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Botanical extracts from St. John's Wort inhibit adipogenesis and induce insulin resistance

2009· article· en· W3173607002 on OpenAlexaboutno aff
Zhaleh Amini, Julie Doucet, David Ribnicky, Jacqueline M. Stephens

Bibliographic record

VenueThe FASEB Journal · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Compound Pharmacology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAdipogenesisInsulin resistanceAdipocyteEndocrinologyInternal medicineGlucose uptakeInsulinObesityAdipose tissueBiologyPharmacologyMedicineChemistry

Abstract

fetched live from OpenAlex

Adipocytes are insulin sensitive cells that play a major role in energy homeostasis. Obesity is the primary disease of fat cells and a major risk factor for the development of Type II diabetes, cardiovascular disease, and metabolic syndrome. Obesity and its related disorders results in dysregulation of the mechanisms that control adipocyte gene expression and function. To identify potential novel therapeutic modulators of adipocytes, we screened 425 botanical extracts for their ability to modulate adipogenesis and insulin sensitivity. We observed that less than 2% of the extracts had substantial effects on adipocyte differentiation. Two of the positive hit botanicals we screened that inhibited adipogenesis were extracts from St. John's Wort (SJW). Our studies revealed that two different SJW extracts inhibited adipogenesis as judged by lipid staining and marker gene expression. We also examined the effects of these SJW extracts on insulin sensitivity in mature adipocytes. One of the SJW extracts substantially inhibited insulin sensitive glucose uptake in a time and dose dependent manner. SJW is commonly used in the treatment of depression. However, our studies have revealed that SJW may have a negative impact on adipocyte related diseases by limiting adipocyte differentiation of preadipocytes and significantly inducing insulin resistance in mature fat cells.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.251
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2009
Admission routes1
Has abstractyes

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