Evidence map›Paper›PMID 39331845›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

A Novel Metabolomic Aging Clock Predicting Health Outcomes and Its Genetic and Modifiable Factors.

Xueqing Jia, Jiayao Fan, Xucheng Wu, Xingqi Cao, Lina Ma, Zeinab Abdelrahman, Fei Zhao, Haitao Zhu, Daniele Bizzarri, Erik B van den Akker and 4 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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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.

Xueqing JiaCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.ORCID 0009-0009-2412-0414
Jiayao FanCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Xucheng WuCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Xingqi CaoCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Lina MaDepartment of Geriatrics, National Clinical Research Center for Geriatric Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Zeinab AbdelrahmanMolecular Epidemiology and Public Health Research Group, Centre for Public Health, Queen's University Belfast, Institute for Clinical Sciences A, Royal Victoria Hospital, Belfast, BT12 6BA, UK.
Fei ZhaoHangzhou Meilian Medical Co., Ltd., Hangzhou, 311200, China.
Haitao ZhuHangzhou Meilian Medical Co., Ltd., Hangzhou, 311200, China.
Daniele BizzarriMolecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, 2333 ZC, The Netherlands.ORCID 0000-0002-6881-273X
Erik B van den AkkerMolecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, 2333 ZC, The Netherlands.ORCID 0000-0002-7693-0728
P Eline SlagboomMolecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, 2333 ZC, The Netherlands.ORCID 0000-0002-2875-4723
Joris DeelenMax Planck Institute for Biology of Ageing, 50931, Cologne, Germany.ORCID 0000-0003-4483-3701
Dan ZhouCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Zuyun LiuCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China.ORCID 0000-0001-6120-5913

Funding

Fundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China 72374180National Natural Science Foundation of China 82204118"Pioneer" and "Leading Goose" R&D Programs of Zhejiang Province 2023C03163Research Center of Prevention and Treatment of Senescence Syndrome, School of Medicine Zhejiang University 2022010002Zhejiang Key Laboratory of Intelligent Preventive Medicine 2020E10004Zhejiang University Global Partnership FundZhejiang University School of Public Health Interdisciplinary Research Innovation Team Development Project
6 · The paper itself

Abstract

Existing metabolomic clocks exhibit deficiencies in capturing the heterogeneous aging rates among individuals with the same chronological age. Yet, the modifiable and non-modifiable factors in metabolomic aging have not been systematically studied. Here, a new aging measure-MetaboAgeMort-is developed using metabolomic profiles from 239,291 UK Biobank participants for 10-year all-cause mortality prediction. The MetaboAgeMort showed significant associations with all-cause mortality, cause-specific mortality, and diverse incident diseases. Adding MetaboAgeMort to a conventional risk factors model improved the predictive ability of 10-year mortality. A total of 99 modifiable factors across seven categories are identified for MetaboAgeMort. Among these, 16 factors representing pulmonary function, body composition, socioeconomic status, dietary quality, smoking status, alcohol intake, and disease status showed quantitatively stronger associations. The genetic analyses revealed 99 genomic risk loci and 271 genes associated with MetaboAgeMort. The tissue-enrichment analysis showed significant enrichment in liver. While the external validation of the MetaboAgeMort is required, this study illuminates heterogeneous metabolomic aging across the same age, providing avenues for identifying high-risk individuals, developing anti-aging therapies, and personalizing interventions, thus promoting healthy aging and longevity.

Indexed as

AgingMetabolomicsAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRisk FactorsUnited Kingdomagingbiological agegenetic determinantmetabolomicmodifiable factormortality

Identifiers

PMID39331845
PMCPMC11578329

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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.