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  • 1. Abbas, S
    et al.
    Linseisen, J
    Rohrmann, S
    Beulens, JWJ
    Buijsse, B
    Amiano, P
    Ardanaz, E
    Balkau, B
    Boeing, H
    Clavel-Chapelon, F
    Fagherazzi, G
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Gavrila, D
    Grioni, S
    Kaaks, R
    Key, TJ
    Khaw, KT
    Kuehn, T
    Mattiello, A
    Molina-Montes, E
    Nilsson, PM
    Overvad, K
    Quiros, JR
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Sacerdote, C
    Saieva, C
    Slimani, N
    Sluijs, I
    Spijkerman, AMW
    Tjonneland, A
    Tumino, R
    van der A, DL
    Zamora-Ros, R
    Sharp, SJ
    Langenberg, C
    Forouhi, NG
    Riboli, E
    Wareham, NJ
    Dietary vitamin D intake and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition: the EPIC-InterAct study2014In: European Journal of Clinical Nutrition, ISSN 0954-3007, E-ISSN 1476-5640, Vol. 68, no 2, p. 196-202Article in journal (Refereed)
    Abstract [en]

    BACKGROUND/OBJECTIVES: Prospective cohort studies have indicated that serum vitamin D levels are inversely related to risk of type 2 diabetes. However, such studies cannot determine the source of vitamin D. Therefore, we examined the association of dietary vitamin D intake with incident type 2 diabetes within the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study in a heterogeneous European population including eight countries with large geographical variation.

    SUBJECTS/METHODS: Using a case-cohort design, 11 245 incident cases of type 2 diabetes and a representative subcohort (N = 15 798) were included in the analyses. Hazard ratios (HR) and 95% confidence intervals (CIs) for type 2 diabetes were calculated using a Prentice-weighted Cox regression adjusted for potential confounders. Twenty-four-hour diet-recall data from a subsample (N = 2347) were used to calibrate habitual intake data derived from dietary questionnaires.

    RESULTS: Median follow-up time was 10.8 years. Dietary vitamin D intake was not significantly associated with the risk of type 2 diabetes. HR and 95% CIs for the highest compared to the lowest quintile of uncalibrated vitamin D intake was 1.09 (0.97-1.22) (P-trend = 0.17). No associations were observed in a sex-specific analysis. The overall pooled effect (HR (95% CI)) using the continuous calibrated variable was 1.00 (0.97-1.03) per increase of 1 mg/day dietary vitamin D.

    CONCLUSIONS: This observational study does not support an association between higher dietary vitamin D intake and type 2 diabetes incidence. This result has to be interpreted in light of the limited contribution of dietary vitamin D on the overall vitamin D status of a person.

  • 2. Ahmad, S
    et al.
    Poveda, A
    Shungin, Dmitry
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Umeå University, Faculty of Medicine, Department of Odontology. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Lund University, Malmö, Sweden.
    Barroso, I
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Biobank Research. Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research.
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Biobank Research. Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Lund University, Malmö, Sweden.
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Lund University, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA.
    Established BMI-associated genetic variants and their prospective associations with BMI and other cardiometabolic traits: the GLACIER Study2016In: International Journal of Obesity, ISSN 0307-0565, E-ISSN 1476-5497, Vol. 40, no 9, p. 1346-1352Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Recent cross-sectional genome-wide scans have reported associations of 97 independent loci with body mass index (BMI). In 3541 middle-aged adult participants from the GLACIER Study, we tested whether these loci are associated with 10-year changes in BMI and other cardiometabolic traits (fasting and 2-h glucose, triglycerides, total cholesterol, and systolic and diastolic blood pressures).

    METHODS: A BMI-specific genetic risk score (GRS) was calculated by summing the BMI-associated effect alleles at each locus. Trait-specific cardiometabolic GRSs comprised only the loci that show nominal association (P⩽0.10) with the respective trait in the original cross-sectional study. In longitudinal genetic association analyses, the second visit trait measure (assessed ~10 years after baseline) was used as the dependent variable and the models were adjusted for the baseline measure of the outcome trait, age, age(2), fasting time (for glucose and lipid traits), sex, follow-up time and population substructure.

    RESULTS: The BMI-specific GRS was associated with increased BMI at follow-up (β=0.014 kg m(-2) per allele per 10-year follow-up, s.e.=0.006, P=0.019) as were three loci (PARK2 rs13191362, P=0.005; C6orf106 rs205262, P=0.043; and C9orf93 rs4740619, P=0.01). Although not withstanding Bonferroni correction, a handful of single-nucleotide polymorphisms was nominally associated with changes in blood pressure, glucose and lipid levels.

    CONCLUSIONS: Collectively, established BMI-associated loci convey modest but statistically significant time-dependent associations with long-term changes in BMI, suggesting a role for effect modification by factors that change with time in this population.

  • 3. Ahmad, Shafqat
    et al.
    Mora, Samia
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. 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.
    Orho-Melander, Marju
    Ridker, Paul M.
    Hu, Frank B.
    Chasman, Daniel I.
    Adiposity and Genetic Factors in Relation to Triglycerides and Triglyceride-Rich Lipoproteins in the Women's Genome Health Study2018In: Clinical Chemistry, ISSN 0009-9147, E-ISSN 1530-8561, Vol. 64, no 1, p. 231-241Article in journal (Refereed)
    Abstract [en]

    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.

  • 4. Ahmad, Shafqat
    et al.
    Mora, Samia
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. 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.
    Orho-Melander, Marju
    Ridker, Paul M.
    Hu, Frank B.
    Chasman, Daniel I.
    Adiposity and Genetic Factors in Relation to Triglycerides and Triglyceride-Rich Lipoproteins in the Women's Genome Health Study2018In: Clinical Chemistry, ISSN 0009-9147, E-ISSN 1530-8561, Vol. 64, no 1, p. 231-241Article in journal (Refereed)
    Abstract [en]

    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.

  • 5. Ahmad, Shafqat
    et al.
    Rukh, Gull
    Varga, Tibor V
    Ali, Ashfaq
    Kurbasic, Azra
    Shungin, Dmitry
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Umeå University, Faculty of Medicine, Department of Odontology. Lund University.
    Ericson, Ulrika
    Koivula, Robert W
    Chu, Audrey Y
    Rose, Lynda M
    Ganna, Andrea
    Qi, Qibin
    Stancakova, Alena
    Sandholt, Camilla H
    Elks, Cathy E
    Curhan, Gary
    Jensen, Majken K
    Tamimi, Rulla M
    Allin, Kristine H
    Jorgensen, Torben
    Brage, Soren
    Langenberg, Claudia
    Aadahl, Mette
    Grarup, Niels
    Linneberg, Allan
    Pare, Guillaume
    Magnusson, Patrik KE
    Pedersen, Nancy L
    Boehnke, Michael
    Hamsten, Anders
    Mohlke, Karen L
    Pasquale, Louis T
    Pedersen, Oluf
    Scott, Robert A
    Ridker, Paul M
    Ingelsson, Erik
    Laakso, Markku
    Hansen, Torben
    Qi, Lu
    Wareham, Nicholas J
    Chasman, Daniel I
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research. Umeå University, Faculty of Medicine, Department of Biobank Research.
    Hu, Frank B
    Renström, Frida
    Orho-Melander, Marju
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Lund University and Harvard University.
    Gene x physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry2013In: PLOS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 9, no 7, p. e1003607-Article in journal (Refereed)
    Abstract [en]

    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.

  • 6. Ahmad, Shafqat
    et al.
    Varga, Tibor V
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Gene x environment interactions in obesity: the state of the evidence2013In: Human Heredity, ISSN 0001-5652, E-ISSN 1423-0062, Vol. 75, no 2-4, p. 106-115Article in journal (Refereed)
    Abstract [en]

    Background/Aims: Obesity is a pervasive and highly prevalent disease that poses substantial health risks to those it affects. The rapid emergence of obesity as a global epidemic and the patterns and distributions of the condition within and between populations suggest that interactions between inherited biological factors (e.g. genes) and relevant environmental factors (e.g. diet and physical activity) may underlie the current obesity epidemic.

    Methods: We discuss the rationale for the assertion that gene x lifestyle interactions cause obesity, systematically appraise relevant literature, and consider knowledge gaps future studies might seek to bridge. Results: We identified >200 relevant studies, of which most are relatively small scale and few provide replication data.

    Conclusion: Although studies on gene x lifestyle interactions in obesity point toward the presence of such interactions, improved data standardization, appropriate pooling of data and resources, innovative study designs, and the application of powerful statistical methods will be required if translatable examples of gene x lifestyle interactions in obesity are to be identified. Future studies, of which most will be observational, should ideally be accompanied by appropriate replication data and, where possible, by analogous findings from experimental settings where clinically relevant traits (e.g. weight regain and weight cycling) are outcomes.

    (C) 2013 S. Karger AG, Basel

  • 7.
    Ahmad, Shafqat
    et al.
    Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden.
    Zhao, Wei
    Philadelphia, PA, US.
    Renström, Frida
    Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden.
    Rasheed, Asif
    Karachi, Pakistan.
    Samuel, Maria
    Karachi, Pakistan.
    Zaidi, Mozzam
    Karachi, Pakistan.
    Shah, Nabi
    Karachi, Pakistan; Abbottabad, Pakistan.
    Mallick, Nadeem Hayyat
    Punjab Institute of Cardiology, Lahore, Pakistan.
    Zaman, Khan Shah
    Karachi, Pakistan.
    Ishaq, Mohammad
    Karachi, Pakistan.
    Rasheed, Syed Zahed
    Karachi, Pakistan.
    Memon, Fazal-ur-Rheman
    Karachi, Pakistan.
    Hanif, Bashir
    Karachi, Pakistan.
    Lakhani, Muhammad Shakir
    Karachi, Pakistan.
    Ahmed, Faisal
    Karachi, Pakistan.
    Kazmi, Shahana Urooj
    Karachi, Pakistan.
    Frossard, Philippe
    Karachi, Pakistan; Nazarbayev University, Astana, Kazakhstan.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA.
    Saleheen, Danish
    Philadelphia, PA, US; Karachi, Pakistan.
    Physical activity, smoking, and genetic predisposition to obesity in people from Pakistan: the PROMIS study2015In: BMC Medical Genetics, ISSN 1471-2350, E-ISSN 1471-2350, Vol. 16, article id 114Article in journal (Refereed)
    Abstract [en]

    Background: Multiple genetic variants have been reliably associated with obesity-related traits in Europeans, but little is known about their associations and interactions with lifestyle factors in South Asians.

    Methods: In 16,157 Pakistani adults (8232 controls; 7925 diagnosed with myocardial infarction [MI]) enrolled in the PROMIS Study, we tested whether: a) BMI-associated loci, individually or in aggregate (as a genetic risk score - GRS), are associated with BMI; b) physical activity and smoking modify the association of these loci with BMI. Analyses were adjusted for age, age(2), sex, MI (yes/no), and population substructure.

    Results: Of 95 SNPs studied here, 73 showed directionally consistent effects on BMI as reported in Europeans. Each additional BMI-raising allele of the GRS was associated with 0.04 (SE = 0.01) kg/m(2) higher BMI (P = 4.5 x 10(-14)). We observed nominal evidence of interactions of CLIP1 rs11583200 (P-interaction = 0.014), CADM2 rs13078960 (P-interaction = 0.037) and GALNT10 rs7715256 (P-interaction = 0.048) with physical activity, and PTBP2 rs11165643 (P-interaction = 0.045), HIP1 rs1167827 (P-interaction = 0.015), C6orf106 rs205262 (P-interaction = 0.032) and GRID1 rs7899106 (P-interaction = 0.043) with smoking on BMI.

    Conclusions: Most BMI-associated loci have directionally consistent effects on BMI in Pakistanis and Europeans. There were suggestive interactions of established BMI-related SNPs with smoking or physical activity.

  • 8. Albrechtsen, A.
    et al.
    Grarup, N.
    Li, Y.
    Sparso, T.
    Tian, G.
    Cao, H.
    Jiang, T.
    Kim, S. Y.
    Korneliussen, T.
    Li, Q.
    Nie, C.
    Wu, R.
    Skotte, L.
    Morris, A. P.
    Ladenvall, C.
    Cauchi, S.
    Stancakova, A.
    Andersen, G.
    Astrup, A.
    Banasik, K.
    Bennett, A. J.
    Bolund, L.
    Charpentier, G.
    Chen, Y.
    Dekker, J. M.
    Doney, A. S. F.
    Dorkhan, M.
    Forsen, T.
    Frayling, T. M.
    Groves, C. J.
    Gui, Y.
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research. Umeå University, Faculty of Medicine, Department of Biobank Research.
    Hattersley, A. T.
    He, K.
    Hitman, G. A.
    Holmkvist, J.
    Huang, S.
    Jiang, H.
    Jin, X.
    Justesen, J. M.
    Kristiansen, K.
    Kuusisto, J.
    Lajer, M.
    Lantieri, O.
    Li, W.
    Liang, H.
    Liao, Q.
    Liu, X.
    Ma, T.
    Ma, X.
    Manijak, M. P.
    Marre, M.
    Mokrosinski, J.
    Morris, A. D.
    Mu, B.
    Nielsen, A. A.
    Nijpels, G.
    Nilsson, P.
    Palmer, C. N. A.
    Rayner, N. W.
    Renstrom, F.
    Ribel-Madsen, R.
    Robertson, N.
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Rossing, P.
    Schwartz, T. W.
    Slagboom, P. E.
    Sterner, M.
    Tang, M.
    Tarnow, L.
    Tuomi, T.
    van't Riet, E.
    van Leeuwen, N.
    Varga, T. V.
    Vestmar, M. A.
    Walker, M.
    Wang, B.
    Wang, Y.
    Wu, H.
    Xi, F.
    Yengo, L.
    Yu, C.
    Zhang, X.
    Zhang, J.
    Zhang, Q.
    Zhang, W.
    Zheng, H.
    Zhou, Y.
    Altshuler, D.
    't Hart, L. M.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Balkau, B.
    Froguel, P.
    McCarthy, M. I.
    Laakso, M.
    Groop, L.
    Christensen, C.
    Brandslund, I.
    Lauritzen, T.
    Witte, D. R.
    Linneberg, A.
    Jorgensen, T.
    Hansen, T.
    Wang, J.
    Nielsen, R.
    Pedersen, O.
    Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes2013In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 56, no 2, p. 298-310Article in journal (Refereed)
    Abstract [en]

    Human complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) > 1% with common metabolic phenotypes. The study comprised three stages. We performed medium-depth (8x) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI > 27.5 kg/m(2) and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p < 0.05) with case-control status, from four selected annotation categories or from loci reported to associate with metabolic traits. These variants were genotyped in 15,989 Danes to search for association with 12 metabolic phenotypes (stage 2). In stage 3, polymorphisms showing potential associations were genotyped in a further 63,896 Europeans. Exome sequencing identified 70,182 polymorphisms with MAF > 1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p = 8.5 x 10(-14)), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 x 10(-11)) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 x 10(-10)). We applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.

  • 9. Ali, Ashfaq
    et al.
    Varga, Tibor V.
    Stojkovic, Ivana A.
    Schulz, Christina-Alexandra
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Biobank Research.
    Barroso, Ines
    Poveda, Alaitz
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Biobank Research.
    Orho-Melander, Marju
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
    Do Genetic Factors Modify the Relationship Between Obesity and Hypertriglyceridemia?: Findings From the GLACIER and the MDC Studies2016In: Circulation: Cardiovascular Genetics, ISSN 1942-325X, E-ISSN 1942-3268, Vol. 9, no 2, p. 162-171Article in journal (Refereed)
    Abstract [en]

    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 Malmo 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=8.4x10(-84)) higher triglyceride concentration and each additional WGRS(TG) unit with 2% (P=7.6x10(-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)xBMI interaction effect (P-interaction=6.0x10(-4)), which was strengthened by including data from the Danish cohorts (P-interaction=6.5x10(-7)). In the meta-analysis of the Swedish cohorts, nominal evidence of a 3-way interaction (WGRS(TG)xBMIxsex) was observed (P-interaction=0.03), where the WGRS(TG)xBMI 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.

  • 10. Allin, Kristine H.
    et al.
    Tremaroli, Valentina
    Caesar, Robert
    Jensen, Benjamin A. H.
    Damgaard, Mads T. F.
    Bahl, Martin I.
    Licht, Tine R.
    Hansen, Tue H.
    Nielsen, Trine
    Dantoft, Thomas M.
    Linneberg, Allan
    Jørgensen, Torben
    Vestergaard, Henrik
    Kristiansen, Karsten
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine.
    Hansen, Torben
    Bäckhed, Fredrik
    Pedersen, Oluf
    Aberrant intestinal microbiota in individuals with prediabetes2018In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 61, no 4, p. 810-820Article in journal (Refereed)
    Abstract [en]

    Aims/hypothesis: Individuals with type 2 diabetes have aberrant intestinal microbiota. However, recent studies suggest that metformin alters the composition and functional potential of gut microbiota, thereby interfering with the diabetes-related microbial signatures. We tested whether specific gut microbiota profiles are associated with prediabetes (defined as fasting plasma glucose of 6.1-7.0 mmol/l or HbA1c of 42-48 mmol/mol [6.0-6.5%]) and a range of clinical biomarkers of poor metabolic health.

