Diabetes Technology and Obesity Medicine

In type 1 diabetes, hybrid closed-loop (HCL) systems have proven effective in improving glucose control without increasing time below range (TBR). We aimed to compare the performance of different HCL systems through a meta-analysis and network meta-analysis of randomized controlled trials (RCTs).

We searched MEDLINE, EMBASE, CENTRAL, PubMed, and conference proceedings up to April 18, 2025, and ultimately included RCTs involving children, adolescents, and adults with type 1 diabetes, with baseline HbA1c 7.0%–8.5%, comparing HCL systems with other forms of insulin therapy, and reporting time in range (TIR).

A total of 32 RCTs were analyzed. Among the 22 RCTs with a mean participant age ≥ 18 years, Omnipod 5, MiniMed 780G, and iLet BP improved TIR by approximately 20% over subcutaneous insulin therapy (SIT) without continuous glucose monitoring (high certainty); of the three, only MiniMed 780G achieved a mean end-of-study TIR > 70%. In the 10 RCTs with participants < 18 years, Control IQ showed the greatest improvement in TIR, although none of the systems reached a mean TIR > 70%. All HCL systems reduced TBR compared with SIT in studies with participants ≥ 18 years but showed similar effects to SIT in younger populations. The risk of severe hypoglycemia and diabetic ketoacidosis was low and similar to other treatment strategies.

In RCTs involving individuals with type 1 diabetes, HCL systems demonstrate a hierarchy of efficacy compared with SIT, which also appear to be influenced by the mean age of participants.

Children and adolescents with type 1 diabetes (T1D) often experience glucose fluctuations during dynamic activities such as summer camps. Evidence on the clinical accuracy and usability of intermittently scanned CGM (isCGM; FreeStyle Libre 2) in these real-world pediatric settings is limited.

To evaluate within-participant changes in continuous glucose monitoring (CGM)–derived glycemic metrics during a pediatric T1D summer camp compared with a pre-camp period under health care professional (HCP)–supervised use of FreeStyle Libre 2, an isCGM system. Secondary aims were to assess sensor accuracy and user satisfaction.

This prospective single-arm observational study enrolled children and adolescents with T1D attending a 3-day residential summer camp. CGM-derived metrics during the on-camp period were compared with those from a pre-camp period within the same participants under structured HCP supervision. Sensor accuracy was assessed using paired capillary blood glucose–sensor glucose measurements, and user satisfaction was evaluated using post-camp questionnaires.

Twenty-one participants were analyzed. The on-camp period showed a significant increase in time in range (54.5%–64.3%, P = 0.031) and a reduction in time above range > 250 mg/dL (17.7%–4.7%, P = 0.049), while time below range remained unchanged. Sensor accuracy, evaluated by mean absolute relative difference, was acceptable within ranges (13.6% ± 10.5% on-camp, 12.7% ± 10.5% pre-camp) reported in pediatric studies with variability.

Glycemic metrics were improved during a pediatric T1D summer camp conducted under a structured, HCP-supervised setting incorporating isCGM, without an increase in hypoglycemia. These findings support the feasibility of supervised, camp-based CGM–supported glucose management in pediatric populations.

Diabetes mellitus is a group of metabolic diseases characterized by chronic hyperglycemia resulting from defects in insulin secretion, insulin action, or both. Massage with aroma oils is commonly used in the management of various diseases, including diabetes. This study aims to evaluate the effect of abdominal massage with eucalyptus aroma oil on blood glucose levels in patients with type 2 diabetes mellitus (T2DM).

In this single group pre-post study, 50 patients with T2DM underwent only one session of abdominal massage with eucalyptus aroma oil for 20 min. Random blood glucose levels, blood pressure, pulse rate, pulmonary function, Trail Making Test A and B, state anxiety, and state mindfulness were measured before and after the intervention.

There was a significant reduction in random blood glucose level, Trail Making Test A, and state mindfulness scores and a significant increase in peak expiratory flow rate with no significant changes in blood pressure, pulse rate, Trail Making Test B, and state anxiety after the intervention compared with before the intervention.

