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Abstract Background Albuminuria is considered an early and sensitive marker of kidney dysfunction, but also an independent cardiovascular risk factor. Considering the possible relationship among metabolic liver disease, cardiovascular disease and chronic kidney disease, we aimed to evaluate the risk of developing albuminuria regarding the presence of epicardial adipose tissue and the steatotic liver disease status. Methods A retrospective long-term longitudinal study including 181 patients was carried out. Epicardial adipose tissue and steatotic liver disease were assessed by computed tomography. The presence of albuminuria at follow-up was defined as the outcome. Results After a median follow up of 11.2 years, steatotic liver disease (HR 3.15; 95% CI, 1.20–8.26; p = 0.02) and excess amount of epicardial adipose tissue (HR 6.12; 95% CI, 1.69–22.19; p = 0.006) were associated with an increased risk of albuminuria after adjustment for visceral adipose tissue, sex, age, weight status, type 2 diabetes, prediabetes, hypertriglyceridemia, hypercholesterolemia, arterial hypertension, and cardiovascular prevention treatment at baseline. The presence of both conditions was associated with a higher risk of developing albuminuria compared to having steatotic liver disease alone (HR 5.91; 95% CI 1.15–30.41, p = 0.033). Compared with the first tertile of visceral adipose tissue, the proportion of subjects with liver steatosis and abnormal epicardial adipose tissue was significantly higher in the second and third tertile. We found a significant correlation between epicardial fat and steatotic liver disease (rho = 0.43 [p

Summary: Background: Tobacco is the main risk factor for developing lung cancer. Yet, while some heavy smokers develop lung cancer at a young age, other heavy smokers never develop it, even at an advanced age, suggesting a remarkable variability in the individual susceptibility to the carcinogenic effects of tobacco. We characterized the germline profile of subjects presenting these extreme phenotypes with Whole Exome Sequencing (WES) and Machine Learning (ML). Methods: We sequenced germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age (extreme cases) or who did not develop lung cancer at an advanced age (extreme controls), selected from databases including over 6600 subjects. We selected individual coding genetic variants and variant-rich genes showing a significantly different distribution between extreme cases and controls. We validated the results from our discovery cohort, in which we analysed by WES extreme cases and controls presenting similar phenotypes. We developed ML models using both cohorts. Findings: Mean age for extreme cases and controls was 50.7 and 79.1 years respectively, and mean tobacco consumption was 34.6 and 62.3 pack-years. We validated 16 individual variants and 33 variant-rich genes. The gene harbouring the most validated variants was HLA-A in extreme controls (4 variants in the discovery cohort, p = 3.46E-07; and 4 in the validation cohort, p = 1.67E-06). We trained ML models using as input the 16 individual variants in the discovery cohort and tested them on the validation cohort, obtaining an accuracy of 76.5% and an AUC-ROC of 83.6%. Functions of validated genes included candidate oncogenes, tumour-suppressors, DNA repair, HLA-mediated antigen presentation and regulation of proliferation, apoptosis, inflammation and immune response. Interpretation: Individuals presenting extreme phenotypes of high and low risk of developing tobacco-associated lung adenocarcinoma show different germline profiles. Our strategy may allow the identification of high-risk subjects and the development of new therapeutic approaches. Funding: See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.