    Methods: In the present case-control study, we analysed the gut microbiota of 134 Danish adults with prediabetes, overweight, insulin resistance, dyslipidaemia and low-grade inflammation and 134 age-and sex-matched individuals with normal glucose regulation.

    Results: We found that five bacterial genera and 36 operational taxonomic units (OTUs) were differentially abundant between individuals with prediabetes and those with normal glucose regulation. At the genus level, the abundance of Clostridium was decreased (mean log2 fold change -0.64 (SEM 0.23), p adj = 0.0497), whereas the abundances of Dorea, [Ruminococcus], Sutterella and Streptococcus were increased (mean log2 fold change 0.51 (SEM 0.12), p adj = 5 x 10-4; 0.51 (SEM 0.11), p adj = 1 x 10-4; 0.60 (SEM 0.21), p adj = 0.0497; and 0.92 (SEM0.21), p adj = 4 x 10-4, respectively). The two OTUs that differed the most were a member of the order Clostridiales (OTU 146564) and Akkermansia muciniphila, which both displayed lower abundance among individuals with prediabetes (mean log2 fold change -1.74 (SEM0.41), p adj = 2 x 10-3 and -1.65 (SEM0.34), p adj = 4 x 10-4, respectively). Faecal transfer from donors with prediabetes or screen-detected, drug-naive type 2 diabetes to germfree Swiss Webster or conventional C57BL/6 J mice did not induce impaired glucose regulation in recipient mice.

    Conclusions/interpretation: Collectively, our data show that individuals with prediabetes have aberrant intestinal microbiota characterised by a decreased abundance of the genus Clostridium and the mucin-degrading bacterium A. muciniphila. Our findings are comparable to observations in overt chronic diseases characterised by low-grade inflammation.

  • 11. Almqvist, Catarina
    et al.
    Adami, Hans-Olov
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Groop, Leif
    Ingelsson, Erik
    Kere, Juha
    Lissner, Lauren
    Litton, Jan-Eric
    Maeurer, Markus
    Michaëlsson, Karl
    Palmgren, Juni
    Pershagen, Göran
    Ploner, Alexander
    Sullivan, Patrick F
    Tybring, Gunnel
    Pedersen, Nancy L
    LifeGene: a large prospective population-based study of global relevance2011In: European Journal of Epidemiology, ISSN 0393-2990, E-ISSN 1573-7284, Vol. 26, no 1, p. 67-77Article in journal (Refereed)
    Abstract [en]

    Studying gene-environment interactions requires that the amount and quality of the lifestyle data is comparable to what is available for the corresponding genomic data. Sweden has several crucial prerequisites for comprehensive longitudinal biomedical research, such as the personal identity number, the universally available national health care system, continuously updated population and health registries and a scientifically motivated population. LifeGene builds on these strengths to bridge the gap between basic research and clinical applications with particular attention to populations, through a unique design in a research-friendly setting. LifeGene is designed both as a prospective cohort study and an infrastructure with repeated contacts of study participants approximately every 5 years. Index persons aged 18-45 years old will be recruited and invited to include their household members (partner and any children). A comprehensive questionnaire addressing cutting-edge research questions will be administered through the web with short follow-ups annually. Biosamples and physical measurements will also be collected at baseline, and re-administered every 5 years thereafter. Event-based sampling will be a key feature of LifeGene. The household-based design will give the opportunity to involve young couples prior to and during pregnancy, allowing for the first study of children born into cohort with complete pre-and perinatal data from both the mother and father. Questions and sampling schemes will be tailored to the participants' age and life events. The target of LifeGene is to enroll 500,000 Swedes and follow them longitudinally for at least 20 years.

  • 12. Atabaki-Pasdar, Naeimeh
    et al.
    Ohlsson, Mattias
    Shungin, Dmitry
    Kurbasic, Azra
    Ingelsson, Erik
    Pearson, Ewan R.
    Ali, Ashfaq
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, SE-205 02, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA.
    Statistical power considerations in genotype-based recall randomized controlled trials2016In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, article id 37307Article in journal (Refereed)
    Abstract [en]

    Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for genemetformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

  • 13. Barroso, I
    et al.
    Luan, J
    Sandhu, MS
    Franks, Paul
    Umeå University, Faculty of Medicine, Public Health and Clinical Medicine.
    Crowley, V
    Meta-analysis of the Gly482Ser variant in PPARGC1A in type 2 diabetes and related phenotypes.2006In: Diabetologia, Vol. 49, no 3, p. 501-5Article in journal (Refereed)
    Abstract [en]

    AIMS/HYPOTHESIS: Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PPARGC1A) is a transcriptional co-activator with a central role in energy expenditure and glucose metabolism. Several studies have suggested that the common PPARGC1A polymorphism Gly482Ser may be associated with risk of type 2 diabetes, with conflicting results. To clarify the role of Gly482Ser in type 2 diabetes and related human metabolic phenotypes we genotyped this polymorphism in a case-control study and performed a meta-analysis of relevant published data. MATERIALS AND METHODS: Gly482Ser was genotyped in a type 2 diabetes case-control study (N=1,096) using MassArray technology. A literature search revealed publications that examined Gly482Ser for association with type 2 diabetes and related metabolic phenotypes. Meta-analysis of the current study and relevant published data was undertaken. RESULTS: In the pooled meta-analysis, including data from this study and seven published reports (3,718 cases, 4,818 controls), there was evidence of between-study heterogeneity (p<0.1). In the fixed-effects meta-analysis, the pooled odds ratio for risk of type 2 diabetes per Ser482 allele was 1.07 (95% CI 1.00-1.15, p=0.044). Elimination of one of the studies from the meta-analysis gave a summary odds ratio of 1.11 (95% CI 1.04-1.20, p=0.004), with no between-study heterogeneity (p=0.475). For quantitative metabolic traits in normoglycaemic subjects, we also found significant between-study heterogeneity. However, no significant association was observed between Gly482Ser and BMI, fasting glucose or fasting insulin. CONCLUSIONS/INTERPRETATION: This meta-analysis of data from the current and published studies supports a modest role for the Gly482Ser PPARGC1A variant in type 2 diabetes risk.

  • 14. Bennet, L.
    et al.
    Groop, L.
    Lindblad, U.
    Agardh, C-D
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Clinical Sciences, Lund University, Malmö, Sweden and Genetic & Molecular Epidemiology Unit, Lund University, Malmö, Sweden and Department of Nutrition, Harvard School of Public Health, Boston Massachusetts, USA.
    Ethnicity is an independent risk indicator when estimating diabetes risk with FINDRISC scores: A cross sectional study comparing immigrants from the Middle East and native Swedes2014In: Primary Care Diabetes, ISSN 1751-9918, E-ISSN 1878-0210, Vol. 8, no 3, p. 231-238Article in journal (Refereed)
    Abstract [en]

    Aims: This study sought to compare type 2 diabetes (T2D) risk indicators in Iraqi immigrants with those in ethnic Swedes living in southern Sweden. Methods: Population-based, cross-sectional cohort study of men and women, aged 30-75 years, born in Iraq or Sweden conducted in 2010-2012 in Malmo, Sweden. A 75 g oral glucose tolerance test was performed and sociodemographic and lifestyle data were collected. T2D risk was assessed by the Finnish Diabetes Risk Score (FINDRISC). Results: In Iraqi versus Swedish participants, T2D was twice as prevalent (11.6 vs. 5.8%, p < 0.001). A large proportion of the excess T2D risk was attributable to larger waist circumference and first-degree family history of diabetes. However, Iraqi ethnicity was a risk factor for T2D independently of other FINDRISC factors (odds ratio (OR) 2.5, 95% CI 1.6-3.9). The FINDRISC algorithm predicted that more Iraqis than Swedes (16.2 vs. 12.3%, p < 0.001) will develop T2D within the next decade. The total annual costs for excess T2D risk in Iraqis are estimated to exceed 2.3 million euros in 2005, not accounting for worse quality of life. Conclusions: Our study suggests that Middle Eastern ethnicity should be considered an independent risk indicator for diabetes. Accordingly, the implementation of culturally tailored prevention programs may be warranted. (C) 2014 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

  • 15. Bennet, L.
    et al.
    Lindblad, U.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    A family history of diabetes determines poorer glycaemic control and younger age of diabetes onset in immigrants from the Middle East compared with native Swedes2015In: Diabetes & Metabolism, ISSN 1262-3636, E-ISSN 1878-1780, Vol. 41, no 1, p. 45-54Article in journal (Refereed)
    Abstract [en]

    Aims. - Immigrant populations from the Middle East develop diabetes earlier than indigenous European populations; however, the underlying etiology is poorly understood. This study looked at the risk factors associated with early diabetes onset and, in non-diabetics, glycaemic control in immigrants from Iraq compared with native Swedes.

    Methods. - This cross-sectional population-based study comprised 1398 Iraqi immigrants and 757 Swedes (ages 30-75 years) residing in the same area of Malmo, Sweden. Outcomes were age at diabetes onset and glycaemic control (HbA(1c)) as assessed by Cox proportional hazards and linear regression, respectively.

    Results. - In Iraqis vs Swedes, clustering in the family history (in two or more relatives) was more prevalent (23.2% vs 3.6%, P<0.001) and diabetes onset occurred earlier (47.6 years vs 53.4 years, P=0.001). Having an Iraqi background independently raised the hazard ratio (HR) for diabetes onset. Diabetes risk due to family history was augmented by obesity, with the highest HRs observed in obese participants with clustering in the family history (HR: 5.1, 95% CI: 3.2-8.2) after adjusting for country of birth and gender. In participants without previously diagnosed diabetes (Iraqis: n=1270; Swedes: n=728), HbA(1c), levels were slightly higher in Iraqis than in Swedes (4.5% vs 4.4%, P=0.038). This difference was explained primarily by clustering in the family history rather than age, obesity, lifestyle or socioeconomic status.

    Conclusion. - The study shows that the greater predisposition to diabetes in Middle Eastern immigrants may be explained by a more extensive family history of the disorder; clinical interventions tailored to Middle Eastern immigrants with such a family history are thus warranted.

  • 16. Bennet, Louise
    et al.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine. Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Department of Diabetes and Endocrinology/Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden; Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA, USA.
    Zöller, Bengt
    Groop, Leif
    Family history of diabetes and its relationship with insulin secretion and insulin sensitivity in Iraqi immigrants and native Swedes: a population-based cohort study2018In: Acta Diabetologica, ISSN 0940-5429, E-ISSN 1432-5233, Vol. 55, no 3, p. 233-242Article in journal (Refereed)
    Abstract [en]

    Aims Middle Eastern immigrants to western countries are at high risk of developing type 2 diabetes. However, the heritability and impact of rst-degree family history (FH) of type 2 diabetes on insulin secretion and action have not been adequately described. Methods Citizens of Malmö, Sweden, aged 30–75 years born in Iraq or Sweden were invited to participate in this population- based study. Insulin secretion (corrected insulin response and oral disposition index) and action (insulin sensitivity index) were assessed by oral glucose tolerance tests.

    Results In total, 45.7% of Iraqis (616/1348) and 27.4% of native Swedes (201/733) had FH in parent(s), sibling(s) or single parent and sibling, i.e., FH+. Approximately 8% of Iraqis and 0.7% of Swedes had ≥ 3 sibling(s) and parent(s) with diabetes, i.e., FH++. Irrespective of family size, prediabetes and diabetes increased with family burden (FH− 29.4%; FH+ 38.8%; FH++ 61.7%) without signi cant di erences across ethnicities. With increasing level of family burden, insulin secretion rather than insulin action decreased. Individuals with a combination of ≥ 3 siblings and parents with diabetes presented with the lowest levels of insulin secretion.

    Conclusions The Iraqi immigrant population often present with a strong familial burden of type 2 diabetes with the worst glycemic control and highest diabetes risk in individuals with ≥ 3 siblings and parents with diabetes. Our data show that in a population still free from diabetes familial burden in uences insulin secretion to a higher degree than insulin action and may be a logical target for intervention. 

  • 17. Bennet, Louise
    et al.
    Groop, Leif
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Nutrition, Harvard School of Public Health, Boston, MA, USA; Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden; Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Malmö, Sweden.
    Country of birth modifies the association of fatty liver index with insulin action in Middle Eastern immigrants to Sweden2015In: Diabetes Research and Clinical Practice, ISSN 0168-8227, E-ISSN 1872-8227, Vol. 110, no 1, p. 66-74Article in journal (Refereed)
    Abstract [en]

    Aims: Non-alcohol fatty liver disease (NAFLD) is a strong risk factor for insulin resistance and type 2 diabetes. The prevalence of NAFLD varies across populations of different ethnic backgrounds but the prevalence in Middle Eastern populations, which are at high risk of type 2 diabetes, is largely unknown. Using fatty liver index (FLI) as a proxy for NAFLD the aim was to calculate the odds of NAFLD (FLI >= 70) given country of origin and further to investigate the associations between ISI and FLI. Methods: In 2010-2012 we conducted a population-based study of individuals aged 30-75 years born in Iraq or Sweden, in whom anthropometrics, fasting blood samples and oral glucose tolerance tests were performed and sociodemography and lifestyle behaviors characterized. Results: A higher proportion of Iraqis (N = 1085) than Swedes (N = 605) had a high probability of NAFLD (FLI >= 70, 32.5 vs. 22.6%, p < 0.001, age-and sex-adjusted data) and ISI was more severely impaired (70.7 vs. 95.9%, p < 0.001). Independently of traditional risk factors for NAFLD, being born in Iraqi increased the risk of FLI >= 70 (OR 1.59: 95% CI 1.15, 2.20). Furthermore, country of birth presented a stronger association between ISI and FLI >= 70 in Iraqis than in Swedes (P-interaction = 0.019). Conclusions: Our data indicate that immigrants from Iraq are at higher risk of NAFLD. The finding that country of birth modifies the relationship of FLI with ISI, suggests that liver fat may be a stronger determinant of impaired insulin action and increased risk of type 2 diabetes in Iraqis than in Swedes.

  • 18. Bennet, Louise
    et al.
    Groop, Leif
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Ethnic differences in the contribution of insulin action and secretion to type 2 diabetes in immigrants from the Middle East compared to native Swedes2014In: Diabetes Research and Clinical Practice, ISSN 0168-8227, E-ISSN 1872-8227, Vol. 105, no 1, p. 79-87Article in journal (Refereed)
    Abstract [en]

    Aims: We investigated insulin action (insulin sensitivity index, ISI) and insulin secretion (oral disposition indices, DIo) and studied metabolic, demographic and lifestyle-related risk factors for type 2 diabetes and insulin action, in the largest non-European immigrant group to Sweden, immigrants from Iraq and native Swedes.

    Methods: Population-based, cross-sectional study conducted 2010-2012 including residents 30-75 years of age born in Iraq or Sweden, in whom oral glucose tolerance tests were performed and sociodemography and lifestyle behaviors were characterized.

    Results: In Iraqis compared to Swedes, ISI was more impaired (76.9 vs. 102.3, p < .001) whereas corrected insulin response CIR was higher (226.6 vs. 188.6, p = .016). However, insulin secretion was inadequate given the substantial insulin resistance, as indicated by lower DIo indices in Iraqis than in Swedes (DIo 12,712.9 vs. 14,659.2, p < .001). The crude ethnic difference in ISI was not offset by traditional risk factors like waist circumference, body mass index or family history of diabetes. In Iraqis, ISI conveyed somewhat higher odds of type 2 diabetes than in Swedes (odds ratio OR 0.98, 95% CI 0.97-0.99) vs. OR 0.95, 0.92-0.99), as indicated by an interaction between country of birth and ISI (P-interaction = .044).

    Conclusion: This study reports ethnic differences in the contribution of insulin action to type 2 diabetes. Our data suggests that the impaired insulin action observed in immigrants from the Middle East to Sweden is not fully explained by established risk factors.

