Evidence map›Paper›PMID 33674838›Full record

ArticleClinical chemistry2021

Association of 152 Biomarker Reference Intervals with All-Cause Mortality in Participants of a General United States Survey from 1999 to 2010.

Nam Pho, Arjun K Manrai, John T Leppert, Glenn M Chertow, John P A Ioannidis, Chirag J Patel

Open access · hybridAbstract read
In one paragraph

Article in Clinical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Observational
  2. 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

6 authors at 2 institutions in 1 country.

Nam PhoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA.
Arjun K ManraiDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA.
John T LeppertDepartment of Medicine, Stanford University School of Medicine, Stanford, CA.
Glenn M ChertowDepartment of Medicine, Stanford University School of Medicine, Stanford, CA.
John P A IoannidisDepartment of Medicine, Stanford University School of Medicine, Stanford, CA.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA.
Harvard University · USStanford University · US

Funding

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
Ancestral Exposures/Modern Responses to EDCsR01ES023254 · NIEHS · UNIVERSITY OF TEXAS AT AUSTIN · PI CREWS, DAVID P, GORE, ANDREA C · 2013 to 2017
$1.9M
Precision Cardiovascular Medicine for Multi-Ethnic PopulationsK01HL138259 · NHLBI · HARVARD MEDICAL SCHOOL · PI MANRAI, ARJUN KUMAR · 2018 to 2022
$801k
NHLBI NIH HHS K01 HL138259NIAID NIH HHS R01 AI127250NIEHS NIH HHS R01 ES023254
6 · The paper itself

Abstract

backgroundPhysicians sometimes consider whether or not to perform diagnostic testing in healthy people, but it is unknown whether nonextreme values of diagnostic tests typically encountered in such populations have any predictive ability, in particular for risk of death. The goal of this study was to quantify the associations among population reference intervals of 152 common biomarkers with all-cause mortality in a representative, nondiseased sample of adults in the United States.

methodsThe study used an observational cohort derived from the National Health and Nutrition Examination Survey (NHANES), a representative sample of the United States population consisting of 6 survey waves from 1999 to 2010 with linked mortality data (unweighted N = 30 651) and a median followup of 6.1 years. We deployed an X-wide association study (XWAS) approach to systematically perform association testing of 152 diagnostic tests with all-cause mortality.

resultsAfter controlling for multiple hypotheses, we found that the values within reference intervals (10-90th percentiles) of 20 common biomarkers used as diagnostic tests or clinical measures were associated with all-cause mortality, including serum albumin, red cell distribution width, serum alkaline phosphatase, and others after adjusting for age (linear and quadratic terms), sex, race, income, chronic illness, and prior-year healthcare utilization. All biomarkers combined, however, explained only an additional 0.8% of the variance of mortality risk. We found modest year-to-year changes, or changes in association from survey wave to survey wave from 1999 to 2010 in the association sizes of biomarkers.

conclusionsReference and nonoutlying variation in common biomarkers are consistently associated with mortality risk in the US population, but their additive contribution in explaining mortality risk is minor.

Indexed as

Cause of DeathNutrition SurveysAdultAgedBiomarkersFemaleHumansMaleMiddle AgedReference ValuesUnited StatesBiomarkers

Identifiers

PMID33674838
PMCPMC8142683
OpenAlexW3106900152

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.