Tag: Appetite and ingestive regulation

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Glioblastoma: Testosterone Supplements Linked to 38% Lower Risk of Death

Researchers at Cleveland Clinic have discovered that androgen hormones such as testosterone can limit the growth of glioblastoma tumors in men. Results published today in Nature show that men receiving testosterone supplements for reasons unrelated to cancer showed a 38% lower risk of death compared to patients not taking these supplements. 

These findings are surprising because testosterone is known to contribute to the growth of other forms of cancer in men, such as prostate cancer, where hormone therapy is used routinely to decrease levels of androgen hormones and block cancer progression. However, these hormones were found to play a very different role in glioblastoma, an aggressive form of brain cancer that is more commonly diagnosed in men. 

“This outcome is a welcome surprise and may potentially offer a lead for new treatments for a kind of cancer that is deadlier in men,” said Anthony Letai, MD, PhD, director of NIH’s National Cancer Institute (NCI).  

In a mouse model of glioblastoma the researchers found that reducing levels of androgen hormones induced overdrive on the hypothalamus-pituitary-adrenal (HPA) axis, a brain circuit that controls reactions to stress and many physiological processes including hormone secretion. This caused a spike in stress hormones that led the brain to reinforce the protective function of the blood-brain barrier and create an immunosuppressive environment in the brain, reducing the ability of immune cells to fight against the tumor. 

“The brain has evolved to keep stuff out and that includes immune cells from elsewhere in the body. It’s a delicate tissue that often doesn’t want huge immune reactions,” said Justin D. Lathia, PhD, professor of cancer sciences and scientific director of the Brain Tumor Center at Cleveland Clinic.

Importantly, this effect was only observed in male mice. In females, changes in testosterone levels did not produce the same effects.

These findings were then confirmed in human samples obtained from 1,300 men with glioblastoma participating in the NIH database Surveillance, Epidemiology, and End Results (SEER). An analysis showed that men who received supplemental testosterone for reasons unrelated to their glioblastoma diagnosis had a 38% lower risk of death than other male patients. 

More research will be needed to better understand the complex pathway activated by testosterone and other androgen hormones. While the current study identified inflammation in the hypothalamus as a potential trigger of HPA axis activation, future work will look for the exact mechanism glioblastoma tumors employ to induce this reaction from an entirely different region of the brain.  

Lathia noted that, although these results do not establish a causal link between testosterone and patient outcomes for men diagnosed with glioblastoma, the study opens the door for future clinical trials that dive deeper into the link between androgen hormones and glioblastoma tumor growth. He added, “An obvious follow-up study would be to find out whether androgen deprivation, which is a common treatment for cancer, is actually detrimental for glioblastoma.” 

 

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Pancreatic Cancer Shares Genetic Drivers with Obesity and Diabetes

Researchers at the University of Birmingham have found that the same genes are active in pancreatic cancer, obesity, and diabetes. Their findings, published in Cancer Medicine, could finally provide an explanation to why metabolic disease is a major risk factor for pancreatic cancer. 

“We know that people with obesity or diabetes tend to have worse outcomes from pancreatic cancer, but the biological reasons have not been clear,” says Animesh Acharjee, PhD, associate professor of integrative analytics and AI at the University of Birmingham and senior author of the study. “Our study shows that the same genes and inflammatory pathways are active in both metabolic disease and pancreatic cancer, which helps explain this link and points to new opportunities for identifying high‑risk patients and developing more targeted treatments.”

Treatment options are currently limited for patients diagnosed with pancreatic cancer, a form of cancer that is often diagnosed at advanced stages. Only about 15% of patients are eligible for surgery, and about 80% of them relapse after treatment. 

Previously, the researchers had identified a series of genes that were consistently altered in metastatic pancreatic tumors. In the current study, they examined whether these same genes also play a role in metabolic disorders such as obesity and diabetes, which are increasingly recognized as risk factors for pancreatic cancer.  

