@article {8554,
	title = {{Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits},
	journal = {Nat Commun},
	volume = {8},
	year = {2017},
	month = {04},
	pages = {14977},
	abstract = {Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87\% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.},
	author = {Justice, A. E. and Winkler, T. W. and Feitosa, M. F. and Graff, M. and Fisher, V. A. and Young, K. and Barata, L. and Deng, X. and Czajkowski, J. and Hadley, D. and Ngwa, J. S. and Ahluwalia, T. S. and Chu, A. Y. and Heard-Costa, N. L. and Lim, E. and Perez, J. and Eicher, J. D. and Kutalik, Z. and Xue, L. and Mahajan, A. and Renstr?m, F. and Wu, J. and Qi, Q. and Ahmad, S. and Alfred, T. and Amin, N. and Bielak, L. F. and Bonnefond, A. and Bragg, J. and Cadby, G. and Chittani, M. and Coggeshall, S. and Corre, T. and Direk, N. and Eriksson, J. and Fischer, K. and Gorski, M. and Neergaard Harder, M. and Horikoshi, M. and Huang, T. and Huffman, J. E. and Jackson, A. U. and Justesen, J. M. and Kanoni, S. and Kinnunen, L. and Kleber, M. E. and Komulainen, P. and Kumari, M. and Lim, U. and Luan, J. and Lyytik?inen, L. P. and Mangino, M. and Manichaikul, A. and Marten, J. and Middelberg, R. P. S. and M?ller-Nurasyid, M. and Navarro, P. and P?russe, L. and Pervjakova, N. and Sarti, C. and Smith, A. V. and Smith, J. A. and Stan??kov?, A. and Strawbridge, R. J. and Stringham, H. M. and Sung, Y. J. and Tanaka, T. and Teumer, A. and Trompet, S. and van der Laan, S. W. and van der Most, P. J. and Van Vliet-Ostaptchouk, J. V. and Vedantam, S. L. and Verweij, N. and Vink, J. M. and Vitart, V. and Wu, Y. and Yengo, L. and Zhang, W. and Hua Zhao, J. and Zimmermann, M. E. and Zubair, N. and Abecasis, G. R. and Adair, L. S. and Afaq, S. and Afzal, U. and Bakker, S. J. L. and Bartz, T. M. and Beilby, J. and Bergman, R. N. and Bergmann, S. and Biffar, R. and Blangero, J. and Boerwinkle, E. and Bonnycastle, L. L. and Bottinger, E. and Braga, D. and Buckley, B. M. and Buyske, S. and Campbell, H. and Chambers, J. C. and Collins, F. S. and Curran, J. E. and de Borst, G. J. and de Craen, A. J. M. and de Geus, E. J. C. and Dedoussis, G. and Delgado, G. E. and den Ruijter, H. M. and Eiriksdottir, G. and Eriksson, A. L. and Esko, T. and Faul, J. D. and Ford, I. and Forrester, T. and Gertow, K. and Gigante, B. and Glorioso, N. and Gong, J. and Grallert, H. and Grammer, T. B. and Grarup, N. and Haitjema, S. and Hallmans, G. and Hamsten, A. and Hansen, T. and Harris, T. B. and Hartman, C. A. and Hassinen, M. and Hastie, N. D. and Heath, A. C. and Hernandez, D. and Hindorff, L. and Hocking, L. J. and Hollensted, M. and Holmen, O. L. and Homuth, G. and Jan Hottenga, J. and Huang, J. and Hung, J. and Hutri-K?h?nen, N. and Ingelsson, E. and James, A. L. and Jansson, J. O. and Jarvelin, M. R. and Jhun, M. A. and J?rgensen, M. E. and Juonala, M. and K?h?nen, M. and Karlsson, M. and Koistinen, H. A. and Kolcic, I. and Kolovou, G. and Kooperberg, C. and Kr?mer, B. K. and Kuusisto, J. and Kval?y, K. and Lakka, T. A. and Langenberg, C. and Launer, L. J. and Leander, K. and Lee, N. R. and Lind, L. and Lindgren, C. M. and Linneberg, A. and Lobbens, S. and Loh, M. and Lorentzon, M. and Luben, R. and Lubke, G. and Ludolph-Donislawski, A. and