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Ying S, Heung T, Thiruvahindrapuram B, Engchuan W, Yin Y, Blagojevic C, Zhang Z, Hegele RA, Yuen RKC, Bassett AS. Polygenic risk for triglyceride levels in the presence of a high impact rare variant. BMC Med Genomics 2023; 16:281. [PMID: 37940981 PMCID: PMC10634078 DOI: 10.1186/s12920-023-01717-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Elevated triglyceride (TG) levels are a heritable and modifiable risk factor for cardiovascular disease and have well-established associations with common genetic variation captured in a polygenic risk score (PRS). In young adulthood, the 22q11.2 microdeletion conveys a 2-fold increased risk for mild-moderate hypertriglyceridemia. This study aimed to assess the role of the TG-PRS in individuals with this elevated baseline risk for mild-moderate hypertriglyceridemia. METHODS We studied a deeply phenotyped cohort of adults (n = 157, median age 34 years) with a 22q11.2 microdeletion and available genome sequencing, lipid level, and other clinical data. The association between a previously developed TG-PRS and TG levels was assessed using a multivariable regression model adjusting for effects of sex, BMI, and other covariates. We also constructed receiver operating characteristic (ROC) curves using logistic regression models to assess the ability of TG-PRS and significant clinical variables to predict mild-moderate hypertriglyceridemia status. RESULTS The TG-PRS was a significant predictor of TG-levels (p = 1.52E-04), along with male sex and BMI, in a multivariable model (pmodel = 7.26E-05). The effect of TG-PRS appeared to be slightly stronger in individuals with obesity (BMI ≥ 30) (beta = 0.4617) than without (beta = 0.1778), in a model unadjusted for other covariates (p-interaction = 0.045). Among ROC curves constructed, the inclusion of TG-PRS, sex, and BMI as predictor variables produced the greatest area under the curve (0.749) for classifying those with mild-moderate hypertriglyceridemia, achieving an optimal sensitivity and specificity of 0.746 and 0.707, respectively. CONCLUSIONS These results demonstrate that in addition to significant effects of sex and BMI, genome-wide common variation captured in a PRS also contributes to the variable expression of the 22q11.2 microdeletion with respect to elevated TG levels.
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Affiliation(s)
- Shengjie Ying
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tracy Heung
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada
- The Dalglish Family 22Q Clinic, University Health Network, Toronto, ON, Canada
| | | | - Worrawat Engchuan
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Yue Yin
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Christina Blagojevic
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Zhaolei Zhang
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Robert A Hegele
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Ryan K C Yuen
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Anne S Bassett
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Clinical Genetics Research Program, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- The Dalglish Family 22Q Clinic, University Health Network, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Toronto General Hospital Research Institute and Campbell Family Mental Health Research Institute, Toronto, ON, Canada.
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Polygenic Risk of Hypertriglyceridemia Is Modified by BMI. Int J Mol Sci 2022; 23:ijms23179837. [PMID: 36077235 PMCID: PMC9456481 DOI: 10.3390/ijms23179837] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 12/03/2022] Open
Abstract
Background: Genetic risk scores (GRSs) have partially improved the understanding of the etiology of moderate hypertriglyceridemia (HTG), which until recently was mainly assessed by secondary predisposing causes. The main objective of this study was to assess whether this variability is due to the interaction between clinical variables and GRS. Methods: We analyzed 276 patients with suspected polygenic HTG. An unweighted GRS was developed with the following variants: c.724C > G (ZPR1 gene), c.56C > G (APOA5 gene), c.1337T > C (GCKR gene), g.19986711A > G (LPL gene), c.107 + 1647T > C (BAZ1B gene) and g.125478730A > T (TRIB gene). Interactions between the GRS and clinical variables (body mass index (BMI), diabetes mellitus, diet, physical activity, alcohol consumption, age and gender) were evaluated. Results: The GRS was associated with triglyceride (TG) concentrations. There was a significant interaction between BMI and GRS, with the intensity of the relationship between the number of alleles and the TG concentration being greater in individuals with a higher BMI. Conclusions: GRS is associated with plasma TG concentrations and is markedly influenced by BMI. This finding could improve the stratification of patients with a high genetic risk for HTG who could benefit from more intensive healthcare interventions.
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Pareek M, Mason RP, Bhatt DL. Icosapent ethyl: safely reducing cardiovascular risk in adults with elevated triglycerides. Expert Opin Drug Saf 2021; 21:31-42. [PMID: 34253137 DOI: 10.1080/14740338.2021.1954158] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION In patients at high cardiovascular risk, the rate of events remains elevated despite traditional, evidence-based lipid-lowering therapy. Residual hypertriglyceridemia is an important contributor to this risk. However, prior medications with triglyceride-lowering effects have not reduced adverse clinical outcomes in the statin era. AREAS COVERED The present review summarizes evidence and recommendations related to triglyceride-lowering therapy in the primary and secondary preventive settings. We provide an overview of findings from recent meta-analyses, important observational studies, and a detailed description of landmark trials, including the Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT). We further review recommendations from current guidelines. EXPERT OPINION Icosapent ethyl is a stable, highly purified ethyl ester of eicosapentaenoic acid that safely and effectively reduces cardiovascular events in the contemporary setting. It is prescribed at a dose of 2 grams twice daily and is indicated in patients at high cardiovascular risk who have fasting or non-fasting triglyceride levels ≥150 mg/dl despite maximally tolerated statin treatment, or in individuals with triglyceride levels ≥500 mg/dl. Conversely, omega-3 fatty acid preparations containing a combination of eicosapentaenoic acid and docosahexaenoic acid are not indicated for reduction of cardiovascular risk and should be actively deprescribed.
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Affiliation(s)
- Manan Pareek
- Heart & Vascular Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Internal Medicine, Yale New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA.,Department of Cardiology, North Zealand Hospital, Hillerød, Denmark
| | - R Preston Mason
- Heart & Vascular Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Elucida Research LLC, Beverly, MA, USA
| | - Deepak L Bhatt
- Heart & Vascular Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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4
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A genetic sum score of effect alleles associated with serum lipid concentrations interacts with educational attainment. Sci Rep 2021; 11:16541. [PMID: 34400708 PMCID: PMC8368036 DOI: 10.1038/s41598-021-95970-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels are influenced by both genes and the environment. The aim was to investigate whether education and income as indicators of socioeconomic position (SEP) interact with lipid-increasing genetic effect allele scores (GES) in a population-based cohort. Using baseline data of 4516 study participants, age- and sex-adjusted linear regression models were fitted to investigate associations between GES and lipids stratified by SEP as well as including GES×SEP interaction terms. In the highest education group compared to the lowest stronger effects per GES standard deviation were observed for HDL-C (2.96 mg/dl [95%-CI: 2.19, 3.83] vs. 2.45 mg/dl [95%-CI: 1.12, 3.72]), LDL-C (6.57 mg/dl [95%-CI: 4.73, 8.37] vs. 2.66 mg/dl [95%-CI: −0.50, 5.76]) and TC (8.06 mg/dl [95%-CI: 6.14, 9.98] vs. 4.37 mg/dl [95%-CI: 0.94, 7.80]). Using the highest education group as reference, interaction terms showed indication of GES by low education interaction for LDL-C (ßGES×Education: −3.87; 95%-CI: −7.47, −0.32), which was slightly attenuated after controlling for GESLDL-C×Diabetes interaction (ßGES×Education: −3.42; 95%-CI: −6.98, 0.18). The present study showed stronger genetic effects on LDL-C in higher SEP groups and gave indication for a GESLDL-C×Education interaction, demonstrating the relevance of SEP for the expression of genetic health risks.
