{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:45:59Z","timestamp":1773189959033,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T00:00:00Z","timestamp":1589932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T00:00:00Z","timestamp":1589932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjusted for intracranial volume (ICV). 93 potential determinants of GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables, lifestyle factors, medication, sleep, and nutrition were obtained from 293 participants from a population-based cohort from Southern Germany. Elastic net regression was used to identify the most important determinants of ICV-adjusted GMV. The four variables age (selected in each of the 1000 splits), glomerular filtration rate (794 splits), diabetes (323 splits) and diabetes duration (122 splits) were identified to be most relevant predictors of GMV adjusted for intracranial volume. The elastic net model showed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p\u2009&lt;\u20090.001). These findings are relevant for preventive and therapeutic considerations and for neuroimaging studies, as they suggest to take information on metabolic status and renal function into account as potential confounders.<\/jats:p>","DOI":"10.1038\/s41598-020-65040-x","type":"journal-article","created":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T10:02:32Z","timestamp":1589968952000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study"],"prefix":"10.1038","volume":"10","author":[{"given":"Franziska","family":"Gali\u00e8","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susanne","family":"Rospleszcz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0244-1024","authenticated-orcid":false,"given":"Daniel","family":"Keeser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebba","family":"Beller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ben","family":"Illigens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Lorbeer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergio","family":"Grosu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonja","family":"Selder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sigrid","family":"Auweter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher L.","family":"Schlett","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wolfgang","family":"Rathmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Schwettmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karl-Heinz","family":"Ladwig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jakob","family":"Linseisen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Annette","family":"Peters","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian","family":"Bamberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Birgit","family":"Ertl-Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophia","family":"Stoecklein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,20]]},"reference":[{"key":"65040_CR1","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/j.neurobiolaging.2005.05.020","volume":"26","author":"KB Walhovd","year":"2005","unstructured":"Walhovd, K. B. et al. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiology of aging 26, 1261\u20131270, https:\/\/doi.org\/10.1016\/j.neurobiolaging.2005.05.020 (2005).","journal-title":"Neurobiology of aging"},{"key":"65040_CR2","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1016\/j.neuroimage.2010.03.004","volume":"51","author":"A Giorgio","year":"2010","unstructured":"Giorgio, A. et al. Age-related changes in grey and white matter structure throughout adulthood. NeuroImage 51, 943\u2013951, https:\/\/doi.org\/10.1016\/j.neuroimage.2010.03.004 (2010).","journal-title":"NeuroImage"},{"key":"65040_CR3","doi-asserted-by":"publisher","first-page":"e22734","DOI":"10.1371\/journal.pone.0022734","volume":"6","author":"Y Taki","year":"2011","unstructured":"Taki, Y. et al. Correlations among brain gray matter volumes, age, gender, and hemisphere in healthy individuals. PloS one 6, e22734, https:\/\/doi.org\/10.1371\/journal.pone.0022734 (2011).","journal-title":"PloS one"},{"key":"65040_CR4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/S0925-4927(99)00052-9","volume":"98","author":"G Bartzokis","year":"2000","unstructured":"Bartzokis, G. et al. Age-related brain volume reductions in amphetamine and cocaine addicts and normal controls: implications for addiction research. Psychiatry Research: Neuroimaging 98, 93\u2013102, https:\/\/doi.org\/10.1016\/S0925-4927(99)00052-9 (2000).","journal-title":"Psychiatry Research: Neuroimaging"},{"key":"65040_CR5","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1001\/archneur.65.10.1363","volume":"65","author":"CA Paul","year":"2008","unstructured":"Paul, C. A. et al. Association of Alcohol Consumption with Brain Volume in the Framingham Study. Archives of neurology 65, 1363\u20131367, https:\/\/doi.org\/10.1001\/archneur.65.10.1363 (2008).","journal-title":"Archives of