Abdominal massage with eucalyptus oil reduces blood glucose levels and improves mindfulness and cognitive task performance in patients with T2DM. A lack of control group and long-term follow-up is limiting the scope of this study. Hence, long-term randomized controlled studies are recommended to validate the results of the study.

The everlasting interest in precision medicine has instigated a need for personalized data collection. Consumer-grade wearable devices and applications can be used to support this need by collecting real-world data. In this article we describe how we collected multimodal data using a consumer-grade continuous glucose monitor, smartwatch with in-house application, and smartphone application in people with diabetes; analyzed data quantity and quality; and evaluated the association of lifestyle variables with glycemic excursions.

Data were collected for 14 days on interstitial glucose concentration, heart rate, acceleration, step count, dietary intake, bouts of physical activity, insulin administration, and mood. Sixty participants were included (52 with type 2 and 8 with type 1 diabetes), with mean (±standard deviation) age of 66.8 ± 10.3 years and BMI of 29.4 ± 4.9 kg/m².

In total, we collected 77,683 CGM measurements, 4073 moods, 3517 meals, 1394 insulin injections, and 1364 bouts of physical activity. Fifty-nine (98.3%) participants finished the study, 45 participants (75.0%) reported data of every modality, and each modality was logged by most participants for at least 9 days. Furthermore, excursions in the glucose levels were significantly associated with dietary intake and indirectly insulin dosing, whereas no association with mood or physical activity was observed.

These findings highlight the effectiveness of consumer-grade wearable devices to collect data from the comfort of the people’s homes and illustrate the potential to continuously evaluate the relationship between lifestyle and health at an individual level.

Digital diabetes apps are associated with improved glycemic control in people with type 1 (T1D) and type 2 (T2D) diabetes mellitus. In this retrospective, real-world data study, we aimed to assess the standalone effect of the mySugr app on glycemic control, without the use of additional connected devices to monitor/manage diabetes therapy.

Data were collected from mySugr Logbook users who registered from January 2018 to July 2025 and provided consent for personal data processing. Eligible users were ≥16 years old upon registration, did not have imported blood glucose (BG) log entries from connected devices, and had manually logged BG for a minimum of two entries per day for 14 days per month, for the first 6 months of logbook use.

Mean monthly BG, coefficient of variation (CV), and proportions of tests below (<70 mg/dL), within (70–180 mg/dL), and above range (>180 mg/dL) were calculated for each user. Mean values for each metric were calculated across users and compared across 6 months of mySugr use. Paired samples t-tests with Bonferroni corrections were performed.

Of the 11,891 users in the sample population, 3523 (29.6%) were people with T1D, 6704 (56.4%) were people with T2D, and 1664 (14.0%) had other/non-disclosed diabetes types. Mean (standard error of measurement [SEM]) monthly BG and CV significantly decreased from the first to the sixth month of mySugr Logbook use (Δ = –6.69 [±0.25] mg/dL and Δ = –1.70 [±0.06]%, respectively; both P < 0.001). Mean (SEM) monthly proportion of tests in range significantly increased (Δ = 4.05 [±0.15]%; P < 0.001).

Highly engaged mySugr Logbook users, who manually logged their BG values without any automatically imported BG data, improved their glycemic outcomes over 6 months, suggesting that the mySugr Logbook may facilitate therapy adherence and constructive lifestyle adjustments.

Diabetic nephropathy (DN) is traditionally diagnosed based on urinary albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). Newer biomarkers, such as urinary cystatin C, neutrophil gelatinase-associated lipocalin (NGAL), interleukin (IL)-18, and angiotensinogen, may offer better prediction of renal dysfunction in diabetes without hypertension.

This 30-month prospective study followed 55 type 2 diabetic patients without hypertension. Urinary NGAL, cystatin C, angiotensinogen, and IL-18 were assessed at baseline, 12, 18, and 30 months to determine their predictive value for progression of renal disease and the trends of the progression of biomarkers. Biomarker levels were correlated with changes in urine ACR and eGFR. Statistical analyses included Pearson’s correlation and linear regression to identify significant predictors.

