» Articles » PMID: 38963649

The Performance of Metabolomics-based Prediction Scores for Mortality in Older Patients with Solid Tumors

Abstract

Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.

Citing Articles

Contextualizing aging clocks and properly describing biological age.

Johnson A, Shokhirev M Aging Cell. 2024; 23(12):e14377.

PMID: 39392224 PMC: 11634725. DOI: 10.1111/acel.14377.


A Novel Metabolomic Aging Clock Predicting Health Outcomes and Its Genetic and Modifiable Factors.

Jia X, Fan J, Wu X, Cao X, Ma L, Abdelrahman Z Adv Sci (Weinh). 2024; 11(43):e2406670.

PMID: 39331845 PMC: 11578329. DOI: 10.1002/advs.202406670.

References
1.
Grace Armitage E, Ciborowski M . Applications of Metabolomics in Cancer Studies. Adv Exp Med Biol. 2017; 965:209-234. DOI: 10.1007/978-3-319-47656-8_9. View

2.
van den Akker E, Trompet S, Barkey Wolf J, Beekman M, Suchiman H, Deelen J . Metabolic Age Based on the BBMRI-NL H-NMR Metabolomics Repository as Biomarker of Age-related Disease. Circ Genom Precis Med. 2020; 13(5):541-547. DOI: 10.1161/CIRCGEN.119.002610. View

3.
Kuiper L, Polinder-Bos H, Bizzarri D, Vojinovic D, Vallerga C, Beekman M . Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk. J Gerontol A Biol Sci Med Sci. 2023; 78(10):1753-1762. PMC: 10562890. DOI: 10.1093/gerona/glad137. View

4.
Lawton M, Brody E . Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969; 9(3):179-86. View

5.
Charlson M, Pompei P, Ales K, MacKenzie C . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987; 40(5):373-83. DOI: 10.1016/0021-9681(87)90171-8. View