Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population

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.

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.

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.

Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes

BACKGROUND

Consistent evidence suggests diabetes-protective effects of dietary fiber intake. However, the underlying mechanisms, particularly the role of gut microbiota and host circulating metabolites, are not fully understood. We aimed to investigate gut microbiota and circulating metabolites associated with dietary fiber intake and their relationships with type 2 diabetes (T2D).

METHODS

This study included up to 11 394 participants from the HCHS/SOL (Hispanic Community Health Study/Study of Latinos). Diet was assessed with two 24-hour dietary recalls at baseline. We examined associations of dietary fiber intake with gut microbiome measured by shotgun metagenomics (350 species/85 genera and 1958 enzymes; n=2992 at visit 2), serum metabolome measured by untargeted metabolomics (624 metabolites; n=6198 at baseline), and associations between fiber-related gut bacteria and metabolites (n=804 at visit 2). We examined prospective associations of serum microbial-associated metabolites (n=3579 at baseline) with incident T2D over 6 years.

RESULTS

We identified multiple bacterial genera, species, and related enzymes associated with fiber intake. Several bacteria (eg, Butyrivibrio, Faecalibacterium) and enzymes involved in fiber degradation (eg, xylanase EC3.2.1.156) were positively associated with fiber intake, inversely associated with prevalent T2D, and favorably associated with T2D-related metabolic traits. We identified 159 metabolites associated with fiber intake, 47 of which were associated with incident T2D. We identified 18 of these 47 metabolites associated with the identified fiber-related bacteria, including several microbial metabolites (eg, indolepropionate and 3-phenylpropionate) inversely associated with the risk of T2D. Both Butyrivibrio and Faecalibacterium were associated with these favorable metabolites. The associations of fiber-related bacteria, especially Faecalibacterium and Butyrivibrio, with T2D were attenuated after further adjustment for these microbial metabolites.

CONCLUSIONS

Among United States Hispanics/Latinos, dietary fiber intake was associated with favorable profiles of gut microbiota and circulating metabolites for T2D. These findings advance our understanding of the role of gut microbiota and microbial metabolites in the relationship between diet and T2D.

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

BACKGROUND

Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

METHODS

A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

RESULTS

Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

CONCLUSIONS

We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

Postpartum defects in inflammatory response after gestational diabetes precede progression to type 2 diabetes: a nested case-control study within the SWIFT study

BACKGROUND

Gestational diabetes (GDM) is a distinctive form of diabetes that first presents in pregnancy. While most women return to normoglycemia after delivery, they are nearly ten times more likely to develop type 2 diabetes than women with uncomplicated pregnancies. Current prevention strategies remain limited due to our incomplete understanding of the early underpinnings of progression.

AIM

To comprehensively characterize the postpartum profiles of women shortly after a GDM pregnancy and identify key mechanisms responsible for the progression to overt type 2 diabetes using multi-dimensional approaches.

METHODS

We conducted a nested case-control study of 200 women from the Study of Women, Infant Feeding and Type 2 Diabetes After GDM Pregnancy (SWIFT) to examine biochemical, proteomic, metabolomic, and lipidomic profiles at 6-9 weeks postpartum (baseline) after a GDM pregnancy. At baseline and annually up to two years, SWIFT administered research 2-hour 75-gram oral glucose tolerance tests. Women who developed incident type 2 diabetes within four years of delivery (incident case group, n = 100) were pair-matched by age, race, and pre-pregnancy body mass index to those who remained free of diabetes for at least 8 years (control group, n = 100). Correlation analyses were used to assess and integrate relationships across profiling platforms.

RESULTS

At baseline, all 200 women were free of diabetes. The case group was more likely to present with dysglycemia (e.g., impaired fasting glucose levels, glucose tolerance, or both). We also detected differences between groups across all omic platforms. Notably, protein profiles revealed an underlying inflammatory response with perturbations in protease inhibitors, coagulation components, extracellular matrix components, and lipoproteins, whereas metabolite and lipid profiles implicated disturbances in amino acids and triglycerides at individual and class levels with future progression. We identified significant correlations between profile features and fasting plasma insulin levels, but not with fasting glucose levels. Additionally, specific cross-omic relationships, particularly among proteins and lipids, were accentuated or activated in the case group but not the control group.

CONCLUSIONS

Overall, we applied orthogonal, complementary profiling techniques to uncover an inflammatory response linked to elevated triglyceride levels shortly after a GDM pregnancy, which is more pronounced in women who progress to overt diabetes.

