Hypothesis-free discovery of novel cancer predictors using machine learning

Iqbal Madakkatel; Amanda L Lumsden; Anwar Mulugeta; Ian Olver; Elina Hyppönen

doi:10.1111/eci.14037

Hypothesis-free discovery of novel cancer predictors using machine learning

Eur J Clin Invest. 2023 Oct;53(10):e14037. doi: 10.1111/eci.14037. Epub 2023 Jun 11.

Authors

Iqbal Madakkatel^{1

2}, Amanda L Lumsden^{1

2}, Anwar Mulugeta^{1

2

3}, Ian Olver⁴, Elina Hyppönen^{1

2}

Affiliations

¹ Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia.
² South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.
³ Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa, Ethiopia.
⁴ School of Psychology, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia.

PMID: 37303098
DOI: 10.1111/eci.14037

Abstract

Background: Cancer is a leading cause of morbidity and mortality worldwide, and better understanding of the risk factors could enhance prevention.

Methods: We conducted a hypothesis-free analysis combining machine learning and statistical approaches to identify cancer risk factors from 2828 potential predictors captured at baseline. There were 459,169 UK Biobank participants free from cancer at baseline and 48,671 new cancer cases during the 10-year follow-up. Logistic regression models adjusted for age, sex, ethnicity, education, material deprivation, smoking, alcohol intake, body mass index and skin colour (as a proxy for sun sensitivity) were used for obtaining adjusted odds ratios, with continuous predictors presented using quintiles (Q).

Results: In addition to smoking, older age and male sex, positively associating features included several anthropometric characteristics, whole body water mass, pulse, hypertension and biomarkers such as urinary microalbumin (Q5 vs. Q1 OR 1.16, 95% CI = 1.13-1.19), C-reactive protein (Q5 vs. Q1 OR 1.20, 95% CI = 1.16-1.24) and red blood cell distribution width (Q5 vs. Q1 OR 1.18, 95% CI = 1.14-1.21), among others. High-density lipoprotein cholesterol (Q5 vs. Q1 OR 0.84, 95% CI = 0.81-0.87) and albumin (Q5 vs. Q1 OR 0.84, 95% CI = 0.81-0.87) were inversely associated with cancer. In sex-stratified analyses, higher testosterone increased the risk in females but not in males (Q5 vs. Q1 OR_females 1.23, 95% CI = 1.17-1.30). Phosphate was associated with a lower risk in females but a higher risk in males (Q5 vs. Q1 OR_females 0.94, 95% CI = 0.90-0.99 vs. OR_males 1.09, 95% CI 1.04-1.15).

Conclusions: This hypothesis-free analysis suggests personal characteristics, metabolic biomarkers, physical measures and smoking as important predictors of cancer risk, with further studies needed to confirm causality and clinical relevance.

Keywords: artificial intelligence; biomarkers; cancer incidence; machine learning; risk factors.

MeSH terms

Biomarkers
C-Reactive Protein
Female
Humans
Male
Neoplasms* / epidemiology
Risk Factors
Smoking / epidemiology

Substances

C-Reactive Protein
Biomarkers

Abstract

MeSH terms

Substances

Grants and funding