Residual β-Cell Function Is Associated With Longer Time in Range in Individuals With Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Little is known about the influence of residual islet function on glycemic control in type 1 diabetes (T1D). We investigated the associations between residual β-cell function and metrics of continuous glucose monitoring (CGM) in individuals with T1D. RESEARCH DESIGN AND METHODS: In this cross-sectional cohort comprising 489 individuals (64% female, age 41.0 ± 14.0 years), T1D duration was 15.0 (interquartile range [IQR] 6.0-29.0) years. Individuals had a time in range (TIR) of 66% (IQR 52-80%) and a urinary C-peptide-to-creatinine ratio (UCPCR) of 0.01 (IQR 0.00-0.41) nmol/mmol. To assess β-cell function, we measured UCPCR (detectable >0.01 nmol/mmol), and to assess α-cell function, fasting plasma glucagon/glucose ratios were measured. CGM was used to record TIR (3.9-10 mmol/L), time below range (TBR) (<3.9 mmol/L), time above range (TAR) (>10 mmol/L), and glucose coefficient of variance (CV). For CGM, 74.7% used FreeStyle Libre 2, 13.8% Medtronic Guardian, and 11.5% Dexcom G6 as their device. RESULTS: The percentage of patients with T1D who had a detectable UCPCR was 49.4%. A higher UCPCR correlated with higher TIR (r = 0.330, P < 0.05), lower TBR (r = -0.237, P < 0.05), lower TAR (r = -0.302, P < 0.05), and lower glucose CV (r = -0.356, P < 0.05). A higher UCPCR correlated negatively with HbA1c levels (r = -0.183, P < 0.05) and total daily insulin dose (r = -0.183, P < 0.05). Glucagon/glucose ratios correlated with longer TIR (r = 0.234, P < 0.05). CONCLUSIONS: Significantly longer TIR, shorter TBR and TAR, and lower CV were observed in individuals with greater UCPCR-assessed β-cell function. Therefore, better CGM-derived metrics in individuals with preserved β-cell function may be a contributor to a lower risk of developing long-term complications.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".