Diabetes mellitus: Classification, mediators, and complications; A gate to identify potential targets for the development of new effective treatments

Nowadays, diabetes mellitus has emerged as a significant global public health concern with a remarkable increase in its prevalence. This review article focuses on the definition of diabetes mellitus and its classification into different types, including type 1 diabetes (idiopathic and fulminant), type 2 diabetes, gestational diabetes, hybrid forms, slowly evolving immune-mediated diabetes, ketosis-prone type 2 diabetes, and other special types. Diagnostic criteria for diabetes mellitus are also discussed. The role of inflammation in both type 1 and type 2 diabetes is explored, along with the mediators and potential anti-inflammatory treatments. Furthermore, the involvement of various organs in diabetes mellitus is highlighted, such as the role of adipose tissue and obesity, gut microbiota, and pancreatic β-cells. The manifestation of pancreatic Langerhans β-cell islet inflammation, oxidative stress, and impaired insulin production and secretion are addressed. Additionally, the impact of diabetes mellitus on liver cirrhosis, acute kidney injury, immune system complications, and other diabetic complications like retinopathy and neuropathy is examined. Therefore, further research is required to enhance diagnosis, prevent chronic complications, and identify potential therapeutic targets for the management of diabetes mellitus and its associated dysfunctions.

Fagopyrum tataricum ethanol extract ameliorates symptoms of hyperglycemia by regulating gut microbiota in type 2 diabetes mellitus mice

Type 2 diabetes mellitus (T2DM) is typically accompanied by sudden weight loss, dyslipidemia-related indicators, decreased insulin sensitivity, and altered gut microbial communities. Fagopyrum tataricum possesses many biological activities, such as antioxidant, hypolipidemic, and hypotensive activities. However, only a few studies have attempted to elucidate the regulatory effects of F. tataricum ethanol extract (FTE) on intestinal microbial communities and its potential relationships with T2DM. In this study, we established a T2DM mouse model and investigated the regulatory effects of FTE on hyperglycemia symptoms and intestinal microbial communities. FTE intervention significantly improved the levels of fasting blood glucose, the area under the curve of oral glucose tolerance test (OGTT), and glycosylated serum protein, as well as pancreas islet function correlation index. In addition, FTE effectively improved hepatic and cecum injuries and insulin secretion due to T2DM. It was also revealed that the potential hypoglycemic mechanism of FTE was involved in the regulation of protein kinase B (AKT-1) and glucose transporter 2 (GLUT-2). Furthermore, compared with the Model group, the FTE-H intervention exhibited a significantly decreased ratio of Firmicutes to Bacteroidetes at the phylum level, reduced relative abundance of pernicious bacteria at the genus level, such as Desulfovibrio, Oscillibacter, Blautia, Parabacteroides, and Erysipelatoclostridium, and ameliorated inflammatory response and insulin resistance. Moreover, the correlation between gut microbiota and hypoglycemic indicators was predicted. The results showed that Lachnoclostridium, Lactobacillus, Oscillibacter, Bilophila, and Roseburia have the potential to be used as bacterial markers for T2DM. In conclusion, our research showed that FTE alleviates hyperglycemia symptoms by regulating the expression of AKT-1 and GLUT-2, as well as intestinal microbial communities in T2DM mice.

Review of the Case Reports on Metformin, Sulfonylurea, and Thiazolidinedione Therapies in Type 2 Diabetes Mellitus Patients

Type 2 diabetes mellitus (T2DM) is the world's most common metabolic disease. The development of T2DM is mainly caused by a combination of two factors: the failure of insulin secretion by the pancreatic β-cells and the inability of insulin-sensitive tissues to respond to insulin (insulin resistance); therefore, the disease is indicated by a chronic increase in blood glucose. T2DM patients can be treated with mono- or combined therapy using oral antidiabetic drugs and insulin-replaced agents; however, the medication often leads to various discomforts, such as abdominal pain, diarrhea or constipation, nausea and vomiting, and hypersensitivity reactions. A biguanide drug, metformin, has been used as a first-line drug to reduce blood sugar levels. Sulfonylureas work by blocking the ATP-sensitive potassium channel, directly inducing the release of insulin from pancreatic β-cells and thus decreasing blood glucose concentrations. However, the risk of the failure of sulfonylurea as a monotherapy agent is greater than that of metformin or rosiglitazone (a thiazolidinedione drug). Sulfonylureas are used as the first-line drug of choice for DM patients who cannot tolerate metformin therapy. Other antidiabetic drugs, thiazolidinediones, work by activating the peroxisome proliferator-activated receptor gamma (PPARγ), decreasing the IR level, and increasing the response of β-cells towards the glucose level. However, thiazolidines may increase the risk of cardiovascular disease, weight gain, water retention, and edema. This review article aims to discuss case reports on the use of metformin, sulfonylureas, and thiazolidinediones in DM patients. The literature search was conducted on the PubMed database using the keywords 'metformin OR sulfonylureas OR thiazolidinediones AND case reports', filtered to 'free full text', 'case reports', and '10 years publication date'. In some patients, metformin may affect sleep quality and, in rare cases, leads to the occurrence of lactate acidosis; thus, patients taking this drug should be monitored for their kidney status, plasma pH, and plasma metformin level. Sulfonylureas and TZDs may cause a higher risk of hypoglycemia and weight gain or edema due to fluid retention. TZDs may be associated with risks of cardiovascular events in patients with concomitant T2DM and chronic obstructive pulmonary disease. Therefore, patients taking these drugs should be closely monitored for adverse effects.

