Identification of two novel subgroups in patients with diabetes mellitus and their association with clinical outcomes: A two-step cluster analysis

Identifying subtypes of type 2 diabetes mellitus based on real-world electronic medical record data in China

AIMS

To replicate the European subtypes of type 2 diabetes mellitus (T2DM) in the Chinese diabetes population and investigate the risk of complications in different subtypes.

METHODS

A diabetes cohort using real-world patient data was constructed, and clustering was employed to subgroup the T2DM patients. Kaplan-Meier analysis and the Cox models were used to analyze the association between diabetes subtypes and the risk of complications.

RESULTS

A total of 2,652 T2DM patients with complete clustering data were extracted. Among them, 466 (17.57 %) were classified as severe insulin-deficient diabetes (SIDD), 502 (18.93 %) as severe insulin-resistant diabetes (SIRD), 672 (25.34 %) as mild obesity-related diabetes (MOD), and 1,012 (38.16 %) as mild age-related diabetes (MARD). The risk of chronic kidney disease (CKD) and diabetic retinopathy (DR) were different in the four subtypes. Compared with MARD, SIRD had a higher risk of CKD (HR 2.40 [1.16, 4.96]), and SIDD had a higher risk of DR (HR 2.16 [1.11, 4.20]). The risk of stroke and coronary events had no difference.

CONCLUSIONS

The European T2DM subtypes can be replicated in the Chinese diabetes population. The risk of CKD and DR varied among different subtypes, indicating that proper interventions can be taken to prevent specific complications in different subtypes.

Retinal and Choroidal Phenotypes Across Novel Subtypes of Type 2 Diabetes Mellitus

PURPOSE

To investigate longitudinal changes in choroidal thickness (CT) and ganglion cell-inner plexiform layer thickness (GC-IPLT) across distinct phenotypes of type 2 diabetes mellitus (T2DM) patients.

DESIGN

Prospective cohort study.

METHODS

T2DM patients were categorized into 5 groups (SAID, SIDD, SIRD, MOD, and MARD) using K-means clustering based on β-cell function and insulin resistance. Swept-source optical coherence tomography measured baseline and 4-year follow-up CT and GC-IPLT. Linear mixed-effects models assessed absolute and relative changes in CT and GC-IPLT across subtypes.

RESULTS

Over a median 4.11-year follow-up, CT and GC-IPLT decreased significantly across all groups. Choroidal thinning rates were most pronounced in SIDD (-6.5 ± 0.53 µm/year and -3.5 ± 0.24%/year) and SAID (-6.27 ± 0.8 µm/year and -3.19 ± 0.37%/year), while MARD showed the slowest thinning rates (-3.63 ± 0.34 µm/year and -1.98 ± 0.25%/year). SIRD exhibited the greatest GC-IPLT loss (-0.66 ± 0.05 µm/year and -0.91 ± 0.07%/year), with the least in SIDD (-0.36 ± 0.05 µm/year and -0.49 ± 0.07%/year), all statistically significant (all P < 0.001). Adjusted for confounding variables, SIDD and SAID groups showed faster CT thinning than MARD [-2.57 µm/year (95% CI: -4.16 to -0.97; P = 0.002) and -2.89 µm/year (95% CI: -4.12 to -1.66; P < 0.001), respectively]. GC-IPLT thinning was notably accelerated in SIRD versus MARD, but slowed in SIDD relative to MARD [differences of -0.16 µm/year (95% CI: -0.3 to -0.03; P = 0.015) and 0.15 µm/year (95% CI: 0.03 to 0.27; P = 0.015), respectively].

CONCLUSIONS

Microvascular damage in the choroid is associated with SIDD patients, whereas early signs of retinal neurodegeneration are evident in SIRD patients. All these changes may precede the onset of DR.

