Causal roles of phenotypic age and metabolic health on dementia: a Mendelian randomisation and structure learning study
- 1University of Manchester
Abstract
BackgroundPhenotypic age acceleration (PhenoAgeAccel), derived from PhenoAge, and MetaboHealth are composite exposures of biological ageing and metabolic health associated with dementia-related outcomes. Whether these associations are causal and reflect the exposures, constituent biomarkers, or both remains unclear.
MethodsThis study included UK Biobank participants of White British genetic ancestry. MetaboHealth was derived from nuclear magnetic resonance (NMR) metabolomics and PhenoAgeAccel from clinical biomarkers and chronological age. Genome-wide association studies (GWAS) were conducted for MetaboHealth (n=272 568) and PhenoAgeAccel (n=274 077). Independent genome-wide significant variants were used as genetic instruments in two-sample Mendelian randomisation (MR) with FinnGen all-cause dementia summary statistics. Inverse-variance weighting was the primary MR method. Causal network analysis estimated relationships among constituent biomarkers and dementia.
FindingsGWAS identified 126 and 141 independent genome-wide significant variants for MetaboHealth and PhenoAgeAccel, of which 109 and 141 were retained as genetic instruments. MR found no evidence of a causal effect of genetically predicted MetaboHealth (per unit: OR 0{middle dot}83, 95% CI 0{middle dot}49-1{middle dot}42; p=0{middle dot}51) or PhenoAgeAccel (per year: OR 0{middle dot}99, 95% CI 0{middle dot}95-1{middle dot}02; p=0{middle dot}44) on all-cause dementia, with consistent findings across sensitivity analyses and robust MR methods. Lower lymphocyte percentage and higher NMR-derived glucose had direct relationships with dementia in the joint constituent-biomarker network.
InterpretationMR provided no evidence that either composite exposure causally influenced dementia. The network prioritised lymphocyte percentage and NMR-derived glucose, supporting examination of composite exposures alongside their constituent biomarkers.
FundingNIHR, UKRI, MRC, UK Dementia Research Institute, Innovate UK, and European Union. Full funding details are provided in the acknowledgements.
Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed from database inception to July 11, 2026, without language restrictions, using combinations of the terms "dementia", "Alzheimers disease", "vascular dementia", "biological ageing", "phenotypic age", "PhenoAge", "PhenoAgeAccel", "MetaboHealth", "metabolomics", "genome-wide association study", "Mendelian randomisation", and "causal network". Previous GWAS characterised the genetic architecture of PhenoAgeAccel and MetaboHealth. Longitudinal studies linked PhenoAgeAccel, derived from PhenoAge, to incident all-cause, young-onset, and late-onset dementia, dementia subtypes, cognition, and brain structure; MetaboHealth was associated with poorer cognitive performance, 10-year cognitive decline, and reduced functional independence. Related PhenoAge-based measures have also been studied in relation to modifiable factors and intervention response. Genetic causal evidence remained limited: one two-sample MR study found no evidence that PhenoAgeAccel affected Alzheimers disease or vascular dementia, while previous causal-discovery research placed Phenotypic Age within a dementia network without examining its constituent biomarkers.
Added value of this studyTo our knowledge, this is the first study to genetically evaluate PhenoAgeAccel and MetaboHealth as composite exposures and then deconstruct their constituent biomarker relationships using causal network analysis in dementia. It also provides the first large-scale GWAS of MetaboHealth using the complete Phase 3 UK Biobank Nightingale dataset. The expanded GWAS identified additional loci and enlarged the available genetic instrument sets, while the network analysis resolved the constituent biomarker structure underlying both composite exposures.
Implications of all available evidenceFor causal investigation, analysing PhenoAgeAccel and MetaboHealth alongside their constituent biomarkers reveals relationships obscured within the composite exposures. This does not preclude their use for prediction or risk stratification. Lymphocyte percentage and NMR-derived glucose therefore warrant further investigation as potential indicators of immune and metabolic pathways relevant to dementia.
