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A Metabolomics Perspective on Early Dementia Diagnosis Study

Thoughts From Ranga

The following is a response to the study in Nature titled, “Early dementia diagnosis: blood proteins reveal at-risk people:” https://www.nature.com/articles/d41586-024-00418-9.

The UK Biobank is one of the several large population cohort datasets that has been mined extensively to derive important insights into health and related outcomes.  A recent analysis evaluated the performance of four existing and widely used dementia risk scores in their ability to predict the 10-year risk of dementia diagnosis.  The study found high error rates and limited utility of these risk scores in identifying individuals for dementia prevention (Kivimaki M et al, 2023).  The four risk prediction tools tested in this study did not use comprehensive molecular profile datasets for model build.

In contrast, in another study of the UK Biobank cohort, the inclusion of plasma metabolomic profiles in combination with a conventional dementia risk (10-year) prediction model was associated with the ability to predict incident dementia and reclassification ability to identify high-risk groups (Zhang X et al., 2022).  Most recently, the use of proteomic signatures within the UK Biobank cohort identified changes in specific proteins with predictive power for dementia diagnosis 10 years before the diagnosis (Guo Y et al, 2024).  These studies highlight the power of molecular omics in identifying high-risk groups compared to conventional benchmarks, with the promise of early diagnosis and the potential to improve treatment and outcomes.

Although the studies referenced above evaluated metabolomic or proteomic profiles as singular molecule omic in building risk prediction models to improve the prediction of high-risk individuals, population cohorts such as UK Biobank, consisting of highly stratified individual-specific multi-modal datasets, offer the best opportunities for integrated multi-omic analysis for the discovery, validation, and eventual development of screening tools for improvement of overall health within populations.

References

  1. Guo Y, You J, Zhang Y, Liu W-S et al. Plasma proteomic profiles predict future dementia in healthy adults.  (2024) Nature Aging. https://doi.org/10.1038/s43587-023-00565-0
  2. Kiviak M, Livingston G, Singh-Manoux A, Mars N, et al. Estimating dementia risk using multifactorial prediction models. (2023) JAMA Network Open.6(6):e2318132.doi:10.1001/jamanetworkopen.2023.18132
  3. Zhang X, Hu W, Wang Y, Wang W et al. Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank. (2022) BMC Medicine.  20:252 https://doi.org/10.1186/s12916-022-02449-3
Ranga Sarangarajan, Ph.D.
Ranga leads Metabolon’s R&D teams to deliver metabolomics data and insights that expand and accelerate the impact of life sciences research in all its applications, including biopharma and diagnostics.

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