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Skals R, Krogager ML, Appel EVR, Schnurr TM, Have CT, Gislason G, Poulsen HE, Køber L, Engstrøm T, Stender S, Hansen T, Grarup N, Lee CJY, Andersson C, Torp-Pedersen C, Weeke PE. Insulin resistance genetic risk score and burden of coronary artery disease in patients referred for coronary angiography. PLoS One 2021; 16:e0252855. [PMID: 34143812 PMCID: PMC8213191 DOI: 10.1371/journal.pone.0252855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 05/24/2021] [Indexed: 11/18/2022] Open
Abstract
AIMS Insulin resistance associates with development of metabolic syndrome and risk of cardiovascular disease. The link between insulin resistance and cardiovascular disease is complex and multifactorial. Confirming the genetic link between insulin resistance, type 2 diabetes, and coronary artery disease, as well as the extent of coronary artery disease, is important and may provide better risk stratification for patients at risk. We investigated whether a genetic risk score of 53 single nucleotide polymorphisms known to be associated with insulin resistance phenotypes was associated with diabetes and burden of coronary artery disease. METHODS AND RESULTS We genotyped patients with a coronary angiography performed in the capital region of Denmark from 2010-2014 and constructed a genetic risk score of the 53 single nucleotide polymorphisms. Logistic regression using quartiles of the genetic risk score was performed to determine associations with diabetes and coronary artery disease. Associations with the extent of coronary artery disease, defined as one-, two- or three-vessel coronary artery disease, was determined by multinomial logistic regression. We identified 4,963 patients, of which 17% had diabetes and 55% had significant coronary artery disease. Of the latter, 27%, 14% and 14% had one, two or three-vessel coronary artery disease, respectively. No significant increased risk of diabetes was identified comparing the highest genetic risk score quartile with the lowest. An increased risk of coronary artery disease was found for patients with the highest genetic risk score quartile in both unadjusted and adjusted analyses, OR 1.21 (95% CI: 1.03, 1.42, p = 0.02) and 1.25 (95% CI 1.06, 1.48, p<0.01), respectively. In the adjusted multinomial logistic regression, patients in the highest genetic risk score quartile were more likely to develop three-vessel coronary artery disease compared with patients in the lowest genetic risk score quartile, OR 1.41 (95% CI: 1.10, 1.82, p<0.01). CONCLUSIONS Among patients referred for coronary angiography, only a strong genetic predisposition to insulin resistance was associated with risk of coronary artery disease and with a greater disease burden.
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Affiliation(s)
- Regitze Skals
- Unit of Clinical Biostatistics, Aalborg University Hospital, Aalborg, Denmark
- * E-mail:
| | | | - Emil Vincent R. Appel
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Theresia M. Schnurr
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Christian Theil Have
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Gunnar Gislason
- Department of Cardiology, Copenhagen University Hospital Gentofte, Hellerup, Denmark
| | - Henrik Enghusen Poulsen
- Department of Clinical Pharmacology, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Lars Køber
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Thomas Engstrøm
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Steen Stender
- Department of Clinical Biochemistry, Copenhagen University Hospital Gentofte, Copenhagen, Denmark
| | - Torben Hansen
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | | | - Charlotte Andersson
- Department of Cardiology, Copenhagen University Hospital Gentofte, Hellerup, Denmark
| | | | - Peter E. Weeke
- Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
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Abdullah MMH, Vazquez-Vidal I, Baer DJ, House JD, Jones PJH, Desmarchelier C. Common Genetic Variations Involved in the Inter-Individual Variability of Circulating Cholesterol Concentrations in Response to Diets: A Narrative Review of Recent Evidence. Nutrients 2021; 13:695. [PMID: 33671529 PMCID: PMC7926676 DOI: 10.3390/nu13020695] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/11/2021] [Accepted: 02/12/2021] [Indexed: 12/21/2022] Open
Abstract
The number of nutrigenetic studies dedicated to the identification of single nucleotide polymorphisms (SNPs) modulating blood lipid profiles in response to dietary interventions has increased considerably over the last decade. However, the robustness of the evidence-based science supporting the area remains to be evaluated. The objective of this review was to present recent findings concerning the effects of interactions between SNPs in genes involved in cholesterol metabolism and transport, and dietary intakes or interventions on circulating cholesterol concentrations, which are causally involved in cardiovascular diseases and established biomarkers of cardiovascular health. We identified recent studies (2014-2020) that reported significant SNP-diet interactions in 14 cholesterol-related genes (NPC1L1, ABCA1, ABCG5, ABCG8, APOA1, APOA2, APOA5, APOB, APOE, CETP, CYP7A1, DHCR7, LPL, and LIPC), and which replicated associations observed in previous studies. Some studies have also shown that combinations of SNPs could explain a higher proportion of variability in response to dietary interventions. Although some findings still need replication, including in larger and more diverse study populations, there is good evidence that some SNPs are consistently associated with differing circulating cholesterol concentrations in response to dietary interventions. These results could help clinicians provide patients with more personalized dietary recommendations, in order to lower their risk for cardiovascular disease.
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Affiliation(s)
| | - Itzel Vazquez-Vidal
- Richardson Centre for Functional Foods & Nutraceuticals, University of Manitoba, Winnipeg, MB R3T 6C5, Canada;
| | - David J. Baer
- United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
| | - James D. House
- Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
| | - Peter J. H. Jones
- Nutritional Fundamentals for Health, Vaudreuil-Dorion, QC J7V 5V5, Canada;
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Williams PT. Gene-environment interactions due to quantile-specific heritability of triglyceride and VLDL concentrations. Sci Rep 2020; 10:4486. [PMID: 32161301 PMCID: PMC7066156 DOI: 10.1038/s41598-020-60965-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 02/17/2020] [Indexed: 12/16/2022] Open
Abstract
"Quantile-dependent expressivity" is a dependence of genetic effects on whether the phenotype (e.g., triglycerides) is high or low relative to its distribution in the population. Quantile-specific offspring-parent regression slopes (βOP) were estimated by quantile regression for 6227 offspring-parent pairs. Quantile-specific heritability (h2), estimated by 2βOP/(1 + rspouse), decreased 0.0047 ± 0.0007 (P = 2.9 × 10-14) for each one-percent decrement in fasting triglyceride concentrations, i.e., h2 ± SE were: 0.428 ± 0.059, 0.230 ± 0.030, 0.111 ± 0.015, 0.050 ± 0.016, and 0.033 ± 0.010 at the 90th, 75th, 50th, 25th, and 10th percentiles of the triglyceride distribution, respectively. Consistent with quantile-dependent expressivity, 11 drug studies report smaller genotype differences at lower (post-treatment) than higher (pre-treatment) triglyceride concentrations. This meant genotype-specific triglyceride changes could not move in parallel when triglycerides were decreased pharmacologically, so that subtracting pre-treatment from post-treatment triglyceride levels necessarily created a greater triglyceride decrease for the genotype with a higher pre-treatment value (purported precision-medicine genetic markers). In addition, sixty-five purported gene-environment interactions were found to be potentially attributable to triglyceride's quantile-dependent expressivity, including gene-adiposity (APOA5, APOB, APOE, GCKR, IRS-1, LPL, MTHFR, PCSK9, PNPLA3, PPARγ2), gene-exercise (APOA1, APOA2, LPL), gene-diet (APOA5, APOE, INSIG2, LPL, MYB, NXPH1, PER2, TNFA), gene-alcohol (ALDH2, APOA5, APOC3, CETP, LPL), gene-smoking (APOC3, CYBA, LPL, USF1), gene-pregnancy (LPL), and gene-insulin resistance interactions (APOE, LPL).
