Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data

Etiologies underlying subtypes of long-standing type 2 diabetes

BACKGROUND

Attempts to subtype, type 2 diabetes (T2D) have mostly focused on newly diagnosed European patients. In this study, our aim was to subtype T2D in a non-white Emirati ethnic population with long-standing disease, using unsupervised soft clustering, based on etiological determinants.

METHODS

The Auto Cluster model in the IBM SPSS Modeler was used to cluster data from 348 Emirati patients with long-standing T2D. Five predictor variables (fasting blood glucose (FBG), fasting serum insulin (FSI), body mass index (BMI), hemoglobin A1c (HbA1c) and age at diagnosis) were used to determine the appropriate number of clusters and their clinical characteristics. Multinomial logistic regression was used to validate clustering results.

RESULTS

Five clusters were identified; the first four matched Ahlqvist et al subgroups: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and a fifth new subtype of mild early onset diabetes (MEOD). The Modeler algorithm allows for soft assignments, in which a data point can be assigned to multiple clusters with different probabilities. There were 151 patients (43%) with membership in cluster peaks with no overlap. The remaining 197 patients (57%) showed extensive overlap between clusters at the base of distributions.

CONCLUSIONS

Despite the complex picture of long-standing T2D with comorbidities and complications, our study demonstrates the feasibility of identifying subtypes and their underlying causes. While clustering provides valuable insights into the architecture of T2D subtypes, its application to individual patient management would remain limited due to overlapping characteristics. Therefore, integrating simplified, personalized metabolic profiles with clustering holds greater promise for guiding clinical decisions than subtyping alone.

Factors influencing optimal diabetes care and clinical outcomes in Thai patients with type 2 diabetes mellitus: a multilevel modelling analysis

BACKGROUND

Increasing levels of poor glycaemic control among Thai patients with type 2 diabetes mellitus (T2DM) motivated us to compare T2DM care between urban and suburban primary care units (PCUs), to identify gaps in care, and to identify significant factors that may influence strategies to enhance the quality of care and clinical outcomes in this population.

METHODS

We conducted a cross-sectional study involving 2160 patients with T2DM treated at four Thai PCUs from 2019 to 2021, comprising one urban and three suburban facilities. Using mixed effects logistic regression, we compared care factors between urban and suburban PCUs.

RESULTS

Patients attending suburban PCUs were significantly more likely to undergo eye (adjusted OR (AOR): 1.83, 95% CI 1.35 to 1.72), foot (AOR: 1.61, 95% CI 0.65 to 4.59) and HbA1c (AOR: 1.66, 95% CI 1.09 to 2.30) exams and achieved all ABC (HbA1c, blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C)) goals (AOR: 2.23, 95% CI 1.30 to 3.83). Conversely, those at an urban PCU were more likely to undergo albuminuria exams. Variables significantly associated with good glycaemic control included age (AOR: 1.51, 95% CI 1.31 to 1.79), T2DM duration (AOR: 0.59, 95% CI 0.41 to 0.88), FAACE (foot, HbA1c, albuminuria, LDL-C and eye) goals (AOR: 1.23, 95% CI 1.12 to 1.36) and All8Q (AOR: 1.20, 95% CI 1.05 to 1.41). Chronic kidney disease (CKD) was significantly linked with high triglyceride and HbA1c levels (AOR: 5.23, 95% CI 1.21 to 7.61). Elevated HbA1c levels, longer T2DM duration, insulin use, high systolic BP and high lipid profile levels correlated strongly with diabetic retinopathy (DR) and CKD progression.

CONCLUSION

This highlights the necessity for targeted interventions to bridge urban-suburban care gaps, optimise drug prescriptions and implement comprehensive care strategies for improved glycaemic control, DR prevention and CKD progression mitigation among in Thai patients with T2DM. The value of the clinical target aggregate (ABC) and the process of care aggregate (FAACE) was also conclusively demonstrated.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Predicting Progression from Gestational Diabetes to Impaired Glucose Tolerance Using Peridelivery Data: An Observational Study

OBJECTIVE

 This article aimed to develop a predictive model to identify persons with recent gestational diabetes mellitus (GDM) most likely to progress to impaired glucose tolerance postpartum.

STUDY DESIGN

 We conducted an observational study among persons with GDM in their most recent pregnancy, defined by Carpenter-Coustan criteria. Participants were followed up from delivery through 1-year postpartum. We used lasso regression with k-fold cross validation to develop a multivariable model to predict progression to impaired glucose tolerance, defined as HbA1c≥5.7%, at 1-year postpartum. Predictive ability was assessed by the area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV).

RESULTS

 Of 203 participants, 71 (35%) had impaired glucose tolerance at 1-year postpartum. The final model had an AUC of 0.79 (95% confidence interval [CI]: 0.72, 0.85) and included eight indicators of weight, body mass index, family history of type 2 diabetes, GDM in a prior pregnancy, GDM diagnosis<24 weeks' gestation, and fasting and 2-hour plasma glucose at 2 days postpartum. A cutoff point of ≥ 0.25 predicted probability had sensitivity of 80% (95% CI: 69, 89), specificity of 58% (95% CI: 49, 67), PPV of 51% (95% CI: 41, 61), and NPV of 85% (95% CI: 76, 91) to identify women with impaired glucose tolerance at 1-year postpartum.

CONCLUSION

 Our predictive model had reasonable ability to predict impaired glucose tolerance around delivery for persons with recent GDM.

KEY POINTS

· We developed a predictive model to identify persons with GDM most likely to develop IGT postpartum.. · The final model had an AUC of 0.79 (95% CI: 0.72, 0.85) and included eight clinical indicators.. · If validated, our model could help prioritize diabetes prevention efforts among persons with GDM..

Diabetes subgroups and sociodemographic inequalities in Mexico: a cross-sectional analysis of nationally representative surveys from 2016 to 2022

Background

Differences in the prevalence of four diabetes subgroups have been reported in Mexico compared to other populations, but factors that may contribute to these differences are poorly understood. Here, we estimate the prevalence of diabetes subgroups in Mexico and evaluate their correlates with indicators of social disadvantage using data from national representative surveys.

Methods

We analyzed serial, cross-sectional Mexican National Health and Nutrition Surveys spanning 2016, 2018, 2020, 2021, and 2022, including 23,354 adults (>20 years). Diabetes subgroups (obesity-related [MOD], severe insulin-deficient [SIDD], severe insulin-resistant [SIRD], and age-related [MARD]) were classified using self-normalizing neural networks based on a previously validated algorithm. We used the density-independent social lag index (DISLI) as a proxy of state-level social disadvantage.

Findings

We identified 4204 adults (median age: 57, IQR: 47-66, women: 64%) living with diabetes, yielding a pooled prevalence of 16.04% [95% CI: 14.92-17.17]. When stratified by diabetes subgroup, prevalence was 6.62% (5.69-7.55) for SIDD, 5.25% (4.52-5.97) for MOD, 2.39% (1.95-2.83) for MARD, and 1.27% (1.00-1.54) for SIRD. SIDD and MOD clustered in Southern Mexico, whereas MARD and SIRD clustered in Northern Mexico and Mexico City. Each standard deviation increase in DISLI was associated with higher odds of SIDD (OR: 1.12, 95% CI: 1.06-1.12) and lower odds of MOD (OR: 0.93, 0.88-0.99). Speaking an indigenous language was associated with higher odds of SIDD (OR: 1.35, 1.16-1.57) and lower odds of MARD (OR 0.58, 0.45-0.74).

Interpretation

Diabetes prevalence in Mexico is rising in the context of regional and sociodemographic inequalities across distinct diabetes subgroups. SIDD is a subgroup of concern that may be associated with inadequate diabetes management, mainly in marginalized states.

