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The science of obesity

2025· review· en· W4415874517 on OpenAlexfundno aff
Warren May, Julia H. Goedecke, M Conradie-Smit

Bibliographic record

VenueSouth African Medical Journal · 2025
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsnot available
FundersObesity Canada
KeywordsObesityAdipose tissueLeptinBody mass indexHormoneConcordancePsychosocialWeight lossAppetite

Abstract

fetched live from OpenAlex

KEY MESSAGES • Obesity arises from a complex interplay of genetic, biological, behavioural, psychosocial and environmental factors. • Obesity has a strong genetic component, with twin studies indicating a 50 - 80% concordance in body mass index (BMI) and regional fat distribution. A Swedish study on identical twins raised apart found no correlation between BMI and their adoptive families but a strong correlation with their biological twin, despite being raised in separate households. • The regulation of appetite, body weight and energy balance is highly complex, governed by a network of hormonal signals from the gut, adipose tissue and other organs, as well as neural signals that shape eating behaviours. Many of these signalling pathways are disrupted in people living with obesity. • Since body weight is homeostatically regulated, weight loss triggers physiological adaptations that promote weight regain. These include a decrease in energy expenditure, and hormonal changes that enhance appetite while reducing satiety. • Adipose tissue influences the central regulation of energy homeostasis, and excess adiposity can become dysfunctional, with production of proinflammatory cytokines and associated metabolic health complications. • Individual variations in body composition, fat distribution and function result in a highly variable threshold at which excess adiposity begins to negatively affect health. • Emerging research in obesity science has widened to include brown fat, the gut microbiome, immune system regulation, and the intricate mechanisms that regulate body weight. • Obesity can be classified as primary, secondary and genetic obesity. • In the current management of primary obesity, prevention (the path in) and treatment (the path out) need to be distinctly separated. • Effective primary obesity treatment requires an integrated approach that addresses the non-modifiable cause (increased appetite) together with modifiable contributors (poor diet quality, increased stress, poor sleep, reduced physical activity and increased sedentary behaviour). Behavioural modification and psychological support provide additional benefit. • Effective treatment in genetic and secondary obesity requires treatment of the underlying causes along with modification of the contributors.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.326
Teacher spread0.303 · 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 designOther design
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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