bims-glumda 2026-10-11 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2026–10–11
fifteen papers selected by
Mott Given

Continuous Glucose Monitoring Studies Should Not Be Exempt From the Reproducibility Revolution.
Accuracy of Continuous Glucose Monitoring During Hospitalization in People With Type 2 Diabetes Requiring Dialysis.
Improving Inpatient Continuous Glucose Monitoring Documentation Through Targeted Nursing Education.
The Case for Evaluating Continuous Glucose Monitoring as a Diagnostic Tool for Diabetes.
Empirical transition probability modeling of continuous glucose monitoring data.
Impact of Ascorbic Acid and Exercise on Accuracy of a Glucose Monitoring System with GDH-FAD Technology in Adult Patients with Type 1 Diabetes Mellitus: A Double-Blinded, Randomized Trial.
Glycaemic Variability and C-Peptide in Bangladeshi Children, Adolescents, and Young Adults With Diabetes: A Prospective Study.
Expanding continuous glucose monitoring's potential from real-time safety to lifestyle support in type 1 diabetes exercise: a proof-of-concept study among adults with low baseline exercise levels.
Continuous Glucose Monitoring and Long-Term Glycemic and Kidney Outcomes.
CGM has Similar Predictive Performance to OGTT for Monitoring Early-Stage Type 1 Diabetes.
CGM-derived disposition index estimates beta cell function in individuals with islet autoimmunity who do not require insulin treatment; changes in the disposition index precede changes identified by CGM metrics 1 year later.
Continuous Glucose Monitoring-Guided Insulin Optimization in Patients Receiving Nocturnal Parenteral Nutrition.
Individualized Therapy Optimization for Type 2 Diabetes: Retrospective Study of Model Development, Internal Validation, and Physician Evaluation.
Association between Continuous Glucose Monitoring and Risk of Acute Diabetes-Related Complications in Pediatric Type 1 Diabetes Mellitus: A Nationwide Cohort Study (Diabetes Metab J 2026 Apr 22 [Epub]. https://doi.org/10.4093/dmj.2025.0794).
Association between Continuous Glucose Monitoring and Risk of Acute Diabetes-Related Complications in Pediatric Type 1 Diabetes Mellitus: A Nationwide Cohort Study (Diabetes Metab J 2026 Apr 22 [Epub]. https://doi.org/10.4093/dmj.2025.0794).

Diabetes Care. 2026 Oct 09. pii: dc261932. [Epub ahead of print]

Continuous Glucose Monitoring Studies Should Not Be Exempt From the Reproducibility Revolution.

John F Mulley.

DOI: https://doi.org/10.2337/dc26-1932
J Diabetes Sci Technol. 2026 Oct 07. 19322968261494587

Accuracy of Continuous Glucose Monitoring During Hospitalization in People With Type 2 Diabetes Requiring Dialysis.

Ray Wang, Mervyn Kyi, Sasha Rawlinson, Tess Cohen, Rahul D Barmanray, Karen M Dwyer, Spiros Fourlanos.

DOI: https://doi.org/10.1177/19322968261494587
Endocr Pract. 2026 Oct 07. pii: S1530-891X(26)01261-9. [Epub ahead of print]

Improving Inpatient Continuous Glucose Monitoring Documentation Through Targeted Nursing Education.

Leticia De Mattei, Mary Kashat, Berk Celik, Daniel Shelden, Michael Brennan.

   OBJECTIVE: To evaluate whether a targeted nursing education intervention improved adoption of a standardized electronic medical record (EMR) documentation workflow for patient-reported continuous glucose monitoring (CGM) values among hospitalized adults with diabetes.
METHODS: We conducted a retrospective pre-post quality improvement study at a 1,131-bed academic institution. Adults aged ≥18 years with type 1 or type 2 diabetes admitted with a functional CGM were included. Pre-intervention data (7/21/24-11/21/24) were compared with a 5-month post-intervention period (11/21/24-04/21/25) following implementation of a nursing practice alert and education initiative. The primary outcome was the proportion of patients with at least 1 CGM value documented in the designated "Patient Performed Glucose" flowsheet.
RESULTS: A total of 224 patients were included (34% type 1, 66% type 2 diabetes) in the primary analysis, and 222 patients were included in the pre-post comparison (94 pre-intervention and 128 post-intervention). Mean age was 63±15 years and median HbA1c 7.4% (IQR 6.4-8.4). Documentation rates increased from 54.3% pre-intervention to 68.8% post-intervention (χ2 p=0.0389). Average documented CGM readings per day did not differ pre- and post-intervention. Among documented readings, level 2 hypoglycemia decreased from 0.47% to 0.19%, whereas hyperglycemic readings increased from 30.8% to 35.1%.
CONCLUSION: A targeted, low-cost nursing education intervention was associated with improved adoption of a standardized inpatient CGM documentation workflow. Further studies are needed to establish optimal documentation practices and determine their clinical impact.

