Findings could lead to clinical laboratory test that can both track the disease’s progress and differentiate it from other forms of dementia
Another research study is underway with hopes of developing a new clinical laboratory blood test to aid in the diagnoses of Alzheimer’s disease and help physicians determine the best course of treatment.
Researchers at the Washington University School of Medicine (WashU Medicine) in St. Louis and Lund University in Sweden have developed a test that focuses on the presence of a protein called MTBR-tau243, a potential biomarker for Alzheimer’s. This protein is correlated to the toxic accumulation of tau aggregates in the brain and the severity of the disease, according to a WashU new release.
Cognitive tests and brain imaging are also used to diagnose the condition. However, existing tests cannot establish how far the illness has progressed. Alzheimer’s therapies are most effective during early stages, so determining the disease’s progression could provide insights doctors need to devise the most effective treatment protocols.
Washington University’s new blood test that identifies MTBR-tau243 protein could lead to new biomarkers as well as identifying how far the disease has progressed.
“This blood test clearly identifies Alzheimer’s tau tangles [aka, neurofibrillary tangles], which is our best biomarker measure of Alzheimer’s symptoms and dementia,” said co-senior author Randall J. Bateman, MD, professor of neurology at WashU Medicine in the WashU news release.
“In clinical practice right now, we don’t have easy or accessible measures of Alzheimer’s tangles and dementia, and so a tangle blood test like this can provide a much better indication if the symptoms are due to Alzheimer’s and may also help doctors decide which treatments are best for their patients,” said co-senior author Randall J. Bateman, MD, professor of neurology at WashU Medicine in a news release. (Photo copyright: Washington University.)
Distinguishing between Alzheimer’s and Other Forms of Dementia
The WashU scientists tested the study participants in three main stages of Alzheimer’s disease:
Pre-symptomatic.
Early stage with mild cognitive impairment.
Late symptomatic where patients have been diagnosed with dementia.
The study included 108 volunteers from WashU Medicine’s Charles F. and Joanne Knight Alzheimer Disease Research Center and a subset of 55 people from the Swedish BioFINDER-2 study, which aims to discover key mechanisms in neurodegenerative disorders. The scientists validated their results in an independent dataset involving 739 other people in the BioFINDER-2 database. The patient information used for the study represented patients from all stages of the disease.
Alzheimer’s disease involves an accumulation of amyloid into plaques in the brain, which turn into tangles of tau proteins. When these tau tangles become detectable, cognitive symptoms begin to occur and exacerbate as the tangles spread. WashU’s new blood test can detect MTBR-tau243 levels in the brain with 92% accuracy. The researchers also found that MTBR-tau243 levels were significantly higher for patients in the mild cognitive stage of the disease and up to 200 times higher for patients in the late symptomatic stage.
“MTBR-tau243 is a chipped (off) piece of the protein in Alzheimer’s tau tangles,” Bateman told Medical News Today. “The blood test measures this piece of tau tangles in the blood as a measure of how many tangles are in the brain.”
The researchers also found that MTBR-tau243 levels were normal in patients with cognitive symptoms attributed to diseases other than Alzheimer’s, suggesting that the test can distinguish Alzheimer’s dementia from other forms of dementia.
“We’re about to enter the era of personalized medicine for Alzheimer’s disease,” said Kanta Horie, PhD, voluntary research associate professor of neurology at WashU Medicine, co-first and co-corresponding author of the study, in the WashU news release.
More Studies Needed
According to the Centers for Disease Control and Prevention (CDC), Alzheimer’s is the seventh leading causes of death in the US. It accounted for more than 120,000 deaths in 2022, the most recent year for available data. With the ebbing of COVID-19, which was ranked number four in 2022, Alzheimer’s is assumed to be higher in ranking for more recent years.
Washington University’s new blood test for Alzheimer’s may one day enable earlier detection of the disease, earlier intervention, and slowing of its advancement. However, more research and trials are needed into the theory behind this study.
“The initial study needs to be replicated in larger and more diverse populations to confirm its accuracy and reliability across different demographics, ethnicities, and stages of the disease,” Manisha Parulekar, MD, director of the Division of Geriatrics at Hackensack University Medical Center, told Medical News Today. “This includes testing individuals with other neurological conditions to ensure specificity. Clear and standardized protocols for blood collection, processing, and analysis must be established to ensure consistent and reproducible results across different laboratories and healthcare settings.”