Diabetes mellitus-Progress and opportunities in the evolving epidemic

Diabetes, a complex multisystem metabolic disorder characterized by hyperglycemia, leads to complications that reduce quality of life and increase mortality. Diabetes pathophysiology includes dysfunction of beta cells, adipose tissue, skeletal muscle, and liver. Type 1 diabetes (T1D) results from immune-mediated beta cell destruction. The more prevalent type 2 diabetes (T2D) is a heterogeneous disorder characterized by varying degrees of beta cell dysfunction in concert with insulin resistance. The strong association between obesity and T2D involves pathways regulated by the central nervous system governing food intake and energy expenditure, integrating inputs from peripheral organs and the environment. The risk of developing diabetes or its complications represents interactions between genetic susceptibility and environmental factors, including the availability of nutritious food and other social determinants of health. This perspective reviews recent advances in understanding the pathophysiology and treatment of diabetes and its complications, which could alter the course of this prevalent disorder.

Correlations between distal sensorimotor polyneuropathy and cardiovascular complications in diabetic patients in the North-Eastern region of Hungary

Distal sensorimotor polyneuropathy (DSPN) is the earliest detectable and the most frequent microvascular complication in diabetes mellitus. Several studies have previously demonstrated correlations between cardiovascular risk factors in diabetic patients and independent risk factors for diabetic neuropathy. Our objective was to retrospectively analyze data from diabetic patients in the North-East region of Hungary who underwent neuropathy screening at the Diabetic Neuropathy Center, University of Debrecen, between 2017 and 2021. We aimed to investigate the correlations between cardiovascular risk factors and microvascular complications among patients with DSPN. The median age of the patients was 67 years, 59,6% were female, and 91,1% had type 2 diabetes. The prevalence of DSPN among the study subjects was 71.7%. A significantly longer duration of diabetes (p<0.01) was noted in patients with DSPN. Those with DSPN demonstrated a significantly higher HbA1c level (p<0.001) and a greater frequency of insulin use (p = 0.001). We observed a significantly elevated albumin/creatinine ratio (p<0.001) and a significantly lower eGFR (p<0.001) in patients with DSPN. Diabetic retinopathy exhibited a significantly higher prevalence in patients with DSPN (p<0.001). A higher prevalence of myocardial infarction (p<0.05), ischemic heart disease (p<0.001), peripheral arterial disease (p<0.05) and a history of atherosclerosis (p<0.05) was observed in patients with DSPN. In a multivariate logistic regression analysis, the following factors were independently associated with the presence of DSPN: higher HbA1c (OR:2.58, 95% CI:1.89-3.52, p<0.001), age (OR:1.03, 95% CI:1.01-1.05, p = 0.006), albumin/creatinine ratio above 3 mg/mmol (OR:1.23, 95% CI:1.06-1.45, p = 0.008), retinopathy (OR:6.06, 95% CI:1.33-27.53, p = 0.02), and composite cardiovascular endpoint (OR:1.95, 95% CI:1.19-3.19, p = 0.008). Our study revealed that age, elevated HbA1c levels, significant albuminuria, retinopathy, and cardiovascular complications may increase the risk of DSPN. Further investigation of these associations is necessary to understand the impact of patient characteristics during the treatment of diabetic neuropathy.

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

PURPOSE

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

METHODS

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

RESULTS

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

CONCLUSION

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus

People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.

Factors associated with diabetic foot ulcers and lower limb amputations in type 1 and type 2 diabetes supported by real-world data from the German/Austrian DPV registry

AIMS

Diabetic foot ulcer (DFU) is a leading cause of lower limb amputations in people with diabetes. This study was aimed to retrospectively analyze factors affecting DFU using real-world data from a large, prospective central-European diabetes registry (DPV [Diabetes-Patienten-Verlaufsdokumentation]).

MATERIALS AND METHODS

We matched adults with type 1 (T1D) or type 2 diabetes (T2D) and DFU to controls without DFU by diabetes type, age, sex, diabetes duration, and treatment year to compare possible risk factors. Cox regression was used to calculate hazard ratios for amputation among those with DFU.

RESULTS

In our cohort (N = 63 464), male sex, taller height, and diabetes complications such as neuropathy, peripheral artery disease, nephropathy, and retinopathy were associated with DFU (all p < .001). Glycated hemoglobin (HbA1c) was related to DFU only in T1D (mean with 95% confidence interval [CI]: 7.8 [6.9-9.0] % vs 7.5 [6.8-8.5] %, p < .001). High triglycerides and worse low-density lipoprotein/high-density lipoprotein ratio were also associated with DFU in T1D, whereas smoking (14.7% vs 13.1%) and alcohol abuse (6.4% vs 3.8%, both p < .001) were associated with DFU in T2D. Male sex, higher Wagner grades, and high HbA1c in both diabetes types and insulin use in T2D were associated with increased hazard ratios for amputations.