Keywords:  continuous glucose monitoring; diabetes; inpatient care; nursing education; quality improvement

DOI:  https://doi.org/10.1016/j.eprac.2026.09.114
J Diabetes Sci Technol. 2026 Oct 08. 19322968261493542

The Case for Evaluating Continuous Glucose Monitoring as a Diagnostic Tool for Diabetes.

David C Klonoff, Viral N Shah, Guillermo E Umpierrez, Curtiss B Cook, G Alexander Fleming, Alberto Gutierrez, Lutz Heinemann, Julia K Mader, Emma Y Zhang, David Kerr.

  Continuous glucose monitoring (CGM) provides information about real-time glycemic exposure that is not captured by measurements of fasting plasma glucose (FPG), changes in glycemia during oral glucose tolerance testing (OGTT), or Hemoglobin A1c (HbA1c). We propose that (1) CGM can identify dysglycemia earlier than existing approaches, (2) CGM may serve as an adjunctive diagnostic tool, and (3) CGM may eventually support independent diagnostic criteria. The strongest case is for Stage 2 type 1 diabetes (T1D), because this stage represents a critical opportunity for intervention before progression to Stage 3 disease. CGM as a diagnostic tool could identify individuals at risk of progression to Stage 3 T1D, particularly those with islet autoantibodies, by detecting progression of dysglycemia, supporting disease staging, and identifying candidates for confirmatory testing, closer monitoring, or future prevention trials. Beyond T1D, CGM might also prove to be a reliable diagnostic test for type 2 diabetes (T2D) in selected populations. Overall, there is a need for prospective clinical studies to compare the value of using CGM-derived metrics directly with existing diagnostic standards, including OGTT and HbA1c. For both T1D and T2D, earlier diagnosis by CGM has the potential to improve patient outcomes by creating opportunities for prevention and treatment early in the natural history of the disease. This article presents an outcome-based framework for deriving and validating CGM-based diagnostic thresholds. The framework emphasizes that future diagnostic criteria should be based on clinically meaningful outcomes, rather than concordance with existing tests.

Keywords:  continuous glucose monitoring; diabetes diagnosis; diagnostic algorithms; dysglycemia; glycemic variability

DOI:  https://doi.org/10.1177/19322968261493542
PLoS One. 2026 ;21(10): e0358361

Empirical transition probability modeling of continuous glucose monitoring data.

Yuanzhi Yu, Margaret Banker, Jennifer Sherr, Denise M Scholtens.

INTRODUCTION: Continuous glucose monitoring (CGM) produces rich time series data. Standard metrics such as time-in-range summarize glycemia but miss the sequence of changes. We aimed to characterize glucose dynamics using empirical transition probability techniques.
MATERIALS AND METHODS: Using publicly available data for 244 participants who enrolled in the Insulin-Only Bionic Pancreas (IOBP2) randomized clinical trial, we mapped each CGM reading to one of five states: < 54, 54-69, 70-180, 181-250, or >250 mg/dL. We counted state changes between consecutive readings collected every 5 minutes. For each participant, we built a 5 × 5 transition probability matrix and averaged matrices within comparator groups. Statistical inference using estimated transition probability matrices was compared to inferences based on standard metrics including mean glucose, standard deviation, coefficient of variation, time-in-range metrics, Glycemia Risk Index (GRI), Mean of Daily Differences (MODD), and Mean Amplitude of Glycemic Excursions (MAGE).
RESULTS: Transition probability analysis showed more frequent moves from hyperglycemia (181-250 or >250 mg/dL) to the in-range (70-180 mg/dL) state for the bionic pancreas that was evaluated in the IOBP2 trial compared to control. This pattern appeared in both adults and in children and persisted through the trial. Conventional CGM metrics also showed treatment differences in mean glucose, GRI, and time in range; MODD, MAGE, and coefficient of variation showed less consistent differences between treatment groups.
CONCLUSION: Transition probability matrices provide an informative summary of glucose dynamics that complements established CGM metrics. In IOBP2, this approach highlighted improved patterns of recovery from hyperglycemia with the bionic pancreas compared with control. Future studies should determine whether transition-based summaries are associated with clinically meaningful outcomes.

