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- Lotta, Luca A., et al.
(författare)
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Association between low-density lipoprotein cholesterol-lowering genetic variants and risk of type 2 diabetes : A meta-analysis
- 2016
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Ingår i: JAMA: The Journal of the American Medical Association. - : American Medical Association (AMA). - 0098-7484 .- 1538-3598. ; 316:13, s. 1383-1391
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Tidskriftsartikel (refereegranskat)abstract
- IMPORTANCE Low-density lipoprotein cholesterol (LDL-C)-lowering alleles in or near NPC1L1 or HMGCR, encoding the respective molecular targets of ezetimibe and statins, have previously been used as proxies to study the efficacy of these lipid-lowering drugs. Alleles near HMGCR are associated with a higher risk of type 2 diabetes, similar to the increased incidence of new-onset diabetes associated with statin treatment in randomized clinical trials. It is unknown whether alleles near NPC1L1 are associated with the risk of type 2 diabetes. OBJECTIVE To investigate whether LDL-C-lowering alleles in or near NPC1L1 and other genes encoding current or prospective molecular targets of lipid-lowering therapy (ie, HMGCR, PCSK9, ABCG5/G8, LDLR) are associated with the risk of type 2 diabetes. DESIGN, SETTING, AND PARTICIPANTS The associations with type 2 diabetes and coronary artery disease of LDL-C-lowering genetic variants were investigated in meta-analyses of genetic association studies. Meta-analyses included 50 775 individuals with type 2 diabetes and 270 269 controls and 60 801 individuals with coronary artery disease and 123 504 controls. Data collection took place in Europe and the United States between 1991 and 2016. EXPOSURES Low-density lipoprotein cholesterol-lowering alleles in or near NPC1L1, HMGCR, PCSK9, ABCG5/G8, and LDLR. MAIN OUTCOMES AND MEASURES Odds ratios (ORs) for type 2 diabetes and coronary artery disease. RESULTS Low-density lipoprotein cholesterol-lowering genetic variants at NPC1L1 were inversely associated with coronary artery disease (OR for a genetically predicted 1-mmol/L [38.7-mg/dL] reduction in LDL-C of 0.61 [95%CI, 0.42-0.88]; P = .008) and directly associated with type 2 diabetes (OR for a genetically predicted 1-mmol/L reduction in LDL-C of 2.42 [95%CI, 1.70-3.43]; P .001). For PCSK9 genetic variants, the OR for type 2 diabetes per 1-mmol/L genetically predicted reduction in LDL-C was 1.19 (95%CI, 1.02-1.38; P = .03). For a given reduction in LDL-C, genetic variants were associated with a similar reduction in coronary artery disease risk (I2 = 0%for heterogeneity in genetic associations; P = .93). However, associations with type 2 diabetes were heterogeneous (I2 = 77.2%; P = .002), indicating gene-specific associations with metabolic risk of LDL-C-lowering alleles. CONCLUSIONS AND RELEVANCE In thismeta-analysis, exposure to LDL-C-lowering genetic variants in or near NPC1L1 and other geneswas associated with a higher risk of type 2 diabetes. These data provide insights into potential adverse effects of LDL-C-lowering therapy.
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| 2. |
- Pennells, Lisa, et al.
(författare)
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Equalization of four cardiovascular risk algorithms after systematic recalibration : individual-participant meta-analysis of 86 prospective studies
- 2019
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Ingår i: European Heart Journal. - : Oxford University Press (OUP). - 0195-668X .- 1522-9645. ; 40:7, s. 621-
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Tidskriftsartikel (refereegranskat)abstract
- Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after ‘recalibration’, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at ‘high’ 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms.Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.
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| 3. |
- Vissers, Linda E.T., et al.
(författare)
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Dairy product intake and risk of type 2 diabetes in EPIC-interact : A mendelian randomization study
- 2019
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Ingår i: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 42:4, s. 568-575
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE To estimate the causal association between intake of dairy products and incident type 2 diabetes. RESEARCH DESIGN AND METHODS The analysis included 21,820 European individuals (9,686 diabetes cases) of the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study. Participants were genotyped, and rs4988235 (LCT-12910C>T), a single nucleotide polymorphism (SNP) for lactase persistence (LP) that enables digestion of dairy sugar, i.e., lactose, was imputed. Baseline dietary intakes were assessed with diet questionnaires. We investigated the associations between imputed SNP dosage for rs4988235 and intake of dairy products and other foods through linear regression. Mendelian randomization (MR) estimates for the milk-diabetes relationship were obtained through a two-stage least squares regression. RESULTS Each additional LP allele was associated with a higher intake of milk (b 17.1 g/day, 95% CI 10.6–23.6) and milk beverages (b 2.8 g/day, 95% CI 1.0–4.5) but not with intake of other dairy products. Other dietary intakes associated with rs4988235 included fruits (b 27.0 g/day, 95% CI 212.4 to 21.7 per additional LP allele), nonalcoholic beverages (b 218.0 g/day, 95% CI 234.4 to 21.6), and wine (b 24.8 g/day, 95% CI 29.1 to 20.6). In instrumental variable analysis, LP-associated milk intake was not associated with diabetes (hazard ratio per 15 g/day 0.99, 95% CI 0.93–1.05). CONCLUSIONS rs4988235 was associated with milk intake but not with intake of other dairy products. This MR study does not suggest that milk intake is associated with diabetes, which is consistent with previous observational and genetic associations. LP may be associated with intake of other foods as well, but owing to the modest associations, we consider it unlikely that this caused the observed null result.
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