Tag: Biomarker

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Prediction of Relapse Using Digital Technology in People in Recovery From Substance Use Disorders: Early Economic Evaluation With a Case Study of the Subreal App

Background: Many people relapse after achieving abstinence in substance use disorders. Health care providers may scan the horizon for new technologies to predict response that allow interventions to be targeted rather than routine. Currently, no such predictive technologies are available in the United Kingdom. The Subreal app is available for use in research contexts, but no clinical data specific to the app are yet available. Early health economic modeling can use data from the literature to explore characteristics essential for the new technology to be cost-effective. This information can guide developers in setting performance targets and pricing and estimating potential cost savings and/or cost-effectiveness for health care providers. Objective: This study was supported by a UK industry funding body to explore the potential of digital technologies such as the Subreal app to offer cost savings or cost-effectiveness for health care providers. We explored the threshold price and clinical effectiveness required to deliver cost savings and cost-effectiveness in 2 subpopulations with substance use disorders in a UK setting. Methods: Deterministic models were used to estimate costs per relapse and quality-adjusted life years over 1-, 5-, and 20-year time horizons for people who have achieved abstinence after treatment for alcohol or opioid misuse. The intervention was a digital technology predicting relapse, provided—in addition to standard care—for 1 year post achievement of abstinence. In Subreal, biomarker data are collected daily through the app, and artificial intelligence–enhanced risk assessment flags patients who require additional support. The comparator was event-driven, reactive response to relapse. Costs and quality-of-life estimates were calculated using Markov models with data from existing published sources. The base-case estimate of 15% reduction in first-year relapse rates was based on a previous study on a similar but simpler digital technology. Results: Digital technologies such as the Subreal app have the potential to be cost-saving from a UK health and social care perspective, especially when used over a longer time horizon. Assuming a reduction of 15% in first-year relapse rates, digital technologies have the potential to be cost-saving, provided that they do not cost more than £300 (US $400.09) and £460 (US $613.47) per patient per annum for alcohol and opioid use disorders, respectively. No cost was included for postalert care, as it was assumed that this could be met within existing resources. Cost savings would be achieved predominantly through a reduction in treatment requirements as fewer people relapse. Price thresholds would reduce correspondingly if a <15% reduction in relapse rates were achieved. Conclusions: Developers of digital technologies that aim to reduce relapse need to focus on the generation of evidence of clinical effectiveness and develop a commercially sustainable pricing model that allows health care providers to benefit from cost savings.
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Blood Biomarker Can Predict Signs of Alzheimer’s Before PET Scans

A blood test that measures plasma phosphorylated tau 217 is capable of predicting future Alzheimer’s disease onset in cognitively normal older adults even when positron emission tomography scans do not show amyloid or tau build up in the brain.

“We used to think that positron emission tomography (PET) scan detection was the earliest sign of Alzheimer’s disease progression, revealing amyloid accumulation in the brain 10 to 20 years before symptoms appear,” said lead author Hyun-Sik Yang, MD, a neurologist with Mass General Brigham Neuroscience Institute, in a press statement.

“But now we are seeing that phosphorylated tau 217 (pTau217) can be detected years earlier, well before clear abnormalities appear on amyloid PET scans.”

In 2025, the FDA approved two blood tests for Alzheimer’s disease, one that compares the ratio of pTau217 to beta amyloid developed by Fujirebio and a pTau181 plasma test developed by Roche. However, both of these tests are only indicated for people who already have some symptoms of the condition.

The current study, published in Nature Communications, aimed to test whether pTau217 in the blood can forecast beta‑amyloid and tau build‑up in the brain before individuals become amyloid‑positive on PET scans.

Overall, 317 older adults, aged 72 years on average, from the Harvard Aging Brain Study were included in the study. About 60% were women. There were no signs of cognitive decline on enrollment, and the group had a high education level. The researchers followed up 245 people in the group with repeat amyloid scans for about six years on average.

The test was able to pick up cases where beta amyloid was visible on brain scans with a high level of accuracy. The study also showed that higher starting pTau217 levels predicted faster amyloid build‑up over time, even after accounting for age, sex, and APOE status

Centiloid units are the standard scale for amyloid on brain scans with 100 centiloids typical of full-blown disease. The team found that the test could also predict future amyloid buildup even if no signs could be seen on initial PET scans. Each one‑percentage‑point increase in pTau217 at baseline was linked to an extra 0.35 centiloid units of amyloid buildup per year. Notably, those with low pTau217 levels on enrollment were still amyloid negative on scans years later.

“What stood out in our study is that even when amyloid scans appear normal in the clinic, the pTau217 biomarker can identify individuals who later become amyloid-positive,” said Yang. “It also shows that those with low pTau217 levels are likely to stay amyloid-negative for several years.”

The post Blood Biomarker Can Predict Signs of Alzheimer’s Before PET Scans appeared first on Inside Precision Medicine.

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<![CDATA[Blood-based biomarkers for Alzheimer disease continue to improve, shares expert researcher. ]]>
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STAT+: University of Michigan wins 2026 STAT Madness for new insights into abdominal aortic aneurysms

An abdominal aortic aneurysm is a life-threatening vascular condition with limited treatment options. 

Now, researchers from the University of Michigan Frankel Cardiovascular Center have identified a driving force behind the condition, opening up a potential target for new therapies. Their paper uncovering the causal link between triglycerides and abdominal aortic aneurysms won the STAT Madness 2026 popular vote. 

Triglycerides have long been considered a biomarker for vascular disease. But using three different mouse models, the Michigan team demonstrated that the common type of fat plays a direct role in aneurysm development, and that lowering triglyceride levels with certain drugs can stop them from forming and rupturing. 

Continue to STAT+ to read the full story…