CONCLUSIONS

Sex, body height, and diabetes complications were associated DFU risk in adults with T1D and T2D. Improvement in glycemic control and lipid levels in T1D and reduction of smoking and drinking in T2D may be appropriate interventions to reduce the risk for DFU or amputations.

Characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups: a retrospective study

BACKGROUND

Glucolipid metabolism plays an important role in the occurrence and development of diabetes mellitus. However, there is limited research on the characteristics of glucolipid metabolism and complications in different subgroups of newly diagnosed diabetes. This study aimed to investigate the characteristics of glucolipid metabolism and complications in novel cluster-based diabetes subgroups and explore the contributions of different glucolipid metabolism indicators to the occurrence of complications and pancreatic function.

METHODS

This retrospective study included 547 newly diagnosed type 2 diabetes patients. Age, body mass index (BMI), glycated hemoglobin (HbA1C), homeostasis model assessment-2 beta-cell function (HOMA2-β), and homeostasis model assessment-2 insulin resistance (HOMA2-IR) were used as clustering variables. The participants were divided into 4 groups by k-means cluster analysis. The characteristics of glucolipid indicators and complications in each subgroup were analyzed. Regression analyses were used to evaluate the impact of glucolipid metabolism indicators on complications and pancreatic function.

RESULTS

Total cholesterol (TC), triglycerides (TG), triglyceride glucose index (TyG), HbA1C, fasting plasma glucose (FPG), and 2-h postprandial plasma glucose (2hPG) were higher in the severe insulin-resistant diabetes (SIRD) and severe insulin-deficient diabetes (SIDD) groups. Fasting insulin (FINS), fasting C-peptide (FCP), 2-h postprandial insulin (2hINS), 2-h postprandial C-peptide (2hCP), and the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) were higher in mild obesity-related diabetes (MOD) and SIRD. 2hCP, FCP, and FINS were positively correlated with HOMA2-β, while FPG, TyG, HbA1C, and TG were negatively correlated with HOMA2-β. FINS, FPG, FCP, and HbA1C were positively correlated with HOMA2-IR, while high-density lipoprotein (HDL) was negatively correlated with HOMA2-IR. FINS (odds ratio (OR),1.043;95% confidence interval (CI) 1.006 ~ 1.081), FCP (OR,2.881;95%CI 2.041 ~ 4.066), and TyG (OR,1.649;95%CI 1.292 ~ 2.104) contributed to increase the risk of nonalcoholic fatty liver disease (NAFLD); 2hINS (OR,1.015;95%CI 1.008 ~ 1.022) contributed to increase the risk of atherosclerotic cardiovascular disease (ASCVD); FCP (OR,1.297;95%CI 1.027 ~ 1.637) significantly increased the risk of chronic kidney disease (CKD).

CONCLUSIONS

There were differences in the characteristics of glucolipid metabolism as well as complications among different subgroups of newly diagnosed type 2 diabetes. 2hCP, FCP, FINS, FPG, TyG, HbA1C, HDL and TG influenced the function of insulin. FINS, TyG, 2hINS, and FCP were associated with ASCVD, NAFLD, and CKD in newly diagnosed T2DM patients.

Precision Medicine in Type 2 Diabetes Mellitus: Utility and Limitations

Type 2 diabetes mellitus (T2DM) is one of the most widespread diseases in Western countries, and its incidence is constantly increasing. Epidemiological studies have shown that in the next 20 years. The number of subjects affected by T2DM will double. In recent years, owing to the development and improvement in methods for studying the genome, several authors have evaluated the association between monogenic or polygenic genetic alterations and the development of metabolic diseases and complications. In addition, sedentary lifestyle and socio-economic and pandemic factors have a great impact on the habits of the population and have significantly contributed to the increase in the incidence of metabolic disorders, obesity, T2DM, metabolic syndrome, and liver steatosis. Moreover, patients with type 2 diabetes appear to respond to antihyperglycemic drugs. Only a minority of patients could be considered true non-responders. Thus, it appears clear that the main aim of precision medicine in T2DM is to identify patients who can benefit most from a specific drug class more than from the others. Precision medicine is a discipline that evaluates the applicability of genetic, lifestyle, and environmental factors to disease development. In particular, it evaluated whether these factors could affect the development of diseases and their complications, response to diet, lifestyle, and use of drugs. Thus, the objective is to find prevention models aimed at reducing the incidence of pathology and mortality and therapeutic personalized approaches, to obtain a greater probability of response and efficacy. This review aims to evaluate the applicability of precision medicine for T2DM, a healthcare burden in many countries.

