| Title: | Multi-dimensional evidence from the UK Biobank shows the impact of diet and macronutrient intake on aging |
| Journal: | Communications Medicine |
| Published: | 4 Feb 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39905241/ |
| DOI: | https://doi.org/10.1038/s43856-025-00754-5 |
Publication 14440
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Abstract
BackgroundThe role of diet in aging is crucial, yet research findings on how specific diets influence human aging remain inconsistent. Understanding the relationship between dietary factors and aging could inform interventions to promote healthier aging outcomes.MethodsWe analyzed data from the UK Biobank baseline survey and a 24-hour dietary assessment survey to investigate the association between diet and aging. The study examined 18 individual food intakes, 6 dietary patterns, 3 macronutrient intakes, and 3 dietary quality scores. High-dimensional Fixed Effects (HDFE) models were used to assess associations between dietary factors and aging measures, including telomere length, phenotypic age, and brain grey/white matter volumes. Multivariable Mendelian Randomization (MVMR) was employed to explore causal links between macronutrient consumption and aging outcomes.ResultsOur results show that healthier diets are generally associated with improved aging outcomes from HDFE analyses. Plant-based food consumption correlates with increased telomere length and reduced phenotypic age, while animal-based food intake is linked to adverse aging effects. MVMR results confirm the causal benefits of carbohydrate intake, including reductions in phenotypic age (β = −0.0025; 95% CI = [−0.0047, −0.0003]; p = 0.0253) and increases in whole-brain grey matter volume (β = 0.0262; 95% CI = [0.007, 0.046]; p = 0.0087). The latter association remains significant after multiple testing correction.ConclusionsThis study underscores the significant role of diet in biological aging and provides robust evidence for the benefits of carbohydrate intake in promoting healthier aging. These findings highlight the potential of dietary interventions to improve aging-related outcomes.</p>
9 Authors
- Chen Zhu
- Youfa Wang
- Xiaosong Yang
- Qiran Zhao
- Wenyan Xu
- Xiaolu Wang
- Yanjun Liang
- Qihui Chen
- Shenggen Fan
1 Application
| Application ID | Title |
|---|---|
| 89068 | Using genetic information to improve the prediction of food choices: A machine learning-based classification approach |
Enabling scientific discoveries that improve human health