Methodological considerations in the assessment of time in range during caloric restriction in type 2 diabetes with obesity

Dear Editor,

We read with interest the article by Kim et al. ¹ evaluating changes in CGM‐derived time in range (TIR) during 12‐week caloric restriction in early type 2 diabetes and obesity. The finding that the C‐peptide index (CPI) strongly predicts TIR, particularly when insulin sensitivity is preserved, offers valuable mechanistic insight. However, we have several methodological concerns that may affect interpretation of the results.

First, although the authors state that CGM data capture rates were calculated, these rates are not reported. The FreeStyle Libre system is an intermittently scanned device, making data completeness heavily dependent on patient scanning frequency. Incomplete or selective scanning can introduce bias, especially for metrics such as time below range and glycemic variability. Consensus guidelines recommend at least 70% sensor use over 14 days for reliable CGM data interpretation ² , and studies indicate that for a 14‐day CGM dataset to be representative of %TIR, at least 70% of data should be available over a minimum of 10 days (R ² > 0.9) ³ . Without reporting the median capture rate or the proportion of participants with adequate data, the reliability of the reported TIR, TAR, and TBR estimates remains unclear. Furthermore, no sensitivity analysis was performed restricting to participants with high capture rates. Future studies should adhere to consensus recommendations for CGM data adequacy and transparently report capture rates.

Second, the multivariable analysis presented in Table 1 (Original text Table 2) ¹ includes only the CPI and Matsuda index as predictors of TIR, despite diabetes duration showing a strong univariable association. Diabetes duration is a well‐established determinant of glycemic control and β‐cell function, and its exclusion from the multivariable model may introduce residual confounding ⁴ . We acknowledge that the original study had a relatively small sample size (n = 24), which reasonably limits the number of covariates that can be included without risking model overfitting. Nevertheless, given that the study enrolled patients with diabetes duration up to 6 years, it would be worth exploring whether adjustment for duration clarifies whether the observed association between CPI and TIR is independent of disease stage, if sample size permits in future larger studies. Reanalysis including duration, and possibly age and baseline HbA1c, in a larger cohort would yield more robust estimates.

Table 1.

Clinical and metabolic correlates of time in range (Original text Table 2) ¹

Variables	β	P	R 2
Univariable analysis
Patient characteristics
Age	−0.120	0.618	0.040
Body mass index	0.959	0.170	0.081
Duration of diabetes	−3.418	0.003	0.246
CGM‐derived glycemic metrics
Mean glucose	−0.468	<0.001	0.813
Coefficient of variation	−0.733	0.015	0.420
OGTT‐based β‐cell function parameters
Fasting glucose	−0.283	<0.001	0.541
Fasting C‐peptide	2.876	0.179	0.370
Fasting insulin	0.350	0.223	0.368
HOMA‐β	0.102	0.002	0.415
HOMA‐IR	0.300	0.735	0.357
Insulinogenic index*	12.367	0.096	0.379
C‐peptide index*	451.037	0.001	0.433
Matsuda index	−0.182	0.520	0.358
Multivariable analysis †
C‐peptide index	446.52	0.001	0.430
Matsuda index	−0.060	0.828

Associations between clinical variables and CGM‐derived time in range (TIR) were evaluated using linear mixed‐effects models with participant‐specific random intercepts, accounting for repeated measurements across study visits.

^*The C‐peptide index and insulinogenic index were calculated to assess the first‐phase insulin secretory response during the OGTT, defined as the ratio of the incremental change in C‐peptide or insulin to the corresponding change in glucose between 0 and 30 min.

^† The multivariable model included the C‐peptide index and the Mat‐suda index.

In summary, while the study by Kim et al. provides important insights into TIR dynamics during caloric restriction, its conclusions would be strengthened by transparent reporting of CGM data capture rates, and by considering diabetes duration in multivariable models when sample size allows. We thank the authors for their valuable contribution and hope these comments assist in refining future analyses.

DISCLOSURE

The author declares no conflicts of interest.

Approval of the research protocol: N/A.

Informed consent: N/A.

Registry and the registration no. of the study: N/A.

Animal studies: N/A.

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no new data were created or analyzed in this study.