Conceptual framework of the study design.

• Main Authors: Florence Guida (3431048), Vanessa Y. Tan (7756985), Laura J. Corbin (6260339), Karl Smith-Byrne (9975677), Karine Alcala (11457761), Claudia Langenberg (160616), Isobel D. Stewart (3368624), Adam S. Butterworth (7658036), Praveen Surendran (7783877), David Achaintre (783484), Jerzy Adamski (127482), Pilar Amiano (23583), Manuela M. Bergmann (7500068), Caroline J. Bull (8654428), Christina C. Dahm (9530014), Audrey Gicquiau (1790689), Graham G. Giles (7242467), Marc J. Gunter (8654437), Toomas Haller (527037), Arnulf Langhammer (75692), Tricia L. Larose (3337074), Börje Ljungberg (72551), Andres Metspalu (61760), Roger L. Milne (7802576), David C. Muller (7730171), Therese H. Nøst (1366620), Elin Pettersen Sørgjerd (9166847), Cornelia Prehn (137977), Elio Riboli (23604), Sabina Rinaldi (115134), Joseph A. Rothwell (548948), Augustin Scalbert (188861), Julie A. Schmidt (10217182), Gianluca Severi (89918), Sabina Sieri (229287), Roel Vermeulen (68721), Emma E. Vincent (7639052), Melanie Waldenberger (236985), Nicholas J. Timpson (7243103), Mattias Johansson (158504)
• Format: Still Image
• Language: unknown
• Published: 2021
• Subjects: Biochemistry; Medicine; Cell Biology; Genetics; Molecular Biology; Biotechnology; Marine Biology; Cancer; Infectious Diseases; Mathematical Sciences not elsewhere classified; sup >&# 8722; related metabolic perturbations; case &# 8211; body mass index; average 8 years; 6 × 10; 5 × 10; 4 × 10; 4 amino acids; predispose kidney cancer; kidney cancer aetiology; incident kidney cancer; developing kidney cancer; including 8 phosphatidylcholines; elevated bmi appeared; diagnostic blood samples; bmi partly attenuated; bmi &# 8212; 1 standard deviation; known risk factors; potentially important role; pc ae c34; palmitoyl )- 2; blood metabolome highlighted; 17 sd change; kidney cancer risk; 1 -( 1; identify circulating metabolites; ci ]: 0; kidney cancer; blood metabolome; blood collection; 1 sd; circulating levels; xlink ">; wide range; robust across; positively associated; poorly understood; participating studies; odds ratio; observed associations; minimal impact; metabolites associated; mendelian randomisation; inversely associated; including glutamate; european descent; confidence interval; also associated; alcohol consumption; 416 metabolites; 2 plasmalogens; 14 glycerophospholipids; Northern Sweden
• Online Access: https://doi.org/10.1371/journal.pmed.1003786.g001

This study includes 3 main analytical steps: (i) the investigation of the associations between circulating levels of metabolites and kidney cancer risk using pre-diagnostic measurements in a case–control study nested within multiple large-scale prospective cohorts; (ii) the assessment of the causal effect of BMI, the leading modifiable risk factor for kidney cancer, on circulating metabolites levels; and (iii) the evaluation of the overlap between the metabolic footprint of BMI and that of kidney cancer risk. The orange X’s indicate the time at which a participant is diagnosed with kidney cancer when his follow-up is stopped. Controls have been selected among participants free of cancer at the time their matched case was diagnosed. Metabolites from all samples have been measured on the Biocrates platform, while only samples from EPIC and NSHDS cohorts have been measured with Metabolon platform. BMI, body mass index; EPIC, The European Prospective Investigation into Cancer and Nutrition; Estonian BB, University of Tartu—Estonian Biobank; HUNT, The Trøndelag Health Study; LC–MS, liquid chromatography–tandem mass spectrometry; MCCS, The Melbourne Collaborative Cohort Study; MR, mendelian randomisation; NSHDS, Northern Sweden Health and Disease study; SNP, single nucleotide polymorphism.