AI Blood Test Could Detect Early Eye Nerve Damage in People with Type 2 Diabetes
Key Takeaways:
- An AI tool called Pro-DRN uses a blood sample to flag people with type 2 diabetes at high risk of diabetic retinal neurodegeneration (DRN), before any damage shows on the retina.
- It was trained on 1,218 participants and validated in 502 people from UK Biobank, identifying 71 proteins linked to DRN – with ACTA2, COL6A3 and HSPG2 the strongest predictors.
- As retinal nerves are among the first tissues affected by diabetes, the test could also hint at wider nerve damage and help target earlier monitoring and future treatments.
A simple blood test to catch nerve damage early
Scientists have developed an AI-assisted prediction tool that can identify people with type 2 diabetes who are at high risk of developing diabetic retinal neurodegeneration (DRN) before symptoms appear. The findings were published in the journal PLOS Medicine.
The work was led by Wei Wang, MD, PhD, associate professor at the Guangdong Provincial Clinical Research Center for Ocular Diseases. According to the authors, the damage that diabetes inflicts on the delicate nerves of the eye appears to leave a detectable molecular trail in the bloodstream long before it becomes visible in the eye itself.
“Our study suggests that early retinal nerve damage in diabetes leaves measurable signals in the blood,” write the authors. “These findings suggest that a simple blood test analyzed with artificial intelligence may help identify people with diabetes who are at highest risk of early retinal nerve damage, well before visible damage appears on the retina.”
Why the retinal nerves matter in diabetes
Type 2 diabetes affects more than half a billion people worldwide, and it carries an increased risk of long-term complications, including progressive neurodegeneration – the gradual deterioration of nerve tissue over time.
The nerves of the retina are among the earliest tissues to be affected. As this damage advances, it can eventually lead to severe visual impairment and the loss of sight. The difficulty for clinicians is one of timing: current diagnostic methods can only detect DRN once the retina has already sustained irreversible damage. By the time the problem is visible, the window for early, protective intervention has often closed.
How the Pro-DRN tool was built
To address this, Wang and colleagues developed a machine learning algorithm called Pro-DRN. They drew on data from 1,218 participants in the Guangzhou Diabetic Eye Study, all of whom had been diagnosed with type 2 diabetes but had not yet developed DRN at the point of enrolment.
The model combined two distinct streams of information. The first was proteomics data – a detailed read-out of the proteins circulating in participants’ blood samples. The second was a series of yearly retinal images, capturing the state of the eye over a six-year follow-up period. By matching the molecular signals in the blood against how each person’s retina changed year on year, the algorithm learned which blood-borne patterns preceded the onset of nerve damage.
The proteins behind the predictions
The analysis surfaced 71 proteins associated with the development of DRN. Of these, three stood out as the most consistent drivers of accurate prediction: ACTA2, COL6A3 and HSPG2. These are key structural components involved in maintaining the integrity of the nerve and muscle tissue in the eye, which helps explain why disturbances in their levels might signal nerve tissue under strain.
Crucially, the team did not rely on a single dataset. The results were validated in an independent cohort of 502 people from UK Biobank, where the core effects and protein signals were reproduced – an important check that the findings were not simply a quirk of the original group.
From research tool to clinical aid
Pro-DRN has been made available as an interactive, web-based risk assessment tool that clinicians can use to support early DRN screening and to monitor how a person’s risk evolves over time. People identified as being at high risk could then benefit from more frequent check-ups and from early interventions aimed at preventing or slowing progressive neurodegeneration, rather than waiting for damage to become apparent.
A window into the wider nervous system
The potential significance of the test reaches beyond the eye. Because DRN is one of the first signs of nerve degeneration brought on by diabetes, detecting it early could also signal the onset of nerve injury elsewhere in the body.
Such damage can contribute to cognitive impairment, dementia and peripheral neuropathy – the latter causing loss of sensation and motor control in the hands, feet and other extremities. Viewed this way, a single eye-focused test could offer valuable insight into the overall health of a person’s nervous system.
New possibilities for treatment and trials
The discoveries also open up two further avenues. The proteins identified as being involved in DRN progression could be investigated as potential targets for the development of new therapies. In addition, the AI-based tool could prove useful for selecting and stratifying participants in clinical trials that are evaluating neuroprotective strategies designed to prevent or delay nerve damage – helping ensure such studies enrol the people most likely to show a measurable benefit.
Looking ahead
For the researchers, the broader ambition is a shift in how diabetic eye care is approached – from reacting to damage that has already occurred towards anticipating who is most vulnerable.