Funding

This research was supported by Instituto Nacional de Geriatría in Mexico.

Differential ischemic stroke risk linked to novel subtypes of type 2 diabetes: insights from a Mendelian randomization analysis

PURPOSE

This study employs a two-sample Mendelian randomization (MR) approach to investigate the variation in ischemic stroke risk across novel subtypes of adult-onset type 2 diabetes.

METHODS

Leveraging pooled genome-wide association study (GWAS) data from the Swedish ANDIS cohort, we explored the association of four newly identified type 2 diabetes subtypes-severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD)-with ischemic stroke risk. The outcome data for ischemic stroke and its three subtypes (large artery, cardioembolic, and small vessel stroke) were sourced from the MEGASTROKE Consortium. Our analysis applied multiple MR methods, focusing on the inverse-variance weighted (IVW) technique, complemented by thorough sensitivity analyses to examine heterogeneity and potential horizontal pleiotropy.

RESULTS

Our findings reveal a significant causal relationship between the SIDD subtype and small vessel stroke (OR = 1.06, 95% CI: 1.01-1.11, p = 0.025), while no causal associations were observed for SIRD with any stroke subtype. MOD was causally linked to small vessel stroke (OR = 1.07, 95% CI: 1.02-1.12, p = 0.004) and large artery stroke (OR = 1.07, 95% CI: 1.01-1.13, p = 0.015). Similarly, MARD showed a causal relationship with small vessel stroke (OR = 1.09, 95% CI: 1.03-1.16, p = 0.006) and overall ischemic stroke risk (OR = 1.04, 95% CI: 1.01-1.08, p = 0.010).

CONCLUSIONS

Our study highlights distinct causal links between specific type 2 diabetes subtypes and ischemic stroke risks, emphasizing the importance of subtype-specific prevention and treatment strategies.

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

AIMS/HYPOTHESIS

This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed.

RESULTS

Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%.

CONCLUSIONS/INTERPRETATION

Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

Effectiveness of an eHealth-Delivered Program to Empower People With Musculoskeletal Pain in Rural Australia: A Randomized Controlled Trial

OBJECTIVE

Our objective was to evaluate the effectiveness of a three-month physiotherapist-delivered eHealth physical activity program compared with usual care to improve function in adults with low back pain or knee osteoarthritis in rural Australia.

METHODS

This was a parallel, two-group, pragmatic, superiority, randomized controlled trial involving three- and six-month posttreatment follow-ups. There was a total of 156 adults with chronic nonspecific low back pain (n = 97) or knee osteoarthritis (n = 59) from rural Australia. The intervention involved an eHealth physical activity and an exercise program that included five to eight teleconsultations with a physiotherapist (primary time point three months) or usual care (eg, general practitioner, physiotherapy, and pain medication). The primary outcome was the Patient-Specific Functional Scale (0-30), with a three-point difference between groups being considered the minimum clinically important difference.

RESULTS

Participants receiving the eHealth intervention (n = 78) reported significantly greater and clinically worthwhile improvements in function (mean between-group difference 3.6; 95% confidence interval [CI] 1.3-5.9) compared to participants receiving usual care (n = 78). Small but statistically significantly greater improvements in disability (7.2 of 100; 95% CI 2.1-12.3) and quality of life (4.5 of 100; 95% CI 0.0-9.0) also favored the eHealth group. No clinical or statistical differences between groups were found for the secondary outcomes of pain, coping skills, and physical activity levels.

CONCLUSION

A physiotherapist-delivered eHealth intervention is effective and provides clinically meaningful improvements in function compared to usual care for people with musculoskeletal pain in rural communities. These findings highlight the potential for eHealth-based programs to improve access to evidence-based exercise interventions for people with musculoskeletal pain in rural communities.

Longitudinal Effects of Glucose-Lowering Medications on β-Cell Responses and Insulin Sensitivity in Type 2 Diabetes: The GRADE Randomized Clinical Trial

OBJECTIVE

To compare the long-term effects of glucose-lowering medications (insulin glargine U-100, glimepiride, liraglutide, and sitagliptin) when added to metformin on insulin sensitivity and β-cell function.

RESEARCH DESIGN AND METHODS

In the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) cohort with type 2 diabetes (n = 4,801), HOMA2 was used to estimate insulin sensitivity (HOMA2-%S) and fasting β-cell function (HOMA2-%B) at baseline and 1, 3, and 5 years on treatment. Oral glucose tolerance test β-cell responses (C-peptide index [CPI] and total C-peptide response [incremental C-peptide/incremental glucose over 120 min]) were evaluated at the same time points. These responses adjusted for HOMA2-%S in regression analysis provided estimates of β-cell function.

RESULTS

HOMA2-%S increased from baseline to year 1 with glargine and remained stable thereafter, while it did not change from baseline in the other treatment groups. HOMA2-%B and C-peptide responses were increased to variable degrees at year 1 in all groups but then declined progressively over time. At year 5, CPI was similar between liraglutide and sitagliptin, and higher for both than for glargine and glimepiride [0.80, 0.87, 0.74, and 0.64 (nmol/L)/(mg/dL) * 100, respectively; P < 0.001], while the total C-peptide response was greatest with liraglutide, followed in descending order by sitagliptin, glargine, and glimepiride [1.54, 1.25, 1.02, and 0.87 (nmol/L)/(mg/dL) * 100, respectively, P < 0.001]. After adjustment for HOMA2-%S to obtain an estimate of β-cell function, the nature of the change in β-cell responses reflected those in β-cell function.

CONCLUSIONS

The differential long-term effects on insulin sensitivity and β-cell function of four different glucose-lowering medications when added to metformin highlight the importance of the loss of β-cell function in the progression of type 2 diabetes.

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.

Unique Challenges in Diabetic Foot Science

In the past 30 years, there has been a rapid influx of information pertaining to the diabetic foot (DF) coming from numerous directions and sources. This article discusses the current state of the DF literature and challenges it presents to clinicians with its associated increase in knowledge on their derivations, complications, and interventions. Further, we attempt to provide tips on how to navigate and criticize the current literature to encourage and maximize positive outcomes in this challenging patient population.

Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease

Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.

Metabolic Profiles of Type 2 Diabetes and Their Association With Renal Complications

CONTEXT

The components of metabolic syndrome (MetS) are interrelated and associated with renal complications in patients with type 2 diabetes (T2D).

OBJECTIVE

We aimed to reveal prevalent metabolic profiles in patients with T2D and identify which metabolic profiles were risk markers for renal progression.

METHODS

A total of 3556 participants with T2D from a hospital (derivation cohort) and 931 participants with T2D from a community survey (external validation cohort) were included. The primary outcome was the onset of diabetic kidney disease (DKD), and secondary outcomes included estimated glomerular filtration rate (eGFR) decline, macroalbuminuria, and end-stage renal disease (ESRD). In the derivation cohort, clusters were identified using the 5 components of MetS, and their relationships with the outcomes were assessed. To validate the findings, participants in the validation cohort were assigned to clusters. Multivariate odds ratios (ORs) of the primary outcome were evaluated in both cohorts, adjusted for multiple covariates at baseline.

RESULTS

In the derivation cohort, 6 clusters were identified as metabolic profiles. Compared with cluster 1, cluster 3 (severe hyperglycemia) had increased risks of DKD (hazard ratio [HR] [95% CI]: 1.72 [1.39-2.12]), macroalbuminuria (2.74 [1.84-4.08]), ESRD (4.31 [1.16-15.99]), and eGFR decline [P < .001]; cluster 4 (moderate dyslipidemia) had increased risks of DKD (1.97 [1.53-2.54]) and macroalbuminuria (2.62 [1.61-4.25]). In the validation cohort, clusters 3 and 4 were replicated to have significantly increased risks of DKD (adjusted ORs: 1.24 [1.07-1.44] and 1.39 [1.03-1.87]).

