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PubMed2024

Multi-ancestry polygenic mechanisms of type 2 diabetes.

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Humans↗

PubMed2025

Proteomic signature of dementia risk in type 2 diabetes.

INTRODUCTION: Type 2 diabetes (T2D) significantly increases dementia risk, yet the molecular mechanisms underlying this association remain unclear. OBJECTIVES: This study aimed to identify protein signatures that distinguish dementia risk in T2D patients, develop a proteomic prediction model, and elucidate biological pathways connecting T2D and dementia. METHODS: We analyzed 2,920 plasma proteins from 52,958 participants (including 3,292 with T2D) in the UK Biobank Pharma Proteomics Project with a median follow-up of 14.6 years. Cox regression models with interaction terms identified T2D-specific protein associations with dementia risk. Machine learning models were developed to predict dementia in T2D patients. Pathway analysis and weighted gene co-expression network analysis identified biological mechanisms linking T2D and dementia. RESULTS: We identified 471 proteins with significant interaction effects between T2D and dementia risk. In non-T2D individuals, elevated levels of neuronal pentraxin receptor (NPTXR, HR = 0.74, 95 %CI:0.66-0.83) and carbonic anhydrase 14 (CA14, HR = 0.67, 95 %CI:0.60-0.75) were exclusively associated with decreased dementia risk. Conversely, in T2D patients, elevated rho guanine nucleotide exchange factor 12 (ARHGEF12, HR = 1.45, 95 %CI:1.10-1.91) was specifically associated with increased dementia risk. A 51-protein model accurately predicted 15-year dementia risk in T2D patients (AUC = 0.835, C-index = 0.829), outperforming conventional clinical risk scores and maintaining high accuracy for Alzheimer's disease and vascular dementia. Pathway analysis revealed enrichment of IL6-JAK-STAT3 signaling in T2D-related dementia, while dysregulation of fatty acid metabolism was specific to T2D-associated Alzheimer's disease. CONCLUSIONS: This large-scale proteomic analysis identifies specific molecular signatures that differentiate dementia risk in diabetic and non-diabetic populations, with potential applications for early risk stratification and targeted interventions. The identified pathways provide novel insights into the pathophysiological processes connecting T2D and dementia and suggest potential therapeutic targets.

Humans↗

PubMed2026

Refining the Genetic Contribution to Type 2 Diabetes Subtypes.

BACKGROUND: Type 2 diabetes (T2D) is a complex and highly heterogeneous disease driven in part by genetic predisposition and can be stratified into clinical subgroups to aid disease management. We recently grouped T2D subjects in the Qatar Biobank (QBB) cohort into Severe Insulin-Deficient Diabetes (SIDD), Severe Insulin-Resistant Diabetes (SIRD), Mild Obesity-Related Diabetes (MOD) and Mild Age-Related Diabetes (MARD) subtypes. Herein, we focused on the genetic makeup of these subtypes. METHODS: We used the QBB cohort (n = 13,808), of whom 2687 were with T2D, and comprehensively assessed polygenic risk scores (PGS) across T2D subtypes, investigated genetic loci associated with each subtype by leveraging the most recent and largest GWAS for T2D, evaluated SNP associations across T2D genetic clusters, and identified protein interaction pathways associated with these distinct T2D subtypes. RESULTS: MOD showed consistently lower PGS compared with other T2D subtypes across all tested scores. SIDD showed more associations with SNPs mapping to residual glycemic cluster compared with other T2D subtypes. The incremental analysis of PGS004838 demonstrated a high ΔAUC of 0.101 for SIDD and a moderate ΔAUC of 0.068 for SIRD, but not for MOD and MARD. Protein interaction analyses identified candidate subtype-associated gene networks linked to pathways related to glucose homeostasis in SIDD, insulin signalling and hepatic metabolism in SIRD, body fat distribution in MOD and vascular-related processes in MARD. CONCLUSION: We found heterogeneous genetic architectures across clinically defined T2D subtypes in a Middle Eastern population. Our findings provide evidence supporting differential polygenic burden, subtype genetic associations and subtype-associated biological pathways across T2D subtypes. These observations support the utility of subtype-based genetic analyses for improving biological understanding of T2D heterogeneity.