“Pro-DRN may help move diabetic eye care from detecting established damage toward earlier, molecularly informed risk stratification, so that closer monitoring and future neuroprotective interventions can be directed to the people most likely to benefit,” Wang and colleagues write.
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New Triple-Hormone Injection Shows Major Weight Loss in People with Type 2 Diabetes and Obesity
Key Takeaways:
- In a phase 3 trial, retatrutide cut body weight more than four times as much as placebo and roughly doubled the reduction in long-term blood sugar.
- The once-weekly jab works through three hormone pathways at once – GLP-1, GIP and glucagon – the last of which may help raise energy expenditure.
- Experts called the results encouraging but stressed that head-to-head trials against existing drugs are still needed.
A new approach to managing type 2 diabetes
A once-weekly injection that works through three hormone pathways at the same time could deliver substantial reductions in both blood sugar and body weight for people with type 2 diabetes, according to phase 3 trial results.
People taking part in the trial who received weekly retatrutide injections over 40 weeks lost more than four times as much weight as those given a placebo, while their average reduction in long-term blood sugar (HbA1c) was more than twice that seen in the placebo group.
How retatrutide works
Retatrutide is described as a triple-hormone drug because it mimics three gut hormones that help regulate appetite, blood sugar and metabolism: GLP-1, GIP and glucagon.
This sets it apart from several medications already in use. Drugs such as Ozempic and Wegovy primarily target the GLP-1 pathway to suppress appetite, while Mounjaro combines GLP-1 with GIP to help control blood-sugar levels. Retatrutide goes a step further by also engaging the glucagon receptor, which is thought to help increase energy expenditure.
Inside the phase 3 trial
The trial, published in the Lancet, randomly assigned 930 adults with type 2 diabetes to receive either 4mg, 9mg or 12mg of retatrutide, or a placebo.
None of the participants were already taking diabetes medicines. All had inadequately controlled blood-sugar levels and a body mass index (BMI) of at least 23.
Throughout the trial, researchers monitored a range of health markers, including HbA1c, weight, cholesterol levels and other indicators, and recorded any side-effects that arose.
What the results showed
After 40 weeks, participants receiving retatrutide saw their HbA1c fall by an average of about 1.7–1.9 percentage points, compared with 0.8 in the placebo group.
The weight loss results were similarly marked. On average, participants taking retatrutide lost about 11.5%–15.3% of their body weight, against 2.6% for those on placebo. Cholesterol and blood pressure also improved among people taking the drug.
Safety and side-effects
Fourteen participants experienced serious adverse events during the trial, including two in the placebo group. For most people, however, side-effects were mild to moderate and eased over time, with gastrointestinal symptoms the most commonly reported.
What the findings could mean
The study authors say this triple-action medication has the potential to improve health outcomes for some people, including greater weight loss, particularly for those who may need more intensive treatment regimens to manage their type 2 diabetes. Further clinical trials are continuing.
The results follow earlier findings from the manufacturer, Eli Lilly, which suggested that retatrutide was highly effective at reducing weight among people with obesity.
What the experts say
Dr Kath McCullough, special adviser on obesity at the Royal College of Physicians, said the findings were very encouraging.
“For many people living with diabetes and obesity, treatments like this could be genuinely life-changing,” she said.
“However, medications are not a silver bullet. While they are proving to be effective, the long-term goal must be to prevent people from needing them in the first place.”
Dr Marie Spreckley, a specialist in prevention of diabetes and related metabolic disorders at IMS Epidemiology, University of Cambridge, said the results were striking: “The magnitude of weight loss observed is particularly notable. However, because this study compared retatrutide with placebo rather than semaglutide or tirzepatide, it is not possible to determine from this data whether retatrutide is superior, equivalent or inferior to currently available therapies. Direct head-to-head trials will be required before firm conclusions can be drawn regarding comparative effectiveness.”
She added that weight loss alone did not necessarily equate to optimal health outcomes, and that people needed support to maintain adequate nutritional intake, preserve muscle mass and maximise long-term health during treatment.
Dr Lucy Chambers, the head of research impact and communications at Diabetes UK, said: “These encouraging findings show that this new class of drug for type 2 diabetes could deliver dual benefits for both weight loss and blood-sugar management. We look forward to further research to understand its long-term effects and how it compares to treatments already available on the NHS.”
Source: The Guardian
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Algorithm-Guided Insulin Dosing Improves Blood Sugar Control in Type 2 Diabetes
Key Takeaways:
- An algorithm paired with continuous glucose monitoring significantly increased time in target glucose range compared with standard self-monitoring approaches
- The tool provides personalised weekly insulin dose recommendations based on recent glucose data, helping to simplify titration
- Early findings suggest strong patient acceptability and potential to enhance diabetes management at scale, though larger trials are needed
A data-driven approach to insulin adjustment
A novel algorithm developed by researchers at the University of Virginia Center for Diabetes Technology has demonstrated encouraging results in supporting people living with Type 2 Diabetes to better manage their blood glucose levels.
