Diabetes Care

Aims/hypothesisGiven the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. MethodsUsing publicly available type 2 diabetes summary statistics from a genomewide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate<0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined colocalisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a colocalisation posterior probability of [&ge;]0.9 was considered a genuine shared association with both diseases. ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability [&ge;]0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (PGM1). In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect. Conclusions/interpretationThat four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases. Research in ContextWhat is already known about this subject? O_LIOther than insulin, there are currently no treatments for both type 1 and type 2 diabetes. C_LIO_LIFindings that genetic variants near the GLIS3 gene increase risk of both type 1 and type 2 diabetes have indicated shared genetic mechanisms at the level of the pancreatic {beta} cell. C_LI What is the key question? O_LIBy examining chromosome regions associated with both diseases, are there any more variants that affect risk of both diseases and could support common mechanisms and repositioning of therapeutics between the diseases? C_LI What are the new findings? O_LIAt current sample sizes, there is evidence that five genetic variants in different chromosome regions impact risk of developing both diseases. C_LIO_LIHowever, four of these variants have the opposite direction of effect in type 1 diabetes compared to type 2 diabetes, with only one, near GLIS3, having a concordant direction of effect. C_LI How might this impact on clinical practise in the foreseeable future? O_LIGenetic findings have furthered research in type 1 and type 2 diabetes independently, and suggest therapeutic strategies. However, our current investigation into their shared genetics suggests that repositioning of current type 2 diabetes treatments into type 1 diabetes may not be straightforward. C_LI

Metabolic dysregulation in multiple tissues alters glucose homeostasis and influences risk for type 2 diabetes (T2D). To identify pathways and tissues influencing T2D-relevant glycemic traits (fasting glucose [FG], fasting insulin [FI], two-hour glucose [2hGlu] and glycated hemoglobin [HbA1c]), we investigated associations of exome-array variants in up to 144,060 individuals without diabetes of multiple ancestries. Single-variant analyses identified novel associations at 21 coding variants in 18 novel loci, whilst gene-based tests revealed signals at two genes, TF (HbA1c) and G6PC (FG, FI). Pathway and tissue enrichment analyses of trait-associated transcripts confirmed the importance of liver and kidney for FI and pancreatic islets for FG regulation, implicated adipose tissue in FI and the gut in 2hGlu, and suggested a role for the non-endocrine pancreas in glucose homeostasis. Functional studies demonstrated that a novel FG/FI association at the liver-enriched G6PC transcript was driven by multiple rare loss-of-function variants. The FG/HbA1c-associated, islet-specific G6PC2 transcript also contained multiple rare functional variants, including two alleles within the same codon with divergent effects on glucose levels. Our findings highlight the value of integrating genomic and functional data to maximize biological inference.\n\nHighlightsO_LI23 novel coding variant associations (single-point and gene-based) for glycemic traits\nC_LIO_LI51 effector transcripts highlighted different pathway/tissue signatures for each trait\nC_LIO_LIThe exocrine pancreas and gut influence fasting and 2h glucose, respectively\nC_LIO_LIMultiple variants in liver-enriched G6PC and islet-specific G6PC2 influence glycemia\nC_LI

BackgroundMaturity-onset diabetes of the young (MODY) is a heterogeneous group of monogenic disorders of impaired glucose-stimulated insulin secretion (GSIS). Mechanisms include {beta}-cell KATP channel dysfunction (e.g., KCNJ11 (MODY13) or ABCC8 (MODY12) mutations); however, no other {beta}-cell channelopathies have been identified in MODY. MethodsA four-generation family with autosomal dominant non-obese, non-ketotic antibody-negative diabetes, without mutations in known MODY genes, underwent exome sequencing. Whole-cell and single-channel K+ currents, Ca2+ handling, and GSIS were determined in cells expressing either mutated or wild-type (WT) protein. ResultsWe identified a novel non-synonymous genetic mutation in KCNK16 (NM_001135105: c.341T>C, p.Leu114Pro) segregating with MODY. KCNK16 is the most abundant and {beta}-cell-restricted K+ channel transcript and encodes the two-pore-domain K+ channel TALK-1. Whole-cell K+ currents in transfected HEK293 cells demonstrated drastic (312-fold increase) gain-of-function with TALK-1 Leu144Pro vs. WT, due to greater single channel activity. Glucose-stimulated cytosolic Ca2+ influx was inhibited in mouse islets expressing TALK-1 Leu114Pro (area under the curve [AUC] at 20mM glucose: Leu114Pro 60.1 vs. WT 89.1; P=0.030) and less endoplasmic reticulum calcium storage (cyclopiazonic acid-induced release AUC: Leu114Pro 17.5 vs. WT 46.8; P=0.008). TALK-1 Leu114Pro significantly blunted GSIS compared to TALK-1 WT in both mouse (52% decrease, P=0.039) and human (38% decrease, P=0.019) islets. ConclusionsOur data identify a novel MODY-associated gene, KCNK16; with a gain-of-function mutation limiting Ca2+ influx and GSIS. A gain-of-function common polymorphism in KCNK16 is associated with type 2 diabetes (T2DM); thus, our findings have therapeutic implications not only for KCNK16-associated MODY but also for T2DM.

