The research, published in The Lancet Regional Health – Europe, pooled data from the German/Austrian/Luxembourgian/Swiss Diabetes Patient Follow-up (DPV) Registry and from two Belgian real-world studies, the FUTURE and RESCUE trials, which together formed a joint Belgian cohort. In total, 4,178 adults with type 1 diabetes who started CGM—either alongside multiple daily insulin injections or insulin pumps—were followed for up to 24 months, alongside 2,104 propensity score-matched controls who did not use CGM. Matching on age, sex, diabetes duration, baseline weight and baseline HbA1c allowed the investigators to isolate, as far as possible in a retrospective design, the effect of CGM itself rather than the effects of simply living with type 1 diabetes through time.

On the surface, the average numbers seemed to confirm the old anxiety. In the DPV cohort, mean weight rose by 1.8 kg over 24 months after CGM initiation, accompanied by a small but statistically significant HbA1c reduction of 0.2 percent (about 1.7 mmol/mol). The Belgian cohort showed a more modest mean weight increase of 1.0 kg with a 0.07 percent HbA1c improvement. Even the matched controls, who never touched a CGM sensor, gained an average of 1.2 kg over the same period. These averages, however, told only part of the story. Real-world evidence had previously suggested an average body mass index increase of roughly 0.4 kg/m² after CGM initiation, and historical data from the landmark Diabetes Control and Complications Trial (DCCT) showed that intensive insulin therapy itself produced weight gains of 1.5 to 1.8 BMI units—a legacy that has coloured expectations of glucose-monitoring technologies ever since.

To move beyond averages, the team applied group-based multi-trajectory modelling, an application of finite mixture modelling that assumes the study population comprises a set of latent subgroups, each following a distinct polynomial trajectory over time, estimated by maximum likelihood. The number of groups and their polynomial orders were selected using the Bayesian information criterion, with a minimum group size of five percent of participants and average posterior probabilities of group membership exceeding 0.7. When applied simultaneously to the two outcome variables—body weight and HbA1c—the model revealed three clearly separable trajectories that replicated across all three cohorts. The first group, labelled “some weight gain with HbA1c decrease,” achieved the largest glycaemic improvements. The second and by far the largest group, “stable weight and HbA1c,” accounted for between 69.8 and 83.2 percent of participants depending on the cohort. The third and clinically most concerning group, “weight gain with stable HbA1c,” represented roughly one in ten CGM users.

The magnitude of change in these subgroups differed sharply. In the DPV-CGM cohort, individuals in the weight-gain group gained an average of 11.1 kg over 24 months while their HbA1c remained essentially unchanged. The Belgian counterpart gained 8.2 kg. Yet the most striking comparison emerged between CGM initiators and their matched controls: while only 9.9 percent of DPV-CGM participants and 10.4 percent of Belgian CGM users fell into the unfavourable weight-gain trajectory, the figure among matched controls without CGM was 20.5 percent—roughly double. Conversely, the proportion achieving the desirable combination of HbA1c improvement with limited weight gain was higher among CGM users. When the two CGM cohorts were pooled and analysed through a multivariate random-effects meta-analysis with hierarchical cohort-level random intercepts, the pooled estimates placed 77.8 percent of CGM initiators in the stable trajectory and only 9.6 percent in the substantial-weight-gain trajectory.

Who ends up on the adverse path? Multinomial logistic regression, using the stable group as reference, identified early weight dynamics as the dominant predictor. Every additional kilogram gained above the cohort average during the first six months after CGM initiation nearly doubled the odds of belonging to the weight-gain-with-stable-HbA1c subgroup, with odds ratios of 1.85 in the DPV-CGM cohort, 1.91 in the Belgian cohort and 1.98 in the combined DPV analysis. Baseline characteristics mattered as well: those in the adverse subgroup were heavier at the outset, with the highest baseline weight and the largest proportion of participants in the obesity category (BMI ≥30 kg/m²), and they tended to be younger with shorter diabetes duration. Meanwhile, individuals in the metabolically favourable subgroup—who lost 2.3 to 2.4 percentage points of HbA1c in the DPV cohort—started with the highest baseline HbA1c (9.6 percent in DPV-CGM) and the lowest baseline weight, and each percentage point of HbA1c above the cohort average reduced the odds of belonging to that group by roughly 95 percent, underscoring how much headroom for improvement distinguished this population.