Lupoli, S. and Madden, P. A. F. and M?nnikk?, R. and Marques-Vidal, P. and Martin, N. G. and McKenzie, C. A. and McKnight, B. and Mellstr?m, D. and Menni, C. and Montgomery, G. W. and Musk, A. B. and Narisu, N. and Nauck, M. and Nolte, I. M. and Oldehinkel, A. J. and Olden, M. and Ong, K. K. and Padmanabhan, S. and Peyser, P. A. and Pisinger, C. and Porteous, D. J. and Raitakari, O. T. and Rankinen, T. and Rao, D. C. and Rasmussen-Torvik, L. J. and Rawal, R. and Rice, T. and Ridker, P. M. and Rose, L. M. and Bien, S. A. and Rudan, I. and Sanna, S. and Sarzynski, M. A. and Sattar, N. and Savonen, K. and Schlessinger, D. and Scholtens, S. and Schurmann, C. and Scott, R. A. and Sennblad, B. and Siemelink, M. A. and Silbernagel, G. and Slagboom, P. E. and Snieder, H. and Staessen, J. A. and Stott, D. J. and Swertz, M. A. and Swift, A. J. and Taylor, K. D. and Tayo, B. O. and Thorand, B. and Thuillier, D. and Tuomilehto, J. and Uitterlinden, A. G. and Vandenput, L. and Vohl, M. C. and V?lzke, H. and Vonk, J. M. and Waeber, G. and Waldenberger, M. and Westendorp, R. G. J. and Wild, S. and Willemsen, G. and Wolffenbuttel, B. H. R. and Wong, A. and Wright, A. F. and Zhao, W. and Zillikens, M. C. and Baldassarre, D. and Balkau, B. and Bandinelli, S. and B?ger, C. A. and Boomsma, D. I. and Bouchard, C. and Bruinenberg, M. and Chasman, D. I. and Chen, Y. D. and Chines, P. S. and Cooper, R. S. and Cucca, F. and Cusi, D. and Faire, U. and Ferrucci, L. and Franks, P. W. and Froguel, P. and Gordon-Larsen, P. and Grabe, H. J. and Gudnason, V. and Haiman, C. A. and Hayward, C. and Hveem, K. and Johnson, A. D. and Wouter Jukema, J. and Kardia, S. L. R. and Kivimaki, M. and Kooner, J. S. and Kuh, D. and Laakso, M. and Lehtim?ki, T. and Marchand, L. L. and M?rz, W. and McCarthy, M. I. and Metspalu, A. and Morris, A. P. and Ohlsson, C. and Palmer, L. J. and Pasterkamp, G. and Pedersen, O. and Peters, A. and Peters, U. and Polasek, O. and Psaty, B. M. and Qi, L. and Rauramaa, R. and Smith, B. H. and S?rensen, T. I. A. and Strauch, K. and Tiemeier, H. and Tremoli, E. and van der Harst, P. and Vestergaard, H. and Vollenweider, P. and Wareham, N. J. and Weir, D. R. and Whitfield, J. B. and Wilson, J. F. and Tyrrell, J. and Frayling, T. M. and Barroso, I. and Boehnke, M. and Deloukas, P. and Fox, C. S. and Hirschhorn, J. N. and Hunter, D. J. and Spector, T. D. and Strachan, D. P. and van Duijn, C. M. and Heid, I. M. and Mohlke, K. L. and Marchini, J. and Loos, R. J. F. and Kilpel?inen, T. O. and Liu, C. T. and Borecki, I. B. and North, K. E. and Cupples, L. A.}
}
@article {8536,
	title = {{Dairy Consumption and Body Mass Index Among Adults: Mendelian Randomization Analysis of 184802 Individuals from 25 Studies},
	journal = {Clin Chem},
	volume = {64},
	year = {2018},
	month = {01},
	pages = {183{\textendash}191},