ObjectivesTo cluster participants with prediabetes with five type 2 diabetes (T2D)-related partitioned polygenetic risk scores (pPRSs) and examine the risk of incident diabetes and the benefit of adherence to healthy lifestyle across clusters. DesignProspective cohort study SettingHispanic Community Health Study/Study of Latinos (HCHS/SOL), US; UK Biobank (UKBB), UK. Participants7,227 US Hispanic/Latinos without diabetes from HCHS/SOL, including 3,677 participants with prediabetes. 400,149 non-Hispanic whites without diabetes from UKBB, including 16,284 participants with prediabetes. Main outcome measuresPrediabetes was defined by fasting plasma glucose (fasting glucose) between 100-125 mg/dL, 2-hour oral glucose tolerance test (OGTT 2h glucose) between 140-199 mg/dL, or hemoglobin A1c (HbA1c) between 5.7% and 6.5%. Diabetes was defined by fasting glucose levels [&ge;]126 mg/dL, 2h glucose after OGTT [&ge;]200 mg/dL, HbA1c [&ge;]6.5%, current use of anti-diabetic medications, or medical record. Five pPRSs representing various pathways related to T2D were calculated based on 94 T2D-related genetic variants. Health lifestyle score was assessed with five modifiable risk factors, including body mass index (BMI), smoking, alcohol drinking, physical activity, and diet for T2D. ResultsUsing K-means consensus clustering on five pRPSs, six clusters of individuals with prediabetes were identified in HCHS/SOL, with each cluster presenting disparate patterns of pPRSs and different patterns of metabolic traits. Except cluster 3 which was not detected, the other five clusters were conformed in participants with prediabetes in UKBB, with each cluster showing the similar patterns of pPRSs to their corresponding cluster in HCHS/SOL. At baseline, proportion of impaired glucose tolerance (IGT)/impaired fasting glucose (IFG) and glycemic traits in HCHS/SOL (fasting glucose, OGTT 2h glucose, and HbA1c) were not significantly different across six clusters (P=0.13, P=0.62, P=0.35, P=0.96, respectively). In UKBB, random glucose and HbA1c at baseline did not show significant difference across five clusters (P=0.43, P=0.71, respectively). Although baseline glycemic traits were similar across clusters, cluster 6, which featured a very low proinsulin score, exhibited elevated risk of incident T2D in both cohorts (risk ratio [RR]=1.39, 95% confidence interval [95% CI]=[1.10, 1.76] vs. cluster 1 in HCHS/SOL; hazard ratio [HR]=1.29, 95% CI=[1.00, 1.69] vs. cluster 1 in UKBB; Combined RR/HR=1.34 [1.13, 1.60]). To explain the elevated risk of incident T2D in cluster 6, interactions between proinsulin score and other three pPRSs (Beta-cell score, Lipodystrophy-like score, Liver-lipid score) and sum score were detected (P for interaction=0.001, 0.04, 0.02 and 0.002, respectively). Cluster 5 showed an increased risk of incident T2D in UKBB (HR=1.35 [1.05, 1.75] vs. cluster 1) and in the combined analysis with HCHS/SOL (RR/HR=1.29 [1.08, 1.53]), although its risk of T2D was not significantly different from cluster 1 in HCHS/SOL (RR=1.23 [0.96, 1.57]). Inverse associations between the lifestyle score and risk of T2D were observed across different clusters, with a suggestively stronger association in Cluster 5 compared to Cluster 1, in both cohorts. Cluster 5 showed reduced risk of incident diabetes caused by healthy lifestyle score (RR=0.65 [0.47, 0.89], HR=0.71 [0.62, 0.81], respectively. Combined RR/HR=0.70 [0.62, 0.79]). Among individuals with a healthy lifestyle, those in Cluster 5 had a similar risk of T2D compared to those in Cluster 1 (combined RR/HR=1.03 [0.91-1.18], P>0.05). ConclusionsThis study identified genetic subtypes of prediabetes which differed in risk of progression to T2D, with two subtypes showing relatively high risk of T2D over time. Favorable relationship between healthy lifestyle and risk of T2D was observed, regardless of their genetic subtypes. Participants in one subtype with higher risk of T2D may realize extra benefits in terms of risk reduction from a healthy lifestyle.

Background Glycated haemoglobin (HbA1c) underpins type 2 diabetes (T2D) and prediabetes management worldwide and reflects both glycaemia and erythrocyte biology. A missense variant in PIEZO1 (rs563555492T), carried by 1 in 12 South Asians, has been associated with a nonglycaemic reduction in HbA1c. We aimed to further characterise this association and evaluate its clinical consequences. Methods We undertook genetic and linked health data analyses across two cohorts: 19,898 (37.4% female) South Indians from the Madras Diabetes Research Foundation (MDRF) and 43,011 (54.4% female) British Bangladeshis and British Pakistanis in Genes & Health. In MDRF, we tested associations with glycaemic and erythrocytic traits using additive genetic models. In Genes & Health we modelled diagnosis of prediabetes, T2D, and diabetic eye disease using flexible parametric survival models. Ten-year absolute risks were estimated for a population aged 40-50 years. Findings PIEZO1 rs563555492T was associated with erythrocytic traits and lower HbA1c, but not with fasting glucose, postprandial glucose, or C-peptide. This variant reduced risk of prediabetes (HR 0.63, 95% CI 0.58-0.69) and T2D (0.85, 0.78-0.93) diagnosis, and increased risk of diabetic eye disease among individuals with T2D (1.20, 1.01-1.43). Modelling suggested approximately 1,019 missed prediabetes and 303 missed T2D diagnoses per 100,000 adults over 10 years. Interpretation An ancestry-enriched PIEZO1 variant is associated with lower HbA1c independent of glycaemia, reduced prediabetes and T2D diagnosis suggesting delayed detection, and increased complication risk. Reliance on HbA1c may systematically underestimate glycaemic risk in a substantial minority of South Asians. Funding The Wellcome Trust; NIHR

Although hyperlipidemia is traditionally considered a risk factor for type-2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL-C increases T2D risk. We thus sought to comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that LDL-C was negatively associated with T2D (OR=0.43[0.41, 0.45] per mmol/L unit of LDL-C), despite positive associations of LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n=431,167) and the GLGC consortium (n=188,577), and T2D from the DIAGRAM consortium (n=898,130). We identified 31 loci associated with lower LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this phenotype, and 9 have previously been implicated in non-alcoholic fatty liver disease. Finally, two-sample Mendelian randomization analyses suggest that low LDL-C causes T2D, although causal interpretations are challenging due to pleiotropy. Our findings extend our current understanding of the higher T2D risk among individuals with low LDL-C, and of the underlying mechanisms, including those underlying the diabetogenic effect of LDL-C-lowering medications.

