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BMC Mol Cell Biol. 2019 Jul 23;20(1):28. doi: 10.1186/s12860-019-0210-7.

Metabolic profiles to predict long-term cancer and mortality: the use of latent class analysis.

Author information

1
School of Cancer & Pharmaceutical Sciences, Translational Oncology and Urology Research, King's College London, London, UK.
2
Regional Oncologic Centre, Uppsala University, Uppsala, Sweden.
3
School of Cancer & Pharmaceutical Sciences, Cancer Bioinformatics, Breast Cancer Now, King's College London, London, UK.
4
School of Cancer & Pharmaceutical Sciences, Translational Oncology and Urology Research, King's College London, London, UK. sundeep.ghuman@kcl.ac.uk.
5
Guy's and St Thomas, NHS Foundation Trust, London, UK. sundeep.ghuman@kcl.ac.uk.
6
Department of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
7
Department of Medicine, Clinical Epidemiological Unit, Karolinska Institutet and CALAB Research, Stockholm, Sweden.
8
Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
9
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Abstract

BACKGROUND:

Metabolites are genetically and environmentally determined. Consequently, they can be used to characterize environmental exposures and reveal biochemical mechanisms that link exposure to disease. To explore disease susceptibility and improve population risk stratification, we aimed to identify metabolic profiles linked to carcinogenesis and mortality and their intrinsic associations by characterizing subgroups of individuals based on serum biomarker measurements. We included 13,615 participants from the Swedish Apolipoprotein MOrtality RISk Study who had measurements for 19 biomarkers representative of central metabolic pathways. Latent Class Analysis (LCA) was applied to characterise individuals based on their biomarker values (according to medical cut-offs), which were then examined as predictors of cancer and death using multivariable Cox proportional hazards models.

RESULTS:

LCA identified four metabolic profiles within the population: (1) normal values for all markers (63% of population); (2) abnormal values for lipids (22%); (3) abnormal values for liver functioning (9%); (4) abnormal values for iron and inflammation metabolism (6%). All metabolic profiles (classes 2-4) increased risk of cancer and mortality, compared to class 1 (e.g. HR for overall death was 1.26 (95% CI: 1.16-1.37), 1.67 (95% CI: 1.47-1.90), and 1.21 (95% CI: 1.05-1.41) for class 2, 3, and 4, respectively).

CONCLUSION:

We present an innovative approach to risk stratify a well-defined population based on LCA metabolic-defined subgroups for cancer and mortality. Our results indicate that standard of care baseline serum markers, when assembled into meaningful metabolic profiles, could help assess long term risk of disease and provide insight in disease susceptibility and etiology.

KEYWORDS:

Biomarkers; Cancer epidemiology; Disease susceptibility; Latent class analysis; Metabolic profiles; Risk stratification

PMID:
31337337
PMCID:
PMC6651931
DOI:
10.1186/s12860-019-0210-7
[Indexed for MEDLINE]
Free PMC Article

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