Precision subclassification of type 2 diabetes: a systematic review

BACKGROUND

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

METHODS

We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

RESULTS

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

CONCLUSION

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Subtypes of Type 2 Diabetes and Incident Cardiovascular Disease Risk: UK Biobank and All of Us Cohorts

OBJECTIVE

To characterize and validate the subtypes of type 2 diabetes (T2D) using a novel clustering algorithm and to further assess their associations with the risk of incident cardiovascular disease (CVD) events.

METHODS

Unsupervised k-means clustering based on glycated hemoglobin level, age at onset of T2D, body mass index, and estimated glomerular filtration rate was conducted among participants with T2D from the UK Biobank (March 13, 2006, to October 1, 2010) and replicated in the All of Us cohort (May 30, 2017, to April 1, 2021).

RESULTS

Five distinct T2D clusters were identified in the UK Biobank and validated in the All of Us cohort, characterizing the phenotypically heterogeneous subtypes. With a median follow-up of 11.69 years for patients with T2D in the UK Biobank, risks of incident CVD events varied considerably between the clusters after adjustment for potential confounders and multiple testing (all P<.001). With cluster 1 characterized by early onset of T2D and mild abnormalities of other variables as the reference, patients in cluster 5 characterized by poor renal function had the highest risk of CVD events (hazard ratio [95% CI], 1.72 [1.45 to 2.03], 2.41 [1.93 to 3.02], and 1.62 [1.35 to 1.94] for composite CVD event, CVD mortality, and CVD incidence, respectively; all P<.001), followed by cluster 4 characterized by poor glycemic control and cluster 3 characterized by severe obesity. No consistently significant difference was found between cluster 2 characterized by late onset of T2D and cluster 1.

CONCLUSION

Our study, using a novel clustering algorithm to identify robust subtypes of T2D, found heterogeneous associations with incident CVD risk among patients with diabetes.

Relationship between caffeine intake and thyroid function: results from NHANES 2007-2012

BACKGROUND

Moderate caffeine intake decreases the risk of metabolic disorders and all-cause mortality, and the mechanism may be related to its ergogenic actions. Thyroid hormones are vital in metabolic homeostasis; however, their association with caffeine intake has rarely been explored.

OBJECTIVE

To investigate the association between caffeine intake and thyroid function.

METHODS

We collected data on demographic background, medical conditions, dietary intake, and thyroid function from the National Health and Nutrition Examination Survey (NHANES) 2007-2012. Subgroups were classified using two-step cluster analysis, with sex, age, body mass index (BMI), hyperglycemia, hypertension, and cardio-cerebral vascular disease (CVD) being used for clustering. Restrictive cubic spline analysis was employed to investigate potential nonlinear correlations, and multivariable linear regression was used to evaluate the association between caffeine consumption and thyroid function.

RESULTS

A total of 2,582 participants were included, and three subgroups with different metabolic features were clustered. In the most metabolically unhealthy group, with the oldest age, highest BMI, and more cases of hypertension, hyperglycemia, and CVD, there was a nonlinear relationship between caffeine intake and serum thyroid stimulating hormone (TSH) level. After adjusting for age, sex, race, drinking, smoking, medical conditions, and micronutrient and macronutrient intake, caffeine intake of less than 9.97 mg/d was positively associated with serum TSH (p = 0.035, standardized β = 0.155); however, moderate caffeine consumption (9.97-264.97 mg/d) indicated a negative association (p = 0.001, standardized β = - 0.152).

CONCLUSIONS

Caffeine consumption had a nonlinear relationship with serum TSH in people with metabolic disorders, and moderate caffeine intake (9.97 ~ 264.97 mg/d) was positively associated with serum TSH.