Humans↗

PubMed2024

Genomic variants associated with type 2 diabetes mellitus among Filipinos.

Type 2 diabetes mellitus leads to debilitating complications that affect the quality of life of many Filipinos. Genetic variability contributes to 30% to 70% of T2DM risk. Determining genomic variants related to type 2 diabetes mellitus susceptibility can lead to early detection to prevent complications. However, interethnic variability in risk and genetic susceptibility exists. This study aimed to identify variants associated with type 2 diabetes mellitus among Filipinos using a case-control design frequency matched for age and sex. A comparison was made between 66 unrelated Filipino adults with type 2 diabetes mellitus and 121 without. Genotyping was done using a candidate gene approach on genetic variants of type 2 diabetes mellitus and its complications involving allelic association and genotypic association studies with correction for multiple testing. Nine (9) significant variants, mostly involved in glucose and energy metabolism, associated with type 2 diabetes mellitus in Filipinos were found. Notably, a CDKAL1 variant (rs7766070) confers the highest level of risk while rs7119 (HMG20A) and rs708272 (CETP) have high risk allele frequencies in this population at 0.77 and 0.66, respectively, making them potentially good markers for type 2 diabetes mellitus screening. The data generated can be valuable in developing genetic risk prediction models for type 2 diabetes mellitus to diagnose and prevent the condition among Filipinos.

Humans↗

PubMed2025

Integrative metabolomic and proteomic analysis of diabetic kidney disease progression with younger-onset type 2 diabetes.

AIM: Younger-onset type 2 diabetes (YT2D) confers a disproportionately high risk of diabetic kidney disease (DKD), yet early biomarkers and underlying mechanisms remain poorly defined. We aimed to identify metabolites associated with DKD progression and integrate metabolomic and proteomic data to elucidate pathways involved in a multi-ethnic Asian cohort. MATERIALS AND METHODS: In this prospective study, 787 YT2D patients (diagnosed at ≤ age 40) were followed for a median of 5.7 years. DKD progression was defined as an annual decline in estimated glomerular filtration rate (eGFR) of ≥3 mL/min/1.73 m2 or ≥ 40% reduction in eGFR from baseline. Plasma metabolites were measured by nuclear magnetic resonance spectroscopy. Multivariable regression analysis was performed in a discovery (N = 550) and internal validation cohort (N = 237). Integrative metabolomic-proteomic analysis (N = 428) was performed using sparse partial least squares discriminant analysis (sPLS-DA). RESULTS: Ninety-eight metabolites were differentially expressed between DKD progressors and non-progressors, of which total branched-chain amino acids (BCAAs) (OR = 0.60, 95% CI 0.46-0.79), valine (OR = 0.62, 95% CI 0.48-0.81), and leucine (OR = 0.56, 95% CI 0.43-0.74) associated with DKD progression, independent of metabolic risk factors. Integrative analysis identified three components comprising 23 proteins and 30 metabolites, involved in the citrate cycle and apoptosis, which improved prediction of DKD progression beyond clinical risk factors (AUC 0.69-0.83). CONCLUSION: Lower plasma BCAA levels are independently associated with DKD progression in YT2D. Integrative multi-omics analysis highlights disruptions in metabolic and apoptotic pathways, providing insights into DKD pathophysiology and potential biomarkers for early risk stratification.

Humans↗

PubMed2024

Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes.