First, the team analyzed genetic data from publicly available datasets to study how six key drivers of pancreatic cancer behave in healthy individuals compared to people with obesity. These included the ITGAM, PECAM1, CCL5, STAT1, STAT2, and CD44 genes, which are involved in inflammation, immune cell recruitment, and lipid metabolism processes. All six genes were found to be upregulated in individuals with obesity. 

Single-cell RNA sequencing of patient tumor samples revealed that a subset of immune cells, including macrophages and monocytes found within the tumor microenvironment, expressed these core six genes at higher levels than other cells. This discovery suggests this group of cells may be key drivers of tumor progression and recurrence and a potential therapeutic target for the development of targeted therapies.  

Taken together, these findings indicate that immune and inflammatory pathways that drive metabolic disease also play a major role in pancreatic cancer, where they could be involved in immune evasion and recurrence after surgery. Future work will investigate whether modulating the activity of these genes could reduce the chronic inflammation and immune dysregulation that drive the recurrence of pancreatic cancer to improve the success rate of this procedure. Targeting these pathways could offer new therapeutic strategies to manage pancreatic cancer, especially in patients with underlying metabolic conditions.

“This study highlights how chronic inflammation and metabolic dysfunction can intersect with cancer biology,” says Simon Jones, PhD, professor in musculoskeletal aging at the University of Birmingham and team lead for the NIHR Biomedical Research Centre. “Understanding these shared mechanisms is essential if we are to improve outcomes for patients who are living with multiple long‑term conditions alongside cancer.”

 

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Advanced Cardiovascular-Kidney-Metabolic Syndrome Linked to Increased Cancer Risk

Researchers from Japan are calling for increased cross-disciplinary collaboration after showing that people with advanced cardiovascular-kidney-metabolic (CKM) syndrome are at increased risk for cancer.

They explain in Circulation: Population Health and Outcomes that CKM syndrome is a conceptual framework, proposed by the American Heart Association (AHA) in 2023, that captures the interconnected nature of cardiovascular, kidney, and metabolic diseases and reflects the shared risk factors and pathophysiological mechanisms of these diseases.

The current study findings suggest that this framework could also “serve as a valuable, non-invasive stratification tool in precision oncology and preventive medicine,” said Hidehiro Kaneko, MD, PhD, the study’s lead author and associate professor in the department of cardiovascular medicine at the University of Tokyo in Japan.

He told Inside Precision Medicine: “By identifying individuals with advanced CKM (particularly stages 3 and 4), clinicians could tailor and potentially intensify cancer screening protocols for these high-risk patients. This enables more personalized surveillance and early detection strategies that bridge the gap between cardiometabolic management and cancer prevention.”

According to AHA statistics, nearly nine out of 10 adults in the United States have at least one component of CKM syndrome, which includes high blood pressure, abnormal cholesterol, diabetes, obesity, and reduced kidney function.

Although these components of CKM syndrome have each been associated with an increased risk for certain cancers, the relationship between CKM stage and the risk for incident cancer is unclear, as current knowledge is largely derived from studies of individual components rather than the integrated syndrome.

To address this, Kaneko and team analyzed administrative claims data for more than 1.3 million people living in Japan. Of these, 12.5% had CKM stage 0, 9.8% had stage 1, 31.7% had stage 2, 36.3% had stage 3, and 9.8% had stage 4.

The researchers report that, over a median follow-up of 3.4 years, increasing baseline CKM stages were associated with significantly greater cancer incidence.

Specifically, the incidence of cancer was 81.2 cases per 10,000 person–years in people with CKM stage 0, increasing to 97.2, 105.1, 250.9, and 257.7 cases per 10,000 person–years in those with CKM stages 1, 2, 3, and 4, respectively.

After adjustment for age, sex, alcohol consumption, and physical inactivity, individuals with CKM stage 1 or 2 at baseline did not have a significantly increased risk for cancer relative to those with CKM stage 0. However, people with CKM stages 3 and 4 had significant 25% and 30% higher risks for cancer, respectively, than those with stage 0.