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Affiliation(s)
- Paul T Williams
- Lawrence Berkeley National Laboratory, Molecular Biophysics & Integrated Bioimaging Division 1 Cyclotron Road, Berkeley, CA, 94720, USA.
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8
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Abstract
Hypertriglyceridemia, a commonly encountered phenotype in cardiovascular and metabolic clinics, is surprisingly complex. A range of genetic variants, from single-nucleotide variants to large-scale copy number variants, can lead to either the severe or mild-to-moderate forms of the disease. At the genetic level, severely elevated triglyceride levels resulting from familial chylomicronemia syndrome (FCS) are caused by homozygous or biallelic loss-of-function variants in LPL, APOC2, APOA5, LMF1, and GPIHBP1 genes. In contrast, susceptibility to multifactorial chylomicronemia (MCM), which has an estimated prevalence of ~1 in 600 and is at least 50-100-times more common than FCS, results from two different types of genetic variants: (1) rare heterozygous variants (minor allele frequency <1%) with variable penetrance in the five causal genes for FCS; and (2) common variants (minor allele frequency >5%) whose individually small phenotypic effects are quantified using a polygenic score. There is indirect evidence of similar complex genetic predisposition in other clinical phenotypes that have a component of hypertriglyceridemia, such as combined hyperlipidemia and dysbetalipoproteinemia. Future considerations include: (1) evaluation of whether the specific type of genetic predisposition to hypertriglyceridemia affects medical decisions or long-term outcomes; and (2) searching for other genetic contributors, including the role of genome-wide polygenic scores, novel genes, non-linear gene-gene or gene-environment interactions, and non-genomic mechanisms including epigenetics and mitochondrial DNA.
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Kelly JM, Ordovas JM, Matuszek G, Smith CE, Huggins GS, Dashti HS, Ichikawa R, Booth SL. The Contribution of Lipids to the Interindividual Response of Vitamin K Biomarkers to Vitamin K Supplementation. Mol Nutr Food Res 2019; 63:e1900399. [PMID: 31533195 PMCID: PMC8815429 DOI: 10.1002/mnfr.201900399] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 08/16/2019] [Indexed: 12/12/2022]
Abstract
SCOPE A better understanding of factors contributing to interindividual variability in biomarkers of vitamin K can enhance the understanding of the equivocal role of vitamin K in cardiovascular disease. Based on the known biology of phylloquinone, the major form of vitamin K, it is hypothesized that plasma lipids contribute to the variable response of biomarkers of vitamin K metabolism to phylloquinone supplementation. METHODS AND RESULTS The association of plasma lipids and 27 lipid-related genetic variants with the response of biomarkers of vitamin K metabolism is examined in a secondary analysis of data from a 3-year phylloquinone supplementation trial in men (n = 66) and women (n = 85). Year 3 plasma triglycerides (TG), but not total cholesterol, LDL-cholesterol, or HDL-cholesterol, are associated with the plasma phylloquinone response (men: β = 1.01, p < 0.001, R2 = 0.34; women: β = 0.61, p = 0.008, R2 = 0.11; sex interaction p = 0.077). Four variants and the TG-weighted genetic risk score are associated with the plasma phylloquinone response in men only. Plasma lipids are not associated with changes in biomarkers of vitamin K function (undercarboxylated osteocalcin and matrix gla protein) in either sex. CONCLUSION Plasma TG are an important determinant of the interindividual response of plasma phylloquinone to phylloquinone supplementation, but changes in biomarkers of vitamin K carboxylation are not influenced by lipids.
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Affiliation(s)
- Jennifer M. Kelly
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
| | - Jose M. Ordovas
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
| | - Gregory Matuszek
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
| | - Caren E. Smith
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
| | - Gordon S. Huggins
- Molecular Cardiology Research Institute Center for Translational Genomics, Tufts Medical Center and Tufts University, Boston, MA
| | - Hassan S. Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Reiko Ichikawa
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
| | - Sarah L. Booth
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA
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Kang M, Sung J. A genome-wide search for gene-by-obesity interaction loci of dyslipidemia in Koreans shows diverse genetic risk alleles. J Lipid Res 2019; 60:2090-2101. [PMID: 31662442 DOI: 10.1194/jlr.p119000226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/21/2019] [Indexed: 11/20/2022] Open
Abstract
Dyslipidemia is a well-established risk factor for CVD. Studies suggest that similar fat accumulation in a given population might result in different levels of dyslipidemia risk among individuals; for example, despite similar or leaner body composition compared with Caucasians, Asians of Korean descent experience a higher prevalence of dyslipidemia. These variations imply a possible role of gene-obesity interactions on lipid profiles. Genome-wide association studies have identified more than 500 loci regulating plasma lipids, but the interaction structure between genes and obesity traits remains unclear. We hypothesized that some loci modify the effects of obesity on dyslipidemia risk and analyzed extensive gene-environment interactions (G×Es) at genome-wide levels to search for replicated gene-obesity interactive SNPs. In four Korean cohorts (n = 18,025), we identified and replicated 20 gene-obesity interactions, including novel variants (SCN1A and SLC12A8) and known lipid-associated variants (APOA5, BUD13, ZNF259, and HMGCR). When we estimated the additional heritability of dyslipidemia by considering G×Es, the gain was substantial for triglycerides (TGs) but mild for LDL cholesterol (LDL-C) and total cholesterol (Total-C); the interaction explained up to 18.7% of TG, 2.4% of LDL-C, and 1.9% of Total-C heritability associated with waist-hip ratio. Our findings suggest that some individuals are prone to develop abnormal lipid profiles, particularly with regard to TGs, even with slight increases in obesity indices; ethnic diversities in the risk alleles might partly explain the differential dyslipidemia risk between populations. Research about these interacting variables may facilitate knowledge-based approaches to personalize health guidelines according to individual genetic profiles.