	abstract = {Associations between dairy intake and body mass index (BMI) have been inconsistently observed in epidemiological studies, and the causal relationship remains ill defined.\ We performed Mendelian randomization (MR) analysis using an established dairy intake-associated genetic polymorphism located upstream of the lactase gene (LCT-13910 C/T, rs4988235) as an instrumental variable (IV). Linear regression models were fitted to analyze associations between (a) dairy intake and BMI, (b) rs4988235 and dairy intake, and (c) rs4988235 and BMI in each study. The causal effect of dairy intake on BMI was quantified by IV estimators among 184802 participants from 25 studies.\ Higher dairy intake was associated with higher BMI ({\^I}{\texttwosuperior} = 0.03 kg/m2 per serving/day; 95\% CI, 0.00-0.06; P = 0.04), whereas the LCT genotype with 1 or 2 T allele was significantly associated with 0.20 (95\% CI, 0.14-0.25) serving/day higher dairy intake (P = 3.15 {\~A}{\textemdash} 10-12) and 0.12 (95\% CI, 0.06-0.17) kg/m2 higher BMI (P = 2.11 {\~A}{\textemdash} 10-5). MR analysis showed that the genetically determined higher dairy intake was significantly associated with higher BMI ({\^I}{\texttwosuperior} = 0.60 kg/m2 per serving/day; 95\% CI, 0.27-0.92; P = 3.0 {\~A}{\textemdash} 10-4).\ The present study provides strong evidence to support a causal effect of higher dairy intake on increased BMI among adults.},
	author = {Huang, T. and Ding, M. and Bergholdt, H. K. M. and Wang, T. and Heianza, Y. and Sun, D. and Frazier-Wood, A. C. and Aslibekyan, S. and North, K. E. and Voortman, T. and Graff, M. and Smith, C. E. and Lai, C. Q. and Varbo, A. and Lemaitre, R. N. and de Jonge, M. E. A. L. and Fumeron, F. and Corella, D. and Wang, C. A. and Tj?nneland, A. and Overvad, K. and S?rensen, T. I. A. and Feitosa, M. F. and Wojczynski, M. K. and K?h?nen, M. and Renstr?m, F. and Psaty, B. M. and Siscovick, D. S. and Barroso, I. and Johansson, I. and Hernandez, D. and Ferrucci, L. and Bandinelli, S. and Linneberg, A. and Zillikens, M. C. and Sandholt, C. H. and Pedersen, O. and Hansen, T. and Schulz, C. A. and Sonestedt, E. and Orho-Melander, M. and Chen, T. A. and Rotter, J. I. and Allison, M. A. and Rich, S. S. and Sorl?, J. V. and Coltell, O. and Pennell, C. E. and Eastwood, P. and Hofman, A. and Uitterlinden, A. G. and van Rooij, F. J. A. and Chu, A. Y. and Rose, L. M. and Ridker, P. M. and Viikari, J. and Raitakari, O. and Lehtim?ki, T. and Mikkil?, V. and Willett, W. C. and Wang, Y. and Tucker, K. L. and Ordovas, J. M. and Kilpel?inen, T. O. and Province, M. A. and Franks, P. W. and Arnett, D. K. and Tanaka, T. and Toft, U. and Ericson, U. and Franco, O. H. and Mozaffarian, D. and Hu, F. B. and Chasman, D. I. and Nordestgaard, B. G. and Ellervik, C. and Qi, L.}
}
@article {8538,
	title = {{Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects},
	journal = {Am J Hum Genet},
	volume = {102},
	year = {2018},
	month = {01},
	pages = {88{\textendash}102},
	abstract = {Bone mineral density (BMD) assessed by DXA is used to evaluate bone health. In children, total body (TB) measurements are commonly used; in older individuals, BMD at the lumbar spine (LS) and femoral neck (FN) is used to diagnose osteoporosis. To date, genetic variants in more than 60 loci have been identified as associated with BMD. To investigate the genetic determinants of TB-BMD variation along the life course and test for age-specific effects, we performed a meta-analysis of 30 genome-wide association studies (GWASs) of TB-BMD including 66,628 individuals overall and divided across five age strata, each spanning 15 years. We identified