Type 2 diabetes (T2D) genome-wide association studies (GWASs) often overlook rare variants as a result of previous imputation panels' limitations and scarce whole-genome sequencing (WGS) data. We used TOPMed imputation and WGS to conduct the largest T2D GWAS meta-analysis involving 51,256 cases of T2D and 370,487 controls, targeting variants with a minor allele frequency as low as 5 × 10-5. We identified 12 new variants, including a rare African/African American-enriched enhancer variant near the LEP gene (rs147287548), associated with fourfold increased T2D risk. We also identified a rare missense variant in HNF4A (p.Arg114Trp), associated with eightfold increased T2D risk, previously reported in maturity-onset diabetes of the young with reduced penetrance, but observed here in a T2D GWAS. We further leveraged these data to analyze 1,634 ClinVar variants in 22 genes related to monogenic diabetes, identifying two additional rare variants in HNF1A and GCK associated with fivefold and eightfold increased T2D risk, respectively, the effects of which were modified by the individual's polygenic risk score. For 21% of the variants with conflicting interpretations or uncertain significance in ClinVar, we provided support of being benign based on their lack of association with T2D. Our work provides a framework for using rare variant GWASs to identify large-effect variants and assess variant pathogenicity in monogenic diabetes genes.

Diabetes Mellitus, Type 2↗

PubMed2026

Cardioprotective glucose-lowering drugs, statins, and secondary major adverse cardiovascular events: a nationwide cohort study of individuals with type 2 diabetes and cardiovascular disease.

BACKGROUND: In type 2 diabetes, cardioprotective glucose-lowering drugs, including sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists, and statins reduce the risk of secondary major adverse cardiovascular events. No trials examined the combination of these drugs as withholding statins in high-risk individuals would be unethical. We tested the hypothesis that cardioprotective glucose-lowering drug and statin combined is associated with lower risk of secondary major adverse cardiovascular events than either drug alone. METHODS: Individuals with type 2 diabetes and established cardiovascular disease from December 2012 through 2021 were identified via national Danish health registers. They were analyzed in an active comparator cohort including 15,404 individuals followed from treatment intensification with cardioprotective glucose-lowering drug or dipeptidyl peptidase-4 inhibitor and additionally in a time-varying cohort including 76,853 individuals with yearly updated treatment and covariate status. The treatment groups were: (i) no cardioprotective drug (no use of cardioprotective glucose-lowering drug or statin), (ii) cardioprotective glucose-lowering drug, (iii) statin, and (iv) cardioprotective glucose-lowering drug and statin. The primary outcome was a new major adverse cardiovascular event (myocardial infarction, stroke, or cardiovascular death). RESULTS: During mean 2.7 and 4.7 years of follow-up, 1,843 and 23,051 had major adverse cardiovascular events in the active comparator and time-varying cohorts. In the active comparator cohort, when compared to nonusers of cardioprotective glucose-lowering drug or statin, multivariable adjusted hazard ratios of major adverse cardiovascular events were 0.82 (95% confidence interval: 0.67 to 1.02) for cardioprotective glucose-lowering drug, 0.85 (0.74 to 0.97) for statin, and 0.71 (0.60 to 0.84) for cardioprotective glucose-lowering drug and statin combined. Corresponding hazard ratios in the time-varying cohort were 0.77 (0.69 to 0.86), 0.73 (0.70 to 0.75), and 0.57 (0.54 to 0.60), respectively. When restricting the active comparator cohort to individuals entering observation between 2019 and 2021 with reduced statistical power, the corresponding hazard ratios were 0.86 (0.57 to 1.31), 0.89 (0.60 to 1.30), and 0.79 (0.54 to 1.14), respectively. In both cohorts p for interaction between cardioprotective glucose-lowering drug and statin was > 0.05. CONCLUSIONS AND RELEVANCE: In individuals with type 2 diabetes and established cardiovascular disease, treatment with a cardioprotective glucose-lowering drug and statin in combination was associated with lower risk of secondary major adverse cardiovascular events than using either drug alone. This is important given the persistently suboptimal uptake of both drug classes in real-world practice. Some biases can never be completely excluded in real-world pharmacotherapy use studies such as this one, including confounding by indication, time-related or immortal time biases, and shifting standards of care, rendering causal interpretation unattainable; however, our results seemed consistent across numerous sensitivity analyses and designs.

Humans↗

PubMed2025

Identification of novel type 1 and type 2 diabetes genes by co-localization of human islet eQTL and GWAS variants with colocRedRibbon.