DOI: https://doi.org/10.1371/journal.pone.0358361
Diabetes Ther. 2026 Oct 09.

Impact of Ascorbic Acid and Exercise on Accuracy of a Glucose Monitoring System with GDH-FAD Technology in Adult Patients with Type 1 Diabetes Mellitus: A Double-Blinded, Randomized Trial.

Ronald Brazg, Tina Mitchell, Jiangfeng Fei, Jiyun Zheng, Fei Gao, Ao Gao, Siting Zhu, Frank Flacke, Leon Shi, Poul Strange.

   INTRODUCTION: The performance of a new continuous glucose monitoring system (CGM) with flavin adenine dinucleotide-dependent glucose dehydrogenase (GDH-FAD) technology in adults with type 1 diabetes mellitus (T1DM) was assessed, with a focus on the impact of ascorbic acid and exercise on the accuracy of glucose measurements relative to Yellow Springs Instrument (YSI) 2300 control.
METHODS: Adults with T1DM aged 18-65 years using a stable glucose management treatment regimen for at least 3 months with multiple daily injections or continuous subcutaneous insulin infusion participated in this study at a single center. Participants attended a screening visit and three in-clinic visits for frequent blood sampling with blood glucose measurements using YSI measurements as the reference measurement as endorsed by the Food and Drug Administration (FDA). Participants had two meals at each clinic visit leading to pronounced glucose excursions. On the second and third in-clinic visits, participants performed moderate exercise in between the two meals and were randomized to receive 1000 mg of ascorbic acid orally at either of those visits. Accuracy evaluation included the proportion of CGM values within 15% of YSI glucose values > 100 mg/dL or within 15 mg/dL of YSI values ≤ 100 mg/dL (%15/15), along with %20/20, %30/30, and %40/40 agreement rates (AR) across CGM ranges. The mean absolute difference (MAD) and mean absolute relative difference (MARD) with and without ascorbic acid and exercise, as well as their differences, are presented across glucose ranges.
RESULTS: Data from 16 participants were analyzed. In the overall CGM range (35-450 mg/dL), the %20/20 AR was 92.0%. The overall changes in MAD between the exercise/nonexercise and the ascorbic acid/nonascorbic acid conditions were -1.3 mg/dL and -1.1 mg/dL. The overall changes in MARD between those two sets of conditions were -1.5% and -1.6%, respectively, none of which were considered to be clinically relevant. There were no serious adverse events.
CONCLUSIONS: This study provides evidence that neither exercise nor ascorbic acid results in any clinically significant interference on glucose measurements using the iCan CGM system.
TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT05348928.

Keywords:  Accuracy; Ascorbic acid; Continuous glucose monitoring; Exercise; Interference; Type 1 diabetes mellitus; iCan i3 CGM

DOI:  https://doi.org/10.1007/s13300-026-01918-9
Endocrinol Diabetes Metab. 2026 Nov;9(6): e70347

Glycaemic Variability and C-Peptide in Bangladeshi Children, Adolescents, and Young Adults With Diabetes: A Prospective Study.

Ajmina Hasan Flabe, Bedowra Zabeen, Shatil Amirat, Jing Luo.

