Research roundup: Saliva testing for diabetes risk and more

Health Tech World explored the latest research developments in the world of health technology.

Saliva testing may reveal early signs of diabetes and obesity

Measuring elevated levels of insulin – called hyperinsulinemia – in blood, is a proven way to measure metabolic health and can show risk of developing future health concerns, including Type 2 diabetes, obesity and heart disease.

Now, a team of UBC Okanagan researchers has found that measuring insulin levels in saliva offers a non-invasive way to do the same test, without the need for needles or lab-based blood work.

It can also be used to detect early metabolic changes linked to obesity and other health risks.

The study included 94 healthy participants with a range of body sizes.

After a period of fasting, each participant drank a standardised meal-replacement shake, then provided saliva samples and underwent a finger-prick blood glucose test.

The researchers say that people living with obesity had much higher insulin levels in their saliva than those who were slightly overweight or had lower body weight, even though their blood sugar levels were the same.

Taking preventive steps at an early stage is important because hyperinsulinemia is a known predictor of several chronic conditions, including Type 2 diabetes, hypertension, cardiovascular disease, stroke, cancer, and most recently, it has been linked to obesity.

The study aimed to help develop a practical non-invasive test for hyperinsulinemia, but they also found an interesting result following the consumption of the meal-replacement drink.

Previous research at UBC Okanagan showed that saliva insulin levels closely follow plasma insulin levels across the day following high and low-carbohydrate mixed meals.

The team says this suggests that saliva insulin may help distinguish between high and low plasma insulin responses, and could play a role in predicting the severity of hyperinsulinemia and possibly insulin resistance.

Brain imaging may identify patients likely to benefit from anxiety care app

By understanding differences in how people’s brains are wired, clinicians may be able to predict who would benefit from a self-guided anxiety care app, according to a new analysis.

The preliminary study suggested that young people with weaker connections between two brain areas involved in both attending to and regulating responses to anxiety were more likely to benefit from a self-guided anxiety care app than those with stronger connections.

The study looked at data from a subset of clinical trial participants who agreed to undergo a brain MRI before using the anxiety care app developed by the investigators.

The app, called Maya, is essentially a course in cognitive behavioural therapy, a gold standard psychotherapeutic intervention that provides users with skills to support them in shifting their thinking, completing challenging behaviors and learning new ways to cope.

The interactive platform guides young adults with anxiety through videos, exercises and educational content.

Initial results of the clinical trial with 59 participants showed that using the app reduced anxiety symptoms for many patients; now the new analysis may help identify which patients benefit most.

For the clinical trial, 59 young adults with anxiety used the app twice a week for six weeks and investigators tracked participants’ symptoms during that time and for an additional six weeks.

Those who used the app experienced reduced anxiety symptoms throughout the 12-week monitoring period.

Some continued to use the app after the initial 6-week trial period, and others experienced lasting improvements in symptoms even after discontinuing use of the app after six weeks.

The investigators were able to use data from 30 participants who had MRIs before using the Maya app to determine if specific patterns of brain activity indicated individuals who were more likely to experience symptom improvements.

The results suggest that young adults experiencing anxiety whose brains were less efficient at regulating their responses to anxiety-provoking information benefited more from learning cognitive behavioral therapy techniques through the app.

In contrast, those with stronger connections in circuits involved with greater attention to potentially threatening or anxiety provoking information were less likely to benefit from using the app.

New mRNA-based therapy that shows promise in heart regeneration after heart attack

Researchers have identified a new strategy that may help repair damaged heart tissue by reactivating an important developmental gene.

A new study describes how a gene known as PSAT1, delivered through synthetic modified messenger RNA (modRNA), can stimulate heart muscle repair and improve cardiac function following a heart attack.

The study represents a major step forward in the effort to develop regenerative therapies for ischemic heart disease.

The researchers synthesized PSAT1-modRNA and delivered it directly into the hearts of adult mice immediately following a heart attack.

The goal was to reawaken regenerative signaling pathways, particularly those related to cell survival, proliferation, and angiogenesis, that are active during development but dormant in adulthood.

The results found that mice treated with PSAT1-modRNA showed robust increases in cardiomyocyte proliferation, reduced tissue scarring, improved blood vessel formation, and significantly enhanced heart function and survival compared to untreated mice.

Mechanistically, PSAT1 was shown to activate the serine synthesis pathway (SSP), a key metabolic network involved in nucleotide synthesis and cellular stress resistance.

SSP activation led to reduced oxidative stress and DNA damage, which are key contributors to cardiomyocyte death following a heart attack.