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Affiliation(s)
- Moonil Kang
- Division of Genome and Health Big Data, Department of Public Health Sciences Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea .,Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
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Abstract
PURPOSE OF REVIEW With improved next-generation sequencing technology, open-access genetic databases and increased awareness of complex trait genetics, we are entering a new era of risk assessment in which genetic-based risk scores (GRSs) will play a clinical role. We review the concepts underlying polygenic models of disease susceptibility and challenges in clinical implementation. RECENT FINDINGS Polygenic risk scores are currently used in genetic research on dyslipidemias and cardiovascular disease (CVD). Although the underlying principles for constructing polygenic scores for lipids are established, the lack of consensus on which score to use is indicated by the large number - about 50 - that have been published. Recently, large-scale polygenic scores for CVD appear to afford superior risk prediction compared to small-scale scores. Despite the potential benefits of GRSs, certain biases towards ethnicity and sex need to be worked through. SUMMARY We are on the verge of clinical application of GRSs to provide incremental information on dyslipidemia and CVD risk above and beyond traditional clinical variables. Additional work is required to develop a consensus of how such scores will be constructed and measured in a validated manner, as well as clinical indications for their use.
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Affiliation(s)
- Jacqueline S Dron
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University
| | - Robert A Hegele
- Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University
- Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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12
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Severe hypertriglyceridemia is primarily polygenic. J Clin Lipidol 2019; 13:80-88. [DOI: 10.1016/j.jacl.2018.10.006] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 10/13/2018] [Accepted: 10/16/2018] [Indexed: 12/22/2022]
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13
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Lukács Krogager M, Skals RK, Appel EVR, Schnurr TM, Engelbrechtsen L, Have CT, Pedersen O, Engstrøm T, Roden DM, Gislason G, Poulsen HE, Køber L, Stender S, Hansen T, Grarup N, Andersson C, Torp-Pedersen C, Weeke PE. Hypertension genetic risk score is associated with burden of coronary heart disease among patients referred for coronary angiography. PLoS One 2018; 13:e0208645. [PMID: 30566436 PMCID: PMC6300273 DOI: 10.1371/journal.pone.0208645] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 11/16/2018] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Recent GWAS studies have identified more than 300 SNPs associated with variation in blood pressure. We investigated whether a genetic risk score constructed from these variants is associated with burden of coronary heart disease. METHODS From 2010-2014, 4,809 individuals admitted to coronary angiography in Capital Region of Copenhagen were genotyped. We calculated hypertension GRS comprised of GWAS identified SNPs associated with blood pressure. We performed logistic regression analyses to estimate the risk of hypertension and prevalent CHD. We also assessed the severity of CHD associated with the GRS. The analyses were performed using GRS quartiles. We used the Inter99 cohort to validate our results and to investigate for possible pleiotropy for the GRS with other CHD risk factors. RESULTS In COGEN, adjusted odds ratios comparing the 2nd, 3rd and 4th cumulative GRS quartiles with the reference were 1.12(95% CI 0.95-1.33), 1.35(95% CI 1.14-1.59) and 1.29(95% CI 1.09-1.53) respectively, for prevalent CHD. The adjusted multinomial logistic regression showed that 3rd and 4th GRS quartiles were associated with increased odds of developing two(OR 1.33, 95% CI 1.04-1.71 and OR 1.36, 95% CI 1.06-1.75, respectively) and three coronary vessel disease(OR 1.77, 95% CI 1.36-2.30 and OR 1.65, 95% CI 1.26-2.15, respectively). Similar results for incident CHD were observed in the Inter99 cohort. The hypertension GRS did not associate with type 2 diabetes, smoking, BMI or hyperlipidemia. CONCLUSION Hypertension GRS quartiles were associated with an increased risk of hypertension, prevalent CHD, and burden of coronary vessel disease in a dose-response pattern. We showed no evidence for pleiotropy with other risk factors for CHD.
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Affiliation(s)
- Maria Lukács Krogager
- Unit of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Regitze Kuhr Skals
- Unit of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark
| | - Emil Vincent R. Appel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Theresia M. Schnurr
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Line Engelbrechtsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christian Theil Have
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Engstrøm
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Dan M. Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Gunnar Gislason
- Department of Cardiology, Copenhagen University Hospital, Herlev and Gentofte, Hellerup, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Danish Heart Foundation, Copenhagen, Denmark
- The National Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Henrik Enghusen Poulsen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Laboratory of Clinical Pharmacology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Pharmacology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Lars Køber
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Steen Stender
- Department of Nutrition, Exercize and Sports, Copenhagen University, Frederiksberg, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Christian Torp-Pedersen
- Unit of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter E. Weeke
- Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Denmark
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Prediction of Blood Lipid Phenotypes Using Obesity-Related Genetic Polymorphisms and Lifestyle Data in Subjects with Excessive Body Weight. Int J Genomics 2018; 2018:4283078. [PMID: 30581838 PMCID: PMC6276413 DOI: 10.1155/2018/4283078] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 09/01/2018] [Accepted: 09/20/2018] [Indexed: 12/26/2022] Open
Abstract
Background and Aim Individual lipid phenotypes including circulating total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and triglycerides (TG) determinations are influenced by gene-environment interactions. The aim of this study was to predict blood lipid level (TC, LDL-c, HDL-c, and TG) variability using genetic and lifestyle data in subjects with excessive body weight-for-height. Methods This cross-sectional study enrolled 304 unrelated overweight/obese adults of self-reported European ancestry. A total of 95 single nucleotide polymorphisms (SNPs) related to obesity and weight loss were analyzed by a targeted next-generation sequencing system. Relevant genotypes of each SNP were coded as 0 (nonrisk) and 1 (risk). Four genetic risk scores (GRS) for each lipid phenotype were calculated by adding the risk genotypes. Information concerning lifestyle (diet, physical activity, alcohol drinking, and smoking) was obtained using validated questionnaires. Total body fat (TFAT) and visceral fat (VFAT) were determined by dual-energy X-ray absorptiometry. Results Overall, 45 obesity-related genetic variants were associated with some of the studied blood lipids. In addition to conventional factors (age, sex, dietary intakes, and alcohol consumption), the calculated GRS significantly contributed to explain their corresponding plasma lipid trait. Thus, HDL-c, TG, TC, and LDL-c serum concentrations were predicted by approximately 28% (optimism-corrected adj. R2 = 0.28), 25% (optimism-corrected adj. R2 = 0.25), 24% (optimism-corrected adj. R2 = 0.24), and 21% (optimism-corrected adj. R2=0.21), respectively. Interestingly, GRS were the greatest contributors to TC (squared partial correlation (PC2) = 0.18) and LDL-c (PC2 = 0.18) features. Likewise, VFAT and GRS had a higher impact on HDL-c (PC2 = 0.09 and PC2 = 0.06, respectively) and TG levels (PC2 = 0.20 and PC2 = 0.07, respectively) than the rest of variables. Conclusions Besides known lifestyle influences, some obesity-related genetic variants could help to predict blood lipid phenotypes.