AimTo clarify whether SARS-CoV-2 infection or vaccination contribute to risk of type 1 diabetes or more severe diabetes onset in children and young adults. MethodsWe analysed cohorts of population-wide registries of young individuals from Norway (N=1,986,970) and Sweden (N=2,100,188). We used regression models to estimate adjusted rate ratios (aRR), treating exposures as time-varying, starting 30 days after registered SARS-CoV-2 positive test or vaccination. FindingsPooled results from Norway and Sweden and age-groups 12-17 and 18-29 years showed no significant increase in type 1 diabetes after documented infections (aRR 1.06, 95%CI:0.77-1.45). There was moderate heterogeneity, with a suggestive increased risk among children in Norway after infection. Pooled results for Norway and Sweden and age-groups 12-17 years and 18-29 years showed no significant association between SARS-CoV-2 vaccination and risk of type 1 diabetes (aRR 1.09, 95%CI: 0.81, 1.48). There was significant heterogeneity, primarily driven by a positive association among children and an inverse association in young adults in Sweden. While the type 1 diabetes incidence increased and diabetes ketoacidosis decreased over time during 2016-2023, no significant break in time-trends were seen after March 2020 for HbA1c, risk or severity of diabetic ketoacidosis, or islet autoantibodies, at diagnosis of type 1 diabetes. InterpretationTaken together, these results do not indicate any consistent, large effects of SARS-CoV-2 infection or -vaccination on risk of type 1 diabetes or severity at disease onset. Suggestive associations in sub-groups should be investigated further in other studies. FundingThe work was done as part of regular work at the institutions where the authors had their primary affiliation, and no specific funding was obtained for these studies.

BackgroundType 2 diabetes (T2DM) incidence is increased after diagnosis of COVID-19. The impact of vaccination on this increase, for how long it persists, and the effect of COVID-19 on other types of diabetes remain unclear. MethodsWith NHS England approval, we studied diabetes incidence following COVID-19 diagnosis in pre-vaccination (N=15,211,471, January 2020-December 2021), vaccinated (N =11,822,640), and unvaccinated (N=2,851,183) cohorts (June-December 2021), using linked electronic health records. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence post-COVID-19 diagnosis with incidence before or without diagnosis up to 102 weeks post-diagnosis. Results were stratified by COVID-19 severity (hospitalised/non-hospitalised) and diabetes type. FindingsIn the pre-vaccination cohort, aHRS for T2DM incidence after COVID-19 (compared to before or without diagnosis) declined from 3.01 (95% CI: 2.76,3.28) in weeks 1-4 to 1.24 (1.12,1.38) in weeks 53-102. aHRS were higher in unvaccinated than vaccinated people (4.86 (3.69,6.41)) versus 1.42 (1.24,1.62) in weeks 1-4) and for hospitalised COVID-19 (pre-vaccination cohort 21.1 (18.8,23.7) in weeks 1-4 declining to 2.04 (1.65,2.51) in weeks 52-102), than non-hospitalised COVID-19 (1.45 (1.27,1.64) in weeks 1-4, 1.10 (0.98,1.23) in weeks 52-102). T2DM persisted for 4 months after COVID-19 for [~]73% of those diagnosed. Patterns were similar for Type 1 diabetes, though excess incidence did not persist beyond a year post-COVID-19. InterpretationElevated T2DM incidence after COVID-19 is greater, and persists longer, in hospitalised than non-hospitalised people. It is markedly less apparent post-vaccination. Testing for T2DM after severe COVID-19 and promotion of vaccination are important tools in addressing this public health problem. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for population-based observational studies published between December 1st 2019 and July 12th 2023 examining associations between SARS-CoV-2 infection or COVID-19 diagnosis (search string: SARS-CoV-2 or COVID* or coronavirus*) and subsequent incident diabetes (search term: diabetes). Of nineteen relevant studies; eight had a composite outcome of diabetes types, six stratified by diabetes type and five pertained to type-1-diabetes (T1DM) only. We did not identify any studies relating to gestational or other types of diabetes. Eleven studies were from the US, three from the UK, two from Germany, one from Canada, one from Denmark and one from South Korea. Most studies described cumulative relative risks (for infection versus no infection) one to two years post-SARS-CoV-2 infection of 1.2 to 2.6, though four studies found no associations with T1DM after the post-acute period. All studies lacked the power to compare diabetes relative risk by type, severity, and vaccination status in population subgroups. One study examined relative risks by vaccination status, but this used a composite outcome of diabetes and hyperlipidaemia and was conducted in a predominantly white male population. Two studies of T1DM found no evidence of elevated risk beyond 30 days after COVID-19 diagnosis, whilst two reported elevated risks at six months. Two studies of type 2 diabetes (T2DM) examined relative risks by time period post-infection: one study of US insurance claims reported a persistent association six months post-infection, whereas a large UK population-based study reported no associations after 12 weeks. However, the latter study used only primary care data, therefore COVID-19 cases were likely to have been under-ascertained. No large studies have investigated the persistence of diabetes diagnosed following COVID-19; key to elucidating the role of stress/steroid-induced hyperglycaemia. Added value of this studyThis study, which is the largest to address the question to date, analysed linked primary and secondary care health records with SARS-CoV-2 testing and COVID-19 vaccination data for 15 million people living in England. This enabled us to compare the elevation in diabetes incidence after COVID-19 diagnosis by diabetes type, COVID-19 severity and vaccination status, overall and in population subgroups. Importantly, excess diabetes incidence by time period since infection could also be quantified. Since healthcare in the UK is universal and free-at-the-point-of-delivery, almost the entire population is registered with primary care. Therefore the findings are likely to be generalisable. We found that, before availability of COVID-19 vaccination, a COVID-19 diagnosis (vs. no diagnosis) was associated with increased T2DM incidence which remained elevated by approximately 30% beyond one year after diagnosis. Though still present (with around 30% excess incidence at eight weeks), these associations were substantially attenuated in unvaccinated compared with vaccinated people. Excess incidence was greater in people hospitalised with COVID-19 than those who were not hospitalised after diagnosis. T1DM incidence was elevated up to, but not beyond, a year post COVID-19. Around 73% of people diagnosed with incident T2DM after COVID-19 still had evidence of diabetes four months after infection. Implications of all the available evidenceThere is a 30-50% elevated T2DM incidence post-COVID-19, but we report the novel finding that there is elevated incidence beyond one-year post-diagnosis. Elevated T1DM incidence did not appear to persist beyond a year, which may explain why previous studies disagree. For the first time in a general-population dataset, we demonstrate that COVID-19 vaccination reduces, but does not entirely ameliorate, excess diabetes incidence after COVID-19. This supports a policy of universal vaccination and suggests that other public health activities, such as enhanced diabetes screening after severe COVID-19, may be warranted, particularly in unvaccinated people.

