Evidence map›Paper›PMID 41326017›Full record

ArticleJMIR aging2025

Associations Between Dietary Habits and Accelerated Aging and the Establishment of an Accelerated Aging Interpretable Risk Prediction Model via Shapley Additive Explanations: Cross-Sectional Study From Two Representative Populations.

Zhengyang Wu, Ning Zhang, Haiwei Wang, Yang Yang, Houhao Shen, Qidi Zhang, Yunxia Cao, Yinan Du, Dongmei Ji

Abstract read
In one paragraph

Article in JMIR aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhengyang Wu *Department of Obstetrics and Gynecology, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, First Affliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-1154-8325
Ning Zhang *Department of Obstetrics and Gynecology, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, First Affliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-5892-2193
Haiwei Wang *School of Basic Medical Sciences, Anhui Medical University, Hefei, China.ORCID https://orcid.org/0009-0001-8694-981X
Yang YangDepartment of Obstetrics and Gynecology, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, First Affliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0009-0009-5175-7250
Houhao ShenSchool of Basic Medical Sciences, Anhui Medical University, Hefei, China.ORCID https://orcid.org/0009-0005-1770-1000
Qidi ZhangSchool of Basic Medical Sciences, Anhui Medical University, Hefei, China.ORCID https://orcid.org/0009-0003-2107-6696
Yunxia CaoDepartment of Obstetrics and Gynecology, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, First Affliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-8715-0874
Yinan DuSchool of Basic Medical Sciences, Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-7678-7546
Dongmei JiDepartment of Obstetrics and Gynecology, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, First Affliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-4597-2036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResearch has revealed potential links between specific dietary habits and accelerated aging. However, most studies focus only on singular diets or lack ethnic diversity.

objectiveThis study aimed to investigate the associations between 5 dietary indices and the risk of accelerated aging and develop an interpretable machine learning (ML) model for accelerated aging prediction.

methodsWe explored associations between dietary indices and the risk of accelerated aging using data from the US National Health and Nutrition Examination Survey (NHANES) and the UK Biobank. A weighted linear regression analysis was used to determine whether accelerated aging was linked to dietary habits, and the covariates were gradually adjusted to ensure that the association was stable. Nonlinear correlations were explored using restricted cubic spline curves. In addition, multiple ML algorithms were used to build predictive models of accelerated aging risk.

resultsExcept for the Dietary Inflammation Index (β=0.35, 95% CI 0.23-0.74), the other 4 dietary indices (Alternative Healthy Eating Index, Alternative Mediterranean Diet, Healthy Eating Index-2020, and Dietary Approaches to Stop Hypertension) were negatively associated with the risk of accelerated aging in NHANES participants. Similar results were observed in UK Biobank participants. Nine ML algorithms were used to develop risk prediction models, among which the gradient boosting decision tree model showed the best overall performance. A web-based prediction platform was developed and made publicly available.

conclusionsSignificant associations between accelerated aging and dietary indices were observed. High compliance with the Dietary Inflammation Index had a promoting effect on accelerated aging, while high compliance with the Alternative Healthy Eating Index, Alternative Mediterranean Diet, Healthy Eating Index-2020, and Dietary Approaches to Stop Hypertension showed varying degrees of protection against accelerated aging.

Indexed as

AgingAging, PrematureFeeding BehaviorAdultAgedCross-Sectional StudiesDiet, HealthyFemaleHumansMachine LearningMaleMiddle AgedNutrition SurveysRisk AssessmentRisk FactorsUnited Kingdomaccelerated agingdietary indicesmachine learningNational Health and Nutrition Examination SurveyNHANESUK Biobank

Identifiers

PMID41326017
PMCPMC12706446

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.