Early onset type 2 diabetes, abbreviated EOT2D, is increasingly recognised by clinicians as a distinct and particularly aggressive clinical phenotype. Because it strikes in the third and fourth decades of life, the disease has decades longer to inflict damage on blood vessels, nerves, kidneys and the heart. People diagnosed young experience more rapid deterioration in blood sugar control, develop complications earlier, and face a lower life expectancy than those whose type 2 diabetes appears later in life. As rates of obesity rise across the globe, the number of young adults affected is climbing steeply, making the search for potent, disease-modifying interventions an urgent priority for health systems worldwide.

The biological rationale behind the trial, known as RESET for Remission, rests on the complementary effects of dietary energy restriction and exercise. Low energy diets have previously been shown to drive glucose levels below the diagnostic threshold for type 2 diabetes without the need for medication, a state defined as remission. Adding structured exercise may deliver synergistic advantages: greater cardiorespiratory fitness, preservation of muscle mass during rapid weight loss, enhanced cardiac function, and reduced insulin resistance. The researchers reasoned that a condition as aggressive as early onset type 2 diabetes merits an intervention powerful enough to arrest its swift evolution, and they designed the study to test whether combining the two approaches would outperform usual care.

The trial was a randomised, open-label, blinded endpoint efficacy study conducted across three centres in England and Canada, affiliated with the University of Leicester, McGill University in Montreal, and the University of Alberta in Edmonton. It enrolled adults aged 18 to 45 with early onset type 2 diabetes and obesity who were not taking insulin therapy. Between September 24, 2021 and June 6, 2025, a total of 96 participants were randomised, 40 in the United Kingdom and 56 in Canada. The intervention group comprised 24 women and 26 men, while the control group had 21 women and 25 men. The mean age of participants was 38 years and the mean body mass index was 35.2 kilograms per square metre. Notably, 86 participants, or 90 percent, attended final assessments, a retention rate that strengthens confidence in the findings.

The intervention itself was demanding but carefully structured. Participants stopped all glucose-lowering and blood pressure-lowering medication, in the absence of albuminuria, as they began an 800 to 900 kilocalorie per day diet providing 30 percent of energy from protein, 50 percent from carbohydrate and 20 percent from fat. They were encouraged to drink at least two litres of calorie-free fluid daily and were given a fibre-based laxative to use as required. The first two weeks consisted of total meal replacement using Optifast. From weeks 3 to 12, the same caloric intake continued with more variety, using Optifast in the United Kingdom and ProtiDiet in Canada, alongside one food-based meal per day supplying 60 to 90 grams of protein from all sources combined, one portion of fruit and two portions of non-starchy vegetables.

Exercise ran in parallel with the dietary phase. During weeks 1 to 12, participants completed twice-weekly supervised sessions covering both aerobic and resistance training, plus one weekly independent exercise session. From weeks 13 to 24, they transitioned to a weight-maintenance diet while continuing independent exercise. The primary outcome was remission at 24 weeks, defined as a glycated haemoglobin level below 6.5 percent, or 48 millimoles per mole, together with the absence of glucose-lowering medication for at least 12 weeks. The primary efficacy and harms analysis included all randomised individuals, with a conservative assumption that missing data meant no remission. Secondary endpoints captured cardiorespiratory fitness, body composition and cardiac structure.

The results were striking across multiple dimensions. Intervention participants, who averaged 100.7 kilograms at baseline, lost a mean of 8.4 kilograms by week 24, reaching an average weight of 92.3 kilograms. Control participants, who averaged 103.2 kilograms at the start, lost only 1.2 kilograms, ending at a mean of 102.0 kilograms. After statistical adjustment, the analysis showed that intervention participants lost 7.5 kilograms more than controls, including 6.6 additional kilograms of fat mass. Critically, the trial demonstrated that fat-free mass and muscle preservation are achievable even during rapid weight loss, a finding the authors attribute to the resistance training component that deliberately targeted muscle during the intensive dietary phase.

Safety data were reassuring. Remission was achieved without any severe adverse events. Most participants in the intervention group, 43 of 50, experienced at least one adverse event, compared with 6 of 46 in the control group, but these were mainly the familiar early effects of very low energy diets, such as constipation, headache, dizziness and fatigue, all of which resolved. This profile mirrors what has been reported in previous meal replacement trials and suggests that, under appropriate supervision, the intervention is tolerable for the majority of young adults willing to undertake it.

