Evidence map›Paper›PMID 36551856›Full record

ArticleBiomedicines2022

Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population.

Kexin Ding, Zechen Zhou, Yujia Ma, Xiaoyi Li, Han Xiao, Yiqun Wu, Tao Wu, Dafang Chen

Abstract read
In one paragraph

Article in Biomedicines, 2022. 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. Review
  2. Review
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.

Kexin DingDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Zechen ZhouDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Yujia MaDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Xiaoyi LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Han XiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Yiqun WuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.ORCID 0000-0002-5554-1678
Tao WuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Dafang ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.ORCID 0000-0001-8226-5413

Funding

National Natural Science Foundation of China 81872692National Natural Science Foundation of China 82073642
6 · The paper itself

Abstract

The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177-13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250-5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes

Indexed as

cardiovascular diseaseChinesegenetic basismetabolic risk factormetabolic subtype

Identifiers

PMID36551856
PMCPMC9775185

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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.