MétaCan
Menu
Back to cohort
Record W2146084458

Understanding the etiology of obesity : a multi-faceted approach

2011· dissertation· en· W2146084458 on OpenAlexaboutno aff
Jennifer Shea

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2011
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsObesityBody mass indexEtiologyPopulationMedicineSingle-nucleotide polymorphismDiseaseAdipokineInternal medicineBioinformaticsGenome-wide association studyEndocrinologyGeneticsBiologyGenotypeEnvironmental healthInsulin resistanceGene
DOInot available

Abstract

fetched live from OpenAlex

Obesity, caused by an excessive accumulation of body fat due to a chronic energy surplus, is a serious public health concern with numerous comorbidities. It is a complex disease with many factors contributing to its manifestation; it is thought that obesity results from the action of multiple genes in combination with lifestyle and environmental factors. At the current time, only a fraction of the genes involved in obesity have been identified. The aims of this thesis were first, to characterize the obesity phenotype in the Newfoundland population and second, shed light on its genetic etiology. This goal was achieved using data from two different studies - the large scale, population-based CODING (Complex Diseases in the Newfoundland Population: Environment and Genetics) Study and an intervention-based, 7-day overfeeding study. -- We have shown that body mass index (BMI) misclassifies adiposity status in nearly one-third of individuals compared to the more accurate reference method, dual energy X-ray absorptiometry (DXA). Furthermore, we found that approximately half of obese subjects were metabolically healthy when using DXA criteria, which was significantly higher than previous reports using BMI. Among BMI-defined normal weight individuals, higher body fat percentage (%BF) determined using DXA was associated with a 3-fold increased risk of cardiometabolic disease. To further understand the genetic etiology, a candidate gene, genetic association approach was utilized. We identified two SNPs (rs10882280 and rs11187545) within RBP4, a newly discovered adipokine, that were associated with increased serum HDL cholesterol but no other obesity-related parameter. No significant associations were observed between genetic variation in another novel adipokine, NAMPT, and parameters of glucose and lipid metabolism, obesity, or systemic inflammation. We also sought to explore the response of lean and obese subjects to a 7-day hypercaloric diet. We found that RBP4 was not regulated by the overfeeding challenge but could serve as a predictor of insulin resistance in lean subjects. In addition, 45 novel obesity candidate genes have been identified that were regulated by the nutritional challenge; of these, six were differentially expressed between lean and obese and as such, represent the most promising targets for downstream work related to 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 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)0.004
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.002
Science and technology studies0.0020.005
Scholarly communication0.0070.007
Open science0.0030.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.275
Teacher spread0.200 · 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
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMemorial University Research Repository (Memorial University)Same topicNutrition, Genetics, and DiseaseFrench-language works237,207