variants associated with TB-BMD at 80 loci, of which 36 have not been previously identified; overall, they explain approximately 10\% of the TB-BMD variance when combining all age groups and influence the risk of fracture. Pathway and enrichment analysis of the association signals showed clustering within gene sets implicated in the regulation of cell growth and SMAD proteins, overexpressed in the musculoskeletal system, and enriched in enhancer and promoter regions. These findings reveal TB-BMD as a relevant trait for genetic studies of osteoporosis, enabling the identification of variants and pathways influencing different bone compartments. Only variants in ESR1 and close proximity to RANKL showed a clear effect dependency on age. This most likely indicates that the majority of genetic variants identified influence BMD early in life and that their effect can be captured throughout the life course.},
	author = {Medina-Gomez, C. and Kemp, J. P. and Trajanoska, K. and Luan, J. and Chesi, A. and Ahluwalia, T. S. and Mook-Kanamori, D. O. and Ham, A. and Hartwig, F. P. and Evans, D. S. and Joro, R. and Nedeljkovic, I. and Zheng, H. F. and Zhu, K. and Atalay, M. and Liu, C. T. and Nethander, M. and Broer, L. and Porleifsson, G. and Mullin, B. H. and Handelman, S. K. and Nalls, M. A. and Jessen, L. E. and Heppe, D. H. M. and Richards, J. B. and Wang, C. and Chawes, B. and Schraut, K. E. and Amin, N. and Wareham, N. and Karasik, D. and Van der Velde, N. and Ikram, M. A. and Zemel, B. S. and Zhou, Y. and Carlsson, C. J. and Liu, Y. and McGuigan, F. E. and Boer, C. G. and B?nnelykke, K. and Ralston, S. H. and Robbins, J. A. and Walsh, J. P. and Zillikens, M. C. and Langenberg, C. and Li-Gao, R. and Williams, F. M. K. and Harris, T. B. and Akesson, K. and Jackson, R. D. and Sigurdsson, G. and den Heijer, M. and van der Eerden, B. C. J. and van de Peppel, J. and Spector, T. D. and Pennell, C. and Horta, B. L. and Felix, J. F. and Zhao, J. H. and Wilson, S. G. and de Mutsert, R. and Bisgaard, H. and Styrk?rsd?ttir, U. and Jaddoe, V. W. and Orwoll, E. and Lakka, T. A. and Scott, R. and Grant, S. F. A. and Lorentzon, M. and van Duijn, C. M. and Wilson, J. F. and Stefansson, K. and Psaty, B. M. and Kiel, D. P. and Ohlsson, C. and Ntzani, E. and van Wijnen, A. J. and Forgetta, V. and Ghanbari, M. and Logan, J. G. and Williams, G. R. and Bassett, J. H. D. and Croucher, P. I. and Evangelou, E. and Uitterlinden, A. G. and Ackert-Bicknell, C. L. and Tobias, J. H. and Evans, D. M. and Rivadeneira, F.}
}
@article {8517,
	title = {{Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry},
	journal = {J Bone Miner Res},
	volume = {34},
	year = {2019},
	month = {07},
	pages = {1284{\textendash}1296},
	abstract = {Hip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with {\^a}{\textasciicircum}{\textonequarter}2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p {\^a}{\textperthousand}¤ 2.6 {\~A}{\textemdash} 10-8 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 {\~A}{\textemdash} 10-5 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12\% to 22\% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility. {\^A}{\textcopyright} 2019 American Society for Bone and Mineral Research.},
