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

Exploring the shared genetic landscape of diabetes and cardiovascular disease: findings and future implications

Diabetes is a rapidly growing global health concern projected to affect one in eight adults by 2045, which translates to roughly 783 million people. The profound metabolic alterations often present in dysglycaemia significantly increase the risk of cardiovascular complications. While genetic susceptibility plays a crucial role in diabetes and its vascular complications, identifying genes and molecular mechanisms that influence both diseases simultaneously has proven challenging. A key reason for this challenge is the pathophysiological heterogeneity underlying these diseases, with multiple processes contributing to different forms of diabetes and specific cardiovascular complications. This molecular heterogeneity has limited the effectiveness of large-scale genome-wide association studies (GWAS) in identifying shared underlying mechanisms. Additionally, our limited knowledge of the causal genes, cell types and disease-relevant states through which GWAS signals operate has hindered the discovery of common molecular pathways. This review highlights recent advances in genetic epidemiology, including studies of causal associations that have uncovered genetic and molecular factors influencing both dysglycaemia and cardiovascular complications. We explore how disease subtyping approaches can be critical in pinpointing the unique molecular signatures underlying both diabetes and cardiovascular complications. Finally, we address critical research gaps and future opportunities to advance our understanding of both diseases and translate these discoveries into tangible benefits for patient care and population health.

Lipid Subclasses Differentiate Insulin Resistance by Triglyceride-Glucose Index

BACKGROUND

Insulin resistance is a key driver of metabolic syndrome and related disorders, yet its underlying metabolic alterations remain incompletely understood. The Triglyceride-Glucose (TyG) index is an emerging, accessible marker for insulin resistance, with growing evidence supporting its clinical utility. This study aimed to characterize the metabolic profiles associated with insulin resistance using the TyG index in a large, population-based cohort, and to identify metabolic pathways potentially implicated in insulin resistance.

METHODS

Here, we conducted a cross-sectional study using data from the Qatar Biobank, including 1255 participants without diabetes classified as insulin-sensitive or insulin-resistant based on TyG index tertiles. Untargeted serum metabolomics profiling was performed using high-resolution mass spectrometry. Our statistical analyses included orthogonal partial least squares discriminate analysis and linear models.

RESULTS

Distinct metabolic signatures differentiated insulin-resistant from insulin-sensitive participants. Phosphatidylethanolamines, phosphatidylinositols, and phosphatidylcholines, were strongly associated with insulin resistance, while plasmalogens and sphingomyelins were consistently linked to insulin sensitivity.

CONCLUSIONS

Lipid-centric pathways emerge as potential biomarkers and therapeutic targets for the early detection and personalized management of insulin resistance and related metabolic disorders. Longitudinal studies are warranted to validate causal relationships.

A polygenic risk score derived from common variants of monogenic diabetes genes is associated with young-onset type 2 diabetes and cardiovascular-kidney complications

AIMS/HYPOTHESIS

Monogenic diabetes is caused by rare mutations in genes usually implicated in beta cell biology. Common variants of monogenic diabetes genes (MDG) may jointly influence the risk of young-onset type 2 diabetes (YOD, diagnosed before the age of 40 years) and cardiovascular and kidney events.

METHODS

Using whole-exome sequencing data, we constructed a weighted polygenic risk score (wPRS) consisting of 135 common variants (minor allele frequency >0.01) of 34 MDG based on r2>0.2 for linkage disequilibrium in a discovery case-control cohort of 453 adults with YOD (median [IQR] age 39.7 [34.9-46.9] years) and 405 without YOD (median [IQR] age 56.7 [50.3-61.0] years), followed by validation in an independent cross-sectional cohort with array-based genotyping for YOD and a prospective cohort of individuals with type 2 diabetes for cardiovascular and kidney events.

RESULTS

In the discovery cohort, the OR of the 135 common variants for YOD ranged from 1.00 to 2.61. In the validation cohort (920 YOD and 4910 non-YOD), top-10%-wPRS was associated with an OR of 1.42 (95% CI 1.03, 1.95, p=0.033) for YOD compared with bottom-10%-wPRS. In 2313 individuals with type 2 diabetes (median [IQR]: age 53.4 [45.4-61.7] years; disease duration 4.0 [1.0-9.0] years) observed for a median (IQR) of 17.5 (14.4-21.8) years, standardised wPRS was associated with increased HR for incident cardiovascular events (1.16 [95% CI 1.06, 1.27], p=0.001), kidney events (1.09 [95% CI 1.02, 1.16], p=0.013) and cardiovascular-kidney events (1.10 [95% CI 1.03, 1.16], p=0.003). Using the 'bottom-20%-wPRS plus baseline disease duration <5 years' group as referent, the 'top-20%-wPRS plus baseline disease duration 5 to <10 years' group had unadjusted and adjusted HR of 1.60 (95% CI 1.17, 2.19, p=0.003) and 1.62 (95% CI 1.16, 2.26, p=0.005), respectively, for cardiovascular-kidney events compared with 1.38 (95% CI 0.97, 1.98, p=0.075) and 1.06 (95% CI 0.72, 1.57, p=0.752) in the 'bottom-20%-wPRS plus baseline disease duration ≥10 years' group.

