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Record W2160335492 · doi:10.6000/1927-5129.2013.09.65

Obesity and CRP, Adiponectin, Leptin, and Lipid Profile in Saudi Arabian Adolescent Females

2013· article· en· W2160335492 on OpenAlexvenueno aff
Sawsan Hassan Mahassni, Rajaa Sebaa

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2013
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAdiponectinWaistLeptinOverweightObesityBody mass indexInternal medicineMedicineWaist–hip ratioEndocrinologyLipid profileBody fat percentageAdipose tissueCholesterolInsulin resistance

Abstract

fetched live from OpenAlex

Overweight and obesity are increasing tremendously in female Saudi Arabian adolescents. Overweight and obesity lead to many medical risks and affects the immune system. In this study, the effects of obesity on the immune system of 100 Saudi female adolescent students were investigated. Using a blood sample from each subject, the following immune related parameters were determined: concentrations of C-reactive protein (CRP), adiponectin and leptin hormones, and the complete lipid profile. Finally, to assess the body weight status of the subjects and to categorize them, the weight, height, and the waist and hip circumferences were measured to calculate the body mass index (BMI), waist-to-hip ratio (WHR), and the waist circumference (WC). Results show highly significant increases for the CRP and leptin and a highly significant decrease of adiponectin with increasing body weight measured by the three methods. As for the lipid profile, both triglycerides and LDL increased while HDL decreased as body weight increased. Cholesterol did not change with changing body weight measured by the three methods. The findings indicate that obesity seriously affects the immune systems of the subjects and confirm the finding of other researchers that obesity is an inflammatory disease, which explains some health complications associated with obesity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2013
Admission routes1
Has abstractyes

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