Association of glycemic coefficient of variation with reduced appendicular skeletal muscle mass index among patients with type 2 diabetes mellitus: a cross‑sectional study

Objective The coefficient of variation (CV) of glycemia, calculated from continuous glucose monitoring (CGM) data, quantifies intraday glycemic variability. However, its association with reduced appendicular skeletal muscle mass in patients with type 2 diabetes mellitus (T2DM) remains unclear. This study aimed to investigate the association between glycemic CV and reduced ASMI. Methods This retrospective cross-sectional study enrolled 1453 hospitalized T2DM patients aged ≥18 years who required intensive insulin pump therapy. Baseline clinical and laboratory data were collected, and glycemic CV was computed from CGM records. ASMI was measured using bioelectrical impedance analysis. Participants were categorized into the reduced ASMI group (n = 341) and normal muscle mass group (n = 1112) according to ASMI values, and further stratified by quartiles of glycemic CV. Multivariable logistic regression was performed to examine the independent association between glycemic CV and reduced ASMI. Restricted cubic spline (RCS) models were constructed to characterize the shape of this association. Subgroup analyses and multiple sensitivity analyses were conducted to verify the robustness of findings. Results Glycemic CV was significantly higher in participants with reduced ASMI. In the fully adjusted multivariable model, elevated glycemic CV was independently associated with increased odds of reduced ASMI (OR = 1.17, 95% CI: 1.04–1.31, P = 0.008). RCS analysis indicated a statistically significant overall association, with no statistically significant nonlinearity (P = 0.148). Subgroup analyses revealed no significant interactions across strata of age, diabetes duration, gender, HbA1c, and visceral fat area. Sensitivity analysis demonstrated that the association between glycemic CV and reduced ASMI disappeared after additional adjustment for BMI (OR = 0.96, 95% CI: 0.52–1.82, P = 0.902), suggesting that the association was sensitive to adjustment for BMI. Conclusions In hospitalized patients with type 2 diabetes receiving intensive insulin pump therapy, elevated glycemic variability was associated with reduced ASMI in the primary model; however, this association was no longer statistically significant after additional adjustment for BMI, indicating that the association was sensitive to adjustment for BMI. Intra-day glycemic variability may provide clues for exploring the mechanisms underlying skeletal muscle mass maintenance in this population.

Authors

Institutions

Publication Details

Journal
Frontiers in Endocrinology
Published
2026-11-02
DOI
https://doi.org/10.3389/fendo.2026.1970132
Primary Topic
Diabetes Management and Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association of glycemic coefficient of variation with reduced appendicular skeletal muscle mass index among patients with type 2 diabetes mellitus: a cross‑sectional study

Yu Yang, Yang Liu, Yunwei Sun, Ping Hu et al.
3 links
Frontiers in Endocrinology
Diabetes Management and Research
article

Association of glycemic coefficient of variation with reduced appendicular skeletal muscle mass index among patients with type 2 diabetes mellitus: a cross‑sectional study

Yu Yang, Yang Liu, Yunwei Sun, Ping Hu, Yan Chen
article en

Abstract

Objective The coefficient of variation (CV) of glycemia, calculated from continuous glucose monitoring (CGM) data, quantifies intraday glycemic variability. However, its association with reduced appendicular skeletal muscle mass in patients with type 2 diabetes mellitus (T2DM) remains unclear. This study aimed to investigate the association between glycemic CV and reduced ASMI. Methods This retrospective cross-sectional study enrolled 1453 hospitalized T2DM patients aged ≥18 years who required intensive insulin pump therapy. Baseline clinical and laboratory data were collected, and glycemic CV was computed from CGM records. ASMI was measured using bioelectrical impedance analysis. Participants were categorized into the reduced ASMI group (n = 341) and normal muscle mass group (n = 1112) according to ASMI values, and further stratified by quartiles of glycemic CV. Multivariable logistic regression was performed to examine the independent association between glycemic CV and reduced ASMI. Restricted cubic spline (RCS) models were constructed to characterize the shape of this association. Subgroup analyses and multiple sensitivity analyses were conducted to verify the robustness of findings. Results Glycemic CV was significantly higher in participants with reduced ASMI. In the fully adjusted multivariable model, elevated glycemic CV was independently associated with increased odds of reduced ASMI (OR = 1.17, 95% CI: 1.04–1.31, P = 0.008). RCS analysis indicated a statistically significant overall association, with no statistically significant nonlinearity (P = 0.148). Subgroup analyses revealed no significant interactions across strata of age, diabetes duration, gender, HbA1c, and visceral fat area. Sensitivity analysis demonstrated that the association between glycemic CV and reduced ASMI disappeared after additional adjustment for BMI (OR = 0.96, 95% CI: 0.52–1.82, P = 0.902), suggesting that the association was sensitive to adjustment for BMI. Conclusions In hospitalized patients with type 2 diabetes receiving intensive insulin pump therapy, elevated glycemic variability was associated with reduced ASMI in the primary model; however, this association was no longer statistically significant after additional adjustment for BMI, indicating that the association was sensitive to adjustment for BMI. Intra-day glycemic variability may provide clues for exploring the mechanisms underlying skeletal muscle mass maintenance in this population.

Frontiers in EndocrinologyVol. 17
Jilin University (CN), Second Affiliated Hospital of Jilin University (CN)
Good health and well-being
Openalex Percentile: Top 13%
Diabetes Management and Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon