Most earlier investigations of the relationship between body mass index and outcomes in peritoneal dialysis have used BMI measured at enrollment or changes between two points. Those approaches cannot fully represent how long a patient remains at a given weight. The new study instead estimated the cumulative duration of BMI exposure at baseline, 12 months and 36 months. Its authors described time above a target range, or TAR, and time in a target range, or TTR, as longitudinal measures intended to capture sustained BMI rather than a single snapshot. The analysis used a landmark cohort design: the first three years defined the exposure period, and mortality was tracked only after participants reached the 36-month landmark.

The investigators scanned 15 integer BMI thresholds, from 18 to 32 kg/m², to identify the level most closely associated with lower mortality. Time above each threshold was calculated by Rosendaal linear interpolation. The lowest estimated hazard ratio occurred at 26 kg/m²: for each 10% increase in TAR above that threshold, the adjusted hazard ratio for death was 0.82, with a 95% confidence interval of 0.73 to 0.92 and a Bonferroni-corrected P value of 0.018. Although every threshold produced a point estimate below 1, only thresholds from 24 through 26 kg/m² remained statistically significant after correction for testing 15 cutoffs. The researchers therefore selected 26 kg/m² as the lower boundary of a candidate range and examined TTR in two prespecified intervals: 24–25.9 and 26–30 kg/m².

Participants entered the study if they were at least 18 years old, had received continuous ambulatory peritoneal dialysis for at least three months and attended a scheduled reassessment between January 2014 and December 2023. The investigators excluded 550 people, including two with missing baseline BMI, 287 with fewer than two BMI measurements during the first three years, 72 who died before the landmark, 75 who transferred to hemodialysis, 23 who received a kidney transplant, 18 who were lost to follow-up and 73 with other reasons for exiting before the landmark. The final group of 670 had a mean age of 48.2 years, and 54.5% were male. Its mean baseline BMI was 21.91 kg/m². Of these participants, 548 spent no time in the 26–30 kg/m² interval during the exposure period, while 122 spent at least some time there.

BMI was calculated from measured weight in kilograms divided by height in metres squared. Heights and weights were recorded by trained personnel using calibrated stadiometers and digital scales, with participants wearing light clothing and no shoes. The investigators used multiple imputation by chained equations to address missing data, generating 20 datasets and combining their estimates with Rubin’s rules. Survival models were fitted in stages: first without adjustment, then for age and diabetes, and finally for estimated glomerular filtration rate, serum albumin, high-density lipoprotein cholesterol, 24-hour urine volume and the cumulative number of peritonitis episodes during the 36-month exposure period. The fully adjusted model also included age and diabetes, making seven covariates in total. Post-hoc power for the primary TTR estimate was 63%.

The TTR results separated the two candidate intervals. Patients with any time in the 24–25.9 kg/m² range did not have a statistically significant association with mortality after full adjustment. The categorical hazard ratio for any versus no time in that lower range was 0.75, with a 95% confidence interval of 0.46 to 1.23, while the continuous hazard ratio per 10-percentage-point increase in TTR was 0.98, with a 95% confidence interval of 0.89 to 1.08. By contrast, any time in the 26–30 kg/m² range was associated with a hazard ratio of 0.27, with a 95% confidence interval of 0.11 to 0.68. For each 10% increase in TTR in that higher range, the adjusted hazard ratio was 0.77, with a 95% confidence interval of 0.62 to 0.96. A restricted cubic spline analysis found no evidence of non-linearity within the selected range, with a P value of 0.21 for non-linearity.