Systematic review of precision subclassification of type 2 diabetes

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Cluster analysis and potential influencing factors of boars with different fertility

The fertility of boars is intimately tied to the pig farm's economic benefits. This study aimed to rapidly categorize boars of different fertility and investigate the factors influencing the categorization using the production data in a large pig farm in northern China, including 11,163 semen collection records of Yorkshire boars (215), 11,163 breeding records and 8770 records of farrowing performance of Yorkshire sows (4505), as well as 4720 records of selection indices (sire line index and dam line index) for boars and sows (215 and 4505) between 2017 and 2020. The boar population was classified by two-step cluster analysis, followed by factor analysis to minimize the dimensionality of data variables and eliminate multicollinearity, and then using ordinal logistic regression model to investigate the risk variables impacting boar fertility categorization. Results showed that the two-step clustering divided the 215 boars into three subgroups: high-fertility (n = 61, 28.4%), medium-fertility (n = 127, 59.1%) and low-fertility (n = 27, 12.6%). The high-fertility boars were shown to be substantially greater than the medium-fertility or low-fertility boars (p < 0.05) in average total litter size, number of born alive, and number of healthy piglets of mated sows. Compared with low-fertility boars, the high-fertility boars were also significantly higher (p < 0.05) in the pregnancy rate and farrowing rate of mated sows. However, the three boar subgroups showed no difference (p > 0.05) in semen quality information (average sperm motility, average sperm density, and average sperm volume). Collinearity diagnosis indicated severe multicollinearity among the 20 data variables, which were reduced to 8 factor variables (factors 1-8) by factor analysis, and further collinearity diagnosis exhibited no multicollinearity among the 8 factor variables. Ordered logistic regression analysis revealed a significant and positive correlation (p < 0.05) of boar fertility with factor 2 (average total litter size, number of born alive, number of healthy piglets), factor 4 (average number of weak piglets and average weak piglet rate), factor 6 (sire line index of boars and dam line index of boars), factor 8 (pregnancy rate and farrowing rate), highlighting factor 2 as the most important factor influencing the classification of boar fertility. Our results indicate that the two-step cluster analysis can be used as a simple and effective method to screen boars with different fertility and that farm producers should pay attention to the recording of the reproductive performance of the mated sows due to its role as the risk factor for classification of boar fertility.

Cardiorenal diseases in type 2 diabetes mellitus: clinical trials and real-world practice

Patients with type 2 diabetes mellitus (T2DM) can have multiple comorbidities and premature mortality due to atherosclerotic cardiovascular disease, hospitalization with heart failure and/or chronic kidney disease. Traditional drugs that lower glucose, such as metformin, or that treat high blood pressure and blood levels of lipids, such as renin-angiotensin-system inhibitors and statins, have organ-protective effects in patients with T2DM. Amongst patients with T2DM treated with these traditional drugs, randomized clinical trials have confirmed the additional cardiorenal benefits of sodium-glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1RA) and nonsteroidal mineralocorticoid receptor antagonists. The cardiorenal benefits of SGLT2i extended to patients with heart failure and/or chronic kidney disease without T2DM, whereas incretin-based therapy (such as GLP1RA) reduced cardiovascular events in patients with obesity and T2DM. However, considerable care gaps exist owing to insufficient detection, therapeutic inertia and poor adherence to these life-saving medications. In this Review, we discuss the complex interconnections of cardiorenal-metabolic diseases and strategies to implement evidence-based practice. Furthermore, we consider the need to conduct clinical trials combined with registers in specific patient segments to evaluate existing and emerging therapies to address unmet needs in T2DM.

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.

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.

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

Several international studies have stratified people with diabetes into phenotypical clusters. However, there has not been a systematic examination of the variation in these clusters across ethnic groups. For example, some clusters appear more frequent among Asians and may have lower weight, age at diagnosis and poorer beta cell function.

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

The current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future.

Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations

Nearly half of all adults with type 2 diabetes mellitus (T2DM) live in India and China. These populations have an underlying predisposition to deficient insulin secretion, which has a key role in the pathogenesis of T2DM. Indian and Chinese people might be more susceptible to hepatic or skeletal muscle insulin resistance, respectively, than other populations, resulting in specific forms of insulin deficiency. Cluster-based phenotypic analyses demonstrate a higher frequency of severe insulin-deficient diabetes mellitus and younger ages at diagnosis, lower β-cell function, lower insulin resistance and lower BMI among Indian and Chinese people compared with European people. Individuals diagnosed earliest in life have the most aggressive course of disease and the highest risk of complications. These characteristics might contribute to distinctive responses to glucose-lowering medications. Incretin-based agents are particularly effective for lowering glucose levels in these populations; they enhance incretin-augmented insulin secretion and suppress glucagon secretion. Sodium-glucose cotransporter 2 inhibitors might also lower blood levels of glucose especially effectively among Asian people, while α-glucosidase inhibitors are better tolerated in east Asian populations versus other populations. Further research is needed to better characterize and address the pathophysiology and phenotypes of T2DM in Indian and Chinese populations, and to further develop individualized treatment strategies.

Novel clusters of newly-diagnosed type 2 diabetes and their association with diabetic retinopathy: a 3-year follow-up study

BACKGROUND

Cluster analysis may assist in stratifying heterogeneous clinical presentations of type 2 diabetes (T2D). However, the association of cluster-based subgroups with diabetes-related outcomes such as diabetic retinopathy remains unclear. This study was aimed to address this issue with novel clusters of T2D derived from four simple parameters.

METHOD

We developed a k-means clustering model in participants with newly diagnosed T2D (N = 1910) from the SENSIBLE and SENSIBLE-Addition studies, based on body mass index (BMI), waist circumference (WC), mean arterial pressure (MAP), and hemoglobin A1c (HbA1c). Diabetic retinopathy was ascertained with the protocol from the Early Treatment of Diabetic Retinopathy Study. Participants (N = 515) without diabetic retinopathy at baseline were followed-up for 3 years. Logistic regression analyses were performed to obtain the odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Three clusters were identified, with cluster 0, 1 and 2 accounting for 48.2, 8.9 and 42.9%, respectively. Participants with T2D were featured by the lowest BMI, WC, MAP, and HbA1c in cluster 0, poor glycemic condition in cluster 1, and the highest BMI, WC, and MAP in cluster 2. Compared with cluster 0, cluster 1 was associated with increased odds of diabetic retinopathy in both the cross-sectional study (OR 6.25, 95% CI: 3.19-12.23) and the cohort study (OR 9.16, 95% CI: 2.08-40.34), while cluster 2 was not. Moreover, most participants remained their clusters unchanged during follow-up.

CONCLUSIONS

Our cluster-based analysis showed that participants with poor glycemic condition rather than high blood pressure and obesity had higher risk of diabetic retinopathy.

Network Pharmacology Integrated with Molecular Docking Elucidates the Mechanism of Wuwei Yuganzi San for the Treatment of Coronary Heart Disease

Introduction: The aim of this study was to investigate the pharmacological mechanism of Wuwei Yuganzi San (WYS) in treating coronary heart disease (CHD) using network pharmacology and molecular docking. Methods: The main active components, related targets, and the target genes related to WYS were investigated by the databases Traditional Chinese Medicine Systems Pharmacology and related articles. Information on the target genes of CHD was acquired through the OMIM database and GeneCards database, and the NCBI Gene Expression Omnibus DataSets (GSE71226) were used to acquire target genes of CHD. A Venn diagram was used to show the common targets of WYS and CHD. The compound-target-disease network was built up by Cytoscape 3.7.2, and the protein–protein interaction (PPI) network was acquired through the STRING database. ClusterProfiler and Pathview packages in RStudio software were used to conduct gene ontology enrichment analysis and KEGG pathway enrichment analysis to reveal the underlying mechanism. Finally, AutoDock Vina software was used to assess the binding affinity of significant ingredients and hub genes. Results: Thirty-four key ingredients of WYS in CHD were screened, which related to 59 targets in CHD. According to the results of enrichment analysis, 59 items in the biological process, 15 items in the molecular function, 10 items in the cellular component, and 52 signaling pathways were associated with efficacy. These processes and pathways were essential for cell survival and were related to several crucial factors of CHD, including a disintegrin and metalloprotease 17 (ADAM17), aldo-keto reductase family 1 member C2 (AKR1C2), albumin (ALB), protein kinase B (AKT1), and alcohol dehydrogenase 1C (ADH1C). Based on the outcomes of the PPI network, we selected ADAM17, AKR1C2, ALB, AKT1, ADH1C, and putative ingredients (sennoside D_qt, quercetin, and procyanidin B-5,3'- O-gallate) to perform molecular docking validation. From the molecular docking outcomes, some vital targets of CHD (including ADAM17, AKR1C2, ALB, AKT1, and ADH1C) could be related to form a stable combination with the putative ingredients of WYS. Conclusions: The network pharmacology and molecular docking study elucidated basically the mechanism of WYS in the treatment of CHD.

