Combined lifestyle factors and the risk of LADA and type 2 diabetes - Results from a Swedish population-based case-control study

Aims: We investigated the risk of latent autoimmune diabetes in adults (LADA) and type 2 diabetes in relation to a healthy lifestyle, the proportion of patients attributable to an unhealthy lifestyle, and the influence of family history of diabetes (FHD) and genetic susceptibility. Methods: The population-based study included incident LADA (n = 571), type 2 diabetes (n = 1962), and matched controls (n = 2217). A healthy lifestyle was defined by BMI < 25 kg/m2, moderate-to-high physical activity, a healthy diet, no smoking, and moderate alcohol consumption. We estimated odds ratios (OR) with 95% confidence intervals (CIs) adjusted for age, sex, education, and FHD. Results: Compared to a poor/moderate lifestyle, a healthy lifestyle was associated with a reduced risk of LADA (OR 0.51, CI 0.34-0.77) and type 2 diabetes (OR 0.09, CI 0.05-0.15). A healthy lifestyle conferred a reduced risk irrespective of FHD and high-risk HLA genotypes. Having a BMI < 25 kg/m2 conferred the largest risk reduction for both LADA (OR 0.54, CI 0.43-0.66) and type 2 diabetes (OR 0.12, CI 0.10-0.15) out of the individual items. Conclusion: People with a healthy lifestyle, especially a healthy body weight, have a reduced risk of LADA including those with genetic susceptibility to diabetes. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Consumption of red meat, genetic susceptibility, and risk of LADA and type 2 diabetes

Purpose Red meat consumption is positively associated with type 1 (T1D) and type 2 (T2D) diabetes. We investigated if red meat consumption increases the risk of latent autoimmune diabetes in adults (LADA) and T2D, and potential interaction with family history of diabetes (FHD), HLA and TCF7L2 genotypes. Methods Analyses were based on Swedish case-control data comprising incident cases of LADA (n = 465) and T2D (n = 1528) with matched, population-based controls (n = 1789; n = 1553 in genetic analyses). Multivariable-adjusted ORs in relation to self-reported processed and unprocessed red meat intake were estimated by conditional logistic regression models. Attributable proportion (AP) due to interaction was used to assess departure from additivity of effects. Results Consumption of processed red meat was associated with increased risk of LADA (per one servings/day OR 1.27, 95% CI 1.07-1.52), whereas no association was observed for unprocessed red meat. For T2D, there was no association with red meat intake once BMI was taken into account. The combination of high (> 0.3 servings/day vs. less) processed red meat intake and high-risk HLA-DQB1 and -DRB1 genotypes yielded OR 8.05 (95% CI 4.86-13.34) for LADA, with indications of significant interaction (AP 0.53, 95% CI 0.32-0.73). Results were similar for the combination of FHD-T1D and processed red meat. No interaction between processed red meat intake and FHD-T2D or risk variants of TCF7L2 was seen in relation to LADA or T2D. Conclusion Consumption of processed but not unprocessed red meat may increase the risk of LADA, especially in individuals with FHD-T1D or high-risk HLA genotypes.Peer reviewe

DNA methylation partially mediates antidiabetic effects of metformin on HbA1c levels in individuals with type 2 diabetes

Aims: Despite metformin being used as first-line pharmacological therapy for type 2 diabetes, its underlying mechanisms remain unclear. We aimed to determine whether metformin altered DNA methylation in newlydiagnosed individuals with type 2 diabetes. Methods and Results: We found that metformin therapy is associated with altered methylation of 26 sites in blood from Scandinavian discovery and replication cohorts (FDR < 0.05), using MethylationEPIC arrays. The majority (88%) of these 26 sites were hypermethylated in patients taking metformin for ~ 3 months compared to controls, who had diabetes but had not taken any diabetes medication. Two of these blood-based methylation markers mirrored the epigenetic pattern in muscle and adipose tissue (FDR < 0.05). Four type 2 diabetes-associated SNPs were annotated to genes with differential methylation between metformin cases and controls, e.g., GRB10, RPTOR, SLC22A18AS and TH2LCRR. Methylation correlated with expression in human islets for two of these genes. Three metformin-associated methylation sites (PKNOX2, WDTC1 and MICB) partially mediate effects of metformin on follow-up HbA1c levels. When combining methylation of these three sites into a score, which was used in a causal mediation analysis, methylation was suggested to mediate up to 32% of metformin’s effects on HbA1c. Conclusion: Metformin-associated alterations in DNA methylation partially mediates metformin’s antidiabetic effects on HbA1c in newly-diagnosed individuals with type 2 diabetes

Interaction Between Overweight and Genotypes of HLA, TCF7L2, and FTO in Relation to the Risk of Latent Autoimmune Diabetes in Adults and Type 2 Diabetes

