NEW ALZHEIMER'S BLOOD TEST MAY TELL WHEN SYMPTOMS ARE AROUND THE CORNER

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From: NIH OLIB (NIH/OD) <olib@od.nih.gov>
Date: Wed, Jul 1, 2026 at 9:05 AM
Subject: NEW ALZHEIMER'S BLOOD TEST MAY TELL WHEN SYMPTOMS ARE AROUND THE CORNER
To: <NIHPRESS@list.nih.gov>

U.S. Department of Health and Human Services

NATIONAL INSTITUTES OF HEALTH NIH News

National Institutes of Health <https://www.nih.gov/>

For Immediate Release: Wednesday, July 4, 2026

 

CONTACT:  NIH Office of Communications, <https://www.hhs.gov/request-for-comment-form/index.html?Agency=NIH>

 

NEW ALZHEIMER'S BLOOD TEST MAY TELL WHEN SYMPTOMS ARE AROUND THE CORNER

A novel biomarker beats the leading diagnostic blood test at predicting disease progression

 

Small loops of genetic material may be strong indicators of imminent Alzheimer’s disease (AD) symptoms. In a National Institutes of Health (NIH)-funded study, researchers showed that elevated levels of certain circular RNAs (circRNAs) in the blood nearly tripled patients’ risk of developing symptoms, suggesting these molecules are more sensitive to symptom onset than traditional AD biomarkers.

 

Current AD blood tests offer reliable diagnoses by detecting markers of amyloid plaques, a hallmark of the disease. However, these tests, which can produce positive results potentially decades before cognitive impairment, are not highly informative of how a patient’s disease will progress. This new research lays groundwork for a kind of test that could potentially predict symptom onset.

 

“In a clinical setting, being able to identify patients on the verge of symptom onset would be invaluable. Having this information could help us select the right patients for clinical trials and better determine which treatments are effective at preventing cognitive decline,” said Richard Hodes, M.D., director of NIH’s National Institute on Aging (NIA).

 

Unlike amyloid plaques, which accumulate slowly in the brain, circRNAs are far more dynamic, reflecting the brain’s more recent activity. In a previous study, Carlos Cruchaga, Ph.D., and colleagues at the Washington University School of Medicine, St. Louis, linked circRNAs in the brain to dementia and neuropathological severity. To learn if these molecules held clinical promise, they needed to see if these associations held up among circRNAs circulating in blood, a far more accessible tissue. 

 

Cruchaga and his co-authors analyzed blood data from more than 1,200 people from multiple independent cohorts, finding a set of 34 circRNAs that were associated with AD. Predictive models based on these associations successfully identified individuals with AD pathology, performing similarly to models trained on the protein pTau217 data — the leading clinical blood-based biomarker for AD. 

 

The circRNA model far surpassed the pTau217 model when looking into the future, however. The 34 circRNAs were stronger predictors of a patient’s progression to symptomatic AD, with additional experiments suggesting that their levels seem to diverge from normal about two to four years prior to symptom onset. Notably, the authors produced similar findings in samples from two independent cohorts.

 

These results may be foundational for tests that could not only help clinicians identify candidates for novel treatments but also monitor their response, especially for drugs that target amyloid plaques. 

 

“Patients being treated with novel Aβ-removal therapies, can become pTau negative but still have Alzheimer’s disease. These circular RNAs may grant us a more complete perspective of someone’s overall disease biology,” said Cruchaga, the study’s corresponding author. 

 

Together with commercial partners, the researchers are currently working to develop translatable clinical assays for blood-based circRNAs. 

 

“It’s nice to have good science and models, but we’re ultimately doing this to help people,” Cruchaga said.

 

NIH supported this research through NIA grants R01AG064614, U01AG084514, R01AG078964, R01AG058501, R01AG071706, P30AG066444, R01AG064877, P30AG066444, P01AG03991, and P01AG026276 and National Institute of Neurological Disorders and Stroke (NINDS) grant P01NS131131.

 

About the National Institute on Aging (NIA): NIA seeks to understand the nature of aging and diseases associated with growing older, with the goal of extending the healthy, active years of life. https://www.nia.nih.gov 

 

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit <https://www.nih.gov>.

 

NIH...Turning Discovery into Health -- Registered, U.S. Patent and Trademark Office

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Reference: Bridget Phillips et al. Blood-Based Circular RNAs for early diagnosis of Alzheimer’s Disease. Nature Medicine. 2026. DOI: 10.1038/s41591-026-04485-5

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Ginkgo autonomous lab

https://cloud.ginkgo.bio/protocols/pichia-protein-expression-labchip?utm_content=380782681&utm_medium=social&utm_source=twitter&hss_channel=tw-21675323 

this can be a good ideas to implement AI predictions. 

alzheimer disease neuralimage initiative

alzheimer disease neuralimage initiative

https://adni.loni.usc.edu/data-samples/adni-data/genetics-related-omics/

HyPhy and SARS-CoV-2

gemini:  

While HyPhy is a cornerstone of molecular evolutionary informatics and a gold standard for tracking natural selection, deploying it for large-scale SARS-CoV-2 mutation analysis exposes several algorithmic, statistical, and architectural limitations.

