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Sökning: WFRF:(Davidson B) > Göteborgs universitet

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1.
  • Blokland, G. A. M., et al. (författare)
  • Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders
  • 2022
  • Ingår i: Biological Psychiatry. - : Elsevier BV. - 0006-3223 .- 1873-2402. ; 91:1, s. 102-117
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. Methods: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. Results: Across disorders, genome-wide significant single nucleotide polymorphism–by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10−8), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10−6) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10−7; rs73033497, p = 8.8 × 10−7; rs7914279, p = 6.4 × 10−7), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10−7) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10−7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10−7) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). Conclusions: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels. © 2021 Society of Biological Psychiatry
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2.
  • Kaptoge, S., et al. (författare)
  • World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions
  • 2019
  • Ingår i: Lancet Global Health. - : Elsevier BV. - 2214-109X. ; 7:10
  • Tidskriftsartikel (refereegranskat)abstract
    • Background To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0.685 (95% CI 0 . 629-0 741) to 0.833 (0 . 783-0- 882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd.
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3.
  • Antoniou, A. C., et al. (författare)
  • Common variants in LSP1, 2q35 and 8q24 and breast cancer risk for BRCA1 and BRCA2 mutation carriers
  • 2009
  • Ingår i: Human Molecular Genetics. - [Antoniou, Antonis C.; McGuffog, Lesley; Peock, Susan; Cook, Margaret; Frost, Debra; Oliver, Clare; Platte, Radka; Pooley, Karen A.; Easton, Douglas F.] Univ Cambridge, Dept Publ Hlth & Primary Care, Canc Res UK Genet Epidemiol Unit, Cambridge, England. [Sinilnikova, Olga M.; Leone, Melanie] Univ Lyon, CNRS, Hosp Civils Lyon,Ctr Leon Berard,UMR5201, Unite Mixte Genet Constitut Canc Frequents, Lyon, France. [Healey, Sue; Spurdle, Amanda B.; Beesley, Jonathan; Chen, Xiaoqing; Chenevix-Trench, Georgia] Queensland Inst Med Res, Brisbane, Qld 4029, Australia. [Nevanlinna, Heli; Heikkinen, Tuomas] Univ Helsinki, Cent Hosp, Dept Obstet & Gynecol, FIN-00290 Helsinki, Finland. [Simard, Jacques] Univ Laval, Quebec City, PQ, Canada. [Simard, Jacques] Univ Quebec, Ctr Hosp, Canada Res Chair Oncogenet, Canc Genom Lab, Quebec City, PQ, Canada. Peter MacCallum Canc Inst, Melbourne, Vic 3002, Australia. [Neuhausen, Susan L.; Ding, Yuan C.] Univ Calif Irvine, Dept Epidemiol, Irvine, CA USA. [Couch, Fergus J.; Wang, Xianshu; Fredericksen, Zachary] Mayo Clin, Rochester, MN USA. [Peterlongo, Paolo; Peissel, Bernard; Radice, Paolo] Fdn IRCCS Ist Nazl Tumori, Milan, Italy. [Peterlongo, Paolo; Radice, Paolo] Fdn Ist FIRC Oncol Molecolare, Milan, Italy. [Bonanni, Bernardo; Bernard, Loris] Ist Europeo Oncol, Milan, Italy. [Viel, Alessandra] IRCCS, Ctr Riferimento Oncol, Aviano, Italy. [Bernard, Loris] Cogentech, Consortium Genom Technol, Milan, Italy. [Szabo, Csilla