	author = {Hsu, Y. H. and Estrada, K. and Evangelou, E. and Ackert-Bicknell, C. and Akesson, K. and Beck, T. and Brown, S. J. and Capellini, T. and Carbone, L. and Cauley, J. and Cheung, C. L. and Cummings, S. R. and Czerwinski, S. and Demissie, S. and Econs, M. and Evans, D. and Farber, C. and Gautvik, K. and Harris, T. and Kammerer, C. and Kemp, J. and Koller, D. L. and Kung, A. and Lawlor, D. and Lee, M. and Lorentzon, M. and McGuigan, F. and Medina-Gomez, C. and Mitchell, B. and Newman, A. and Nielson, C. and Ohlsson, C. and Peacock, M. and Reppe, S. and Richards, J. B. and Robbins, J. and Sigurdsson, G. and Spector, T. D. and Stefansson, K. and Streeten, E. and Styrkarsdottir, U. and Tobias, J. and Trajanoska, K. and Uitterlinden, A. and Vandenput, L. and Wilson, S. G. and Yerges-Armstrong, L. and Young, M. and Zillikens, M. C. and Rivadeneira, F. and Kiel, D. P. and Karasik, D.}
}
@article {8532,
	title = {{Omega-3 Fatty Acids and Genome-Wide Interaction Analyses Reveal DPP10-Pulmonary Function Association},
	journal = {Am J Respir Crit Care Med},
	volume = {199},
	year = {2019},
	month = {03},
	pages = {631{\textendash}642},
	abstract = {Omega-3 polyunsaturated fatty acids (n-3 PUFAs) have anti-inflammatory properties that could benefit adults with comprised pulmonary health.\ To investigate n-3 PUFA associations with spirometric measures of pulmonary function tests (PFTs) and determine underlying genetic susceptibility.\ Associations of n-3 PUFA biomarkers ({\^I}{\textpm}-linolenic acid, eicosapentaenoic acid, docosapentaenoic acid [DPA], and docosahexaenoic acid [DHA]) were evaluated with PFTs (FEV1, FVC, and FEV1/FVC) in meta-analyses across seven cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (N = 16,134 of European or African ancestry). PFT-associated n-3 PUFAs were carried forward to genome-wide interaction analyses in the four largest cohorts (N = 11,962) and replicated in one cohort (N = 1,687). Cohort-specific results were combined using joint 2 degree-of-freedom (2df) meta-analyses of SNP associations and their interactions with n-3 PUFAs.\ DPA and DHA were positively associated with FEV1 and FVC (P < 0.025), with evidence for effect modification by smoking and by sex. Genome-wide analyses identified a novel association of rs11693320-an intronic DPP10 SNP-with FVC when incorporating an interaction with DHA, and the finding was replicated (P2df = 9.4 {\~A}{\textemdash} 10-9 across discovery and replication cohorts). The rs11693320-A allele (frequency, {\^a}{\textasciicircum}{\textonequarter}80\%) was associated with lower FVC (PSNP = 2.1 {\~A}{\textemdash} 10-9; {\^I}{\texttwosuperior}SNP = -161.0 ml), and the association was attenuated by higher DHA levels (PSNP{\~A}{\textemdash}DHA interaction = 2.1 {\~A}{\textemdash} 10-7; {\^I}{\texttwosuperior}SNP{\~A}{\textemdash}DHA interaction = 36.2 ml).\ We corroborated beneficial effects of n-3 PUFAs on pulmonary function. By modeling genome-wide n-3 PUFA interactions, we identified a novel DPP10 SNP association with FVC that was not detectable in much larger studies ignoring this interaction.},