CONCLUSION

We identified 6 prevalent metabolic profiles in patients with T2D. Severe hyperglycemia and moderate dyslipidemia were validated as significant risk markers for DKD.

Treatment responses to basal insulin glargine 300 U/ml and glargine 100 U/ml in newly defined subphenotypes of type 2 diabetes: A post hoc analysis of the EDITION 3 randomized clinical trial

INTRODUCTION

To compare responses to basal insulin glargine 300 U/ml (IGlar-300) and 100 U/ml (IGlar-100) in newly defined subphenotypes of type 2 diabetes.

METHODS

Insulin-naive participants (n = 858) from the EDITION 3 trial were assigned to subphenotypes 'Mild Age-Related Diabetes (MARD)', 'Mild Obesity Diabetes (MOD)', 'Severe Insulin Resistant Diabetes (SIRD)' and 'Severe Insulin Deficient Diabetes (SIDD)'. Key variables were analysed at baseline and 26 weeks.

RESULTS

Participants were comprised of MOD 56.1% (n = 481), SIDD 22.1% (n = 190), MARD 18.2% (n = 156) and SIRD 3.0% (n = 26). After 26 weeks a similar decrease in glycated haemoglobin (HbA1c) and fasting plasma glucose (FPG) of 16-19 mmol/mol and 1.4-1.7 mmol/L, respectively, occurred in MARD and MOD with both insulins. SIDD had the most elevated HbA1c and FPG (80-83 mmol/mol/11.1-11.4 mmol/L) and reduction in both HbA1c and FPG was greater with IGlar-100 than with IGlar-300 (-18 vs. -15 mmol/mol and -1.6 vs. -1.3 mmol/L, respectively; each p = .03). In SIDD, despite receiving the highest basal insulin doses, HbA1c decline (57-60 mmol/mol/7.3-7.6%) was suboptimal at week 26. In MOD and SIDD lower incidences with IGlar-300 were found for level 1 nocturnal hypoglycaemia [odds ratio (OR) 0.59, 95% confidence intervals (CI) 0.36-0.97; OR 0.49, 95% CI 0.24-0.99]. In addition, fewer level 2 hypoglycaemia episodes occurred at any time with IGlar-300 in SIDD (OR 0.31, 95% CI 0.13-0.77).

CONCLUSION

Both insulins produce comparable outcomes in type 2 diabetes subphenotypes, but in SIDD, add-on treatment to basal insulin is required to achieve glycaemic targets.

Impact of chiglitazar on glycemic control in type 2 diabetic patients with metabolic syndrome and insulin resistance: A pooled data analysis from two phase III trials

BACKGROUND

To evaluate the glycemic control effects of vhiglitazar (carfloglitazar), a novel peroxisome proliferator-activated receptor pan-agonist, in patients with type 2 diabetes mellitus (T2DM) with metabolic syndrome (MetS) or insulin resistance (IR) using pooled data analysis of two phase III clinical trials.

METHODS

Data were collected from two randomized phase III clinical trials in China, comparing chiglitazar to placebo or sitagliptin in T2DM patients. The MetS was defined by the Adult Treatment Panel III MetS criteria, and IR was defined by homeostatic model assessment for insulin resistance (HOMA-IR) ≥4.31 (male) or 4.51 (female). The main end point of this analysis was glycemic control in the different arms within each subgroup.

RESULTS

In the MetS subgroup, changes in glycated hemoglobin (HbA1c) from baseline at week 24 in the chiglitazar 32 mg, chiglitazar 48 mg, and sitagliptin 100 mg arms were -1.44%, -1.68%, and -1.37%, respectively; p < .05 was obtained when chiglitazar 48 mg was compared with sitagliptin. In the IR subgroup, the changes in HbA1c were -1.58%, -1.56%, and -1.26% in chiglitazar 32 mg, chiglitazar 48 mg, and sitagliptin 100 mg arms, respectively; p < .05 was obtained when chiglitazar 32 mg was compared with sitaligptin. The two doses of chiglitazar demonstrated a greater reduction in fasting plasma glucose and 2 h postprandial plasma glucose than sitagliptin in the pooled population and in the MetS and IR subgroups.

CONCLUSIONS

Chiglitazar shows promising efficacy for glycemic control in patients with T2DM associated with MetS or IR. Further prospective trials are required to validate these findings.

Analysis of type 2 diabetes heterogeneity with a tree-like representation: insights from the prospective German Diabetes Study and the LURIC cohort

BACKGROUND

Heterogeneity in type 2 diabetes can be represented by a tree-like graph structure by use of reversed graph-embedded dimensionality reduction. We aimed to examine whether this approach can be used to stratify key pathophysiological components and diabetes-related complications during longitudinal follow-up of individuals with recent-onset type 2 diabetes.

METHODS

For this cohort analysis, 927 participants aged 18-69 years from the German Diabetes Study (GDS) with recent-onset type 2 diabetes were mapped onto a previously developed two-dimensional tree based on nine simple clinical and laboratory variables, residualised for age and sex. Insulin sensitivity was assessed by a hyperinsulinaemic-euglycaemic clamp, insulin secretion was assessed by intravenous glucose tolerance test, hepatic lipid content was assessed by 1 H magnetic resonance spectroscopy, serum interleukin (IL)-6 and IL-18 were assessed by ELISA, and peripheral and autonomic neuropathy were assessed by functional and clinical measures. Participants were followed up for up to 16 years. We also investigated heart failure and all-cause mortality in 794 individuals with type 2 diabetes undergoing invasive coronary diagnostics from the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort.

FINDINGS

There were gradients of clamp-measured insulin sensitivity (both dimensions: p<0·0001) and insulin secretion (pdim1<0·0001, pdim2=0·00097) across the tree. Individuals in the region with the lowest insulin sensitivity had the highest hepatic lipid content (n=205, pdim1<0·0001, pdim2=0·037), pro-inflammatory biomarkers (IL-6: n=348, pdim1<0·0001, pdim2=0·013; IL-18: n=350, pdim1<0·0001, pdim2=0·38), and elevated cardiovascular risk (nevents=143, pdim1=0·14, pdim2<0·00081), whereas individuals positioned in the branch with the lowest insulin secretion were more prone to require insulin therapy (nevents=85, pdim1=0·032, pdim2=0·12) and had the highest risk of diabetic sensorimotor polyneuropathy (nevents=184, pdim1=0·012, pdim2=0·044) and cardiac autonomic neuropathy (nevents=118, pdim1=0·0094, pdim2=0·06). In the LURIC cohort, all-cause mortality was highest in the tree branch showing insulin resistance (nevents=488, pdim1=0·12, pdim2=0·0032). Significant gradients differentiated individuals having heart failure with preserved ejection fraction from those who had heart failure with reduced ejection fraction.

INTERPRETATION

These data define the pathophysiological underpinnings of the tree structure, which has the potential to stratify diabetes-related complications on the basis of routinely available variables and thereby expand the toolbox of precision diabetes diagnosis.

FUNDING

German Diabetes Center, German Federal Ministry of Health, Ministry of Culture and Science of the state of North Rhine-Westphalia, German Federal Ministry of Education and Research, German Diabetes Association, German Center for Diabetes Research, European Community, German Research Foundation, and Schmutzler Stiftung.

Novel subgroups of obesity and their association with outcomes: a data-driven cluster analysis

BACKGROUND

Obesity is associated with various complications and decreased life expectancy, and substantial heterogeneity in complications and outcomes has been observed. However, the subgroups of obesity have not yet been clearly defined. This study aimed to identify the subgroups of obesity especially those for target of interventions by cluster analysis.