   OBJECTIVES: To characterise the magnitude of glycaemic variability and to examine the factors associated with it, with particular attention to residual beta-cell function measured by fasting C-peptide, in children, adolescents, and young adults with diabetes in Bangladesh.
DESIGN AND SETTING: Prospective observational study at the BADAS Paediatric Diabetes Care and Research Centre, BIRDEM, Dhaka, using 14 days of blinded professional continuous glucose monitoring (CGM). Participants were enrolled between March and November 2024.
PARTICIPANTS: Ninety-seven participants aged 1-25 years (68 with Type 1 and 29 with Type 2 diabetes) had at least seven valid CGM days and a paired HbA1c within 2 weeks of CGM initiation. Fasting C-peptide was available for 46 participants.
MAIN OUTCOME MEASURES: The coefficient of variation (CV) of glucose as the primary metric of variability; time in range, time above range, and time below range as secondary metrics; and independent predictors of CV on multivariable regression.
RESULTS: Mean CV was 39.4% (SD 9.2), and 59 of 97 participants (60.8%) were at or above the recommended target of 36%. CV was higher in Type 1 than Type 2 diabetes (41.9% vs. 33.3%). Lower fasting C-peptide was independently associated with higher CV after adjustment for diabetes type, age, and insulin dose (-1.94 percentage points per ng/mL, 95% CI -3.35 to -0.53; model R-squared 0.52), with a graded relationship across pre-specified C-peptide categories for CV, time in range, and time below range.
CONCLUSIONS: Glycaemic variability substantially exceeds international targets in this setting. Fasting C-peptide, available for fewer than half of the cohort, showed a graded association with variability that is best regarded as hypothesis-generating; if confirmed in larger studies, it could offer a low-cost means of identifying children at highest variability risk where CGM access is limited.

Keywords:  Bangladesh; C‐peptide; coefficient of variation; continuous glucose monitoring; glycaemic variability; low‐ and middle‐income countries; paediatric diabetes

DOI:  https://doi.org/10.1002/edm2.70347
Hum Factors Health. 2026 Dec;10 100154

Expanding continuous glucose monitoring's potential from real-time safety to lifestyle support in type 1 diabetes exercise: a proof-of-concept study among adults with low baseline exercise levels.

Young In Chung, Garrett I Ash, Sangchoon Jeon, Reshma Ramachandran, Matthew Stults-Kolehmainen, Elias K Spanakis, Lisa M Fucito.

   Objective: This proof-of-concept study sought to explore how data from CGM and exercise can be aggregated and analyzed to capture clinically meaningful glycemic patterns and actionable feedback. To this end, we examined how exercise days were associated with glucose outcomes derived from continuous glucose monitoring (CGM) among adults with type 1 diabetes (T1D) who had low baseline exercise levels.
Methods: Secondary analyses were conducted on data from a 10-week digital app-based exercise intervention. Participants (N = 17; 52.9% Female; M [SD] age = 43.4 years [13.5]; M [SD] diabetes duration = 23.1 years [15.2]) received an exercise app with videos, text-based exercise coaching, a web-based self-monitoring diary, and a monthly session with personalized integrated feedback of CGM and other psychosocial data (exercise, mood, and sleep). To identify meaningful post-exercise periods, glucose patterns were examined across the 24-hour period, followed by iterative testing of different analytic windows. The final models focused on evening and overnight periods (18:00-06:00) to capture immediate and delayed glycemic effects. CGM-derived outcomes - mean glucose, time in range (TIR; 70-180 mg/dL), time in hyperglycemia (≥250 mg/dL), and time in hypoglycemia (≤70 mg/dL) - were analyzed using generalized linear mixed models. Logistic mixed models estimated odds of hyperglycemia and hypoglycemia, adjusting for age, diabetes duration, BMI, weekday, and HbA1c.
Results: Relative to non-exercise days, exercise days were associated with lower mean glucose (-3.13 mg/dL, p = .001) and higher TIR (3.36%, p < .001), corresponding to ~25 additional minutes per night in range. Exercise days were associated with reduced time in hyperglycemia (-2.16%, p < .001) and lower odds of hyperglycemia (OR = 0.68 [0.58-0.78], p < .001). Exercise was associated with a small but statistically significant increase in percent time below 70 mg/dL (0.81%, p = .002); however, the interval-level odds model did not show a statistically significant increase in hypoglycemia risk (OR = 0.85 [0.71-1.00], p = .057).
Conclusions: In this exploratory study, exercise was associated with greater evening and overnight glucose stability among adults with T1D who were physically inactive at baseline (i.e., 0 min of recorded exercise per week). These preliminary findings suggest the analytic feasibility of linking CGM and exercise data to identify clinically meaningful glycemic patterns. This lays the groundwork for an integrated behavioral support system that provides interpretable, actionable feedback to support patients' diabetes self-management.

Keywords:  Continuous glucose monitoring; Exercise; Type 1 diabetes; Wearable electronic devices

DOI:  https://doi.org/10.1016/j.hfh.2026.100154
JAMA Netw Open. 2026 Oct 01. 9(10): e2637698

Continuous Glucose Monitoring and Long-Term Glycemic and Kidney Outcomes.