    (C) 2014 The Authors. Published by Elsevier Ireland Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

  • 19. Berndt, Sonja I.
    et al.
    Gustafsson, Stefan
    Maegi, Reedik
    Ganna, Andrea
    Wheeler, Eleanor
    Feitosa, Mary F.
    Justice, Anne E.
    Monda, Keri L.
    Croteau-Chonka, Damien C.
    Day, Felix R.
    Esko, Tonu
    Fall, Tove
    Ferreira, Teresa
    Gentilini, Davide
    Jackson, Anne U.
    Luan, Jian'an
    Randall, Joshua C.
    Vedantam, Sailaja
    Willer, Cristen J.
    Winkler, Thomas W.
    Wood, Andrew R.
    Workalemahu, Tsegaselassie
    Hu, Yi-Juan
    Lee, Sang Hong
    Liang, Liming
    Lin, Dan-Yu
    Min, Josine L.
    Neale, Benjamin M.
    Thorleifsson, Gudmar
    Yang, Jian
    Albrecht, Eva
    Amin, Najaf
    Bragg-Gresham, Jennifer L.
    Cadby, Gemma
    den Heijer, Martin
    Eklund, Niina
    Fischer, Krista
    Goel, Anuj
    Hottenga, Jouke-Jan
    Huffman, Jennifer E.
    Jarick, Ivonne
    Johansson, Asa
    Johnson, Toby
    Kanoni, Stavroula
    Kleber, Marcus E.
    Koenig, Inke R.
    Kristiansson, Kati
    Kutalik, Zoltn
    Lamina, Claudia
    Lecoeur, Cecile
    Li, Guo
    Mangino, Massimo
    McArdle, Wendy L.
    Medina-Gomez, Carolina
    Mueller-Nurasyid, Martina
    Ngwa, Julius S.
    Nolte, Ilja M.
    Paternoster, Lavinia
    Pechlivanis, Sonali
    Perola, Markus
    Peters, Marjolein J.
    Preuss, Michael
    Rose, Lynda M.
    Shi, Jianxin
    Shungin, Dmitry
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Smith, Albert Vernon
    Strawbridge, Rona J.
    Surakka, Ida
    Teumer, Alexander
    Trip, Mieke D.
    Tyrer, Jonathan
    Van Vliet-Ostaptchouk, Jana V.
    Vandenput, Liesbeth
    Waite, Lindsay L.
    Zhao, Jing Hua
    Absher, Devin
    Asselbergs, Folkert W.
    Atalay, Mustafa
    Attwood, Antony P.
    Balmforth, Anthony J.
    Basart, Hanneke
    Beilby, John
    Bonnycastle, Lori L.
    Brambilla, Paolo
    Bruinenberg, Marcel
    Campbell, Harry
    Chasman, Daniel I.
    Chines, Peter S.
    Collins, Francis S.
    Connell, John M.
    Cookson, William O.
    de Faire, Ulf
    de Vegt, Femmie
    Dei, Mariano
    Dimitriou, Maria
    Edkins, Sarah
    Estrada, Karol
    Evans, David M.
    Farrall, Martin
    Ferrario, Marco M.
    Ferrieres, Jean
    Franke, Lude
    Frau, Francesca
    Gejman, Pablo V.
    Grallert, Harald
    Groenberg, Henrik
    Gudnason, Vilmundur
    Hall, Alistair S.
    Hall, Per
    Hartikainen, Anna-Liisa
    Hayward, Caroline
    Heard-Costa, Nancy L.
    Heath, Andrew C.
    Hebebrand, Johannes
    Homuth, Georg
    Hu, Frank B.
    Hunt, Sarah E.
    Hyppoenen, Elina
    Iribarren, Carlos
    Jacobs, Kevin B.
    Jansson, John-Olov
    Jula, Antti
    Kahonen, Mika
    Kathiresan, Sekar
    Kee, Frank
    Khaw, Kay-Tee
    Kivimaki, Mika
    Koenig, Wolfgang
    Kraja, Aldi T.
    Kumari, Meena
    Kuulasmaa, Kari
    Kuusisto, Johanna
    Laitinen, Jaana H.
    Lakka, Timo A.
    Langenberg, Claudia
    Launer, Lenore J.
    Lind, Lars
    Lindstrom, Jaana
    Liu, Jianjun
    Liuzzi, Antonio
    Lokki, Marja-Liisa
    Lorentzon, Mattias
    Madden, Pamela A.
    Magnusson, Patrik K.
    Manunta, Paolo
    Marek, Diana
    Maerz, Winfried
    Leach, Irene Mateo
    McKnight, Barbara
    Medland, Sarah E.
    Mihailov, Evelin
    Milani, Lili
    Montgomery, Grant W.
    Mooser, Vincent
    Muehleisen, Thomas W.
    Munroe, Patricia B.
    Musk, Arthur W.
    Narisu, Narisu
    Navis, Gerjan
    Nicholson, George
    Nohr, Ellen A.
    Ong, Ken K.
    Oostra, Ben A.
    Palmer, Colin N. A.
    Palotie, Aarno
    Peden, John F.
    Pedersen, Nancy
    Peters, Annette
    Polasek, Ozren
    Pouta, Anneli
    Pramstaller, Peter P.
    Prokopenko, Inga
    Puetter, Carolin
    Radhakrishnan, Aparna
    Raitakari, Olli
    Rendon, Augusto
    Rivadeneira, Fernando
    Rudan, Igor
    Saaristo, Timo E.
    Sambrook, Jennifer G.
    Sanders, Alan R.
    Sanna, Serena
    Saramies, Jouko
    Schipf, Sabine
    Schreiber, Stefan
    Schunkert, Heribert
    Shin, So-Youn
    Signorini, Stefano
    Sinisalo, Juha
    Skrobek, Boris
    Soranzo, Nicole
    Stancakova, Alena
    Stark, Klaus
    Stephens, Jonathan C.
    Stirrups, Kathleen
    Stolk, Ronald P.
    Stumvoll, Michael
    Swift, Amy J.
    Theodoraki, Eirini V.
    Thorand, Barbara
    Tregouet, David-Alexandre
    Tremoli, Elena
    Van der Klauw, Melanie M.
    van Meurs, Joyce B. J.
    Vermeulen, Sita H.
    Viikari, Jorma
    Virtamo, Jarmo
    Vitart, Veronique
    Waeber, Gerard
    Wang, Zhaoming
    Widen, Elisabeth
    Wild, Sarah H.
    Willemsen, Gonneke
    Winkelmann, Bernhard R.
    Witteman, Jacqueline C. M.
    Wolffenbuttel, Bruce H. R.
    Wong, Andrew
    Wright, Alan F.
    Zillikens, M. Carola
    Amouyel, Philippe
    Boehm, Bernhard O.
    Boerwinkle, Eric
    Boomsma, Dorret I.
    Caulfield, Mark J.
    Chanock, Stephen J.
    Cupples, L. Adrienne
    Cusi, Daniele
    Dedoussis, George V.
    Erdmann, Jeanette
    Eriksson, Johan G.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Froguel, Philippe
    Gieger, Christian
    Gyllensten, Ulf
    Hamsten, Anders
    Harris, Tamara B.
    Hengstenberg, Christian
    Hicks, Andrew A.
    Hingorani, Aroon
    Hinney, Anke
    Hofman, Albert
    Hovingh, Kees G.
    Hveem, Kristian
    Illig, Thomas
    Jarvelin, Marjo-Riitta
    Joeckel, Karl-Heinz
    Keinanen-Kiukaanniemi, Sirkka M.
    Kiemeney, Lambertus A.
    Kuh, Diana
    Laakso, Markku
    Lehtimaki, Terho
    Levinson, Douglas F.
    Martin, Nicholas G.
    Metspalu, Andres
    Morris, Andrew D.
    Nieminen, Markku S.
    Njolstad, Inger
    Ohlsson, Claes
    Oldehinkel, Albertine J.
    Ouwehand, Willem H.
    Palmer, Lyle J.
    Penninx, Brenda
    Power, Chris
    Province, Michael A.
    Psaty, Bruce M.
    Qi, Lu
    Rauramaa, Rainer
    Ridker, Paul M.
    Ripatti, Samuli
    Salomaa, Veikko
    Samani, Nilesh J.
    Snieder, Harold
    Sorensen, Thorkild I. A.
    Spector, Timothy D.
    Stefansson, Kari
    Tonjes, Anke
    Tuomilehto, Jaakko
    Uitterlinden, Andre G.
    Uusitupa, Matti
    van der Harst, Pim
    Vollenweider, Peter
    Wallaschofski, Henri
    Wareham, Nicholas J.
    Watkins, Hugh
    Wichmann, H-Erich
    Wilson, James F.
    Abecasis, Goncalo R.
    Assimes, Themistocles L.
    Barroso, Ines
    Boehnke, Michael
    Borecki, Ingrid B.
    Deloukas, Panos
    Fox, Caroline S.
    Frayling, Timothy
    Groop, Leif C.
    Haritunian, Talin
    Heid, Iris M.
    Hunter, David
    Kaplan, Robert C.
    Karpe, Fredrik
    Moffatt, Miriam F.
    Mohlke, Karen L.
    O'Connell, Jeffrey R.
    Pawitan, Yudi
    Schadt, Eric E.
    Schlessinger, David
    Steinthorsdottir, Valgerdur
    Strachan, David P.
    Thorsteinsdottir, Unnur
    van Duijn, Cornelia M.
    Visscher, Peter M.
    Di Blasio, Anna Maria
    Hirschhorn, Joel N.
    Lindgren, Cecilia M.
    Morris, Andrew P.
    Meyre, David
    Scherag, Andr
    McCarthy, Mark I.
    Speliotes, Elizabeth K.
    North, Kari E.
    Loos, Ruth J. F.
    Ingelsson, Erik
    Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture2013In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 45, no 5, p. 501-U69Article in journal (Refereed)
    Abstract [en]

    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.

  • 20. Brage, Sören
    et al.
    Brage, Niels
    Ekelund, Ulf
    Luan, Jian'an
    Franks, Paul
    Umeå University, Faculty of Medicine, Public Health and Clinical Medicine, Medicine.
    Froberg, Karsten
    Wareham, Nicholas J
    Effect of combined movement and heart rate monitor placement on physical activity estimates during treadmill locomotion and free-living.2006In: Eur J Appl Physiol, ISSN 1439-6319, Vol. 96, no 5, p. 517-24Article in journal (Refereed)
  • 21. Brage, Søren
    et al.
    Ekelund, Ulf
    Brage, Niels
    Hennings, Mark A
    Froberg, Karsten
    Franks, Paul
    Umeå University, Faculty of Medicine, Public Health and Clinical Medicine, Medicine.
    Wareham, Nicholas J
    Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity.2007In: J Appl Physiol, ISSN 8750-7587, Vol. 103, no 2, p. 682-92Article in journal (Refereed)
  • 22. Brand, J. S.
    et al.
    Onland-Moret, N. C.
    Eijkemans, M. J. C.
    Tjonneland, A.
    Roswall, N.
    Overvad, K.
    Fagherazzi, G.
    Clavel-Chapelon, F.
    Dossus, L.
    Lukanova, Annekatrin
    Umeå University, Faculty of Medicine, Department of Medical Biosciences. Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg.
    Grote, V.
    Bergmann, M. M.
    Boeing, H.
    Trichopoulou, A.
    Tzivoglou, M.
    Trichopoulos, D.
    Grioni, S.
    Mattiello, A.
    Masala, G.
    Tumino, R.
    Vineis, P.
    Bueno-de-Mesquita, H. B.
    Weiderpass, E.
    Redondo, M. L.
    Sanchez, M. J.
    Castano, J. M. Huerta
    Arriola, L.
    Ardanaz, E.
    Duell, E. J.
    Rolandsson, O.
    Franks, P. W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Butt, S.
    Nilsson, P.
    Khaw, K. T.
    Wareham, N.
    Travis, R.
    Romieu, I.
    Gunter, M. J.
    Riboli, E.
    van der Schouw, Y. T.
    Diabetes and Onset of Natural Menopause: Results From the European Prospective Investigation Into Cancer and Nutrition EDITORIAL COMMENT2015In: Obstetrical and Gynecological Survey, ISSN 0029-7828, E-ISSN 1533-9866, Vol. 70, no 8, p. 507-508Article in journal (Other academic)
    Abstract [en]

    The age at natural menopause (ANM) in the Western world ranges from 40 to 60 years, with an average onset of 51 years. The exact mechanisms underlying the timing of ANM are not completely understood. Both genetic and environmental factors are involved. The best-established environmental factor affecting ANM is smoking; menopause occurs 1 to 2 years earlier in smokers. In addition to genetic and environmental factors, chronic metabolic diseases may influence ANM. Some evidence suggests that diabetes may accelerate menopausal onset. With more women of childbearing age receiving a diagnosis of diabetes, it is important to examine the impact of diabetes on reproductive health. This study was designed to determine whether ANM occurs at an earlier age among women who have diabetes before menopause than in women without diabetes. Data were obtained from the European Prospective Investigation into Cancer and Nutrition (EPIC) study, a large multicenter prospective cohort study investigating the relationship between diet, lifestyle, and genetic factors and the incidence of cancer and other chronic diseases. A cohort of 519,978 men and women, mostly aged 27 to 70 years, were recruited primarily from the general population between 1992 and 2000. A total of 367,331 women participated in the EPIC study. After exclusions, 258,898 of these women met study inclusion criteria. Diabetes status at baseline and menopausal age were based on self-report and were obtained through questionnaires. Participants were asked if they had ever been diagnosed with diabetes and if so at what age. Associations of diabetes and age at diabetes diagnosis with ANM were estimated using time-dependent Cox regression analyses, with stratification by center and adjustments for age, smoking, reproductive, and known diabetes risk factors including smoking and with age from birth to menopause or censoring as the underlying time scale. Overall, there was no statistically significant lower risk of becoming menopausal among women with diabetes than women with no diabetes; the hazard ratio (HR) was 0.94, with a 95% confidence interval (CI) of 0.89 to 1.01. However, compared with women with no diabetes, women with diabetes before the age of 20 years had an earlier menopause (10-20 years [HR, 1.43; 95% CI, 1.02-2.01] and <10 years [HR, 1.59; 95% CI, 1.03-2.43]), whereas women with diabetes at age 50 years or older had a later menopause (HR, 0.81; 95% CI, 0.70-0.95). No association with ANM was found for diabetes onset between the ages 20 and 50 years. Strengths of the study include its large sample size and the measurement of a broad set of potential confounders. However, there were several limitations. First, results may have been underestimated because of survival bias. Second, the sequence of menopause and diabetes in women with a late age at diabetes is uncertain, as both events occur in a short period, and both diabetes and menopause status were based on self-report, not verified by medical records. Third, no distinction was made between types 1 and 2 diabetes. Although there is no overall association between diabetes and age at menopause, the data suggest that early-onset diabetes may accelerate menopause. The delaying effect of late-onset diabetes on ANM is not in agreement with other studies suggesting the opposite association.

  • 23.
    Brand, J. S.
    et al.
    Utrecht, The Netherlands.
    Onland-Moret, N. C.
    Utrecht, The Netherlands.
    Eijkemans, M. J. C.
    Utrecht, The Netherlands.
    Tjönneland, A.
    Copenhagen, Denmark .
    Roswall, N.
    Copenhagen, Denmark .
    Overvad, K.
    Aarhus, Denmark .
    Fagherazzi, G.
    Villejuif, France.
    Clavel-Chapelon, F.
    Villejuif, France.
    Dossus, L.
    Villejuif, France.
    Lukanova, Annekatrin
    Umeå University, Faculty of Medicine, Department of Medical Biosciences. Heidelberg, Germany .
    Grote, V.
    Heidelberg, Germany .
    Bergmann, M. M.
    Potsdam, Germany.
    Boeing, H.
    Potsdam, Germany.
    Trichopoulou, A.
    Athens, Greece.
    Tzivoglou, M.
    Athens, Greece.
    Trichopoulos, D.
    Athens, Greece; Boston, MA 02115, USA.
    Grioni, S.
    Milan, Italy.
    Mattiello, A.
    Naples, Italy.
    Masala, G.
    Florence, Italy.
    Tumino, R.
    Ragusa, Italy.
    Vineis, P.
    Torino, Italy; London, UK.
    Bueno-De-Mesquita, H. B.
    The Netherlands; London, United Kingdom; Kuala Lumpur, Malaysia .
    Weiderpass, E.
    Norway; Stockholm, Sweden; Helsinki, Finland .
    Redondo, M. L.
    Asturias, Spain.
    Sanchez, M. J.
    Spain.
    Huerta Castano, J. M.
    Spain.
    Arriola, L.
    San Sebastian, Spain.
    Ardanaz, E.
    Spain.
    Duell, E. J.
    Barcelona, Spain.
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Franks, Paul
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Malmö, Sweden.
    Butt, S.
    Malmö, Sweden.
    Nilsson, P.
    Malmö, Sweden.
    Khaw, K. T.
    Cambridge, UK.
    Wareham, N.
    Cambridge, UK.
    Travis, R.
    Oxford, UK.
    Romieu, I.
    Lyon, France.
    Gunter, M. J.
    London, UK .
    Riboli, E.
    London, UK .
    van der Schouw, Y. T.
    Utrecht, The Netherlands.
    Diabetes and onset of natural menopause: results from the European Prospective Investigation into Cancer and Nutrition2015In: Human Reproduction, ISSN 0268-1161, E-ISSN 1460-2350, Vol. 30, no 6, p. 1491-1498Article in journal (Refereed)
    Abstract [en]

    STUDY QUESTION: Do women who have diabetes before menopause have their menopause at an earlier age compared with women without diabetes? SUMMARY ANSWER: Although there was no overall association between diabetes and age at menopause, our study suggests that early-onset diabetes may accelerate menopause. WHAT IS KNOWN ALREADY: Today, more women of childbearing age are being diagnosed with diabetes, but little is known about the impact of diabetes on reproductive health. STUDY DESIGN, SIZE, DURATION: We investigated the impact of diabetes on age at natural menopause (ANM) in 258 898 women from the European Prospective Investigation into Cancer and Nutrition (EPIC), enrolled between 1992 and 2000. PARTICIPANTS/MATERIALS, SETTING, METHODS: Determinant and outcome information was obtained through questionnaires. Time-dependent Cox regression analyses were used to estimate the associations of diabetes and age at diabetes diagnosis with ANM, stratified by center and adjusted for age, smoking, reproductive and diabetes risk factors and with age from birth to menopause or censoring as the underlying time scale. MAIN RESULTS AND THE ROLE OF CHANCE: Overall, no association between diabetes and ANM was found (hazard ratio (HR) = 0.94; 95% confidence interval (CI) 0.89-1.01). However, women with diabetes before the age of 20 years had an earlier menopause (10-20 years: HR = 1.43; 95% CI 1.02-2.01, <10 years: HR = 1.59; 95% CI 1.03-2.43) compared with non-diabetic women, whereas women with diabetes at age 50 years and older had a later menopause (HR = 0.81; 95% CI 0.70-0.95). None of the other age groups were associated with ANM. LIMITATIONS, REASONS FOR CAUTION: Strengths of the study include the large sample size and the broad set of potential confounders measured. However, results may have been underestimated due to survival bias. We cannot be sure about the sequence of the events in women with a late age at diabetes, as both events then occur in a short period. We could not distinguish between type 1 and type 2 diabetes. WIDER IMPLICATIONS OF THE FINDINGS: Based on the literature, an accelerating effect of early-onset diabetes on ANM might be plausible. A delaying effect of late-onset diabetes on ANM has not been reported before, and is not in agreement with recent studies suggesting the opposite association.

