Evidence map›Paper›PMID 28097619›Full record

ReviewCurrent environmental health reports2017

Lipid and Creatinine Adjustment to Evaluate Health Effects of Environmental Exposures.

Katie M O'Brien, Kristen Upson, Jessie P Buckley

Abstract readReview
In one paragraph

Review in Current environmental health reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 1 pooled it
–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

52 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

3 authors.

Katie M O'BrienBiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 TW Alexander Dr, Research Triangle Park, Durham, NC, 27709, USA. obrienkm2@niehs.nih.gov.
Kristen UpsonEpidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, 111 TW Alexander Dr, Research Triangle Park, Durham, NC, 27709, USA.
Jessie P BuckleyDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Funding

Intramural NIH HHS Z99 ES999999
6 · The paper itself

Abstract

purpose of reviewUrine- and serum-based biomarkers are useful for assessing individuals' exposure to environmental factors. However, variations in urinary creatinine (a measure of dilution) or serum lipid levels, if not adequately corrected for, can directly impact biomarker concentrations and bias exposure-disease association measures. RECENT

findingsRecent methodological literature has considered the complex relationships between creatinine or serum lipid levels, exposure biomarkers, outcomes, and other potentially relevant factors using directed acyclic graphs and simulation studies. The optimal measures of urinary dilution and serum lipids have also been investigated. Existing evidence supports the use of covariate-adjusted standardization plus creatinine adjustment for urinary biomarkers and standardization plus serum lipid adjustment for lipophilic, serum-based biomarkers. It is unclear which urinary dilution measure is best, but all serum lipid measures performed similarly. Future research should assess methods for pooled biomarkers and for studying diseases and exposures that affect creatinine or serum lipids directly.

Indexed as

BiomarkersCreatinineEnvironmental ExposureEnvironmental MonitoringEnvironmental PollutantsHumansLipidsBiomarkersCreatinineEnvironmental PollutantsLipidsBiomarkersCreatinine adjustmentEnvironmental exposuresLipid adjustment

Identifiers

PMID28097619
PMCPMC5323273

What OpenQuestion holds

Textmetadata
LicenceTDM
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.