Objective: We investigated potential interactions between body mass index (BMI) and genotypes of human leukocyte antigen (HLA), TCF7L2-rs7903146, and FTO-rs9939609 in relation to the risk of latent autoimmune diabetes in adults (LADA) and type 2 diabetes. Methods: We pooled data from two population-based studies: (i) a Swedish study with incident cases of LADA [positive for glutamic acid decarboxylase autoantibodies (GADA); n = 394) and type 2 diabetes (negative for GADA; n = 1290) and matched controls without diabetes (n = 2656) and (ii) a prospective Norwegian study that included incident cases of LADA (n = 131) and type 2 diabetes (n = 1901) and 886,120 person-years of follow-up. Analyses were adjusted for age, sex, physical activity, and smoking. Interaction between overweight (BMI >= 25 kg/m(2)) and HLA/TCF7L2/FTO high-risk genotypes was assessed by attributable proportion due to interaction (AP). Results: The combination of overweight and high-risk genotypes of HLA, TCF7L2, and FTO was associated with pooled relative risk (RRpooled) of 7.59 (95% CI, 5.27 to 10.93), 2.65 (95% CI, 1.97 to 3.56), and 2.21 (95% CI, 1.60 to 3.07), respectively, for LADA, compared with normal-weight individuals with low/intermediate genetic risk. There was a significant interaction between overweight and HLA (AP, 0.29; 95% CI, 0.10 to 0.47), TCF7L2 (AP, 0.31; 95% CI, 0.09 to 0.52), and FTO (AP, 0.38; 95% CI, 0.15 to 0.61). The highest risk of LADA was seen in overweight individuals homozygous for the DR4 genotype [RR, 26.76 (95% CI, 15.42 to 46.43); AP, 0.58 (95% CI, 0.32 to 0.83) (Swedish data)]. Overweight and TCF7L2 also significantly interacted in relation to type 2 diabetes (AP, 0.26; 95% CI, 0.19 to 0.33), but no interaction was observed with high-risk genotypes of HLA or FTO. Conclusions: Overweight interacts with HLA high-risk genotypes but also with genes associated with type 2 diabetes in the promotion of LADA.Peer reviewe

A novel rare CUBN variant and three additional genes identified in Europeans with and without diabetes : results from an exome-wide association study of albuminuria

Aims/hypothesisIdentifying rare coding variants associated with albuminuria may open new avenues for preventing chronic kidney disease and end-stage renal disease, which are highly prevalent in individuals with diabetes. Efforts to identify genetic susceptibility variants for albuminuria have so far been limited, with the majority of studies focusing on common variants.MethodsWe performed an exome-wide association study to identify coding variants in a two-stage (discovery and replication) approach. Data from 33,985 individuals of European ancestry (15,872 with and 18,113 without diabetes) and 2605 Greenlanders were included.ResultsWe identified a rare (minor allele frequency [MAF]: 0.8%) missense (A1690V) variant in CUBN (rs141640975, =0.27, p=1.3x10(-11)) associated with albuminuria as a continuous measure in the combined European meta-analysis. The presence of each rare allele of the variant was associated with a 6.4% increase in albuminuria. The rare CUBN variant had an effect that was three times stronger in individuals with type 2 diabetes compared with those without (p(interaction)=7.0x10(-4), with diabetes=0.69, without diabetes=0.20) in the discovery meta-analysis. Gene-aggregate tests based on rare and common variants identified three additional genes associated with albuminuria (HES1, CDC73 and GRM5) after multiple testing correction (p(Bonferroni)Peer reviewe

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Genetic Discrimination Between LADA and Childhood-Onset Type 1 Diabetes Within the MHC

OBJECTIVE The MHC region harbors the strongest loci for latent autoimmune diabetes in adults (LADA); however, the strength of association is likely attenuated compared with that for childhood-onset type 1 diabetes. In this study, we recapitulate independent effects in the MHC class I region in a population with type 1 diabetes and then determine whether such conditioning in LADA yields potential genetic discriminators between the two subtypes within this region. RESEARCH DESIGN AND METHODS Chromosome 6 was imputed using SNP2HLA, with conditional analysis performed in type 1 diabetes case subjects (n = 1,985) and control subjects (n = 2,219). The same approach was applied to a LADA cohort (n = 1,428) using population-based control subjects (n = 2,850) and in a separate replication cohort (656 type 1 diabetes case, 823 LADA case, and 3,218 control subjects). RESULTS The strongest associations in the MHC class II region (rs3957146, beta [SE] = 1.44 [0.05]), as well as the independent effect of MHC class I genes, on type 1 diabetes risk, particularly HLA-B*39 (beta [SE] = 1.36 [0.17]), were confirmed. The conditional analysis in LADA versus control subjects showed significant association in the MHC class II region (rs3957146, beta [SE] = 1.14 [0.06]); however, we did not observe significant independent effects of MHC class I alleles in LADA. CONCLUSIONS In LADA, the independent effects of MHC class I observed in type 1 diabetes were not observed after conditioning on the leading MHC class II associations, suggesting that the MHC class I association may be a genetic discriminator between LADA and childhood-onset type 1 diabetes.Peer reviewe

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

Aims/hypothesis: As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. Methods: We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case–control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30–75 ml min−1 [1.73 m]−2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. Results: Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets. Conclusions/interpretation: Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30–75 ml min−1 [1.73 m]−2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers

Genome-Wide Association Study and Functional Characterization Identifies Candidate Genes for Insulin-Stimulated Glucose Uptake

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in \u3e55,000 participants from three ancestry groups. We identified ten new loci (P \u3c 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits

Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis