Evidence map›Paper›PMID 32391803›Full record

ArticleAging2020

Prediction of chronological and biological age from laboratory data.

Luke Sagers, Luke Melas-Kyriazi, Chirag J Patel, Arjun K Manrai

Abstract read
In one paragraph

Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Trial
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  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022
    Article
  13. Review
  14. Article
  15. Article
  16. Article
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  19. A high-throughput phenotyping algorithm is portable from adult to pediatric populations.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  20. 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

4 authors.

Luke SagersComputational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, USA.
Luke Melas-KyriaziComputational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, USA.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Arjun K ManraiComputational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, USA.

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
TrainingU54HG007963 · NHGRI · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S., MURPHY, SHAWN N · 2014 to 2018
$18.3M
The Integration of Trans-omics for Precision Medicine (TOPMED) and Other Heart, Lung, Blood and Sleep (HLBS) Data Sets with the Data CommonsOT3HL142480 · NHLBI · HARVARD MEDICAL SCHOOL · PI AVILLACH, PAUL · 2017 to 2022
$8.8M
Big Data Analysis of HIV Risk and Epidemiology in Sub-Saharan AfricaR01AI127250 · NIAID · STANFORD UNIVERSITY · PI BENDAVID, ERAN, PATEL, CHIRAG J. · 2017 to 2020
$2.7M
Precision Cardiovascular Medicine for Multi-Ethnic PopulationsK01HL138259 · NHLBI · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR · 2018 to 2022
$801k
NHGRI NIH HHS U54 HG007963NHLBI NIH HHS K01 HL138259NHLBI NIH HHS OT3 HL142480NIAID NIH HHS R01 AI127250NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

Aging has pronounced effects on blood laboratory biomarkers used in the clinic. Prior studies have largely investigated one biomarker or population at a time, limiting a comprehensive view of biomarker variation and aging across different populations. Here we develop a supervised machine learning approach to study aging using 356 blood biomarkers measured in 67,563 individuals across diverse populations. Our model predicts age with a mean absolute error (MAE), or average magnitude of prediction errors, in held-out data of 4.76 years and an R

Indexed as

Machine LearningAdolescentAdultAgedAged, 80 and overAgingBiomarkersChildChild, PreschoolFemaleHumansInfantMaleMiddle AgedPredictive Value of TestsRetrospective StudiesBiomarkersbig databiomarkerscomputational modelsdiversitymachine learning

Identifiers

PMID32391803
PMCPMC7244024

What OpenQuestion 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 OpenQuestion graph.