Article in press ; Dynamic sitting, such as fidgeting and desk work, might be associated with health, but remains difficult toidentify out of accelerometry data. We examined, in a laboratory study, whether dynamic sitting can beidentified out of triaxial activity counts. Among 18 participants (56% men, 27.3 ± 6.5 years), up to 236 countsper minute were recorded in the anteroposterior and mediolateral axes during dynamic sitting using a hip-worn accelerometer. Subsequently, we examined in 621 participants (38% men, 80.0 ± 4.7 years) from theAGES-Reykjavik Study whether dynamic sitting was associated with cardio-metabolic health. Compared toparticipants who recorded the fewest dynamic sitting minutes (Q1), those with more dynamic sittingminutes had a lower BMI (Q2=−1.39 (95%CI =−2.33;–0.46); Q3=−1.87 (−2.82;–0.92); Q4=−3.38 (−4.32;–2.45)), a smaller waist circumference (Q2=−2.95 (−5.44;–0.46); Q3=−3.47 (−6.01;–0.93); Q4=−8.21 (−10.72;–5.71)), and a lower odds for the metabolic syndrome (Q2= 0.74 [0.45;1.20] Q3= 0.58 [0.36;0.95]; Q4=0.36[0.22;0.59]). Our findings suggest that dynamic sitting might be identified using accelerometry and that thisbehaviour was associated with health. This might be important given the large amounts of time peoplespend sitting. Future studies with a focus on validation, causation and physiological pathways are needed tofurther examine the possible relevance of dynamic sitting. ; This work was supported by the National Institute on Aging;National Institutes of Health [N01-AG-12100];FP7 People: Marie-Curie Actions (FP7- PEOPLE-2011-CIG) [PCIG09-GA-2011-293621];Icelandic Heart Association; Icelandic Parliament. ; Peer reviewed
To access publisher's full text version of this article click on the hyperlink below ; BACKGROUND: Height and body mass index (BMI) are associated with higher ovarian cancer risk in the general population, but whether such associations exist among BRCA1/2 mutation carriers is unknown. METHODS: We applied a Mendelian randomisation approach to examine height/BMI with ovarian cancer risk using the Consortium of Investigators for the Modifiers of BRCA1/2 (CIMBA) data set, comprising 14,676 BRCA1 and 7912 BRCA2 mutation carriers, with 2923 ovarian cancer cases. We created a height genetic score (height-GS) using 586 height-associated variants and a BMI genetic score (BMI-GS) using 93 BMI-associated variants. Associations were assessed using weighted Cox models. RESULTS: Observed height was not associated with ovarian cancer risk (hazard ratio [HR]: 1.07 per 10-cm increase in height, 95% confidence interval [CI]: 0.94-1.23). Height-GS showed similar results (HR = 1.02, 95% CI: 0.85-1.23). Higher BMI was significantly associated with increased risk in premenopausal women with HR = 1.25 (95% CI: 1.06-1.48) and HR = 1.59 (95% CI: 1.08-2.33) per 5-kg/m2 increase in observed and genetically determined BMI, respectively. No association was found for postmenopausal women. Interaction between menopausal status and BMI was significant (Pinteraction < 0.05). CONCLUSION: Our observation of a positive association between BMI and ovarian cancer risk in premenopausal BRCA1/2 mutation carriers is consistent with findings in the general population. ; Cancer Research - UK European Community's Seventh Framework Programme Cancer Research UK National Institutes of Health Post-Cancer GWAS initiative Department of Defence Canadian Institutes of Health Research (CIHR) Ministry of Economic Development, Innovation and Export Trade Komen Foundation for the Cure Breast Cancer Research Foundation Ovarian Cancer Research Fund Government of Canada through Genome Canada Canadian Institutes of Health Research Ministry of Economy, Science and ...
Publisher's version (útgefin grein) ; Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health. ; This work has been supported by MRC grant MC_U106179472 (FD, KO, JRBP), Samuel Oschin Comprehensive Cancer Institute Developmental Funds, Center for Bioinformatics and Functional Genomics and Department of Biomedical Sciences Developmental Funds (MRJ), NCI P30CA177558 (CH), NCI UM1CA186107 (PK), European Regional Development Fund (Project No. 2014-2020.4.01.15-0012) and the European Union's Horizon 2020 research and innovation program under grant agreements No 692065 (TL, RM, AS) and 692145 (RM), NICHD R01HD065029 (RS), Estonian Ministry of Education and Research (grant IUT34-16 to TL), NICHD R01HD057450 (MU), NICHD P50HD044405 (AD), NICHD R01HD057223 (AD), R01HD085227 (MGH, AD), deCode Genetics (GT, UT, KS, US), Raine Medical Research Foundation Priming Grant (BHM), SCGOPHCG RAC 2015-16/034 (SGW, BGAS), 2016-17/018 (BGAS), NIHR BRC, Wellcome Trust, MRC (TDS), Eris M. Field Chair in Diabetes Research (MOG), NIDDK P30 DK063491 (MOG), NIDDK U01DK094431, U01DK048381 (DE), NICHD U10HD38992 (RL), Estonian Ministry of Education and Research (grant IUT34-16), Enterprise Estonia (grant EU48695); the EU-FP7 Marie Curie Industry-Academia Partnerships and Pathways (IAPP, grant SARM, EU324509 to AS), Wellcome (090532, 098381, 203141); European Commission (ENGAGE: HEALTH-F4-2007-201413 to MIM), MRC G0802782, MR/M012638/1 (SF), Li Ka Shing Foundation, WT-SSI/John Fell Funds, NIHR Biomedical Research Centre, Oxford, Widenlife and NICHD 5P50HD028138-27 (CML), NICHD R01HD065029, ADA 1-10-CT-57, Harvard Clinical and Translational Science Center, from the National Center for Research Resources 1UL1 RR025758 (CKW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Peer Reviewed