I.] Mayo Clin, Coll Med, Dept Lab Med & Pathol, Rochester, MN USA. [Foretova, Lenka] Masaryk Mem Canc Inst, Dept Canc Epidemiol & Genet, Brno, Czech Republic. [Zikan, Michal] Charles Univ Prague, Dept Biochem & Expt Oncol, Fac Med 1, Prague, Czech Republic. [Claes, Kathleen] Ghent Univ Hosp, Ctr Med Genet, B-9000 Ghent, Belgium. [Greene, Mark H.; Mai, Phuong L.] US Natl Canc Inst, Clin Genet Branch, Rockville, MD USA. [Rennert, Gad; Lejbkowicz, Flavio] CHS Natl Canc Control Ctr, Haifa, Israel. [Rennert, Gad; Lejbkowicz, Flavio] Carmel Hosp, Dept Community Med & Epidemiol, Haifa, Israel. [Rennert, Gad; Lejbkowicz, Flavio] B Rappaport Fac Med, Haifa, Israel. [Andrulis, Irene L.; Glendon, Gord] Canc Care Ontario, Ontario Canc Genet Network, Toronto, ON M5G 2L7, Canada. [Andrulis, Irene L.] Mt Sinai Hosp, Fred A Litwin Ctr Canc Genet, Samuel Lunenfeld Res Inst, Toronto, ON, Canada. [Andrulis, Irene L.] Univ Toronto, Dept Mol Genet, Toronto, ON, Canada. [Gerdes, Anne-Marie; Thomassen, Mads] Odense Univ Hosp, Dept Biochem Pharmacol & Genet, DK-5000 Odense, Denmark. [Sunde, Lone] Aarhus Univ Hosp, Dept Clin Genet, DK-8000 Aarhus, Denmark. [Caligo, Maria A.] Univ Pisa, Div Surg Mol & Ultrastructural Pathol, Dept Oncol, Pisa, Italy. [Caligo, Maria A.] Pisa Univ Hosp, Pisa, Italy. [Laitman, Yael; Kontorovich, Tair; Cohen, Shimrit; Friedman, Eitan] Chaim Sheba Med Ctr, Susanne Levy Gertner Oncogenet Unit, IL-52621 Tel Hashomer, Israel. [Kaufman, Bella] Chaim Sheba Med Ctr, Inst Oncol, IL-52621 Tel Hashomer, Israel. [Kaufman, Bella; Friedman, Eitan] Tel Aviv Univ, Sackler Sch Med, IL-69978 Tel Aviv, Israel. [Dagan, Efrat; Baruch, Ruth Gershoni] Rambam Med Ctr, Genet Inst, Haifa, Israel. [Harbst, Katja] Lund Univ, Dept Oncol, S-22100 Lund, Sweden. [Barbany-Bustinza, Gisela; Rantala, Johanna] Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden. [Ehrencrona, Hans] Uppsala Univ, Dept Genet & Pathol, Uppsala, Sweden. [Karlsson, Per] Sahlgrenska Univ, Dept Oncol, Gothenburg, Sweden. [Domchek, Susan M.; Nathanson, Katherine L.] Univ Penn, Philadelphia, PA 19104 USA. [Osorio, Ana; Benitez, Javier] Ctr Invest Biomed Red Enfermedades Raras CIBERERE, Inst Salud Carlos III, Madrid, Spain. [Osorio, Ana; Benitez, Javier] Spanish Natl Canc Ctr CNIO, Human Canc Genet Programme, Human Genet Grp, Madrid, Spain. [Blanco, Ignacio] Catalan Inst Oncol ICO, Canc Genet Counseling Program, Barcelona, Spain. [Lasa, Adriana] Hosp Santa Creu & Sant Pau, Genet Serv, Barcelona, Spain. [Hamann, Ute] Deutsch Krebsforschungszentrum, Neuenheimer Feld 580 69120, D-6900 Heidelberg, Germany. [Hogervorst, Frans B. L.] Netherlands Canc Inst, Dept Pathol, Family Canc Clin, NL-1066 CX Amsterdam, Netherlands. [Rookus, Matti A.] Netherlands Canc Inst, Dept Epidemiol, Amsterdam, Netherlands. [Collee, J. Margriet] Erasmus Univ, Dept Clin Genet, Rotterdam Family Canc Clin, Med Ctr, NL-3000 DR Rotterdam, Netherlands. [Devilee, Peter] Dept Genet Epidemiol, Leiden, Netherlands. [Wijnen, Juul] Leiden Univ, Med Ctr, Ctr Human & Clin Genet, Leiden, Netherlands. [Ligtenberg, Marjolijn J.] Radboud Univ Nijmegen, Med Ctr, Dept Human Genet, NL-6525 ED Nijmegen, Netherlands. [van der Luijt, Rob B.] Univ Utrecht, Med Ctr, Dept Clin Mol Genet, NL-3508 TC Utrecht, Netherlands. [Aalfs, Cora M.] Univ Amsterdam, Acad Med Ctr, Dept