When the team analyzed the data by cancer type, they found that the incidence of colorectal, stomach, lung, renal pelvis and ureter, pancreatic, non-Hodgkin lymphoma, bladder, liver, kidney, thyroid, leukemia, and gallbladder cancers increased progressively with higher baseline CKM stages. There was a similar pattern for prostate cancer in men and for breast, cervical, and uterine cancers in women.

Conversely, there was no clear association between baseline CKM stage and the incidence of esophageal cancer, malignant melanoma, or Hodgkin lymphoma.

The associations between CKM stage 3 or 4 and cancer risk were stronger in men than in women and in people younger than 65 years of age relative to older individuals. However, people aged 65 years and older were also at increased risk for cancer even when they had CKM stage 1 or 2 relative to stage 0, whereas younger individuals were not.

Kaneko said that the study highlights a critical need for increased awareness of the link between CKM syndrome and cancer.

“While physicians and the public generally understand that interconnected metabolic and kidney conditions lead to heart disease and stroke, the integrated CKM syndrome is rarely viewed as a significant driver of cancer,” he remarked. “Our study demonstrates a clear, stage-dependent increase in incident cancer risk as CKM progresses, underscoring the need to recognize cancer as a major potential consequence of this multisystem syndrome.”

Kaneko suggested that “this can be addressed by shifting public health messaging to emphasize that proactive lifestyle modifications—such as weight management, a healthy diet, and regular exercise—provide dual protection against both cardiovascular events and cancer.”

He added: “Within the medical community, we should promote the CKM staging framework as a comprehensive health assessment tool, encouraging cross-disciplinary collaboration among cardiologists, nephrologists, endocrinologists, and oncologists to manage these overlapping risks holistically.”

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STAT+: French regulator fines Novo and Lilly over weight loss ad campaigns

As competition mounts in the red-hot market for weight loss drugs, France’s medicines regulator fined Novo Nordisk approximately $2 million for running “misleading” advertisements for its Wegovy and Saxenda medications.

At the same time, the National Agency for Medicines and Health Products Safety also fined Eli Lilly roughly $127,000 over advertising for its Mounjaro obesity treatment that purportedly amounted to indirect promotion of a medicine for which a prescription is required.

The penalties reflect increasing concern among regulators that weight loss medicines may be misused and, as result, promotions run by pharmaceutical companies are being closely scrutinized. Two years ago, the regulator issued a bulletin on the risks associated with the drugs, especially inappropriate use.

Continue to STAT+ to read the full story…

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Biomarker of Epigenetic Aging Could Signal Depression

Research led by New York University suggests a marker of epigenetic aging could be linked to depression.

The team found that accelerated aging of a type of white blood cell called a monocyte was significantly associated with the psychological and cognitive expressions of depression in a group of women with and without HIV.

“Depression is not a one-size-fits-all disorder—it can look really different from person to person, which is why it’s so important to consider varied presentations and not just a clinical label,” said lead researcher Nicole Beaulieu Perez, PhD, assistant professor at NYU Rory Meyers College of Nursing, in a press statement.

“Our study reveals unique biological underpinnings of mental health that are often obscured by broad diagnostic categories.”

As reported in The Journals of Gerontology Series A, the researchers analyzed blood samples and depression scores from 440 women, 261 living with HIV and 179 without, from the Women’s Interagency HIV Study. They tested women with HIV as people with this disease and others affecting the immune system are at greater risk of depression than the general public.

The team looked at biological aging using two epigenetic clocks: a broad multi-tissue clock and a monocyte-specific clock that measures chemical modifications to DNA in these cells.

Depression was measured using the CES-D questionnaire, which separates physical, bodily expressions of depression such as fatigue, appetite loss, and agitation from psychological and cognitive expressions of the disorder such as hopelessness, anhedonia, and feelings of failure.