Sensitivity analyses broadly supported the main association. Complete-case analyses, recalculation of TTR with a step-function rather than linear interpolation, and Firth penalized Cox regression produced consistent results. In exploratory competing-risks analyses, each 10% increase in TTR within the 26–30 kg/m² range was associated with lower cardiovascular mortality, with a subdistribution hazard ratio of 0.68 and a 95% confidence interval of 0.53 to 0.88. The same exposure was not significantly associated with infection-related mortality; its subdistribution hazard ratio was 0.93, with a 95% confidence interval of 0.71 to 1.21. Those cause-specific analyses were underpowered and should not be treated as independent proof of a cardiovascular mechanism. The study also calculated E-values of 1.9 for the continuous association and 6.8 for the categorical comparison, estimating how strong an unmeasured confounder would need to be to explain each result.

Several potential explanations were discussed, but none was directly tested. A higher BMI could partly reflect greater nutritional reserve and muscle mass, potentially helping patients withstand protein-energy wasting, a common and dangerous complication of peritoneal dialysis. Other possibilities included metabolic effects of adipose tissue, inflammation and fluid status. BMI cannot distinguish fat, lean tissue and retained fluid, and the investigators had no direct body-composition or overhydration measurements. Twenty-four-hour urine volume served only as a partial adjustment for fluid status. Peritoneal transport status was also missing for roughly one-fifth of the cohort and was omitted from the primary model because the limited number of deaths constrained the number of supported covariates. The findings therefore cannot show that excess adiposity itself improved survival.

Important design limitations constrain the interpretation. The retrospective, single-center design cannot establish causality, and 75 deaths provided only 10.7 events per covariate and about 63% post-hoc power for the primary effect estimate. Survivor bias was possible because the landmark design excluded patients who died, transferred dialysis modality, received a transplant or otherwise left before 36 months; these exclusions may have selected relatively healthier survivors. Unmeasured confounding and measurement error also remain plausible, particularly because diet, physical activity, dialysis adequacy and medication data were unavailable. BMI was measured at only three time points, so interpolation may have missed short-term fluctuations. Exploratory subgroup and interaction analyses were underpowered and should not be used to identify patient subgroups. Larger multicenter cohorts with more frequent BMI measurements, validated body-composition data and adequate numbers of deaths are needed before the observed 26–30 kg/m² range can be considered a clinical target or generalized beyond this Chinese population.

Subject of Research: Longitudinal BMI time-in-range and mortality in adults receiving peritoneal dialysis

Article Title: The obesity paradox in peritoneal dialysis: reassessing optimal BMI via longitudinal time in range analysis in a single-center cohort

Article References: Wang, M., Zeng, X., Liu, D., Chen, P., & Peng, Z. (2026). The obesity paradox in peritoneal dialysis: reassessing optimal BMI via longitudinal time in range analysis in a single-center cohort. Frontiers in Nutrition, 13, 1982039. https://doi.org/10.3389/fnut.2026.1982039

Image Credits: AI Generated

DOI: 10.3389/fnut.2026.1982039

Keywords: peritoneal dialysis, body mass index, time in range, all-cause mortality, obesity paradox, retrospective cohort, Chinese patients, nutritional reserve, landmark analysis, obesity, paradox, peritoneal

The study followed 97 children, adolescents, and young adults aged 1 to 25 years who attended routine follow-up at the BADAS Paediatric Diabetes Care and Research Centre at BIRDEM in Dhaka between March and November 2024. Each participant wore a blinded professional continuous glucose monitor, the Abbott FreeStyle Libre Pro iQ, on the upper arm for 14 days. Unlike the consumer devices increasingly common in high-income countries, the blinded sensor recorded interstitial glucose every 15 minutes without displaying any readings to the wearer. That design choice matters scientifically: when patients can see their glucose in real time, they tend to correct with extra insulin, adjust meals, or change activity, which artificially smooths the very fluctuations researchers want to measure. By keeping the sensor invisible, the team captured a less confounded estimate of true day-to-day glycaemic instability.

