The treatment at the center of the research is immune checkpoint blockade, a class of drugs that releases the molecular brakes that tumors place on the immune system. Checkpoint inhibitors have transformed the prognosis for several cancers, including melanoma and lung cancer, yet a substantial proportion of patients derive little or no benefit. For years, researchers have tried to identify the factors that separate responders from non-responders, and among the most intriguing candidates has been the gut microbiome, the vast community of microbes that lives in the gastrointestinal tract and communicates constantly with the immune system. Diet, in turn, is one of the most powerful levers shaping the composition and activity of that microbial community.

Daniela Quail, Associate Professor in the Department of Physiology at McGill and Principal Investigator at the Rosalind & Morris Goodman Cancer Institute, co-led the investigation alongside McGill researcher Logan Walsh and CR-CHUM collaborators Bertrand Routy and Arielle Elkrief. The team set out to test how different diets influence responses to checkpoint inhibitors, using mice as a model system. Their experimental design was unusually broad: rather than examining a single dietary manipulation, the researchers compared twelve distinct diets, allowing them to disentangle the effects of specific nutritional patterns on both the microbiome and immunotherapy outcomes.

The results defied expectations. Some of the diets that would typically be labeled unhealthy or fattening, and that are associated with weight gain, actually improved the animals’ responses to immunotherapy. Even more striking, the benefit appeared after relatively short periods of exposure, indicating that the relevant mechanism does not require the long-term metabolic changes that come with chronic obesity. As Quail noted, the findings highlighted a benefit of some obesogenic diets, a result she described as surprising because it complicates the conventional message that healthy eating is uniformly the best prescription for every aspect of health.

According to Quail, there is a synergistic relationship between the bacteria that reside in the gastrointestinal tract and the food we eat. Even when the gut contains the right composition of bacteria, the foods consumed to nourish those populations can determine whether a patient responds to treatment. In other words, the microbiome is not merely a static list of species but a dynamic metabolic engine whose output depends on the fuel it receives. That output, the study suggests, can either support or undermine the immune activation that checkpoint inhibitors are designed to unleash against tumors.

This framing helps resolve the obesity paradox in a way that previous studies could not. If higher body mass index were simply a marker of better immunotherapy response, one might expect that any route to weight gain would confer an advantage. The McGill team’s data instead indicate that the link between obesity and immunotherapy efficacy may have less to do with body weight than previously thought, and more to do with how diet and gut bacteria synergize to influence the immune system. Two patients with identical BMI values could harbor very different microbial communities, shaped by very different eating patterns, and therefore respond very differently to the same drug.

The researchers are careful to emphasize what the findings do not mean. Nothing in the study suggests that people with cancer should adopt diets associated with weight gain, which carries well-established health risks including cardiovascular disease, diabetes, and worse outcomes for many cancers. The obesogenic diets in the mouse experiments improved immunotherapy responses through specific, identifiable biological pathways, and the goal now is to isolate those pathways. If the team can determine exactly which dietary components, and which microbial metabolites, drive the enhanced immune response, clinicians might eventually be able to reproduce the benefit without the harmful consequences of an obesogenic diet.

That translational ambition is what makes the study significant beyond its immediate findings. Immunotherapy response is notoriously difficult to predict, and interventions aimed at the microbiome, such as fecal transplants or probiotic supplements, have produced mixed results in clinical trials. The McGill work suggests a reason for those mixed results: supplying the right bacteria may not be enough if the diet fails to nourish them in the right way. A rational microbiome-directed nutritional intervention would need to account for both halves of the synergy, pairing microbial composition with the specific nutrients that prompt those microbes to produce immune-supporting compounds.

The study, titled Diet–microbiome synergy underlies obesity-associated immunotherapy efficacy, was led by McGill PhD graduate Lysanne Desharnais together with Quail and their colleagues, and was published in Nature. Funding came from the Canadian Institutes of Health Research, the Canadian Cancer Society, the Terry Fox Research Institute, and the Canada Foundation for Innovation, with additional support from philanthropic partners including La Vie en Rose and the Bachynski Family Foundation. The breadth of that support reflects the interdisciplinary nature of the work, which sits at the intersection of immunology, microbiology, nutrition science, and clinical oncology.

For patients and clinicians, the immediate takeaway is one of cautious optimism rather than actionable change. No dietary recommendation for cancer patients should change on the basis of a mouse study, however elegant, and the well-known dangers of obesity-promoting diets remain firmly in place. But the research opens a concrete research agenda: identify the microbial metabolites generated under specific dietary conditions, test whether they enhance checkpoint inhibitor efficacy, and develop targeted supplements or dietary components that deliver the benefit safely. If that program succeeds, the obesity paradox will have served its purpose, transforming from a confusing clinical observation into a roadmap for making one of cancer medicine’s most powerful tools work for many more patients.

Subject of Research: How diet and gut microbiome interactions influence responses to immune checkpoint inhibitor cancer immunotherapy

Article Title: Focusing on the ‘obesity paradox’ to improve cancer treatment success

Article References: Focusing on the ‘obesity paradox’ to improve cancer treatment success. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: obesity paradox, cancer immunotherapy, immune checkpoint inhibitors, gut microbiome, diet, McGill University, Nature, oncology, microbiome-directed nutrition, tumor immunology, Daniela Quail, cancer research

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