Effects of Continuous Glucose Monitoring Versus Blood Glucose Monitoring During a Carbohydrate-Restricted Nutrition Intervention in People With Type 2 Diabetes: 6-Month Follow-up Outcomes From a Randomized Clinical Trial
Bibliographic record
Abstract
OBJECTIVES: Low and very-low carbohydrate eating patterns can improve glycemia in people with type 2 diabetes (T2D). Continuous glucose monitoring (CGM) may also help improve glycemic outcomes, like time in range (TIR). This research evaluated differences in diabetes-related outcomes when people with T2D used CGM or blood glucose monitoring (BGM) to support dietary choices and medication management for 6 months during a virtual, medically supervised ketogenic diet program (MSKDP). Three-month primary outcomes are published, and here we report 6-month follow-up outcomes. METHODS: The IGNITE study (Impact of Glucose moNitoring and nutrItion on Time in rangE) randomized participants to use CGM (N = 81) or BGM (N = 82) to support care during 6 months in a MSKDP. Glycemia, diabetes medications, dietary intake, ketones, and weight were assessed at baseline (Base) and month 6 (M6); differences between and within arms were evaluated. RESULTS: Adults (N = 163) with mean (SD) T2D duration of 9.7 (7.7) years and HbA1c of 8.1% (1.2%) participated. From Base to M6, TIR improved from 61% to 87% for CGM and from 63% to 88% for BGM (P < .001), with no difference in changes between arms (P = .99). HbA1c decreased at least 1.3% from Base to M6 in both arms (P < .001). Diabetes medications were deintensified in both arms based on medication effect scores (P < .01). Energy and carbohydrate intake decreased (P < .001) and participants in both arms had clinically meaningful weight loss (P < .001). CONCLUSIONS: The CGM and BGM arms achieved similar and significant improvements in glycemia and other diabetes-related outcomes after 6 months in this MSKDP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".