The Drosophila ortholog of TMEM18 regulates insulin and glucagon-like signaling

Wiemerslage, Lyle; Gohel, Priya; Maestri, Giulia; Hilmarsson, Torfi Geir; Mickael, Michel‐Edwar; Fredriksson, Robert; Williams, Michael J.; Schiöth, Helgi B.

doi:10.1530/joe-16-0040

Cited by 19 publications

(16 citation statements)

References 47 publications

(53 reference statements)

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“…Therefore, we speculated that the locus near TMEM18 (rs6548238) may increase diabetes risk via inducing obesity and insulin resistance. An experimental study in Drosophila found that TMEM18 affected substrate levels via insulin and glucagon signaling [24]. Further functional studies are needed to understand the mechanisms by which the locus near TMEM18 (rs6548238) regulates the development of obesity and type 2 diabetes.…”

Section: Discussionmentioning

confidence: 99%

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

et al. 2020

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Background: Several obesity susceptibility loci in genes, including GNPDA2, SH2B1, TMEM18, MTCH2, CDKAL1, FAIM2, and MC4R, have been identified by genome-wide association studies. The purpose of this study was to investigate whether these loci are associated with the concurrence of obesity and type 2 diabetes in Chinese Han patients. Methods: Using the SNaPshot technique, we genotyped seven single nucleotide polymorphisms (SNPs) in 439 Chinese patients living in Northeast China who presented at The Second Hospital of Jilin University. We analyzed the associations between these seven alleles and clinical characteristics. Results: Risk alleles near TMEM18 (rs6548238) were associated with increased waist circumference, waist/hip ratio, body mass index (BMI), fasting plasma glucose, hemoglobin A1c, diastolic blood pressure, triglycerides, total cholesterol, and low-density lipoprotein-cholesterol; risk alleles of CDKAL1 (rs7754840) were associated with increased waist circumference and waist/hip ratio; and FAIM2 (rs7138803) risk alleles were linked to increased BMI, diastolic blood pressure, and triglycerides (all P < 0.05). After adjusting for sex and age, loci near TMEM18 (rs6548238) and FAIM2 (rs7138803), but not SH2B1 (rs7498665), near GNPDA2 (rs10938397), MTCH2 (rs10838738) and near MC4R (rs12970134), were associated with increased risk for type 2 diabetes in obese individuals. Conclusion: We found that loci near TMEM18 (rs6548238), CDKAL1 (rs7754840), and FAIM2 (rs7138803) may be associated with obesity-related indicators, and loci near TMEM18 (rs6548238) and FAIM2 (rs7138803) may increase susceptibility of concurrent type 2 diabetes associated with obesity.

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Section: Discussionmentioning

confidence: 99%

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

et al. 2020

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“…Although variants related to this gene have been associated with BMI and insulin concentrations, little is known of its function. A study in Drosophila melanogaster implicated TMEM18 in insulin/glucagon regulation [ 64 ]. The variant rs11676272 is among the SNPs most strongly associated with childhood adiposity and is also nominally associated with T1D.…”

Section: Discussionmentioning

confidence: 99%

Childhood adiposity and risk of type 1 diabetes: A Mendelian randomization study

et al. 2017

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BackgroundThe incidence of type 1 diabetes (T1D) is increasing globally. One hypothesis is that increasing childhood obesity rates may explain part of this increase, but, as T1D is rare, intervention studies are challenging to perform. The aim of this study was to assess this hypothesis with a Mendelian randomization approach that uses genetic variants as instrumental variables to test for causal associations.Methods and findingsWe created a genetic instrument of 23 single nucleotide polymorphisms (SNPs) associated with childhood adiposity in children aged 2–10 years. Summary-level association results for these 23 SNPs with childhood-onset (<17 years) T1D were extracted from a meta-analysis of genome-wide association study with 5,913 T1D cases and 8,828 reference samples. Using inverse-variance weighted Mendelian randomization analysis, we found support for an effect of childhood adiposity on T1D risk (odds ratio 1.32, 95% CI 1.06–1.64 per standard deviation score in body mass index [SDS-BMI]). A sensitivity analysis provided evidence of horizontal pleiotropy bias (p = 0.04) diluting the estimates towards the null. We therefore applied Egger regression and multivariable Mendelian randomization methods to control for this type of bias and found evidence in support of a role of childhood adiposity in T1D (odds ratio in Egger regression, 2.76, 95% CI 1.40–5.44). Limitations of our study include that underlying genes and their mechanisms for most of the genetic variants included in the score are not known. Mendelian randomization requires large sample sizes, and power was limited to provide precise estimates. This research has been conducted using data from the Early Growth Genetics (EGG) Consortium, the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, the Tobacco and Genetics (TAG) Consortium, and the Social Science Genetic Association Consortium (SSGAC), as well as meta-analysis results from a T1D genome-wide association study.ConclusionsThis study provides genetic support for a link between childhood adiposity and T1D risk. Together with evidence from observational studies, our findings further emphasize the importance of measures to reduce the global epidemic of childhood obesity and encourage mechanistic studies.

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“…One of these is the transmembrane protein 18 ( TMEM18 ) gene. TMEM18 is expressed throughout the body and plays a role in the regulation of body weight, appetite, and even in the development of obesity [48,49]. Another gene on chromosome 18 is the melanocortin-4 receptor ( MC4R ), whose risk allele is associated with 0.23 kg/m 2 higher BMI [42].…”

Section: Genetics and Obesitymentioning

confidence: 99%

A Scientific Perspective of Personalised Gene-Based Dietary Recommendations for Weight Management

Drabsch

Holzapfel

2019

Nutrients

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Various studies showed that a “one size fits all” dietary recommendation for weight management is questionable. For this reason, the focus increasingly falls on personalised nutrition. Although there is no precise and uniform definition of personalised nutrition, the inclusion of genetic variants for personalised dietary recommendations is more and more favoured, whereas scientific evidence for gene-based dietary recommendations is rather limited. The purpose of this article is to provide a science-based viewpoint on gene-based personalised nutrition and weight management. Most of the studies showed no clinical evidence for gene-based personalised nutrition. The Food4Me study, e.g., investigated four different groups of personalised dietary recommendations based on dietary guidelines, and physiological, clinical, or genetic parameters, and resulted in no difference in weight loss between the levels of personalisation. Furthermore, genetic direct-to-consumer (DTC) tests are widely spread by companies. Scientific organisations clearly point out that, to date, genetic DTC tests are without scientific evidence. To date, gene-based personalised nutrition is not yet applicable for the treatment of obesity. Nevertheless, personalised dietary recommendations on the genetic landscape of a person are an innovative and promising approach for the prevention and treatment of obesity. In the future, human intervention studies are necessary to prove the clinical evidence of gene-based dietary recommendations.

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The Drosophila ortholog of TMEM18 regulates insulin and glucagon-like signaling

Cited by 19 publications

References 47 publications

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

Obesity-related loci in TMEM18, CDKAL1 and FAIM2 are associated with obesity and type 2 diabetes in Chinese Han patients

Childhood adiposity and risk of type 1 diabetes: A Mendelian randomization study

A Scientific Perspective of Personalised Gene-Based Dietary Recommendations for Weight Management

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