Genetic polymorphism in TCF7L2 and risk of hypertension in Thai elderly subjects

Rattanatham, Rujikorn; Settasatian, Nongnuch; Settasatian, Chatri; Decharatchakul, Nisa; Sarutipaiboon, I.; Kanyalert, Sakda; Boonpalit, Nuthatai

doi:10.1016/j.atherosclerosis.2017.06.585

Cited by 3 publications

(4 citation statements)

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“…The effect size of TCF7L2 variants is similar for LADA and T2D (Cervin et al, 2008;Lukacs et al, 2012;Szepietowska et al, 2010;Zampetti et al, 2010) Obesity rs12255372, rs7903146 TCF7L2 variants do not have a direct relationship with adiposity but influence glycemic indexes, increasing T2D risk in subjects with and without overweight condition (Kimura et al, 2018;Vazquez-Roque et al, 2011;Wrzosek et al, 2019) Metabolic syndrome (MetS) rs12255372, rs7903146 rs7903146 increases the risk of hypertension in patients with T2D; TCF7L2 variants increase glycemic indexes and T2D risk in MetS patients (Katsoulis et al, 2018;Phillips et al, 2012;Rattanatham et al, 2017) Diabetic nephropathy rs7903146, rs7901695 TCF7L2 variants are significantly associated with CKD progression (Franceschini et al, 2012;Köttgen et al, 2008) Small-bowel Crohn's disease (CD) rs3814570, rs10885394, rs10885395…”

Section: Rs12255372 Rs7903146mentioning

confidence: 99%

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The broad pathogenetic role of TCF7L2 in human diseases beyond type 2 diabetes

Bosque-Plata

Hernández-Cortés

Gragnoli

2021

Journal Cellular Physiology

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The TCF7L2 protein is a key transcriptional effector of the Wnt/β-catenin signaling pathway, regulating gene expression. It was initially identified in cancer research and embryologic developmental studies. Later, the TCF7L2 gene was linked to type 2 diabetes (T2D), implicating TCF7L2 and Wnt-signaling in metabolic disorders and homeostasis. In fact, TCF7L2-T2D variants confer the greatest relative risk for T2D, unquestionably predicting conversion to T2D in individuals with impaired glucose tolerance. We aim to describe the relevance of TCF7L2 in other human disorders. The TCF7L2-single nucleotide polymorphisms (SNPs) and T2D-risk association have been replicated in numerous follow-up studies, and research has now been performed in several other diseases. In this article, we discuss common TCF7L2-T2D variants within the framework of their association with human diseases. The TCF7L2 functional regions need to be further investigated because the molecular and cellular mechanisms through which TCF7L2 contributes to risk associations with different diseases are still not fully elucidated. In this review, we show the association of common TCF7L2-T2D variants with many types of diseases. However, the role of rare genetic variations in the TCF7L2 gene in distinct diseases and ethnic groups has not been explored, and understanding their impact on specific phenotypes will be of clinical relevance. This offers an excellent opportunity to gain a clearer picture of the role that the TCF7L2 gene plays in the pathophysiology of human diseases. The potential pleiotropic role of TCF7L2 may underlie a possible pathway for comorbidity in human disorders.

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Section: Rs12255372 Rs7903146mentioning

confidence: 99%

“…The presence of both variants in MetS is a powerful predictor of glucose impairment, increasing the risk more than fourfold (Katsoulis et al, 2018 ). Furthermore, it has been found that the variant rs7903146 increases the risk of hypertension in patients with T2D (Rattanatham et al, 2017 ).…”

Section: Metabolic Syndrome (Mets)mentioning

confidence: 99%

The broad pathogenetic role of TCF7L2 in human diseases beyond type 2 diabetes

Bosque-Plata

Hernández-Cortés

Gragnoli

2021

Journal Cellular Physiology

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“…TCF7L2 is a wellestablished susceptibility gene for type 2 DM [79], and given the high comorbidity rate and possibly shared pathophysiology between DM and hypertension [80], further studies into this target may be warranted. In addition, a study of the Thai elderly population suggested that the SNP rs290487 in TCF7L2 may contribute to risks of hypertension regardless of type 2 DM [81]. Another cohort study concluded that both a parental history of diabetes and the TCF7L2 at-risk variant were associated with a higher incidence of hypertension after controlling for other cardiometabolic risk factors [82].…”

Section: Hypertensionmentioning

confidence: 99%

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Zhao

Shi

2022

Pharmaceutics

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Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles with outcomes defined as whether the drug treats the studied disease. The goal is to “learn” the expression patterns associated with treatment. Then, the fitted ML models were applied to expression profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may ‘re-discover’ known drug targets from an external database (OpenTargets). Indeed, we found evidence of significant enrichment across all diseases under study. A further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with the treatment of RA, which was supported by a study showing that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate the expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing drug targets.

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“…TCF7L2 is a well-established susceptibility gene for type 2 DM [59], and given the high comorbidity rate and possibly shared pathophysiology between DM and hypertension [60], further studies into this target may be warranted. In addition, a study of the Thai elderly population suggested that the SNP rs290487 in TCF7L2 may contribute to risks of hypertension regardless of Type 2 DM [61]. Another cohort study concluded that both a parental history of diabetes and the TCF7L2 at-risk variant were associated with a higher incidence of hypertension, after controlling for other cardiometabolic risk factors [62].…”

Section: Hypertensionmentioning

confidence: 99%

Prediction of drug targets for specific diseases leveraging gene perturbation data: A machine learning approach

Zhao

Shi

2021

Preprint

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Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations.We present a novel computational target discovery approach based on machine learning(ML) models. ML models are first trained on drug-induced expression profiles, with outcomes defined as whether the drug treats the studied disease. The goal is to “learn” expression patterns associated with treatment. The fitted ML models were then applied to expression profiles from gene perturbations(over-expression[OE]/knockdown[KD]). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential.The methodology was applied to predict targets for hypertension, diabetes mellitus(DM), rheumatoid arthritis(RA) and schizophrenia(SCZ). We validated our approach by evaluating whether the identified targets may ‘re-discover’ known drug targets from an external database(OpenTargets). We indeed found evidence of significant enrichment across all diseases under study. Further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with treatment of RA, which was supported by a study showing PSMB8 inhibitors(PR-957) ameliorated experimental RA in mice.In conclusion, we propose a new ML approach to integrate expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing targets.

show abstract

Genetic polymorphism in TCF7L2 and risk of hypertension in Thai elderly subjects

Cited by 3 publications

References 0 publications

The broad pathogenetic role of TCF7L2 in human diseases beyond type 2 diabetes

The broad pathogenetic role of TCF7L2 in human diseases beyond type 2 diabetes

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Prediction of drug targets for specific diseases leveraging gene perturbation data: A machine learning approach

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