The system works in combination with a continuous glucose monitor and provides tailored recommendations for insulin dose adjustments. Rather than relying solely on manual interpretation of glucose readings, the algorithm analyses patterns over time and offers structured, data-informed guidance.
In a clinical trial involving 30 participants, individuals were randomly assigned to one of two approaches over a 16-week period:
- Algorithm-guided insulin adjustment using continuous glucose monitoring data
- Traditional self-monitoring of blood glucose with independent dose adjustment
The results showed a marked improvement in glycaemic control among those using the algorithm. Participants in this group increased their average time spent within a safe blood glucose range from 54.1% to 75.3%. By contrast, those relying on self-monitoring alone saw a more modest increase from 50.2% to 55.3%.
Moving beyond traditional insulin management
The findings highlight the growing role of digital health tools in diabetes care. According to Marc D. Breton, the study’s lead author:
“These results clearly show that diabetes technology and advanced algorithms can be leveraged to great effects, well beyond the classical paradigm of automated insulin delivery. As continuous glucose monitoring and connected medical devices become ubiquitous, we have the opportunity to provide highly personalized advice and monitoring to people with diabetes and guide their use of insulin and medications. Showing the impact of these technologies in early insulin therapy (only one dose a day) opens the door to helping the vast majority of people using insulin, well beyond what we were able to achieve with automated insulin delivery.”
This perspective reflects a broader shift towards personalised, technology-enabled care. Rather than fully automated systems alone, there is increasing interest in decision-support tools that augment clinical judgement and patient self-management.
Addressing the challenges of insulin titration
For many people living with type 2 diabetes, treatment often begins with oral or non-insulin therapies. However, as the condition progresses, insulin may become necessary to maintain adequate glycaemic control.
Adjusting insulin doses – a process known as titration – can be complex and burdensome. It typically requires frequent monitoring, interpretation of glucose patterns, and iterative dose changes. Importantly, there is no universally standardised titration protocol, which can create variability in care and outcomes.
To address this, Anas El Fathi developed the algorithm with the aim of streamlining and improving this process. The system evaluates two weeks of continuous glucose monitoring data and generates weekly recommendations for insulin dose adjustments, offering a structured and personalised approach.
Strong acceptance and clinical potential
The study also explored how well the technology was received by participants. According to Ralf Nass:
“From a medical point of view, it was fascinating to see that the algorithm was not only better than the standardized insulin titration recommendations, but also how well the technology was accepted by the participants with type 2 diabetes. This type of technology has the potential to help physicians enable their patients to achieve better glycemic control faster by using a personalized approach.”
This combination of improved outcomes and user acceptability is particularly important, as adherence and engagement remain key challenges in long-term diabetes management.
Future directions – towards more personalised diabetes care
While the results are promising, the researchers emphasise that further validation is required. Larger and longer clinical trials will be needed to confirm the effectiveness of the algorithm across more diverse populations.
Looking ahead, the integration of more advanced data-driven approaches may further enhance personalisation. Breton noted:
“It is only the very beginning of these efforts. With early demonstration behind us, we can focus on robust approaches that will be effective with more varied populations. Integrating recently developed data-driven methodologies, especially digital twins, to further improve our capacity to tailor diabetes managements to individuals is likely to once more revolutionize diabetes care.”
Such developments could represent a significant step forward in precision medicine for people living with diabetes.
Study publication and funding
The findings have been published in the peer-reviewed journal Diabetes Technology & Therapeutics, with the article available as open access.
The research team included El Fathi, Nass, Carol J. Levy, Camilla Levister, Grenye O’Malley, Nirali A. Shah, Shaziah Hassan, Cheryl Quainoo, Chaitanya L.K. Koravi, Taylor N. Nguyen, Giulio Matteo Santini, Emma Emory, Carlene Alix, Dillon K. Flanagan, David Fulkerson, Mary Clancy Oliveri, Christian Laugesen, Jonas K. Lineolov, Peter W. Hansen and Breton.
The clinical trial was supported by a grant from Novo Nordisk.
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Genetic Risk Scores Offer Improved Prediction of Obesity, Type 2 Diabetes and Long-Term Health Outcomes
Key Takeaways:
- A new polygenic risk score integrates genetic data from over 8.5 million people to better predict obesity and type 2 diabetes risk
- The model goes beyond traditional measures such as body mass index by incorporating multiple aspects of metabolic function
- Individuals with higher genetic risk were more likely to develop complications and require interventions such as GLP-1 therapy or bariatric surgery
A more comprehensive approach to metabolic risk
Obesity and type 2 diabetes are complex metabolic conditions influenced by a combination of environmental, behavioural and genetic factors. While traditional clinical measures such as body mass index have long been used to assess risk, they do not fully capture the biological complexity underlying these conditions.
In a new study published in Cell Metabolism, researchers from Mass General Brigham have developed an advanced polygenic risk score designed to improve prediction of both obesity and type 2 diabetes, as well as their long-term health consequences. Polygenic risk scores work by aggregating the effects of many genetic variants across the genome, providing an estimate of an individual’s predisposition to developing a given condition.
“Our intention was to not only capture the risk of being diagnosed with obesity or diabetes, but also to better predict health consequences across the life course by integrating many aspects of metabolic function,” said co-first author Min Seo Kim, MD, MSc. “In the future, this genomic approach could complement established clinical risk factors to inform patient care and preventative strategies.”
Building a next-generation polygenic risk score
The research team constructed two distinct metabolic risk scores – one optimised for obesity and another for type 2 diabetes. Unlike conventional models, these scores incorporate genetic signals linked to 20 different traits associated with metabolic health. These include factors such as fat distribution, insulin regulation and glucose control.
To build these models, the investigators drew on genome-wide association studies conducted across some of the largest biobank datasets globally, encompassing more than 8.5 million individuals. This scale allowed the researchers to capture a broad and diverse range of genetic influences.
Importantly, the model moves beyond reliance on body mass index alone, reflecting a growing recognition that metabolic health cannot be fully understood through weight-based measures in isolation.
Predicting disease progression and clinical outcomes
Beyond predicting the likelihood of developing obesity or type 2 diabetes, the new polygenic risk scores demonstrated the ability to forecast downstream health outcomes.
The researchers found that individuals identified as high risk were more likely to go on to develop complications such as cardiovascular disease and stroke. Even among people who were initially healthy, those with a high genetic risk score were approximately twice as likely to require clinical interventions over time.
Specifically, individuals with higher polygenic risk scores were about twice as likely to receive GLP-1 receptor agonist medications or undergo bariatric surgery compared with those with average risk scores, over a median follow-up period of 5.5 years.
These findings suggest that genetic profiling could help identify people at risk earlier in the disease trajectory, potentially enabling more proactive and targeted care.
Improved performance across diverse populations
A notable strength of the study lies in its use of multi-ancestry genetic data. By incorporating genome-wide association studies from a wide range of populations, including African, East Asian, South Asian and Middle Eastern groups, the researchers were able to develop risk scores that performed better across diverse populations than earlier models.
Historically, many genetic prediction tools have been less accurate in non-European populations due to limited representation in genomic datasets. This study represents a step towards addressing that imbalance and improving equity in precision medicine.
Towards more personalised prevention and treatment
The research team emphasises that this work is part of a broader effort to refine understanding of the genetic subtypes of obesity and type 2 diabetes. Improved classification of these conditions could support more precise patient stratification in clinical trials and, ultimately, more tailored interventions in routine care.
“We want clinicians to be able to think about metabolic conditions in terms beyond body mass index, with a focus more broadly on underlying genetic susceptibility,” said co-senior author Akl Fahed, MD, MPH, of the Cardiovascular Research Center at Massachusetts General Hospital and an interventional cardiologist with the Mass General Brigham Heart and Vascular Institute. “Early identification of people who are likely to have a worse trajectory of poor metabolic health, before they even develop these conditions, can help us improve prevention and clinical interventions. That is how we can cure disease, and that is the bold mission that we are after.”
Implications for clinical practice
While further validation and implementation work will be required, the findings highlight the potential role of genomic tools in enhancing current approaches to metabolic disease prevention and management. By complementing existing clinical risk factors, polygenic risk scores could support earlier identification of people at risk and enable more personalised, proactive care pathways.
As healthcare systems increasingly move towards precision medicine, integrating genetic insights with clinical decision-making may become an important step in improving outcomes for people living with obesity and type 2 diabetes.
CCH insights:
This is exciting research, and a big step towards precision obesity prevention, as it gives us an individual risk score for obesity and diabetes for each patient. However, it is only half the story – ideally we’d also like to be able to determine what type of interventions will work best for each individual (in terms of diet, lifestyle and medicine) in order to optimise their chances of good metabolic health and achieving a healthy weight. Hopefully the ability to do this is not too far away.
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