The central measure was the coefficient of variation, calculated as the standard deviation of glucose readings divided by the mean glucose and expressed as a percentage. International consensus guidelines, including those from the International Society for Paediatric and Adolescent Diabetes, recommend keeping this value at or below 36 percent, because higher variability is linked to more hypoglycaemic episodes, particularly dangerous overnight drops in blood sugar. In the Dhaka cohort, the mean coefficient of variation was 39.4 percent, with individual values ranging from 19.3 to 59.1 percent. Nearly 61 percent of participants exceeded the recommended threshold, meaning suboptimal variability was the norm rather than the exception. That mean is substantially above the 32 to 37 percent range typically reported in well-resourced paediatric cohorts, and because those comparison studies mostly used real-time devices that tend to lower observed variability, the true gap is likely even wider.

The contrast between diabetes types was stark. Participants with Type 1 diabetes, who made up just over 70 percent of the cohort, had a mean coefficient of variation of 41.9 percent, compared with 33.3 percent among those with Type 2 diabetes, a difference of 8.6 percentage points. More than three-quarters of the Type 1 group exceeded the variability target, against roughly a quarter of the Type 2 group. Time-in-range, the proportion of readings between 70 and 180 milligrams per decilitre, averaged just 52.5 percent in the Type 1 group versus 70.3 percent in the Type 2 group. Notably, time spent below range, which captures hypoglycaemia, was statistically similar between the two groups, at around 10 to 11 percent each, indicating that dangerous low blood sugar is not a concern confined to Type 1 diabetes in this population.

The study’s most intriguing finding concerns C-peptide, a byproduct of insulin production that serves as a marker of residual beta-cell function. When insulin-producing cells in the pancreas are destroyed, as in Type 1 diabetes, C-peptide falls to undetectable levels; when they survive, as in Type 2 diabetes or early Type 1 disease, measurable amounts persist. Fasting C-peptide was available for 46 of the 97 participants, and its relationship with glucose stability was striking. Higher C-peptide correlated strongly with lower variability, and in a multivariable regression adjusting for diabetes type, age, and insulin dose, each additional nanogram per millilitre of C-peptide was associated with a 1.94 percentage point reduction in the coefficient of variation.

Stratifying participants into three C-peptide categories revealed a clear dose-response pattern. The 20 participants with near-complete beta-cell depletion, defined as C-peptide below 0.2 nanograms per millilitre, had a mean coefficient of variation of 44.8 percent, time-in-range of only 42.1 percent, and time below range of 13.6 percent. The 13 participants with the best-preserved beta-cell function, above 1.0 nanograms per millilitre, achieved a mean variability of 32.2 percent and time-in-range of 65.8 percent, approaching international targets despite having no access to advanced insulin delivery technology. Time below range was nearly doubled in the lowest category compared with the highest, showing that hypoglycaemia risk, not just hyperglycaemia, tracks inversely with residual insulin secretion. The statistical trend across categories was significant for variability, time-in-range, time below range, and mean glucose.

The biology behind this relationship is coherent. Preserved endogenous insulin secretion provides meal-responsive, physiologically titrated insulin delivery that no injection regimen fully replicates, buffering the post-meal spikes that drive variability upward. Intact beta cells also support counter-regulatory glucagon responses that restrain hypoglycaemic swings. What makes the finding clinically important is its practicality: a single fasting C-peptide measurement costs roughly 5 to 10 US dollars in Bangladesh and can be run in ordinary hospital biochemistry laboratories, without the stimulation protocols or antibody assays that are often unavailable in low-resource settings. If confirmed in larger samples, the authors suggest, the test could help clinicians identify children who need prioritised access to glucose monitoring, more frequent clinical contact, or structured hypoglycaemia counselling.

The researchers are careful to frame the C-peptide results as hypothesis-generating. Because diabetes type was itself classified partly using fasting C-peptide, the two variables are partially collinear, and the association is best interpreted as a gradient of residual beta-cell function rather than a fully independent effect. Sensitivity analyses restricted to participants with Type 1 diabetes alone, and to those with at least 10 days of valid sensor data, produced consistent results, but C-peptide was missing for more than half the cohort, and although those with and without measurements differed on no measured baseline characteristic, residual selection bias cannot be excluded. The study was also single-centre, conducted at a tertiary referral facility where patients receive specialist care and may not represent all children with diabetes in Bangladesh. The 14-day monitoring window cannot capture seasonal or illness-related patterns, and the cross-sectional design precludes conclusions about cause and effect.

