Loci for insulin processing and secretion provide insight into type 2 diabetes risk

Broadaway, K. Alaine; Yin, Xianyong; Williamson, Alice; Parsons, Victoria A.; Wilson, Emily; Moxley, Anne H.; Vadlamudi, Swarooparani; Varshney, Arushi; Jackson, Anne; Ahuja, Vasudha; Bornstein, Stefan R.; Corbin, Laura J; Delgado, Graciela; Dwivedi, Om Prakash; Silva, Lilian Fernandes; Frayling, Timothy M.; Grallert, Harald; Gustafsson, Stefan; Hakaste, Liisa; Hammar, Ulf; Herder, Christian; Herrmann, Joerg; Højlund, Kurt; Hughes, David A.; Kleber, Marcus E.; Lindgren, Cecilia M.; Liu, Ching‐Ti; Luan, Jian’an; Malmberg, Anni; Moissl, Angela P.; Morris, Andrew P.; Perakakis, Nikolaos; Peters, Annette; Petrie, John R.; Roden, Michael; Schwarz, Peter E. H.; Sharma, Sapna; Silveira, Angela; Strawbridge, Rona J.; Tuomi, Tiinamaija; Wood, Andrew R.; Wu, Peitao; Zethelius, Björn; Baldassarre, Damiano; Eriksson, Johan G.; Fall, Tove; Florez, José C.; Fritsche, Andreas; Gigante, Bruna; Hamsten, Anders; Kajantie, Eero; Laakso, Markku; Lahti, Jari; Lawlor, Deborah A; Lind, Lars; März, Winfried; Meigs, James B.; Sundström, Johan; Timpson, Nicholas J.; Wagner, Róbert; Walker, Mark; Wareham, Nicholas J.; Watkins, Hugh; Barroso, Inês; O’Rahilly, Stephen; Grarup, Niels; Parker, Stephen C. J.; Boehnke, Michael; Langenberg, Claudia; Wheeler, Eleanor; Mohlke, Karen L.

doi:10.1016/j.ajhg.2023.01.002

Cited by 17 publications

(12 citation statements)

References 98 publications

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“…We noted substantial agreement between DEGAS and RePACT, for example the transcription factor SIX3 was enriched in T2D-β obese-DEGAS cells and in Fang et al’s obese trajectory β-cells 5 . SIX3 has been implicated as a diabetes risk gene and is important for β-cell function 44, 45 . Additionally, both approaches highlighted the association of DLK1 with obesity, but only DEGAS identified DLK1 association with T2D.…”

Section: Discussionmentioning

confidence: 99%

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Roy,

Syed,

Lazaro

et al. 2024

Preprint

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Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet β-cells are the sole source of endogenous insulin and our understanding of β-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in β-cell function and survival. However, it is difficult to identify which β-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related β-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct β-cell subpopulations. A singular cluster of T2D-associated β-cells was identified; however, β-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. DLK1 was heterogeneously expressed among β-cells and appeared depleted from T2D islets. In conclusion, DEGAS has the potential to advance our holistic understanding of the β-cell transcriptomic phenotypes, including features that distinguish β-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

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Section: Discussionmentioning

confidence: 99%

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Roy,

Syed,

Lazaro

et al. 2024

Preprint

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“…The primary MR analysis was only performed for antidiabetic drugs that demonstrated expected associations with these positive control outcomes. Details about genetic association data for these positive control outcomes, e.g., sample size, inclusion/exclusion criteria, covariates, are listed in Table 1 [29,30,[33][34][35][36].…”

Section: Methodsmentioning

confidence: 99%

“… Age, sex and PCs [ 17 ] Positive control outcomes T2DM Participants from 18 studies 26,676 cases/ 132,532 controls (NA) Self-reported diabetes diagnosed by a physician, self-reported antidiabetic drug use, fasting-glucose ≥ 7.0mmol/L, non-fasting glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, a diagnosis of diabetes in the hospital discharge register Age, sex and PCs [ 33 ] HOMA-IR Participants from 20 studies 36,466 Individuals with diagnosed type-1 or type-2 diabetes, with fasting glucose ≥ 7.0mmol/L, on diabetes treatment, with pregnancy, with outliers ± 3 s.d. ; non-fasting individuals HOMA-IR=(fasting insulin [mU/L] × fasting glucose [nmol/L]) × 22.5 Age, sex, study site, geographic covariates and age squared [ 34 ] Fasting proinsulin Participants from 16 studies 45,861 Individuals with self-reported or diagnosed diabetes, antidiabetic drug use, fasting-glucose ≥ 7.0mmol/L, 2-hour glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5% (NA) Age, sex, PCs and other study-specific covariates [ 36 ] BMI* Participants from 125 studies 322,154 Individuals with missing information (including weight, height, BMI, age or sex), with age < 18 years Self-reported or measured height and weight. BMI = weight (kg) / height (m) 2 Age, age squared and study-specific covariates such as PCs [ 29 ] Hip circumference* Participants from 57 studies 213,038 Individuals with missing information (including hip circumference, weight, height, BMI, age or sex), with age < 18 years Self-reported or measured metrics …”