Perhaps the most provocative finding came from the direct comparison of matched populations within the DPV registry. In a multinomial model fitting both CGM users and controls, individuals who initiated CGM were almost three times more likely (odds ratio 2.80, 95 percent confidence interval 1.56–5.04) to belong to the group achieving substantial HbA1c reductions without excessive weight gain than their non-CGM counterparts. The fitted models showed strong explanatory power, with Nagelkerke pseudo R² values between 0.58 and 0.66. Complementary cross-lagged panel analysis, which examines bidirectional influences between two variables across repeated time points, found that weight and HbA1c largely predicted their own future values, with little cross-variable coupling—except in the adverse DPV-CGM subgroup, where higher HbA1c at 12 months predicted greater weight at 24 months, at a rate of nearly one additional kilogram per percentage point of HbA1c above the mean. This hints at a dynamic interplay between deteriorating glucose control and accelerating weight gain in vulnerable patients.

The clinical interpretation offered by the researchers is twofold. First, the notion that CGM inherently causes weight gain does not survive scrutiny when individual trajectories rather than population averages are examined. Prior real-world reports of an average BMI increase of about 0.4 kg/m² after CGM initiation—equivalent to roughly 1.1 kg in a person of 1.7 metres—are, in this analysis, driven largely by a small minority, while the substantial majority maintain stable weight and glycaemia. Second, and more urgently actionable, the first six months after CGM initiation constitute a critical early-warning window. A patient who gains approximately 1.5 kg in that period nearly doubles their risk of joining the subgroup that ultimately gains 8 to 11 kg over two years. The authors argue that clinicians should treat early weight trajectory as a red flag and consider timely interventions for this subset—potentially including adjunctive weight-management pharmacotherapy or, in selected cases, referral for metabolic surgery—to prevent the emergence of what the field calls “double diabetes,” a phenotype combining the insulin deficiency of type 1 diabetes with the insulin resistance and central adiposity of type 2 diabetes, and with it an elevated risk of cardiovascular and microvascular complications.

The study’s design carries both strengths and caveats worth weighing. Its reliance on robust longitudinal real-world data from three European sources lends ecological validity that randomised trials, with their tightly protocolised conditions and shorter horizons, often lack. The consistency of the three-trajectory structure across independently collected cohorts, confirmed formally by meta-analytic pooling, strengthens the case that these patterns reflect genuine biology and behaviour rather than statistical artefact. Yet the retrospective design leaves open the possibility of unmeasured confounding, including social determinants of health, physical activity and dietary habits that could shape both weight and glucose trajectories. The predominantly European cohorts, with limited ethnic diversity and similar healthcare structures, may constrain generalisability. Moreover, most participants used multiple daily injections rather than insulin pumps, and fewer than two percent were on hybrid closed-loop systems, which have demonstrated glycaemic benefits beyond what CGM paired with injections can achieve—making evaluation of weight outcomes under automated insulin delivery an obvious priority for future research.

For the millions of adults with type 1 diabetes weighing whether to adopt CGM, and for the clinicians advising them, the message is clearer than it has ever been. Continuous glucose monitoring, by replacing episodic fingerstick snapshots with a continuous stream of interstitial glucose data, enables finer insulin titration, better detection of overnight and postprandial excursions, and generally more confident self-management—all without, for the great majority of users, a penalty in body weight. Indeed, the data suggest the technology may be part of the solution to the growing problem of overweight and obesity in type 1 diabetes, since people without CGM were twice as likely to drift onto a substantial weight-gain path. What remains is a shift in clinical practice: away from population averages and toward personalised monitoring of individual trajectories, with early weight trends after CGM initiation serving as a simple, inexpensive signal of who needs intensified metabolic support before excess weight becomes entrenched.

Type 1 Diabetes, characterized by the autoimmune destruction of insulin-producing beta cells in the pancreas, presents unique challenges for patients and healthcare providers alike. Insulin therapy has traditionally been the cornerstone of treatment; however, recent advancements in pharmacotherapy, such as the introduction of SGLT2 inhibitors, have sparked interest in revising treatment paradigms. Ipragliflozin, one such agent, has shown promise in enhancing glycemic control and aiding weight management in Type 2 Diabetes patients, raising questions about its effectiveness in Type 1 Diabetes.