Further investigation revealed that PSAT1 is transcriptionally regulated by YAP1, a known driver of regenerative signaling.

PSAT1 in turn promotes nuclear translocation of β-catenin, a protein critical for cell cycle re-entry in cardiomyocytes.

Importantly, the study also demonstrated that inhibition of SSP negated the beneficial effects of PSAT1, highlighting the pathway’s central role in heart repair.

The researchers say that the findings suggest that PSAT1 is a master regulator of cardiac repair after injury, and that by activating PSAT1 through modRNA, it is possible to jumpstart regenerative programmes in the heart that are otherwise inaccessible in adult tissues.

The implications of the study are wide-ranging.

ModRNA technology, which has recently transformed vaccine development, provides a flexible and efficient platform for delivering genes such as PSAT1 with high specificity and limited side effects.

In addition, unlike viral gene therapies, modRNA does not integrate into the genome, reducing the risk of long-term complications.

Looking ahead, the researchers plan to evaluate the safety, durability, and delivery optimisation of PSAT1-based therapies in larger animal models.

They also aim to refine control over the timing and localisation of gene expression, which are key considerations for clinical translation.

Smart wound monitor poised to improve chronic infection care

Researchers have developed a wearable wound monitoring device with integrated sensors that could reduce infection risks by minimising the need for frequent physical contact.

The proof-of-concept device is designed for reuse, making it more cost-effective and practical than disposable smart bandages and other emerging wound monitoring technologies.

Lead inventor Dr Peter Francis Mathew Elango of RMIT University said the device used advanced integrated sensor technology – including inflammation, pH and temperature sensors – to continuously track key healing indicators.

High temperatures signal inflammation or infection, while changes in pH levels can indicate different stages of wound healing.

An RMIT-patented technology platform underpins this innovation, with flexible sensors that can be placed on or next to a wound under dressings.

“The high-resistivity silicon-based sensor technology is our platform IP that has been proven to be efficient at multiple biomarker detection related to different ailments,” said team leader Professor Madhu Bhaskaran.

AI model detects hidden diabetes risk by reading glucose spikes

Scientists have discovered that artificial intelligence can use a combination of other data – including real-time glucose levels from wearable monitors – to provide a more nuanced view of diabetes risk.

To diagnose either type 2 diabetes or pre-diabetes, clinicians typically rely on a lab value known as HbA1c. This test captures a person’s average blood glucose levels over the previous few months.

However, HbA1c cannot predict who is at highest risk of progressing from healthy to prediabetic, or from prediabetic to full-blown diabetes.

The new model uses continuous glucose monitor (CGM) data alongside gut microbiome, diet, physical activity and genetic information. It flags early signs of diabetes risk that standard HbA1c tests may miss.

“We showed that two people with the same HbA1c score can have very different underlying risk profiles,” says co-senior author Giorgio Quer, the director of artificial intelligence and assistant professor of Digital Medicine at Scripps Research.

While some variation in blood sugar is completely normal – especially after eating – frequent or exaggerated glucose spikes can be a sign that the body is struggling to manage sugar effectively.

In healthy individuals, blood sugar typically rises and falls smoothly.

But in people at risk for diabetes, these spikes can become sharper, more frequent or slower to resolve, even before routine lab tests like HbA1c pick up a problem.

The new study shows that tracking these day-to-day dynamics provides a much more detailed view of a person’s metabolic health, and might help identify trouble earlier.

Using the data, the researchers trained an AI model to distinguish people with type 2 diabetes from healthy individuals.

One of the clearest signals of diabetes risk that the researchers found was the time it takes for a blood sugar spike to return to normal.

In people with type 2 diabetes, it often took 100 minutes or more for blood sugar to decrease after a spike, while healthier individuals returned to baseline much faster.

The study also found that people with a more diverse gut microbiome and higher activity level tended to have better glucose control, while a higher resting heart rate was linked to diabetes.

Importantly, the AI model didn’t just detect risk in people with already elevated HbA1c.

When applied to pre-diabetic individuals, it found that some looked metabolically similar to those with diabetes, while others resembled healthy individuals, despite having similar lab values.

This level of granularity could help clinicians personalize treatment—focusing on lifestyle changes or early therapies for patients with the highest risk of disease progression.

While the current study was a snapshot in time, the researchers are continuing to follow participants to see whether the model’s predictions translate to real-world disease progression.

They also validated the model using a separate set of patient data from Israel, strengthening its potential for broader clinical use.

The team envisions future versions of the model being used by clinicians, or even individuals using CGMs at home, to assess metabolic risk and monitor how daily choices affect diabetes.