ObjectiveTo define phenotypes of gastric myoelectrical abnormalities and relation to symptoms in people with longstanding T1D, compared to matched healthy controls, using a novel non-invasive body surface gastric mapping (BSGM) device. Research design and methodsBSGM was performed on people with T1D of >10 years duration and matched controls, employing Gastric Alimetry (Alimetry, New Zealand), comprising a high-resolution 64-channel array, validated symptom logging App, and wearable reader. Results32 people with T1D were recruited (15 with a high symptom burden), and 32 controls. Those with symptoms showed more unstable gastric myoelectrical activity, (Gastric Alimetry Rhythm Index 0.39 vs 0.51, p=0.017; and lower average spatial covariance 0.48 vs 0.51, p=0.009) compared with controls. Those with T1D and symptoms also had higher prevalence of peripheral neuropathy (67% vs 6%, p=0.001), anxiety/depression diagnoses (27% vs 0%, p=0.001), and mean HbA1c levels (76 vs 56 mmol/mol, p<0.001). BSGM defined distinct phenotypes in participants including those with markedly unstable gastric rhythms (4/32, 12.5%), and abnormally high gastric frequencies (10/32, 31%). Deviation in gastric frequency was positively correlated with symptoms of bloating, upper gut pain, nausea and vomiting, and fullness and early satiation (r>0.35, p<0.05) ConclusionGastroduodenal symptoms in people with longstanding T1D correlate with gastric myoelectrical abnormalities on BSGM evaluation, in addition to glycemic control, psychological comorbidities, and peripheral neuropathy. BSGM using the Gastric Alimetry device identified a range of myoelectrical phenotypes, representing both myogenic and neurogenic mechanisms, which represent targets for diagnosis, monitoring and therapy.

ImportanceTwenty-three percent of 37.3M adults in the USA with diabetes are estimated to be undiagnosed, leading to potentially avoidable sequelae and morbidity. ObjectiveTo explore the utility of a polygenic risk score (PRS) at identifying individuals with undiagnosed diabetes and prediabetes. Design, Setting and ParticipantsIndividuals without doctor-diagnosed diabetes at study baseline in the UK Biobank (UKB) with HbA1c and BMI measurements. Participants were restricted to white individuals to use an ancestry-appropriate PRS. Undiagnosed diabetes and prediabetes were defined using HbA1c ([&ge;]6.5% and [&ge;]5.7 - <6.5%, respectively). ExposuresA diabetes PRS comprising 13,863 SNPs derived from the 23andMe Research Cohort, and measured BMI among UKB participants. ResultsOf 412,439 individuals self-reporting an absence of diagnosed diabetes and who had BMI and HbA1c measurements at baseline, 2,934 (0.7%) had undiagnosed diabetes, representing 11.9% of all (diagnosed and undiagnosed) diabetes. Nearly half (1,362, 46%) of undiagnosed diabetes cases were among individuals in the top 25% of the PRS distribution. Overweight individuals (BMI [&ge;]25 - <30 kg/m2) who were in the top 12.5% of the PRS distribution had a similar frequency of undiagnosed diabetes (0.8-1.6% frequency) as individuals with obesity (BMI [&ge;]30kg/m2) in the lowest 12.5% of the PRS distribution (0.7-1.7% frequency). Combining overweight and obesity with the PRS identified nearly all cases of undiagnosed diabetes: individuals with a BMI [&ge;]25 kg/m2 (66% of the study population) or those in the top 54-69% of the PRS identified 98-99% of undiagnosed cases. Of the 199 undiagnosed diabetes cases occurring among individuals with a normal BMI (<25kg/m2), two-thirds were among individuals in the top 50% of the PRS. Prediabetes was common (14%), with measured BMI and PRS providing additive risk. Among those in the top 12.5% PRS with BMI [&ge;]35kg/m2, 6.3% developed incident diabetes over 4 years follow-up, as compared to 0% among the bottom 12.5% PRS with BMI<25kg/m2. ConclusionsA diabetes PRS is informative at identifying undiagnosed cases. PRS may have broader utility in detecting individuals with asymptomatic disease. Key PointsO_ST_ABSQuestionC_ST_ABSDoes a polygenic risk score (PRS) have utility in identifying individuals with undiagnosed type 2 diabetes (T2D)? FindingsIn this analysis of 412,439 individuals without doctor-diagnosed diabetes, a T2D PRS performed additively to body mass index (BMI) at identifying individuals with undiagnosed diabetes. Selecting individuals on the basis of overweight/obesity or a T2D PRS identified almost all cases of undiagnosed diabetes. The majority of undiagnosed diabetes cases among individuals with normal weight occurred among those at elevated polygenic risk. MeaningA T2D PRS identifies cases of undiagnosed diabetes among individuals with and without overweight or obesity.

AimsGlycated Hemoglobin A1c (HbA1c) is widely used for the diagnosis and management of type 2 diabetes mellitus (T2D), with regular testing in primary care recommended every three to six months. We aimed to identify distinct, long-term HbA1c trajectories following a T2D diagnosis and investigate how these glycaemic control trajectories were associated with health-related traits and T2D complications. MethodsA cohort of 12,435 unrelated individuals of European ancestry with T2D was extracted from the UK Biobank data linked to primary care records. Latent class growth mixture modelling was applied to identify classes with similar HbA1c trajectories over the 10-years following T2D diagnosis. We tested for associations of HbA1c class membership with sociodemographic factors, biomarkers, polygenic scores, and T2D-related outcomes, using logistic regression and Cox proportional hazards models. ResultsSix HbA1c trajectory classes were identified. The largest class (76.8%) maintained low and stable HbA1c levels over time. The other five classes demonstrated higher and more variable trajectories and included: two with parabolic shapes (starting low and distinguished by the height of their peaks), two with high initial HbA1c levels that declined over time (one rapidly, one slowly), and one class with a rapid increase in HbA1c five years after diagnosis. Younger age at T2D diagnosis, higher fasting glucose levels, higher random glucose levels, and higher body mass index polygenic score were associated with membership of these five classes. These classes were also more likely to be prescribed glucose-lowering medication at diagnosis and had fewer primary care visits in the month and year prior to diagnosis. Relative to the low and stable class, these five showed increased risks of T2D complications, including stroke (HR=1.55 [1.31-1.84]), kidney disease (HR=1.39 [1.27-1.53]), all-cause mortality (HR=1.36 [1.23-1.51]), and progression to combination therapy (HR=3.22 [3.04-3.41]) or insulin (HR=3.21 [2.89-3.55]). ConclusionIndividuals with T2D who show higher and more variable HbA1c trajectories are at increased risk of developing T2D-related complications. Early identification of patients at risk, based on factors such as age at diagnosis and previous healthcare utilisation could improve patient outcomes.

Aims/HypothesisGCK-MODY (Glucokinase-Maturity Onset Diabetes of the Young) causes lifelong, mild hyperglycaemia with high penetrance. Variation in glycaemic phenotype among carriers remains unexplained. We hypothesised that polygenic background contributes to this variability. MethodsTo test whether polygenic background contributes to the GCK-MODY clinical phenotype, we analysed polygenic risk scores (PGS) for nine diabetes-related traits in 901 clinically referred individuals with GCK-MODY. We compared these to 7,645 non-diabetic controls and assessed associations between PGSs and glycaemic measures. Additionally, we evaluated 158 unselected GCK variant carriers from the UK Biobank to examine polygenic effects independent of clinical referral. ResultsWe observed independent polygenic enrichment for HbA1c (including both glycaemic and non- glycaemic components), fasting glucose, and type 2 diabetes in clinically referred GCK-MODY individuals (0.16-0.33 SD higher, all P < 0.003), but not for type 1 diabetes. In contrast, no such enrichment was seen in GCK pathogenic variant carriers from a clinically unselected population- based cohort. In both settings, HbA1c PGSs were associated with measured HbA1c levels in GCK carriers ({beta} = 0.91- 0.97, all P < 0.009), with effect sizes similar to those in non-carriers. GCK-MODY cases in the top HbA1c quintile had a 3-to-6-fold risk of exceeding the diabetes diagnostic HbA1c threshold ([&ge;] 48 mmol/mol) in clinically selected and clinically unselected cohort respectively. Conclusions/interpretationOur findings suggest that polygenic background and GCK variants interact to modify the glycaemic expression of GCK-MODY, influencing clinical diagnosis despite high penetrance. Our study highlights the importance of integrating both monogenic and polygenic factors to better understand phenotypic variability in monogenic diseases. Research in ContextO_ST_ABSWhat is already known about the subject?C_ST_ABSO_LIAlthough GCK-MODY shows high penetrance, individuals vary in their glycaemic phenotype, and the cause of this variability remains unclear. C_LIO_LIPolygenic background has previously been found to modify disease risk and phenotypic variability in other lower penetrant forms of MODY but its contribution to the clinical variability in GCK-MODY is largely unexplored C_LI What is the key question?O_LIDoes polygenic background for diabetes-related traits contribute to variation in the GCK- MODY phenotype? C_LI What are the new findings?O_LIWe identified that clinically referred GCK-MODY cases had an independent enrichment of HbA1c, fasting glucose, and type 2 diabetes polygenic background, potentially increasing the likelihood of clinical referral. C_LIO_LIHigher HbA1c polygenic risk in GCK-MODY was associated with elevated measured HbA1c levels and an increased probability of exceeding the diagnostic threshold for diabetes. C_LI How might this impact on clinical practice in the foreseeable future?O_LIPolygenic background shapes the clinical expression of GCK-MODY, supporting the integration of monogenic and polygenic information to explain variability in monogenic disease. C_LI

BackgroundType 2 diabetes (T2D) results from a complex interplay between genetic predisposition and lifestyle factors. Both genetic susceptibility and unhealthy lifestyle are known to be associated with elevated T2D risk. However, their combined effects on T2D risk are not well studied. We aimed to determine whether unhealthy modifiable health behaviors were associated with similar increases in the risk of incident T2D among individuals with different levels of genetic risk. MethodsWe performed a genetic risk score (GRS) by lifestyle interaction analysis within 332,251 non-diabetic individuals at baseline from the UK Biobank. Multi-ancestry GRS were calculated by summing the effects of 783 T2D-associated variants and ranked into tertiles. We used baseline self-reported data on smoking, BMI, physical activity level, and diet quality to categorize participants as having a healthy, intermediate, or unhealthy lifestyle. Cox proportional hazards regression models were used to generate adjusted hazards ratios (HR) of T2D risk and associated 95% confidence intervals (CI). ResultsDuring follow-up (median 13.6 years), 13,128 (4.0%) participants developed T2D. GRS (P < 0.001) and lifestyle classification (P < 0.001) were independently associated with increased risk for T2D. Compared with healthy lifestyle, unhealthy lifestyle was associated with increased T2D risk in all genetic risk strata, with adjusted HR ranging from 7.11 (low genetic risk) to 16.33 (high genetic risk). ConclusionsHigh genetic risk and unhealthy lifestyle were the most significant contributors to the development of T2D. Individuals at all levels of genetic risk can greatly mitigate their risk for T2D through lifestyle modifications.

