Genetic Variation in Targets of Antidiabetic Drugs and Alzheimer Disease Risk

Abstract

Background and Objectives:Previous studies have highlighted anti-diabetic drugs as repurposing candidates for Alzheimer´s disease (AD), but the disease-modifying effects are still unclear.METHODS:A two-sample Mendelian randomization (MR) study design was applied to examine the association between genetic variation in the targets of four anti-diabetic drug classes and AD risk. Genetic summary statistics for blood glucose were analyzed using UK Biobank data of 326,885 participants, while summary statistics for AD were retrieved from previous genome-wide association studies (GWAS) comprising 24,087 clinically diagnosed AD cases and 55,058 controls. Positive control analysis on type 2 diabetes (T2DM), insulin secretion, insulin resistance, and obesity-related traits was conducted to validate the selection of instrumental variables (IVs).RESULTS:In the positive control analysis, genetic variation in sulfonylurea targets was associated with higher insulin secretion, a lower risk of T2DM, and an increment in body mass index, waist circumference, and hip circumference, consistent with drug mechanistic actions and previous trial evidence. In the primary analysis, genetic variation in sulfonylurea targets was associated with a lower risk of AD (OR=0.38 per 1 mmol/L decrement in blood glucose, 95%CI=0.19-0.72, P=0.0034). These results for sulfonylureas were largely unchanged in the sensitivity analysis using a genetic variant, rs757110, that has been validated to modulate the target proteins of sulfonylureas (OR=0.35 per 1 mmol/L decrement in blood glucose, 95%CI=0.15-0.82, P=0.016). An association between genetic variations in the GLP-1 analogue target and a lower risk of AD was also observed (OR=0.32 per 1 mmol/L decrement in blood glucose, 95%CI=0.13-0.79, P=0.014). However, this result should be interpreted with caution because the positive control analyses for GLP-1 analogues did not comply with a weight-loss effect as shown in previous clinical trials. Results regarding other drug classes were inconclusive.DISCUSSION:Genetic variation in sulfonylurea targets was associated with a lower risk of AD, and future studies are warranted to clarify the underlying mechanistic pathways between sulfonylureas and AD.

Multisite chronic pain and the risk of autoimmune diseases: A Mendelian randomization study

“…Finally, to evaluate the strength of the selected SNPs, the F statistics were calculated using the formula F = R 2 (N–k–1)/[(1–R 2 ) k], where R 2 is the proportion of variability explained by each SNP, N is the sample size of the GWAS, and k is the number of SNPs. When the F-statistic is <10, it indicates that IV is a weak instrument ( 39 ).…”

Neurofilament Light: a biomarker of neurodegeneration correlated with Type 2 diabetes and other related diseases

“…Besides, many studies have shown that diabetes drugs (such as metformin, sulfonylureas, and pioglitazone etc.) not only play a role in blood glycemic control, but also can impact on neuroprotection, neurogenesis, neuroinflammation, synaptic plasticity, and proteins aggregationthrough different mechanisms and linking pathways, in order to reduce the risk of suffering from AD [39][40][41][42][43][44]. Moreover, early and effective control of hyperglycemia has a positive impact on reducing the risk of AD and delaying the progress of AD, and neurodegeneration plays an important role in this process.…”

Genetic Prediction of Antiglycemic Drug Targets and Risk of Epilepsy: A Mendelian Randomization Study

“…Mendelian randomization analysis is currently being used to investigate the relationship between antihypertensive drugs and psychiatric disorders 17 , drug targets for the treatment of Parkinson's disease 18 , and the risk of lipid-lowering drugs, antiglycemic drugs, and Alzheimer's disease 19,20 . In this study, to explore the effects of different antiglycemic drug treatments on epilepsy, we used the public GTEx -V8 database to con rm the proxy antiglycemic drug target genes of instrumental variables and performed two-sample Mendelian randomization analysis.…”