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15
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Abstract
PURPOSE OF REVIEW Rare large-effect genetic variants underlie monogenic dyslipidemias, whereas common small-effect genetic variants - single nucleotide polymorphisms (SNPs) - have modest influences on lipid traits. Over the past decade, these small-effect SNPs have been shown to cumulatively exert consistent effects on lipid phenotypes under a polygenic framework, which is the focus of this review. RECENT FINDINGS Several groups have reported polygenic risk scores assembled from lipid-associated SNPs, and have applied them to their respective phenotypes. For lipid traits in the normal population distribution, polygenic effects quantified by a score that integrates several common polymorphisms account for about 20-30% of genetic variation. Among individuals at the extremes of the distribution, that is, those with clinical dyslipidemia, the polygenic component includes both rare variants with large effects and common polymorphisms: depending on the trait, 20-50% of susceptibility can be accounted for by this assortment of genetic variants. SUMMARY Accounting for polygenic effects increases the numbers of dyslipidemic individuals who can be explained genetically, but a substantial proportion of susceptibility remains unexplained. Whether documenting the polygenic basis of dyslipidemia will affect outcomes in clinical trials or prospective observational studies remains to be determined.
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16
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Engelbrechtsen L, Mahendran Y, Jonsson A, Gjesing AP, Weeke PE, Jørgensen ME, Færch K, Witte DR, Holst JJ, Jørgensen T, Grarup N, Pedersen O, Vestergaard H, Torekov S, Kanters JK, Hansen T. Common variants in the hERG (KCNH2) voltage-gated potassium channel are associated with altered fasting and glucose-stimulated plasma incretin and glucagon responses. BMC Genet 2018; 19:15. [PMID: 29548277 PMCID: PMC5857134 DOI: 10.1186/s12863-018-0602-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 03/13/2018] [Indexed: 01/29/2023] Open
Abstract
Background Patients with long QT syndrome due to rare loss-of-function mutations in the human ether-á-go-go-related gene (hERG) have prolonged QT interval, risk of arrhythmias, increased secretion of insulin and incretins and impaired glucagon response to hypoglycemia. This is caused by a dysfunctional Kv11.1 voltage-gated potassium channel. Based on these findings in patients with rare variants in hERG, we hypothesized that common variants in hERG may also lead to alterations in glucose homeostasis. Subsequently, we aimed to evaluate the effect of two common gain-of-function variants in hERG (rs36210421 and rs1805123) on QT interval and plasma levels of glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP), insulin and glucagon during an oral glucose tolerance test (OGTT). We used two population-based cohorts for evaluation of the effect of common variants in hERG on QT-interval and circulation levels of incretins, insulin and glucagon. The Danish population-based Inter99 cohort (n = 5895) was used to assess the effect of common variants on QT-interval. The Danish ADDITION-PRO cohort was used (n = 1329) to study genetic associations with levels of GLP-1, GIP, insulin and glucagon during an OGTT. Results Carriers of either the minor A-allele of rs36210421 or the minor G-allele of rs1805123 had ~ 2 ms shorter QT interval per risk allele (p = 0.025 and p = 1.9 × 10− 7). Additionally, both variants were associated with alterations in pancreatic and gut hormone release among carriers. The minor A- allele of rs36210421 was associated with increased GLP-1 and decreased GIP response to oral glucose stimulation, whereas the minor G-allele of rs1805123 is associated with decreased fasting plasma insulin and glucagon release. A genetic risk score combining the two gene variants revealed reductions in glucose-stimulated GIP, as well as suppressed glucagon response to increased glucose levels during an OGTT. Conclusions Two common missense polymorphisms of the Kv11.1 voltage-gated hERG potassium channel are associated with alterations in circulating levels of GIP and glucagon, suggesting that hERG potassium channels play a role in fasting and glucose-stimulated release of GIP and glucagon. Trial registration ClinicalTrials.gov (NCT00289237). Trial retrospectively registered at February 9, 2006. Studies were approved by the Ethical Committee of the Central Denmark Region (journal no. 20080229) and by the Copenhagen County Ethical Committee (KA 98155). Electronic supplementary material The online version of this article (10.1186/s12863-018-0602-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Line Engelbrechtsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark. .,Danish Diabetes Academy, Odense, Denmark.
| | - Yuvaraj Mahendran
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark.,Danish Diabetes Academy, Odense, Denmark
| | - Anna Jonsson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark
| | - Anette Prior Gjesing
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark
| | - Peter E Weeke
- Department of Cardiology, Rigshospitalet, Copenhagen, Denmark
| | - Marit E Jørgensen
- National Institute of Public Health, University of Southern Denmark, Odense, Denmark.,Steno Diabetes Center, Gentofte, Denmark
| | | | - Daniel R Witte
- Section of General Practice, Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Jens J Holst
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark.,Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Jørgensen
- Research Centre for Prevention and Health, The Capital Region of Denmark, Hillerød, Denmark.,Department of Public health, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark.,Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark.,Steno Diabetes Center, Gentofte, Denmark
| | - Signe Torekov
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark.,Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jørgen K Kanters
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark.,Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, Maersk Tower 8. floor, -2200, Copenhagen, DK, Denmark
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Influence of polygenic risk scores on lipid levels and dyslipidemia in a psychiatric population receiving weight gain-inducing psychotropic drugs. Pharmacogenet Genomics 2018; 27:464-472. [PMID: 28945215 DOI: 10.1097/fpc.0000000000000313] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Dyslipidemia represents a major health issue in psychiatry. We determined whether weighted polygenic risk scores (wPRSs) combining multiple single-nucleotide polymorphisms (SNPs) associated with lipid levels in the general population are associated with lipid levels [high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), and triglycerides] and/or dyslipidemia in patients receiving weight gain-inducing psychotropic drugs. We also determined whether genetics improve the predictive power of dyslipidemia. PATIENTS AND METHODS The influence of wPRS on lipid levels was firstly assessed in a discovery psychiatric sample (n=332) and was then tested for replication in an independent psychiatric sample (n=140). The contribution of genetic markers to predict dyslipidemia was evaluated in the combined psychiatric sample. RESULTS wPRSs were significantly associated with the four lipid traits in the discovery (P≤0.02) and in the replication sample (P≤0.03). Patients whose wPRS was higher than the median wPRS had significantly higher LDL, TC, and triglyceride levels (0.20, 0.32 and 0.26 mmol/l, respectively; P≤0.004) and significantly lower HDL levels (0.13 mmol/l; P<0.0001) compared with others. Adding wPRS to clinical data significantly improved dyslipidemia prediction of HDL (P=0.03) and a trend for improvement was observed for the prediction of TC dyslipidemia (P=0.08). CONCLUSION Population-based wPRSs have thus significant effects on lipid levels in the psychiatric population. As genetics improved the predictive power of dyslipidemia development, only 24 patients need to be genotyped to prevent the development of one case of HDL hypocholesterolemia. If confirmed by further prospective investigations, the present results could be used for individualizing psychotropic treatment.