METHODS

In this study, an unsupervised, data-driven cluster analysis of 9,494 individuals with obesity (body mass index ≥ 35 kg/m2) was performed using the data of ICD-10, drug, and medical procedure from the healthcare claims database. The prevalence and clinical characteristics of the complications such as diabetes in each cluster were evaluated using the prescription records. Additionally, renal and life prognoses were compared among the clusters.

RESULTS

We identified seven clusters characterised by different combinations of complications and several complications were observed exclusively in each cluster. Notably, the poorest prognosis was observed in individuals who rarely visited a hospital after being diagnosed with obesity, followed by those with cardiovascular complications and diabetes.

CONCLUSIONS

In this study, we identified seven subgroups of individuals with obesity using population-based data-driven cluster analysis. We clearly demonstrated important target subgroups for intervention as well as a metabolically healthy obesity group.

Secondary analysis of newly diagnosed type 2 diabetes subgroups and treatment responses in the MARCH cohort

OBJECTIVE

To incorporate new clusters in the MARCH (Metformin and AcaRbose in Chinese patients as the initial Hypoglycemic treatment) cohort of newly diagnosed type 2 diabetes (T2D) patients and compare the anti-glycemic effects of metformin and acarbose across different clusters.

METHODS

K-means cluster analysis was performed based on six clinical indicators. The diabetic clusters in the MARCH cohort were retrospectively associated with the response to metformin and acarbose.

RESULTS

A total of 590 newly diagnosed T2D patients were classified by data-driven clusters into the MARD (mild obesity-related diabetes) (34.1 %), MOD (mild obesity-related diabetes) (34.1 %), SIDD (severe insulin-deficient diabetes) (20.3 %) and SIRD (severe insulin-resistant diabetes) (11.5 %) subgroups at baseline. At 24 and 48 weeks, 346 participants had finished the follow-up. After the adjustment of age, gender, weight, baseline HbA1c, baseline fasting glucose and 2-h postprandial blood glucose (2hPG), metformin mainly decreased the fasting glucose (0.07 ± 0.89 vs -0.26 ± 0.83, P = 0.043) in the MARD subgroup presented with OGTT (oral glucose tolerance test) results compared with acarbose group at 24 weeks. Acarbose led to a greater decrease in 2hPG in the MOD subgroup compared with metformin group (0.08 ± 0.86 vs -0.24 ± 0.92, P = 0.037) at 24 weeks. There was a also significant interaction between cluster and treatment efficacy in HbA1c (glycated hemoglobin) reduction in metformin and acarbose groups at 24 and 48 weeks (pinteraction<0.001).

CONCLUSIONS

Metformin and acarbose affected different metabolic variables depending on the diabetes subtype.

Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology

The limited understanding of the heterogeneity in the treatment response to antidiabetic drugs contributes to metabolic deterioration and cardiovascular complications1,2, stressing the need for more personalized treatment1. Although recent attempts have been made to classify diabetes into subgroups, the utility of such stratification in predicting treatment response is unknown3. We enrolled participants with type 2 diabetes (n = 239, 74 women and 165 men) and features of severe insulin-deficient diabetes (SIDD) or severe insulin-resistant diabetes (SIRD). Participants were randomly assigned to treatment with the glucagon-like peptide 1 receptor agonist semaglutide or the sodium-glucose cotransporter 2 inhibitor dapagliflozin for 6 months (open label). The primary endpoint was the change in glycated haemoglobin (HbA1c). Semaglutide induced a larger reduction in HbA1c levels than dapagliflozin (mean difference, 8.2 mmol mol-1; 95% confidence interval, -10.0 to -6.3 mmol mol-1), with a pronounced effect in those with SIDD. No difference in adverse events was observed between participants with SIDD and those with SIRD. Analysis of secondary endpoints showed greater reductions in fasting and postprandial glucose concentrations in response to semaglutide in participants with SIDD than in those with SIRD and a more pronounced effect on postprandial glucose by dapagliflozin in participants with SIDD than in those with SIRD. However, no significant interaction was found between drug assignment and the SIDD or SIRD subgroup. In contrast, continuous measures of body mass index, blood pressure, insulin secretion and insulin resistance were useful in identifying those likely to have the largest improvements in glycaemic control and cardiovascular risk factors by adding semaglutide or dapagliflozin. Thus, systematic evaluation of continuous pathophysiological variables can guide the prediction of the treatment response to these drugs and provide more information than stratified subgroups ( NCT04451837 ).

Chronic kidney disease stage is associated with the number of risk factors in type 2 diabetes patients (STages Of NEphropathy in type 2 diabetes and Heart Failure - STONE HF)

AIMS

To study the association between risk factors and chronic kidney disease (CKD), and characterize medication use in Finnish primary care type 2 diabetes (T2D) patients.

METHODS

Data on clinical characteristics, laboratory measurements, and medications were collected from medical records. The primary outcome measure was notable CKD (stage 3-5, eGFR <60 ml/min/1.73 m2) and/or increased albuminuria. The explanatory variables were individual risk factors and risk factor groups based on their number (0-2, 3-4, 5-6, >7). Spearman's rank correlation coefficient and risk ratio analysis were used to analyze the association between the number of risk factors and CKD stage, and between the number of risk factors and notable CKD, respectively.

RESULTS

Altogether, 1335 patients with T2D in 60 Finnish primary care centers were recruited for this cross-sectional study. Three-quarters of T2D patients had 3 risk factors and 36% had ≥ 5 risk factors. Compared to patients with 0-2 risk factors, patients with 3-4, 5-6, and ≥ 7 risk factors had a 5.5-fold, 9.9-fold, and 15.9-fold risk of notable CKD (p < 0.001), respectively. Heart failure was most strongly associated with notable CKD (risk ratio, 3.7; p < 0.001).

CONCLUSIONS

Number of risk factors was strongly associated with advanced-stage CKD.

Combining diabetes, sex, and menopause as meaningful clinical features associated with NASH and liver fibrosis in individuals with class II and III obesity: A retrospective cohort study

OBJECTIVE

Steatotic liver disease (SLD) is frequent in individuals with obesity. In this study, type 2 diabetes (T2D), sex, and menopausal status were combined to refine the stratification of obesity regarding the risk of advanced SLD and gain further insight into disease physiopathology.

METHODS

This study enrolled 1446 participants with obesity from the ABOS cohort (NCT01129297), who underwent extensive phenotyping, including liver histology and transcriptome profiling. Hierarchical clustering was applied to classify participants. The prevalence of metabolic disorders associated with steatohepatitis (NASH) and liver fibrosis (F ≥ 2) was determined within each identified subgroup and aligned to clinical and biological characteristics.

RESULTS

The prevalence of NASH and F ≥ 2 was, respectively, 9.5% (N = 138/1446) and 11.7% (N = 159/1365) in the overall population, 20.3% (N = 107/726) and 21.1% (N = 106/502) in T2D patients, and 3.4% (N = 31/920) and 6.1% (N = 53/863) in non-T2D patients. NASH and F ≥ 2 prevalence was 15.4% (33/215) and 15.5% (32/206) among premenopausal women with T2D vs. 29.5% (33/112) and 30.3% (N = 36/119) in postmenopausal women with T2D (p < 0.01); and 21.0% (21/100) / 27.0% (24/89) in men with T2D ≥ age 50 years and 17.9% (17/95) / 18.5% (17/92) in men with T2D < age 50 years (NS). The distinct contribution of menopause was confirmed by the interaction between sex and age with respect to NASH among T2D patients (p = 0.048). Finally, several NASH-associated biological traits (lower platelet count; higher serum uric acid; gamma-glutamyl transferase; aspartate aminotransferase) and liver expressed genes AKR1B10 and CCL20 were significantly associated with menopause in women with T2D but not with age in men with T2D.