The study cohort had a mean age of 48.93 years, with 60% being male. The mean eGFR at baseline was 111.8 mL/min/1.73m², and urine ACR significantly increased over 30 months (52.2 mg/g to 111.7 mg/g, P = 0.013). Urinary cystatin C showed the strongest correlation with ACR progression (r = 0.6814, P < 0.0001), explaining 46.4% of the variance. IL-18 also correlated with ACR changes (r = 0.4245, P = 0.0012). In contrast, NGAL and angiotensinogen did not significantly predict ACR progression or eGFR decline. Elevated HbA1c levels were associated with worsening albuminuria (r = 0.2833, P = 0.0361).

Urinary cystatin C and IL-18 are strong early predictors of albuminuria progression in type 2 diabetic patients without hypertension. NGAL and angiotensinogen did not show predictive value for early renal decline. Further large-scale studies are needed to validate these findings and refine biomarker-driven approaches to DN management.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are highly effective pharmacotherapies for obesity, offering substantial weight loss and improvement in obesity-related complications. However, their high cost and limited insurance coverage necessitate precise, data-driven strategies for patient selection.

To develop and validate machine learning (ML) models that predict eligibility for GLP-1 RA therapy using body mass index (BMI) and comorbidity profiles, leveraging both synthetic and real-world patient data.

A rule-based algorithm, grounded in clinical guidelines, was used to generate a synthetic training dataset (n = 256), while a real-world test set (n = 287) was derived from electronic health records. Patients were classified as eligible for GLP-1 RA therapy or recommended for generic alternatives based on BMI and major obesity-related complications (e.g., type 2 diabetes, cardiovascular disease, metabolic dysfunction-associated steatohepatits [MASH], sleep apnea, transplant status). Four models, baseline classifier, logistic regression, decision tree, and random forest, were trained and evaluated using cross-validation and independent testing. The rule-based algorithm was also applied directly to the test set.

Decision tree, random forest, and logistic regression models achieved near-perfect accuracy (≥99%) on both datasets. Key predictors of eligibility included type 2 diabetes, cardiovascular disease, MASH, sleep apnea, and transplant status. The rule-based model also performed comparably well, demonstrating its clinical validity and interpretability.

ML and rule-based models can accurately identify patients eligible for GLP-1 RA therapy, supporting scalable, equitable, and cost-conscious obesity treatment strategies. These tools offer a transparent framework for optimizing access to high-cost antiobesity medications in real-world clinical settings.

Chronic inflammation, particularly C-reactive protein (CRP), is a key indicator of obesity, which is believed to contribute to insulin resistance and type 2 diabetes. However, no known studies reported the effect of yoga and naturopathy (YN) on CRP levels in severe obesity. This study aimed to assess the efficacy of YN on CRP levels in an individual with severe obesity and prediabetes.

A 25-year-old male with prediabetic obesity visited our hospital with complaints of a gradual increase in bodyweight, depression, and emotional instability over the past 3 years. He experienced palpitations, excessive sweating, breathlessness, and struggled with regular physical activity. His weight has gradually increased over the last 3 years; he gained almost 73 kg. He underwent YN intervention for 14 days of supervised care at the hospital, followed by 3 months of follow-up at home and 1 month of supervised care at the hospital.

The study showed a reduction in weight, body mass index (BMI), mid-arm, mid-thigh, waist, hip, and neck circumferences, waist-hip ratio, CRP levels, lipid profile, and Depression Anxiety Stress Scales 21 Item (DASS-21), and no changes found in blood glucose and glycosylated hemoglobin levels in the post-test assessments compared with the pretest assessments.

Yoga and Naturopathy might be effective in reducing CRP, stress, anxiety, and depression, and in improving obesity related parameters in an individual with severe obesity and prediabetes. However, randomized controlled trials with a large sample size are recommended to validate the results of this study.

With the advent and increasing availability of continuous glucose monitoring (CGM), patients are now able to more easily and frequently determine their blood glucose levels than they would have using the finger prick blood test. Health care professionals can also review the patient’s prandial glucose profiles stored in the CGM system without intentional checking of glucose levels. However, given the large amount of data stored, methods for the utilization of such information need to be developed for better diabetes management.