	author = {Xu, J. and Gaddis, N. C. and Bartz, T. M. and Hou, R. and Manichaikul, A. W. and Pankratz, N. and Smith, A. V. and Sun, F. and Terzikhan, N. and Markunas, C. A. and Patchen, B. K. and Schu, M. and Beydoun, M. A. and Brusselle, G. G. and Eiriksdottir, G. and Zhou, X. and Wood, A. C. and Graff, M. and Harris, T. B. and Ikram, M. A. and Jacobs, D. R. and Launer, L. J. and Lemaitre, R. N. and O{\textquoteright}Connor, G. T. and Oelsner, E. C. and Psaty, B. M. and Vasan, R. S. and Rohde, R. R. and Rich, S. S. and Rotter, J. I. and Seshadri, S. and Smith, L. J. and Tiemeier, H. and Tsai, M. Y. and Uitterlinden, A. G. and Voruganti, V. S. and Xu, H. and Zilh?o, N. R. and Fornage, M. and Zillikens, M. C. and London, S. J. and Barr, R. G. and Dupuis, J. and Gharib, S. A. and Gudnason, V. and Lahousse, L. and North, K. E. and Steffen, L. M. and Cassano, P. A. and Hancock, D. B.}
}
@article {8660,
	title = {{Genome-wide meta-analysis of muscle weakness identifies 15 susceptibility loci in older men and women},
	journal = {Nat Commun},
	volume = {12},
	year = {2021},
	month = {01},
	pages = {654},
	abstract = {Low muscle strength is an important heritable indicator of poor health linked to morbidity and mortality in older people. In a genome-wide association study meta-analysis of 256,523 Europeans aged 60 years and over from 22 cohorts we identify 15 loci associated with muscle weakness (European Working Group on Sarcopenia in Older People definition: n = 48,596 cases, 18.9\% of total), including 12 loci not implicated in previous analyses of continuous measures of grip strength. Loci include genes reportedly involved in autoimmune disease (HLA-DQA1 p = 4 {\texttimes} 10-17), arthritis (GDF5 p = 4 {\texttimes} 10-13), cell cycle control and cancer protection, regulation of transcription, and others involved in the development and maintenance of the musculoskeletal system. Using Mendelian randomization we report possible overlapping causal pathways, including diabetes susceptibility, haematological parameters, and the immune system. We conclude that muscle weakness in older adults has distinct mechanisms from continuous strength, including several pathways considered to be hallmarks of ageing.},
	author = {Jones, G. and Trajanoska, K. and Santanasto, A. J. and Stringa, N. and Kuo, C. L. and Atkins, J. L. and Lewis, J. R. and Duong, T. and Hong, S. and Biggs, M. L. and Luan, J. and Sarnowski, C. and Lunetta, K. L. and Tanaka, T. and Wojczynski, M. K. and Cvejkus, R. and Nethander, M. and Ghasemi, S. and Yang, J. and Zillikens, M. C. and Walter, S. and Sicinski, K. and Kague, E. and Ackert-Bicknell, C. L. and Arking, D. E. and Windham, B. G. and Boerwinkle, E. and Grove, M. L. and Graff, M. and Spira, D. and Demuth, I. and Van der Velde, N. and de Groot, L. C. P. G. M. and Psaty, B. M. and Odden, M. C. and Fohner, A. E. and Langenberg, C. and Wareham, N. J. and Bandinelli, S. and van Schoor, N. M. and Huisman, M. and Tan, Q. and Zmuda, J. and Mellstr{\"o}m, D. and Karlsson, M. and Bennett, D. A. and Buchman, A. S. and De Jager, P. L. and Uitterlinden, A. G. and V{\"o}lker, U. and Kocher, T. and Teumer, A. and Rodrigu{\'e}z-Ma{\~n}as, L. and Garc{\'\i}a, F. J. and Carnicero, J. A. and Herd, P. and Bertram, L. and Ohlsson, C. and Murabito, J. M. and Melzer, D. and Kuchel, G. A. and Ferrucci, L. and Karasik, D. and Rivadeneira, F. and Kiel, D. P. and Pilling, L. C.}
}
