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Nine weeks of bedtime diazoxide is well tolerated and improves β‐cell function in subjects with Type 2 diabetes

2003· article· en· W2041567320 on OpenAlexfundno aff
Elisabeth Qvigstad, M. Kollind, Valdemar Grill

Bibliographic record

VenueDiabetic Medicine · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsnot available
FundersBayer CanadaSt. Olavs Hospital Universitetssykehuset i TrondheimNorges Forskningsråd
KeywordsDiazoxideBedtimeMedicineInternal medicineEndocrinologyMetforminType 2 diabetesGlucagonPlaceboInsulinDiabetes mellitus

Abstract

fetched live from OpenAlex

AIMS: To test whether a bedtime dose of diazoxide can improve daytime beta-cell function without side-effects in Type 2 diabetes. METHODS: A double-blind randomized study was performed in 27 Type 2 diabetic subjects (17 male, 10 female) who were treated with bedtime insulin and metformin. Subjects received either bedtime diazoxide, 100 mg, or placebo for 9 weeks. Duplicate C-peptide glucagon tests were performed before and in the last days of intervention. RESULTS: No side-effects of diazoxide were detected. Treatment with diazoxide did not incur any increase in bedtime insulin. C-peptide responses to glucagon tended to increase: 0.15 +/- 0.06 nmol/l vs. -0.01 +/- 0.04 nmol/l for placebo, P < 0.06 for difference. Corresponding effects on insulin were 66.2 +/- 41.7 pmol/l for diazoxide vs. -84.2 +/- 51.5 for placebo, P < 0.03. Treatment with diazoxide decreased fasting glucagon levels by 41% vs. placebo, P < 0.03. Glycated haemoglobin (HbA1c) levels were not affected, whereas levels of blood glucose post breakfast were higher during diazoxide (1.34 +/- 0.43 mmol/l, P < 0.01 vs. placebo). CONCLUSIONS: Bedtime treatment with diazoxide in Type 2 diabetic subjects on bedtime insulin and metformin has no significant side-effects, does not increase bedtime insulin supplementation, tends to ameliorate beta-cell function but fails to improve metabolic control.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.543

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 studies0.0000.000
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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Citations28
Published2003
Admission routes1
Has abstractyes

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