Section: Methodsmentioning

confidence: 99%

Repurposing antidiabetic drugs for rheumatoid arthritis: results from a two-sample Mendelian randomization study

et al. 2023

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Despite increasing therapeutic options to treat rheumatoid arthritis (RA), many patients fail to reach treatment targets. The use of antidiabetic drugs like thiazolidinediones has been associated with lower RA risk. We aimed to explore the repurposing potential of antidiabetic drugs in RA prevention by assessing associations between genetic variation in antidiabetic drug target genes and RA using Mendelian randomization (MR). A two-sample MR design was used to estimate the association between the antidiabetic drug and RA risk using summary statistics from genome-wide association studies (GWAS). We selected independent genetic variants from the gene(s) that encode the target protein(s) of the investigated antidiabetic drug as instruments. We extracted the associations of instruments with blood glucose concentration and RA from the UK Biobank and a GWAS meta-analysis of clinically diagnosed RA, respectively. The effect of genetic variation in the drug target(s) on RA risk was estimated by the Wald ratio test or inverse-variance weighted method. Insulin and its analogues, thiazolidinediones, and sulfonylureas had valid genetic instruments (n = 1, 1, and 2, respectively). Genetic variation in thiazolidinedione target (gene: PPARG) was inversely associated with RA risk (odds ratio [OR] 0.38 per 0.1mmol/L glucose lowering, 95% confidence interval [CI] 0.20–0.73). Corresponding ORs (95%CIs) were 0.83 (0.44–1.55) for genetic variation in the targets of insulin and its analogues (gene: INSR), and 1.12 (0.83, 1.49) 1.25 (0.78-2.00) for genetic variation in the sulfonylurea targets (gene: ABCC8 and KCNJ11). In conclusion, genetic variation in the thiazolidinedione target is associated with a lower RA risk. The underlying mechanisms warrant further exploration.

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“…The original GWAS consisted solely of individuals from the European population. The MAGIC GWAS involved a total of 55,172, 55,535, and 45,826 participants for insulin fold change, insulin sensitivity index, and fasting proinsulin values, respectively [10,11]. And data of fasting insulin, fasting glucose, 2-hour glucose and glycated haemoglobin was obtained from a total of 281,416 participants.…”

Section: Data Sourcesmentioning

confidence: 99%

Association Between Glycemic Traits and Oesophageal Cancer: a Multivariable Mendelian Randomization Study

Chen,

Zeng,

et al. 2023

Preprint

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Background Observational studies indicate a potential link between increased blood glucose levels and the development of oesophageal cancer. However, the causal relationship between glycemic traits and oesophageal cancer has not been clarified yet. The aim of this research was to examine if there is a genetic link between glycemic characteristics and oesophageal cancer through the use of Mendelian randomization analysis. Methods We conducted a TSMR analysis and MVMR using publicly available GWAS data on the European population. The primary analysis utilized the IVW method, which was subsequently validated through comprehensive complementary and sensitivity analyses. Results IVW analysis revealed a substantial correlation (OR = 1.612, P = 0.0228) between type 2 diabetes (T2DM) and oesophageal cancer. After accounting for body mass index (BMI), this association remained to be statistically significant in multivariate MR analyses (OR = 1.662, P = 0.0416). There was only a small amount of evidence indicating a possible link between oesophageal cancer and other glycemic traits after excluding the outliers. Conclusions Our study further supports the evidence that European individuals with T2DM are at an increased risk of developing oesophageal cancer. It implies that pharmacological or lifestyle treatments for individuals with T2DM could potentially be advantageous in the prevention of oesophageal tumor development.

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Loci for insulin processing and secretion provide insight into type 2 diabetes risk

Cited by 17 publications

References 98 publications

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Repurposing antidiabetic drugs for rheumatoid arthritis: results from a two-sample Mendelian randomization study

Association Between Glycemic Traits and Oesophageal Cancer: a Multivariable Mendelian Randomization Study

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