Amidst a backdrop of rising diabetes prevalence, the identification of potential risks associated with treatment options has become essential. Diabetic ketoacidosis, a life-threatening complication marked by hyperglycemia and ketone body accumulation, poses a serious risk for individuals with Type 1 Diabetes. On the other hand, severe hypoglycemia, characterized by critically low blood glucose levels, also remains a significant concern, often leading to severe neurological impairment or even death if untreated.

In this study, Kawamura et al. meticulously analyzed data from a vast Japanese database to explore the comparative risks of DKA and severe hypoglycemia in patients treated with a combination of ipragliflozin and insulin versus those receiving insulin therapy alone. By examining a carefully curated cohort, their investigation aimed to elucidate whether the addition of ipragliflozin could mitigate or exacerbate these risks.

The methodology employed in this research is noteworthy, as it reflects a robust approach to studying real-world scenarios rather than relying solely on controlled clinical trials, which often have stringent inclusion criteria. The authors employed statistical techniques to adjust for confounding factors, ensuring the validity of their findings. This aspect of the study underscores the importance of real-world evidence in understanding the complexities of diabetes management.

As the research progressed, the findings revealed a nuanced picture. While the combination therapy of ipragliflozin and insulin demonstrated potential benefits in terms of glycemic control, the data suggested a concerning uptick in DKA episodes among patients utilizing this treatment regimen. Conversely, incidents of severe hypoglycemia appeared to be lower in the combination therapy group, indicating a potential protective effect against this dangerous complication.

The implications of these findings extend far beyond the immediate context of the study. For clinicians, the insights derived from Kawamura et al.’s work may catalyze a re-evaluation of treatment strategies for Type 1 Diabetes. The dual-edged sword of improved glycemic control versus the heightened risk of DKA necessitates a careful balancing act in clinical practice, prompting further research into patient selection criteria and monitoring protocols.

Moreover, the study invites patients and healthcare providers alike to engage in informed discussions about treatment goals and preferences. The risks associated with DKA, particularly in combination therapy, highlight the necessity for comprehensive patient education. Patients must be aware of the symptoms of DKA and understand their individual risk profiles, empowering them to make informed decisions regarding their treatment options.

As researchers continue to uncover the multifaceted nature of diabetes management, the importance of patient-centered care becomes increasingly clear. It is essential for healthcare professionals to tailor treatment regimens to suit individual patient needs, taking into account their unique medical histories, lifestyle factors, and preferences. This approach not only enhances adherence but also fosters a collaborative dynamic between patients and providers.

Looking forward, the authors of this study call for further investigation into the long-term outcomes associated with the ipragliflozin and insulin combination therapy. The need for larger, multicenter trials is crucial in validating the findings of this Japanese study across diverse populations. As the medical community strives to optimize diabetes management, understanding the intricate relationship between treatment modalities and patient outcomes remains a top priority.

In conclusion, the research conducted by Kawamura and colleagues represents a significant contribution to the field of diabetes studies, shedding light on the implications of combining ipragliflozin with insulin therapy in Type 1 Diabetes management. The delicate balance between combating DKA risks while minimizing severe hypoglycemia events presents both challenges and opportunities, paving the way for a deeper understanding of how best to manage this complex disease. With continued research and application of these findings in clinical practice, there is hope that patients will receive more personalized, effective treatment regimens that enhance their quality of life while minimizing complications.

In an era where chronic diseases like diabetes exert a monumental toll on public health, it remains essential to prioritize research that elucidates new pathways for therapy. The collaborative efforts of researchers, clinicians, and patients will undoubtedly drive advancements, ultimately transforming the landscape of diabetes care for future generations.

Subject of Research: Risks of DKA and Severe Hypoglycemia in Type 1 Diabetes with Ipragliflozin/Insulin Therapy

Article Title: Diabetic Ketoacidosis and Severe Hypoglycemia Risks with Ipragliflozin/Insulin Versus Insulin in Type 1 Diabetes: A Japanese Real-World Database Study

Article References:

Kawamura, T., Lee, T., Shintani-Tachi, M. et al. Diabetic Ketoacidosis and Severe Hypoglycemia Risks with Ipragliflozin/Insulin Versus Insulin in Type 1 Diabetes: A Japanese Real-World Database Study.
Diabetes Ther (2025). https://doi.org/10.1007/s13300-025-01815-7

Image Credits: AI Generated

DOI: https://doi.org/10.1007/s13300-025-01815-7

Keywords: Type 1 Diabetes, Diabetic Ketoacidosis, Severe Hypoglycemia, Ipragliflozin, Insulin, Real-World Database Study