Evidence map›Paper›PMID 37604298›Full record

ArticleThe American journal of clinical nutrition2023

Adherence to Life's Essential 8 and incident chronic kidney disease: a prospective study of 147,988 UK Biobank participants.

Rui Tang, Xuan Wang, Xiang Li, Hao Ma, Zhaoxia Liang, Yoriko Heianza, Lu Qi

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The American journal of clinical nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
10.7field-weighted citation impact, top 1% 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

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

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  19. Cardiovascular health and risks of atrial fibrillation and its prognosis.American journal of preventive cardiology · 2025
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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

7 authors at 2 institutions in 2 countries.

Rui TangDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.
Xuan WangDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.
Xiang LiDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.
Hao MaDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.
Zhaoxia LiangDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States; Department of Obstetrical, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Yoriko HeianzaDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Lu QiDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States. Electronic address: lqi1@tulane.edu.
Tulane University · USHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe American Heart Association recently updated Life's Essential 8 (LE8) score. This amalgamation of health factors, recognized for their individual associations with chronic kidney disease (CKD) risk, provides a robust tool to assess overall cardiovascular health (CVH), which could potentially be extrapolated to predict CKD risk.

objectivesThis study aimed to investigate the association between levels of CVH, as measured by the LE8 score, and risk of CKD in the UK Biobank.

methodsA total of 147,988 participants free of CKD and cardiovascular disease from the UK Biobank were included in this prospective study. CVH levels were categorized as low (0-49), moderate (50-79), and high (80-100) using LE8 score. An adjusted Cox proportional hazard model was used to investigate the association between LE8 and CKD. The population attributable-risk (PAR) was also calculated.

resultsDuring a median follow-up of 10 y, 1936 CKD cases were documented. A higher LE8 score was associated with a significant lower risk of CKD (P < 0.001), and a linear dose-response relationship was observed. Similar patterns were also found in the associations of the LE8 behavior and biological subscale scores with CKD. Compared with participants with a low CVH category, participants with a moderate CVH were associated with a 39% lower risk of developing CKD (hazard ratio [HR]: 0.61; 95% confidence interval [CI]: 0.52, 0.72); and those with a high CVH had a 57% lower risk of CKD incidence (HR: 0.43; 95% CI: 0.35, 0.53) after adjustment for covariates. Among 8 distinct metrics of LE8 score, the BMI metric had the highest PAR (24.6%; 95% CI: 18.8, 30.2). Of the total CKD risk, 3.2% (95% CI: 1.4, 5.0) was attributable to inadequate or excessive sleep duration.

conclusionsHigh CVH, defined by LE8, is significantly associated with a lower risk of CKD. These results suggest that promoting optimal cardiovascular health may lower the burden of CKD.

Indexed as

Renal Insufficiency, ChronicAdultAgedBiological Specimen BanksCardiovascular DiseasesFemaleHumansIncidenceMaleMiddle AgedProportional Hazards ModelsProspective StudiesRisk FactorsUK BiobankUnited Kingdomcardiovascular healthCKDLife’s Essential 8lifestylePARUK Biobank

Identifiers

PMID37604298
PMCPMC10579043
OpenAlexW4386003355

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.