BackgroundThis study evaluates longitudinal associations between glycaemic control (mean and within-patient variability of glycated haemaglobin (HbA1c) levels) in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD), focusing on the timings of these diagnoses. MethodsIn UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with T2D disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered. ResultsT2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 year median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis (/3 = 0.51, 95% CI: [0.17, 0.86]). Retrospectively, looking across all follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13-1.19) times higher in post-T2D MDD. ConclusionsThe timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

AimsWe investigated the relationship between polygenic score (PGS) for BMI and other adiposity PGS with age at type 2 diabetes onset in white European (EUR) and south Asian (SAS) ancestries, and the mediating role of BMI. MethodsIn this retrospective study, using polygenic score (PGS) for BMI, clinically measured BMI and age at type 2 diabetes onset, we conducted mediation analysis separately for SAS (n=3,901, Genes & Health) and EUR (n=729, UK Biobank) aged 40 years or older. For SAS, we also used multivariable linear regression with backward selection to identify the best adiposity PGS (waist circumference (WC), waist-to-hip ratio (WHR), visceral adipose tissue (VAT), body fat (BF), trunk fat (TF) and gluteofemoral fat (GFAT), hand-grip strength (HGS)) predicting age at type 2 diabetes onset. ResultsA one SD increment in BMI-PGS was associated with earlier type 2 diabetes onset by -0.73 years (95%CI -1.01; -0.45) in SAS and -0.57 years (95%CI -1.05; -0.08) in EUR. BMI fully mediated the PGS effect in EUR (100%) and only partially in SAS (28%). Alongside BMI-PGS, WC-PGS and TF-PGS were good at discriminating measured BMI, WC and WHTR in SAS and were correlated with BMI-PGS. Other best predictors of early onset type 2 diabetes in SAS were WC-PGS, WHR-PGS, BF-PGS and GFAT-PGS, which differed between SAS subgroups and by sex. ConclusionsThese findings underscore the importance of incorporating adiposity-related genetics in predicting type 2 diabetes onset among SAS and demonstrate the limitations of using BMI alone to capture associated risk, particularly in diverse populations with typically lower BMI. Research in contextWhat is already known about this subject? O_LISouth Asians develop type 2 diabetes, on average, a decade earlier than white Europeans and at lower BMI levels. C_LIO_LIThe well-established association between BMI and type 2 diabetes risk in white Europeans is more complex in south Asians, who have different patterns of adipose tissue distribution that are not well reflected in BMI. C_LIO_LIThe contribution of adiposity-related polygenic scores (PGS) to type 2 diabetes onset has not been examined across ancestries. C_LI What is the key question? O_LIWhat is the association between adiposity PGS and type 2 diabetes onset across ancestries, what how much of this effect is mediated by BMI? C_LI What are the new findings? O_LIHigher BMI-PGS is associated with earlier type 2 diabetes onset in both south Asians and white Europeans; this relationship is fully mediated by BMI in white Europeans but only partially in south Asians. C_LIO_LIOther PGS for central adiposity are significant predictors of early type 2 diabetes onset and central adiposity anthropometrics in south Asians. C_LI How might this impact on clinical practice in the foreseeable future? O_LIThese findings highlight the importance of incorporating adiposity-related genetics in predicting type 2 diabetes onset in diverse populations and underscore the need to move beyond BMI when assessing metabolic risk and potentially evaluating the effectiveness of interventions. C_LI

BackgroundType 2 diabetes (T2D) is a multi-organ disease defined by hyperglycemia resulting from different disease mechanisms. Using clinical parameters measured at diagnosis (age, BMI, HbA1c, HOMA2-B, HOMA2-IR and GAD autoantibodies) adult patients with diabetes have been reproducibly clustered into five subtypes, that differed clinically with respect to disease progression and outcomes.1 In this study we use genetic information to investigate if these subtypes have distinct underlying genetic drivers. MethodsGenome-wide association (GWAS) and genetic risk score (GRS) analysis was performed in Swedish (N=12230) and Finnish (N=4631) cohorts. Family history was recorded by questionnaires. ResultsSevere insulin-deficient diabetes (SIDD) and mild obesity-related diabetes (MOD) groups had the strongest family history of T2D. A GRS including known T2D loci was strongly associated with SIDD (OR per 1 SD increment [95% CI]=1.959 [1.814-2.118]), MOD (OR 1.726 [1.607-1.855]) and mild age-related diabetes (MARD) (OR 1.771 [1.671-1.879]), whereas it was less strongly associated with severe insulin-resistant diabetes (SIRD, OR 1.244 [1.157-1.337]), which was similar to severe autoimmune diabetes (SAID, OR 1.282 [1.160-1.418]). SAID showed strong association with the GRS for T1D, whereas the non-autoimmune subtype SIDD was most strongly associated with the GRS for insulin secretion rate (P<7.43x10-9). SIRD showed no association with variants in TCF7L2 or any GRS reflecting insulin secretion. Instead, only SIRD was associated with GRS for fasting insulin (P=3.10x10-8). Finally, a T2D locus, rs10824307 near the ZNF503 gene was uniquely associated with MOD (ORmeta=1.266 (1.170-1.369), P=4.3x10-9). ConclusionsNew diabetes subtypes have partially different genetic backgrounds and subtype-specific risk loci can be identified. Especially the SIRD subtype stands out by having lower heritability and less involvement of beta-cell related pathways in its pathogenesis. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSIn March 2018 we suggested a novel subclassification of diabetes into five subtypes. This classification was based on clustering using clinical parameters commonly measured at diabetes diagnosis (age at diabetes onset, HbA1c, bodymass index, presence of GAD autoantibodies and HOMA2 indices for insulin resistance and secretion). These subtypes differed with respect to clinical characteristics, disease progression and risk of complications, but it remained unclear to what extent these subtypes have different underlying pathologies. In our original publication we analysed a small set of genetic risk variants for diabetes and found differential associations between subtypes, suggesting potential aetiological differences. Added value of this studyIn this study we have conducted a full genome analysis of the original ANDIS cohort, including genome-wide association studies and polygenic risk score analysis with replication in an independent cohort. We have also compared heritability and prevalence of having a family history of diabetes in the subtypes. Implications of all the available evidenceWe demonstrate that stratification into subtypes facilitates identification of genetic risk loci and that the aetiology of the subtypes is at least partially distinct. These results are especially important for the future study and treatment of individuals belonging to the severe insulin-resistant diabetes (SIRD) subtype, whose pathogenesis appears to differ substantially from that of traditional T2D.