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BibTeX
@misc{baousi2026causal,
title = {{Causal roles of phenotypic age and metabolic health on dementia: a Mendelian randomisation and structure learning study}},
author = {A. Baousi and K. Dobinda and J. Zhu and X. Yu and K. Muir and A. Lophatananon and B. McMillan and P. Clarkson and E. Y. H. Tang and H. Guo},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.01.26360731},
url = {https://www.medrxiv.org/content/10.64898/2026.09.01.26360731v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Causal roles of phenotypic age and metabolic health on dementia: a Mendelian randomisation and structure learning study
AU - A. Baousi
AU - K. Dobinda
AU - J. Zhu
AU - X. Yu
AU - K. Muir
AU - A. Lophatananon
AU - B. McMillan
AU - P. Clarkson
AU - E. Y. H. Tang
AU - H. Guo
PY - 2026
DA - 2026/09/01
PB - medRxiv
DO - 10.64898/2026.09.01.26360731
UR - https://www.medrxiv.org/content/10.64898/2026.09.01.26360731v1
AB - BackgroundPhenotypic age acceleration (PhenoAgeAccel), derived from PhenoAge, and MetaboHealth are composite exposures of biological ageing and metabolic health associated with dementia-related outcomes. Whether these associations are causal and reflect the exposures, constituent biomarkers, or both remains unclear. MethodsThis study included UK Biobank participants of White British genetic ancestry. MetaboHealth was derived from nuclear magnetic resonance (NMR) metabolomics and PhenoAgeAccel from clinical biomarkers and chronological age. Genome-wide association studies (GWAS) were conducted for MetaboHealth (n=272 568) and PhenoAgeAccel (n=274 077). Independent genome-wide significant variants were used as genetic instruments in two-sample Mendelian randomisation (MR) with FinnGen all-cause dementia summary statistics. Inverse-variance weighting was the primary MR method. Causal network analysis estimated relationships among constituent biomarkers and dementia. FindingsGWAS identified 126 and 141 independent genome-wide significant variants for MetaboHealth and PhenoAgeAccel, of which 109 and 141 were retained as genetic instruments. MR found no evidence of a causal effect of genetically predicted MetaboHealth (per unit: OR 0{middle dot}83, 95% CI 0{middle dot}49-1{middle dot}42; p=0{middle dot}51) or PhenoAgeAccel (per year: OR 0{middle dot}99, 95% CI 0{middle dot}95-1{middle dot}02; p=0{middle dot}44) on all-cause dementia, with consistent findings across sensitivity analyses and robust MR methods. Lower lymphocyte percentage and higher NMR-derived glucose had direct relationships with dementia in the joint constituent-biomarker network. InterpretationMR provided no evidence that either composite exposure causally influenced dementia. The network prioritised lymphocyte percentage and NMR-derived glucose, supporting examination of composite exposures alongside their constituent biomarkers. FundingNIHR, UKRI, MRC, UK Dementia Research Institute, Innovate UK, and European Union. Full funding details are provided in the acknowledgements. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed from database inception to July 11, 2026, without language restrictions, using combinations of the terms "dementia", "Alzheimers disease", "vascular dementia", "biological ageing", "phenotypic age", "PhenoAge", "PhenoAgeAccel", "MetaboHealth", "metabolomics", "genome-wide association study", "Mendelian randomisation", and "causal network". Previous GWAS characterised the genetic architecture of PhenoAgeAccel and MetaboHealth. Longitudinal studies linked PhenoAgeAccel, derived from PhenoAge, to incident all-cause, young-onset, and late-onset dementia, dementia subtypes, cognition, and brain structure; MetaboHealth was associated with poorer cognitive performance, 10-year cognitive decline, and reduced functional independence. Related PhenoAge-based measures have also been studied in relation to modifiable factors and intervention response. Genetic causal evidence remained limited: one two-sample MR study found no evidence that PhenoAgeAccel affected Alzheimers disease or vascular dementia, while previous causal-discovery research placed Phenotypic Age within a dementia network without examining its constituent biomarkers. Added value of this studyTo our knowledge, this is the first study to genetically evaluate PhenoAgeAccel and MetaboHealth as composite exposures and then deconstruct their constituent biomarker relationships using causal network analysis in dementia. It also provides the first large-scale GWAS of MetaboHealth using the complete Phase 3 UK Biobank Nightingale dataset. The expanded GWAS identified additional loci and enlarged the available genetic instrument sets, while the network analysis resolved the constituent biomarker structure underlying both composite exposures. Implications of all available evidenceFor causal investigation, analysing PhenoAgeAccel and MetaboHealth alongside their constituent biomarkers reveals relationships obscured within the composite exposures. This does not preclude their use for prediction or risk stratification. Lymphocyte percentage and NMR-derived glucose therefore warrant further investigation as potential indicators of immune and metabolic pathways relevant to dementia.
ER -