Evidence map›Paper›PMID 39506848›Full record

ArticleAlzheimer's research & therapy2024

Facial aging, cognitive impairment, and dementia risk.

Xinming Xu, Guliyeerke Jigeer, David Andrew Gunn, Yizhou Liu, Xinrui Chen, Yi Guo, Yaqi Li, Xuelan Gu, Yanyun Ma, Jiucun Wang and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
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

14 authors.

Xinming Xu *Department of Nutrition and Food Hygiene, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, 130 Dongan Road, Shanghai, 200030, China.ORCID 0009-0005-3121-8138
Guliyeerke Jigeer *Department of Nutrition and Food Hygiene, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, 130 Dongan Road, Shanghai, 200030, China.
David Andrew GunnUnilever R&D Colworth Science Part, Sharnbrook, Bedfordshire, MK44 1LQ, UK.
Yizhou LiuState Key Laboratory of Genetic Engineering, School of Life Sciences & Human Phenome Institute, Fudan University, Shanghai, 200438, China.
Xinrui ChenState Key Laboratory of Genetic Engineering, School of Life Sciences & Human Phenome Institute, Fudan University, Shanghai, 200438, China.
Yi GuoDepartment of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, 200030, China.
Yaqi LiDepartment of Nutrition and Food Hygiene, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, 130 Dongan Road, Shanghai, 200030, China.
Xuelan GuUnilever R&D Shanghai, Shanghai, 200335, China.
Yanyun MaUnilever R&D Colworth Science Part, Sharnbrook, Bedfordshire, MK44 1LQ, UK.
Jiucun WangState Key Laboratory of Genetic Engineering, School of Life Sciences & Human Phenome Institute, Fudan University, Shanghai, 200438, China.
Sijia WangCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institute for Biological Sciences, CAS-MPG Partner Institute for Computational Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
Liang SunDepartment of Nutrition and Food Hygiene, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, 130 Dongan Road, Shanghai, 200030, China. sun_liang@fudan.edu.cn.
Xu LinShanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. xlin@sibs.ac.cn.
Xiang GaoDepartment of Nutrition and Food Hygiene, Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, 130 Dongan Road, Shanghai, 200030, China. xiang_gao@fudan.edu.cn.ORCID 0000-0003-2617-6509

Funding

National Natural Science Foundation of China 81970684Shanghai Municipal Science and Technology Major Project 2017SHZDZX01Strategic Priority CAS Project XDB38010300the Startup grant at Fudan University JIF201036Y
6 · The paper itself

Abstract

backgroundFacial aging, cognitive impairment, and dementia are all age-related conditions. However, the temporal relation between facial age and future risk of dementia was not systematically examined.

objectivesTo investigate the relationship between facial age (both subjective/perceived and objective) and cognitive impairment and/or dementia risk.

methodsThe study included 195,329 participants (age ≥ 60 y) from the UK Biobank (UKB) with self-perceived facial age and 612 participants from the Nutrition and Health of Aging Population in China Project (NHAPC) study (age ≥ 56 y) with objective assessment of facial age. Cox proportional hazards model was used to prospectively examine the hazard ratios (HRs) and their 95% confidence intervals (CIs) of self-perceived facial age and dementia risk in the UKB, adjusting for age, sex, education, APOE ε4 allele, and other potential confounders. Linear and logistic regressions were performed to examine the cross-sectional association between facial age (perceived and objective) and cognitive impairment in the UKB and NHAPC, with potential confounders adjusted.

resultsDuring a median follow-up of 12.3 years, 5659 dementia cases were identified in the UKB. The fully-adjusted HRs comparing high vs. low perceived facial age were 1.61 (95% CI, 1.33 ~ 1.96) for dementia (P-trend ≤ 0.001). Subjective facial age and cognitive impairment was also observed in the UKB. In the NHAPC, facial age, as assessed by three objective wrinkle parameters, was associated with higher odds of cognitive impairment (P-trend < 0.05). Specifically, the fully-adjusted OR for cognitive impairment comparing the highest versus the lowest quartiles of crow's feet wrinkles number was 2.48 (95% CI, 1.06 ~ 5.78).

conclusionsHigh facial age was associated with cognitive impairment, dementia and its subtypes after adjusting for conventional risk factors for dementia. Facial aging may be an indicator of cognitive decline and dementia risk in older adults, which can aid in the early diagnosis and management of age-related conditions.

Indexed as

AgingCognitive DysfunctionDementiaAgedAged, 80 and overChinaCross-Sectional StudiesFaceFemaleHumansMaleMiddle AgedProspective StudiesRisk FactorsUnited KingdomCognitive impairmentDementiaFacial age

Identifiers

PMID39506848
PMCPMC11539626

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LicenceCC BY-NC-ND
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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.