CONCLUSIONS

This study unveiled a remarkably high prevalence of advanced SLD after menopause in women with T2D, associated with a dysfunctional biological liver profile.

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.

Personalized glucose-lowering effect of chiglitazar in type 2 diabetes

Chiglitazar (carfloglitazar) is a peroxisome proliferator-activated receptor pan-agonist presenting non-inferior glucose-lowering efficacy with sitagliptin in patients with type 2 diabetes. To delineate the subgroup of patients with greater benefit from chiglitazar, we conducted a machine learning-based post-hoc analysis in two randomized controlled trials. We established a character phenomap based on 13 variables and estimated HbA1c decline to the effects of chiglitazar in reference to sitagliptin. Out of 1,069 patients, 63.3% were found to have greater reduction in HbA1c levels with chiglitazar, while 36.7% showed greater reduction with sitagliptin. This distinction in treatment response was statistically significant between groups (pinteraction<0.001). To identify patients who would gain the most glycemic control benefit from chiglitazar, we developed a machine learning model, ML-PANPPAR, which demonstrated robust performance using sex, BMI, HbA1c, HDL, and fasting insulin. The phenomapping-derived tool successfully identified chiglitazar responders and enabled personalized drug allocation in patients with drug-naïve diabetes.

[Clinical features and complication rates in type 2 diabetes mellitus clusters on five variables: glycated hemoglobin, age at diagnosis, body mass index, HOMA-IR, HOMA-B]

BACKGROUND

Diabetes mellitus (T2DM) is a serious medical and social problem. Now they are studying the possibility of a new stratification of diabetes. The possibility of cluster analysis for different durations of diabetes, in different cohorts to identify phenotypic clusters of T2DM and validation by reproducing clusters is relevant.

AIM

Identify clusters of type 2 diabetes mellitus in patients with different disease duration based on five variables: HbA1c, age at diagnosis, BMI, HOMA-IR, HOMA-B and study the clinical features and complication rates in each cluster in the Novosibirsk region.

MATERIALS AND METHODS

Cluster analysis of K-means was performed in 2131 patients with T2DM, aged 44 to 70 years, with a duration of diabetes of 6.42±5.66 years, living in the Novosibirsk region based on 5 variables - HbA1c, age at -diagnosis, BMI, HOMA-IR, HOMA-B. All patients a complete clinical and laboratory examination. The insulin resistance index in the HOMA (HOMA-IR, u) and the β-cell function assessment index (HOMA-B) were calculated using the calculator -version 2.2.3 at www.dtu.ox.ac.uk.

RESULTS

Cluster analysis revealed: Cluster 1 included 455 patients with preserved β-cell function (HOMA-B 82.97±23.28%), moderate insulin resistance (HOMA-IR 5.57±4.72) and higher diastolic BP; Cluster 2 in 1658 patients with reduced function of β-cells (HOMA-B 21.71±12.51%), the lowest indices of insulin resistance (HOMA-IR 3.50±2.48) and was characterized by a longer duration of diabetes, high fasting glycemia , HbA1c, higher eGFR and MAU, men compared with women had a 31% higher risk of developing diabetic neuropathy and 28% more diabetic nephropathy; Cluster 3 in 18 patients with high function of β-cells (HOMA-B 228.53±63.32%), severe insulin resistance (HOMA-IR 6.92±4.77), features were high incidence of men, shorter duration of diabetes, lower fasting glycemia and HbA1c, lower diastolic BP and eGFR, high incidence of early development of diabetic retinopathy after 4.00±3.6 years.

CONCLUSION

Cluster analysis in patients with different durations of diabetes mellitus confirmed the possibility of using cluster analysis to identify T2DM phenotypes in the Russian population. The clusters differed in the clinical characteristics of patients, the frequency and risk of diabetic complications. These results have potential value for early stratification of therapy.

Precision medicine for cardiometabolic disease: a framework for clinical translation

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

A Longitudinal Clinical Trajectory Analysis Examining the Accumulation of Co-morbidity in People with Type 2 Diabetes (T2D) Compared with Non-T2D Individuals

BACKGROUND

Type 2 diabetes mellitus (T2D) is commonly associated with an increasing complexity of multimorbidity. While some progress has been made in identifying genetic and non-genetic risk factors for T2D, understanding the longitudinal clinical history of individuals before/after T2D diagnosis may provide additional insights.

METHODS

In this study, we utilised longitudinal data from the DARE (Diabetes Alliance for Research in England) study to examine the trajectory of clinical conditions in individuals with and without T2D. Data from 1932 individuals (T2D n = 1196 vs. matched non-T2D controls n = 736) were extracted and subjected to trajectory analysis over a period of up to 50 years (25 years pre-diagnosis/25 years post-diagnosis). We also analysed the cumulative proportion of people with diagnosed coronary artery disease (CAD) in their general practice (GP) record with an analysis of lower respiratory tract infection (RTI) as a comparator group.

RESULTS

The mean age of diagnosis of T2D was 52.6 (95% confidence interval 52.0-53.4) years. In the years leading up to T2D diagnosis, individuals who eventually received a T2D diagnosis consistently exhibited a considerable increase in several clinical phenotypes. Additionally, immediately prior to T2D diagnosis, a significantly greater prevalence of hypertension (35%)/RTI (34%)/heart conditions (17%)/eye, nose, throat infection (19%) and asthma (12%) were observed. The corresponding trajectory of each of these conditions was much less dramatic in the matched controls. Post-T2D diagnosis, proportions of T2D individuals exhibiting hypertension/chronic kidney disease/retinopathy/infections climbed rapidly before plateauing. At the last follow-up by quintile of disadvantage, the proportion (%) of people with diagnosed CAD was 6.4% for quintile 1 (least disadvantaged) and 11% for quintile 5 (F = 3.4, p = 0.01 for the difference between quintiles).

CONCLUSION

These findings provide novel insights into the onset/natural progression of T2D, suggesting an early phase of inflammation-related disease activity before any clinical diagnosis of T2D is made. Measures that reduce social inequality have the potential in the longer term to reduce the social gradient in health outcomes reported here.

Unsupervised cluster analysis of clinical and metabolite characteristics in patients with chronic complications of T2DM: an observational study of real data

Introduction

The aim of this study was to cluster patients with chronic complications of type 2 diabetes mellitus (T2DM) by cluster analysis in Dalian, China, and examine the variance in risk of different chronic complications and metabolic levels among the various subclusters.

Methods

2267 hospitalized patients were included in the K-means cluster analysis based on 11 variables [Body Mass Index (BMI), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Glucose, Triglycerides (TG), Total Cholesterol (TC), Uric Acid (UA), microalbuminuria (mAlb), Insulin, Insulin Sensitivity Index (ISI) and Homa Insulin-Resistance (Homa-IR)]. The risk of various chronic complications of T2DM in different subclusters was analyzed by multivariate logistic regression, and the Kruskal-Wallis H test and the Nemenyi test examined the differences in metabolites among different subclusters.