The authors emphasised the breadth of the benefits beyond blood sugar control. In their summary of the RESET for Remission findings, they report that combining structured aerobic and resistance exercise with a low-calorie diet produced high remission rates, induced fat loss while preserving muscle mass, improved muscle quality and function, enhanced cardiovascular health, and led to meaningful improvements in patient-reported outcomes. They noted that their remission rates sit at the upper end of those previously reported in the literature for trials of similar duration, and that this is the first time such effects have been demonstrated in a multi-ethnic population with early onset type 2 diabetes through a combined low energy diet and structured exercise intervention.

The researchers are candid about the challenges that remain. All weight loss programmes, including low energy diets used for type 2 diabetes remission, are typically followed by a period of partial or full weight regain for most people. Because weight loss driven by lower energy intake alone reduces both fat and fat-free mass, and there is some evidence that weight regain may preferentially restore fat over fat-free mass, there is a long-term risk of net muscle loss and increased sarcopenia. Preserving muscle during remission-oriented weight loss, they argue, may have important implications for long-term metabolic health, physical function and disease trajectory, though this requires confirmation in longer-term studies. They also highlight that their approach may be particularly valuable for those seeking or with potential for pregnancy, a group in which medication options are limited. As the global prevalence of early onset type 2 diabetes continues to rise, the team concludes that the findings present an opportunity to re-evaluate treatment paradigms and support scalable implementation of integrated dietary and exercise interventions, pending further evaluation in real-world settings.

Subject of Research: Remission of early onset type 2 diabetes through a combined low energy diet and structured exercise intervention

Article Title: Study shows low energy diet combined with structured exercise reverses early onset type 2 diabetes in half of participants

Article References: Study shows low energy diet combined with structured exercise reverses early onset type 2 diabetes in half of participants. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: type 2 diabetes, diabetes remission, low energy diet, structured exercise, early onset diabetes, randomised controlled trial, weight loss, resistance training, McGill University, University of Leicester, EASD, The Lancet Regional Health Americas

A team at the Montréal Neurological Institute of McGill University has now published a detailed account of how they have managed this complexity, both inside their own laboratory and for the wider community. Writing in Nature Protocols, Zhen-Qi Liu, Vincent Bazinet and colleagues, led by Bratislav Misic, describe netneurotools, an open-source Python toolkit that has been continuously developed and maintained by the laboratory’s trainees since its inception. The paper is both a practical protocol for carrying out network neuroscience analyses and a manifesto for a different way of building scientific software, one in which the informal, ad hoc scripts that every laboratory accumulates are treated as a legitimate, shareable scientific resource.

The philosophy behind the toolkit is disarmingly simple. The authors describe netneurotools as the Swiss army knife of the laboratory: a collection of functions and routines that the group uses constantly but that belong to no established pipeline or package. Where large neuroimaging platforms excel at well-defined tasks such as preprocessing functional magnetic resonance imaging or reconstructing diffusion data, they are not designed to interoperate with one another. The gaps between them, the authors argue, are precisely where trainees are forced to improvise isolated heuristics and workarounds. netneurotools formalizes those improvisations, turning scattered personal scripts into documented, tested, reusable code that anyone can pick up.

Technically, the toolkit is built on the familiar foundations of the scientific Python ecosystem, drawing on array programming libraries such as NumPy, the algorithms of SciPy, machine learning utilities from scikit-learn, graph structures from NetworkX, and file-reading capabilities from nibabel and nilearn. It extends these foundations with capabilities that are specific to network neuroscience. These include utilities for handling cortical surface meshes and transforming data between the many parcellation schemes that fragment the field, from volumetric atlases to multi-resolution cortical subdivisions. Because a brain map computed on one parcellation cannot be directly compared with a map on another, robust surface-based resampling and interpolation are among the most valuable functions the package provides, sparing researchers from the error-prone manual conversions that have long been a rite of passage in the field.

