Evidence map›Paper›PMID 39605023›Full record

ArticleBMC public health2024

Evaluating a new obesity indicator for stroke risk prediction: comparative cohort analysis in rural settings of two nations.

Feifei Yao, Jing Cui, Yuncheng Shen, Yuting Jiang, Yuanyuan Li, Xiaona Liu, Hongqi Feng, Zhe Jiao, Chang Liu, Fulan Hu and 2 more

Abstract readComparative Study
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 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

23 citing papers in PubMed, 2 syntheses or guidelines 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

12 authors.

Feifei YaoCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Jing CuiCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Yuncheng ShenCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Yuting JiangCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Yuanyuan LiCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Xiaona LiuCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Hongqi FengCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Zhe JiaoCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Chang LiuCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China.
Fulan HuDepartment of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China. hufu1525@163.com.
Wei ZhangCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China. zwhxd@126.com.
Dianjun SunCenter for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Baojian Road 157, Harbin, Heilongjiang Province, 150081, People's Republic of China. hrbmusdj@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile the TyG index has been studied in relation to stroke risk, there is a lack of research integrating fat distribution indicators like Body Roundness Index (BRI) and Fat Mass Index (FMI). Additionally, comparative studies across multiple regions are scarce. This study investigates the association between obesity-related parameters and stroke incidence, examining the mediation effects of multimorbidity, using data from rural areas in China and the United Kingdom.

methodsThis cohort study included 60,685 participants (6,980 from China and 53,705 from UK). The obesity-related parameters were calculated using established formulas. The TyG index was determined as ln [TG (mg/dL) × GLU (mg/dL) / 2]. Additionally, composite indices were created by multiplying the TyG index by BMI, WC, FMI, and RBI to assess obesity-related risks. Cox regression analyses were employed on the relationship between Triglyceride Glucose index related parameters and stroke risk. Multiple mediation analysis was applied to assess the contributions of multimorbidity to obesity indicators in stroke occurrence.

resultsAfter excluding those who developed stroke within two years of enrollment, the Chinese cohort (6,638 subjects, median follow-up 4.33 years) had 237 ischemic and 21 hemorrhagic strokes. The UK cohort (53,631 subjects, median follow-up 13.85 years) had 742 ischemic and 316 hemorrhagic strokes. Chinese residents had lower BMI but higher visceral obesity (BRI), higher prevalence of multimorbidity, and higher stroke incidence compared to UK residents. Cox analyses demonstrated significant associations between BMI/TyG indices and ischemic stroke in both Chinese and UK populations, which diminished after adjusting for multimorbidity. In the Chinese rural cohort, only TyG-BRI (HR:1.13, 95%CI:0.99-1.30) approached statistical significance after full adjustment for mediators. In contrast, in the UK cohort, significant associations persisted for most TyG Index indicators when full adjustment for mediators, including BMI (HR: 1.17, 95% CI: 1.09-1.26), TyG-BMI (HR: 1.16, 95% CI: 1.07-1.26), TyG-WC (HR: 1.13, 95% CI: 1.03-1.25), TyG-FMI (HR: 1.17, 95% CI: 1.07-1.28), and TyG-RBI (HR: 1.15, 95% CI: 1.06-1.24). TyG-BRI also showed the best predictive performance for ischemic stroke in Chinese rural residents (AUC > 0.7) and exhibited an almost linear relationship with ischemic stroke occurrence. Additionally, TyG-BRI presented a U-shaped relationship with the risk of hemorrhagic stroke incidence in the UK (p

conclusionsChinese rural residents exhibit higher levels of visceral obesity compared to residents in UK, leading to greater stroke susceptibility mediated by multimorbidity. These findings underscore the importance of comprehensive management of multimorbidity for stroke prevention. The TyG-BRI may serve as a promising predictor of ischemic stroke incidence among rural community residents.

Indexed as

ObesityRural PopulationStrokeAdultAgedBody Mass IndexChinaCohort StudiesFemaleHumansIncidenceMaleMiddle AgedRisk AssessmentRisk FactorsUnited KingdomMultimorbidityObesityRural residentsStrokeTyG index

Identifiers

PMID39605023
PMCPMC11600789

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Registered trials

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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.