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Ahmad S, Mora S, Franks PW, Orho-Melander M, Ridker PM, Hu FB, Chasman DI. Adiposity and Genetic Factors in Relation to Triglycerides and Triglyceride-Rich Lipoproteins in the Women's Genome Health Study. Clin Chem 2017; 64:231-241. [PMID: 29097515 DOI: 10.1373/clinchem.2017.280545] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 10/11/2017] [Indexed: 02/07/2023]
Abstract
BACKGROUND Previous results from Scandinavian cohorts have shown that obesity accentuates the effects of common genetic susceptibility variants on increased triglycerides (TG). Whether such interactions are present in the US population and further selective for particular TG-rich lipoprotein subfractions is unknown. METHODS We examined these questions using body mass index (BMI) and waist circumference (WC) among women of European ancestry from the Women's Genome Health Study (WGHS) (n = 21840 for BMI; n = 19313 for WC). A weighted genetic risk score (TG-wGRS) based on 40 published TG-associated single-nucleotide polymorphisms was calculated using published effect estimates. RESULTS Comparing overweight (BMI ≥ 25 kg/m2) and normal weight (BMI < 25 kg/m2) WGHS women, each unit increase of TG-wGRS was associated with TG increases of 1.013% and 1.011%, respectively, and this differential association was significant (Pinteraction = 0.014). Metaanalyses combining results for WGHS BMI with the 4 Scandinavian cohorts (INTER99, HEALTH2006, GLACIER, MDC) (total n = 40026) yielded a more significant interaction (Pinteraction = 0.001). Similarly, we observed differential association of the TG-wGRS with TG (Pinteraction = 0.006) in strata of WC (<80 cm vs ≥80 cm). Metaanalysis with 2 additional cohorts reporting WC (INTER99 and HEALTH2006) (total n = 27834) was significant with consistent effects (Pinteraction = 0.006). We also observed highly significant interactions of the TG-wGRS across the strata of BMI with very large, medium, and small TG-rich lipoprotein subfractions measured by nuclear magnetic resonance spectroscopy (all Pinteractions < 0.0001). The differential effects were strongest for very large TG-rich lipoprotein. CONCLUSIONS Our results support the original findings and suggest that obese individuals may be more susceptible to aggregated genetic risk associated with common TG-raising alleles, with effects accentuated in the large TG-rich lipoprotein subfraction.
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Affiliation(s)
- Shafqat Ahmad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; .,Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Samia Mora
- Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Paul W Franks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden.,Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University, Umeå, Sweden
| | - Marju Orho-Melander
- Diabetes and Cardiovascular Disease-Genetic Epidemiology, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Paul M Ridker
- Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Daniel I Chasman
- Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA;
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Sarzynski MA, Loos RJF, Lucia A, Pérusse L, Roth SM, Wolfarth B, Rankinen T, Bouchard C. Advances in Exercise, Fitness, and Performance Genomics in 2015. Med Sci Sports Exerc 2017; 48:1906-16. [PMID: 27183119 DOI: 10.1249/mss.0000000000000982] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This review of the exercise genomics literature encompasses the highest-quality articles published in 2015 across seven broad topics: physical activity behavior, muscular strength and power, cardiorespiratory fitness and endurance performance, body weight and adiposity, insulin and glucose metabolism, lipid and lipoprotein metabolism, and hemodynamic traits. One study used a quantitative trait locus for wheel running in mice to identify single nucleotide polymorphisms (SNPs) in humans associated with physical activity levels. Two studies examined the association of candidate gene ACTN3 R577X genotype on muscular performance. Several studies examined gene-physical activity interactions on cardiometabolic traits. One study showed that physical inactivity exacerbated the body mass index (BMI)-increasing effect of an FTO SNP but only in individuals of European ancestry, whereas another showed that high-density lipoprotein cholesterol (HDL-C) SNPs from genome-wide association studies exerted a smaller effect in active individuals. Increased levels of moderate-to-vigorous-intensity physical activity were associated with higher Matsuda insulin sensitivity index in PPARG Ala12 carriers but not Pro12 homozygotes. One study combined genome-wide and transcriptome-wide profiling to identify genes and SNPs associated with the response of triglycerides (TG) to exercise training. The genome-wide association study results showed that four SNPs accounted for all of the heritability of △TG, whereas the baseline expression of 11 genes predicted 27% of △TG. A composite SNP score based on the top eight SNPs derived from the genomic and transcriptomic analyses was the strongest predictor of ΔTG, explaining 14% of the variance. The review concludes with a discussion of a conceptual framework defining some of the critical conditions for exercise genomics studies and highlights the importance of the recently launched National Institutes of Health Common Fund program titled "Molecular Transducers of Physical Activity in Humans."
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Affiliation(s)
- Mark A Sarzynski
- 1Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC; 2The Genetics of Obesity and Related Metabolic Traits Program, The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY; 3Universidad Europea and Research Institute, Hospital 12 de Octubre (i+12), Madrid, SPAIN; 4Faculty of Medicine, Department of Kinesiology, Laval University, Ste-Foy, Québec, CANADA; 5Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD; 6Department of Sport Medicine, Humboldt University and Charité University School of Medicine, Berlin, GERMANY; and 7Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
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20
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Pereira NL. Genetic Risk and Altering Lipids With Lifestyle Changes and Metformin: Is Fate Modifiable? CIRCULATION. CARDIOVASCULAR GENETICS 2016; 9:469-471. [PMID: 27998943 DOI: 10.1161/circgenetics.116.001646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Naveen L Pereira
- From the Department of Cardiology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN.
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21
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Justesen JM, Andersson EA, Allin KH, Sandholt CH, Jørgensen T, Linneberg A, Jørgensen ME, Hansen T, Pedersen O, Grarup N. Increasing insulin resistance accentuates the effect of triglyceride-associated loci on serum triglycerides during 5 years. J Lipid Res 2016; 57:2193-2199. [PMID: 27777317 PMCID: PMC5321221 DOI: 10.1194/jlr.p068379] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 10/18/2016] [Indexed: 11/20/2022] Open
Abstract
Blood concentrations of triglycerides are influenced by genetic factors as well as a number of environmental factors, including adiposity and glucose homeostasis. The aim was to investigate the association between a serum triglyceride weighted genetic risk score (wGRS) and changes in fasting serum triglyceride level over 5 years and to test whether the effect of the wGRS was modified by 5 year changes of adiposity, insulin resistance, and lifestyle factors. A total of 3,474 nondiabetic individuals from the Danish Inter99 cohort participated in both the baseline and 5 year follow-up physical examinations and had information on the wGRS comprising 39 genetic variants. In a linear regression model adjusted for age, sex, and baseline serum triglyceride, the wGRS was associated with increased serum triglyceride levels over 5 years [per allele effect = 1.3% (1.0-1.6%); P = 1.0 × 10-17]. This triglyceride-increasing effect of the wGRS interacted with changes in insulin resistance (Pinteraction = 1.5 × 10-6). This interaction indicated that the effect of the wGRS was stronger in individuals who became more insulin resistant over 5 years. In conclusion, our findings suggest that increased genetic risk load is associated with a larger increase in fasting serum triglyceride levels in nondiabetic individuals during 5 years of follow-up. This effect of the wGRS is accentuated by increasing insulin resistance.
