| 1. |
- Ahmad, Shafqat, et al.
(författare)
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Gene × physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry. : combined analysis of 111,421 individuals of European ancestry
- 2013
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Ingår i: PLoS Genetics. - : Public Library of Science (PLoS). - 1553-7404. ; 9:7, s. 1003607-1003607
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Tidskriftsartikel (refereegranskat)abstract
- Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age(2), sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.
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| 2. |
- Ahmad, Shafqat, et al.
(författare)
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Gene x physical activity interactions in obesity : combined analysis of 111,421 individuals of European ancestry
- 2013
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Ingår i: PLOS Genetics. - : Public Library of Science. - 1553-7390 .- 1553-7404. ; 9:7, s. e1003607-
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Tidskriftsartikel (refereegranskat)abstract
- Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age(2), sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS x physical activity interaction effect estimate (P-interaction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, P-interaction = 0.014 vs. n = 71,611, P-interaction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (P-interaction = 0.003) and the SEC16B rs10913469 (P-interaction = 0.025) variants showed evidence of SNP x physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.
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| 3. |
- Berndt, Sonja I., et al.
(författare)
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Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:5, s. 501-U69
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Tidskriftsartikel (refereegranskat)abstract
- Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.
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| 4. |
- Campbell, William, et al.
(författare)
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Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models
- 2016
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier BV. - 0895-4356 .- 1878-5921. ; 69, s. 89-95
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Tidskriftsartikel (refereegranskat)abstract
- Objective: We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Study Design and Setting: Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. Results: We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. Conclusion: We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance.
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| 5. |
- Cornelis, Marilyn C, et al.
(författare)
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Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior
- 2016
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Ingår i: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 25:24, s. 5472-5482
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Tidskriftsartikel (refereegranskat)abstract
- Caffeine is the most widely consumed psychoactive substance in the world and presents with wide interindividual variation in metabolism. This variation may modify potential adverse or beneficial effects of caffeine on health. We conducted a genome-wide association study (GWAS) of plasma caffeine, paraxanthine, theophylline, theobromine and paraxanthine/caffeine ratio among up to 9,876 individuals of European ancestry from six population-based studies. A single SNP at 6p23 (near CD83) and several SNPs at 7p21 (near AHR), 15q24 (near CYP1A2) and 19q13.2 (near CYP2A6) met GW-significance (P < 5 × 10(-8)) and were associated with one or more metabolites. Variants at 7p21 and 15q24 associated with higher plasma caffeine and lower plasma paraxanthine/caffeine (slow caffeine metabolism) were previously associated with lower coffee and caffeine consumption behavior in GWAS. Variants at 19q13.2 associated with higher plasma paraxanthine/caffeine (slow paraxanthine metabolism) were also associated with lower coffee consumption in the UK Biobank (n = 94 343, P < 1.0 × 10(-6)). Variants at 2p24 (in GCKR), 4q22 (in ABCG2) and 7q11.23 (near POR) that were previously associated with coffee consumption in GWAS were nominally associated with plasma caffeine or its metabolites. Taken together, we have identified genetic factors contributing to variation in caffeine metabolism and confirm an important modulating role of systemic caffeine levels in dietary caffeine consumption behavior. Moreover, candidate genes identified encode proteins with important clinical functions that extend beyond caffeine metabolism.
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| 6. |
- Do, Ron, et al.
(författare)
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Common variants associated with plasma triglycerides and risk for coronary artery disease
- 2013
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:11, s. 1345-
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Tidskriftsartikel (refereegranskat)abstract
- Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 x 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
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| 7. |
- Fall, Tove, 1979-, et al.
(författare)
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Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes
- 2016
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 59:10, s. 2114-2124
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesisIdentification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction.MethodsIn this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies.ResultsOut of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes.Conclusions/interpretationWe found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes.Access to research materialsMetabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS).
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| 8. |
- Figarska, Sylwia M., et al.