CONCLUSIONS/INTERPRETATION

Common variants of MDG increased risk for YOD and cardiovascular-kidney events.

Type 2 diabetes: a contemporary view from the Asian lens

PURPOSE OF REVIEW

The aim of this article was to review the up-to-date evidence with regards to the unique features of the Type 2 diabetes (T2D) pathophysiology, complications, response to therapy with the possibility of precision medicine guiding therapeutic decision making in Asia.

RECENT FINDINGS

Asia is the epicenter of diabetes. There have been marked advances with genotyping and phenotyping of the Asian patient with T2D, particularly with young onset diabetes where early beta cell failure and rapid progression of complications are more frequent. As Asians have lower muscle mass and higher adiposity, sarcopenia is increasingly associated with diabetes. Response to lifestyle and pharmacotherapy are generally similar, but unique features exist with different populations. Across Asia, use of guideline directed medical therapy for cardio-renal protection are recommended, but uptake of these newer agents are suboptimal and barriers exist with regards to standardized care.

SUMMARY

Although many similarities have been observed across Asia, due to the heterogeneity of populations within Asia, further research is required to streamline and pave the way towards precision medicine. There is an urgent need for region wide consensus to minimize barriers to diabetes care and stigma in diabetes terminology across Asia.

Clustering-based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks

AIMS/INTRODUCTION

This study aimed to identify low- and high-risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering-based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions.

MATERIALS AND METHODS

Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow-up data. K-means clustering was performed on prediabetes participants using significant features determined through logistic regression and LASSO. Cluster stability was assessed using mean Jaccard similarity, silhouette score, and the elbow method.

RESULTS

We identified two stable clusters representing high- and low-risk diabetes groups in both biobanks. The high-risk clusters showed higher diabetes incidence, with 15.7% in TWB and 13.0% in UKB, compared to 7.3% and 9.1% in the low-risk clusters, respectively. Notably, males were predominant in the high-risk groups, constituting 76.6% in TWB and 52.7% in UKB. In TWB, the high-risk group also exhibited significantly higher BMI, fasting glucose, and triglycerides, while UKB showed marginal significance in BMI and other metabolic indicators. Current smoking was significantly associated with increased diabetes risk in the TWB high-risk group (P < 0.001). Kaplan-Meier curves indicated significant differences in diabetes complication incidences between clusters.

CONCLUSIONS

UL effectively identified risk-specific groups within prediabetes populations, with high-risk groups strongly associated male gender, higher BMI, smoking, and metabolic markers. Tailored preventive strategies, particularly for young males in Taiwan, are crucial to reducing T2D risk.

Microbial and proteomic signatures of type 2 diabetes in an Arab population

BACKGROUND

The rising prevalence of Type 2 diabetes mellitus (T2D) in the Qatari population presents a significant public health challenge, highlighting the need for innovative approaches to early detection and management. While most efforts are centered on using blood samples for biomarker discovery, the use of saliva remains underexplored.

METHODS

Using noninvasive saliva samples from 2974 Qatari subjects, we analyzed the microbial communities from diabetic, pre-diabetic, and non-diabetic participants based on their HbA1C levels. The salivary microbiota was assessed in all subjects by sequencing the V1-V3 regions of 16S rRNA gene. For the proteomics profiling, we randomly selected 50 gender and age-matched non-diabetic and diabetic subjects and compared their proteome with SOMAscan. Microbiota and proteome profiles were then integrated to reveal candidate biomarkers for T2D.

RESULTS

Our results indicate that the salivary microbiota of pre-diabetic and diabetic individuals differs significantly from that of non-diabetic subjects. Specifically, a significant increase in the abundance of Campylobacter, Dorea, and Bacteroidales was observed in the diabetic subjects compared to their non-diabetic controls. Metabolic pathway prediction analysis for these bacteria revealed a significant overrepresentation of genes associated with fatty acid and lipid biosynthesis, as well as aromatic amino acid metabolism in the diabetic group. Additionally, we observed distinct differences in salivary proteomic profiles between diabetic and non-diabetic subjects. Notably, levels of Haptoglobin, Plexin-C1, and MCL-1 were elevated, while Osteopontin (SPP1), Histone1H3A (HIST3H2A), and Histone H1.2 were reduced in diabetic individuals. Furthermore, integrated correlation analysis of salivary proteome and microbiota data demonstrated a strong positive correlation between HIST1H3A and HIST3H2A with Porphyromonas sp., all of which were decreased in the diabetic group.

