Evidence map›Paper›PMID 39994951›Full record

ArticleJournal of clinical hypertension (Greenwich, Conn.)2025

Directional Temporal Relationship Between Hypertension and Non-Alcoholic Fatty Liver Disease: A Cross-Lagged Cohort Study.

Jianmin Tang, Halengbieke Aheyeerke, Xuetong Ni, Tengrui Cao, Xuan Wang, Shuo Chen, Yumei Han, Linrun Kong, Xinghua Yang

Abstract read
In one paragraph

Article in Journal of clinical hypertension (Greenwich, Conn.), 2025. 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
–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

2 citing papers in PubMed.

  1. Article
  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

9 authors.

Jianmin TangSchool of Public Health, Capital Medical University, Beijing, China.
Halengbieke AheyeerkeSchool of Public Health, Capital Medical University, Beijing, China.
Xuetong NiSchool of Public Health, Capital Medical University, Beijing, China.
Tengrui CaoSchool of Public Health, Capital Medical University, Beijing, China.
Xuan WangSchool of Public Health, Capital Medical University, Beijing, China.
Shuo ChenBeijing Medical Examination Center, Beijing, China.
Yumei HanBeijing Medical Examination Center, Beijing, China.
Linrun KongBeijing Medical Examination Center, Beijing, China.
Xinghua YangSchool of Public Health, Capital Medical University, Beijing, China.ORCID 0000-0001-5608-0255

Funding

Beijing Natural Science Foundation of China 7202010
6 · The paper itself

Abstract

The temporal relationship between non-alcoholic fatty liver disease (NAFLD) and hypertension (HTN) remains unclear despite their known association. Using data from the Beijing Health Management Cohort (BHMC) with a 5-year follow-up, we investigated these bidirectional links through Cox proportional hazards regression and a cross-lagged panel model (CLPM), adjusting for confounders. Systolic/diastolic blood pressure (SBP/DBP) and hepatic steatosis index (HSI) were treated as continuous variables to enhance biological interpretability. Cox regression revealed that HTN increased the risk of NAFLD (hazard ratio [HR]: 1.15, 95% confidence interval [CI]: 1.01-1.30, p < 0.05) among participants without NAFLD at baseline, while NAFLD elevated the risk of HTN (HR: 1.11, 95% CI: 1.02-1.21, p < 0.05) among those without HTN at baseline. However, CLPM involving 7349 participants identified a unidirectional temporal relationship from HTN to NAFLD, regression coefficients β

Indexed as

HypertensionNon-alcoholic Fatty Liver DiseaseAdultAgedBeijingBlood PressureChinaCohort StudiesFemaleFollow-Up StudiesHumansMaleMiddle AgedProportional Hazards ModelsRisk FactorsTime Factorscohort studycross‐lagged panel modelhypertensionnon‐alcoholic fatty liver disease

Identifiers

PMID39994951
PMCPMC11850434

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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.