent form of monogenic diabetes is MODY, which may account for 0.5-5% of patients diagnosed with non-autoimmune diabetes but is probably underdiagnosed owing to insufficient genetic testing. Most patients with neonatal diabetes or MODY have autosomal dominant diabetes. More than 40 subtypes of monogenic diabetes have been identified to date, the most prevalent being deficiencies of GCK and HNF1A. Precision medicine approaches (including specific treatments for hyperglycaemia, monitoring associated extra-pancreatic phenotypes and/or following up clinical trajectories, especially during pregnancy) are available for some forms of monogenic diabetes (including GCK- and HNF1A-diabetes) and increase patients' quality of life. Next-generation sequencing has made genetic diagnosis affordable, enabling effective genomic medicine in monogenic diabetes.

Trajectory of glycated haemoglobin over time, using real-world data, in type 2 diabetes patients with obesity on a U-100 basal-bolus insulin regimen

AIMS

To identify patient clusters with poor glucose control among type 2 diabetes mellitus (T2DM) patients with obesity who are receiving basal-bolus insulin and to identify the potential therapeutic inertia factors associated with poor control.

METHODS

Glycated haemoglobin (HbA1c) trajectories across a 3-year period were structured at 6-month intervals for a retrospective cohort of T2DM patients with obesity on basal-bolus insulin from the Veterans' Health Administration database. Based on each patient's longitudinal HbA1c features, an unsupervised clustering procedure was used to determine the numbers of clusters and associated trajectory patterns. Multinomial logistic regression was used to examine the association between HbA1c trajectory clusters and patient characteristics/treatment patterns.

RESULTS

A total of 51 273 patients were included, of whom 11.2% were in a subgroup with persistent missingness of HbA1c values. For those with sufficient HbA1c observations, cluster analysis indicated six distinct HbA1c trajectories: stable low (35.8%); stable high (20.8%); descending low (10.5%); ascending low (10.2%); descending high (5.7%); and ascending high (5.7%). Being of Black ethnicity, not initiating noninsulin antihyperglycaemic agents (sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists or thiazolidinediones) or concentrated insulin, low adherence (measured by proportion of days covered), and reduced insulin prescription refills were factors associated with poorer HbA1c clusters; similar factors were associated with persistent HbA1c missingness.

CONCLUSION

The present study found the potential for therapeutic inertia among a significant proportion of T2DM patients with obesity on basal-bolus insulin. Subgrouping T2DM patients based on HbA1c missingness and HbA1c trajectories can inform disease management strategies.

Inflammatory and immune etiology of type 2 diabetes

Type 2 diabetes (T2D) represents a global threat affecting millions of patients worldwide. However, its causes remain incompletely dissected and we lack the tools to predict which individuals will develop T2D. Although there is a clear proven clinical association of T2D with metabolic disorders such as obesity and nonalcoholic fatty liver disease (NAFLD), the existence of a significant number of nondiabetic obese subjects suggests yet-uncovered features of such relationships. Here, we propose that a significant proportion of individuals may harbor an immune profile that renders them susceptible to developing T2D. We note the heterogeneity of circulating monocytes and tissue macrophages in organs that are key to metabolic disorders such as liver, white adipose tissue (WAT), and endocrine pancreas, as well as their contribution to T2D genesis.

Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes

BACKGROUND

The prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes.

METHODS

Data on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and β-cell function, systolic and diastolic blood pressure, and BMI. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes.

RESULTS

Six risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low β-cell function (LRLB), low-risk high β-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk β-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years.

CONCLUSIONS

Phenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.

New clusters of serum electrolytes aid in stratification of diabetes and metabolic risk

BACKGROUND

Serum electrolytes were found to associate with type 2 diabetes. Our study aimed to stratify nondiabetes by clusters based on multiple serum electrolytes and evaluate their associations with risk of developing diabetes and longitudinal changes in glucose and lipid metabolic traits.

METHODS

We performed a data-driven cluster analysis in 4937 nondiabetes individuals aged ≥40 years at baseline from a cohort follow-up for an average of 4.4 years. Cluster analysis was based on seven commonly measured serum electrolytes (iron, chlorine, magnesium, sodium, potassium, calcium, and phosphorus) by using the k-means method.

RESULTS

A total of 4937 nondiabetes individuals were classified into three distinct clusters, with 1635 (33.1%) assigned to Cluster A, 1490 (30.2%) to Cluster B, and 1812 (36.7%) to Cluster C. Individuals in Cluster A had higher serum chlorine, were older, and more were women. Individuals in Cluster B had higher serum iron and body mass index (BMI). Individuals in Cluster C had higher serum phosphorus, were younger, and had lower BMI. Cluster B had 1.41-fold higher risk of developing diabetes and Cluster C's risk was 1.33-fold higher compared with Cluster A. Over an average follow-up of 4.4 years, Cluster A showed a moderate and stable BMI, Cluster B showed an accelerated deterioration in glucose metabolism, and Cluster C showed the most sharply increased serum low-density lipoprotein cholesterol level.

CONCLUSIONS

Clusters based on seven common serum electrolytes differed in diabetes risk and progression of glucose and lipid metabolic traits. Serum electrolytes clusters could provide a powerful tool to differentiate individuals into different risk stratification for developing type 2 diabetes.