neurology"},{"key":"65040_CR6","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1016\/j.numecd.2017.10.007","volume":"27","author":"MRP Markus","year":"2017","unstructured":"Markus, M. R. P. et al. Prediabetes is associated with lower brain gray matter volume in the general population. The Study of Health in Pomerania (SHIP). Nutrition, Metabolism and Cardiovascular Diseases 27, 1114\u20131122, https:\/\/doi.org\/10.1016\/j.numecd.2017.10.007 (2017).","journal-title":"Nutrition, Metabolism and Cardiovascular Diseases"},{"key":"65040_CR7","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1016\/j.neuroimage.2006.01.003","volume":"31","author":"PJ Gianaros","year":"2006","unstructured":"Gianaros, P. J., Greer, P. J., Ryan, C. M. & Jennings, J. R. Higher blood pressure predicts lower regional grey matter volume: Consequences on short-term information processing. NeuroImage 31, 754\u2013765, https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.003 (2006).","journal-title":"NeuroImage"},{"key":"65040_CR8","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1038\/ijo.2012.13","volume":"37","author":"SJ Brooks","year":"2013","unstructured":"Brooks, S. J. et al. Late-life obesity is associated with smaller global and regional gray matter volumes: a voxel-based morphometric study. International journal of obesity (2005) 37, 230\u2013236, https:\/\/doi.org\/10.1038\/ijo.2012.13 (2013).","journal-title":"International journal of obesity (2005)"},{"key":"65040_CR9","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.bbr.2015.09.005","volume":"296","author":"NY Arnardottir","year":"2016","unstructured":"Arnardottir, N. Y. et al. Association of change in brain structure to objectively measured physical activity and sedentary behavior in older adults: Age, Gene\/Environment Susceptibility-Reykjavik Study. Behavioural brain research 296, 118\u2013124, https:\/\/doi.org\/10.1016\/j.bbr.2015.09.005 (2016).","journal-title":"Behavioural brain research"},{"key":"65040_CR10","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, 301\u2013320, https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00503.x (2005).","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"issue":"Suppl 1","key":"65040_CR11","doi-asserted-by":"publisher","first-page":"S19","DOI":"10.1055\/s-2005-858235","volume":"67","author":"R Holle","year":"2005","unstructured":"Holle, R., Happich, M., Lowel, H. & Wichmann, H. E. KORA\u2013a research platform for population based health research. Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) 67(Suppl 1), S19\u201325, https:\/\/doi.org\/10.1055\/s-2005-858235 (2005).","journal-title":"Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))"},{"key":"65040_CR12","doi-asserted-by":"publisher","first-page":"158","DOI":"10.2337\/db16-0630","volume":"66","author":"F Bamberg","year":"2017","unstructured":"Bamberg, F. et al. Subclinical Disease Burden as Assessed by Whole-Body MRI in Subjects With Prediabetes, Subjects With Diabetes, and Normal Control Subjects From the General Population: The KORA-MRI Study. Diabetes 66, 158\u2013169, https:\/\/doi.org\/10.2337\/db16-0630 (2017).","journal-title":"Diabetes"},{"key":"65040_CR13","doi-asserted-by":"publisher","unstructured":"Beller, E. et al. T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions-an MRI evaluation study. Neuroradiology, https:\/\/doi.org\/10.1007\/s00234-018-2121-2 (2018).","DOI":"10.1007\/s00234-018-2121-2"},{"key":"65040_CR14","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith, S. M. Fast robust automated brain extraction. Hum Brain Mapp 17, 143\u2013155, https:\/\/doi.org\/10.1002\/hbm.10062 (2002).","journal-title":"Hum Brain Mapp"},{"key":"65040_CR15","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273\u2013289, https:\/\/doi.org\/10.1006\/nimg.2001.0978 (2002).","journal-title":"NeuroImage"},{"key":"65040_CR16","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825\u2013841 (2002).","journal-title":"Neuroimage"},{"key":"65040_CR17","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1089\/brain.2016.0429","volume":"6","author":"C Zhang","year":"2016","unstructured":"Zhang, C. et al. Sex and Age Effects of Functional Connectivity in Early Adulthood. Brain Connect 6, 700\u2013713, https:\/\/doi.org\/10.1089\/brain.2016.0429 (2016).","journal-title":"Brain Connect"},{"key":"65040_CR18","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1016\/j.neuroimage.2004.06.018","volume":"23","author":"RL Buckner","year":"2004","unstructured":"Buckner, R. L. et al. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage 23, 724\u2013738, https:\/\/doi.org\/10.1016\/j.neuroimage.2004.06.018 (2004).","journal-title":"NeuroImage"},{"key":"65040_CR19","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.3174\/ajnr.A4299","volume":"36","author":"TI Hansen","year":"2015","unstructured":"Hansen, T. I., Brezova, V., Eikenes, L., Haberg, A. & Vangberg, T. R. How Does the Accuracy of Intracranial Volume Measurements Affect Normalized Brain Volumes? Sample Size Estimates Based on 966 Subjects from the HUNT MRI Cohort. AJNR Am J Neuroradiol 36, 1450\u20131456, https:\/\/doi.org\/10.3174\/ajnr.A4299 (2015).","journal-title":"AJNR Am J Neuroradiol"},{"key":"65040_CR20","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1146\/annurev-clinpsy-032816-045037","volume":"14","author":"DB Dwyer","year":"2018","unstructured":"Dwyer, D. B., Falkai, P. & Koutsouleris, N. Machine learning approaches for clinical psychology and psychiatry. Annual review of clinical psychology 14, 91\u2013118 (2018).","journal-title":"Annual review of clinical psychology"},{"key":"65040_CR21","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1198\/016214501753382273","volume":"96","author":"J Fan","year":"2001","unstructured":"Fan, J. & Li, R. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American statistical Association 96, 1348\u20131360 (2001).","journal-title":"Journal of the American statistical Association"},{"key":"65040_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i03","volume":"45","author":"S van Buuren","year":"2011","unstructured":"van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software 45, 1\u201367, https:\/\/doi.org\/10.18637\/jss.v045.i03 (2011).","journal-title":"Journal of Statistical Software"},{"key":"65040_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. Journal of statistical software 33, 1 (2010).","journal-title":"Journal of statistical software"},{"key":"65040_CR24","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1038\/ki.1992.126","volume":"41","author":"S Rudberg","year":"1992","unstructured":"Rudberg, S., Persson, B. & Dahlquist, G. Increased glomerular filtration rate as a predictor of diabetic nephropathy - An 8-year prospective study. Kidney International 41, 822\u2013828, https:\/\/doi.org\/10.1038\/ki.1992.126 (1992).","journal-title":"Kidney International"},{"key":"65040_CR25","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1111\/j.1532-5415.2004.52508.x","volume":"52","author":"M Kurella","year":"2004","unstructured":"Kurella, M., Chertow, G. M., Luan, J. & Yaffe, K. Cognitive Impairment in Chronic Kidney Disease. Journal of the American Geriatrics Society 52, 1863\u20131869, https:\/\/doi.org\/10.1111\/j.1532-5415.2004.52508.x (2004).","journal-title":"Journal of the American Geriatrics Society"},{"key":"65040_CR26","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1053\/j.ajkd.2005.10.029","volume":"47","author":"A Martinez-Vea","year":"2006","unstructured":"Martinez-Vea, A. et al. Silent cerebral white matter lesions and their relationship with vascular risk factors in middle-aged predialysis patients with CKD. American journal of kidney diseases: the official journal of the National Kidney Foundation 47, 241\u2013250, https:\/\/doi.org\/10.1053\/j.ajkd.2005.10.029 (2006).","journal-title":"American journal of kidney diseases: the official journal of the National Kidney Foundation"},{"key":"65040_CR27","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jstrokecerebrovasdis.2010.03.015","volume":"21","author":"W Takahashi","year":"2012","unstructured":"Takahashi, W., Tsukamoto, Y., Takizawa, S., Kawada, S. & Takagi, S. Relationship Between Chronic Kidney Disease and White Matter Hyperintensities on Magnetic Resonance Imaging. Journal of Stroke and Cerebrovascular Diseases 21, 18\u201323, https:\/\/doi.org\/10.1016\/j.jstrokecerebrovasdis.2010.03.015 (2012).","journal-title":"Journal of Stroke and Cerebrovascular Diseases"},{"key":"65040_CR28","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.1161\/STROKEAHA.107.493593","volume":"38","author":"M Khatri","year":"2007","unstructured":"Khatri, M. et al. Chronic kidney disease is associated with white matter hyperintensity volume: the Northern Manhattan Study (NOMAS). Stroke 38, 3121\u20133126 (2007).","journal-title":"Stroke"},{"key":"65040_CR29","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s11011-013-9438-7","volume":"28","author":"LJ Zhang","year":"2013","unstructured":"Zhang, L. J. et al. Predominant gray matter volume loss in patients with end-stage renal disease: a voxel-based morphometry study. Metabolic brain disease 28, 647\u2013654 (2013).","journal-title":"Metabolic brain disease"},{"key":"65040_CR30","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1016\/j.jdiacomp.2017.09.001","volume":"31","author":"G Wu","year":"2017","unstructured":"Wu, G., Lin, L., Zhang, Q. & Wu, J. Brain gray matter changes in type 2 diabetes mellitus: A meta-analysis of whole-brain voxel-based morphometry study. Journal of diabetes and its complications 31, 1698\u20131703, https:\/\/doi.org\/10.1016\/j.jdiacomp.2017.09.001 (2017).","journal-title":"Journal of diabetes and its complications"},{"key":"65040_CR31","doi-asserted-by":"publisher","first-page":"527","DOI":"10.7326\/0003-4819-101-4-527","volume":"101","author":"M Brownlee","year":"1984","unstructured":"Brownlee, M., Vlassara, H. & Cerami, A. Nonenzymatic glycosylation and the pathogenesis of diabetic complications. Annals of internal medicine 