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Record W2119245928 · doi:10.2337/diabetes.52.6.1347

Combined Infusion of Epinephrine and Norepinephrine During Moderate Exercise Reproduces the Glucoregulatory Response of Intense Exercise

2003· article· en· W2119245928 on OpenAlexaff
Stuart H. Kreisman, Jeffrey B. Halter, Mladen Vranić, Errol B. Marliss

Bibliographic record

VenueDiabetes · 2003
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of TorontoRoyal Victoria Hospital
Fundersnot available
KeywordsEpinephrineNorepinephrineInternal medicineMedicineEndocrinologyAnesthesiaDopamine

Abstract

fetched live from OpenAlex

Intense exercise (IE) (>80% O(2max)) causes a seven- to eightfold increase in glucose production (R(a)) and a fourfold increase in glucose uptake (R(d)), resulting in hyperglycemia, whereas moderate exercise (ME) causes both to double. If norepinephrine (NE) plus epinephrine (Epi) infusion during ME produces the plasma levels and R(a) of IE, this would prove them capable of mediating these responses. Male subjects underwent 40 min of 53% O(2max) exercise, eight each with saline (control [CON]), or with combined NE + Epi (combined catecholamine infusion [CCI]) infusion from min 26-40. In CON and CCI, NE levels reached 7.3 +/- 0.7 and 33.1 +/- 2.9 nmol/l, Epi 0.94 +/- 0.08 and 7.06 +/- 0.44 nmol/l, and R(a) 3.8 +/- 0.4 and 12.9 +/- 0.8 mg. kg(-1). min(-1) (P < 0.001), respectively, at 40 min. R(d) increased to 3.5 +/- 0.4 vs. 11.2 +/- 0.8 mg. kg(-1). min(-1) and glycemia 5.2 +/- 0.2 mmol/l in CON vs. 6.5 +/- 0.2 mmol/l in CCI (P < 0.001). The glucagon-to-insulin ratio did not differ. Comparing CCI data to those from 14-min IE (n = 16), peak NE (33.6 +/- 5.1 nmol/l), Epi (5.32 +/- 0.93 nmol/l), and R(a) (13.0 +/- 1.0 mg. kg(-1). min(-1)) were comparable. The induced increments in NE, Epi, and R(a), all of the same magnitude as in IE, strongly support that circulating catecholamines can be the prime regulators of R(a) in IE.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.215
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations56
Published2003
Admission routes1
Has abstractyes

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