Evidence mapPaperPMID 35330504Full record

ArticleJournal of personalized medicine2022

Individualized Biological Age as a Predictor of Disease: Korean Genome and Epidemiology Study (KoGES) Cohort.

Seokyung An, Choonghyun Ahn, Sungji Moon, Eun Ji Sim, Sue-Kyung Park

Open access · goldAbstract read
In one paragraph

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.8field-weighted citation impact, top 16% of its field
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

8 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Readmission and In-Hospital Outcomes After Transcatheter Aortic Valve Replacement in Patients With Dementia.Cardiovascular revascularization medicine : including molecular interventions · 2023
    Article
  6. Use of Personal Resources May Influence the Rate of Biological Aging Depending on Individual Typology.European journal of investigation in health, psychology and education · 2022
    Article
  7. Article
  8. 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

5 authors at 1 institution in 1 country.

Seokyung AnDepartment of Biomedical Science, Graduate School, Seoul National University, Seoul 03080, Korea.ORCID 0000-0002-1971-1094
Choonghyun AhnDepartment of Biomedical Science, Graduate School, Seoul National University, Seoul 03080, Korea.
Sungji MoonDepartment of Preventive Medicine, College of Medicine, Seoul National University, Seoul 03080, Korea.ORCID 0000-0002-6668-3065
Eun Ji SimDepartment of Preventive Medicine, College of Medicine, Seoul National University, Seoul 03080, Korea.
Sue-Kyung ParkDepartment of Preventive Medicine, College of Medicine, Seoul National University, Seoul 03080, Korea.ORCID 0000-0001-5002-9707
Seoul National University · KR

Funding

Korea Health Industry Development Institute HI16C1127
6 · The paper itself

Abstract

Chronological age (CA) predicts health status but its impact on health varies with anthropometry, socioeconomic status (SES), and lifestyle behaviors. Biological age (BA) is, therefore, considered a more precise predictor of health status. We aimed to develop a BA prediction model from self-assessed risk factors and validate it as an indicator for predicting the risk of chronic disease. A total of 101,980 healthy participants from the Korean Genome and Epidemiology Study were included in this study. BA was computed based on body measurements, SES, lifestyle behaviors, and presence of comorbidities using elastic net regression analysis. The effects of BA on diabetes mellitus (DM), hypertension (HT), combination of DM and HT, and chronic kidney disease were analyzed using Cox proportional hazards regression. A younger BA was associated with a lower risk of DM (HR = 0.63, 95% CI: 0.55-0.72), hypertension (HR = 0.74, 95% CI: 0.68-0.81), and combination of DM and HT (HR = 0.65, 95% CI: 0.47-0.91). The largest risk of disease was seen in those with a BA higher than their CA. A consistent association was also observed within the 5-year follow-up. BA, therefore, is an effective tool for detecting high-risk groups and preventing further risk of chronic diseases through individual and population-level interventions.

Indexed as

biological agechronic diseasemachine learningpersonalized lifestyle medicineprecision medicine

Identifiers

PMID35330504
PMCPMC8955355
OpenAlexW4220976984

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.