Statin Use Associates With Risk of Type 2 Diabetes via Epigenetic Patterns at ABCG1

Liu, Yuwei; Shen, Yu; Guo, Tao; Parnell, Laurence D.; Westerman, Kenneth; Smith, Caren E.; Ordovás, José M.; Lai, Chao‐Qiang

doi:10.3389/fgene.2020.00622

Cited by 14 publications

(8 citation statements)

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“…However, a precise role of cg06500161 in lipid metabolism remains to be elucidated, as does that of lipid-lowering drugs in the relationship. The previously hypothesized influence of statins on methylation at this site [ 64 ], and its mediation on the association between statins and type 2 diabetes [ 65 ], suggest that methylation at cg06500161 could lie on the causal path between the apparent mitigating effect of lipid-lowering drugs and its association with MI.…”

Section: Discussionmentioning

confidence: 99%

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

et al. 2021

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Background The discovery of robust and trans-ethnically replicated DNA methylation markers of metabolic phenotypes, has hinted at a potential role of epigenetic mechanisms in lipid metabolism. However, DNA methylation and the lipid compositions and lipid concentrations of lipoprotein sizes have been scarcely studied. Here, we present an epigenome-wide association study (EWAS) (N = 5414 total) of mostly lipid-related metabolic measures, including a fine profiling of lipoproteins. As lipoproteins are the main players in the different stages of lipid metabolism, examination of epigenetic markers of detailed lipoprotein features might improve the diagnosis, prognosis, and treatment of metabolic disturbances. Results We conducted an EWAS of leukocyte DNA methylation and 226 metabolic measurements determined by nuclear magnetic resonance spectroscopy in the population-based KORA F4 study (N = 1662) and replicated the results in the LOLIPOP, NFBC1966, and YFS cohorts (N = 3752). Follow-up analyses in the discovery cohort included investigations into gene transcripts, metabolic-measure ratios for pathway analysis, and disease endpoints. We identified 161 associations (p value < 4.7 × 10−10), covering 16 CpG sites at 11 loci and 57 metabolic measures. Identified metabolic measures were primarily medium and small lipoproteins, and fatty acids. For apolipoprotein B-containing lipoproteins, the associations mainly involved triglyceride composition and concentrations of cholesterol esters, triglycerides, free cholesterol, and phospholipids. All associations for HDL lipoproteins involved triglyceride measures only. Associated metabolic measure ratios, proxies of enzymatic activity, highlight amino acid, glucose, and lipid pathways as being potentially epigenetically implicated. Five CpG sites in four genes were associated with differential expression of transcripts in blood or adipose tissue. CpG sites in ABCG1 and PHGDH showed associations with metabolic measures, gene transcription, and metabolic measure ratios and were additionally linked to obesity or previous myocardial infarction, extending previously reported observations. Conclusion Our study provides evidence of a link between DNA methylation and the lipid compositions and lipid concentrations of different lipoprotein size subclasses, thus offering in-depth insights into well-known associations of DNA methylation with total serum lipids. The results support detailed profiling of lipid metabolism to improve the molecular understanding of dyslipidemia and related disease mechanisms.

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Section: Discussionmentioning

confidence: 99%

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

et al. 2021

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“…In brief, ∼500,000 SNPs were genotyped on the Affymetrix GeneChip® Human Mapping 500K Array (Santa Clara, CA) and filtered at the sample and SNP level. QC steps have been described in detail ( Liu et al, 2020 ). SNP IDs, loci, and allelic information were annotated using the 1,000 Genomes Phase 3 downloaded from dbSNP (downloaded date: April 13, 2018) and human genome build GRCh37/hg19.…”

Section: Methodsmentioning

confidence: 99%

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene–Gene and Gene–Diet Interactions

et al. 2022

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Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual’s risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants’ obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity.Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

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“…To control plasma cholesterol levels, statin is one of the most prescribed medications. However, it has been observed to contribute to the increased risk of diabetes and the mechanism that involves this process remains unclear [68]. The ABCG1, ABCA1…”

Section: Discussionmentioning

confidence: 99%

“…and ACSL3 genes were inversely associated with the use of statins, methylation in CpG islands and diabetic markers under study. These genes stood out for being involved in cholesterol metabolism and glycemic pathways [68]. There are also reports of epigenetic effects of angiotensin receptor blocking medications such as Enalapril, Lisinopril and Captopril.…”

Section: Discussionmentioning

confidence: 99%

Association Between Methylation Levels of LPL, ADRB3 and MTHFR Genes in Leukocytes from Individuals with Isolated or Associated Morbidities and Inflammation: A Cross-Sectional Study

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Lima

Luna

et al. 2021

Preprint

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Epigenetic alterations such as DNA methylation have been associated with the etiology of inflammation-related diseases. The present study evaluated the association between the methylation levels of LPL , ADRB3 and MTHFR genes and the stage of inflammation in individuals with isolated or associated morbidities, as well as in individuals without morbidities. This is a cross-sectional population-based study, in which 261 adults, between 20 and 59 years, individuals of both sex were selected. Inflammatory parameters were evaluated in blood, and the evaluation of methylation levels in the promoter of LPL , ADRB3 and MTHFR genes was performed in peripheral blood leukocytes. For statistical treatment, normality analysis was performed using the Lilliefors test, multiple linear regression, in addition to odds ratio. For all tests, the significance level adopted was 5%. In individuals with isolated morbidities, a positive association was observed between CRP values (2.49mg/L±3.7) and LPL gene methylation levels (35%±18) (p=.003) and with associated morbidities, females had higher levels of LPL gene methylation (40% ± 20) (p = 0.041) and for MTHFR gene methylation levels (35% ± 18) a positive association was found with MDA values (3.02µmol/L± 0.8) (p=.032). In addition, the use of medications did not influence the level of methylation for any of the three genes analyzed. Among individuals with isolated or associated morbidities, there was an association between the methylation levels of the LPL gene with the CRP values and females, and furthermore with the MTHFR gene and MDA. This study could help to understand the etiology and treatment of different morbidities, enabling the discovery of new combats resources.

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Statin Use Associates With Risk of Type 2 Diabetes via Epigenetic Patterns at ABCG1

Cited by 14 publications

References 50 publications

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene–Gene and Gene–Diet Interactions

Association Between Methylation Levels of LPL, ADRB3 and MTHFR Genes in Leukocytes from Individuals with Isolated or Associated Morbidities and Inflammation: A Cross-Sectional Study

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