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van der Schouw, Yvonne T.ORCID iD iconorcid.org/0000-0002-4605-435X
Publications (3 of 3) Show all publications
Forouhi, N. G., Imamura, F., Sharp, S. J., Koulman, A., Schulze, M. B., Zheng, J., . . . Wareham, N. J. (2016). Association of Plasma Phospholipid n-3 and n-6 Polyunsaturated Fatty Acids with Type 2 Diabetes: The EPIC-InterAct Case-Cohort Study. PLoS Medicine, 13(7), Article ID e1002094.
Open this publication in new window or tab >>Association of Plasma Phospholipid n-3 and n-6 Polyunsaturated Fatty Acids with Type 2 Diabetes: The EPIC-InterAct Case-Cohort Study
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2016 (English)In: PLoS Medicine, ISSN 1549-1277, E-ISSN 1549-1676, Vol. 13, no 7, article id e1002094Article in journal (Refereed) Published
Abstract [en]

Background Whether and how n-3 and n-6 polyunsaturated fatty acids (PUFAs) are related to type 2 diabetes (T2D) is debated. Objectively measured plasma PUFAs can help to clarify these associations.

Methods and Findings Plasma phospholipid PUFAs were measured by gas chromatography among 12,132 incident T2D cases and 15,919 subcohort participants in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study across eight European countries. Country-specific hazard ratios (HRs) were estimated using Prentice-weighted Cox regression and pooled by random-effects meta-analysis. We also systematically reviewed published prospective studies on circulating PUFAs and T2D risk and pooled the quantitative evidence for comparison with results from EPIC-InterAct. In EPIC-InterAct, among long-chain n-3 PUFAs, a-linolenic acid (ALA) was inversely associated with T2D (HR per standard deviation [SD] 0.93; 95% CI 0.88-0.98), but eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were not significantly associated. Among n-6 PUFAs, linoleic acid (LA) (0.80; 95% CI 0.77-0.83) and eicosadienoic acid (EDA) (0.89; 95% CI 0.85-0.94) were inversely related, and arachidonic acid (AA) was not significantly associated, while significant positive associations were observed with.-linolenic acid (GLA), dihomo-GLA, docosatetraenoic acid (DTA), and docosapentaenoic acid (n6-DPA), with HRs between 1.13 to 1.46 per SD. These findings from EPIC-InterAct were broadly similar to comparative findings from summary estimates from up to nine studies including between 71 to 2,499 T2D cases. Limitations included potential residual confounding and the inability to distinguish between dietary and metabolic influences on plasma phospholipid PUFAs.

Conclusions These large-scale findings suggest an important inverse association of circulating plant-origin n-3 PUFA (ALA) but no convincing association of marine-derived n3 PUFAs (EPA and DHA) with T2D. Moreover, they highlight that the most abundant n6-PUFA (LA) is inversely associated with T2D. The detection of associations with previously less well-investigated PUFAs points to the importance of considering individual fatty acids rather than focusing on fatty acid class.

National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:umu:diva-126528 (URN)10.1371/journal.pmed.1002094 (DOI)000383354300017 ()27434045 (PubMedID)
Available from: 2016-10-26 Created: 2016-10-10 Last updated: 2018-06-09Bibliographically approved
Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., . . . McCarthy, M. I. (2016). The genetic architecture of type 2 diabetes. Nature, 536(7614), 41-47
Open this publication in new window or tab >>The genetic architecture of type 2 diabetes
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2016 (English)In: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 536, no 7614, p. 41-47Article in journal (Refereed) Published
Abstract [en]

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

National Category
Medical Genetics
Identifiers
urn:nbn:se:umu:diva-127978 (URN)10.1038/nature18642 (DOI)000380999200026 ()27398621 (PubMedID)
Available from: 2016-12-13 Created: 2016-11-21 Last updated: 2018-06-09Bibliographically approved
Kengne, A. P., Beulens, J. W. J., Peelen, L. M., Moons, K. G. M., van der Schouw, Y. T., Schulze, M. B., . . . Wareham, N. J. (2014). Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes & Endocrinology, 2(1), 19-29
Open this publication in new window or tab >>Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models
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2014 (English)In: Lancet Diabetes & Endocrinology, ISSN 2213-8587, Vol. 2, no 1, p. 19-29Article in journal (Refereed) Published
Abstract [en]

Background The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations. Methods We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27 779 individuals from eight European countries, of whom 12 403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs >= 60 years), BMI (<25 kg/m(2) vs >= 25 kg/m(2)), and waist circumference (men <102 cm vs >= 102 cm; women <88 cm vs >= 88 cm). Findings We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0.76 (95% CI 0.72-0.80) to 0.81 (0.77-0.84) overall, from 0.73 (0.70-0.76) to 0.79 (0.74-0.83) in men, and from 0.78 (0.74-0.82) to 0.81 (0.80-0.82) in women. We noted significant heterogeneity in discrimination (p(heterogeneity) <0.0001) in all but one model. Calibration was good for most models, and consistent across countries (p(heterogeneity) >0.05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups. Interpretation Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.

National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:umu:diva-90878 (URN)10.1016/S2213-8587(13)70103-7 (DOI)000336720400018 ()
Available from: 2014-07-03 Created: 2014-07-01 Last updated: 2018-06-07Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4605-435X

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