ContextIndividuals with youth-onset type 2 diabetes mellitus (T2DM) display substantial, but unexplained, heterogeneity in their clinical presentations and risk of complications such as diabetic neuropathy. Data-driven clustering may be useful in characterizing this heterogeneity. ObjectiveTo identify data-driven subphenotypes of newly diagnosed youth-onset T2DM and study their association with distal symmetric polyneuropathy (DSPN) at time of diagnosis. DesignCross-sectional SettingUSA Participants641 individuals with newly diagnosed T2DM aged 10-19 years from the SEARCH for Diabetes in Youth Study and the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study. Exposure(s)Body mass index, HbA1c, fasting C-peptide, systolic blood pressure, diastolic blood pressure, LDL cholesterol and HDL cholesterol Main Outcome MeasuresData-driven subphenotypes were identified from k-means clustering. The cross-sectional association of subphenotypes with DSPN, based on expert examination scores ([&ge;]2.5) from the Michigan Neuropathy Screening Instrument, were assessed using Poisson regressions with robust standard errors. ResultsAmong 641 youth-onset T2DM, 58.2% were female, with 38.2% of participants [&le;]13 years having average BMI of 34.5 kg/m2 (SD: 6.5 kg/m2), and average HbA1c of 6.1% (IQR: 5.6-7.0). Three youth-onset subphenotypes were identified: mild obesity related diabetes (yMOD, 48.5%), severe insulin deficient diabetes (ySIDD, 18.7%) and severe insulin resistant diabetes (ySIRD, 32.7%). After adjusting for covariates, the prevalence of abnormal DSPN were 2.58 (95%CI: 1.74, 3.81) and 2.02 (95%CI: 1.40, 2.93) times among those classified as the ySIDD and ySIRD subphenotypes, relative to the yMOD subphenotype. ConclusionsYouth-onset T2DM consisted of heterogeneous clinical subphenotypes with differences prevalence of DSPN. Management of youth-onset T2DM may need to consider strategies tailored to each subphenotype.

Aims/HypothesisFunctional variants in the TAS2R38 bitter taste receptor influence bitter taste perception and inform dietary and lifestyle behaviors that impact glucose homeostasis. Experimental data suggest that TAS2R38 receptors also mediate postprandial GLP-1 secretions from intestinal L-cells, though human study data are limited. To further establish the role of TAS2R38 in glucose homeostasis in humans, we tested whether functional variants conferring greater TAS2R38 sensitivity associate with lower blood glucose, particularly in the postprandial state, independent of dietary- and lifestyle-mediated effects of TAS2R38 on glucose levels. MethodsWe analyzed participants without type 2 diabetes of European ancestry in the UK Biobank. We used known functional variants in TAS2R38 to assign canonical haplotypes (AVI, PAV) and diplotypes conferring low (AVI/AVI nontasters), moderate (AVI/PAV tasters) or high (PAV/PAV supertasters) TAS2R38 receptor sensitivity and bitter taste perception. Linear models were used to quantify the associations of TAS2R38 diplotype with random glucose, and glucose levels over various time windows spanning postprandial and fasting states, adjusting for demographics and BMI, then sequentially for dietary and lifestyle factors. We used variants in other bitter taste receptors (TAS2R14 and TAS2R19), related to similar dietary and lifestyle behaviors as TAS2R38 but without hypothesized roles in glucose metabolism, to serve as negative controls. ResultsAmong 218,688 individuals, 34%, 49% and 18% were AVI/AVI nontasters, AVI/PAV tasters and PAV/PAV supertasters, respectively. In BMI-adjusted models, each additional PAV haplotype associated with decreases in random glucose levels (beta [95% CI] = -0.07 [-0.13, -0.01] mg/dL; P = 0.021). In analyses stratified by fasting time, associations were only significant in the 0-2 hr postprandial window (-0.24 [-0.39, -0.08] mg/dL per PAV haplotype; P = 0.003). These associations replicated (at the variant-level) in a published GWAS meta-analysis of 2-hr OGTT glucose and persisted after further adjustment for dietary and lifestyle behaviors (adjusted beta = -0.22; P = 0.004). Variants in TAS2R14 and TAS2R19 were related to similar behavioral traits as TAS2R38 but were not associated with 0-2 hr glucose, supporting behaviorally-independent effects of TAS2R38 diplotypes on 0-2 hr glucose. Conclusions/interpretationFunctional variants conferring greater TAS2R38 receptor sensitivity were associated with lower glucose levels in the postprandial state. These findings align with experimental evidence supporting a functional role for TAS2R38 in postprandial glycemia, reinforcing it as a potential target for type 2 diabetes prevention and treatment. Research in ContextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LITAS2R38 is a specialized G-protein coupled receptor that mediates bitter taste perception in the mouth and has been discovered to act as a peripheral nutrient sensor in intestinal L-cells in the gut. C_LIO_LIFunctional variants in TAS2R38 conferring its sensitivity give rise to three canonical diplotypes with well-defined effects on bitter taste perception and dietary and lifestyle behaviors relevant to glucose homeostasis. C_LIO_LIExperimental studies show that TAS2R38 receptors in human enteroendocrine L-cells contribute to the postprandial secretion of incretin hormone, glucagon-like peptide-1 (GLP-1). C_LI What is the key question?O_LIAre functional variants in the TAS2R38 bitter taste receptor associated with glucose homeostasis in humans, particularly in the postprandial state? C_LI What are the new findings?O_LIIn a large cohort of adults without type 2 diabetes, individuals carrying two copies of the haplotype encoding more sensitive TAS2R38 receptors (PAV) had significantly lower postprandial glucose levels than those with two copies of the non-functional haplotype (AVI), regardless of dietary or lifestyle behaviors, with differences following a dose-response relationship per PAV haplotype. C_LI How might this impact on clinical practice in the foreseeable future?O_LIThese findings provide evidence to support direct TAS2R38 actions in postprandial glycemia, which could inform subsequent experimental work to leverage TAS2R38 as a therapeutic target for impaired glucose regulation. C_LI