Network analysis itself forms a second major pillar. The toolkit implements routines for generating group-representative structural brain networks using distance-dependent consensus thresholding, an approach designed to respect the fact that anatomical connection probability falls with physical distance in the brain. It provides algorithms for randomizing weighted networks while preserving key topological properties, a crucial step in any null-model-based analysis, including a simulated annealing method developed by the same group for rigorously controlling network structure. It also implements a library of network communication models, which ask how signals could theoretically travel along the wiring of the brain, from classical shortest-path routing inspired by the Floyd, Roy and Warshall algorithms to navigation strategies and diffusion-style models that better capture the biology of neural signaling.

Statistical machinery rounds out the package. Network neuroscience increasingly relies on spatial statistics, because brain measures are arranged in space and neighboring regions are not independent. netneurotools includes implementations of spatial autocorrelation measures such as Moran’s I and Geary’s C, along with bivariate extensions that quantify spatial associations between two brain maps. It offers null models that preserve the spatial autocorrelation of data before statistical testing, a safeguard against the inflated significance that naive permutation schemes can produce. Dominance analysis, a technique from psychology for assessing the relative importance of correlated predictors in regression, is also available, addressing a common challenge when multiple brain properties compete to explain a neural phenomenon.

The protocol paper walks readers through complete workflows that chain these functions together to answer neurobiologically meaningful questions. Example analyses include relating brain network organization to microarchitectural features such as receptor distributions and cell-type composition, examining how strongly the brain’s structural wiring constrains its functional dynamics across different imaging modalities, and generating spatially informed null models for testing whether an observed pattern of structure-function coupling is unusual. Workflow diagrams in the paper show how data flow from raw parcellated imaging outputs, through the toolkit’s conversion, modeling and statistical layers, to interpretable figures, giving trainees a template they can adapt to their own projects rather than a black box they must trust blindly.

Beyond its technical content, the article makes a cultural argument that is likely to resonate far beyond one laboratory. The authors position netneurotools as a necessary counterweight to out-of-the-box software packages, arguing that smaller, ad hoc functions deserve recognition as real scientific contributions. By opening a window into the inner workings of a laboratory, the toolkit invites a new kind of discourse among research groups, one in which the unglamorous glue code that actually holds a project together is shared, critiqued and improved collectively. The package has been open to contributions from neuroscientists across the globe since its inception, and its development by trainees reflects a deliberate pedagogical choice: writing and maintaining shared infrastructure is itself a form of scientific training.

The timing of this publication is significant. A recent assessment of open-source neuroscience software described the field’s dependence on volunteer-maintained tools as precarious, and the proliferation of analysis pipelines has made reproducibility a persistent concern. By documenting their toolkit in a peer-reviewed protocols journal, the Misic laboratory is making a case that sustainability in computational neuroscience depends not only on large, polished platforms but also on transparent, community-maintained collections of mid-sized tools that bridge the gaps between them. The approach aligns with the FAIR principles for research software, which call for software to be findable, accessible, interoperable and reusable.

For a field whose data keep multiplying in modality and scale, the message is practical and quietly radical at once. The connectome may be the most complicated object ever mapped, but the daily work of studying it is made of thousands of small, concrete operations: converting a file, resampling a surface, rewiring a network, testing a spatial statistic. netneurotools gathers those operations into one open, living toolbox, and in doing so suggests that the health of network neuroscience may depend as much on how generously its practitioners share their everyday tools as on any single breakthrough analysis.

Subject of Research: An open-source, trainee-developed Python toolkit for network neuroscience analysis and brain imaging data integration

Article Title: netneurotools: a trainee-oriented approach to network neuroscience

Article References: Liu, Z.-Q., Bazinet, V., Hansen, J. Y., Milisav, F., Luppi, A. I., Ceballos, E. G., Farahani, A., Suarez, L. E., Shafiei, G., Markello, R. D., & Misic, B. (2026). netneurotools: a trainee-oriented approach to network neuroscience. Nature Protocols. https://doi.org/10.1038/s41596-026-01446-7

Image Credits: AI Generated

DOI: 10.1038/s41596-026-01446-7

Keywords: netneurotools, network neuroscience, Python toolkit, brain imaging, connectomics, open-source software, brain networks, spatial statistics, null models, FAIR principles, McGill University, Nature Protocols