The primary challenges stem from a fundamental mismatch: HyPhy was historically designed to extract selection signals from deeply diverged inter-species data, whereas SARS-CoV-2 genomic tracking involves massive datasets with low sequence divergence.

The core weaknesses and limitations of using HyPhy for SARS-CoV-2 mutation analysis can be categorized across four operational domains:

1. Scale and Memory Bottlenecks (The "Big Data" Problem)

• The Sampling Explosion: Throughout the pandemic, millions of SARS-CoV-2 genomes have been deposited into databases like GISAID. HyPhy’s maximum-likelihood ($ML$) or Bayesian engines (even fast approximations like FUBAR) cannot directly swallow alignments of tens of thousands—let alone millions—of sequences due to exponential memory scaling.

• Heavy Pre-Filtering Overhead: To utilize HyPhy, researchers must heavily subsample datasets down to a few hundred or thousand representative sequences per Pango lineage (often using tools like hyphy cln to strip identical sequences). This aggressive subsampling risks dropping rare, emerging mutations before they reach a critical mass to trigger selection flags.

• Tree Invariance Assumptions: HyPhy typically requires a fixed, pre-computed guide tree (often generated by IQ-TREE or FastTree). If the underlying tree topology contains errors—which is common when resolving polytomies in massive SARS-CoV-2 phylogenies—HyPhy’s subsequent selection estimates inherit those biases.

2. Low Divergence and the "Phylogenetic Signal" Deficit

• The Star-Like Phylogeny Problem: SARS-CoV-2 genomes are closely related, often differing by only a handful of single-nucleotide polymorphisms ($SNPs$) across the 30kb genome. Because mutations accumulate slowly relative to the massive explosion of cases, the underlying tree structures contain vast unresolved polytomies (nodes with many concurrent child branches). HyPhy relies on historical branch lengths and substitution counts to calculate rates; when branches have zero or near-zero lengths, the statistical power to resolve selection pressures drops significantly.

• High Variance in $\omega$ ($dN/dS$) Ratios: HyPhy estimates selective pressure by calculating the ratio of non-synonymous to synonymous substitution rates ($\omega = dN/dS$). At individual codon sites with very low numbers of total mutations, the variance of these estimates becomes massive. A single sequencing artifact or random drift event can artificially spike the $dN$ value, leading to a false-positive flag for positive selection.

3. Confounding Neutral Processes

• Founder Effects vs. True Adaptation: Because SARS-CoV-2 exhibits intense super-spreading dynamics, specific viral variants frequently experience massive population expansions purely due to demographic luck (e.g., an infected individual attending a large event). HyPhy models often struggle to distinguish a mutation that is actively driving fitness (true positive selection) from a neutral mutation that rode along on an expanding lineage due to a founder effect.

• Strong Mutation Biases: SARS-CoV-2 evolution is intensely dominated by specific host-mediated mutational biases, most notably a high frequency of $C \to U$ transitions driven by host APOBEC deaminase enzymes. Standard codon substitution models in HyPhy assume that mutations are primarily driven by replication errors. If a site experiences a high frequency of $C \to U$ transitions due to host editing, HyPhy may misinterpret this directional mutational pressure as a strong signal of positive Darwinian selection.

4. Algorithmic Specifics & Structural Blind Spots

• Blindness to Insertions and Deletions (Indels): Critical evolutionary milestones in SARS-CoV-2 variants—such as the recurrent recurrent deletion regions ($RDRs$) in the Spike protein N-terminal domain seen in Omicron lineages—are heavily driven by indels. Because HyPhy’s core statistical engines treat gaps (-) as missing data or strip them out during standard codon alignment preprocessing, it completely misses the selective forces operating on these structural alterations.

• Epistasis and Complex Interaction Limitations: Standard site-level models (MEME, FEL) look at each codon position in total isolation. However, SARS-CoV-2 fitness is deeply epistatic; for instance, the impact of a mutation in the Receptor Binding Domain ($RBD$) often depends entirely on compensating mutations elsewhere in the Spike or Scaffold proteins. While HyPhy features co-evolutionary tools (like Bayesian Graphical Models), running them at scale across large SARS-CoV-2 alignments to detect multi-site epistatic networks is computationally prohibitive.

Summary of Common HyPhy Methods vs. SARS-CoV-2 Context

Method	Intended Strength	SARS-CoV-2 Limitation
MEME	Finds episodic selection on individual branches.	High rate of false positives on short, low-divergence branches.
FUBAR	Extremely fast Bayesian calculation for large alignments.	Scaling fails if data isn't aggressively down-sampled first.
GARD	Detects recombination breakpoints.	Overwhelmed by the low diversity of intra-host recombinant lineages (like XBB), as it needs distinct parental signatures to map breakpoints accurately.