  • 24. Brand, Judith S.
    et al.
    van der Schouw, Yvonne T.
    Onland-Moret, N. Charlotte
    Sharp, Stephen J.
    Ong, Ken K.
    Khaw, Kay-Tee
    Ardanaz, Eva
    Amiano, Pilar
    Boeing, Heiner
    Chirlaque, Maria-Dolores
    Clavel-Chapelon, Francoise
    Crowe, Francesca L.
    de Lauzon-Guillain, Blandine
    Duell, Eric J.
    Fagherazzi, Guy
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Grioni, Sara
    Groop, Leif C.
    Kaaks, Rudolf
    Key, Timothy J.
    Nilsson, Peter M.
    Overvad, Kim
    Palli, Domenico
    Panico, Salvatore
    Quiros, J. Ramon
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Sacerdote, Carlotta
    Sanchez, Maria-Jose
    Slimani, Nadia
    Teucher, Birgit
    Tjonneland, Anne
    Tumino, Rosario
    van der A, Daphne L.
    Feskens, Edith J. M.
    Langenberg, Claudia
    Forouhi, Nita G.
    Riboli, Elio
    Wareham, Nicholas J.
    Age at Menopause, Reproductive Life Span, and Type 2 Diabetes Risk2013In: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 36, no 4, p. 1012-1019Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE-Age at menopause is an important determinant of future health outcomes, but little is known about its relationship with type 2 diabetes. We examined the associations of menopausal age and reproductive life span (menopausal age minus menarcheal age) with diabetes risk.

    RESEARCH DESIGN AND METHODS-Data were obtained from the InterAct study, a prospective case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition. A total of 3,691 postmenopausal type 2 diabetic case subjects and 4,408 subcohort members were included in the analysis, with a median follow-up of 11 years. Prentice weighted Cox proportional hazards models were adjusted for age, known risk factors for diabetes, and reproductive factors, and effect modification by BMI, waist circumference, and smoking was studied.

    RESULTS-Mean (SD) age of the subcohort was 59.2 (5.8) years. After multivariable adjustment, hazard ratios (HRs) of type 2 diabetes were 1.32 (95% CI 1.04-1.69), 1.09 (0.90-1.31), 0.97 (0.86-1.10), and 0.85 (0.70-1.03) for women with menopause at ages <40, 40-44, 45-49, and >= 55 years, respectively, relative to those with menopause at age 50-54 years. The HR per SD younger age at menopause was 1.08 (1.02-1.14). Similarly, a shorter reproductive life span was associated with a higher diabetes risk (HR per SD lower reproductive life span 1.06 [ 1.01-1.12]). No effect modification by BMI, waist circumference, or smoking was observed (P interaction all > 0.05).

    CONCLUSIONS-Early menopause is associated with a greater risk of type 2 diabetes. Diabetes Care 36:1012-1019, 2013

  • 25.
    Brito, Ema C
    et al.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Commentary on viewpoint: perspective on the future use of genomics in exercise prescription2008In: Journal of applied physiology, ISSN 8750-7587, E-ISSN 1522-1601, Vol. 104, no 4, p. 1248-1248Article in journal (Other academic)
  • 26.
    Brito, Ema C
    et al.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Lyssenko, V
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Berglund, G
    Nilsson, PM
    Groop, L
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Previously associated type 2 diabetes variants may interact with physical activity to modify the risk of impaired glucose regulation and type 2 diabetes: a study of 16,003 Swedish adults2009In: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 58, no 6, p. 1411-1418Article in journal (Refereed)
  • 27. Brunkwall, Louise
    et al.
    Chen, Yan
    Hindy, George
    Rukh, Gull
    Ericson, Ulrika
    Barroso, Ines
    Johansson, Ingegerd
    Umeå University, Faculty of Medicine, Department of Odontology.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA.
    Orho-Melander, Marju
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Biobank Research. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.
    Sugar-sweetened beverage consumption and genetic predisposition to obesity in 2 Swedish cohorts2016In: American Journal of Clinical Nutrition, ISSN 0002-9165, E-ISSN 1938-3207, Vol. 104, no 3, p. 809-815Article in journal (Refereed)
    Abstract [en]

    Background: The consumption of sugar-sweetened beverages (SSBs), which has increased substantially during the last decades, has been associated with obesity and weight gain.

    Objective: Common genetic susceptibility to obesity has been shown to modify the association between SSB intake and obesity risk in 3 prospective cohorts from the United States. We aimed to replicate these findings in 2 large Swedish cohorts.

    Design: Data were available for 21,824 healthy participants from the Malmö Diet and Cancer study and 4902 healthy participants from the Gene-Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk Study. Self-reported SSB intake was categorized into 4 levels (seldom, low, medium, and high). Unweighted and weighted genetic risk scores (GRSs) were constructed based on 30 body mass index [(BMI) in kg/m2]-associated loci, and effect modification was assessed in linear regression equations by modeling the product and marginal effects of the GRS and SSB intake adjusted for age-, sex-, and cohort-specific covariates, with BMI as the outcome. In a secondary analysis, models were additionally adjusted for putative confounders (total energy intake, alcohol consumption, smoking status, and physical activity).

    Results: In an inverse variance-weighted fixed-effects meta-analysis, each SSB intake category increment was associated with a 0.18 higher BMI (SE = 0.02; P = 1.7 × 10−20n = 26,726). In the fully adjusted model, a nominal significant interaction between SSB intake category and the unweighted GRS was observed (P-interaction = 0.03). Comparing the participants within the top and bottom quartiles of the GRS to each increment in SSB intake was associated with 0.24 (SE = 0.04; P = 2.9 × 10−8n = 6766) and 0.15 (SE = 0.04; P = 1.3 × 10−4n = 6835) higher BMIs, respectively.

    Conclusions: The interaction observed in the Swedish cohorts is similar in magnitude to the previous analysis in US cohorts and indicates that the relation of SSB intake and BMI is stronger in people genetically predisposed to obesity.

  • 28. Buijsse, B.
    et al.
    Boeing, H.
    Drogan, D.
    Schulze, M. B.
    Feskens, E. J.
    Amiano, P.
    Barricarte, A.
    Clavel-Chapelon, F.
    de Lauzon-Guillain, B.
    Fagherazzi, G.
    Fonseca-Nunes, A.
    Franks, Paul
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Huerta, J. M.
    Jakobsen, M. U.
    Kaaks, R.
    Key, T. J.
    Khaw, K. T.
    Masala, G.
    Moskal, A.
    Nilsson, P. M.
    Overvad, K.
    Pala, V.
    Panico, S.
    Redondo, M. L.
    Ricceri, F.
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Sanchez, M-J
    Sluijs, I.
    Spijkerman, A. M.
    Tjonneland, A.
    Tumino, R.
    van der A, D. L.
    van der Schouw, Y. T.
    Langenberg, C.
    Sharp, S. J.
    Forouhi, N. G.
    Riboli, E.
    Wareham, N. J.
    Consumption of fatty foods and incident type 2 diabetes in populations from eight European countries2015In: European Journal of Clinical Nutrition, ISSN 0954-3007, E-ISSN 1476-5640, Vol. 69, no 4, p. 455-461Article in journal (Refereed)
    Abstract [en]

    BACKGROUND/OBJECTIVES:

    Diets high in saturated and trans fat and low in unsaturated fat may increase type 2 diabetes (T2D) risk, but studies on foods high in fat per unit weight are sparse. We assessed whether the intake of vegetable oil, butter, margarine, nuts and seeds and cakes and cookies is related to incident T2D.

    SUBJECTS/METHODS:

    A case-cohort study was conducted, nested within eight countries of the European Prospective Investigation into Cancer (EPIC), with 12 403 incident T2D cases and a subcohort of 16 835 people, identified from a cohort of 340 234 people. Diet was assessed at baseline (1991-1999) by country-specific questionnaires. Country-specific hazard ratios (HRs) across four categories of fatty foods (nonconsumers and tertiles among consumers) were combined with random-effects meta-analysis.

    RESULTS:

    After adjustment not including body mass index (BMI), nonconsumers of butter, nuts and seeds and cakes and cookies were at higher T2D risk compared with the middle tertile of consumption. Among consumers, cakes and cookies were inversely related to T2D (HRs across increasing tertiles 1.14, 1.00 and 0.92, respectively; P-trend <0.0001). All these associations attenuated upon adjustment for BMI, except the higher risk of nonconsumers of cakes and cookies (HR 1.57). Higher consumption of margarine became positively associated after BMI adjustment (HRs across increasing consumption tertiles: 0.93, 1.00 and 1.12; P-trend 0.03). Within consumers, vegetable oil, butter and nuts and seeds were unrelated to T2D.

    CONCLUSIONS:

    Fatty foods were generally not associated with T2D, apart from weak positive association for margarine. The higher risk among nonconsumers of cakes and cookies needs further explanation.

  • 29. Cooper, A. J.
    et al.
    Forouhi, N. G.
    Ye, Z.
    Buijsse, B.
    Arriola, L.
    Balkau, B.
    Barricarte, A.
    Beulens, J. W. J.
    Boeing, H.
    Buchner, F. L.
    Dahm, C. C.
    de Lauzon-Guillain, B.
    Fagherazzi, G.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Gonzalez, C.
    Grioni, S.
    Kaaks, R.
    Key, T. J.
    Masala, G.
    Navarro, C.
    Nilsson, P.
    Overvad, K.
    Panico, S.
    Ramon Quiros, J.
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Roswall, N.
    Sacerdote, C.
    Sanchez, M-J
    Slimani, N.
    Sluijs, I.
    Spijkerman, A. M. W.
    Teucher, B.
    Tjonneland, A.
    Tumino, R.
    Sharp, S. J.
    Langenberg, C.
    Feskens, E. J. M.
    Riboli, E.
    Wareham, N. J.
    Fruit and vegetable intake and type 2 diabetes: EPIC-InterAct prospective study and meta-analysis2012In: European Journal of Clinical Nutrition, ISSN 0954-3007, E-ISSN 1476-5640, Vol. 66, no 10, p. 1082-1092Article, review/survey (Refereed)
    Abstract [en]

    Fruit and vegetable intake (FVI) may reduce the risk of type 2 diabetes (T2D), but the epidemiological evidence is inconclusive. The aim of this study is to examine the prospective association of FVI with T2D and conduct an updated meta-analysis. In the European Prospective Investigation into Cancer-InterAct (EPIC-InterAct) prospective case-cohort study nested within eight European countries, a representative sample of 16 154 participants and 12 403 incident cases of T2D were identified from 340 234 individuals with 3.99 million person-years of follow-up. For the meta-analysis we identified prospective studies on FVI and T2D risk by systematic searches of MEDLINE and EMBASE until April 2011. In EPIC-InterAct, estimated FVI by dietary questionnaires varied more than twofold between countries. In adjusted analyses the hazard ratio (95% confidence interval) comparing the highest with lowest quartile of reported intake was 0.90 (0.80-1.01) for FVI; 0.89 (0.76-1.04) for fruit and 0.94 (0.84-1.05) for vegetables. Among FV subtypes, only root vegetables were inversely associated with diabetes 0.87 (0.77-0.99). In meta-analysis using pooled data from five studies including EPIC-InterAct, comparing the highest with lowest category for FVI was associated with a lower relative risk of diabetes (0.93 (0.87-1.00)). Fruit or vegetables separately were not associated with diabetes. Among FV subtypes, only green leafy vegetable (GLV) intake (relative risk: 0.84 (0.74-0.94)) was inversely associated with diabetes. Subtypes of vegetables, such as root vegetables or GLVs may be beneficial for the prevention of diabetes, while total FVI may exert a weaker overall effect.

  • 30. Corbin, Laura J.
    et al.
    Tan, Vanessa Y.
    Hughes, David A.
    Wade, Kaitlin H.
    Paul, Dirk S.
    Tansey, Katherine E.
    Butcher, Frances
    Dudbridge, Frank
    Howson, Joanna M.
    Jallow, Momodou W.
    John, Catherine
    Kingston, Nathalie
    Lindgren, Cecilia M.
    O'Donavan, Michael
    O'Rahilly, Stephen
    Owen, Michael J.
    Palmer, Colin N. A.
    Pearson, Ewan R.
    Scott, Robert A.
    van Heel, David A.
    Whittaker, John
    Frayling, Tim
    Tobin, Martin D.
    Wain, Louise V.
    Smith, George Davey
    Evans, David M.
    Karpe, Fredrik
    McCarthy, Mark I.
    Danesh, John
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK; Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, SE-205 02, Sweden; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
    Timpson, Nicholas J.
    Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference2018In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 711Article, review/survey (Refereed)
    Abstract [en]

    Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.

  • 31. Cornelis, M C
    et al.
    Byrne, E M
    Esko, T
    Nalls, M A
    Ganna, A
    Paynter, N
    Monda, K L
    Amin, N
    Fischer, K
    Renstrom, F
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research. Umeå University, Faculty of Medicine, Department of Biobank Research.
    Ngwa, J S
    Huikari, V
    Cavadino, A
    Nolte, I M
    Teumer, A
    Yu, K
    Marques-Vidal, P
    Rawal, R
    Manichaikul, A
    Wojczynski, M K
    Vink, J M
    Zhao, J H
    Burlutsky, G
    Lahti, J
    Mikkilä, V
    Lemaitre, R N
    Eriksson, J
    Musani, S K
    Tanaka, T
    Geller, F
    Luan, J
    Hui, J
    Mägi, R
    Dimitriou, M
    Garcia, M E
    Ho, W-K
    Wright, M J
    Rose, L M
    Magnusson, P K E
    Pedersen, N L
    Couper, D
    Oostra, B A
    Hofman, A
    Ikram, M A
    Tiemeier, H W
    Uitterlinden, A G
    van Rooij, F J A
    Barroso, I
    Johansson, Ingegerd
    Umeå University, Faculty of Medicine, Department of Odontology. Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research. Umeå University, Faculty of Medicine, Department of Biobank Research.
    Xue, L
    Kaakinen, M
    Milani, L
    Power, C
    Snieder, H
    Stolk, R P
    Baumeister, S E
    Biffar, R
    Gu, F
    Bastardot, F
    Kutalik, Z
    Jacobs, D R
    Forouhi, N G
    Mihailov, E
    Lind, L
    Lindgren, C
    Michaëlsson, K
    Morris, A
    Jensen, M
    Khaw, K-T
    Luben, R N
    Wang, J J
    Männistö, S
    Perälä, M-M
    Kähönen, M
    Lehtimäki, T
    Viikari, J
    Mozaffarian, D
    Mukamal, K
    Psaty, B M
    Döring, A
    Heath, A C
    Montgomery, G W
    Dahmen, N
    Carithers, T
    Tucker, K L
    Ferrucci, L
    Boyd, H A
    Melbye, M
    Treur, J L
    Mellström, D
    Hottenga, J J
    Prokopenko, I
    Tönjes, A
    Deloukas, P
    Kanoni, S
    Lorentzon, M
    Houston, D K
    Liu, Y
    Danesh, J
    Rasheed, A
    Mason, M A
    Zonderman, A B
    Franke, L
    Kristal, B S
    Karjalainen, J
    Reed, D R
    Westra, H-J
    Evans, M K
    Saleheen, D
    Harris, T B
    Dedoussis, G
    Curhan, G
    Stumvoll, M
    Beilby, J
    Pasquale, L R
    Feenstra, B
    Bandinelli, S
    Ordovas, J M
    Chan, A T
    Peters, U
    Ohlsson, C
    Gieger, C
    Martin, N G
    Waldenberger, M
    Siscovick, D S
    Raitakari, O
    Eriksson, J G
    Mitchell, P
    Hunter, D J
    Kraft, P
    Rimm, E B
    Boomsma, D I
    Borecki, I B
    Loos, R J F
    Wareham, N J
    Vollenweider, P
    Caporaso, N
    Grabe, H J
    Neuhouser, M L
    Wolffenbuttel, B H R
    Hu, F B
    Hyppönen, E
    Järvelin, M-R
    Cupples, L A
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Nutrition, Harvard School of Public Health, Boston, MA, USA; Lund Univ, Dept Clin Sci, Malmo, Sweden.
    Ridker, P M
    van Duijn, C M
    Heiss, G
    Metspalu, A
    North, K E
    Ingelsson, E
    Nettleton, J A
    van Dam, R M
    Chasman, D I
    Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption2015In: Molecular Psychiatry, ISSN 1359-4184, E-ISSN 1476-5578, Vol. 20, no 5, p. 647-656Article in journal (Refereed)
    Abstract [en]

    Coffee, a major dietary source of caffeine, is among the most widely consumed beverages in the world and has received considerable attention regarding health risks and benefits. We conducted a genome-wide (GW) meta-analysis of predominately regular-type coffee consumption (cups per day) among up to 91 462 coffee consumers of European ancestry with top single-nucleotide polymorphisms (SNPs) followed-up in ~30 062 and 7964 coffee consumers of European and African-American ancestry, respectively. Studies from both stages were combined in a trans-ethnic meta-analysis. Confirmed loci were examined for putative functional and biological relevance. Eight loci, including six novel loci, met GW significance (log10Bayes factor (BF)>5.64) with per-allele effect sizes of 0.03-0.14 cups per day. Six are located in or near genes potentially involved in pharmacokinetics (ABCG2, AHR, POR and CYP1A2) and pharmacodynamics (BDNF and SLC6A4) of caffeine. Two map to GCKR and MLXIPL genes related to metabolic traits but lacking known roles in coffee consumption. Enhancer and promoter histone marks populate the regions of many confirmed loci and several potential regulatory SNPs are highly correlated with the lead SNP of each. SNP alleles near GCKR, MLXIPL, BDNF and CYP1A2 that were associated with higher coffee consumption have previously been associated with smoking initiation, higher adiposity and fasting insulin and glucose but lower blood pressure and favorable lipid, inflammatory and liver enzyme profiles (P<5 × 10(-8)).Our genetic findings among European and African-American adults reinforce the role of caffeine in mediating habitual coffee consumption and may point to molecular mechanisms underlying inter-individual variability in pharmacological and health effects of coffee.