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Affiliation(s)
- Johanne M Justesen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ehm A Andersson
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Kristine H Allin
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Camilla H Sandholt
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Faculty of Medicine, University of Aalborg, Aalborg, Denmark
| | - Allan Linneberg
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark; Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Marit E Jørgensen
- Steno Diabetes Center, Gentofte, Denmark; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Vishram JKK, Hansen TW, Torp-Pedersen C, Madsbad S, Jørgensen T, Fenger M, Lyngbæk S, Jeppesen J. Relationship Between Two Common Lipoprotein Lipase Variants and the Metabolic Syndrome and Its Individual Components. Metab Syndr Relat Disord 2016; 14:442-448. [PMID: 27676127 DOI: 10.1089/met.2016.0030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Common lipoprotein lipase (LPL) variants are important determinants of triglycerides (TG) and high-density lipoprotein (HDL) cholesterol (C) concentrations. High TG/low HDL-C tend to cluster with hypertension, glucose intolerance, and abdominal obesity and comprise the metabolic syndrome (MetS). The role of LPL variants as a cause of MetS is unclear. This study investigated the relationship between two common LPL variants and the presence of MetS and its individual components. METHODS Cross-sectional study, including 2348 Danish women (50.7%) and men, age 41-72 years, without known cardiovascular disease. Carrier status for the two common LPL variants: 447Ter (low TG/high HDL-C) and 291Ser (high TG/low HDL-C) was determined. The prevalence of MetS according to the National Cholesterol Education Program criteria was 16.6%. RESULTS Of the 2348 participants, 19.8% had the 447Ter variant and 4.9% had the 291Ser variant. Compared with the reference variant, the prevalence of MetS was lower in carriers of the 447Ter variant (11.2% vs. 17.9%, P < 0.001) but with no difference in carriers of the 291Ser variant (18.4% vs. 16.5%, P = 0.59). Adjusted for age, sex, smoking, physical activity, alcohol consumption, and highest sex-specific insulin quartile, the relative risk of MetS was 0.63 (95% confidence interval [CI] 0.45-0.89, P < 0.01) for carriers of the 447Ter variant and 1.20 (95% CI 0.70-2.03, P > 0.05) for carriers of the 291Ser variant. Both LPL variants were associated with high TG/low HDL-C (P < 0.01), but not with the MetS components waist circumference, hypertension, and glucose intolerance (P > 0.05). CONCLUSION The two common LPL variants were associated with MetS through their effect on high TG/low HDL-C.
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Affiliation(s)
- Julie K K Vishram
- 1 Research Centre for Prevention and Health, Glostrup Hospital, University of Copenhagen , Glostrup, Denmark .,2 Department of Internal Medicine, Næstved Hospital, University of Copenhagen , Næstved, Denmark
| | | | | | - Sten Madsbad
- 5 Faculty of Health Sciences, University of Copenhagen , Copenhagen, Denmark .,6 Department of Endocrinology and Internal Medicine, Hvidovre Hospital, University of Copenhagen , Hvidovre, Denmark
| | - Torben Jørgensen
- 1 Research Centre for Prevention and Health, Glostrup Hospital, University of Copenhagen , Glostrup, Denmark .,5 Faculty of Health Sciences, University of Copenhagen , Copenhagen, Denmark
| | - Mogens Fenger
- 5 Faculty of Health Sciences, University of Copenhagen , Copenhagen, Denmark .,7 Department of Clinical Biochemistry, Hvidovre Hospital, University of Copenhagen , Hvidovre, Denmark
| | - Stig Lyngbæk
- 8 Department of Medicine, Glostrup Hospital, University of Copenhagen , Glostrup, Denmark
| | - Jørgen Jeppesen
- 5 Faculty of Health Sciences, University of Copenhagen , Copenhagen, Denmark .,8 Department of Medicine, Glostrup Hospital, University of Copenhagen , Glostrup, Denmark
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23
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Svendstrup M, Sandholt CH, Andersson Galijatovic EA, Linneberg A, Jørgensen T, Sørensen TIA, Pedersen O, Grarup N, Hansen T, Vestergaard H. Genetic risk scores link body fat distribution with specific cardiometabolic profiles. Obesity (Silver Spring) 2016; 24:1778-85. [PMID: 27311925 DOI: 10.1002/oby.21473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 01/18/2016] [Accepted: 01/19/2016] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Forty-nine known single nucleotide polymorphisms (SNPs) associating with body mass index (BMI)-adjusted waist-hip-ratio (WHR) (WHRadjBMI) were recently suggested to cluster into three groups with different associations to cardiometabolic traits. Genetic risk scores of the clusters on the risk of incident diabetes and associations with detailed cardiometabolic phenotypes were tested. METHODS In a prospective study of 6,121 Inter99 individuals, the risk of incident diabetes using Cox proportional hazards regression was evaluated. Using linear regession, the associations between genetic risk scores and anthropometry and blood samples at fasting and during an oral glucose tolerance test were tested. Analyses were adjusted for age, sex, and BMI. RESULTS Cluster 1 associated with an increased risk of diabetes (HR = 1.05, P = 2.74 × 10(-) (4) ) and with a poor metabolic profile, including fasting serum triglyceride (β = 0.98% mmol/L, P = 3.33 × 10(-) (8) ) and Matsuda index (β = -0.74%, P = 1.29 × 10(-) (4) ). No similar associations for Clusters 2 and 3 were found. The three clusters showed different patterns of association with waist circumference, hip circumference, and height. CONCLUSIONS Our results suggest that the 49 WHRadjBMI-associated SNPs affect metabolic health differently depending on the cluster of SNPs. The clusters further associate differently with anthropometric measures.
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Affiliation(s)
- Mathilde Svendstrup
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Camilla H Sandholt
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Ehm Astrid Andersson Galijatovic
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Allan Linneberg
- Research Centre for Prevention and Health, Glostrup Hospital, Glostrup, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup Hospital, Glostrup, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I A Sørensen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Oluf Pedersen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center, Gentofte, Denmark
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24
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Ali A, Varga TV, Stojkovic IA, Schulz CA, Hallmans G, Barroso I, Poveda A, Renström F, Orho-Melander M, Franks PW. Do Genetic Factors Modify the Relationship Between Obesity and Hypertriglyceridemia? ACTA ACUST UNITED AC 2016; 9:162-71. [DOI: 10.1161/circgenetics.115.001218] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 01/27/2016] [Indexed: 12/11/2022]
Abstract
Background—
Obesity is a major risk factor for dyslipidemia, but this relationship is highly variable. Recently published data from 2 Danish cohorts suggest that genetic factors may underlie some of this variability.