101, 527\u2013537 (1984).","journal-title":"Annals of internal medicine"},{"key":"65040_CR32","doi-asserted-by":"publisher","first-page":"4343","DOI":"10.1210\/jc.2017-01922","volume":"102","author":"EJ Barrett","year":"2017","unstructured":"Barrett, E. J. et al. Diabetic Microvascular Disease: An Endocrine Society Scientific Statement. The. Journal of clinical endocrinology and metabolism 102, 4343\u20134410, https:\/\/doi.org\/10.1210\/jc.2017-01922 (2017).","journal-title":"Journal of clinical endocrinology and metabolism"},{"key":"65040_CR33","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.2337\/db14-0568","volume":"63","author":"A Kleinridders","year":"2014","unstructured":"Kleinridders, A., Ferris, H. A., Cai, W. & Kahn, C. R. Insulin Action in Brain Regulates Systemic Metabolism and Brain Function. Diabetes 63, 2232, https:\/\/doi.org\/10.2337\/db14-0568 (2014).","journal-title":"Diabetes"},{"key":"65040_CR34","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.2337\/dc13-0143","volume":"36","author":"C Moran","year":"2013","unstructured":"Moran, C. et al. Brain Atrophy in Type 2 Diabetes: Regional distribution and influence on cognition. Diabetes Care 36, 4036\u20134042, https:\/\/doi.org\/10.2337\/dc13-0143 (2013).","journal-title":"Diabetes Care"},{"key":"65040_CR35","doi-asserted-by":"publisher","unstructured":"Kharabian Masouleh, S. et al. Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults. Journal of cerebral blood flow and metabolism: official journal of the International Society of Cerebral Blood Flow and Metabolism, 271678x17729111, https:\/\/doi.org\/10.1177\/0271678x17729111 (2017).","DOI":"10.1177\/0271678x17729111"},{"key":"65040_CR36","doi-asserted-by":"publisher","first-page":"148","DOI":"10.3389\/fnagi.2014.00148","volume":"6","author":"LA Meusel","year":"2014","unstructured":"Meusel, L. A. et al. A systematic review of type 2 diabetes mellitus and hypertension in imaging studies of cognitive aging: time to establish new norms. Frontiers in Aging Neuroscience 6, 148, https:\/\/doi.org\/10.3389\/fnagi.2014.00148 (2014).","journal-title":"Frontiers in Aging Neuroscience"},{"key":"65040_CR37","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1006\/nimg.2001.0848","volume":"14","author":"JC Baron","year":"2001","unstructured":"Baron, J. C. et al. In Vivo Mapping of Gray Matter Loss with Voxel-Based Morphometry in Mild Alzheimer\u2019s Disease. NeuroImage 14, 298\u2013309, https:\/\/doi.org\/10.1006\/nimg.2001.0848 (2001).","journal-title":"NeuroImage"},{"key":"65040_CR38","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1093\/schbul\/sbq150","volume":"38","author":"C Schuster","year":"2012","unstructured":"Schuster, C. et al. Gray Matter Volume Decreases in Elderly Patients with Schizophrenia: A Voxel-based Morphometry Study. Schizophrenia Bulletin 38, 796\u2013802, https:\/\/doi.org\/10.1093\/schbul\/sbq150 (2012).","journal-title":"Schizophrenia Bulletin"},{"key":"65040_CR39","doi-asserted-by":"publisher","first-page":"e0184858","DOI":"10.1371\/journal.pone.0184858","volume":"12","author":"R Lorbeer","year":"2017","unstructured":"Lorbeer, R. et al. Lack of association of MRI determined subclinical cardiovascular disease with dizziness and vertigo in a cross-sectional population-based study. PloS one 12, e0184858, https:\/\/doi.org\/10.1371\/journal.pone.0184858 (2017).","journal-title":"PloS one"},{"key":"65040_CR40","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.1093\/ije\/dyv341","volume":"45","author":"JP Vandenbroucke","year":"2016","unstructured":"Vandenbroucke, J. P., Broadbent, A. & Pearce, N. Causality and causal inference in epidemiology: the need for a pluralistic approach. International Journal of Epidemiology 45, 1776\u20131786, https:\/\/doi.org\/10.1093\/ije\/dyv341 (2016).","journal-title":"International Journal of Epidemiology"},{"key":"65040_CR41","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1093\/ije\/dyw314","volume":"45","author":"DA Lawlor","year":"2017","unstructured":"Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. International Journal of Epidemiology 45, 1866\u20131886, https:\/\/doi.org\/10.1093\/ije\/dyw314 (2017).","journal-title":"International Journal of Epidemiology"},{"key":"65040_CR42","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine 12, e1001779 (2015).","journal-title":"PLoS medicine"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-65040-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-65040-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-65040-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T11:24:37Z","timestamp":1670325877000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-020-65040-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,20]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["65040"],"URL":"https:\/\/doi.org\/10.1038\/s41598-020-65040-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,20]]},"assertion":[{"value":"1 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8363"}}