CONCLUSION

This is the first study to assess the salivary microbiota in T2D patients from a large cohort of the Qatari population. We found significant differences in the salivary microbiota of pre-diabetic and diabetic individuals compared to non-diabetic controls. Our study is also the first to assess the salivary proteome using SOMAScan in diabetic and non-diabetic subjects. Integration of the microbiota and proteome profiles revealed a unique signature for T2D that can be used as potential T2D biomarkers.

Longitudinal clinical and proteomic diabetes signatures in women with a history of gestational diabetes

We characterized the longitudinal serum protein signatures of women 6 and 10 years after having gestational diabetes mellitus (GDM) to identify factors associated with the development of type 2 diabetes mellitus (T2D) and prediabetes in this at-risk post-GDM population, aiming to discover potential biomarkers for early diagnosis and prevention of T2D. Our study identified 75 T2D-associated serum proteins and 23 prediabetes-associated proteins, some of which were validated in an independent T2D cohort. Machine learning (ML) performed on the longitudinal proteomics highlighted protein signatures associated with progression to post-GDM diabetes. We also proposed prognostic biomarker candidates that were differentially regulated in healthy participants at 6 years postpartum who later progressed to having T2D. Our longitudinal study revealed T2D risk factors for post-GDM populations who are relatively young and healthy, providing insights for clinical decisions and early lifestyle interventions.

An artificial metabzyme for tumour-cell-specific metabolic therapy

Metabolic dysregulation constitutes a pivotal feature of cancer progression. Enzymes with multiple metal active sites play a major role in this process. Here we report the first metabolic-enzyme-like FeMoO4 nanocatalyst, dubbed 'artificial metabzyme'. It showcases dual active centres, namely, Fe2+ and tetrahedral Mo4+, that mirror the characteristic architecture of the archetypal metabolic enzyme xanthine oxidoreductase. Employing spatially dynamic metabolomics in conjunction with the assessments of tumour-associated metabolites, we demonstrate that FeMoO4 metabzyme catalyses the metabolic conversion of tumour-abundant xanthine into uric acid. Subsequent metabolic adjustments orchestrate crosstalk with immune cells, suggesting a potential therapeutic pathway for cancer. Our study introduces an innovative paradigm in cancer therapy, where tumour cells are metabolically reprogrammed to autonomously modulate and directly interface with immune cells through the intervention of an artificial metabzyme, for tumour-cell-specific metabolic therapy.

Comorbidities confound metabolomics studies of human disease

The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC-MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the 14 common chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (93%, 219/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.

The impact of steatotic liver disease on coronary artery disease through changes in the plasma lipidome

Steatotic liver disease has been shown to associate with cardiovascular disease independently of other risk factors. Lipoproteins have been shown to mediate some of this relationship but there remains unexplained variance. Here we investigate the plasma lipidomic changes associated with liver steatosis and the mediating effect of these lipids on coronary artery disease (CAD). In a population of 2579 Swedish participants of ages 50 to 65 years, lipids were measured by mass spectrometry, liver fat was measured using computed tomography (CT), and CAD status was defined as the presence of coronary artery calcification (CAC score > 0). Lipids associated with liver steatosis and CAD were identified and their mediating effects between the two conditions were investigated. Out of 458 lipids, 284 were found to associate with liver steatosis and 19 of them were found to also associate with CAD. Two fatty acids, docosatrienoate (22:3n6) and 2-hydroxyarachidate, presented the highest mediating effect between steatotic liver disease and CAD. Other mediators were also identified among sphingolipids and glycerophospholipids, although their mediating effects were attenuated when adjusting for circulating lipoproteins. Further research should investigate the role of docosatrienoate (22:3n6) and 2-hydroxyarachidate as mediators between steatotic liver disease and CAD alongside known risk factors.

Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Urine complement proteins are associated with kidney disease progression of type 2 diabetes in Korean and American cohorts

Background

Mechanisms of progression of diabetic kidney disease (DKD) are not completely understood. This study uses untargeted and targeted mass spectrometry-based proteomics in two independent cohorts on two continents to decipher the mechanisms of DKD in patients with type 2 diabetes.

