Evidence map›Paper›PMID 30814477›Full record

ArticleObesity facts2019

The Prevalence of Metabolically Healthy and Unhealthy Obesity according to Different Criteria.

Chunxiao Liu, Chunxiu Wang, Shaochen Guan, Hongjun Liu, Xiaoguang Wu, Zhongying Zhang, Xiang Gu, Yanlei Zhang, Yan Zhao, Lap Ah Tse and 1 more

Abstract read
In one paragraph

Article in Obesity facts, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing 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

55 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Observational
  4. Article
  5. Exploring obesity phenotypes: a longitudinal perspective.Reviews in endocrine & metabolic disorders · 2025
    Review
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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

11 authors.

Chunxiao LiuEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Chunxiu WangEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Shaochen GuanEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Hongjun LiuEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Xiaoguang WuEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Zhongying ZhangEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Xiang GuEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Yanlei ZhangEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Yan ZhaoEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China.
Lap Ah TseJockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Xianghua FangEvidence-Based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China, fangxianghua@xwhosp.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveObesity-related disease risks may vary depending on whether the subject has metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO). At least 5 definitions/criteria of obesity and metabolic disorders have been documented in the literature, yielding uncertainties in a reliable international comparison of obesity phenotype prevalence. This report aims to compare differences in MHO and MUO prevalence according to the 5 most frequently used definitions.

methodsA random sample of 4,757 adults aged 35 years and older (male 51.1%) was enrolled. Obesity was defined either according to body mass index or waist circumference, and the definitions of metabolic abnormalities were derived from 5 different criteria.

resultsIn MHO, the highest prevalence was obtained when using the homeostasis model assessment (HOMA) criteria (13.6%), followed by the Chinese Diabetes Society (11.4%), Adult Treatment Panel III (10.3%), Wildman (5.2%), and Karelis (4.2%) criteria; however, the MUO prevalence had an opposite trend to MHO prevalence. The magnitude of differences in the age-specific prevalence of MHO and MUO varied greatly and ranked in different orders. The proportion of insulin resistance for MHO and MUO individuals differed significantly regardless of which metabolic criterion was used.

conclusionThe prevalence of MHO and MUO in the Chinese population varies according to different definitions of obesity and metabolic disorders.

Indexed as

AdolescentAdultAgedAged, 80 and overAsian PeopleBody Mass IndexChinaDiagnosis, DifferentialDiagnostic Techniques, EndocrineFemaleHealth StatusHumansInsulin ResistanceMaleMiddle AgedObesityMetabolic syndromeObesityPhenotypePrevalence

Identifiers

PMID30814477
PMCPMC6465689

What OpenQuestion holds

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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.