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

BACKGROUND

This study aimed to cluster patients with diabetes and explore the association between duration of diabetes and diabetes treatment choices in each cluster.

METHODS

A Two-Step cluster analysis was performed on 1332 Chinese patients with diabetes based on six parameters (glutamate decarboxylase antibodies, age at disease onset, body mass index, glycosylated hemoglobin, homeostatic model assessment 2 to estimate β-cell function and insulin resistance). Associations between the duration of diabetes and diabetes treatment choices in each cluster of patients were analyzed using Kaplan-Meier survival curves and logistic regression models.

RESULTS

The following five replicable clusters were identified: severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). There were significant differences in blood pressure, blood lipids, and diabetes-related complications among the clusters (all P < 0.05). Early in the course of disease (≤5 years), compared with the other subgroups, the SIRD, MOD, and MARD populations were more likely to receive non-insulin hypoglycemic agents for glycemic control. Among the non-insulin hypoglycemic drug options, SIRD had higher rates of receiving metformin, alpha-glucosidase inhibitor (AGI), and glucagon-like peptide-1 drug; the MOD and MARD groups both received metformin, AGI and sodium-glucose cotransporter 2 inhibitor (SGLT-2i) drug ratio was higher. While the SAID and SIDD groups were more inclined to receive insulin therapy than the other subgroups, with SAID being more pronounced. With prolonged disease course (>5 years), only the MOD group was able to accept non-insulin hypoglycemic drugs to control the blood sugar levels, and most of them are still treated with metformin, AGI, and SGLT-2i drugs. While the other four groups required insulin therapy, with SIDD being the most pronounced.

CONCLUSIONS

Clustering of patients with diabetes with a data-driven approach yields consistent results. Each diabetes cluster has significantly different disease characteristics and risk of diabetes complications. With the development of the disease course, each cluster receives different hypoglycemic treatments.

The association of adiponectin with risk of pre-diabetes and diabetes in different subgroups: cluster analysis of a general population in south China

OBJECTIVE

Adiponectin is an adipocyte-derived hormone with an important role in glucose metabolism. The present study explored the effect of adiponectin in diverse population groups on pre-diabetes and newly diagnosed diabetes.

METHODS

A total of 3300 individuals were enrolled and their data were collected in the analyses dataset from December 2018 to October 2019. Cluster analysis was conducted based on age, BMI, waistline, body fat, systolic blood pressure, triglycerides, and glycosylated hemoglobin 1c. Cluster analysis divided the participants into four groups: a young-healthy group, an elderly-hypertension group, a high glucose-lipid group, and an obese group. Odds ratio (OR) and 95% CIs were calculated using multivariate logistic regression analysis.

RESULTS

Compared with the first quartile of adiponectin, the risk of pre-diabetes of fourth quartile was decreased 61% (aOR = 0.39, 95% CI (0.20-0.73)) in the young-healthy group; and the risk of diabetes of fourth quartile was decreased 85% (aOR = 0.15, 95% CI (0.02-0.67)) in the obese group. There were no significant correlations between the adiponectin level and diabetes/pre-diabetes in the other two groups. Additionally, receiver operating characteristic curve analysis indicated that adiponectin could significantly improve the diagnosis based on models in the young-healthy group (from 0.640 to 0.675) and the obese group (from 0.714 to 0.761).

CONCLUSIONS

Increased adiponectin levels were associated with decreased risk of pre-diabetes in the young-healthy population, and with a decreased the risk of diabetes in the obese population. An increased adiponectin level is an independent protective factor for pre-diabetes and diabetes in a specific population in south China.