Pharmacometabolomic Approach to Investigate the Response to Metformin in Patients with Type 2 Diabetes: A Cross-Sectional Study

Metformin constitutes the foundation therapy in type 2 diabetes (T2D). Despite its multiple beneficial effects and widespread use, there is considerable inter-individual variability in response to metformin. Our objective is to identify metabolic signatures associated with poor and good responses to metformin, which may improve our ability to predict outcomes for metformin treatment. In this cross-sectional study, clinical and metabolic data for 119 patients with type 2 diabetes taking metformin were collected from the Qatar Biobank. Patients were empirically dichotomized according to their HbA1C levels into good and poor responders. Differences in the level of metabolites between these two groups were compared using orthogonal partial least square discriminate analysis (OPLS-DA) and linear models. Good responders showed increased levels of sphingomyelins, acylcholines, and glutathione metabolites. On the other hand, poor responders showed increased levels of metabolites resulting from glucose metabolism and gut microbiota metabolites. The results of this study have the potential to increase our knowledge of patient response variability to metformin and carry significant implications for enabling personalized medicine.

Response to insulin glargine 100 U/mL treatment in newly-defined subgroups of type 2 diabetes: Post hoc pooled analysis of insulin-naïve participants from nine randomised clinical trials

AIMS

To assess insulin glargine 100 U/mL (IGlar-100) treatment outcomes according to newly-defined subgroups of type 2 diabetes mellitus (T2DM).

METHODS

Insulin-naïve T2DM participants (n = 2684) from nine randomised clinical trials initiating IGlar-100 were pooled and assigned to subgroups "Mild Age-Related Diabetes (MARD)", "Mild Obesity Diabetes (MOD)", "Severe Insulin Resistant Diabetes (SIRD)", and "Severe Insulin Deficient Diabetes (SIDD)", according to age at onset of diabetes, baseline HbA1c, BMI, and fasting C-peptide using sex-specific nearest centroid approach. HbA1c, FPG, hypoglycemia, insulin dose, and body weight were analysed at baseline and 24 weeks.

RESULTS

Subgroup distribution was MARD 15.3 % (n = 411), MOD 39.8 % (n = 1067), SIRD 10.5 % (n = 283), SIDD 34.4 % (n = 923). From baseline HbA1c 8.0-9.6% adjusted least square mean reductions after 24 weeks were similar between subgroups (1.4-1.5 %). SIDD was less likely to achieve HbA1c < 7.0 % (OR: 0.40 [0.29, 0.55]) than MARD. While the final IGlar-100 dose (0.36 U/kg) in MARD was lower than in other subgroups (0.46-0.50 U/kg), it had the highest hypoglycemia risk. SIRD had lowest hypoglycemia risk and SIDD exhibited greatest body weight gain.

CONCLUSIONS

IGlar-100 lowered hyperglycemia similarly in all T2DM subgroups, but level of glycemic control, insulin dose, and hypoglycemia risk differed between subgroups.

The Metabolic Switch of Physical Activity in Non-Obese Insulin Resistant Individuals

Healthy non-obese insulin resistant (IR) individuals are at higher risk of metabolic syndrome. The metabolic signature of the increased risk was previously determined. Physical activity can lower the risk of insulin resistance, but the underlying metabolic pathways remain to be determined. In this study, the common and unique metabolic signatures of insulin sensitive (IS) and IR individuals in active and sedentary individuals were determined. Data from 305 young, aged 20-30, non-obese participants from Qatar biobank, were analyzed. The homeostatic model assessment of insulin resistance (HOMA-IR) and physical activity questionnaires were utilized to classify participants into four groups: Active Insulin Sensitive (ISA, n = 30), Active Insulin Resistant (IRA, n = 20), Sedentary Insulin Sensitive (ISS, n = 21) and Sedentary Insulin Resistant (SIR, n = 23). Differences in the levels of 1000 metabolites between insulin sensitive and insulin resistant individuals in both active and sedentary groups were compared using orthogonal partial least square discriminate analysis (OPLS-DA) and linear models. The study indicated significant differences in fatty acids between individuals with insulin sensitivity and insulin resistance who engaged in physical activity, including monohydroxy, dicarboxylate, medium and long chain, mono and polyunsaturated fatty acids. On the other hand, the sedentary group showed changes in carbohydrates, specifically glucose and pyruvate. Both groups exhibited alterations in 1-carboxyethylphenylalanine. The study revealed different metabolic signature in insulin resistant individuals depending on their physical activity status. Specifically, the active group showed changes in lipid metabolism, while the sedentary group showed alterations in glucose metabolism. These metabolic discrepancies demonstrate the beneficial impact of moderate physical activity on high risk insulin resistant healthy non-obese individuals by flipping their metabolic pathways from glucose based to fat based, ultimately leading to improved health outcomes. The results of this study carry significant implications for the prevention and treatment of metabolic syndrome in non-obese individuals.

Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial

AIMS/HYPOTHESIS

Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673).

METHODS

Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection.

RESULTS

A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10^-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID.

CONCLUSIONS/INTERPRETATION

We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping.

TRIAL REGISTRATION

ORIGIN trial, ClinicalTrials.gov NCT00069784.