Evidence mapPaperPMID 40087481Full record

ArticleScientific reports2025

Pre-frailty is associated with higher risk of gastroesophageal reflux disease: a large prospective cohort study.

Lei Peng, Xueqin Li, Jinfeng Qi, Yangang Shan, Liming Zhang, Zhenqing Yang, Xucheng Wu, George O Agogo, Zuyun Liu, Genxiang Mao and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 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

11 authors.

Lei PengDepartment of Gastroenterology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China.
Xueqin LiCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Jinfeng QiDepartment of Gastroenterology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China.
Yangang ShanDepartment of Outpatient, Shanxian Center Hospital, Heze, 274399, Shandong, China.
Liming ZhangCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Zhenqing YangCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Xucheng WuCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
George O AgogoStatsDecide Analytics and Consulting Ltd, Nakuru, Kenya.
Zuyun LiuCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China. Zuyun.liu@outlook.com.
Genxiang MaoZhejiang Key Laboratory of Geriatrics and Geriatrics Institute of Zhejiang Province, Zhejiang Hospital, Hangzhou, 310030, China. maogenxiang@163.com.
Honglei WuDepartment of Gastroenterology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China. whl20011996@163.com.

Funding

Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province 2020E10004
6 · The paper itself

Abstract

To investigate the prospective association of frailty status, especially the early stage, with the long-term risk of Gastroesophageal reflux disease (GERD) in a large prospective cohort. We included participants who were free of GERD and cancer at baseline and use of aspirin and non-steroidal anti-inflammatory drugs (NSAIDs) from the UK Biobank (UKB). Frailty status was assessed using Fried phenotype including five items (weight loss, exhaustion, low grip strength, low physical activity, slow walking pace) and classified as non-frail, pre-frail, and frail. The outcome was incident GERD. The frailty status was assessed using Cox proportional hazard model. Among 327,965 participants (mean age 56.6 years) at baseline, 151,689 (46.3%) were pre-frail and 14,288 (4.4%) were frail. During a median of 13.5-years of follow-up, 31,027 (9.5%) participants developed GERD. Compared with non-frail participants, pre-frail (hazard ratios [HR] = 1.21, 95% confidence interval [CI] 1.18-1.24) and frail (HR = 1.60, 95% CI 1.52-1.68) participants had significantly higher risks of GERD. Among the five indicators of frailty, exhaustion demonstrated the strongest association with the risk of GERD (HR = 1.42, 95% CI 1.38-1.47). Subgroup analysis showed strong associations among younger (< 60 years), female, high-educated, unemployed participants, and those who had BMI < 18.5 kg/m

Indexed as

FrailtyGastroesophageal RefluxAdultAgedFemaleHumansMaleMiddle AgedProportional Hazards ModelsProspective StudiesRisk FactorsUnited KingdomAgingCohort studyFrailtyGastroesophageal reflux disease

Identifiers

PMID40087481
PMCPMC11909115

What Socratic 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 Socratic graph.