title: The interplay between lipid profiles, glucose, BMI and risk of kidney cancer in the Swedish AMORIS study.
abstract: With exception of cholesterol and total fat intake, associations between lipid biomarkers and kidney cancer have not often been researched. We aimed to assess possible links between lipid profiles and kidney cancer risk in a large prospective cohort study, while also taking into account glucose levels and BMI. A cohort based on 542,924 persons with baseline information on glucose, triglycerides (TGs), total cholesterol (TC) and creatinine was selected from the Swedish Apolipoprotein Mortality Risk study. A subgroup of 85,621 also had baseline measurements of HDL, LDL, apolipoprotein A-I and apoB. Multivariate Cox proportional hazard models were used to analyze associations between quartiles and dichotomized values of these lipid components and kidney cancer risk. During a mean follow-up of 13 years, 958 persons developed kidney cancer. TGs were the only lipid component for which a statistically significant association was found with kidney cancer risk when using both quartiles and a clinical cutoff (hazard ratio: 1.25 (95% CI: 0.99-1.60), 1.29 (1.01-1.66) and 1.66 (1.30-2.13) for the 2nd, 3rd and 4th quartile, compared to the 1st, with p-value for trend: <0.001). The association remained after exclusion of the 95% percentile of TG. Quartiles of glucose were also positively associated with kidney cancer risk, whereas quartiles of TC were negatively associated with kidney cancer risk. This detailed analysis of lipid components only showed a consistent relation between TG levels and kidney cancer risk. Further mechanistic studies are required to assess links between lipid abnormalities and kidney cancer.
authors.affiliation: King's College London, School of Medicine, Division of Cancer Studies, Cancer Epidemiology Group, London, UK. mieke.vanhemelrijck@kcl.ac.uk
authors.forename: Mieke
authors.initials: M
authors.lastname: Van Hemelrijck
authors.forename: Hans
authors.initials: H
authors.lastname: Garmo
authors.forename: Niklas
authors.initials: N
authors.lastname: Hammar
authors.forename: Ingmar
authors.initials: I
authors.lastname: Jungner
authors.forename: Göran
authors.initials: G
authors.lastname: Walldius
authors.forename: Mats
authors.initials: M
authors.lastname: Lambe
authors.forename: Lars
authors.initials: L
authors.lastname: Holmberg
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016632:Apolipoprotein A-I / Q000097:blood; D001055:Apolipoproteins B / Q000097:blood; D015992:Body Mass Index; D002784:Cholesterol / Q000378:metabolism*; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D015331:Cohort Studies; D005260:Female; D005947:Glucose / Q000378:metabolism*; D006801:Humans; D007680:Kidney Neoplasms / Q000453:epidemiology* / Q000378:metabolism* / Q000503:physiopathology; D050356:Lipid Metabolism; D008297:Male; D008875:Middle Aged; D016016:Proportional Hazards Models; D012307:Risk Factors; D014280:Triglycerides / Q000378:metabolism*
chemical_list: D016632:Apolipoprotein A-I; D001055:Apolipoproteins B; D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D014280:Triglycerides; D002784:Cholesterol; D005947:Glucose