Even so, the findings carry weight for a region where diabetes in young people is rising and monitoring technology remains scarce and unevenly distributed. The authors point out that the elevated variability likely reflects a combination of resource-limited care realities: limited access to rapid-acting and long-acting analogue insulins, inconsistent meal timing, infrequent clinical contact, and practical barriers to rapid dose adjustment. Notably, even the Type 2 subgroup, which retained meaningful insulin secretion, showed variability exceeding values reported in comparably managed high-income country cohorts, suggesting the resource environment imposes a burden that operates across diabetes types. The team calls for multicentre studies across South Asia and sub-Saharan Africa to establish regionally appropriate benchmarks, for intervention trials testing whether variability-focused protocols reduce both high and low glucose excursions, and for cost-effectiveness analyses comparing professional glucose monitoring against standard HbA1c-only care. In the meantime, they argue, reducing variability, not merely lowering average HbA1c, should become an explicit treatment goal for young people with diabetes of both types in resource-limited settings.

Subject of Research: Glycaemic variability and residual beta-cell function in young people with Type 1 and Type 2 diabetes in Bangladesh

Article Title: Glycaemic Variability and C‐Peptide in Bangladeshi Children, Adolescents, and Young Adults With Diabetes: A Prospective Study

Article References: Flabe, A. H., Zabeen, B., Amirat, S., & Luo, J. (2026). Glycaemic Variability and C‐Peptide in Bangladeshi Children, Adolescents, and Young Adults With Diabetes: A Prospective Study. Endocrinology, Diabetes & Metabolism, 9(6), Article e70347. https://doi.org/10.1002/edm2.70347

Image Credits: AI Generated

DOI: 10.1002/edm2.70347

Keywords: continuous glucose monitoring, glycaemic variability, C-peptide, Type 1 diabetes, Type 2 diabetes, paediatric diabetes, hypoglycaemia, time-in-range, Bangladesh, low- and middle-income countries, beta-cell function, HbA1c

The protocol, published in Health Science Reports and registered with the international PROSPERO registry, describes a mixed-methods systematic review and meta-analysis that will pull together randomised trials, real-world cohort studies, qualitative interviews and implementation research on hybrid closed-loop systems such as CamAPS FX, Medtronic MiniMed platforms, Tandem Control-IQ and Omnipod 5. What makes the effort timely is the sheer speed of adoption. In England and Wales, the 2025 National Pregnancy in Diabetes Audit recorded that nearly 45 percent of women with type 1 diabetes at 28 weeks of pregnancy were using the CamAPS FX closed-loop system, with another 12 percent on a different hybrid platform. Yet that figure comes from a single publicly funded health system, and the researchers caution it cannot be extrapolated to countries where reimbursement, regulatory approval and clinical workforce differ wildly.

The stakes are high because type 1 diabetes in pregnancy remains stubbornly dangerous despite decades of specialist care. Women with the condition face elevated risks of pre-eclampsia, preterm birth, caesarean delivery, excessively large babies, neonatal hypoglycaemia and admission of their newborns to intensive care. The mothers themselves risk severe hypoglycaemia, diabetic ketoacidosis and progression of retinal and kidney damage. Large national audits continue to report worse outcomes than in the general obstetric population, even as technology use climbs. The core problem is physiological: insulin sensitivity shifts across gestation, glucose variability increases, and the relentless self-management burden falls on women, families and care teams already stretched thin.

Continuous glucose monitoring has transformed how clinicians measure that burden. The metric at the heart of the new review is time in range, specifically the pregnancy-specific target of 3.5 to 7.8 millimoles per litre, a window far tighter than the standard adult range. Landmark studies such as CONCEPTT and subsequent large cohort analyses established that more time in this range and less glucose variability translate directly into fewer large-for-gestational-age births, fewer hypoglycaemic newborns and fewer intensive care admissions. But even sensor-augmented pumps and monitored injection regimens still depend heavily on users and clinicians manually adjusting insulin doses, leaving dangerous highs and lows to persist precisely when insulin requirements are changing fastest.

Automated insulin delivery systems promise to close that gap. These platforms couple a glucose sensor, an insulin pump and an embedded algorithm that continuously modulates insulin delivery in response to sensor readings, adjusting basal rates and, in some systems, delivering automated correction doses. In non-pregnant populations, trials and meta-analyses show such systems increase time in range and reduce time spent dangerously low without clear increases in severe hypoglycaemia or ketoacidosis. Pregnancy, however, is not simply an extension of ordinary type 1 diabetes care. Many commercial algorithms were never designed around pregnancy-specific glucose targets, raising unresolved questions about device suitability, target settings, the best timing for initiation, clinician oversight, use during labour and adaptation to rapidly shifting insulin needs.

The clinical evidence so far is promising but fragmented. Randomised trials including AiDAPT, which tested CamAPS FX, and CRISTAL, which evaluated the MiniMed 780G, have reported improved glycaemic outcomes in selected settings, but they differ in device, comparator, algorithm targets, timing of initiation and support models. Observational cohorts and case series add real-world texture but vary enormously in size, baseline technology use and outcome definitions. The protocol’s authors argue that commercial automated insulin delivery in pregnancy must not be treated as a single homogeneous intervention; the meaning of any pooled result depends on which system was used, when it was started, how it was supported and which outcomes were measured.

That heterogeneity shapes the review’s unusually rigorous methodology. The team will not pool randomised and non-randomised studies together, and pairwise meta-analyses will be run separately for each comparator: closed-loop versus sensor-augmented pump therapy, versus monitored multiple daily injections, and versus clearly defined usual care. The primary outcome, pregnancy-specific time in range, will be harmonised by prioritising second-trimester estimates, with third- and first-trimester values used in descending order of preference. Network meta-analysis is deliberately not planned, because differences in devices, populations, timing and care models could violate the statistical assumptions such analyses require. Rare events such as diabetic ketoacidosis, severe hypoglycaemia and perinatal mortality will be handled with sparse-data methods or synthesised narratively when estimates are unstable.

What truly distinguishes the protocol is its breadth beyond glucose numbers. The review will capture maternal outcomes including hypertensive disorders, retinopathy progression and mode of delivery; neonatal outcomes from birthweight centiles to intensive care admission; and early postpartum outcomes grouped into the first 48 hours, the first two weeks after discharge and out to 12 weeks, a period when breastfeeding, sleep disruption and collapsing insulin requirements create new hazards. Psychosocial measures, including diabetes distress, fear of hypoglycaemia, sleep quality, anxiety and depression, will be extracted with full attention to instrument validation. Crucially, qualitative studies of women’s and clinicians’ lived experiences will be synthesised thematically and then integrated with the quantitative findings through structured side-by-side comparison, so that numbers can be explained, contextualised or challenged by human experience.

Implementation realities also take centre stage. The review will document device and consumable costs, reimbursement arrangements, out-of-pocket expenses, clinician workload, training demands, uptake and retention rates, and reported barriers to equitable access, always tied to the specific jurisdiction and regulatory context of each study. The authors are explicit that access, equity and workload will be treated as descriptive domains, with causal claims made only where study designs genuinely support them. Certainty in the quantitative evidence will be graded using the GRADE framework, while confidence in qualitative findings will be assessed with GRADE-CERQual, allowing the two evidence streams to be weighed on their own terms.

