Evidence map›Paper›PMID 30919901›Full record

ArticleThe Journal of nutrition2019

The Serum Metabolome Identifies Biomarkers of Dietary Acid Load in 2 Studies of Adults with Chronic Kidney Disease.

Casey M Rebholz, Aditya Surapaneni, Andrew S Levey, Mark J Sarnak, Lesley A Inker, Lawrence J Appel, Josef Coresh, Morgan E Grams

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of nutrition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.4field-weighted citation impact, top 12% 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

14 citing papers in PubMed, 23 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Serum Metabolomic Markers of Dietary Potassium and Risk of CKD.Clinical journal of the American Society of Nephrology : CJASN · 2025
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Metabolomics of Dietary Acid Load and Incident Chronic Kidney Disease.Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation · 2022
    Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. 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

8 authors at 2 institutions in 2 countries.

Casey M RebholzDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Aditya SurapaneniDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Andrew S LeveyDivision of Nephrology, Tufts Medical Center, Boston, MA.
Mark J SarnakDivision of Nephrology, Tufts Medical Center, Boston, MA.
Lesley A InkerDivision of Nephrology, Tufts Medical Center, Boston, MA.
Lawrence J AppelDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Josef CoreshDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Morgan E GramsDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Johns Hopkins University · USTufts Medical Center · US

Funding

Multi-Omics and Chronic Kidney Disease: Correlation with HistologyR01DK108803 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Morgan Erika Grams, EUGENE P. RHEE · 2016 to 2026
$6.4M
Proteomic Approach to CKD Biomarker Discovery and ValidationU01DK085689 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI CORESH, JOSEF · 2009 to 2019
$5.6M
Beyond the Glomerulus: Novel Clinical Markers of Kidney Health and PrognosisU01DK102730 · NIDDK · TUFTS MEDICAL CENTER · PI GUTIERREZ, ORLANDO M, IX, JOACHIM H · 2016 to 2020
$1.9M
Effects of Age and Race on GFR Estimation in a Population-based CohortR01DK087961 · NIDDK · TUFTS MEDICAL CENTER · PI LEVEY, ANDREW S, SHLIPAK, MICHAEL G · 2011 to 2015
$1.2M
Metabolomics for Identifying Biomarkers of Dietary Intake & Kidney Disease ProgressionK01DK107782 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI REBHOLZ, CASEY MARIE · 2016 to 2020
$778k
NIDDK NIH HHS K01 DK107782NIDDK NIH HHS R01 DK087961NIDDK NIH HHS R01 DK108803NIDDK NIH HHS U01 DK085689NIDDK NIH HHS U01 DK102730
6 · The paper itself

Abstract

backgroundDietary acid load is a clinically important aspect of the diet that reflects the balance between acid-producing foods, for example, meat and cheese, and base-producing foods, for example, fruits and vegetables.

methodsWe used metabolomics to identify blood biomarkers of dietary acid load in 2 independent studies of chronic kidney disease patients: the African American Study of Kidney Disease and Hypertension (AASK, n = 689) and the Modification of Diet in Renal Disease (MDRD, n = 356) study. Multivariable linear regression was used to assess the cross-sectional association between serum metabolites whose identity was known (outcome) and dietary acid load (exposure), estimated with net endogenous acid production (NEAP) based on 24-h urine urea nitrogen and potassium, and adjusted for age, sex, race, randomization group, measured glomerular filtration rate, log-transformed urine protein-to-creatinine ratio, history of cardiovascular disease, BMI, and smoking status.

resultsOut of the 757 known, nondrug metabolites identified in AASK, 26 were significantly associated with NEAP at the Bonferroni threshold for significance (P < 6.6 × 10-5). Twenty-three of the 26 metabolites were also identified in the MDRD study, and 13 of the 23 (57%) were significantly associated with NEAP (P < 2.2 × 10-3), including 5 amino acids (S-methylmethionine, indolepropionylglycine, indolepropionate, N-methylproline, N-δ-acetylornithine), 2 cofactors and vitamins (threonate, oxalate), 1 lipid (chiro-inositol), and 5 xenobiotics (methyl glucopyranoside, stachydrine, catechol sulfate, hippurate, and tartronate). Higher levels of all 13 replicated metabolites were associated with lower NEAP in both AASK and the MDRD study.

conclusionMetabolomic profiling of serum specimens from kidney disease patients in 2 study populations identified 13 replicated metabolites associated with dietary acid load. Additional studies are needed to validate these compounds in healthy populations. These 13 compounds may potentially be used as objective markers of dietary acid load in future nutrition research studies.

Indexed as

DietAcidsAgedBiomarkersCross-Sectional StudiesFemaleFood AnalysisHumansHypertensionMaleMetabolomeMiddle AgedRenal Insufficiency, ChronicAcidsBiomarkersbiomarkersdietary acid loaddietary assessmentkidney diseasemetabolites

Identifiers

PMID30919901
PMCPMC6461721
OpenAlexW2926536807

What OpenQuestion holds

Textmetadata
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.