Methods and Results—
We tested whether established triglyceride-associated loci modify the relationship of body mass index (BMI) and triglyceride concentrations in 2 Swedish cohorts (the Gene–Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk [GLACIER Study; N=4312] and the Malmö Diet and Cancer Study [N=5352]). The genetic loci were amalgamated into a weighted genetic risk score (WGRS
TG
) by summing the triglyceride-elevating alleles (weighted by their established marginal effects) for all loci. Both BMI and the WGRS
TG
were strongly associated with triglyceride concentrations in GLACIER, with each additional BMI unit (kg/m
2
) associated with 2.8% (
P
=8.4×10
–84
) higher triglyceride concentration and each additional WGRS
TG
unit with 2% (
P
=7.6×10
–48
) higher triglyceride concentration. Each unit of the WGRS
TG
was associated with 1.5% higher triglyceride concentrations in normal weight and 2.4% higher concentrations in overweight/obese participants (
P
interaction
=0.056). Meta-analyses of results from the Swedish cohorts yielded a statistically significant WGRS
TG
×BMI interaction effect (
P
interaction
=6.0×10
–4
), which was strengthened by including data from the Danish cohorts (
P
interaction
=6.5×10
–7
). In the meta-analysis of the Swedish cohorts, nominal evidence of a 3-way interaction (WGRS
TG
×BMI×sex) was observed (
P
interaction
=0.03), where the WGRS
TG
×BMI interaction was only statistically significant in females. Using protein–protein interaction network analyses, we identified molecular interactions and pathways elucidating the metabolic relationships between BMI and triglyceride-associated loci.
Conclusions—
Our findings provide evidence that body fatness accentuates the effects of genetic susceptibility variants in hypertriglyceridemia, effects that are most evident in females.
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Affiliation(s)
- Ashfaq Ali
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Tibor V. Varga
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Ivana A. Stojkovic
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Christina-Alexandra Schulz
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Göran Hallmans
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Inês Barroso
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Alaitz Poveda
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Frida Renström
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Marju Orho-Melander
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
| | - Paul W. Franks
- From the Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit (A.A., T.V.V., A.P., F.R., P.W.F.) and Department of Clinical Sciences, Diabetes & Cardiovascular Disease-Genetic Epidemiology (I.A.S., C.-A.S., M.O.-M.), Lund University, Malmö, Sweden; Department of Systems Medicine, Steno Diabetes Center, Gentofte, Denmark (A.A.); Department of Biobank Research (G.H., F.R.) and Department of Public Health & Clinical Medicine (P.W.F.), Umeå University, Umeå, Sweden; Human
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25
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Klimentidis YC, Arora A. Interaction of Insulin Resistance and Related Genetic Variants With Triglyceride-Associated Genetic Variants. ACTA ACUST UNITED AC 2016; 9:154-61. [PMID: 26850992 DOI: 10.1161/circgenetics.115.001246] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 01/27/2016] [Indexed: 12/24/2022]
Abstract
BACKGROUND Several studies suggest that some triglyceride-associated single-nucleotide polymorphisms (SNPs) have pleiotropic and opposite effects on glycemic traits. This potentially implicates them in pathways such as de novo lipogenesis, which is presumably upregulated in the context of insulin resistance. We therefore tested whether the association of triglyceride-associated SNPs with triglyceride levels differs according to one's level of insulin resistance. METHODS AND RESULTS In 3 cohort studies (combined n=12 487), we tested the interaction of established triglyceride-associated SNPs (individually and collectively) with several traits related to insulin resistance, on triglyceride levels. We also tested the interaction of triglyceride SNPs with fasting insulin-associated SNPs, individually and collectively, on triglyceride levels. We find significant interactions of a weighted genetic risk score for triglycerides with insulin resistance on triglyceride levels (Pinteraction=2.73×10(-11) and Pinteraction=2.48×10(-11) for fasting insulin and homeostasis model assessment of insulin resistance, respectively). The association of the triglyceride genetic risk score with triglyceride levels is >60% stronger among those in the highest tertile of homeostasis model assessment of insulin resistance compared with those in the lowest tertile. Individual SNPs contributing to this trend include those in/near GCKR, CILP2, and IRS1, whereas PIGV-NROB2 and LRPAP1 display an opposite trend of interaction. In the pooled data set, we also identify a SNP-by-SNP interaction involving a triglyceride-associated SNP, rs4722551 near MIR148A, with a fasting insulin-associated SNP, rs4865796 in ARL15 (Pinteraction=4.1×10(-5)). CONCLUSIONS Our findings may thus provide genetic evidence for the upregulation of triglyceride levels in insulin-resistant individuals, in addition to identifying specific genetic loci and a SNP-by-SNP interaction implicated in this process.
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Affiliation(s)
- Yann C Klimentidis
- From the Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson.
| | - Amit Arora
- From the Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson
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26
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Akoudad S, Ikram MA, Portegies MLP, Adams HH, Bos D, Hofman A, Koudstaal PJ, Uitterlinden AG, van der Lugt A, van Duijn CM, Vernooij MW. Genetic loci for serum lipid fractions and intracerebral hemorrhage. Atherosclerosis 2016; 246:287-92. [PMID: 26820804 DOI: 10.1016/j.atherosclerosis.2016.01.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 01/11/2016] [Accepted: 01/15/2016] [Indexed: 11/26/2022]
Abstract
BACKGROUND Serum total cholesterol and its fractions are inversely associated with intracerebral hemorrhages (ICH) and their potential subclinical precursor, cerebral microbleeds. To ascertain whether there is a genetic basis for this inverse association, we studied established genetic loci for serum total, LDL, and HDL cholesterol, and triglycerides in their association with ICH and microbleeds. METHODS Data on 161 genetic variants for serum lipids was collected in 9011 stroke-free participants (mean age 65.8, SD 10.2; 57.9% women) of the population-based Rotterdam Study. Participants were followed from baseline (1997-2005) up to 2013 for the occurrence of ICH. A subset of 4179 participants underwent brain MRI for microbleed assessment between 2005 and 2011. We computed genetic risk scores (GRS) for the joint effect of lipid variants. Cox proportional hazards and logistic regression models were used to investigate the association of GRS of lipid fractions with ICH and microbleeds. RESULTS After a mean follow-up of 8.7 (SD 4.1) years, 67 (0.7%) participants suffered an ICH. Microbleed prevalence was 19.6%. Higher genetic load for high serum total and LDL cholesterol was associated with an increased risk of ICH. Higher genetic load for high serum LDL cholesterol was borderline associated with a higher prevalence of multiple lobar microbleeds. CONCLUSIONS Genetic susceptibility for high serum total and LDL cholesterol is positively associated with incident ICH and borderline associated with multiple lobar microbleeds. We did not find a genetic basis for the previously reported inverse association between serum lipid levels and ICH.
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Affiliation(s)
- Saloua Akoudad
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Radiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Neurology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Radiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Neurology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Marileen L P Portegies
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Neurology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Hieab H Adams
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Radiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Radiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Peter J Koudstaal
- Department of Neurology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Andre G Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Aad van der Lugt
- Department of Radiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Radiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands.
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