Evidence map›Paper›PMID 34163194›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2021

Transition Patterns of Weight Status and Their Predictive Lipid Markers Among Chinese Adults: A Longitudinal Cohort Study Using the Multistate Markov Model.

Xiao Tang, Hongya Zhang, Yanxiang Zhao, Fang Lei, Qigui Liu, Dongmei Hu, Guorong Li, Guirong Song

Open access · goldAbstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 34% of its field
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

5 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Study on Dynamic Progression and Risk Assessment of Metabolic Syndrome Based on Multi-State Markov Model.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022
    Article
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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

8 authors at 2 institutions in 2 countries.

Xiao TangDepartment of Health Statistics, School of Public Health, Dalian Medical University, Dalian City, Liaoning Province, 116044, People's Republic of China.ORCID 0000-0003-1941-9437
Hongya ZhangDepartment of Health Statistics, School of Public Health, Dalian Medical University, Dalian City, Liaoning Province, 116044, People's Republic of China.
Yanxiang ZhaoDepartment of Mathematics, George Washington University, Washington, DC, USA.
Fang LeiDepartment of Health Statistics, School of Public Health, Dalian Medical University, Dalian City, Liaoning Province, 116044, People's Republic of China.
Qigui LiuDepartment of Health Statistics, School of Public Health, Dalian Medical University, Dalian City, Liaoning Province, 116044, People's Republic of China.
Dongmei HuDepartment of Health Statistics, School of Public Health, Dalian Medical University, Dalian City, Liaoning Province, 116044, People's Republic of China.
Guorong LiDepartment of Health Statistics, School of Public Health, Dalian Medical University, Dalian City, Liaoning Province, 116044, People's Republic of China.
Guirong SongDepartment of Health Statistics, School of Public Health, Dalian Medical University, Dalian City, Liaoning Province, 116044, People's Republic of China.
Dalian Medical University · CNGeorge Washington University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundObesity is well recognized as a risk factor for cardiometabolic diseases. The development of obesity is a dynamic process that can be described as a multistate process with an emphasis on transitions between weight states. However, it is still unclear what convenient biomarkers predict transitions between weight states. The aim of this study was to show the dynamic nature of weight status in adults stratified by age and sex and to explore blood markers of metabolic syndrome (MetS) that predict transitions between weight states.

methodsThis study involved 9795 individuals aged 18 to 56 at study entry who underwent at least two health check-ups in the eight-year period of study in the health check-up centre of our institution. Weight, height and biochemical indices were measured at each check-up. The participants were divided into four groups based on age and sex (young male, middle-aged male, young female and middle-aged female groups). A multistate Markov model containing 3 states (healthy weight, overweight and obesity) was adopted to study the longitudinal weight data.

resultsYoung people were more likely to transit between weight states than middle-aged people, and middle-aged people were more resistant to recover from worse states. The mean sojourn time in obesity was greatest in the middle-aged male group (6.23 years), and the predicted rate of obesity beginning with healthy weight was greatest in the young male group (13.7%). In multivariate models, age group and triglyceride (TG) and high-density lipoprotein cholesterol (HDL) levels were significant for specific transitions in females, whereas age group and HDL levels were significant in males. In females, if HDL levels increased 1 mmol/L, the probability of progression from healthy weight to overweight decreased by 37.0% (HR= 0.63), and the probabilities of recovery (overweight to healthy weight and obesity to overweight) increased by 62.0% (HR= 1.62) and 1.23-fold (HR= 2.23), respectively. In males, if TG levels increased 1 mmol/L, the risk of progression from healthy weight to overweight increased by 24.0% (HR= 1.24). Each unit increase in HDL levels was associated with a 0.99-fold (HR= 1.99) increase in the chance of recovery from overweight to healthy weight and with a 0.37-fold (HR= 0.63) decrease in the risk of progression from healthy weight to overweight.

conclusionThe weight status of young people was less stable than that of middle-aged people. Males were more likely to become overweight and more resistant to recover from worse states than females. Young males with healthy weight were more likely to develop obesity than other healthy weight groups. Blood lipid levels, especially HDL, were predictors of weight transitions in adults. Prevention and intervention measures should be applied early.

Indexed as

high-density lipoprotein cholesterollow density lipoprotein cholesterolmultistate Markov modelobesitytriglyceride

Identifiers

PMID34163194
PMCPMC8215687
OpenAlexW3171935472

What OpenQuestion holds

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LicenceCC BY-NC
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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.