Low-Carbohydrate and Ketogenic Diets in Adults with Type 1 and Type 2 Diabetes
Notice bibliographique
Résumé
Low-carbohydrate diets (LCDs) have increasingly gained interest in the diabetes community over the last two decades. The ketogenic diet (KD) is a variation of a LCD which is very-low in carbohydrates (CHO) and high in fat. These diets continue to intrigue individuals despite the lack of strong, long-term evidence. The first part of this thesis aims to better understand the experience of adults with diabetes with following the KD, such as reasons to start the diet, motivators, support systems, sources of information, and challenges. Methods: In this qualitative study, adults living with type 1 (T1D) or type 2 (T2D) diabetes and following the KD for ≥3 months were recruited. 14 semistructured interviews were conducted in-person, audio-recorded, and transcribed. Thematic analysis by concept mapping was conducted. Results: Participants were 54.5±10.1 years old and followed the KD for 6 to 19 (median 5) months; 43% were male and 79% had type 2 diabetes. The main motivation to start the KD was to improve glycemic control or to reduce/stop taking diabetes medications. Social disapproval and lack of support from a health-care professional were the main challenges, which were prevailed by self-reported benefits such as improved glycemic control, weight loss, and increased satiety. Conclusion: A wide range of self-reported benefits strongly motivated individuals to follow the KD despite the lack of safety information and/or support. Furthermore, there is a particular concern of the safety of LCDs, particularly the KD, in individuals with T1D where injected insulin doses need to match CHO intake for proper glycemic control. In addition, higher fat intake, as in LCDs and KDs, may aggravate blood lipids in individuals with T1D, who already have an increased risk of cardiovascular (CV) events. Manuscript 2 aims to evaluate the relationship of LCD, assessed using a LCD score, with glycemic control and CV risk factors in adults with T1D. Methods: This cross-sectional study used data collected in a T1D registry in Québec, including self-reported or measured anthropometric data, history of moderate and severe hypoglycemic episodes, impaired awareness of hypoglycemia (Clarke score ≥4), and biochemical data (hemoglobin A1c (HbA1c), LDL-cholesterol, and non-HDL-cholesterol). 24-hour dietary recalls were collected and ranked by each macronutrient in order to calculate the LCD score. Participants were divided into quartiles (Q) based on LCD scores. Results: 285 adults (aged 48.2±15.0 years; T1D duration of 25.9±16.2 years) were included. Overall, participants reported low carbohydrate and fiber intakes and high fat intake compared to recommendations. Mean carbohydrate intake ranged from 31.2±6.9% (Q1) to 56.5±6.8% of total energy (Q4). Compared to Q4, more people in Q1 reported HbA1c ≤7% (Q1: 53.4% vs Q4: 29.4%; P=0.011). Compared to Q3, more people in Q1 reported no history of severe hypoglycemia (Q1: 60.0% vs Q3: 31.0%; P=0.004). There were no differences between quartiles for frequency of moderate hypoglycemia events (P=0.784), impaired awareness of hypoglycemia (P=0.269) and lipid profile: LDL-cholesterol (P=0.290) and non-HDL-cholesterol (P=0.118). Conclusions: Low carbohydrate intake is associated with a higher probability of reaching HbA1c target and lower frequency of history of severe hypoglycemia, but not with moderate hypoglycemia frequency, impaired hypoglycemia awareness, nor CV risk factors.LCDs appear to have benefits on glycemic control in adults with both T1D and T2D. As LCDs continue to gain interest in the diabetes community, it is important to acknowledge possible adverse effects on glycemic control as well as lifestyle and social challenges when following these diets. Further long-term studies of the effect of LCDs in T1D and T2D are needed to help HCPs establish clinical recommendations for individuals wishing to follow these diets
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».