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Record W3157256053 · doi:10.22317/jcms.v7i2.928

LEP and LEPR Polymorphisms Influences Anthropometric Outcome in Response to 8 Weeks of Combined Training in Obese boys

2021· article· en· W3157256053 on OpenAlexaff
Ali Akbar Jahandideh, Hadi Rohani, Hamid Rajabi, Mohammad Shariatzadeh, Sahar Razmjou

Bibliographic record

VenueJournal of Contemporary Medical Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsGenotypingAlleleLeptinObesityInternal medicineOverweightFat massEndocrinologyAnthropometryMedicineBody mass indexGenotypeAerobic exercisePolymorphism (computer science)BiologyGeneticsGene

Abstract

fetched live from OpenAlex

Objectives: The purpose of the present study was to investigate whether LEP19 G>A and LEPR 668 A>G polymorphisms, would influence the effect of an 8-week combined aerobic and resistance training. Methods: Thirty obese boys (BMIz>+2) aged 11-13 (12.66±0.47) were recruited from three middle schools in Quchan. The changes in body composition parameters and metabolic factors in response to 8-weeks combined aerobic and resistance training program were analyzed regarding LEP and LEPR polymorphism. DNA was extracted from cheek cells donated by the 30 participants and genotyping was carried out using PCR. Results: Our results suggest that carriers of rs2167270G allele and rs1137101A allele were characterized by a greater reduction in body mass and WHR (P< 0.05). Also, a significant decrease was observed in leptin levels in carriers of rs2167270G allele after the training program (P=0.031). Moreover, the LEP and LEPR polymorphisms were associated with changes in lipid profile in response to training. Conclusion: In response to 8 weeks of regular physical activity, obese boys with G (rs2167270) and A (rs1137101) alleles had the best likelihood of losing weight which was associated with a decrease in body mass, fat mass (%), WHR and leptin concentrations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.114
GPT teacher head0.402
Teacher spread0.289 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes1
Has abstractyes

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