SEARCHCOHORTneurology, epidemiology

cohort·neurology, epidemiology, public health, biomedical research, research methods·PMC10168570

Machine learning analysis of the UK Biobank reveals IGF-1 and inflammatory biomarkers predict Parkinson’s disease risk

PLOS ONE · 9 authors, 3 centres

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TL;DR

Using an integrated machine learning algorithm (IDEARS) on 334,062 UK Biobank participants, the study found that male gender, elevated IGF-1, and neutrophil/lymphocyte ratio were the top-ranked non-genetic risk factors for Parkinson's disease.

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Male gender was the highest-ranked risk factor, followed by elevated IGF-1, lymphocyte count, and neutrophil/lymphocyte ratio (NLR). IGF-1 and NLR were elevated in both sexes before PD diagnosis, and IGF-1 was elevated up to 10 years before disease onset. Features aligned with frailty, including reduced walking pace and hand grip strength, also ranked highly. The findings suggest that elevated IGF-1 and NLR may serve as prospective biomarkers for PD risk.

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cohort

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neurology

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ORIGINAL PAPER

PMC10168570

Machine learning analysis of the UK Biobank reveals IGF-1 and inflammatory biomarkers predict Parkinson’s disease risk

PLOS ONE

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ABSTRACT

Abstract Introduction Parkinson’s disease (PD) is the most common movement disorder, and its prevalence is increasing rapidly worldwide with an ageing population. The UK Biobank is the world’s largest and most comprehensive longitudinal study of ageing community volunteers. The cause of the common form of PD is multifactorial, but the degree of causal heterogeneity among patients or the relative importance of one risk factor over another is unclear. This is a major impediment to the discovery of disease-modifying therapies. Methods We used an integrated machine learning algorithm (IDEARS) to explore the relative effects of 1,753 measured non-genetic variables in 334,062 eligible UK Biobank participants, including 2,719 who had developed PD since their recruitment into the study. Results Male gender was the highest-ranked risk factor, followed by elevated serum insulin-like growth factor 1 (IGF-1), lymphocyte count, and neutrophil/lymphocyte ratio. A group of factors aligned with the symptoms of frailty also ranked highly. IGF-1 and neutrophil/lymphocyte ratio were also elevated in both sexes before PD diagnosis and at the point of diagnosis. Discussion The use of machine learning with the UK Biobank provides the best opportunity to explore the multidimensional nature of PD. Our results suggest that novel risk biomarkers, including elevated IGF-1 and NLR, may play a role in, or are indicative of PD pathomechanisms. In particular, our results are consistent with PD being a central manifestation of a systemic inflammatory disease. These biomarkers may be used clinically to predict future PD risk, improve early diagnosis and provide new therapeutic avenues.

CITATION EXPORT

Michael Allwright, Hamish Mundell, Greg Sutherland et al.. Machine learning analysis of the UK Biobank reveals IGF-1 and inflammatory biomarkers predict Parkinson’s disease risk. PLOS ONE. (2023). https://doi.org/10.1371/journal.pone.0285416

AUTHORS

9 · 3 CENTRES

Michael Allwright, Hamish Mundell, Greg Sutherland, Paul Austin, Boris Guennewig, Jacopo Sabbatinelli, Jacopo Sabbatinelli, Jacopo Sabbatinelli, Jacopo Sabbatinelli

1Brain and Mind Centre and School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
2Charles Perkins Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
3Universita Politecnica delle Marche, ITALY