Over 1,000 genetic variants have been associated with diabetes by genome-wide association studies (GWASs), but for most, their functional impact is unknown; only 7% alter gene expression in pancreatic islets in expression quantitative trait locus (eQTL) studies. To fill this gap, we developed a co-localization pipeline, colocRedRibbon, that prefilters eQTLs by the direction of effect on gene expression and shortlists overlapping eQTL and GWAS variants prior to co-localization. Applying colocRedRibbon to recent diabetes and glycemic trait GWASs, we identified 292 co-localizing gene regions, including 24 co-localizations for type 1 diabetes and 268 for type 2 diabetes and glycemic traits, representing a 4-fold increase. A low-frequency type 2 diabetes protective variant increases islet MYO5C expression, and a type 1 diabetes protective variant increases FUT2 expression. These novel co-localizations advance the understanding of diabetes genetics and its impact on human islet biology. colocRedRibbon has broad applicability to co-localize GWASs and various QTLs.

Humans↗

PubMed2025

Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities.

Genomics can provide insight into the etiology of type 2 diabetes and its comorbidities, but assigning functionality to non-coding variants remains challenging. Polygenic scores, which aggregate variant effects, can uncover mechanisms when paired with molecular data. Here, we test polygenic scores for type 2 diabetes and cardiometabolic comorbidities for associations with 2,922 circulating proteins in the UK Biobank. The genome-wide type 2 diabetes polygenic score associates with 617 proteins, of which 75% also associate with another cardiometabolic score. Partitioned type 2 diabetes scores, which capture distinct disease biology, associate with 342 proteins (20% unique). In this work, we identify key pathways (e.g., complement cascade), potential therapeutic targets (e.g., FAM3D in type 2 diabetes), and biomarkers of diabetic comorbidities (e.g., EFEMP1 and IGFBP2) through causal inference, pathway enrichment, and Cox regression of clinical trial outcomes. Our results are available via an interactive portal ( https://public.cgr.astrazeneca.com/t2d-pgs/v1/ ).

Humans↗

PubMed2026

Multi-omic signatures of genetic mechanisms inform on type 2 diabetes biology and patient heterogeneity.

Type 2 diabetes (T2D) is a heterogeneous disease shaped by genetic pathways related to insulin resistance and β-cell dysfunction, but how this heterogeneity is reflected molecularly remains unclear. We integrated partitioned polygenic scores (pPS) with proteomic and metabolomic profiling to define molecular signatures of T2D and their clinical relevance. We analyzed UK Biobank participants with genomic, proteomic, and metabolomic data. In a disease-free training subset, we used LASSO regression to identify multi-omic signatures associated with each pPS by jointly modeling proteins and metabolites. In an independent testing set, we constructed multi-omic scores and examined their associations with clinical traits and diabetes-related outcomes. Mediation analyses were used to investigate putative causal pathways. Key findings were evaluated in the Multi-Ethnic Study of Atherosclerosis (MESA). We identified distinct multi-omic signatures that capture the molecular architecture of T2D genetic risk across physiological subtypes. Compared with genetic scores alone, multi-omic pPS showed larger effect sizes and better disease discrimination. These scores recapitulated subtype-specific physiology and were associated with T2D risk. The Beta-Cell 2 multi-omic score showed marked stratification for insulin use, which was replicated in MESA, where it also predicted future insulin use. Mediation analyses implicated lipoprotein remodeling and fatty acid metabolism in the Lipodystrophy 1 cluster, accounting for 30-45% of the total effect of pPS on T2D risk. Integrating process-specific genetic risk with circulating multi-omic profiles reveals biologically distinct endotypes of T2D and supports a framework for improved patient stratification and risk assessment.

Journal Article↗

PubMed2026

Efficacy and Safety of Once-Weekly Semaglutide 2.0 mg as an Add-On to Dose-Reduced Insulin Glargine versus Dose-Titrated Insulin Glargine in People With Type 2 Diabetes and Overweight (SUSTAIN OPTIMIZE).

