Evidence map›Paper›PMID 33152050›Full record

ArticlePloS one2020

Commonly used clinical chemistry tests as mortality predictors: Results from two large cohort studies.

Lars Lind, Daniela Zanetti, Marieann Högman, Lars Sundman, Erik Ingelsson

Abstract read
In one paragraph

Article in PloS one, 2020. 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
–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

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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.

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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

5 authors.

Lars LindDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.
Daniela ZanettiDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States of America.ORCID 0000-0002-1225-1021
Marieann HögmanDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.
Lars SundmanDepartment of Public Health Medicine, County Council of Gävleborg, Gävle, Sweden.
Erik IngelssonDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States of America.

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to functionR01DK106236 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2016 to 2020
$2.4M
Causal associations of circulating biomarkers with cardiovascular diseaseR01HL135313 · NHLBI · STANFORD UNIVERSITY · PI INGELSSON, ERIK · 2017 to 2019
$1.6M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHLBI NIH HHS R01 HL135313NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK106236
6 · The paper itself

Abstract

backgroundThe normal ranges for clinical chemistry tests are usually defined by cut-offs given by the distribution in healthy individuals. This approach does however not indicate if individuals outside the normal range are more prone to disease.

methodsWe studied the associations and risk prediction of 11 plasma and serum biomarkers with all-cause mortality in two population-based cohorts: a Swedish cohort (X69) initiated in 1969, and the UK Biobank (UKB) initiated in 2006-2010, with up to 48- and 9-years follow-up, respectively.

resultsIn X69 and in UKB, 18,529 and 425,264 individuals were investigated, respectively. During the follow-up time, 14,475 deaths occurred in X69 and 17,116 in UKB. All evaluated tests were associated with mortality in X69 (P<0.0001, except bilirubin P<0.005). For calcium, blood urea nitrogen, bilirubin, hematocrit, uric acid, and iron, U-shaped associations were seen (P<0.0001). For leukocyte count, gamma-glutamyl transferase, alkaline phosphatases and lactate dehydrogenase, linear positive associations were seen, while for albumin the association was negative. Similar associations were seen in UKB. Addition of all biomarkers to a model with classical risk factors improved mortality prediction (delta C-statistics: +0.009 in X69 and +0.023 in UKB, P<0.00001 in both cohorts).

conclusionsCommonly used clinical chemistry tests were associated with all-cause mortality both in the medium- and long-term perspective, and improved mortality prediction beyond classical risk factors. Since both linear and U-shaped relationships were found, we propose to define the normal range of a clinical chemistry test based on its association with mortality, rather than from the distribution.

Indexed as

MortalityBiological Specimen BanksBiomarkersClinical Chemistry TestsCohort StudiesHumansKaplan-Meier EstimatePrincipal Component AnalysisReproducibility of ResultsRisk FactorsYoung AdultBiomarkers

Identifiers

PMID33152050
PMCPMC7644047

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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.