  • 32. Dawed, A. Y.
    et al.
    Franks, Paul
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Lund Univ, Dept Clin Sci, Skane Univ Hosp, Genet & Mol Epidemiol Unit,Diabet Ctr, Malmö, Sweden; Harvard Univ, Sch Publ Hlth, Dept Nutr, Boston, MA 02115 USA.
    Walker, M.
    Mari, A.
    Pearson, E. R.
    Determinants of glucagon-like peptide-1 (GLP-1) response in prediabetes and diabetes: an IMI-DIRECT study2016In: Diabetic Medicine, ISSN 0742-3071, E-ISSN 1464-5491, Vol. 33, no Special Issue, Meeting Abstract: A15, p. 10-10Article in journal (Other academic)
  • 33. Dawed, Adem Y.
    et al.
    Ali, Ashfaq
    Zhou, Kaixin
    Pearson, Ewan R.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, USA.
    Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes2017In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 60, no 11, p. 2231-2239Article in journal (Refereed)
    Abstract [en]

    Aims/hypothesis: There is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence. Methods: We (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS). Results: From the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 x 10(-04)) and G6PC (p = 4.77 x 10(-04)). Conclusions/interpretation: We have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.

  • 34. Del Gobbo, Liana C.
    et al.
    Imamura, Fumiaki
    Aslibekyan, Stella
    Marklund, Matti
    Virtanen, Jyrki K.
    Wennberg, Maria
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research.
    Yakoob, Mohammad Y.
    Chiuve, Stephanie E.
    dela Cruz, Luicito
    Frazier-Wood, Alexis C.
    Fretts, Amanda M.
    Guallar, Eliseo
    Matsumoto, Chisa
    Prem, Kiesha
    Tanaka, Tosh
    Wu, Jason H. Y.
    Zhou, Xia
    Helmer, Catherine
    Ingelsson, Erik
    Yuan, Jian-Min
    Barberger-Gateau, Pascale
    Campos, Hannia
    Chaves, Paulo H. M.
    Djousse, Luc
    Giles, Graham G.
    Gomez-Aracena, Jose
    Hodge, Allison M.
    Hu, Frank B.
    Jansson, Jan-Håkan
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Johansson, Ingegerd
    Umeå University, Faculty of Medicine, Department of Odontology.
    Khaw, Kay-Tee
    Koh, Woon-Puay
    Lemaitre, Rozenn N.
    Lind, Lars
    Luben, Robert N.
    Rimm, Eric B.
    Riserus, Ulf
    Samieri, Cecilia
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Siscovick, David S.
    Stampfer, Meir
    Steffen, Lyn M.
    Steffen, Brian T.
    Tsai, Michael Y.
    van Dam, Rob M.
    Voutilainen, Sari
    Willett, Walter C.
    Woodward, Mark
    Mozaffarian, Dariush
    omega-3 Polyunsaturated Fatty Acid Biomarkers and Coronary Heart Disease Pooling Project of 19 Cohort Studies2016In: JAMA Internal Medicine, ISSN 2168-6106, E-ISSN 2168-6114, Vol. 176, no 8, p. 1155-1166Article in journal (Refereed)
    Abstract [en]

    IMPORTANCE The role of omega-3 polyunsaturated fatty acids for primary prevention of coronary heart disease (CHD) remains controversial. Most prior longitudinal studies evaluated self-reported consumption rather than biomarkers. OBJECTIVE To evaluate biomarkers of seafood-derived eicosapentaenoic acid (EPA; 20: 5 omega-3), docosapentaenoic acid (DPA; 22: 5 omega-3), and docosahexaenoic acid (DHA; 22: 6 omega-3) and plant-derived alpha-linolenic acid (ALA; 18: 3 omega-3) for incident CHD. DATA SOURCES A global consortium of 19 studies identified by November 2014. STUDY SELECTION Available prospective (cohort, nested case-control) or retrospective studies with circulating or tissue omega-3 biomarkers and ascertained CHD. DATA EXTRACTION AND SYNTHESIS Each study conducted standardized, individual-level analysis using harmonized models, exposures, outcomes, and covariates. Findings were centrally pooled using random-effects meta-analysis. Heterogeneity was examined by age, sex, race, diabetes, statins, aspirin, omega-6 levels, and FADS desaturase genes. MAIN OUTCOMES AND MEASURES Incident total CHD, fatal CHD, and nonfatal myocardial infarction (MI). RESULTS The 19 studies comprised 16 countries, 45 637 unique individuals, and 7973 total CHD, 2781 fatal CHD, and 7157 nonfatal MI events, with omega-3 measures in total plasma, phospholipids, cholesterol esters, and adipose tissue. Median age at baseline was 59 years (range, 18-97 years), and 28 660 (62.8%) were male. In continuous (per 1-SD increase) multivariable-adjusted analyses, the omega-3 biomarkers ALA, DPA, and DHA were associated with a lower risk of fatal CHD, with relative risks (RRs) of 0.91 (95% CI, 0.84-0.98) for ALA, 0.90 (95% CI, 0.85-0.96) for DPA, and 0.90 (95% CI, 0.84-0.96) for DHA. Although DPA was associated with a lower risk of total CHD (RR, 0.94; 95% CI, 0.90-0.99), ALA (RR, 1.00; 95% CI, 0.95-1.05), EPA (RR, 0.94; 95% CI, 0.87-1.02), and DHA (RR, 0.95; 95% CI, 0.91-1.00) were not. Significant associations with nonfatal MI were not evident. Associations appeared generally stronger in phospholipids and total plasma. Restricted cubic splines did not identify evidence of nonlinearity in dose responses. CONCLUSIONS AND RELEVANCE On the basis of available studies of free-living populations globally, biomarker concentrations of seafood and plant-derived omega-3 fatty acids are associated with a modestly lower incidence of fatal CHD.

  • 35. Ding, Ming
    et al.
    Huang, Tao
    Bergholdt, Helle K. M.
    Nordestgaard, Borge G.
    Ellervik, Christina
    Qi, Lu
    Frazier-Wood, Alexis C.
    Aslibekyan, Stella
    North, Kari E.
    Voortman, Trudy
    Graff, Mariaelisa
    Smith, Caren E.
    Lai, Chao-Qiang
    Varbo, Anette
    Lemaitre, Rozenn N.
    de Jonge, Ester A. L.
    Fumeron, Frederic
    Corella, Dolores
    Wang, Carol A.
    Tjonneland, Anne
    Overvad, Kim
    Sorensen, Thorkild I. A.
    Feitosa, Mary F.
    Wojczynski, Mary K.
    Kahonen, Mika
    Ahmad, Shafqat
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Biobank Research. Department of Clinical Sciences, Lund University, Malmö, Sweden.
    Psaty, Bruce M.
    Siscovick, David S.
    Barroso, Ines
    Johansson, Ingegerd
    Umeå University, Faculty of Medicine, Department of Biobank Research.
    Hernandez, Dena
    Ferrucci, Luigi
    Bandinelli, Stefania
    Linneberg, Allan
    Sandholt, Camilla Helene
    Pedersen, Oluf
    Hansen, Torben
    Schulz, Christina-Alexandra
    Sonestedt, Emily
    Orho-Melander, Marju
    Chen, Tzu-An
    Rotter, Jerome I.
    Allison, Mathew A.
    Rich, Stephen S.
    Sorli, Jose V.
    Coltell, Oscar
    Pennell, Craig E.
    Eastwood, Peter R.
    Hofman, Albert
    Uitterlinden, Andre G.
    Zillikens, MCarola
    van Rooij, Frank J. A.
    Chu, Audrey Y.
    Rose, Lynda M.
    Ridker, Paul M.
    Viikari, Jorma
    Raitakari, Olli
    Lehtimaki, Terho
    Mikkila, Vera
    Willett, Walter C.
    Wang, Yujie
    Tucker, Katherine L.
    Ordovas, Jose M.
    Kilpelainen, Tuomas O.
    Province, Michael A.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA; Department of Clinical Sciences, Lund University, Malmö, Sweden.
    Arnett, Donna K.
    Tanaka, Toshiko
    Toft, Ulla
    Ericso, Ulrika
    Franco, Oscar H.
    Mozaffarian, Dariush
    Hu, Frank B.
    Chasman, Daniel I.
    Dairy consumption, systolic blood pressure, and risk of hypertension: Mendelian randomization study2017In: BMJ. British Medical Journal, E-ISSN 1756-1833, Vol. 356, article id j1000Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE To examine whether previous observed inverse associations of dairy intake with systolic blood pressure and risk of hypertension were causal. DESIGN Mendelian randomization study using the single nucleotide polymorphism rs4988235 related to lactase persistence as an instrumental variable. SETTING CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium. PARTICIPANTS Data from 22 studies with 171 213 participants, and an additional 10 published prospective studies with 26 119 participants included in the observational analysis. MAIN OUTCOME MEASURES The instrumental variable estimation was conducted using the ratio of coefficients approach. Using metaanalysis, an additional eight published randomized clinical trials on the association of dairy consumption with systolic blood pressure were summarized. RESULTS Compared with the CC genotype (CC is associated with complete lactase deficiency), the CT/TT genotype (TT is associated with lactose persistence, and CT is associated with certain lactase deficiency) of LCT-13910 (lactase persistence gene) rs4988235 was associated with higher dairy consumption (0.23 (about 55 g/day), 95% confidence interval 0.17 to 0.29) serving/day; P<0.001) and was not associated with systolic blood pressure (0.31, 95% confidence interval -0.05 to 0.68 mm Hg; P=0.09) or risk of hypertension (odds ratio 1.01, 95% confidence interval 0.97 to 1.05; P=0.27). Using LCT-13910 rs4988235 as the instrumental variable, genetically determined dairy consumption was not associated with systolic blood pressure (beta=1.35, 95% confidence interval -0.28 to 2.97 mm Hg for each serving/day) or risk of hypertension (odds ratio 1.04, 0.88 to 1.24). Moreover, meta-analysis of the published clinical trials showed that higher dairy intake has no significant effect on change in systolic blood pressure for interventions over one month to 12 months (intervention compared with control groups: beta=-0.21, 95% confidence interval -0.98 to 0.57 mm Hg). In observational analysis, each serving/day increase in dairy consumption was associated with -0.11 (95% confidence interval -0.20 to -0.02 mm Hg; P=0.02) lower systolic blood pressure but not risk of hypertension (odds ratio 0.98, 0.97 to 1.00; P=0.11). CONCLUSION The weak inverse association between dairy intake and systolic blood pressure in observational studies was not supported by a comprehensive instrumental variable analysis and systematic review of existing clinical trials.

  • 36. Do, Ron
    et al.
    Willer, Cristen J.
    Schmidt, Ellen M.
    Sengupta, Sebanti
    Gao, Chi
    Peloso, Gina M.
    Gustafsson, Stefan
    Kanoni, Stavroula
    Ganna, Andrea
    Chen, Jin
    Buchkovich, Martin L.
    Mora, Samia
    Beckmann, Jacques S.
    Bragg-Gresham, Jennifer L.
    Chang, Hsing-Yi
    Demirkan, Ayse
    Den Hertog, Heleen M.
    Donnelly, Louise A.
    Ehret, Georg B.
    Esko, Tonu
    Feitosa, Mary F.
    Ferreira, Teresa
    Fischer, Krista
    Fontanillas, Pierre
    Fraser, Ross M.
    Freitag, Daniel F.
    Gurdasani, Deepti
    Heikkila, Kauko
    Hyppoenen, Elina
    Isaacs, Aaron
    Jackson, Anne U.
    Johansson, Asa
    Johnson, Toby
    Kaakinen, Marika
    Kettunen, Johannes
    Kleber, Marcus E.
    Li, Xiaohui
    Luan, Jian'an
    Lyytikainen, Leo-Pekka
    Magnusson, Patrik K. E.
    Mangino, Massimo
    Mihailov, Evelin
    Montasser, May E.
    Mueller-Nurasyid, Martina
    Nolte, Ilja M.
    O'Connell, Jeffrey R.
    Palmer, Cameron D.
    Perola, Markus
    Petersen, Ann-Kristin
    Sanna, Serena
    Saxena, Richa
    Service, Susan K.
    Shah, Sonia
    Shungin, Dmitry
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Umeå University, Faculty of Medicine, Department of Odontology. Lunds universitet.
    Sidore, Carlo
    Song, Ci
    Strawbridge, Rona J.
    Surakka, Ida
    Tanaka, Toshiko
    Teslovich, Tanya M.
    Thorleifsson, Gudmar
    Van den Herik, Evita G.
    Voight, Benjamin F.
    Volcik, Kelly A.
    Waite, Lindsay L.
    Wong, Andrew
    Wu, Ying
    Zhang, Weihua
    Absher, Devin
    Asiki, Gershim
    Barroso, Ines
    Been, Latonya F.
    Bolton, Jennifer L.
    Bonnycastle, Lori L.
    Brambilla, Paolo
    Burnett, Mary S.
    Cesana, Giancarlo
    Dimitriou, Maria
    Doney, Alex S. F.
    Doering, Angela
    Elliott, Paul
    Epstein, Stephen E.
    Eyjolfsson, Gudmundur Ingi
    Gigante, Bruna
    Goodarzi, Mark O.
    Grallert, Harald
    Gravito, Martha L.
    Groves, Christopher J.
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research.
    Hartikainen, Anna-Liisa
    Hayward, Caroline
    Hernandez, Dena
    Hicks, Andrew A.
    Holm, Hilma
    Hung, Yi-Jen
    Illig, Thomas
    Jones, Michelle R.
    Kaleebu, Pontiano
    Kastelein, John J. P.
    Khaw, Kay-Tee
    Kim, Eric
    Klopp, Norman
    Komulainen, Pirjo
    Kumari, Meena
    Langenberg, Claudia
    Lehtimaki, Terho
    Lin, Shih-Yi
    Lindstrom, Jaana
    Loos, Ruth J. F.
    Mach, Francois
    McArdle, Wendy L.
    Meisinger, Christa
    Mitchell, Braxton D.
    Mueller, Gabrielle
    Nagaraja, Ramaiah
    Narisu, Narisu
    Nieminen, Tuomo V. M.
    Nsubuga, Rebecca N.
    Olafsson, Isleifur
    Ong, Ken K.
    Palotie, Aarno
    Papamarkou, Theodore
    Pomilla, Cristina
    Pouta, Anneli
    Rader, Daniel J.
    Reilly, Muredach P.
    Ridker, Paul M.
    Rivadeneira, Fernando
    Rudan, Igor
    Ruokonen, Aimo
    Samani, Nilesh
    Scharnagl, Hubert
    Seeley, Janet
    Silander, Kaisa
    Stancakova, Alena
    Stirrups, Kathleen
    Swift, Amy J.
    Tiret, Laurence
    Uitterlinden, Andre G.
    van Pelt, L. Joost
    Vedantam, Sailaja
    Wainwright, Nicholas
    Wijmenga, Cisca
    Wild, Sarah H.
    Willemsen, Gonneke
    Wilsgaard, Tom
    Wilson, James F.
    Young, Elizabeth H.
    Zhao, Jing Hua
    Adair, Linda S.
    Arveiler, Dominique
    Assimes, Themistocles L.
    Bandinelli, Stefania
    Bennett, Franklyn
    Bochud, Murielle
    Boehm, Bernhard O.
    Boomsma, Dorret I.
    Borecki, Ingrid B.
    Bornstein, Stefan R.
    Bovet, Pascal
    Burnier, Michel
    Campbell, Harry
    Chakravarti, Aravinda
    Chambers, John C.
    Chen, Yii-Der Ida
    Collins, Francis S.
    Cooper, Richard S.
    Danesh, John
    Dedoussis, George
    de Faire, Ulf
    Feranil, Alan B.
    Ferrieres, Jean
    Ferrucci, Luigi
    Freimer, Nelson B.
    Gieger, Christian
    Groop, Leif C.
    Gudnason, Vilmundur
    Gyllensten, Ulf
    Hamsten, Anders
    Harris, Tamara B.
    Hingorani, Aroon
    Hirschhorn, Joel N.
    Hofman, Albert
    Hovingh, G. Kees
    Hsiung, Chao Agnes
    Humphries, Steve E.
    Hunt, Steven C.
    Hveem, Kristian
    Iribarren, Carlos
    Jarvelin, Marjo-Riitta
    Jula, Antti
    Kahonen, Mika
    Kaprio, Jaakko
    Kesaniemi, Antero
    Kivimaki, Mika
    Kooner, Jaspal S.
    Koudstaal, Peter J.
    Krauss, Ronald M.
    Kuh, Diana
    Kuusisto, Johanna
    Kyvik, Kirsten O.
    Laakso, Markku
    Lakka, Timo A.
    Lind, Lars
    Lindgren, Cecilia M.
    Martin, Nicholas G.
    Maerz, Winfried
    McCarthy, Mark I.
    McKenzie, Colin A.
    Meneton, Pierre
    Metspalu, Andres
    Moilanen, Leena
    Morris, Andrew D.
    Munroe, Patricia B.
    Njolstad, Inger
    Pedersen, Nancy L.
    Power, Chris
    Pramstaller, Peter P.
    Price, Jackie F.
    Psaty, Bruce M.
    Quertermous, Thomas
    Rauramaa, Rainer
    Saleheen, Danish
    Salomaa, Veikko
    Sanghera, Dharambir K.
    Saramies, Jouko
    Schwarz, Peter E. H.
    Sheu, Wayne H-H
    Shuldiner, Alan R.
    Siegbahn, Agneta
    Spector, Tim D.
    Stefansson, Kari
    Strachan, David P.
    Tayo, Bamidele O.
    Tremoli, Elena
    Tuomilehto, Jaakko
    Uusitupa, Matti
    van Duijn, Cornelia M.
    Vollenweider, Peter
    Wallentin, Lars
    Wareham, Nicholas J.
    Whitfield, John B.
    Wolffenbuttel, Bruce H. R.
    Altshuler, David
    Ordovas, Jose M.
    Boerwinkle, Eric
    Palmer, Colin N. A.
    Thorsteinsdottir, Unnur
    Chasman, Daniel I.
    Rotter, Jerome I.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Lunds universitet, Harvard University.
    Ripatti, Samuli
    Cupples, L. Adrienne
    Sandhu, Manjinder S.
    Rich, Stephen S.
    Boehnke, Michael
    Deloukas, Panos
    Mohlke, Karen L.
    Ingelsson, Erik
    Abecasis, Goncalo R.
    Daly, Mark J.
    Neale, Benjamin M.
    Kathiresan, Sekar
    Common variants associated with plasma triglycerides and risk for coronary artery disease2013In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 45, no 11, p. 1345-+Article in journal (Refereed)
    Abstract [en]