Accelerated monocyte aging was significantly associated with the psychological and cognitive expressions of depression and with anhedonia specifically, even after adjusting for HIV status, race, and ethnicity. The broader multi-tissue Horvath clock showed no association with any depression domain, suggesting it is the monocyte-specific aging signal, not generalized biological aging, that tracks with mood and cognitive symptoms.

Diagnosis of depression relies largely on self-reported symptoms and not a specific physiological test. The finding that monocyte aging maps onto cognitive and mood symptoms rather than physical ones is counterintuitive, since monocytes are inflammatory cells that one might expect to track physical, inflammation-driven complaints like fatigue.

The study is small and cross-sectional, so causality cannot yet be established, but if the claims of the study were validated it could help to personalize treatment for depression in the future.

“The dynamics of monocyte aging and depression warrant further study to clarify mechanistic links,” conclude the authors.

“Our findings bring us a step closer to this goal of precision mental health care, especially for high-risk populations, by providing a biological framework that could guide future diagnosis and treatment,” adds Beaulieu Perez.

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Data-Driven Tool Identifies Individuals at Highest Risk of Obesity-Related Disease

A new clinical risk model may transform how obesity is managed, by identifying which individuals are most likely to develop serious complications, regardless of their body mass index (BMI).

Developed by researchers at Queen Mary University of London and the Berlin Institute of Health, the tool, called OBSCORE, uses just 20 routinely collected clinical variables to predict the future risk of 18 obesity-related conditions, ranging from type 2 diabetes to cardiovascular disease.

Published in Nature Medicine, the study challenges the long-standing reliance on BMI as the primary metric for assessing obesity-related health risk.

Moving beyond BMI

BMI has long served as a simple proxy for obesity, but it fails to capture the biological heterogeneity of patients. Two individuals with similar BMI can have vastly different risks of developing complications.

The new model addresses this limitation directly. As described in the study, it “provides information beyond BMI” by integrating multiple dimensions of health into a unified risk score.

These include demographic data, clinical biomarkers, disease history, and lifestyle factors, variables already commonly available in healthcare settings.

The findings show that BMI alone is a poor discriminator of risk. The model consistently outperformed BMI-based approaches across all tested outcomes.

Large-scale data enables precision risk prediction

To build the model, researchers analyzed health data from nearly 200,000 individuals with overweight or obesity from the UK Biobank.

Using an interpretable machine learning framework, they screened more than 2,000 potential predictors and distilled them into a core set of 20 features that best predicted long-term health outcomes.

The resulting OBSCORE model estimates the 10-year risk of developing 18 conditions, including cardiovascular disease, kidney disease, sleep apnea, and metabolic disorders.

The model demonstrated strong predictive performance, with median concordance indices around 0.75 across outcomes, indicating robust discrimination between high- and low-risk individuals.

Hidden high-risk individuals

One of the most striking findings is that high-risk individuals are not always those with the highest BMI.

A substantial proportion of individuals classified as high risk fell into the “overweight” category (BMI 27–30 kg/m²), rather than obesity. In some outcomes, up to ~40% of those in the highest risk group had BMI below the obesity threshold.

This reveals a critical gap in current clinical practice: individuals who may benefit from intervention could be overlooked simply because they do not meet BMI-based criteria.

On the other hand, some individuals with obesity may have relatively low risk and may not require intensive intervention.

Strong risk stratification across diseases

Beyond prediction, the scientists believe that OBSCORE enables meaningful risk stratification. Individuals in the highest risk group showed dramatically higher rates of disease compared to those in the lowest group.

For example, the study reports:

  • Up to 89-fold higher risk for chronic kidney disease
  • 42-fold higher risk for type 2 diabetes
  • 47-fold higher risk for cardiovascular mortality

These differences exceed those observed when comparing individuals based solely on BMI categories, underscoring the added value of multidimensional risk assessment.

Clinical and healthcare implications

The implications of these findings are significant, particularly in the context of emerging obesity therapies.

