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Record W4407883604 · doi:10.1186/s12889-025-21571-4

Prevalence of metabolic syndrome and its association with selected factors among people with psychiatric conditions in Ethiopia: a systematic review and meta-analysis

2025· review· en· W4407883604 on OpenAlexaboutno aff
Sintayehu Simie Tsega, Ermiyas Alemayehu, Anteneh Mengist Dessie, Denekew Tenaw Anley, Rahel Mulatie Anteneh, Natnael Moges, Melkamu Aderajew Zemene, Asaye Alamneh Gebeyehu, Melaku Ashagrie Belete, Zufan Alamrie Asmare, Natnael Kebede, Ermias Sisay Chanie

Bibliographic record

VenueBMC Public Health · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisBiostatisticsAssociation (psychology)PsychiatryPublic healthEnvironmental healthEpidemiologyMetabolic syndromeSystematic reviewGerontologyMEDLINEObesityInternal medicinePathologyPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Metabolic syndrome is a cluster of metabolic risk factors, including glucose intolerance, dyslipidemia, central obesity, high triglyceride levels, and low levels of high-density lipoprotein. It is the commonest type of co-morbidity among people with psychiatric conditions particularly in low and middle-income countries due to poor health care systems and financial burden. Metabolic syndrome among people with psychiatric conditions may be due to prolonged use of psychiatric medications, diminished quality of life, and personal and behavioral-related factors. Except for single studies with fluctuating reports, there is no nationwide study conducted on the prevalence of metabolic syndrome among people with psychiatric conditions in Ethiopia. Thus, this review aims to estimate the pooled prevalence of metabolic syndrome and its association with selected factors among people with psychiatric conditions in Ethiopia. METHODS: We conducted a thorough search of PubMed, Scopus, Wiley online library, African journals online, and Google Scholar. For analysis, STATA version 14 software was used. A funnel plot and Egger's regression test statistic were used to find the potential reporting bias. A fixed effect model was used to contrast summary effects, odds ratios, and 95% confidence intervals all over research findings. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of each included study. RESULTS: Eight articles were included in the final review after retrieving 9,714 articles through electronic database searching. By using the national cholesterol education adult treatment panel criteria, the pooled prevalence of metabolic syndrome among people with psychiatric conditions in Ethiopia was found to be 37.33% (95%CI: 24.52-50.14). Being female AOR = 2.66; 95% CI: 0.89, 7.92), urban residency (AOR = 2.84; 95% CI: 0.56, 14.45), physical inactivity (AOR = 3.80; 95% CI: 1.61, 8.98), alcohol consumption (AOR = 4.53; 95% CI: 1.62, 12.71) and body mass index higher than the normal range (AOR = 4.66; 95% CI: 1.22, 17.85) were the factors significantly associated with metabolic syndrome among people with psychiatric conditions. According to the review, schizophrenic-form disorder, delusional disorder, major depressive disorder, schizophrenia, bipolar disorder, and schizoaffective disorder were the frequently reported psychiatric conditions. CONCLUSION: This systematic review and meta-analysis revealed that the magnitude of metabolic syndrome among people with psychiatric conditions in Ethiopia was high and female gender, physical inactivity, alcohol consumption, and body mass index higher than the normal range were the factors that determined the occurrence of metabolic syndrome. Thus, policymakers, clinicians, and other concerned stakeholders must reinforce effective strategies in the control, timely screening, prevention, and management of metabolic syndrome among people with psychiatric conditions. PROTOCOL REGISTRATION: PROSPERO CRD42023405293.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.023
Bibliometrics0.0100.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.325
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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