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Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank

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Abstract

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients’ outcomes allowing healthy aging.

Genotype, clinical and demographic data of subjects (aged 60–73 years) from UK Biobank were utilized. FP was defined on Fried’s criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals.

Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively.

This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy.

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Data availability

Data supporting the results reported in the manuscript are obtained from UK Biobank (www.ukbiobank.ac.uk), a major biomedical database, approved under project # 54423.

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Acknowledgements

This research has been conducted using data from UK Biobank (www.ukbiobank.ac.uk), a major biomedical database, approved under project # 54423.

Funding

This work was supported by China Medical University Hospital, Taichung, Taiwan (C1100708016) and National Science and Technology Council, Taiwan (NSTC 111-2634-F-002-017). The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Watson Hua-Sheng Tseng & Oscar K. Lee

  2. Bioinformatics and Biostatistics Core, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan

    Amrita Chattopadhyay, Nam Nhut Phan & Eric Y. Chuang

  3. Graduate Institute of Biomedical Electronics and Bioinformatics, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

    Eric Y. Chuang

  4. Center for Translational Genomics and Regenerative Medicine, China Medical University Hospital, Taichung, Taiwan

    Oscar K. Lee

  5. Stem Cell Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Oscar K. Lee

  6. Department of Orthopedics, China Medical University Hospital, Taichung, Taiwan

    Oscar K. Lee

  7. Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan

    Eric Y. Chuang

Authors
  1. Watson Hua-Sheng Tseng
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  2. Amrita Chattopadhyay
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  3. Nam Nhut Phan
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  4. Eric Y. Chuang
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  5. Oscar K. Lee
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Corresponding authors

Correspondence to Amrita Chattopadhyay or Oscar K. Lee.

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Watson Hua-Sheng Tseng and Amrita Chattopadhyay are co-first authors with equal contribution.

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Tseng, W.HS., Chattopadhyay, A., Phan, N.N. et al. Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank. GeroScience 46, 1211–1228 (2024). https://doi.org/10.1007/s11357-023-00874-7

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