Background and aims: Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. Methods: We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk. Results: In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03–5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as “high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II (“high triglycerides & liver enzymes”) was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). Conclusions: Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.
- Hormone-sensitive cancers
- Metabolic subgroup profile
- Self-organizing map
ASJC Scopus subject areas
- Endocrinology, Diabetes and Metabolism