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Record W1556370317

Genetic endocrinology of the metabolic syndrome

2009· book· en· W1556370317 on OpenAlexaff
Santiago Rodrı́guez, Tom R. Gaunt, Abdollahi, S Sonnenberg, Inm Day

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMetabolic syndromeDyslipidemiaCandidate geneInsulin resistanceBioinformaticsDiseaseBiologyObesityGeneticsMedicineGeneEndocrinologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Cardiovascular disease and mortality risk are significantly increased in people with metabolic syndrome, a cluster of interrelated metabolic disorders including obesity, insulin resistance, glucose intolerance, dyslipidemia and hypertension. A complex interplay between predisposing and protective factors ultimately determines whether an individual will develop this set of disorders or not. Genetic factors are one of the significant contributors that predispose to, or protect against, each component of the metabolic syndrome. As in other complex diseases and traits, such genetic factors are likely to be multiple and interacting, with individual polymorphisms producing only a moderate effect. The identification of genetic variants influencing the metabolic syndrome is of great importance to understanding pathogenesis, identifying groups of individuals with different relative risk, and developing or improving therapies against this cluster of metabolic disorders. This has greatly stimulated both theoretical and applied genetic research in recent years. A range of new analytical tools has been developed for the dissection of complex traits. Applied genetic analyses have identified large numbers of candidate markers and chromosomal regions (more than 600 for obesity, which represents only one of the disorders of this cluster). In this chapter, the authors present a basic overview of the genetic approaches currently used for the identification of candidate genetic factors involved in the metabolic syndrome. The authors also summarise current evidence suggesting that genetic variants within elements of the endocrine system are directly involved in the risk of the metabolic syndrome. The authors focused their attention on endocrine pathways for which candidate genetic variants have been identified, and they introduced the foundations of a new hypothesis which postulates the involvement of a network of endocrine genetic setpoints as a combined contributor to the risk of the metabolic syndrome.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.234
Teacher spread0.225 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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