Andrew J Karter, Melissa M Parker, Holly Finertie, Joshua R Nugent, Lisa K Gilliam, Ajit Mahapatra.

Importance: Real-time continuous glucose monitoring (rtCGM) improves short-term glycemic control in diabetes. However, scant evidence exists regarding rtCGM's longer-term benefits for glycemia or kidney health.
Objective: To evaluate whether rtCGM initiation and sustained use is associated with improved glycemic control and kidney markers in high-risk adults with type 2 diabetes (T2D).
Design, Setting, and Participants: This comparative effectiveness research study compared glycemic control and kidney markers among high-risk adults with T2D initiating and sustaining use of rtCGM (rtCGM arm) vs no initiation (controls) using inverse probability-weighted marginal structural models to estimate per-protocol outcomes of an emulated target trial. Target trial eligibility criteria were as follows: age of 19 years or older, T2D, no prior CGM use, and poor glycemic control (hemoglobin A1c [HbA1c] >8%) or emergency department or inpatient treatment for hypoglycemia. Baseline was defined by the rtCGM initiation date and applied to temporally matched CGM-naive controls who met the eligibility criteria, sampled monthly from risk sets from January 1, 2015, to June 30, 2023. Data were censored at nonadherence to the target trial protocol (eg, rtCGM arm discontinued use or controls initiated CGM), coverage gaps, death, or after 36 months of follow-up. Data analysis was performed July 2025 to March 2026.
Exposure: rtCGM use.
Main Outcomes and Measures: Cumulative differences for HbA1c, urinary albumin-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR).
Results: The study included 25 648 individuals (2771 in the rtCGM arm and 22 877 in the control arm) who were target trial eligible (mean [SD] age, 61.5 [13.1] years; 13 885 [54.7%] male). The mean (SD) baseline HbA1c level was 9.5% (1.5%). After 36 months of follow-up, the rtCGM arm had an approximately 0.8-percentage point lower HbA1c level than controls (7.83% [95% CI, 7.55%-8.11%] vs 8.58% [95% CI, 8.49%-8.66%], P < .001). There was substantial attrition during follow-up; 1228 (44.3%) in the rtCGM arm were censored due to discontinuation and 4284 (18.7%) in the control group were censored due to CGM initiation. There were no significant differences in UACR between arms initially; however, by the end of 36 months, rtCGM initiators had a UACR of 125.3 mg/g (95% CI, -196.6 to -54.0 mg/g; P = .001) lower than that of controls. There were no differences in eGFR during follow-up.
Conclusions and Relevance: In this comparative effectiveness study using a target trial emulation, sustained, longer-term use of rtCGM was associated with durable improvements in glycemic control and later regression of UACR. By addressing glycemic deterioration, rtCGM may extend the time before pharmacologic escalation is required and potentially delay diabetic kidney disease progression.

DOI: https://doi.org/10.1001/jamanetworkopen.2026.37698
J Clin Endocrinol Metab. 2026 Oct 07. pii: dgag394. [Epub ahead of print]

CGM has Similar Predictive Performance to OGTT for Monitoring Early-Stage Type 1 Diabetes.

Andrea K Steck, Fran Dong, Laura Pyle, Tim Vigers, Darrell M Wilson, Kimber M Simmons, Marian J Rewers, Brigitte I Frohnert.

   OBJECTIVE: Compare continuous glucose monitoring (CGM) and other metabolic markers in progression to stage 3 type 1 diabetes (T1D) in early-stage T1D and evaluate CGM time above 140 mg/dl (TA140) across different stages.
METHODS: In a combined dataset from three prospective studies, 152 early-stage T1D individuals had baseline CGM, oral glucose tolerance test (OGTT) and HbA1c with 54 (36%) progressing to stage 3. ROC curves were generated to compare the areas under the curve (AUC) for stage 3 prediction. Risk of progression was estimated by Accelerated Failure Time (AFT) and Kaplan-Meier analyses.
RESULTS: TA140 > 10% and 2 hr glucose>140 mg/dl had the exact same specificity (83%), negative predictive value (88%) and sensitivity (55%) for stage 3 prediction at 2 years. In univariable AFT, the HRs for stage 3 at 5 years were significant for all CGM metrics tested (all p<=0.008), while significant OGTT metrics included 2hr-glucose, AUC_glucose, AUC_C-peptide and peak_C-peptide (all p<=0.021). In multivariable AFT, younger age, male sex, HbA1c>=5.7%, TA140 > 15% and 2-hr glucose>140 mg/dl were strong predictors of accelerated progression to stage 3. Cumulative incidence curves for stage 3 were similar for TA140 > 15%, HbA1c>=5.7% and 2hr-glucose>140 mg/dl. Among 406 longitudinal CGMs, participants at stage 3 had higher median TA140 (17.2%, IQR 7.9-33.2) compared to stage 2 (8.6%, IQR 3.8-15.2) and stage 1 (3.2%, IQR 1.3-7.7) (all pairwise p<=0.024).
CONCLUSIONS: In this largest dataset of individuals at early-stage T1D, we define CGM-derived TA140 criteria for the various stages. Furthermore, we show that TA140 has similar performance to a 2-hour OGTT value for predicting stage 3.