AIMS: Type 2 diabetes (T2D) management with basal insulin can lead to hypoglycaemia and weight gain. SUSTAIN OPTIMIZE compared once-weekly semaglutide 2.0 mg as add-on to dose-reduced insulin glargine (Sema+IGlarreduced) versus dose-titrated IGlar (IGlartitrated) on glycated haemoglobin (HbA1c), body weight (BW), daily insulin dose, and participant satisfaction. MATERIALS AND METHODS: SUSTAIN OPTIMIZE was a 40-week, phase 3b, open-label, randomised study. Adults with T2D, overweight (body mass index ≥ 25 kg/m2), and treatment with basal insulin ≤ 40 units/day were randomised 1:1 into Sema+IGlarreduced or IGlartitrated. The primary endpoint was change in HbA1c using a non-inferiority approach. Secondary endpoints assessed superiority of Sema+IGlarreduced versus IGlartitrated in reducing HbA1c, BW, daily insulin dose, and improving Diabetes Treatment Satisfaction Questionnaire change version (DTSQc) scores. RESULTS: Overall, 573 participants were randomised. Sema+IGlarreduced achieved both non-inferiority and superiority versus IGlartitrated in HbA1c reduction (estimated treatment difference [ETD]: -0.74%; 95% confidence interval [CI95]: -0.90, -0.59) and superiority in BW change (ETD: -8.5 kg; CI95: -9.5, -7.4), relative daily insulin dose change (ETD: -121.9%; CI95: -143.1, -100.6), and DTSQc scores (ETD: 2.6; CI95: 1.6, 3.5) (p < 0.0001 for all endpoints). No new safety concerns were identified. Severe hypoglycaemia was reduced (rate ratio: 0.45; CI95: 0.23, 0.87; p = 0.02), while gastrointestinal events were higher for Sema+IGlarreduced (310 vs. 32 events). CONCLUSIONS: Once-weekly subcutaneous semaglutide 2.0 mg as add-on to dose-reduced IGlar achieved superior reductions in HbA1c, BW, and daily insulin dose in people with T2D and overweight, while reducing their risk for severe hypoglycaemia compared to dose-titrated IGlar alone.

Adult↗

PubMed2026

Disentangling Sex Differences in Sulfonylurea Drug Response With Genome-Wide Association Studies in Individuals With Type 2 Diabetes.

Sulfonylureas are a cornerstone of type 2 diabetes therapy despite interindividual variability in response. Despite well-documented sex-based differences, pharmacogenomic and genome-wide association studies (GWAS) have largely overlooked sex as a biological variable. We conducted the first sex-stratified GWAS of hemoglobin A1c (HbA1c) response to sulfonylureas in Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial participants (N = 871). Variants meeting genome-wide (P < 5.0 × 10-8) and suggestive (P < 5.0 × 10-6) significance were assessed for replication in the Pharmacogenomics of Metformin (PMET1) cohort. Replicated variants were further analyzed in the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) cohort to assess acute insulin and glucose responses to a single glipizide dose. Genome-wide significant loci with sex-specific effects were identified: KAZN, KIF2B, SLC39A10, and SPINK5 (combined-sex); CRACR2A, KCNK2, and TENM2 (male-only); and NACPH2 (female-only). Two suggestive variants in the TMEM64/NECAB1 locus, associated with reduced HbA1c response to sulfonylureas in the male-only ACCORD analysis, were directly replicated in the PMET1 male-only cohort. In SUGAR-MGH, one replicated variant (rs6471250-C) was significantly associated with reduced peak insulin in males (P = 0.035) but not females (P = 0.40), demonstrating sex-specific functional effects. This study identified statistically supported and biologically plausible loci with prior evidence linking nearby genes to pathways relevant to sulfonylurea action, including insulin secretion, insulin regulation/sensitivity, calcium signaling, potassium-channel biology, and glucose transport. The findings highlight sex-specific differences in sulfonylurea response, providing mechanistic insights and underscoring the importance of sex-specific precision medicine. Identification of genetic variants influencing sex-specific response could inform dosing to optimize sulfonylureas.

