MétaCan
Menu
← Back to cohort
Record W4405894109 · doi:10.2196/60838

Exploring Molecular Genetics Research on Obesity in Malaysia: Protocol for a Scoping Review

2024· review· en· W4405894109 on OpenAlexvenueno aff
Liyana Ahmad Zamri, Norhashimah Abu Seman, Nur Azlin Zainal Abidin, Siti Sarah Hamzah

Bibliographic record

VenueJMIR Research Protocols · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsChecklistScopusSystematic reviewPopulationGrey literatureData extractionMEDLINEMedicinePublic healthFamily medicineGerontologyPsychologyBiologyEnvironmental healthPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity presents a growing challenge to public health, and its intricate association with genetics continues to be a compelling field of study. In countries such as Malaysia, where diverse genetic backgrounds converge, exploring the molecular genetics of obesity is even more imperative. OBJECTIVE: This scoping review aimed to explore the literature on molecular genetics of obesity in Malaysia. Specifically, we sought to characterize existing studies, identify the genetic determinants of obesity, and assess their association with obesity predisposition in the population. METHODS: This scoping review followed the methodology of the Joanna Briggs Institute and used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist as its guiding framework. Searches were conducted using electronic databases such as PubMed, ScienceDirect, and Scopus, filtering for human studies published until March 2024. Eligible studies included peer-reviewed articles on the Malaysian population irrespective of age or sex. This review excluded review articles, book chapters, non-peer-reviewed conference proceedings, gray literature, and preclinical studies, and the reference lists of the retrieved studies were manually examined to ensure thorough inclusion. The articles were subjected to a 2-stage screening process (title/abstract and full text) conducted by 2 reviewers to assess eligibility. Eligible articles were then extracted following a data extraction framework and organized into a charting table. Only studies investigating the genetics of obesity in Malaysian populations were included. RESULTS: As of March 2024, our extensive search strategy has yielded 572 records. After removing 153 duplicates, 419 records were screened by title and abstract, resulting in 47 selected for full-text review. Of these, 34 were chosen for data extraction and detailed analysis. These studies predominantly involved participants from major ethnic groups (Malay, Chinese, and Indian) recruited from local health centers and university communities. The articles primarily explored the relationship between specific gene variants and obesity or obesity-related health parameters. This ongoing research is expected to be completed with a comprehensive scoping review by April 2025. CONCLUSIONS: This review provides valuable insights into the genetic determinants of obesity in Malaysia, despite limitations such as no quality appraisal being conducted for the included studies and the search strategy being restricted to selected databases, potentially omitting relevant studies. However, this review ensured reliability and reproducibility by adhering to the Joanna Briggs Institute and PRISMA-ScR guidelines. Ultimately, this study advances the understanding of local research and sets the foundation for future molecular genetic studies to improve obesity risk prediction and management in Malaysia's multiethnic population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60838.

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)0.068
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.080
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.070
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0090.014
Bibliometrics0.0170.013
Science and technology studies0.0050.004
Scholarly communication0.0070.008
Open science0.0050.007
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0800.014

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 head0.738
GPT teacher head0.667
Teacher spread0.071 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Research Protocols→Same topicNutrition, Genetics, and Disease→French-language works237,207→