BackgroundGenetic risk scores (GRS) for type 1 diabetes (T1D) have been developed primarily in European populations, limiting their generalisability across ancestries. Indians differ from Europeans in clinical characteristics of T1D and overall genetic architecture, yet systematic evaluation of T1D GRS performance in multi-regional Indian cohorts is lacking. MethodsThe study included 597 T1D patients and 3347 non-diabetic controls from different regions in India. Genotyping, imputation, quality control analysis, and construction of the 67-SNPs T1D GRS were performed using standardised pipelines. Discriminative performance was assessed using Receiver Operative Curve-Area under Curve (ROC-AUC) analysis, and optimal thresholds were derived using Youdens index. HLA-DQ diplotype frequencies were compared, and association analysis was conducted using multivariable logistic regression. FindingsT1D GRS showed consistent discriminative performance across Indian cohorts [ROC-AUC=0.84 (range=0{middle dot}78-0{middle dot}87)], supporting its comprehensive use for T1D classification in India. Notably, its performance was lower in islet cell autoantibody (IA) negative compared with IA positive T1D patients (ROC-AUC, 0{middle dot}75 vs 0{middle dot}85) and in adult-onset than in childhood-onset patients (0{middle dot}74 vs 0{middle dot}84). We observed a lower frequency of protective HLA-DQ diplotypes and a strong association of HLA-DQ81 containing diplotypes in childhood-onset T1D. Application of an India-specific T1D GRS score improved the sensitivity than the European cut-off. InterpretationT1D GRS is a valuable unified diagnostic tool in Indians, but its performance varies by islet cell autoantibody status and age at onset, likely reflecting population-specific HLA architecture. European-derived T1D GRS thresholds under-classify the genetic risk, highlighting the importance of ancestry-aware optimisation in Indians. FundingCDRC grant CDRC202111026 and CSIR Intramural Grant P50. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies have shown that a 67-SNPs T1D genetic risk score (GRS) can distinguish T1D patients from non-diabetic controls and other forms of diabetes, but its performance varies across ancestries. Islet cell autoantibodies (IA) have important diagnostic value for classifying type 1 diabetes (T1D). However, their prevalence in India varies widely, with up to one-quarter of patients testing negative, limiting their clinical utility. Evidence supporting the use of the T1D GRS in India, combined with IA antibodies status is limited to a single cohort representing one linguistic group. The applicability of T1D GRS across multi-centric clinical settings has not been systematically evaluated. Added value of this studyThis study validates the 67-SNPs T1D GRS across multiple Indian cohorts representing major linguistic groups, supporting its use as a unified diagnostic tool. Differences in T1DGRS performance between childhood-and adult-onset T1D are linked to enrichment of protective HLA-DQ diplotypes in adult-onset disease, providing genetic insight into disease heterogeneity. The study also demonstrates that European-derived GRS thresholds systematically under-classify genetic risk in Indians and the population-specific threshold is essential. Implications of all the available evidenceThe European-derived T1D GRS can be applied across Indian clinical settings with consistent discriminative performance. However, its utility is influenced by islet cell autoantibody status and the age at onset of disease. Ancestry-aware threshold optimisation substantially improves diagnostic accuracy and is essential for equitable implementation of T1D GRS in Indians. Larger studies are needed to identify population-specific risk variants and further refine genetic tools for clinical diagnosis.

As part of the American Diabetes Association Precision Medicine in Diabetes Initiative (PMDI) - a partnership with the European Association for the Study of Diabetes (EASD) - this systematic review is part of a comprehensive evidence evaluation in support of the 2nd International Consensus Report on Precision Diabetes Medicine. Here, we sought to synthesize evidence from empirical research papers published through September 1st, 2021 to evaluate and identify prognostic conditions, risk factors, and biomarkers among women and children affected by gestational diabetes mellitus (GDM), focusing on clinical endpoints of cardiovascular disease (CVD) and type 2 diabetes (T2D) among women with a history of GDM; and adiposity and cardiometabolic profile among offspring exposed to GDM in utero. We identified a total of 107 observational studies and 12 randomized controlled trials testing the effect of pharmaceutical and/or lifestyle interventions. Broadly, current literature indicates that greater GDM severity, higher maternal body mass index, belonging to racial/ethnic minority group; and unhealthy lifestyle behaviors would predict a womans risk of incident T2D and CVD, and an unfavorable cardiometabolic profile among offspring. However, the level of evidence is low (Level 4 according to the Diabetes Canada 2018 Clinical Practice Guidelines for diabetes prognosis) largely because most studies leveraged retrospective data from large registries that are vulnerable to residual confounding and reverse causation bias; and prospective cohort studies that may suffer selection and attrition bias. Moreover, for the offspring outcomes, we identified a relatively small body of literature on prognostic factors indicative of future adiposity and cardiometabolic risk. Future high-quality prospective cohort studies in diverse populations with granular data collection on prognostic factors, clinical and subclinical outcomes, high fidelity of follow-up, and appropriate analytical approaches to deal with structural biases are warranted.