Aims To investigate glycaemic variability (GV) patterns in patients with type 1 diabetes (T1D), type 2 diabetes (T2D), and latent autoimmune diabetes in adults (LADA). Materials and Methods A total of 842 subjects (510 T1D, 105 LADA, 227 T2D) were enrolled and underwent 1 week of continuous glucose monitoring (CGM). Clinical characteristics and CGM parameters were compared among T1D, LADA, and T2D. LADA patients were divided into two subgroups based on glutamic acid decarboxylase autoantibody titres (≥180 U/mL [LADA‐1], <180 U/mL [LADA‐2]) and compared. The C‐peptide cut‐offs for predicting a coefficient of variation (CV) of glucose ≥36% and a time in range (TIR) > 70% were determined using receiver operating characteristic analysis. Results Twenty‐seven patients (9 T1D, 18 T2D) were excluded due to insufficient CGM data. Sex, diabetes duration and HbA1c were comparable among the three groups. Fasting and 2‐h postprandial C‐peptide (FCP, 2hCP) increased sequentially across T1D, LADA, and T2D. T1D and LADA patients had comparable TIR and GV, whereas those with T2D had much higher TIR and lower GV (p < 0.001). The GV of LADA‐1 was close to that of T1D, while the GV of LADA‐2 was close to that of T2D. CP exhibited the strongest negative correlation with GV. The cut‐offs of FCP/2hCP for predicting a CV ≥ 36% and TIR >70% were 121.6/243.1 and 128.9/252.8 pmol/L, respectively. Conclusions GV presented a continuous spectrum across T1D, LADA‐1, LADA‐2, and T2D. More frequent glucose monitoring is suggested for patients with impaired insulin secretion. Clinical Trail Registration Chinese Clinical Trial Registration (ChiCTR) website approved by WHO; http://www.chictr.org.cn/ ‐ ChiCTR2200065036

BackgroundWe aimed to explore the performance of detrended fluctuation function (DFF) in distinguishing patients with latent autoimmune diabetes in adults (LADA) from type 2 diabetes mellitus (T2DM) with glucose data derived from continuous glucose monitoring.MethodsIn total, 71 LADA and 152 T2DM patients were enrolled. Correlations between glucose parameters including time in range (TIR), mean glucose, standard deviation (SD), mean amplitude of glucose excursions (MAGE), coefficient of variation (CV), DFF and fasting and 2-hour postprandial C-peptide (FCP, 2hCP) were analyzed and compared. Receiver operating characteristics curve (ROC) analysis and 10-fold cross-validation were employed to explore and validate the performance of DFF in diabetes classification respectively.ResultsPatients with LADA had a higher mean glucose, lower TIR, greater SD, MAGE and CV than those of T2DM (P&lt;0.001). DFF achieved the strongest correlation with FCP (r = -0.705, P&lt;0.001) as compared with TIR (r = 0.485, P&lt;0.001), mean glucose (r = -0.337, P&lt;0.001), SD (r = -0.645, P&lt;0.001), MAGE (r = -0.663, P&lt;0.001) and CV (r = -0.639, P&lt;0.001). ROC analysis showed that DFF yielded the greatest area under the curve (AUC) of 0.862 (sensitivity: 71.2%, specificity: 84.9%) in differentiating LADA from T2DM as compared with TIR, mean glucose, SD, MAGE and CV (AUC: 0.722, 0.650, 0.800, 0.820 and 0.807, sensitivity: 71.8%, 47.9%, 63.6%, 72.7% and 78.8%, specificity: 67.8%, 83.6%, 80.9%, 80.3% and 72.4%, respectively). The kappa test indicated a good consistency between DFF and the actual diagnosis (kappa = 0.551, P&lt;0.001). Ten-fold cross-validation showed a stable performance of DFF with a mean AUC of 0.863 (sensitivity: 78.8%, specificity: 77.8%) in 10 training sets and a mean AUC of 0.866 (sensitivity: 80.9%, specificity: 84.1%) in 10 test sets.ConclusionsA more violent glucose fluctuation pattern was marked in patients with LADA than T2DM. We first proposed the possible role of DFF in distinguishing patients with LADA from T2DM in our study population, which may assist in diabetes classification.

Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.