Clin Genet, NL-1105 AZ Amsterdam, Netherlands. [Waisfisz, Quinten] Vrije Univ Amsterdam, Med Ctr, Dept Clin Genet, Amsterdam, Netherlands. [van Roozendaal, Cornelis E. P.] Univ Med Ctr, Dept Clin Genet, Maastricht, Netherlands. [Evans, D. Gareth; Lalloo, Fiona] Cent Manchester Univ Hosp, NHS Fdn Trust, Manchester Acad Hlth Sci Ctr, Manchester, Lancs, England. [Eeles, Rosalind] Inst Canc Res, Translat Canc Genet Team, London SW3 6JB, England. [Eeles, Rosalind] Royal Marsden NHS Fdn Trust, London, England. [Izatt, Louise] Guys Hosp, Clin Genet, London SE1 9RT, England. [Davidson, Rosemarie] Ferguson Smith Ctr Clin Genet, Glasgow, Lanark, Scotland. [Chu, Carol] Yorkshire Reg Genet Serv, Leeds, W Yorkshire, England. [Eccles, Diana] Princess Anne Hosp, Wessex Clin Genet Serv, Southampton, Hants, England. [Cole, Trevor] Birmingham Womens Hosp Healthcare, NHS Trust, W Midlands Reg Genet Serv, Birmingham, W Midlands, England. [Hodgson, Shirley] Univ London, Dept Canc Genet, St Georges Hosp, London, England. [Godwin, Andrew K.; Daly, Mary B.] Fox Chase Canc Ctr, Philadelphia, PA 19111 USA. [Stoppa-Lyonnet, Dominique] Univ Paris 05, Paris, France. [Stoppa-Lyonnet, Dominique] Inst Curie, INSERM U509, Serv Genet Oncol, Paris, France. [Buecher, Bruno] Inst Curie, Dept Genet, Paris, France. [Bressac-de Paillerets, Brigitte; Remenieras, Audrey; Lenoir, Gilbert M.] Inst Cancrol Gustave Roussy, Dept Genet, Villejuif, France. [Bressac-de Paillerets, Brigitte] Inst Cancerol Gustave Roussy, INSERM U946, Villejuif, France. [Caron, Olivier] Inst Cancerol Gustave Roussy, Dept Med, Villejuif, France. [Lenoir, Gilbert M.] Inst Cancerol Gustave Roussy, CNRS FRE2939, Villejuif, France. [Sevenet, Nicolas; Longy, Michel] Inst Bergonie, Lab Genet Constitutionnelle, Bordeaux, France. [Longy, Michel] Inst Bergonie, INSERM U916, Bordeaux, France. [Ferrer, Sandra Fert] Hop Hotel Dieu, Ctr Hosp, Lab Genet Chromosom, Chambery, France. [Prieur, Fabienne] CHU St Etienne, Serv Genet Clin Chromosom, St Etienne, France. [Goldgar, David] Univ Utah, Dept Dermatol, Salt Lake City, UT 84112 USA. [Miron, Alexander; Yassin, Yosuf] Dana Farber Canc Inst, Boston, MA 02115 USA. [John, Esther M.] No Calif Canc Ctr, Fremont, CA USA. [John, Esther M.] Stanford Univ, Sch Med, Stanford, CA 94305 USA. [Buys, Saundra S.] Univ Utah, Hlth Sci Ctr, Huntsman Canc Inst, Salt Lake City, UT USA. [Hopper, John L.] Univ Melbourne, Melbourne, Australia. [Terry, Mary Beth] Columbia Univ, New York, NY USA. [Singer, Christian; Gschwantler-Kaulich, Daphne; Staudigl, Christine] Med Univ Vienna, Div Special Gynecol, Dept OB GYN, Vienna, Austria. [Hansen, Thomas V. O.] Univ Copenhagen, Rigshosp, Dept Clin Biochem, DK-2100 Copenhagen, Denmark. [Barkardottir, Rosa Bjork] Landspitali Univ Hosp, Dept Pathol, Reykjavik, Iceland. [Kirchhoff, Tomas; Pal, Prodipto; Kosarin, Kristi; Offit, Kenneth] Mem Sloan Kettering Canc Ctr, Dept Med, Clin Genet Serv, New York, NY 10021 USA. [Piedmonte, Marion] Roswell Pk Canc Inst, GOG Stat & Data Ctr, Buffalo, NY 14263 USA. [Rodriguez, Gustavo C.] Evanston NW Healthcare, NorthShore Univ Hlth Syst, Evanston, IL 60201 USA. [Wakeley, Katie] Tufts Univ, New England Med Ctr, Boston, MA 02111 USA. [Boggess, John F.] Univ N Carolina, Chapel Hill, NC 27599 USA. [Basil, Jack] St Elizabeth Hosp, Edgewood, KY 41017 USA. [Schwartz, Peter E.] Yale Univ, Sch Med, New Haven, CT 06510 USA. [Blank, Stephanie V.] New York Univ, Sch Med, New York, NY 10016 USA. [Toland, Amanda E.] Ohio State Univ, Dept Internal Med, Columbus, OH 43210 USA. [Toland, Amanda E.] Ohio State Univ, Div Human Canc Genet, Ctr Comprehens Canc, Columbus, OH 43210 USA. [Montagna, Marco; Casella, Cinzia] IRCCS, Ist Oncologico Veneto, Immunol & Mol Oncol Unit, Padua, Italy. [Imyanitov, Evgeny N.] NN Petrov Inst Res Inst, St Petersburg, Russia. [Allavena, Anna] Univ Turin, Dept Genet Biol & Biochem, Turin, Italy. [Schmutzler, Rita K.; Versmold, Beatrix; Arnold, Norbert] Univ Cologne, Dept Obstet & Gynaecol, Div Mol Gynaeco Oncol, Cologne, Germany. [Engel, Christoph] Univ Leipzig, Inst Med Informat Stat & Epidemiol, Leipzig, Germany. [Meindl, Alfons] Tech Univ Munich, Dept Obstet & Gynaecol, Munich, Germany. [Ditsch, Nina] Univ Munich, Dept Obstet & Gynecol, Munich, Germany. Univ Schleswig Holstein, Dept Obstet & Gynaecol, Campus Kiel, Germany. [Niederacher, Dieter] Univ Duesseldorf, Dept Obstet & Gynaecol, Mol Genet Lab, Dusseldorf, Germany. [Deissler, Helmut] Univ Ulm, Dept Obstet & Gynaecol, Ulm, Germany. [Fiebig, Britta] Univ Regensburg, Inst Human Genet, Regensburg, Germany. [Suttner, Christian] Univ Heidelberg, Inst Human Genet, Heidelberg, Germany. [Schoenbuchner, Ines] Univ Wurzburg, Inst Human Genet, D-8700 Wurzburg, Germany. [Gadzicki, Dorothea] Med Univ, Inst Cellular & Mol Pathol, Hannover, Germany. [Caldes, Trinidad; de la Hoya, Miguel] Hosp Clinico San Carlos 28040, Madrid, Spain. : Oxford University Press. - 0964-6906 .- 1460-2083. ; 18:22, s. 4442-4456
  • Tidskriftsartikel (refereegranskat)abstract
    • Genome-wide association studies of breast cancer have identified multiple single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risks in the general population. In a previous study, we demonstrated that the minor alleles at three of these SNPs, in FGFR2, TNRC9 and MAP3K1, also confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. Three additional SNPs rs3817198 at LSP1, rs13387042 at 2q35 and rs13281615 at 8q24 have since been reported to be associated with breast cancer in the general population, and in this study we evaluated their association with breast cancer risk in 9442 BRCA1 and 5665 BRCA2 mutation carriers from 33 study centres. The minor allele of rs3817198 was associated with increased breast cancer risk only for BRCA2 mutation carriers [hazard ratio (HR) = 1.16, 95% CI: 1.07-1.25, P-trend = 2.8 × 10-4]. The best fit for the association of SNP rs13387042 at 2q35 with breast cancer risk was a dominant model for both BRCA1 and BRCA2 mutation carriers (BRCA1: HR = 1.14, 95% CI: 1.04-1.25, P = 0.0047; BRCA2: HR = 1.18 95% CI: 1.04-1.33, P = 0.0079). SNP rs13281615 at 8q24 was not associated with breast cancer for either BRCA1 or BRCA2 mutation carriers, but the estimated association for BRCA2 mutation carriers (per-allele HR = 1.06, 95% CI: 0.98-1.14) was consistent with odds ratio estimates derived from population-based case-control studies. The LSP1 and 2q35 SNPs appear to interact multiplicatively on breast cancer risk for BRCA2 mutation carriers. There was no evidence that the associations vary by mutation type depending on whether the mutated protein is predicted to be stable or not. 