Humans↗

PubMed2026

Elucidating shared genetic signals between type 2 diabetes and three neurodegenerative dementia phenotypes.

Type 2 diabetes (T2D) and dementia frequently co-occur, yet the biological mechanisms underlying this comorbidity remain incompletely understood. Here, we systematically investigate shared genetic signals between T2D and three forms of neurodegenerative dementia (Alzheimer disease, Lewy body dementia, and sporadic frontotemporal dementia) using large-scale genome-wide association studies of clinically diagnosed individuals. We identify five genomic regions harboring shared association signals between T2D and at least one dementia subtype. Among these, the APOE locus was common to all dementia subtypes, whereas the remaining four loci (GBA, CRY2/PEX16/MAPK8IP1, INO80E, and NSF) were each shared exclusively between T2D and one dementia subtype. Integrating multi-omics data across several disease-relevant tissues and orthogonal lines of functional evidence, we prioritize 26 candidate genes through which these shared genetic loci potentially mediate their effect. Pathway enrichment highlights lipid and lipoprotein regulatory biology as a central shared axis. Mendelian randomization analyses using genetically regulated gene expression in relevant tissues indicate pleiotropic mechanisms with divergent phenotypic consequences. Our findings identify shared genetic loci between T2D and neurodegenerative dementia, revealing systemic metabolic-neurodegenerative trade-offs and highlighting key genes that underpin the comorbidity, providing a framework for improved understanding of age-related multi-morbidity.

Alzheimer disease↗

PubMed2026

Large-scale multi-omics enhance risk prediction for type 2 diabetes.

BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.

Humans↗

PubMed2026

Factors Associated With Progression From Level 1 to Level 2 Sensor-Detected Hypoglycaemia in People With Type 1 and Insulin-Treated Type 2 Diabetes: A Post Hoc Analysis From the Hypo-METRICS Study.

AIMS: We investigated the proportion of sensor-detected hypoglycaemic (SDH) events progressing to level 2, and associated variables. MATERIALS AND METHODS: We used data from Hypo-METRICS, which recruited people with type 1 (pwT1D) and insulin-treated type 2 diabetes (pwT2D) with ≥ 1 hypoglycaemic event in preceding 3 months, wearing blinded continuous glucose monitoring devices (CGM), additional to usual monitoring modality, and FitBits for 10 weeks. We defined: L1: SDH < 3.9 mmol/L for ≥ 15 min but ≥ 3.0 mmol/L; L2: SDH < 3.9 mmol/L with ≥ 15 min of sensor glucose < 3.0 mmol/L; L2 ratio = L2/(L1 + L2), restricted to participants with both event types during the study period. Associations of selected variables on L2 ratio was assessed using beta regression and purposeful variable selection. RESULTS: We analysed 23 768 SDH events from 212 pwT1D and 213 pwT2D. PwT1D were predominantly female (53% vs. 42% in T2D, p = 0.023) and younger (median age 49 vs. 62 years, p < 0.001), with a higher L2 ratio (16% vs. 14%, p = 0.03). Coefficient of variation (CV), OR = 1.07, 95% CI = 1.06-1.09 (p < 0.001), and mean sensor glucose, OR = 1.13, 95% CI = 1.08-1.18 (p < 0.001) were the principal predictors in T1D and T2D respectively; mean L1-SDH duration, weekly L1-SDH frequency, personal CGM usage, impaired awareness were not. Progression was higher during sleep than wakefulness (21% vs. 11%, p < 0.001); L2 ratios during sleep did not differ by awareness status. CONCLUSIONS: Approximately one sixth of SDH events progressed to L2, with a higher proportion in T1D than T2D. The principal determinants were CV in T1D and mean glucose in T2D. Awareness status was not associated with progression risk during sleep.

Humans↗

PubMed1992

Role of glucose and insulin resistance in development of type 2 diabetes mellitus: results of a 25-year follow-up study.