    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.

  • 37. Donnelly, Louise A.
    et al.
    Zhou, Kaixin
    Doney, Alex S. F.
    Jennison, Chris
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine. Department of Clinical Science, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, USA.
    Pearson, Ewan R.
    Rates of glycaemic deterioration in a real-world population with type 2 diabetes2018In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 61, no 3, p. 607-615Article in journal (Refereed)
    Abstract [en]

    Aims/hypothesis: There is considerable variability in how diabetes progresses after diagnosis. Progression modelling has largely focused on 'time to failure' methods, yet determining a 'coefficient of failure' has many advantages. We derived a rate of glycaemic deterioration in type 2 diabetes, using a large real-world cohort, and aimed to investigate the clinical, biochemical, pharmacological and immunological variables associated with fast and slow rates of glycaemic deterioration. Methods: An observational cohort study was performed using the electronic medical records from participants in the Genetics of Diabetes Audit and Research in Tayside Study (GoDARTS). A model was derived based on an individual's observed HbA(1c) measures from the first eligible HbA(1c) after the diagnosis of diabetes through to the study end (defined as insulin initiation, death, leaving the area or end of follow-up). Each HbA(1c) measure was time-dependently adjusted for the effects of non-insulin glucose-lowering drugs, changes in BMI and corticosteroid use. GAD antibody (GADA) positivity was defined as GAD titres above the 97.5th centile of the population distribution. Results: The mean (95% CI) glycaemic deterioration for type 2 diabetes and GADA-positive individuals was 1.4 (1.3, 1.4) and 2.8 (2.4, 3.3) mmol/mol HbA(1c) per year, respectively. A younger age of diagnosis, lower HDL-cholesterol concentration, higher BMI and earlier calendar year of diabetes diagnosis were independently associated with higher rates of glycaemic deterioration in individuals with type 2 diabetes. The rate of deterioration in those diagnosed at over 70 years of age was very low, with 66% having a rate of deterioration of less than 1.1 mmol/mol HbA(1c) per year, and only 1.5% progressing more rapidly than 4.4 mmol/mol HbA(1c) per year. Conclusions/interpretation: We have developed a novel approach for modelling the progression of diabetes in observational data across multiple drug combinations. This approach highlights how glycaemic deterioration in those diagnosed at over 70 years of age is minimal, supporting a stratified approach to diabetes management.

  • 38. Dupuis, Josée
    et al.
    Langenberg, Claudia
    Prokopenko, Inga
    Saxena, Richa
    Soranzo, Nicole
    Jackson, Anne U
    Wheeler, Eleanor
    Glazer, Nicole L
    Bouatia-Naji, Nabila
    Gloyn, Anna L
    Lindgren, Cecilia M
    Mägi, Reedik
    Morris, Andrew P
    Randall, Joshua
    Johnson, Toby
    Elliott, Paul
    Rybin, Denis
    Thorleifsson, Gudmar
    Steinthorsdottir, Valgerdur
    Henneman, Peter
    Grallert, Harald
    Dehghan, Abbas
    Hottenga, Jouke Jan
    Franklin, Christopher S
    Navarro, Pau
    Song, Kijoung
    Goel, Anuj
    Perry, John R B
    Egan, Josephine M
    Lajunen, Taina
    Grarup, Niels
    Sparsø, Thomas
    Doney, Alex
    Voight, Benjamin F
    Stringham, Heather M
    Li, Man
    Kanoni, Stavroula
    Shrader, Peter
    Cavalcanti-Proença, Christine
    Kumari, Meena
    Qi, Lu
    Timpson, Nicholas J
    Gieger, Christian
    Zabena, Carina
    Rocheleau, Ghislain
    Ingelsson, Erik
    An, Ping
    O'Connell, Jeffrey
    Luan, Jian'an
    Elliott, Amanda
    McCarroll, Steven A
    Payne, Felicity
    Roccasecca, Rosa Maria
    Pattou, François
    Sethupathy, Praveen
    Ardlie, Kristin
    Ariyurek, Yavuz
    Balkau, Beverley
    Barter, Philip
    Beilby, John P
    Ben-Shlomo, Yoav
    Benediktsson, Rafn
    Bennett, Amanda J
    Bergmann, Sven
    Bochud, Murielle
    Boerwinkle, Eric
    Bonnefond, Amélie
    Bonnycastle, Lori L
    Borch-Johnsen, Knut
    Böttcher, Yvonne
    Brunner, Eric
    Bumpstead, Suzannah J
    Charpentier, Guillaume
    Chen, Yii-Der Ida
    Chines, Peter
    Clarke, Robert
    Coin, Lachlan J M
    Cooper, Matthew N
    Cornelis, Marilyn
    Crawford, Gabe
    Crisponi, Laura
    Day, Ian N M
    de Geus, Eco J C
    Delplanque, Jerome
    Dina, Christian
    Erdos, Michael R
    Fedson, Annette C
    Fischer-Rosinsky, Antje
    Forouhi, Nita G
    Fox, Caroline S
    Frants, Rune
    Franzosi, Maria Grazia
    Galan, Pilar
    Goodarzi, Mark O
    Graessler, Jürgen
    Groves, Christopher J
    Grundy, Scott
    Gwilliam, Rhian
    Gyllensten, Ulf
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research.
    Hadjadj, Samy
    Hammond, Naomi
    Han, Xijing
    Hartikainen, Anna-Liisa
    Hassanali, Neelam
    Hayward, Caroline
    Heath, Simon C
    Hercberg, Serge
    Herder, Christian
    Hicks, Andrew A
    Hillman, David R
    Hingorani, Aroon D
    Hofman, Albert
    Hui, Jennie
    Hung, Joe
    Isomaa, Bo
    Johnson, Paul R V
    Jørgensen, Torben
    Jula, Antti
    Kaakinen, Marika
    Kaprio, Jaakko
    Kesaniemi, Y Antero
    Kivimaki, Mika
    Knight, Beatrice
    Koskinen, Seppo
    Kovacs, Peter
    Kyvik, Kirsten Ohm
    Lathrop, G Mark
    Lawlor, Debbie A
    Le Bacquer, Olivier
    Lecoeur, Cécile
    Li, Yun
    Lyssenko, Valeriya
    Mahley, Robert
    Mangino, Massimo
    Manning, Alisa K
    Martínez-Larrad, María Teresa
    McAteer, Jarred B
    McCulloch, Laura J
    McPherson, Ruth
    Meisinger, Christa
    Melzer, David
    Meyre, David
    Mitchell, Braxton D
    Morken, Mario A
    Mukherjee, Sutapa
    Naitza, Silvia
    Narisu, Narisu
    Neville, Matthew J
    Oostra, Ben A
    Orrù, Marco
    Pakyz, Ruth
    Palmer, Colin N A
    Paolisso, Giuseppe
    Pattaro, Cristian
    Pearson, Daniel
    Peden, John F
    Pedersen, Nancy L
    Perola, Markus
    Pfeiffer, Andreas F H
    Pichler, Irene
    Polasek, Ozren
    Posthuma, Danielle
    Potter, Simon C
    Pouta, Anneli
    Province, Michael A
    Psaty, Bruce M
    Rathmann, Wolfgang
    Rayner, Nigel W
    Rice, Kenneth
    Ripatti, Samuli
    Rivadeneira, Fernando
    Roden, Michael
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Sandbaek, Annelli
    Sandhu, Manjinder
    Sanna, Serena
    Sayer, Avan Aihie
    Scheet, Paul
    Scott, Laura J
    Seedorf, Udo
    Sharp, Stephen J
    Shields, Beverley
    Sigurethsson, Gunnar
    Sijbrands, Eric J G
    Silveira, Angela
    Simpson, Laila
    Singleton, Andrew
    Smith, Nicholas L
    Sovio, Ulla
    Swift, Amy
    Syddall, Holly
    Syvänen, Ann-Christine
    Tanaka, Toshiko
    Thorand, Barbara
    Tichet, Jean
    Tönjes, Anke
    Tuomi, Tiinamaija
    Uitterlinden, André G
    van Dijk, Ko Willems
    van Hoek, Mandy
    Varma, Dhiraj
    Visvikis-Siest, Sophie
    Vitart, Veronique
    Vogelzangs, Nicole
    Waeber, Gérard
    Wagner, Peter J
    Walley, Andrew
    Walters, G Bragi
    Ward, Kim L
    Watkins, Hugh
    Weedon, Michael N
    Wild, Sarah H
    Willemsen, Gonneke
    Witteman, Jaqueline C M
    Yarnell, John W G
    Zeggini, Eleftheria
    Zelenika, Diana
    Zethelius, Björn
    Zhai, Guangju
    Zhao, Jing Hua
    Zillikens, M Carola
    Borecki, Ingrid B
    Loos, Ruth J F
    Meneton, Pierre
    Magnusson, Patrik K E
    Nathan, David M
    Williams, Gordon H
    Hattersley, Andrew T
    Silander, Kaisa
    Salomaa, Veikko
    Smith, George Davey
    Bornstein, Stefan R
    Schwarz, Peter
    Spranger, Joachim
    Karpe, Fredrik
    Shuldiner, Alan R
    Cooper, Cyrus
    Dedoussis, George V
    Serrano-Ríos, Manuel
    Morris, Andrew D
    Lind, Lars
    Palmer, Lyle J
    Hu, Frank B
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Ebrahim, Shah
    Marmot, Michael
    Kao, W H Linda
    Pankow, James S
    Sampson, Michael J
    Kuusisto, Johanna
    Laakso, Markku
    Hansen, Torben
    Pedersen, Oluf
    Pramstaller, Peter Paul
    Wichmann, H Erich
    Illig, Thomas
    Rudan, Igor
    Wright, Alan F
    Stumvoll, Michael
    Campbell, Harry
    Wilson, James F
    Bergman, Richard N
    Buchanan, Thomas A
    Collins, Francis S
    Mohlke, Karen L
    Tuomilehto, Jaakko
    Valle, Timo T
    Altshuler, David
    Rotter, Jerome I
    Siscovick, David S
    Penninx, Brenda W J H
    Boomsma, Dorret I
    Deloukas, Panos
    Spector, Timothy D
    Frayling, Timothy M
    Ferrucci, Luigi
    Kong, Augustine
    Thorsteinsdottir, Unnur
    Stefansson, Kari
    van Duijn, Cornelia M
    Aulchenko, Yurii S
    Cao, Antonio
    Scuteri, Angelo
    Schlessinger, David
    Uda, Manuela
    Ruokonen, Aimo
    Jarvelin, Marjo-Riitta
    Waterworth, Dawn M
    Vollenweider, Peter
    Peltonen, Leena
    Mooser, Vincent
    Abecasis, Goncalo R
    Wareham, Nicholas J
    Sladek, Robert
    Froguel, Philippe
    Watanabe, Richard M
    Meigs, James B
    Groop, Leif
    Boehnke, Michael
    McCarthy, Mark I
    Florez, Jose C
    Barroso, Inês
    New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk2010In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 42, no 2, p. 105-116Article in journal (Refereed)
    Abstract [en]

    Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

  • 39. Ehret, Georg B.
    et al.
    Ferreira, Teresa
    Chasman, Daniel I.
    Jackson, Anne U.
    Schmidt, Ellen M.
    Johnson, Toby
    Thorleifsson, Gudmar
    Luan, Jian'an
    Donnelly, Louise A.
    Kanoni, Stavroula
    Petersen, Ann -Kristin
    Pihurl, Vasyl
    Strawbridge, Rona J.
    Shungin, Dmitry
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine. Umeå University, Faculty of Medicine, Department of Odontology. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden.
    Hughes, Maria F.
    Meirelles, Osorio
    Kaakinen, Marika
    Bouatia-Naji, Nabila
    Kristiansson, Kati
    Shah, Sonia
    Kleber, Marcus E.
    Guo, Xiuqing
    Lyytikainen, Leo-Pekka
    Fava, Cristiano
    Eriksson, Nidas
    Nolte, Ilja M.
    Magnusson, Patrik K.
    Salfati, Elias L.
    Rallidis, Loukianos S.
    Theusch, Elizabeth
    Smith, Andrew J. P.
    Folkersen, Lasse
    Witkowska, Kate
    Pers, Tune H.
    Joehanes, Roby
    Kim, Stuart K.
    Lataniotis, Lazaros
    Jansen, Rick
    Johnson, Andrew D.
    Warren, Helen
    Kim, Young Jin
    Zhao, Wei
    Wu, Ying
    Tayo, Bamidele O.
    Bochud, Murielle
    Absher, Devin
    Adair, Linda S.
    Amin, Najaf
    Arkingl, Dan E.
    Axelsson, Tomas
    Baldassarre, Damian
    Balkau, Beverley
    Bandinelli, Stefania
    Barnes, Michael R.
    Barroso, Ines
    Bevan, Stephen
    Bis, Joshua C.
    Bjornsdottir, Gyda
    Boehnke, Michael
    Boerwinkle, Eric
    Bonnycastle, Lori L.
    Boomsma, Dorret I.
    Bornstein, Stefan R.
    Brown, Morris J.
    Burnier, Michel
    Cabrera, Claudia P.
    Chambers, John C.
    Chang, I-Shou
    Cheng, Ching-Yu
    Chines, Peter S.
    Chung, Ren-Hua
    Collins, Francis S.
    Connell, John M.
    Doring, Angela
    Dallongeville, Jean
    Danesh, John
    de Faire, Ulf
    Delgado, Graciela
    Dominiczak, Anna F.
    Doney, Alex S. F.
    Drenos, Fotios
    Edkins, Sarah
    Eicher, John D.
    Elosua, Roberto
    Enroth, Stefan
    Erdmann, Jeanette
    Eriksson, Per
    Esko, Tonu
    Evangelou, Evangelos
    Evans, Alun
    Fai, Tove
    Farra, Martin
    Felixl, Janine F.
    Ferrieres, Jean
    Ferrucci, Luigi
    Fornage, Myriam
    Forrester, Terrence
    Franceschinil, Nora
    Franco, Oscar H.
    Franco-Cereceda, Anders
    Fraser, Ross M.
    Ganesh, Santhi K.
    Gao, He
    Gertow, Karl
    Gianfagna, Francesco
    Gigante, Bruna
    Giulianini, Franco
    Goe, Anuj
    Goodall, Alison H.
    Goodarzi, Mark
    Gorski, Mathias
    Grassler, Jurgen
    Groves, Christopher J.
    Gudnason, Vilmundur
    Gyllensten, Ulf
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Biobank Research.
    Hartikainen, Anna-Liisa
    Hassinen, Maija
    Havulinna, Aki S.
    Hayward, Caroline
    Hercberg, Serge
    Herzig, Karl-Heinz
    Hicks, Andrew A.
    Hingorani, Aroon D.
    Hirschhorn, Joel N.
    Hofmanl, Albert
    Holmen, Jostein
    Holmen, Oddgeir Lingaas
    Hottenga, Jouke-Jan
    Howard, Phil
    Hsiung, Chao A.
    Hunt, Steven C.
    Ikram, M. Arfan
    Illig, Thomas
    Iribarren, Carlos
    Jensen, Richard A.
    Kahonen, Mika
    Kang, Hyun Min
    Kathiresan, Sekar
    Keating, Brendan J.
    Khaw, Kay-Tee
    Kim, Yun Kyoung
    Kim, Eric
    Kivimaki, Mika
    Klopp, Norman
    Kolovou, Genovefa
    Komulainen, Pirjo
    Kooner, Jaspal S.
    Kosova, Gulum
    Krauss, Ronald M.
    Kuh, Diana
    Kutalik, Zoltan
    Kuusisto, Johanna
    Kvaloy, Kirsti
    Lakka, Timo A.
    Lee, Nanette R.
    Lee, I-Te
    Lee, Wen-Jane
    Levy, Daniel
    Li, Xiaohui
    Liang, Kae-Woei
    Lin, Honghuang
    Lin, Li
    Lindstrom, Jaana
    Lobbens, Stephane
    Mannisto, Satu
    Muller, Gabriele
    Muller-Nurasyid, Martina
    Mach, Francois
    Markus, Hugh S.
    Marouli, Eirini
    McCarthy, Mark I.
    McKenzie, Colin A.
    Meneton, Pierre
    Menni, Cristina
    Metspalu, Andres
    Mijatovic, Vladan
    Moilanen, Leena
    Montasser, May E.
    Morris, Andrew D.
    Morrison, Alanna C.
    Mulas, Antonella
    Nagaraja, Ramaiah
    Narisu, Narisu
    Nikus, Kjell
    O'Donnell, Christopher J.
    O'Reilly, Paul F.
    Ong, Ken K.
    Paccaud, Fred
    Palmer, Cameron D.
    Parsa, Afshin
    Pedersen, Nancy L.
    Penninx, Brenda W.
    Perola, Markus
    Peters, Annette
    Poulter, Neil
    Pramstaller, Peter P.
    Psaty, Bruce M.
    Quertermous, Thomas
    Rao, Dabeeru C.
    Rasheed, Asif
    Rayner, N. William
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Biobank Research. Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden.
    Rettig, Rainer
    Rice, Kenneth M.
    Roberts, Robert
    Rose, Lynda M.
    Rossouw, Jacques
    Samani, Nilesh J.
    Sanna, Serena
    Saramies, Jouko
    Schunkert, Heribert
    Sebert, Sylvain
    Sheu, Wayne H-H
    Shin, Young-Ah
    Sim, Xueling
    Smit, Johannes H.
    Smith, Albert V.
    Sosa, Maria X.
    Spector, Tim D.
    Stancakova, Alena
    Stanton, Alice V.
    Stirrups, Kathleen E.
    Stringham, Heather M.
    Sundstrom, Johan
    Swift, Amy J.
    Syvanen, Ann-Christine
    Tai, E-Shyong
    Tanaka, Toshiko
    Tarasov, Kirill V.
    Teumer, Alexander
    Thorsteinsdottir, Unnur
    Tobin, Martin D.
    Tremoli, Elena
    Uitterlinden, Andre G.
    Uusitupa, Matti
    Vaez, Ahmad
    Vaidya, Dhananjay
    van Duijn, Cornelia M.
    van Iperen, Erik P. A.
    Vasan, Ramachandran S.
    Verwoert, Germaine C.
    Virtamo, Jarmo
    Vitart, Veronique
    Voight, Benjamin F.
    Vollenweider, Peter
    Wagner, Aline
    Wain, Louise V.
    Wareham, Nicholas J.
    Watldns, Hugh
    Weder, Alan B.
    Westra, Harm Jan
    Wilks, Rainford
    Wilsgaard, Tom
    Wilson, James F.
    Wong, Tien Y.
    Yang, Tsun-Po
    Yao, Jie
    Yengo, Loic
    Zhang, Weihua
    Zhao, Jing Hua
    Zhu, Xiaofeng
    Bovet, Pascal
    Cooper, Richard S.
    Mohlke, Karen L.
    Saleheen, Danish
    Lee, Jong-Young
    Elliott, Paul
    Gierman, Hinco J.
    Willer, Cristen J.
    Franke, Lude
    Hovingh, G. Kees
    Taylor, Kent D.
    Dedoussis, George
    Sever, Peter
    Wong, Andrew
    Lind, Lars
    Assimes, Themistocles L.
    Njolstad, Inger
    Schwarz, Peter E. H.
    Langenberg, Claudia
    Snieder, Harold
    Caulfield, Mark J.
    Melander, E.
    Laakso, Markku
    Saltevo, Juha
    Rauramaa, Rainer
    Tuomilehto, Jaakko
    Ingelsson, Erik
    Lehtimaki, Terho
    Hveem, Kristian
    Palmas, Walter
    Marz, Winfried
    Kumar, Meena
    Salomaa, Veikko
    Chen, Yii-Der I.
    Rotter, Jerome I.
    Froguel, Philippe
    Jarvelin, Marjo-Riitta
    Lakatta, Edward G.
    Kuulasmaa, Kari
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
    Hamsten, Anders
    Wichmann, H-Erich
    Palmer, Colin N. A.
    Stefansson, Kari
    Ridker, Paul M.
    Loos, Ruth J. F.
    Chalcravarti, Aravinda
    Deloukas, Panos
    Morris, Andrew P.
    Newton-Cheh, Christopher
    Munroe, Patricia B.
    The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals2016In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 48, no 10, p. 1171-1184Article in journal (Refereed)
    Abstract [en]

    To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

  • 40. Ekelund, U.
    et al.
    Palla, L.
    Brage, S.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Lund University, Malmö, Sweden.
    Peters, T.
    Balkau, B.
    Diaz, M. J. T.
    Huerta, J. M.
    Agnoli, C.
    Arriola, L.
    Ardanaz, E.
    Boeing, H.
    Clavel-Chapelon, F.
    Crowe, F.
    Fagherazzi, G.
    Groop, L.
    Hainaut, P.
    Johnsen, N. Fons
    Kaaks, R.
    Khaw, K. T.
    Key, T. J.
    de Lauzon-Guillain, B.
    May, A.
    Monninkhof, E.
    Navarro, C.
    Nilsson, P.
    Ostergaard, J. Nautrup
    Norat, T.
    Overvad, K.
    Palli, D.
    Panico, S.
    Redondo, M. L.
    Ricceri, F.
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Romaguera, D.
    Romieu, I.
    Sanchez Perez, M. J.
    Slimani, N.
    Spijkerman, A.
    Teucher, B.
    Tjonneland, A.
    Travier, N.
    Tumino, R.
    Vos, W.
    Vigl, M.
    Sharp, S.
    Langenberg, C.
    Forouhi, N.
    Riboli, E.
    Feskens, E.
    Wareham, N. J.
    Physical activity reduces the risk of incident type 2 diabetes in general and in abdominally lean and obese men and women: the EPIC-InterAct Study2012In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 55, no 7, p. 1944-1952Article in journal (Refereed)
    Abstract [en]

    We examined the independent and combined associations of physical activity and obesity with incident type 2 diabetes in men and women. The InterAct case-cohort study consists of 12,403 incident type 2 diabetes cases and a randomly selected subcohort of 16,154 individuals, drawn from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. Physical activity was assessed by a four-category index. Obesity was measured by BMI and waist circumference (WC). Associations between physical activity, obesity and case-ascertained incident type 2 diabetes were analysed by Cox regression after adjusting for educational level, smoking status, alcohol consumption and energy intake. In combined analyses, individuals were stratified according to physical activity level, BMI and WC. A one-category difference in physical activity (equivalent to approximately 460 and 365 kJ/day in men and women, respectively) was independently associated with a 13% (HR 0.87, 95% CI 0.80, 0.94) and 7% (HR 0.93, 95% CI 0.89, 0.98) relative reduction in the risk of type 2 diabetes in men and women, respectively. Lower levels of physical activity were associated with an increased risk of diabetes across all strata of BMI. Comparing inactive with active individuals, the HRs were 1.44 (95% CI 1.11, 1.87) and 1.38 (95% CI 1.17, 1.62) in abdominally lean and obese inactive men, respectively, and 1.57 (95% CI 1.19, 2.07) and 1.19 (95% CI 1.01, 1.39) in abdominally lean and obese inactive women, respectively. Physical activity is associated with a reduction in the risk of developing type 2 diabetes across BMI categories in men and women, as well as in abdominally lean and obese men and women.

  • 41.
    Ekelund, Ulf
    et al.
    Medical Research Council Epidemiology Unit, Cambridge, U.K..
    Franks, Paul
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Sharp, Stephen
    Medical Research Council Epidemiology Unit, Cambridge, U.K..
    Brage, Søren
    Medical Research Council Epidemiology Unit, Cambridge, U.K..
    Wareham, Nicholas J.
    Medical Research Council Epidemiology Unit, Cambridge, U.K..
    Increase in physical activity energy expenditure is associated with reduced metabolic risk independent of change in fatness and fitness2007In: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 30, no 8, p. 2101-2106Article in journal (Refereed)
    Abstract [en]

    Objective: We sought to examine whether change in physical activity energy expenditure (PAEE) is associated with change in metabolic risk factors and whether this association is independent of change in fat mass and aerobic fitness.

    Research design and methods: In a population-based sample of 176 men and 217 women followed prospectively for 5.6 years, we measured PAEE by individually calibrated heart rate monitoring, aerobic fitness, total body fat (fat mass), and metabolic risk factors (blood pressure, fasting triglycerides, HDL cholesterol, insulin, and 2-h glucose) at baseline and follow-up.

    Results: A 100 J · kg fat-free mass (FFM)−1 · min−1 increase in PAEE from baseline to follow-up reduced triglycerides by 3.5% (95% CI 0.03–5.7) in men and 3.2% (0.02–5.4) in women, fasting insulin by 5.3% (1.0–7.5) in men and women, and 2-h glucose by 3.2% (0.3–5.3) in men and 3.1% (0.3–5.2) in women, after adjustment for sex, age, smoking status, aerobic fitness, baseline phenotype, and change in fat mass. In general, the magnitudes of association for change in fat mass with metabolic risk factors were two to three times stronger than for PAEE.

    Conclusions: Increasing levels of physical activity may protect against metabolic disease even in the absence of improved aerobic fitness and reduced body fatness. Therefore, the combination of increasing levels of physical activity and avoidance of gain in fat mass is likely to be the most successful approach for preventing cardiovascular and metabolic disease.

  • 42. Elgzyri, T
    et al.
    Parikh, H
    Zhou, Y
    Nitert, M Dekker
    Ronn, T
    Segerstrom, AB
    Ling, C
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Wollmer, P
    Eriksson, KF
    Groop, L
    Hansson, O
    First-degree relatives of type 2 diabetic patients have reduced expression of genes involved in fatty acid metabolism in skeletal muscle2012In: Journal of Clinical Endocrinology and Metabolism, ISSN 0021-972X, E-ISSN 1945-7197, Vol. 97, no 7, p. E1332-E1337Article in journal (Refereed)
    Abstract [en]

    Context: First-degree relatives of patients with type 2 diabetes (FH+) have been shown to have decreased energy expenditure and decreased expression of mitochondrial genes in skeletal muscle. In previous studies, it has been difficult to distinguish whether mitochondrial dysfunction and differential regulation of genes are primary (genetic) or due to reduced physical activity, obesity, or other correlated factors.

    Objective: The aim of this study was to investigate whether mitochondrial dysfunction is a primary defect or results from an altered metabolic state.

    Design: We compared gene expression in skeletal muscle from 24 male subjects with FH and 26 without FH matched for age, glucose tolerance, VO2peak (peak oxygen uptake), and body mass index using microarrays. Additionally, type fiber composition, mitochondrial DNA content, and citrate synthase activity were measured. The results were followed up in an additional cohort with measurements of in vivo metabolism. Results: FH+ vs. FH- subjects showed reduced expression of mitochondrial genes (P = 2.75 x 10(-6)), particularly genes involved in fatty acid metabolism (P = 4.08 x 10(-7)), despite similar mitochondrial DNA content. Strikingly, a 70% reduced expression of the monoamine oxidase A(MAOA) gene was found in FH+ vs. FH- individuals (P = 0.0009). Down-regulation of the genes involved in fat metabolism was associated with decreased in vivo fat oxidation and increased glucose oxidation examined in an additional cohort of elderly men.

    Conclusions: These results suggest that genetically altered fatty acid metabolism predisposes to type 2 diabetes and propose a role for catecholamine-metabolizing enzymes like MAOA in the regulation of energy metabolism. (J Clin Endocrinol Metab 97: E1332-E1337, 2012)

  • 43. Elks, Cathy E.
    et al.
    Ong, Ken K.
    Scott, Robert A.
    van der Schouw, Yvonne T.
    Brand, Judith S.
    Wark, Petra A.
    Amiano, Pilar
    Balkau, Beverley
    Barricarte, Aurelio
    Boeing, Heiner
    Fonseca-Nunes, Ana
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Lunds universitet.
    Grioni, Sara
    Halkjaer, Jytte
    Kaaks, Rudolf
    Key, Timothy J.
    Khaw, Kay Tee
    Mattiello, Amalia
    Nilsson, Peter M.
    Overvad, Kim
    Palli, Domenico
    Quiros, J. Ramon
    Rinaldi, Sabina
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Romieu, Isabelle
    Sacerdote, Carlotta
    Sanchez, Maria-Jose
    Spijkerman, Annemieke M. W.
    Tjonneland, Anne
    Tormo, Maria-Jose
    Tumino, Rosario
    Daphne, L. Van der A.
    Forouhi, Nita G.
    Sharp, Stephen J.
    Langenberg, Claudia
    Riboli, Elio
    Wareham, Nicholas J.
    Age at Menarche and Type 2 Diabetes Risk The EPIC-InterAct study2013In: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 36, no 11, p. 3526-3534Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE Younger age at menarche, a marker of pubertal timing in girls, is associated with higher risk of later type 2 diabetes. We aimed to confirm this association and to examine whether it is explained by adiposity. RESEARCH DESIGN AND METHODS The prospective European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study consists of 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,154 individuals from 26 research centers across eight European countries. We tested the association between age at menarche and incident type 2 diabetes using Prentice-weighted Cox regression in 15,168 women (n = 5,995 cases). Models were adjusted in a sequential manner for potential confounding and mediating factors, including adult BMI. RESULTS Mean menarcheal age ranged from 12.6 to 13.6 years across InterAct countries. Each year later menarche was associated with 0.32 kg/m(2) lower adult BMI. Women in the earliest menarche quintile (8-11 years, n = 2,418) had 70% higher incidence of type 2 diabetes compared with those in the middle quintile (13 years, n = 3,634), adjusting for age at recruitment, research center, and a range of lifestyle and reproductive factors (hazard ratio [HR], 1.70; 95% CI, 1.49-1.94; P < 0.001). Adjustment for BMI partially attenuated this association (HR, 1.42; 95% CI, 1.18-1.71; P < 0.001). Later menarche beyond the median age was not protective against type 2 diabetes. CONCLUSIONS Women with history of early menarche have higher risk of type 2 diabetes in adulthood. Less than half of this association appears to be mediated by higher adult BMI, suggesting that early pubertal development also may directly increase type 2 diabetes risk.

  • 44.
    Eriksson, Margareta K.
    et al.
    Umeå University, Faculty of Medicine, Department of Community Medicine and Rehabilitation, Physiotherapy.
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Eliasson, Mats
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    A 3-year randomized trial of lifestyle intervention for cardiovascular risk reduction in the primary care setting: the Swedish Björknäs study2009In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, no 4, p. e5195-Article in journal (Refereed)
    Abstract [en]

    Background Successfully transferring the findings of expensive and tightly controlled programmes of intensive lifestyle modification to the primary care setting is necessary if such knowledge is to be of clinical utility. The objective of this study was to test whether intensive lifestyle modification, shown previously in tightly-controlled clinical trials to be efficacious for diabetes risk-reduction among high-risk individuals, can reduce cardiovascular risk factor levels in the primary care setting. 

    Methodology / Principal Findings The Swedish Björknäs study was a randomized controlled trial conducted from 2003 to 2006 with follow-up on cardiovascular risk factors at 3, 12, 24 and 36 months. A total of 151 middle-aged men and women at moderate- to high-risk of cardiovascular disease from northern Sweden were randomly assigned to either an intensive lifestyle intervention (n=75) or control (n=76) group. The intervention was based broadly on the protocol of the Diabetes Prevention Program. The three-month intervention period was administered in the primary care setting and consisted of supervised exercise sessions and diet counselling, followed by regular group meetings during three years. The control group was given general advice about diet and exercise and received standard clinical care. Outcomes were changes in anthropometrics, aerobic fitness, self-reported physical activity, blood pressure, and metabolic traits. At 36 months post-randomisation, intensive lifestyle modification reduced waist circumference (–2.2cm: p=0.001), waist-hip ratio (–0.02: p<0.0001), systolic blood pressure (–4.9mmHg: p=0.036), and diastolic blood pressure (–1.6mmHg: p=0.005), and improved aerobic fitness (5%; p=0.038). Changes in lipid or glucose values did not differ statistically between groups. At 36 months, self-reported time spent exercising and total physical activity had increased more in the intervention group than in the control group (p<0.001).