This study shows the retrospective estimation of carbohydrate amount using pre- and postprandial blood glucose levels via the following derived equation: (patient’s current carbohydrate-to-insulin ratio) × [injected bolus insulin + (postprandial blood glucose − preprandial blood glucose)/correction factor]. Accordingly, the retrospective estimates are systemically biased when the carbohydrate-to-insulin ratio (CIR) is not set appropriately. Insufficient adjustment of the correction factor (CF) also results in retrospective estimation bias for meals with large differences in pre- and postprandial blood glucose levels. We compared these retrospective estimates with patient-measured preprandial carbohydrate estimates to assess the bias. By using only meal data with small differences in pre- and postprandial blood glucose levels, the influence of CF on the bias was limited, and only the systemic bias caused by CIR was assessed. In the presence of a statistically significant systemic bias, the data suggest that the current CIR is set inappropriately.

We present the method by assessing CIR in a patient with newly diagnosed type 1 diabetes. CIR parameter assessments at 7 and 10 months from diagnosis are presented.

A novel method for statistically assessing CIR was introduced. This method can support clinical decision-making when adjusting CIR for patients with insulin-dependent diabetes who engage in carbohydrate counting.

Individuals with type 1 diabetes (T1D) require close glucose monitoring to prevent both short- and long-term complications. Physical activity (PA) is a significant source of variability in metabolic dynamics, leading to glycemic fluctuations that depend on the type, intensity, and duration of the exercise. Accurately monitoring and classifying the type of PA is crucial for optimizing glycemic control and minimizing the risk of hypoglycemia.

This study utilizes the largest clinical trial of PA in people with T1D to date, the Type 1 Diabetes and Exercise Initiative (T1DEXI), which included both structured and unstructured PA sessions, to develop an online classification approach for identifying the type of PA (aerobic, interval, resistance). A computationally efficient convolutional neural network (CNN) was trained on time–frequency representations (spectrograms) of step count and heart rate signals, readily available from wearable devices, from the structured PA sessions of the T1DEXI dataset. The proposed methodology presents an ad hoc process for designing the spectrograms based on the CNN architecture to optimize the classifier’s performance.

The CNN-based classification approach was implemented using spectrograms of 5- and 30-min signals, resulting in two classifiers that achieve high classification accuracy when evaluated on the structured PA sessions. The 5-min classifier was then applied to unstructured PA sessions, where the predicted distribution of glucose changes for the activity types was consistent with clinical evidence.

These results demonstrate the potential of the proposed approach for its integration into decision support systems or automated insulin delivery systems, enabling improved glucose management during exercise in T1D.

Young adults (YA) with type 1 diabetes struggle with glycemic control and diabetes distress. As social media (SM) is highly used by YA, we explored how YA with diabetes use SM, identifying associations with demographics and diabetes measures.

We developed and emailed a survey to YA seen in clinic, aged 18–25 years with diabetes ≥1 year, querying SM, demographic, and diabetes characteristics, as well as diabetes distress (Problem Areas in Diabetes [PAID]). We divided the sample into lower (<3 h/day) and higher (3+ h/day) SM use and compared characteristics using t-tests and Chi-square.

Of 1176 YA approached, 385 (33%) provided evaluable responses. Mean age was 22 years; 61% were female and 86% non-Hispanic white (NHW); 83% used a pump and 96% used a continuous glucose monitor. Mean HbA1c was 7.4% ± 1.3% (56.3 ± 14.2 mmol/mol). Almost all (98%) used SM; Instagram (56%) and TikTok (24%) were most popular. The lower (65%) and higher (35%) SM use groups differed by age (23 vs. 22.3 years, P = 0.02), race/ethnicity (90 vs 78.5% NHW, P = 0.002), HbA1c (7.3% vs. 7.6% [55.2 vs. 59.6], P = 0.007), and PAID score (24.5 vs. 29.7, P = 0.02), respectively. Diabetes content represented <12% of overall SM engagement, while 27.4% of sample reported SM as their primary source of diabetes technology knowledge.

YA with higher SM use were younger, had higher HbA1c, reported more diabetes distress, and were less likely to be NHW. Engagement with diabetes SM was relatively low, representing a potential opportunity to disseminate education and support to YA with diabetes.