Evidence map›Paper›PMID 35972662›Full record

ArticleGeroScience2023

High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile.

Mary Revelas, Anbupalam Thalamuthu, Anna Zettergren, Christopher Oldmeadow, Jenna Najar, Nazib M Seidu, Nicola J Armstrong, Carlos Riveros, John B Kwok, Peter R Schofield and 12 more

Abstract read
In one paragraph

Article in GeroScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  5. Review
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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

22 authors.

Mary RevelasCentre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine & Health, UNSW, Sydney, Australia. m.revelas@student.unsw.edu.au.ORCID 0000-0002-5249-022X
Anbupalam ThalamuthuCentre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine & Health, UNSW, Sydney, Australia.
Anna ZettergrenNeuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.
Christopher OldmeadowHunter Medical Research Institute, Newcastle, NSW, Australia.
Jenna NajarNeuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.
Nazib M SeiduNeuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.
Nicola J ArmstrongCentre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine & Health, UNSW, Sydney, Australia.
Carlos RiverosHunter Medical Research Institute, Newcastle, NSW, Australia.
John B KwokNeuroscience Research Australia, Sydney, NSW, Australia.
Peter R SchofieldNeuroscience Research Australia, Sydney, NSW, Australia.
Julian N TrollorCentre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine & Health, UNSW, Sydney, Australia.
Margda WaernNeuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.
Margaret J WrightQueensland Brain Institute, University of Queensland, Brisbane, Australia.
Henrik ZetterbergDepartment of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
David AmesUniversity of Melbourne Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, VIC, Australia.
Kaj BelnnowDepartment of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
Henry BrodatyCentre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine & Health, UNSW, Sydney, Australia.
Rodney J ScottFaculty of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia.
Ingmar SkoogNeuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.
John R AttiaHunter Medical Research Institute, Newcastle, NSW, Australia.
Perminder S SachdevCentre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine & Health, UNSW, Sydney, Australia.
Karen A MatherCentre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine & Health, UNSW, Sydney, Australia.

Funding

The effects of iron on oxidative stress and Alzheimer's biomarkers in amyloid-positive and negative elderly normalR01AG068398 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHIANG, GLORIA CHIA-YI · 2020 to 2024
$2.8M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NIA NIH HHS R01 AG068398Wellcome Trust
6 · The paper itself

Abstract

Healthy metabolic measures in humans are associated with longevity. Dysregulation leads to metabolic syndrome (MetS) and negative health outcomes. Recent exceptional longevity (EL) genome wide association studies have facilitated estimation of an individual's polygenic risk score (PRS) for EL. We tested the hypothesis that individuals with high ELPRS have a low prevalence of MetS. Participants were from five cohorts of middle-aged to older adults. The primary analyses were performed in the UK Biobank (UKBB) (n = 407,800, 40-69 years). Replication analyses were undertaken using three Australian studies: Hunter Community Study (n = 2122, 55-85 years), Older Australian Twins Study (n = 539, 65-90 years) and Sydney Memory and Ageing Study (n = 925, 70-90 years), as well as the Swedish Gothenburg H70 Birth Cohort Studies (n = 2273, 70-93 years). MetS was defined using established criteria. Regressions and meta-analyses were performed with the ELPRS and MetS and its components. Generally, MetS prevalence (22-30%) was higher in the older cohorts. In the UKBB, high EL polygenic risk was associated with lower MetS prevalence (OR = 0.94, p = 1.84 × 10

Indexed as

LongevityMetabolic SyndromeAgedAustraliaGenome-Wide Association StudyHumansMetabolomeMiddle AgedRisk FactorsLongevityMetabolic syndromePRS

Identifiers

PMID35972662
PMCPMC9886704

What OpenQuestion holds

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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.