Methods

We conducted untargeted mass spectrometry on urine samples collected at the time of kidney biopsy from Korean patients with type 2 diabetes and biopsy-proven diabetic nephropathy at Seoul National University Hospital (SNUH-DN cohort; n = 64). These findings were validated using targeted mass spectrometry in urine samples from a Chronic Renal Insufficiency Cohort subgroup with type 2 diabetes and DKD (CRIC-T2D; n = 282). Urinary biomarkers/pathways associated with kidney disease progression (doubling of serum creatinine, ≥50% decrease in estimated glomerular filtration rates, or the development of end-stage kidney disease) were identified.

Results

SNUH-DN patients had an estimated glomerular filtration rate (eGFR) of 55 mL/min/1.73 m 2 (interquartile range [IQR], 44-75) and random urine protein-to-creatinine ratio of 3.1 g/g (IQR, 1.7-7.0). Urine proteins clustered into two groups, with cluster 2 having a 4.6-fold greater hazard (95% confidence interval [CI], 1.9-11.5) of disease progression than cluster 1 in multivariable-adjusted, time-to-event analyses. Proteins in cluster 2 mapped to 10 pathways, four of the top five of which were complement or complement-related. A high complement score, constructed from urine complement protein abundance, was strongly correlated to 4 of 5 histopathologic DN features and was associated with a 2.4-fold greater hazard (95% CI, 1.0-5.4) of disease progression than a low complement score. Targeted mass spectrometry of the CRIC-T2D participants, who had an eGFR of 42 mL/min/1.73 m 2 (IQR, 37-49) and 24-hr urine protein of 0.48 g (IQR, 0.10-1.87), showed that the complement score similarly segregated them into rapid and slow DKD progression groups. In both cohorts, the complement score had a linear association with disease progression.

Conclusions

Urinary proteomic profiling confirms the association between the complement pathway and rapid DKD progression in two independent cohorts. These results suggest a need to further investigate complement pathway inhibition as a novel treatment for DKD.

Proteomics profiling and association with cardiorenal complications in type 2 diabetes subtypes in Asian population

AIM

Among multi-ethnic Asians, type 2 diabetes (T2D) clustered in three subtypes; mild obesity-related diabetes (MOD), mild age-related diabetes with insulin insufficiency (MARD-II) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII) had differential cardio-renal complication risk. We assessed the proteomic profiles to identify subtype specific biomarkers and its association with diabetes complications.

METHODS

1448 plasma proteins at baseline were measured and compared across the T2D subtypes. Multivariable cox regression was used to assess associations between significant proteomics features and cardio-renal complications.

RESULTS

Among 645 T2D participants (SIRD-RII [19%], MOD [45%], MARD-II [36%]), 295 proteins expression differed significantly across the groups. These proteins were enriched in cell adhesion, neurogenesis and inflammatory response processes. In SIRD-RII group, ADH4, ACY1, THOP1, IGFBP2, NEFL, ENTPD2, CALB1, HAO1, CTSV, ITGAV, SCLY, EDA2R, ERBB2 proteins significantly associated with progressive CKD and LILRA5 protein with incident heart failure (HF). In MOD group, TAFA5, RSPO3, EDA2R proteins significantly associated with incident HF. In MARD-II group, FABP4 protein significantly associated with progressive CKD and PTPRN2 protein with major adverse cardiovascular events. Genetically determined NEFL and CALB1 were associated with kidney function decline.

CONCLUSIONS

Each T2D subtype has unique proteomics signature and association with clinical outcomes and underlying mechanisms.

Circulating Metabolite Profiles and Risk of Coronary Heart Disease Among Racially and Geographically Diverse Populations

BACKGROUND

Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations.

METHODS

Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings.

RESULTS

Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; P<0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD.

CONCLUSIONS

In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.

European and African-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites

Initially focused on the European population, multiple genome-wide association studies (GWAS) of complex diseases, such as type-2 diabetes (T2D), have now extended to other populations. However, to date, few ancestry-matched omics datasets have been generated or further integrated with the disease GWAS to nominate the key genes and/or molecular traits underlying the disease risk loci. In this study, we generated and integrated plasma proteomics and metabolomics with array-based genotype datasets of European (EUR) and African (AFR) ancestries to identify ancestry-specific muti-omics quantitative trait loci (QTLs). We further applied these QTLs to ancestry-stratified T2D risk to pinpoint key proteins and metabolites underlying the disease-associated genetic loci. We nominated five proteins and four metabolites in the European group and one protein and one metabolite in the African group to be part of the molecular pathways of T2D risk in an ancestry-stratified manner. Our study demonstrates the integration of genetic and omic studies of different ancestries can be used to identify distinct effector molecular traits underlying the same disease across diverse populations. Specifically, in the AFR proteomic findings on T2D, we prioritized the protein QSOX2; while in the AFR metabolomic findings, we pinpointed the metabolite GlcNAc sulfate conjugate of C21H34O2 steroid. Neither of these findings overlapped with the corresponding EUR results.

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.