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Identifying Common Genetic Risk Factors of Diabetic Neuropathies.

ABSTRACT: Type 2 diabetes mellitus (T2DM) is a global public health problem of epidemic proportions, with 60-70% of affected individuals suffering from associated neurovascular complications that act on multiple organ systems. The most common and clinically significant neuropathies of T2DM include uremic neuropathy, peripheral neuropathy, and cardiac autonomic neuropathy. These conditions seriously impact an individual's quality of life and significantly increase the risk of morbidity and mortality. Although advances in gene sequencing technologies have identified several genetic variants that may regulate the development and progression of T2DM, little is known about whether or not the variants are involved in disease progression and how these genetic variants are associated with diabetic neuropathy specifically. Significant missing heritability data and complex disease etiologies remain to be explained. This article is the first to provide a review of the genetic risk variants implicated in the diabetic neuropathies and to highlight potential commonalities. We thereby aim to contribute to the creation of a genetic-metabolic model that will help to elucidate the cause of diabetic neuropathies, evaluate a patient's risk profile, and ultimately facilitate preventative and targeted treatment for the individual.

SUBMITTER: Witzel II

PROVIDER: S-EPMC4447004 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Identifying Common Genetic Risk Factors of Diabetic Neuropathies.

Witzel Ini-Isabée II Jelinek Herbert F HF Khalaf Kinda K Lee Sungmun S Khandoker Ahsan H AH Alsafar Habiba H

Frontiers in endocrinology 20150528

Type 2 diabetes mellitus (T2DM) is a global public health problem of epidemic proportions, with 60-70% of affected individuals suffering from associated neurovascular complications that act on multiple organ systems. The most common and clinically significant neuropathies of T2DM include uremic neuropathy, peripheral neuropathy, and cardiac autonomic neuropathy. These conditions seriously impact an individual's quality of life and significantly increase the risk of morbidity and mortality. Altho ...[more]

PMID: 26074879

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Project description:BackgroundAntidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.MethodsGenome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.ResultsNeither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.ConclusionsThis study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

Project description:Neuropathic pain is a frequent feature of diabetic peripheral neuropathy (DPN) and small fiber neuropathy (SFN). Resolving the genetic architecture of these painful neuropathies will lead to better disease management strategies, counselling and intervention. Our aims were to profile ten sodium channel genes (SCG) expressed in a nociceptive pathway in painful and painless DPN and painful and painless SFN patients, and to provide a perspective for clinicians who assess patients with painful peripheral neuropathy. Between June 2014 and September 2016, 1125 patients with painful-DPN (n = 237), painless-DPN (n = 309), painful-SFN (n = 547) and painless-SFN (n = 32), recruited in four different centers, were analyzed for SCN3A, SCN7A-SCN11A and SCN1B-SCN4B variants by single molecule Molecular inversion probes-Next Generation Sequence. Patients were grouped based on phenotype and the presence of SCG variants. Screening of SCN3A, SCN7A-SCN11A, and SCN1B-SCN4B revealed 125 different (potential) pathogenic variants in 194 patients (17.2%, n = 194/1125). A potential pathogenic variant was present in 18.1% (n = 142/784) of painful neuropathy patients vs. 15.2% (n = 52/341) of painless neuropathy patients (17.3% (n = 41/237) for painful-DPN patients, 14.9% (n = 46/309) for painless-DPN patients, 18.5% (n = 101/547) for painful-SFN patients, and 18.8% (n = 6/32) for painless-SFN patients). Of the variants detected, 70% were in SCN7A, SCN9A, SCN10A and SCN11A. The frequency of SCN9A and SCN11A variants was the highest in painful-SFN patients, SCN7A variants in painful-DPN patients, and SCN10A variants in painless-DPN patients. Our findings suggest that rare SCG genetic variants may contribute to the development of painful neuropathy. Genetic profiling and SCG variant identification should aid in a better understanding of the genetic variability in patients with painful and painless neuropathy, and may lead to better risk stratification and the development of more targeted and personalized pain treatments.

Dataset Information

Identifying Common Genetic Risk Factors of Diabetic Neuropathies.

Publications

Identifying Common Genetic Risk Factors of Diabetic Neuropathies.

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