The anticipated limitations are candidly acknowledged: likely few randomised trials, inconsistent definitions for neonatal hypoglycaemia and device-related adverse events, off-label use of systems with non-pregnancy-specific settings, and qualitative evidence skewed toward specialist early-adopter centres. Yet the strengths are substantial, including prospective registration, alignment with PRISMA-P reporting standards, no language restrictions on searches extending back to 2010, and dual independent screening and appraisal at every stage. When the completed review arrives, it should give clinicians, guideline developers and health services something the field currently lacks: a clear, device-specific, globally honest account of whether automated insulin delivery improves diabetic pregnancies, and what it takes, technically, financially and organisationally, to deliver that benefit safely and equitably.

Subject of Research: A mixed-methods systematic review protocol evaluating commercial automated insulin delivery systems in type 1 diabetes pregnancy

Article Title: Commercial Automated Insulin Delivery Systems in Type 1 Diabetes Pregnancy: Protocol for a Mixed‐Methods Systematic Review and Meta‐Analysis of Glycaemic, Perinatal, Psychosocial and Implementation Outcomes

Article References: Li, J., Sheklabadi, E., Goldstein, R. F., Ng, A. H., Teede, H., & Naderpoor, N. (2026). Commercial Automated Insulin Delivery Systems in Type 1 Diabetes Pregnancy: Protocol for a Mixed‐Methods Systematic Review and Meta‐Analysis of Glycaemic, Perinatal, Psychosocial and Implementation Outcomes. Endocrinology, Diabetes & Metabolism, 9(5), Article e70309. https://doi.org/10.1002/edm2.70309

Image Credits: AI Generated

DOI: 10.1002/edm2.70309

Keywords: type 1 diabetes, pregnancy, automated insulin delivery, hybrid closed-loop, continuous glucose monitoring, time in range, perinatal outcomes, CamAPS FX, systematic review, meta-analysis, health technology, implementation science

The trial builds on a 13-week multicenter randomized controlled trial conducted in the United States and France, in which 194 adults aged 18 to 70 were assigned either to the Omnipod 5 System or to continue their existing non-automated insulin pump therapy with continuous glucose monitoring. In that original phase, the automated system clearly outperformed conventional pumps: participants spent 61.2 percent of their time in the target glucose range of 70 to 180 milligrams per deciliter, compared with 43.8 percent for controls, a difference of more than four hours per day spent in the healthy range. The extension study followed 75 of the 76 French participants who agreed to continue, including 23 who had originally served as controls and now transitioned to the automated system.

The technology itself represents a notable engineering achievement. Omnipod 5 is the first tubeless automated insulin delivery system, combining a disposable, wearable insulin pump called a Pod with a built-in control algorithm, a wirelessly connected smartphone application, and the Dexcom G6 continuous glucose monitor. Operating in Automated Mode, the system delivers tiny micro-boluses of insulin every five minutes, continuously nudging glucose toward a user-selected target that can be customized in 10 milligram per deciliter increments between 110 and 150 milligrams per deciliter. This closed-loop design replaces much of the guesswork of manual pump therapy with an algorithm that responds to every fluctuation the sensor detects.

The results after one year were striking. Participants spent a median of 99.7 percent of the study period wearing and using the system, and a median of 95.6 percent of the time in Automated Mode rather than reverting to manual control, a strong signal of satisfaction and trust in the algorithm. Most of the time, 63.2 percent, users chose the most ambitious 110 milligram per deciliter target. Compared with their pre-trial baseline, participants increased their time in range by an average of 17.9 percentage points, equivalent to 4.3 additional hours per day in the healthy glucose zone, while their mean glucose fell by nearly 28 milligrams per deciliter. Time spent above range dropped by 17.7 percentage points, and critically, time spent in hypoglycemia did not increase at all.