Glycated Hemoglobin (HbA1c) as a Biomarker for Diabetic Foot Peripheral Neuropathy

BACKGROUND

Diabetic peripheral neuropathy (DPN) is known to predict foot ulceration, lower-extremity amputation and mortality. Patients with diabetes mellitus have a predisposition toward developing chronic inflammatory demyelinating polyneuropathy, and this may also facilitate the formation of diabetic foot and cutaneous impairment, which are considered one of the most serious impairments of diabetes mellitus, with a prevalence of 4-10% in this population. Biomarkers research provides opportunities for the early diagnosis of these complications for specific treatments useful to prevent amputation and, therefore, physical inability and mental disturbance. The recent literature has suggested that glycemic levels may be a novel factor in the pathogenesis of diabetic foot complications and is an important mediator of axonal dysfunction. The aim of this systematic literary review is to determine whether hemoglobin A1c (HbA1c) is a positive predictor for diabetic foot peripheral neuropathy and its complications, such as foot cutaneous impairments. There is a lack of consensus regarding the effect of glycemic variability on diabetic foot peripheral neuropathy, unlike other complications such as retinopathy, nephropathy or micro/macrovascular pathology Methods: Relevant articles were searched in the Medline database using PubMed and Scopus and relevant keywords. The primary search terms used were "glycated hemoglobin" OR "HbA1c" AND "diabetic neuropathies" AND "Foot".

RESULTS

A number of articles (336) were initially identified while searching the scientific literature regarding this topic, and 32 articles were selected and included in this review.

CONCLUSIONS

This review highlights the role of HbA1c in diabetic foot peripheral neuropathy. Biomarkers play an important role in the decision-making process, and HbA1c levels are extensively used for diabetic foot clinical outcomes and settings, but biomarker research in diabetic foot peripheral neuropathy is in its infancy and will require careful attention to a number of factors and associations, since the consequences of DPN also include neurological alterations. HbA1c is an accurate and easy-to-administer test and can be an effective biomarker in establishing the diagnosis of diabetes, but future research should focus on standardizing the HbA1c level and selecting which DPN value and its correlated complications, such as foot cutaneous impairments, are the most informative.

Novel Subgroups and Chronic Complications of Diabetes in Middle-Aged and Elderly Chinese:A Prospective Cohort Study

BACKGROUND

Diabetes mellitus, especially type 2 diabetes mellitus (T2DM), is regarded as highly heterogeneous. Novel diabetes phenotypes by cluster analysis have been proposed in Europeans but may show different cluster features in Asians. The applicability of cluster analysis in middle-aged and elderly Chinese community T2DM patients needs further investigation.

METHODS

Participants were recruited from Jiading community in Shanghai, China. We adopted k-means cluster analysis in 1130 patients (aged ≥ 40 years) with newly-diagnosed T2DM at baseline. Cluster analysis was performed based on seven variables, including fasting plasma glucose, 2 hours postprandial blood glucose, age at diagnosis, body mass index, hemoglobin A1c, homoeostatic model assessment estimates of β-cell function and insulin resistance. All subjects were re-examined at 4.4 years later. Metabolic associated fatty liver disease was diagnosed using B-ultrasound, hepatic fibrosis by non-invasive scores, renal and cardiovascular status by subclinical biomarkers. Multivariable logistic regression models were used to compare the risks of complications between clusters.

RESULTS

Patients were classified into 4 clusters. 381 (33.7%), 456 (40.4%), 87 (7.7%), and 206 (18.2%) patients were separately assigned to mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), severe insulin-deficient and insulin-resistant diabetes (SIDRD), or severe obesity-related and insulin-resistant diabetes (SOIRD), respectively. Participants in MARD, SOIRD, and SIDRD clusters were associated with significantly increased risks of different complications. SOIRD and SIDRD showed novel features in Chinese T2DM patients that were different from those in Europeans.

CONCLUSIONS

The refined diabetes phenotypic approach was applicable to Chinese middle-aged and elderly T2DM patients. Patients in different clusters presented significantly different characteristics, progression of metabolic features, and risks of diabetic complications.