Highly effective drugs such as GLP-1 receptor agonists and dual incretin therapies have transformed treatment options, but their high cost and limited availability make patient prioritization essential.

As the authors note, current systems lack robust frameworks to identify which patients should receive treatment.

OBSCORE offers a potential solution by enabling risk-based allocation of interventions, ensuring that treatment is directed toward those most likely to benefit.

This could improve clinical outcomes while optimizing healthcare resource use.

Toward implementation in clinical practice

One of the key strengths of OBSCORE is its practicality. Unlike many predictive models, it relies on a small number of variables that are already routinely collected, making it suitable for integration into electronic health records.

The researchers envision the model being used as a decision-support tool in clinical settings, complementing rather than replacing existing frameworks.

External validation in independent cohorts—including populations of different ancestry, demonstrated strong generalizability, further supporting its potential for real-world deployment.

Limitations and next steps

Despite its promise, the model requires further validation in broader populations, including younger individuals and more diverse healthcare settings.

Additionally, while OBSCORE effectively stratifies risk, translating these predictions into actionable treatment thresholds will require clinical consensus and cost-effectiveness analyses.

The authors also emphasize that the model identifies predictive, not necessarily causal, factors, and should be interpreted accordingly.

Taken together, the findings mark a shift toward precision medicine in obesity, moving from simplistic metrics like BMI to data-driven, individualized risk assessment.

By capturing the complex interplay of metabolic, clinical, and behavioral factors, OBSCORE could enable earlier intervention, better targeting of therapies, and improved long-term outcomes for patients living with overweight and obesity.

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Machine Learning Tool Helps Improve Type 1 Diabetes Prediction

A machine learning model can improve genetic prediction of type 1 diabetes by as much as 10%, show results from a University of California, San Diego study.

The researchers used the machine‑learning model T1GRS to improve on a gold standard polygenic genetic risk score used to predict who is likely to develop the condition called GRS2.

Type 1 diabetes is an autoimmune condition that impacts around 2 million people in the U.S. While it is a multifactorial condition, genetics plays a big role and around 50% of a person’s susceptibility comes from genetics.

“The natural history of type 1 diabetes suggests that the disease occurs in genetically susceptible individuals exposed to environmental triggers, leading to the development of islet-specific autoantibodies and autoreactive T cells and progressive loss of insulin secretory function, although the underlying etiology is not fully understood,” write lead author Kyle Gaulton, PhD, associate professor of pediatrics at UC San Diego School of Medicine, and colleagues in Nature Genetics.

The GRS2 polygenic risk score has been widely tested and can be used to predict newborns who are at high risk of developing type 1 diabetes. While early prediction can’t necessarily stop the disease it can help to prevent emergencies like diabetic ketoacidosis at diagnosis, allow families time to prepare and could allow use of therapies to delay onset of the condition.

In this study, Gaulton and colleagues carried out a genome‑wide association study in 20,355 people with type 1 diabetes and 797,363 non‑diabetic Europeans, as well as a further analysis around the MHC region in 10,107 diabetic and 19,639 nondiabetic individuals.

“The MHC has ‘blocks’ of co-inherited genetic information that are very highly enriched in individuals with type 1 diabetes,” said co-first author Emily Griffin, PhD, a postdoctoral fellow in Gaulton’s lab. “If you have them, it doesn’t mean that you’re going to get diabetes, but if you don’t have them, it means you have a very low chance of getting diabetes.”

Overall 160 risk signals were identified, and the team trained their T1GRS model to predict who was likely to develop type 1 diabetes based on their genetics. The model was able to improve on the GRS2 model predictions by up to 10% in both populations of European and African American ancestry.

Overall the new score correctly flagged about 89 of 100 people with type 1 diabetes while correctly reassuring about 84 of 100 people without the disease.

“Our results highlight the value of combining the results of genetic association studies with machine learning methods to improve the prediction of complex diseases,” conclude the authors.

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