(författare)
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Proteomic profiles before and during weight loss : Results from randomized trial of dietary intervention
- 2020
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Ingår i: Scientific Reports. - : NATURE PUBLISHING GROUP. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Inflammatory and cardiovascular biomarkers have been associated with obesity, but little is known about how they change upon dietary intervention and concomitant weight loss. Further, protein biomarkers might be useful for predicting weight loss in overweight and obese individuals. We performed secondary analyses in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized intervention trial that included healthy 609 adults (18-50 years old) with BMI 28-40 kg/m(2), to evaluate associations between circulating protein biomarkers and BMI at baseline, during a weight loss diet intervention, and to assess predictive potential of baseline blood proteins on weight loss. We analyzed 263 plasma proteins at baseline and 6 months into the intervention using the Olink Proteomics CVD II, CVD III and Inflammation arrays. BMI was assessed at baseline, after 3 and 6 months of dietary intervention. At baseline, 102 of the examined inflammatory and cardiovascular biomarkers were associated with BMI (>90% with successful replication in 1,584 overweight/obese individuals from a community-based cohort study) and 130 tracked with weight loss shedding light into the pathophysiology of obesity. However, out of 263 proteins analyzed at baseline, only fibroblast growth factor 21 (FGF-21) predicted weight loss, and none helped individualize dietary assignment.
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| 9. |
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| 10. |
- Ganna, Andrea, et al.
(författare)
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5 year mortality predictors in 498 103 UK Biobank participants : a prospective population-based study
- 2015
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Ingår i: The Lancet. - 0140-6736 .- 1474-547X. ; 386:9993, s. 533-540
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Tidskriftsartikel (refereegranskat)abstract
- Background To our knowledge, a systematic comparison of predictors of mortality in middle-aged to elderly individuals has not yet been done. We investigated predictors of mortality in UK Biobank participants during a 5 year period. We aimed to investigate the associations between most of the available measurements and 5 year all-cause and cause-specific mortality, and to develop and validate a prediction score for 5 year mortality using only self-reported information. Methods Participants were enrolled in the UK Biobank from April, 2007, to July, 2010, from 21 assessment centres across England, Wales, and Scotland with standardised procedures. In this prospective population-based study, we assessed sex-specific associations of 655 measurements of demographics, health, and lifestyle with all-cause mortality and six cause-specific mortality categories in UK Biobank participants using the Cox proportional hazard model. We excluded variables that were missing in more than 80% of the participants and all cardiorespiratory fitness test measurements because summary data were not available. Validation of the prediction score was done in participants enrolled at the Scottish centres. UK life tables and census information were used to calibrate the score to the overall UK population. Findings About 500 000 participants were included in the UK Biobank. We excluded participants with more than 80% variables missing (n=746). Of 498 103 UK Biobank participants included (54% of whom were women) aged 37-73 years, 8532 (39% of whom were women) died during a median follow-up of 4.9 years (IQR 4.33-5.22). Self-reported health (C-index including age 0.74 [95% CI 0.73-0.75]) was the strongest predictor of all-cause mortality in men and a previous cancer diagnosis (0.73 [0.72-0.74]) was the strongest predictor of all-cause mortality in women. When excluding individuals with major diseases or disorders (Charlson comorbidity index >0; n=355 043), measures of smoking habits were the strongest predictors of all-cause mortality. The prognostic score including 13 self-reported predictors for men and 11 for women achieved good discrimination (0.80 [0.77-0.83] for men and 0.79 [0.76-0.83] for women) and significantly outperformed the Charlson comorbidity index (p<0.0001 in men and p=0.0007 in women). A dedicated website allows the interactive exploration of all results along with calculation of individual risk through an online questionnaire. Interpretation Measures that can simply be obtained by questionnaires and without physical examination were the strongest predictors of all-cause mortality in the UK Biobank population. The prediction score we have developed accurately predicts 5 year all-cause mortality and can be used by individuals to improve health awareness, and by health professionals and organisations to identify high-risk individuals and guide public policy.
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