Keywords:  CGM; early-stage type 1 diabetes; metabolic markers; prediction of type 1 diabetes; type 1 diabetes staging

DOI:  https://doi.org/10.1210/clinem/dgag394
Diabetologia. 2026 Oct 07.

CGM-derived disposition index estimates beta cell function in individuals with islet autoimmunity who do not require insulin treatment; changes in the disposition index precede changes identified by CGM metrics 1 year later.

Hadija Marchiori, Michele Schiavon, Eileen Tichy, Michele Alguard, Marcia DeSousa, Simone Del Favero, Jason L Gaglia, Jennifer L Sherr, Chiara Dalla Man, Alfonso Galderisi.

   AIMS/HYPOTHESIS: Monitoring of beta cell function in individuals with islet autoimmunity who do not require insulin treatment (preclinical type 1 diabetes) is normally done via OGTTs, posing major challenges given the invasiveness of the test. Here, we validated a disposition index (DI) derived from continuous glucose monitoring (CGM) metrics (DICGM), which is a proxy of beta cell function, against the gold-standard OGTT DI (DIMM), in a cohort of individuals with one or more islet autoantibodies who do not require insulin treatment.
METHODS: Participants underwent a 3 h 10-point OGTT with measurement of glucose, insulin and C-peptide while wearing a blinded CGM. The sensor was started 24 h prior to the OGTT. DICGM was computed using CGM data during the 3 h OGTT and two blood glucose calibrations, and compared with the DI from the oral minimal model (DIMM), which uses plasma glucose, insulin and C-peptide. The association between the baseline DICGM and duration of glucose levels above 7.8 mmol/l (140 mg/dl) (TA140) at 1 year was explored.
RESULTS: Twenty-four participants (median age 16.8 years [IQR 13.5-27.7] ) were enrolled, of whom 18 completed both the OGTT (ten with pre-stage 1, seven with stage 1 and one with stage 3a type 1 diabetes) and CGM at baseline, and had repeat CGM 1 year later. The baseline DICGM was highly correlated with the DIMM (r=0.90, p<0.001). Lower baseline DICGM was associated with higher 1 year TA140 (r=-0.61, p=0.014) and higher 1 year mean sensor glucose (r=-0.62, p=0.020).
CONCLUSIONS/INTERPRETATION: Beta cell function can be estimated using CGM during a glucose load. DICGM correlates with a 1 year clinically relevant outcome (TA140) in individuals with islet autoimmunity who do not require insulin therapy.

Keywords:  Beta cell function; CGM; Continuous glucose monitoring; Digital biomarkers; Model identification; Preclinical T1D; Stage 1 T1D; Stage 2 T1D; Type 1 diabetes

DOI:  https://doi.org/10.1007/s00125-026-06895-z
Diabetes Care. 2026 Oct 08. pii: dc260936. [Epub ahead of print]

Continuous Glucose Monitoring-Guided Insulin Optimization in Patients Receiving Nocturnal Parenteral Nutrition.

Laurianne Kerlogot, Vincent Rigalleau, Gauthier Pellet, Constance Chevrier, Coraline Baldé, Eva Wilsius, Marion Pidoux, Marion Jeamart, Florian Poullenot.