Perhaps the most dramatic shift appeared in a standard laboratory measure of long-term glucose control. The average hemoglobin A1c of the cohort fell from 8.33 percent at baseline to 7.18 percent after 12 months, an improvement of 1.14 percentage points that remained statistically robust at every quarterly checkpoint. At the start of the study, not a single participant met the internationally recommended target of an A1c below 7 percent; by the end, 30 participants, or 40.5 percent of the cohort, had crossed that threshold. The proportion meeting the stricter goal of below 8 percent rose from roughly one in four to more than 90 percent of all participants. Notably, participants with the highest starting A1c levels experienced the greatest improvements, suggesting the system holds particular value for those struggling most with conventional therapy.

Safety data were equally encouraging. Over the full year, researchers recorded just nine adverse events, a rate of 12 per 100 person-years, including a single episode of prolonged hyperglycemia. There were no cases of severe hypoglycemia, diabetic ketoacidosis, or other serious glycemic emergencies, rates well below population-level figures reported across Europe. Seventeen device deficiencies were reported among 13 participants, most involving the handheld controller, but none resulted in serious harm. Body mass index rose slightly, by 0.7 kilograms per square meter, an increase the authors judged unlikely to be clinically meaningful.

The durability of these gains matters because time in range is increasingly recognized as a predictor of long-term outcomes. A growing body of evidence links greater time within the target glucose range with reduced risks of the microvascular complications of diabetes, including retinopathy, nephropathy, and neuropathy. The proportion of participants in this study meeting international consensus targets of at least 70 percent time in range together with less than 4 percent time below range rose more than fourteen-fold, from 1.4 percent at baseline to 20 percent at one year. Although the study was not designed to measure complication rates directly, the authors argue that sustained glycemic improvements of this magnitude may translate into clinically meaningful long-term protection.

Patient-reported measures reinforced the clinical picture. Scores on the Diabetes Quality of Life questionnaire and the Hypoglycemia Confidence Scale improved during the randomized phase and were maintained at both six and twelve months of the extension, indicating that users did not merely tolerate the technology over time but continued to feel confident managing hypoglycemia and less burdened by intensive treatment. The near-universal uptake is itself telling: all but one of the 76 eligible participants opted to continue into the extension, and only one dropped out, because of a move abroad.

The study does have limits. All participants were French adults who were already experienced insulin pump users with baseline A1c values between 7 and 11 percent, so the findings may not generalize to people using multiple daily injections or those with very different starting glucose control. Outcomes were exploratory, without pre-specified hypotheses or formal sample-size considerations, and a slight dip in time in range from the end of the randomized phase to the end of the extension may reflect less intensive clinical monitoring, underscoring that ongoing professional support still matters. Even so, the authors conclude that the short-term glycemic, safety, and quality-of-life benefits observed in the controlled trial were faithfully maintained over a full year of routine use, adding to a mounting body of evidence that automated insulin delivery deserves a place as a first-line therapy for people with type 1 diabetes.

Subject of Research: Twelve-month extended use of the tubeless Omnipod 5 automated insulin delivery system in adults with type 1 diabetes

Article Title: Extended Use of the Omnipod 5 Automated Insulin Delivery System in Adults With Type 1 Diabetes: 12‐Month Extension of a Randomized Controlled Trial

Article References: Renard, E., Weinstock, R. S., Penfornis, A., Riveline, J.-P., Thivolet, C., Ly, T. T., & for the OP5‐003 Research Group (2026). Extended Use of the Omnipod 5 Automated Insulin Delivery System in Adults With Type 1 Diabetes: 12‐Month Extension of a Randomized Controlled Trial. Endocrinology, Diabetes & Metabolism, 9(5), Article e70321. https://doi.org/10.1002/edm2.70321

Image Credits: AI Generated

DOI: 10.1002/edm2.70321

Keywords: type 1 diabetes, automated insulin delivery, Omnipod 5, continuous glucose monitoring, time in range, HbA1c, closed-loop insulin pump, hypoglycemia, diabetic ketoacidosis, patient-reported outcomes, randomized controlled trial, diabetes technology