    Conclusion / Significance  A program of intensive lifestyle modification undertaken in the primary health care setting can favourably influence cardiovascular risk-factor profiles in high-risk individuals.

  • 45. Estampador, Angela C.
    et al.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA; Oxford Center for Diabetes, Endocrinology, and Metabolism, Radcliff Department of Medicine, University of Oxford, Oxford, UK.
    Precision Medicine in Obesity and Type 2 Diabetes: The Relevance of Early-Life Exposures2018In: Clinical Chemistry, ISSN 0009-9147, E-ISSN 1530-8561, Vol. 64, no 1, p. 130-141Article, review/survey (Refereed)
    Abstract [en]

    BACKGROUND: Type 2 diabetes is highly prevalent and devastating. Obesity is a diabetogenic factor, driving insulin resistance and a compensatory demand for increased insulin secretion from the pancreatic β cells; a failure to address this demand results in diabetes. Accordingly, primary and secondary prevention of obesity are at the core of diabetes prevention programs. The development of obesity and declining β-cell function often span many years or decades before diabetes is clinically manifest. Thus, characterizing the early-life process and risk factors that set disease trajectories may yield novel targets for early intervention and help improve the accuracy of prediction algorithms, factors germane to the emerging field of precision medicine.

    CONTENT: Here, we overview the concepts of precision medicine and fetal programming. We discuss the barriers to preventing obesity and type 2 diabetes in adulthood and present the rationale for considering early-life events in this context. In so doing, we discuss proof-of-concept studies and cutting-edge technological developments that are likely to transform current thinking on the etiology and pathogenesis of obesity and type 2 diabetes. We also review the factors hampering progress, including the success and failures of pregnancy intervention trials.

    SUMMARY: Obesity and type 2 diabetes are among the major health and economic burdens of our time. Defeating these diseases is likely to require life-course approaches, which may include aggressive interventions informed by biomarker profiling undertaken during early life.

  • 46.
    Estampador, Angela C.
    et al.
    Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden .
    Pomeroy, Jeremy
    Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden ; Phoenix Epidemiology and Clinical Research Branch, National Institutes of Health, Phoenix, AZ .
    Renström, Frida
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden .
    Nelson, Scott M.
    Reproductive and Maternal Medicine, Faculty of Medicine, University of Glasgow, Glasgow, U.K..
    Mogren, Ingrid
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Obstetrics and Gynaecology.
    Persson, Margareta
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Obstetrics and Gynaecology. Dalarna University, School of Health and Social Studies, Falun, Sweden.
    Sattar, Naveed
    British Heart Foundation Cardiovascular Research Center, University of Glasgow, Glasgow, U.K..
    Domellöf, Magnus
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden ; Department of Nutrition, Harvard School of Public Health, Boston, MA.
    Infant body composition and adipokine concentrations in relation to maternal gestational weight gain2014In: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 37, no 5, p. 1432-1438Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: To investigate associations of maternal gestational weight gain and body composition and their impact on offspring body composition and adipocytokine, glucose, and insulin concentrations at age 4 months. RESEARCH DESIGN AND METHODS: This was a prospective study including 31 mother-infant pairs (N = 62). Maternal body composition was assessed using doubly labeled water. Infant body composition was assessed at 4 months using air displacement plethysmography, and venous blood was assayed for glucose, insulin, adiponectin, interleukin-6 (IL-6), and leptin concentrations. RESULTS: Rate of gestational weight gain in midpregnancy was significantly associated with infant fat mass (r = 0.41, P = 0.03); rate of gestational weight in late pregnancy was significantly associated with infant fat-free mass (r = 0.37, P = 0.04). Infant birth weight was also strongly correlated with infant fat-free mass at 4 months (r = 0.63, P = 0.0002). Maternal BMI and maternal fat mass were strongly inversely associated with infant IL-6 concentrations (r = -0.60, P = 0.002 and r = -0.52, P = 0.01, respectively). Infant fat-free mass was inversely related to infant adiponectin concentrations (r = -0.48, P = 0.008) and positively correlated with infant blood glucose adjusted for insulin concentrations (r = 0.42, P = 0.04). No significant associations for leptin were observed. CONCLUSIONS: Timing of maternal weight gain differentially impacts body composition of the 4-month-old infant, which in turn appears to affect the infant's glucose and adipokine concentrations.

  • 47. Fawcett, Katherine A
    et al.
    Wheeler, Eleanor
    Morris, Andrew P
    Ricketts, Sally L
    Hallmans, Göran
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Nutritional Research.
    Rolandsson, Olov
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Family Medicine.
    Daly, Allan
    Wasson, Jon
    Permutt, Alan
    Hattersley, Andrew T
    Glaser, Benjamin
    Franks, Paul W
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    McCarthy, Mark I
    Wareham, Nicholas J
    Sandhu, Manjinder S
    Barroso, Inês
    Detailed investigation of the role of common and low-frequency WFS1 variants in type 2 diabetes risk2010In: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 59, no 3, p. 741-746Article in journal (Refereed)
    Abstract [en]

    We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations will require large sample sizes (>100,000) or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on type 2 diabetes risk in white U.K. populations, highlighting the complexities of undertaking association studies with low-frequency variants identified by resequencing.

  • 48. Fitipaldi, Hugo
    et al.
    McCarthy, Mark I.
    Florez, Jose C.
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
    A Global Overview of Precision Medicine in Type 2 Diabetes2018In: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 67, no 10, p. 1911-1922Article in journal (Refereed)
    Abstract [en]

    The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; "big data" from electronic medical records, health insurance databases, and other platforms becoming increasingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better understand diabetes and many other complex traits. Identifying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and management of the disease. This emerging area is broadly termed "precision medicine." In this Perspective, we give an overview of the evidence and barriers to the development and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our understanding of diabetes and ultimately our ability to tackle the disease more effectively than ever before.

  • 49. Flannick, Jason
    et al.
    Fuchsberger, Christian
    Mahajan, Anubha
    Teslovich, Tanya M.
    Agarwala, Vineeta
    Gaulton, Kyle J.
    Caulkins, Lizz
    Koesterer, Ryan
    Ma, Clement
    Moutsianas, Loukas
    McCarthy, Davis J.
    Rivas, Manuel A.
    Perry, John R. B.
    Sim, Xueling
    Blackwell, Thomas W.
    Robertson, Neil R.
    Rayner, N. William
    Cingolani, Pablo
    Locke, Adam E.
    Tajes, Juan Fernandez
    Highland, Heather M.
    Dupuis, Josee
    Chines, Peter S.
    Lindgren, Cecilia M.
    Hartl, Christopher
    Jackson, Anne U.
    Chen, Han
    Huyghe, Jeroen R.
    De Bunt, Martijn Van
    Pearson, Richard D.
    Kumar, Ashish
    Muller-Nurasyid, Martina
    Grarup, Niels
    Stringham, Heather M.
    Gamazon, Eric R.
    Lee, Jaehoon
    Chen, Yuhui
    Scott, Robert A.
    Below, Jennifer E.
    Chen, Peng
    Huang, Jinyan
    Go, Min Jin
    Stitzel, Michael L.
    Pasko, Dorota
    Parker, Stephen C. J.
    Varga, Tibor V.
    Green, Todd
    Beer, Nicola L.
    Day-Williams, Aaron G.
    Ferreira, Teresa
    Fingerlin, Tasha
    Horikoshi, Momoko
    Hu, Cheng
    Huh, Iksoo
    Ikram, Mohammad Kamran
    Kim, Bong-Jo
    Kim, Yongkang
    Kim, Young Jin
    Kwon, Min-Seok
    Lee, Juyoung
    Lee, Selyeong
    Lin, Keng-Han
    Maxwell, Taylor J.
    Nagai, Yoshihiko
    Wang, Xu
    Welch, Ryan P.
    Yoon, Joon
    Zhang, Weihua
    Barzilai, Nir
    Voight, Benjamin F.
    Han, Bok-Ghee
    Jenkinson, Christopher P.
    Kuulasmaa, Teemu
    Kuusisto, Johanna
    Manning, Alisa
    Ng, Maggie C. Y.
    Palmer, Nicholette D.
    Balkau, Beverley
    Stancakova, Alena
    Abboud, Hanna E.
    Boeing, Heiner
    Giedraitis, Vilmantas
    Prabhakaran, Dorairaj
    Gottesman, Omri
    Scott, James
    Carey, Jason
    Kwan, Phoenix
    Grant, George
    Smith, Joshua D.
    Neale, Benjamin M.
    Purcell, Shaun
    Butterworth, Adam S.
    Howson, Joanna M. M.
    Lee, Heung Man
    Lu, Yingchang
    Kwak, Soo-Heon
    Zhao, Wei
    Danesh, John
    Lam, Vincent K. L.
    Park, Kyong Soo
    Saleheen, Danish
    So, Wing Yee
    Tam, Claudia H. T.
    Afzal, Uzma
    Aguilar, David
    Arya, Rector
    Aung, Tin
    Chan, Edmund
    Navarro, Carmen
    Cheng, Ching-Yu
    Palli, Domenico
    Correa, Adolfo
    Curran, Joanne E.
    Rybin, Dennis
    Farook, Vidya S.
    Fowler, Sharon P.
    Freedman, Barry I.
    Griswold, Michael
    Hale, Daniel Esten
    Hicks, Pamela J.
    Khor, Chiea-Chuen
    Kumar, Satish
    Lehne, Benjamin
    Thuillier, Dorothee
    Lim, Wei Yen
    Liu, Jianjun
    Loh, Marie
    Musani, Solomon K.
    Puppala, Sobha
    Scott, William R.
    Yengo, Loic
    Tan, Sian-Tsung
    Taylor, Herman A.
    Thameem, Farook
    Wilson, Gregory
    Wong, Tien Yin
    Njolstad, Pal Rasmus
    Levy, Jonathan C.
    Mangino, Massimo
    Bonnycastle, Lori L.
    Schwarzmayr, Thomas
    Fadista, Joao
    Surdulescu, Gabriela L.
    Herder, Christian
    Groves, Christopher J.
    Wieland, Thomas
    Bork-Jensen, Jette
    Brandslund, Ivan
    Christensen, Cramer
    Koistinen, Heikki A.
    Doney, Alex S. F.
    Kinnunen, Leena
    Esko, Tonu
    Farmer, Andrew J.
    Hakaste, Liisa
    Hodgkiss, Dylan
    Kravic, Jasmina
    Lyssenko, Valeriya
    Hollensted, Mette
    Jorgensen, Marit E.
    Jorgensen, Torben
    Ladenvall, Claes
    Justesen, Johanne Marie
    Karajamaki, Annemari
    Kriebel, Jennifer
    Rathmann, Wolfgang
    Lannfelt, Lars
    Lauritzen, Torsten
    Narisu, Narisu
    Linneberg, Allan
    Melander, Olle
    Milani, Lili
    Neville, Matt
    Orho-Melander, Marju
    Qi, Lu
    Qi, Qibin
    Roden, Michael
    Rolandsson, Olov
    Swift, Amy
    Rosengren, Anders H.
    Stirrups, Kathleen
    Wood, Andrew R.
    Mihailov, Evelin
    Blancher, Christine
    Carneiro, Mauricio O.
    Maguire, Jared
    Poplin, Ryan
    Shakir, Khalid
    Fennell, Timothy
    DePristo, Mark
    De Angelis, Martin Hrabe
    Deloukas, Panos
    Gjesing, Anette P.
    Jun, Goo
    Nilsson, Peter M.
    Murphy, Jacquelyn
    Onofrio, Robert
    Thorand, Barbara
    Hansen, Torben
    Meisinger, Christa
    Hu, Frank B.
    Isomaa, Bo
    Karpe, Fredrik
    Liang, Liming
    Peters, Annette
    Huth, Cornelia
    O'Rahilly, Stephen P.
    Palmer, Colin N. A.
    Pedersen, Oluf
    Rauramaa, Rainer
    Tuomilehto, Jaakko
    Salomaa, Veikko
    Watanabe, Richard M.
    Syvanen, Ann-Christine
    Bergman, Richard N.
    Bharadwaj, Dwaipayan
    Bottinger, Erwin P.
    Cho, Yoon Shin
    Chandak, Giriraj R.
    Chan, Juliana Cn
    Chia, Kee Seng
    Daly, Mark J.
    Ebrahim, Shah B.
    Langenberg, Claudia
    Elliott, Paul
    Jablonski, Kathleen A.
    Lehman, Donna M.
    Jia, Weiping
    Ma, Ronald Cw
    Pollin, Toni I.
    Sandhu, Manjinder
    Tandon, Nikhil
    Froguel, Philippe
    Barroso, Ines
    Teo, Yik Ying
    Zeggini, Eleftheria
    Loos, Ruth J. F.
    Small, Kerrin S.
    Ried, Janina S.
    DeFronzo, Ralph A.
    Grallert, Harald
    Glaser, Benjamin
    Metspalu, Andres
    Wareham, Nicholas J.
    Walker, Mark
    Banks, Eric
    Gieger, Christian
    Ingelsson, Erik
    Im, Hae Kyung
    Illig, Thomas
    Franks, Paul W.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Buck, Gemma
    Trakalo, Joseph
    Buck, David
    Prokopenko, Inga
    Magi, Reedik
    Lind, Lars
    Farjoun, Yossi
    Owen, Katharine R.
    Gloyn, Anna L.
    Strauch, Konstantin
    Tuomi, Tiinamaija
    Kooner, Jaspal Singh
    Lee, Jong-Young
    Park, Taesung
    Donnelly, Peter
    Morris, Andrew D.
    Hattersley, Andrew T.
    Bowden, Donald W.
    Collins, Francis S.
    Atzmon, Gil
    Chambers, John C.
    Spector, Timothy D.
    Laakso, Markku
    Strom, Tim M.
    Bell, Graeme I.
    Blangero, John
    Duggirala, Ravindranath
    Tai, EShyong
    McVean, Gilean
    Hanis, Craig L.
    Wilson, James G.
    Seielstad, Mark
    Frayling, Timothy M.
    Meigs, James B.
    Cox, Nancy J.
    Sladek, Rob
    Lander, Eric S.
    Gabriel, Stacey
    Mohlke, Karen L.
    Meitinger, Thomas
    Groop, Leif
    Abecasis, Goncalo
    Scott, Laura J.
    Morris, Andrew P.
    Kang, Hyun Min
    Altshuler, David
    Burtt, Noel P.
    Florez, Jose C.
    Boehnke, Michael
    McCarthy, Mark I.
    Sequence data and association statistics from 12,940 type 2 diabetes cases and controls2017In: Scientific Data, E-ISSN 2052-4463, Vol. 4, article id 170179Article in journal (Refereed)
    Abstract [en]

    To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (> 80% of low-frequency coding variants in similar to 82 K Europeans via the exome chip, and similar to 90% of low-frequency non-coding variants in similar to 44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

  • 50. Florez, J
    et al.
    Jablonski, K
    McAteer, J
    Sandhu, M
    Wareham, N
    Barroso, I
    Franks, Paul
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine.
    Altshuler, D
    Knowler, W
    Testing of diabetes-associated WFS1 polymorphisms in the Diabetes Prevention Program2007In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 51, no 3, p. 451-457Article in journal (Refereed)
    Abstract [en]

    Aims/hypothesis: Wolfram syndrome (diabetes insipidus, diabetes mellitus, optic atrophy and deafness) is caused by mutations in the WFS1 gene. Recently, single nucleotide polymorphisms (SNPs) in WFS1 have been reproducibly associated with type 2 diabetes. We therefore examined the effects of these variants on diabetes incidence and response to interventions in the Diabetes Prevention Program (DPP), in which a lifestyle intervention or metformin treatment was compared with placebo.

    Methods: We genotyped the WFS1 SNPs rs10010131, rs752854 and rs734312 (H611R) in 3,548 DPP participants and performed Cox regression analysis using genotype, intervention and their interactions as predictors of diabetes incidence. We also evaluated the effect of these SNPs on insulin resistance and beta cell function at 1 year.

    Results: Although none of the three SNPs was associated with diabetes incidence in the overall cohort, white homozygotes for the previously reported protective alleles appeared less likely to develop diabetes in the lifestyle arm. Examination of the publicly available Diabetes Genetics Initiative genome-wide association dataset revealed that rs10012946, which is in strong linkage disequilibrium with the three WFS1 SNPs (r 2 = 0.88–1.0), was associated with type 2 diabetes (allelic odds ratio 0.85, 95% CI 0.75–0.97, p = 0.026). In the DPP, we noted a trend towards increased insulin secretion in carriers of the protective variants, although for most SNPs this was seen as compensatory for the diminished insulin sensitivity.

    Conclusions/interpretation: The previously reported protective effect of select WFS1 alleles may be magnified by a lifestyle intervention. These variants appear to confer an improvement in beta cell function.

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