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4.
  • Hageman, S., et al. (författare)
  • SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe
  • 2021
  • Ingår i: European Heart Journal. - : Oxford University Press (OUP). - 0195-668X .- 1522-9645. ; 42:25, s. 2439-2454
  • Tidskriftsartikel (refereegranskat)abstract
    • Aims The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe. Methods and results We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low- risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries. Conclusion SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe.
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5.
  • 2019
  • Tidskriftsartikel (refereegranskat)
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6.
  • Antoniou, A. C., et al. (författare)
  • Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers : Implications for risk prediction
  • 2010
  • Ingår i: Cancer Research. - : American Association for Cancer Research. - 0008-5472 .- 1538-7445. ; 70:23, s. 9742-9754
  • Tidskriftsartikel (refereegranskat)abstract
    • The known breast cancer susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1, LSP1, and 2q35 confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of 3 additional single nucleotide polymorphisms (SNPs), rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11, and rs10941679 at 5p12, and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased breast cancer risk for BRCA2 carriers (per-allele HR = 1.10, 95% CI: 1.03-1.18, P = 0.006 and HR = 1.09, 95% CI: 1.01-1.19, P = 0.03, respectively). Neither SNP was associated with breast cancer risk for BRCA1 carriers, and rs6504950 was not associated with breast cancer for either BRCA1 or BRCA2 carriers. Of the 9 polymorphisms investigated, 7 were associated with breast cancer for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, P = 7 × 10-11 - 0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (P = 0.0049, 0.03, respectively). All risk-associated polymorphisms appear to interact multiplicatively on breast cancer risk for mutation carriers. Based on the joint genotype distribution of the 7 risk-associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e., between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing breast cancer by age 80, compared with 42% to 50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences might be sufficient to influence the clinical management of mutation carriers.
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7.
  • Di Angelantonio, E., et al. (författare)
  • Association of Cardiometabolic Multimorbidity With Mortality
  • 2015
  • Ingår i: JAMA. - : American Medical Association (AMA). - 0098-7484 .- 1538-3598. ; 314:1, s. 52-60
  • Tidskriftsartikel (refereegranskat)abstract
    • IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.
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8.
  • Ely, K. S., et al. (författare)
  • A reporting format for leaf-level gas exchange data and metadata
  • 2021
  • Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541. ; 61
  • Tidskriftsartikel (refereegranskat)abstract
    • Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy?s ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.
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9.
  • Kaptoge, S., et al. (författare)
  • Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation
  • 2023
  • Ingår i: The Lancet Diabetes and Endocrinology. - : Elsevier. - 2213-8587 .- 2213-8595. ; 11:10, s. 731-742
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods: For this observational study, we conducted a combined analysis of individual-participant data from 19 high-income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961–2007, median latest follow-up years 1980–2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings: For participants with diabetes, we observed a linear dose–response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43–2·97) when diagnosed at 30–39 years, 2·26 (2·08–2·45) at 40–49 years, 1·84 (1·72–1·97) at 50–59 years, 1·57 (1·47–1·67) at 60–69 years, and 1·39 (1·29–1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years than an individual without diabetes. Using EU death rates, the corresponding estimates were 13, 9, or 5 years earlier. Interpretation: Every decade of earlier diagnosis of diabetes was associated with about 3–4 years of lower life expectancy, highlighting the need to develop and implement interventions that prevent or delay the onset of diabetes and to intensify the treatment of risk factors among young adults diagnosed with diabetes. Funding: British Heart Foundation, Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.
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