Results

Four subclusters were identified by clustering analysis, and each subcluster had significant features and was labeled with a different level of risk. Cluster 1 contained 1112 inpatients (49.05%), labeled as "Low-Risk"; cluster 2 included 859 (37.89%) inpatients, the label characteristics as "Medium-Low-Risk"; cluster 3 included 134 (5.91%) inpatients, labeled "Medium-Risk"; cluster 4 included 162 (7.15%) inpatients, and the label feature was "High-Risk". Additionally, in different subclusters, the proportion of patients with multiple chronic complications was different, and the risk of the same chronic complication also had significant differences. Compared to the "Low-Risk" cluster, the other three clusters exhibit a higher risk of microangiopathy. After additional adjustment for 20 covariates, the odds ratios (ORs) and 95% confidence intervals (95%CI) of the "Medium-Low-Risk" cluster, the "Medium-Risk" cluster, and the"High-Risk" cluster are 1.369 (1.042, 1.799), 2.188 (1.496, 3.201), and 9.644 (5.851, 15.896) (all p<0.05). Representatively, the "High-Risk" cluster had the highest risk of DN [OR (95%CI): 11.510(7.139,18.557), (p<0.05)] and DR [OR (95%CI): 3.917(2.526,6.075), (p<0.05)] after 20 variables adjusted. Four metabolites with statistically significant distribution differences when compared with other subclusters [Threonine (Thr), Tyrosine (Tyr), Glutaryl carnitine (C5DC), and Butyryl carnitine (C4)].

Conclusion

Patients with chronic complications of T2DM had significant clustering characteristics, and the risk of target organ damage in different subclusters was significantly different, as were the levels of metabolites. Which may become a new idea for the prevention and treatment of chronic complications of T2DM.

Stem Cell-Derived Islet Transplantation in Patients With Type 2 Diabetes: Can Diabetes Subtypes Guide Implementation?

Historically, only patients with brittle diabetes or severe recurrent hypoglycemia have been considered for islet transplantation (ITx). This population has been selected to optimize the risk-benefit profile, considering risks of long-term immunosuppression and limited organ supply. However, with the advent of stem cell (SC)-derived ITx and the potential for immunosuppression-free ITx, consideration of a broader recipient cohort may soon be justified. Simultaneously, the classical categorization of diabetes is being challenged by growing evidence in support of a clustering of disease subtypes that can be better categorized by the All New Diabetics in Scania (ANDIS) classification system. Using the ANDIS classification, 5 subtypes of diabetes have been described, each with unique causes and consequences. We evaluate consideration for ITx in the context of this broader patient population and the new classification of diabetes subtypes. In this review, we evaluate considerations for ITx based on novel diabetes subtypes, including their limitations, and we elaborate on unique transplant features that should now be considered to enable ITx in these "unconventional" patient cohorts. Based on evidence from those receiving whole pancreas transplant and our more than 20-year experience with ITx, we offer recommendations and potential research avenues to justify implementation of SC-derived ITx in broader populations of patients with all types of diabetes.

Impact of follow ups, time interval and study duration in diffusion & myelin MRI clinical study in MS

It is currently unknown how quantitative diffusion and myelin MRI designs affect the results of a longitudinal study. We used two independent datasets containing 6 monthly MRI measurements from 20 healthy controls and 20 relapsing-remitting multiple sclerosis (RR-MS) patients. Six designs were tested, including 3 MRI acquisitions, either over 6 months or over a shorter study duration, with balanced (same interval) or unbalanced (different interval) time intervals between MRI acquisitions. First, we show that in RR-MS patients, the brain changes over time obtained with 3 MRI acquisitions were similar to those observed with 5 MRI acquisitions and that designs with an unbalanced time interval showed the highest similarity, regardless of study duration. No significant brain changes were found in the healthy controls over the same periods. Second, the study duration affects the sample size in the RR-MS dataset; a longer study requires more subjects and vice versa. Third, the number of follow-up acquisitions and study duration affect the sensitivity and specificity of the associations with clinical parameters, and these depend on the white matter bundle and MRI measure considered. Together, this suggests that the optimal design depends on the assumption of the dynamics of change in the target population and the accuracy required to capture these dynamics. Thus, this work provides a better understanding of key factors to consider in a longitudinal study and provides clues for better strategies in clinical trial design.

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.

Precision gestational diabetes treatment: a systematic review and meta-analyses

BACKGROUND

Gestational Diabetes Mellitus (GDM) affects approximately 1 in 7 pregnancies globally. It is associated with short- and long-term risks for both mother and baby. Therefore, optimizing treatment to effectively treat the condition has wide-ranging beneficial effects. However, despite the known heterogeneity in GDM, treatment guidelines and approaches are generally standardized. We hypothesized that a precision medicine approach could be a tool for risk-stratification of women to streamline successful GDM management. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, with more rapid normalization of hyperglycaemia, could have benefits for both mother and fetus.

METHODS

We conducted two systematic reviews, to identify precision markers that may predict effective lifestyle and pharmacological interventions.

RESULTS

There was a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We found a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). This included previous history of GDM, Body Mass Index and blood glucose concentrations at diagnosis.

CONCLUSIONS

Clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown. These will be important to consider in future studies.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Individualised prescription of medications for treatment of obesity in adults

Obesity continues to increase in prevalence globally, driven by changes in environmental factors which have accelerated the development of obesity in individuals with an underlying predisposition to weight gain. The adverse health effects and increased risk for chronic disease associated with obesity are ameliorated by weight loss, with greater benefits from larger amounts of weight reduction. Obesity is a heterogeneous condition, with the drivers, phenotype and complications differing substantially between individuals. This raises the question of whether treatments for obesity, specifically pharmacotherapy, can be targeted based on individual characteristics. This review examines the rationale and the clinical data evaluating this strategy in adults. Individualised prescribing of obesity medication has been successful in rare cases of monogenic obesity where medications have been developed to target dysfunctions in leptin/melanocortin signalling pathways but has been unsuccessful in polygenic obesity due to a lack of understanding of how the gene variants associated with body mass index affect phenotype. At present, the only factor consistently associated with longer-term efficacy of obesity pharmacotherapy is early weight loss outcome, which cannot inform choice of therapy at the time of medication initiation. The concept of matching a therapy for obesity to the characteristics of the individual is appealing but as yet unproven in randomised clinical trials. With increasing technology allowing deeper phenotyping of individuals, increased sophistication in the analysis of big data and the emergence of new treatments, it is possible that precision medicine for obesity will eventuate. For now, a personalised approach that takes into account the person's context, preferences, comorbidities and contraindications is recommended.

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Current insights and emerging trends in early-onset type 2 diabetes

Type 2 diabetes diagnosed in childhood or early adulthood is termed early-onset type 2 diabetes. Cases of early-onset type 2 diabetes are increasing rapidly globally, alongside rising obesity. Compared with a diagnosis later in life, an earlier-onset diagnosis carries an unexplained excess risk of microvascular complications, adverse cardiovascular outcomes, and earlier death. Women with early-onset type 2 diabetes also have a higher risk of adverse pregnancy outcomes. The high burden of complications renders individuals with early-onset type 2 diabetes at future risk of multimorbidity and interventions to reverse these concerning trends should be a priority. Within the early-onset cohort, disease pathophysiology and interventions have been better studied in paediatric-onset (<19 years) type 2 diabetes compared to adults; however, young adults aged 19-39 years (a larger number proportionally) are not well characterised and are also invisible in the current evidence base supporting management, which is derived from trials in later-onset type 2 diabetes. Young adults with type 2 diabetes face challenges in self-management that older individuals are less likely to experience (being in education or of working age, higher diabetes distress, and possible obesity-related stigma and diabetes-related stigma). There is a major research gap as to the optimal strategies to deploy in managing type 2 diabetes in adolescents and young adults, given that current models of care appear to not work as well in this age group. In the face of manifold risk factors (obesity, female sex, social deprivation, non-White European ethnicity, and genetic risk factors) prevention strategies with tailored lifestyle interventions, where needed, are likely to have greater success, but more evidence is needed. In this Review, we draw on evidence from both adolescents and young adults to provide a contemporary update on the current insights and emerging trends in early-onset type 2 diabetes.