Type 2 diabetes mellitus is characterised by resistance of peripheral tissues to insulin and a relative deficiency of insulin secretion. To find out which is the earliest or primary determinant of disease, we used a minimum model of glucose disposal and insulin secretion based on intravenous glucose tolerance tests to estimate insulin sensitivity (SI), glucose effectiveness (ie, insulin-independent glucose removal rate, SG), and first-phase and second-phase beta-cell responsiveness in normoglycaemic offspring of couples who both had type 2 diabetes. 155 subjects from 86 families were followed-up for 6-25 years. More than 10 years before the development of diabetes, subjects who developed the disease had lower values of both SI (mean 3.2 [SD 2.4] vs 8.1 [6.7] 10(-3) I min-1 pmol-1 insulin; p < 0.0001) and SG (1.6 [0.9] vs 2.3 [1.2] 10(-2) min-1, p < 0.0001) than did those who remained normoglycaemic). For the subjects with both SI and SG below the group median, the cumulative incidence of type 2 diabetes during the 25 years was 76% (95% confidence interval 54-99). By contrast, no subject with both SI and SG above the median developed the disease. Subjects with low SI/high SG or high SI/low SG had intermediate risks. Insulin secretion, especially first phase, tended to be increased rather than decreased in this prediabetic phase and was appropriate for the level of insulin resistance. The development of type 2 diabetes is preceded by and predicted by defects in both insulin-dependent and insulin-independent glucose uptake; the defects are detectable when the patients are normoglycaemic and in most cases more than a decade before diagnosis of disease.

Adolescent↗

PubMed2026

A narrative review of what cohorts have taught us and how they have laid the foundation for much of our understanding of type 2 diabetes.

This narrative review provides a historical perspective on how observational research on type 2 diabetes has been developed and consolidated over the last 50 years and how well-designed cohort studies will provide us with knowledge for research and practice in the future and aid guideline development. We have included data from a large number of cohorts from every continent that have been used to study the development and/or progression of type 2 diabetes, including cohorts that are general population-based, disease-based, intervention-based and registry-based. We have structured the results from the past 50 years based on the following themes: diagnosis and screening, complications, risk factors and pathophysiology. We also discuss the strengths and weaknesses of observational research when compared with other research designs. Finally, we discuss the emerging and future directions for type 2 diabetes research using cohorts, which include novel developments, such as artificial intelligence, precision health and the exposome. We conclude that cohort research has significantly advanced our understanding of type 2 diabetes and aided guideline development, and complements experimental work, such as human randomised controlled trials and animal studies. Both approaches are essential and complementary in our pursuit to provide a more comprehensive understanding of the development and progression of type 2 diabetes, and to change dogma, practice and policies for better outcomes.

Humans↗

PubMed2026

Causality between genetically predicted type 2 diabetes and ankle fracture risk: A 2-sample Mendelian randomization study.

It has been proven that diabetes mellitus plays an important role in the occurrence and development of joint fractures. In this study, a 2-sample Mendelian randomization (MR) analysis was conducted to investigate the causal relationship between diabetes and ankle fractures. We pooled the data from the published genome-wide association studies. Diabetes mellitus type 2 was derived from pooled genome-wide association study data of 655,666 European individuals (61,714 patients and 1178 controls). Data on ankle fractures were derived from pooled genome-wide association study data in a total of 460,340 European individuals (6479 patients and 453,861 controls). Using diabetes-associated loci as instrumental variables, we used inverse variance weighting, MR-Egger, weighted median, simple multivariate analysis and weighted multivariate analysis to evaluate the association between diabetes and ankle fracture risk. Reverse MR analysis was performed on the Diabetes mellitus type 2 that were found to be causally associated with ankle fractures in forward MR analysis. Sensitivity analysis was used to evaluate the robustness of the results. Statistical analysis showed a significant causal relationship between diabetes and ankle fractures (inverse variance weighting: OR = 1.07, 95% CI = 1.01-1.32, P = .02). Diabetes mellitus is associated with an increased risk of ankle fracture. The results of MR analysis can be used as a guide for the screening of diabetes and ankle fractures, which is helpful to improve the awareness of screening, early diagnosis and early treatment.

Humans↗