OBJECTIVE: To evaluate whether a continuous glucose monitoring (CGM) system (CGMS)-guided insulin optimization can improve glucose control and nutritional status in insulin-treated patients receiving long-term nocturnal parenteral nutrition (PN).
RESEARCH DESIGN AND METHODS: In this prospective single-center study, 12 insulin-treated adults with chronic intestinal failure undergoing nocturnal cyclic PN underwent 30-day CGM assessment before and after a diabetologist-led optimization of insulin therapy.
RESULTS: Glucose profiles improved after the intervention: less time >250 mg/dL (27% to 17%; -139 ± 113 min/day; P = 0.02), more time in range (44% to 59%; +206 ± 118 min/day; P = 0.002), and without more hypoglycemia. HbA1c decreased by -0.7% (-8 mmol/mol; P = 0.04), and glycosuria resolved in all affected patients (P = 0.03). Body weight increased by +5 kg (interquartile range 4-7.75; P = 0.006), as did serum albumin (+8 g/L; P = 0.006). Malnutrition resolved among 8 of 11 participants (P < 0.05).
CONCLUSIONS: CGMS-guided insulin optimization was associated with improvements in glucose control and nutritional status.

DOI: https://doi.org/10.2337/dc26-0936
JMIR Form Res. 2026 Oct 09. 10 e92877

Individualized Therapy Optimization for Type 2 Diabetes: Retrospective Study of Model Development, Internal Validation, and Physician Evaluation.

Arina Lozhkina, Camillo Dario Piazza, Zeina Gabr, Maurice Rupp, David Herzig, Lia Bally, Andre Jaun.

   Background: Type 2 diabetes is a widespread chronic condition in which blood glucose and body weight management constitute essential therapeutic targets. Emerging technologies have the potential to aid complex therapeutic pharmacotherapy choices that are optimally tailored to individual needs.
Objective: In this study, we developed and evaluated an AI model combining guidelines with clinical features and continuous glucose monitoring (CGM) to optimize therapeutic decision-making.
Methods: Therapeutic guidelines were first encoded using a rule-based model and trained on a feed-forward neural network to predict the probability of therapeutic success for individual treatment recommendations. This approach relied on real-world evidence from a specialist diabetes outpatient clinic, using historical clinical data generated between 2009 and 2023. We used data from 533 patients with a diagnosis of type 2 diabetes and complete baseline data for weight and hemoglobin A1c within relevant therapy windows, resulting in a total of 853 treatment regimens. Transfer learning was used to optimize for glucose-lowering therapies that led to successful treatment outcomes, defined as an absolute 0.3% reduction in hemoglobin A1c (when it is over 6.5%) without weight gain in patients with a BMI over 28 kg/m2. Recommendations that deviated from the guidelines were described using Shapley values and tested in digital twins for statistical significance. Four CGM-derived glucose-insulin response dynamic factors served as additional biomarkers.
Results: Dual glycemic and weight targets were achieved in actual clinical practice in 51.2% (131/256) of cases, increasing to 54% (20/37) when clinical guidelines were followed. After selecting outcomes in the test set that followed individualized recommendations, this increased further to 58% (21/36) when using only phenotypic markers and to 65% (22/34) when adding CGM-derived dynamic factors.
Conclusions: Tested on the limited number of patients available, our findings show that our AI model was associated with improved retrospective outcomes compared to the guidelines in complex type 2 diabetes cases by integrating multiple data sources, drawing on experiential clinical insights, and selecting treatments most likely to meet each patient's clinical targets for glucose and weight control. Future research is needed with a larger dataset.

Keywords:  AI; artificial intelligence; continuous glucose monitoring; glucose-lowering pharmacotherapy; precision medicine; type 2 diabetes

DOI:  https://doi.org/10.2196/92877
Diabetes Metab J. 2026 Oct 08.

Association between Continuous Glucose Monitoring and Risk of Acute Diabetes-Related Complications in Pediatric Type 1 Diabetes Mellitus: A Nationwide Cohort Study (Diabetes Metab J 2026 Apr 22 [Epub]. https://doi.org/10.4093/dmj.2025.0794).

Junfeng Yuan.

DOI: https://doi.org/10.4093/dmj.2026.0461
Diabetes Metab J. 2026 Oct 08.

Association between Continuous Glucose Monitoring and Risk of Acute Diabetes-Related Complications in Pediatric Type 1 Diabetes Mellitus: A Nationwide Cohort Study (Diabetes Metab J 2026 Apr 22 [Epub]. https://doi.org/10.4093/dmj.2025.0794).

Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim.

DOI: https://doi.org/10.4093/dmj.2026.0684