Towards Precision Medicine in Gestational Diabetes: Pathophysiology and Glycemic Patterns in Pregnant Women With Obesity

AIMS

Precision medicine has revolutionized our understanding of type 1 diabetes and neonatal diabetes but has yet to improve insight into gestational diabetes mellitus (GDM), the most common obstetric complication and strongly linked to obesity. Here we explored if patterns of glycaemia (fasting, 1 hour, 2 hours) during the antenatal oral glucose tolerance test (OGTT), reflect distinct pathophysiological subtypes of GDM as defined by insulin secretion/sensitivity or lipid profiles.

METHODS

867 pregnant women with obesity (body mass index ≥ 30 kg/m2) from the UPBEAT trial (ISRCTN 89971375) were assessed for GDM at 28 weeks' gestation (75 g oral glucose tolerance test OGTT; World Health Organization criteria). Lipid profiling of the fasting plasma OGTT sample was undertaken using direct infusion mass spectrometry and analyzed by logistic/linear regression, with and without adjustment for confounders. Insulin secretion and sensitivity were characterized by homeostatic model assessment 2b and 2s, respectively.

RESULTS

In women who developed GDM (n = 241), patterns of glycaemia were associated with distinct clinical and biochemical characteristics and changes to lipid abundance in the circulation. Severity of glucose derangement, rather than pattern of postload glycaemia, was most strongly related to insulin action and lipid abundance/profile. Unexpectedly, women with isolated postload hyperglycemia had comparable insulin secretion and sensitivity to euglycemic women, potentially indicative of a novel mechanistic pathway.

CONCLUSIONS

Patterns of glycemia during the OGTT may contribute to a precision approach to GDM as assessed by differences in insulin resistance/secretion. Further research is indicated to determine if isolated postload hyperglycemia reflects a different mechanistic pathway for targeted management.

Ethnic differences in the association between age at diagnosis of diabetes and the risk of cardiovascular complications: a population-based cohort study

BACKGROUND

We examined ethnic differences in the association between age at diagnosis of diabetes and the risk of cardiovascular complications.

METHODS

We conducted a population-based cohort study in Ontario, Canada among individuals with diabetes and matched individuals without diabetes (2002-18). We fit Cox proportional hazards models to determine the associations of age at diagnosis and ethnicity (Chinese, South Asian, general population) with cardiovascular complications. We tested for an interaction between age at diagnosis and ethnicity.

RESULTS

There were 453,433 individuals with diabetes (49.7% women) and 453,433 matches. There was a significant interaction between age at diagnosis and ethnicity (P < 0.0001). Young-onset diabetes (age at diagnosis < 40) was associated with higher cardiovascular risk [hazard ratios: Chinese 4.25 (3.05-5.91), South Asian: 3.82 (3.19-4.57), General: 3.46 (3.26-3.66)] than usual-onset diabetes [age at diagnosis ≥ 40 years; Chinese: 2.22 (2.04-2.66), South Asian: 2.43 (2.22-2.66), General: 1.83 (1.81-1.86)] versus ethnicity-matched individuals. Among those with young-onset diabetes, Chinese ethnicity was associated with lower overall cardiovascular [0.44 (0.32-0.61)] but similar stroke risks versus the general population; while South Asian ethnicity was associated with lower overall cardiovascular [0.75 (0.64-0.89)] but similar coronary artery disease risks versus the general population. In usual-onset diabetes, Chinese ethnicity was associated with lower cardiovascular risk [0.44 (0.42-0.46)], while South Asian ethnicity was associated with lower cardiovascular [0.90 (0.86-0.95)] and higher coronary artery disease [1.08 (1.01-1.15)] risks versus the general population.

CONCLUSIONS

There are important ethnic differences in the association between age at diagnosis and risk of cardiovascular complications.

Inferring regulators of cell identity in the human adult pancreas

Cellular identity during development is under the control of transcription factors that form gene regulatory networks. However, the transcription factors and gene regulatory networks underlying cellular identity in the human adult pancreas remain largely unexplored. Here, we integrate multiple single-cell RNA-sequencing datasets of the human adult pancreas, totaling 7393 cells, and comprehensively reconstruct gene regulatory networks. We show that a network of 142 transcription factors forms distinct regulatory modules that characterize pancreatic cell types. We present evidence that our approach identifies regulators of cell identity and cell states in the human adult pancreas. We predict that HEYL, BHLHE41 and JUND are active in acinar, beta and alpha cells, respectively, and show that these proteins are present in the human adult pancreas as well as in human induced pluripotent stem cell (hiPSC)-derived islet cells. Using single-cell transcriptomics, we found that JUND represses beta cell genes in hiPSC-alpha cells. BHLHE41 depletion induced apoptosis in primary pancreatic islets. The comprehensive gene regulatory network atlas can be explored interactively online. We anticipate our analysis to be the starting point for a more sophisticated dissection of how transcription factors regulate cell identity and cell states in the human adult pancreas.

Machine learning based study for the classification of Type 2 diabetes mellitus subtypes

PURPOSE

Data-driven diabetes research has increased its interest in exploring the heterogeneity of the disease, aiming to support in the development of more specific prognoses and treatments within the so-called precision medicine. Recently, one of these studies found five diabetes subgroups with varying risks of complications and treatment responses. Here, we tackle the development and assessment of different models for classifying Type 2 Diabetes (T2DM) subtypes through machine learning approaches, with the aim of providing a performance comparison and new insights on the matter.

METHODS

We developed a three-stage methodology starting with the preprocessing of public databases NHANES (USA) and ENSANUT (Mexico) to construct a dataset with N = 10,077 adult diabetes patient records. We used N = 2,768 records for training/validation of models and left the remaining (N = 7,309) for testing. In the second stage, groups of observations -each one representing a T2DM subtype- were identified. We tested different clustering techniques and strategies and validated them by using internal and external clustering indices; obtaining two annotated datasets Dset A and Dset B. In the third stage, we developed different classification models assaying four algorithms, seven input-data schemes, and two validation settings on each annotated dataset. We also tested the obtained models using a majority-vote approach for classifying unseen patient records in the hold-out dataset.

RESULTS

From the independently obtained bootstrap validation for Dset A and Dset B, mean accuracies across all seven data schemes were [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]), respectively. Best accuracies were [Formula: see text] and [Formula: see text]. Both validation setting results were consistent. For the hold-out dataset, results were consonant with most of those obtained in the literature in terms of class proportions.

CONCLUSION

The development of machine learning systems for the classification of diabetes subtypes constitutes an important task to support physicians for fast and timely decision-making. We expect to deploy this methodology in a data analysis platform to conduct studies for identifying T2DM subtypes in patient records from hospitals.

Clusters of prediabetes and type 2 diabetes stratify all-cause mortality in a cohort of participants undergoing invasive coronary diagnostics

BACKGROUND

Heterogeneous metabolic clusters have been identified in diabetic and prediabetic states. It is not known whether such pathophysiologic clusters impact survival in at-risk persons being evaluated for coronary heart disease.

METHODS

The LURIC Study recruited patients referred for coronary angiography at a median age of 63 (IQR 56-70) with a follow-up of 16.1 (IQR 9.6, 17.7) years. Clustering of 1269 subjects without diabetes was performed with oGTT-derived glucose and insulin; fasting triglyceride, high-density lipoprotein, BMI, waist and hip circumference. Patients with T2D (n = 794) were clustered using age, BMI, glycemia, homeostasis model assessment, and islet autoantibodies. Associations of clusters with mortality were analysed using Cox regression.

RESULTS

Individuals without diabetes were classified into six subphenotypes, with 884 assigned to subjects at low-risk (cluster 1,2,4) and 385 at high-risk (cluster 3,5,6) for diabetes. We found significantly increased mortality in clusters 3 (hazard ratio (HR)1.42), 5 (HR 1.43), and 6 (HR 1.46) after adjusting for age, BMI, HbA1c and sex. In the T2D group, 508 were assigned to mild age-related diabetes (MARD), 183 to severe insulin-resistant diabetes (SIRD), 84 to mild obesity-related diabetes (MOD), 19 to severe insulin-deficient diabetes (SIDD). Compared to the low-risk non-diabetes group, crude mortality was not different in MOD. Increased mortality was found for MARD (HR 2.2), SIRD (HR 2.2), and SIDD (HR 2.5).

CONCLUSIONS

Metabolic clustering successfully stratifies survival even among persons undergoing invasive coronary diagnostics. Novel clustering approaches based on glucose metabolism can identify persons who require special attention as they are at risk of increased mortality.

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.

Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population

Adult-onset diabetes mellitus (here: aDM) is not a uniform disease entity. In European populations, five diabetes subgroups have been identified by cluster analysis using simple clinical variables; these may elucidate diabetes aetiology and disease prognosis. We aimed at reproducing these subgroups among Ghanaians with aDM, and establishing their importance for diabetic complications in different health system contexts. We used data of 541 Ghanaians with aDM (age: 25-70 years; male sex: 44%) from the multi-center, cross-sectional Research on Obesity and Diabetes among African Migrants (RODAM) Study. Adult-onset DM was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L, documented use of glucose-lowering medication or self-reported diabetes, and age of onset ≥ 18 years. We derived subgroups by cluster analysis using (i) a previously published set of variables: age at diabetes onset, HbA1c, body mass index, HOMA-beta, HOMA-IR, positivity of glutamic acid decarboxylase autoantibodies (GAD65Ab), and (ii) Ghana-specific variables: age at onset, waist circumference, FPG, and fasting insulin. For each subgroup, we calculated the clinical, treatment-related and morphometric characteristics, and the proportions of objectively measured and self-reported diabetic complications. We reproduced the five subgroups: cluster 1 (obesity-related, 73%) and cluster 5 (insulin-resistant, 5%) with no dominant diabetic complication patterns; cluster 2 (age-related, 10%) characterized by the highest proportions of coronary artery disease (CAD, 18%) and stroke (13%); cluster 3 (autoimmune-related, 5%) showing the highest proportions of kidney dysfunction (40%) and peripheral artery disease (PAD, 14%); and cluster 4 (insulin-deficient, 7%) characterized by the highest proportion of retinopathy (14%). The second approach yielded four subgroups: obesity- and age-related (68%) characterized by the highest proportion of CAD (9%); body fat-related and insulin-resistant (18%) showing the highest proportions of PAD (6%) and stroke (5%); malnutrition-related (8%) exhibiting the lowest mean waist circumference and the highest proportion of retinopathy (20%); and ketosis-prone (6%) with the highest proportion of kidney dysfunction (30%) and urinary ketones (6%). With the same set of clinical variables, the previously published aDM subgroups can largely be reproduced by cluster analysis in this Ghanaian population. This method may generate in-depth understanding of the aetiology and prognosis of aDM, particularly when choosing variables that are clinically relevant for the target population.

Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH

AIMS/HYPOTHESIS

Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes.

METHODS

One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals.

RESULTS

Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAFAfr]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9×10-9); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAFAfr=0.0536), was associated with a reduced response to metformin (p=2.4×10-8), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increase in HbA1c of 0.08% and non-carriers had an HbA1c increase of 0.01% after 1 year of treatment (p=3.3×10-3). We also identified associations between type 2 diabetes-associated variants and glycaemic response, including the type 2 diabetes-protective C allele of rs703972 near ZMIZ1 and increased levels of active glucagon-like peptide 1 (GLP-1) (p=1.6×10-5), supporting the role of alterations in incretin levels in type 2 diabetes pathophysiology.

CONCLUSIONS/INTERPRETATION

We present a well-phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common glucose-lowering medications and provide insight into mechanisms of action of type 2 diabetes-related variation.

DATA AVAILABILITY

The complete summary statistics from this study are available at the Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org ) and the GWAS Catalog ( www.ebi.ac.uk/gwas/ , accession IDs: GCST90269867 to GCST90269899).

Development and economic assessment of machine learning models to predict glycosylated hemoglobin in type 2 diabetes

Background: Glycosylated hemoglobin (HbA1c) is recommended for diagnosing and monitoring type 2 diabetes. However, the monitoring frequency in real-world applications has not yet reached the recommended frequency in the guidelines. Developing machine learning models to screen patients with poor glycemic control in patients with T2D could optimize management and decrease medical service costs. Methods: This study was carried out on patients with T2D who were examined for HbA1c at the Sichuan Provincial People's Hospital from April 2018 to December 2019. Characteristics were extracted from interviews and electronic medical records. The data (excluded FBG or included FBG) were randomly divided into a training dataset and a test dataset with a radio of 8:2 after data pre-processing. Four imputing methods, four screening methods, and six machine learning algorithms were used to optimize data and develop models. Models were compared on the basis of predictive performance metrics, especially on the model benefit (MB, a confusion matrix combined with economic burden associated with therapeutic inertia). The contributions of features were interpreted using SHapley Additive exPlanation (SHAP). Finally, we validated the sample size on the best model. Results: The study included 980 patients with T2D, of whom 513 (52.3%) were defined as positive (need to perform the HbA1c test). The results indicated that the model trained in the data (included FBG) presented better forecast performance than the models that excluded the FBG value. The best model used modified random forest as the imputation method, ElasticNet as the feature screening method, and the LightGBM algorithms and had the best performance. The MB, AUC, and AUPRC of the best model, among a total of 192 trained models, were 43475.750 (¥), 0.972, 0.944, and 0.974, respectively. The FBG values, previous HbA1c values, having a rational and reasonable diet, health status scores, type of manufacturers of metformin, interval of measurement, EQ-5D scores, occupational status, and age were the most significant contributors to the prediction model. Conclusion: We found that MB could be an indicator to evaluate the model prediction performance. The proposed model performed well in identifying patients with T2D who need to undergo the HbA1c test and could help improve individualized T2D management.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine

OBJECTIVE

Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model.

METHODS

Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink).

RESULTS

Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1).

CONCLUSIONS

Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment

Among 20 leading global risk factors for years of life lost in 2040, reference forecasts point to three metabolic risks-high blood pressure, high BMI, and high fasting plasma glucose-as being the top risk variables. Building upon these and other risk factors, the concept of metabolic health is attracting much attention in the scientific community. It focuses on the aggregation of important risk factors, which allows the identification of subphenotypes, such as people with metabolically unhealthy normal weight or metabolically healthy obesity, who strongly differ in their risk of cardiometabolic diseases. Since 2018, studies that used anthropometrics, metabolic characteristics, and genetics in the setting of cluster analyses proposed novel metabolic subphenotypes among patients at high risk (eg, those with diabetes). The crucial point now is whether these subphenotyping strategies are superior to established cardiometabolic risk stratification methods regarding the prediction, prevention, and treatment of cardiometabolic diseases. In this Review, we carefully address this point and conclude, firstly, regarding cardiometabolic risk stratification, in the general population both the concept of metabolic health and the cluster approaches are not superior to established risk prediction models. However, both subphenotyping approaches might be informative to improve the prediction of cardiometabolic risk in subgroups of individuals, such as those in different BMI categories or people with diabetes. Secondly, the applicability of the concepts by treating physicians and communication of the cardiometabolic risk with patients is easiest using the concept of metabolic health. Finally, the approaches to identify cardiometabolic risk clusters in particular have provided some evidence that they could be used to allocate individuals to specific pathophysiological risk groups, but whether this allocation is helpful for prevention and treatment still needs to be determined.