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Record W2808977183 · doi:10.2337/db18-290-or

Relationship between Amount and Type of Dietary Fat, Postprandial Glycemia, and Insulin Requirements in Type 1 Diabetes

2018· article· en· W2808977183 on OpenAlexaboutno aff
Kirstine Bell, Sally Duke, Kylie Alexander, Margaret McGill, Jencia Wong, Gregory Fulcher, Stephen M. Twigg, Jennie Brand‐Miller, Garry M. Steil

Bibliographic record

VenueDiabetes · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
Fundersnot available
KeywordsInsulinGlycemicPostprandialMedicineType 1 diabetesEndocrinologyMealInternal medicineType 2 diabetesDiabetes mellitusBolus (digestion)

Abstract

fetched live from OpenAlex

ADA recommends individuals with type 1 diabetes (T1D) are taught to adjust insulin for dietary fat however optimal adjustments are not yet clear. This study aimed to determine 1) the relationship between the ‘amount’ and ‘type’ of dietary fat and glycemia and 2) the optimal insulin adjustments for dietary fat. Six adults with T1D using insulin pump therapy attended the research clinic on 9 to 12 occasions. On the first 6 visits, participants consumed meals containing 45g CHO with either 0g, 20g, 40g, or 60g fat and either saturated (SFA), monounsaturated (MUFA) or polyunsaturated (PUFA) fat. Insulin was dosed using individual insulin: carbohydrate ratio as a dual-wave 50/50% split over 2h. On subsequent visits, participants repeated the 20 to 60g fat meals with the insulin dose estimated using a model predictive bolus, with up to 2 repeats/meal until glycemic control achieved. With the same insulin dose, mean 5h incremental area under the curve (iAUC) was increased by 46%, 23% and 139% for the 3 fat loads relative to the 0g fat meal (0g: 509 ± 153; 20g: 742 ± 742; 40g: 626 ± 74; 60g: 1216 ± 434mmol/L.min; ns). The type of fat made small but non-significant differences to the 5h iAUC. To achieve glycemic control, on average, participants required 20-60% more insulin, delivered as a dual-wave over 1.25-2h. This study provides foundation for mealtime insulin dosing recommendations for dietary fat in T1D. Disclosure K. Bell: Other Relationship; Self; Novo Nordisk Inc.. S. Duke: None. K.M. Alexander: None. M. McGill: Advisory Panel; Self; Abbott. Speaker's Bureau; Self; AstraZeneca. Advisory Panel; Self; Merck Sharp & Dohme Corp.. Speaker's Bureau; Self; Merck Sharp & Dohme Corp. J. Wong: Speaker's Bureau; Self; Novo Nordisk A/S, AstraZeneca, Eli Lilly and Company. G. Fulcher: Other Relationship; Self; Novo Nordisk Inc., Janssen Scientific Affairs, LLC., Boehringer Ingelheim GmbH, Merck Sharp & Dohme Corp. S.M. Twigg: Advisory Panel; Self; Abbott. Consultant; Self; Abbott. Advisory Panel; Self; Novo Nordisk Inc., Sanofi-Aventis, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company. Speaker's Bureau; Self; AstraZeneca, Merck Sharp & Dohme Corp.. Other Relationship; Self; Abbott. J. Brand-Miller: Board Member; Self; Glycemic Index Foundation. Other Relationship; Self; Hachette Australia. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. Other Relationship; Self; Pam Krauss Books. G.M. Steil: Consultant; Self; Profusa, Eli Lilly and Company.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.341
Teacher spread0.266 · 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 designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

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