Single genetic test can identify multiple pathogens and can be used by the UCSF clinical laboratory team to help physicians identify difficult to diagnose diseases
Continuing improvements in gene sequencing technologies and analytical software tools are enabling clinical laboratorians to diagnosis patients who have challenging symptoms. One such example is a new genomic test developed by researchers at University California, San Francisco (UCSF). The single test analyzes both RNA and DNA to detect almost any type of pathogen that may be the cause of specific illnesses.
The test uses a genomic sequencing technique known as metagenomics next-generation sequencing (mNGS). It works by sequencing genetic material found in blood, tissue, or body fluid samples and compares the sequenced data against a broad database of known pathogens to seek a match. Instead of looking for just one pathogen at a time, mNGS analyzes all of the nucleic acids, RNA, and DNA present in a sample simultaneously to detect nearly all pathogens, including viruses, bacteria, fungi, and parasites.
The mNGS test is not intended to replace existing clinical laboratory tests, but to help physicians diagnose an illness in cases where patients are experiencing severe symptoms, and where initial, commonplace tests are ineffective. In such cases, medical professionals require additional information to achieve a proper diagnosis.
“Our technology is deceptively simple,” said Charles Chiu, MD, PhD (above), professor of laboratory medicine and infectious diseases at UCSF and senior author of the studies in a news release. “By replacing multiple tests with a single test, we can take the lengthy guesswork out of diagnosing and treating infections.” The new technology may help physicians diagnose patients who have challenging symptoms and where current clinical laboratory testing is ineffective at identifying specific pathogens. (Photo copyright: University California San Francisco.)
Diagnostic Armamentarium for Physicians
According to an article published by the American Society for Microbiology (ASM) titled, “Metagenomic Next Generation Sequencing: How Does It Work and Is It Coming to Your Clinical Microbiology Lab?” mNGS is “running all nucleic acids in a sample, which may contain mixed populations of microorganisms, and assigning these to their reference genomes to understand which microbes are present and in what proportions. The ability to sequence and identify nucleic acids from multiple different taxa [plural for taxon] for metagenomic analysis makes this a powerful new platform that can simultaneously identify genetic material from entirely different kingdoms of organisms.”
The researchers developed the mNGS test years ago and it has produced promising results, including:
Diagnosing cases of encephalitis in transplant recipients to yellow fever in their organ donors.
Helping to identify the cause of a meningitis outbreak in Mexico among surgical patients.
Detecting a case of leptospirosis in a patient who was in a medically induced coma, which prompted doctors to prescribe penicillin and resulted in the full recovery of the patient.
Identifying the cause of neurological infections such as meningitis and encephalitis. The test successfully diagnosed 86% of neurological infections in more than 4,800 spinal fluid samples.
“Our mNGS test performs better than any other category of test for neurologic infections,” said Charles Chiu, MD, PhD, professor of laboratory medicine and infectious diseases at UCSF and senior author of the two studies, in a UCSF news release. “The results support its use as a critical part of the diagnostic armamentarium for physicians who are working up patients with infectious diseases.”
FDA Breakthrough Device Designation
The UCSF test has not yet been approved by the federal Food and Drug Administration (FDA), but it was granted a “breakthrough device” designation by the agency. This classification authorizes labs to use the test as a valid diagnosis method due to its potential ability to benefit patients.
Chiu told NBC News that the test costs about $3,000 per sample and fewer than 10 labs routinely use it due to several issues.
“Traditionally, it’s been used as a test of last resort, but that’s primarily because of issues involving, for instance, the cost of the test, the fact that it’s only available in specialized reference laboratories, and it also is quite laborious to run,” he said.
This type of lab testing is not feasible for most hospitals as it is costly and complicated, and because physicians may need assistance from clinical laboratory personnel who have the appropriate expertise to properly read test results.
“This just is not something that a clinical lab will be doing until somebody commercially puts it in a box with an easy button,” Susan Butler-Wu, PhD, associate professor of clinical pathology at the University of Southern California (USC), told NBC News. “It’s not a one-stop shop. It just can be helpful as an additional tool.”
Although the technology has some limitations, Chiu says the research performed by his team “raises the possibility that we perhaps should be considering running this test earlier” in symptomatic patients. He hopes the test will be used on a widespread basis in hospitals to diagnose various illnesses in the future.
“We need to get the cost down and we need to get the turnaround times down as well,” he told NBC.
Definitive Tool for Pathogen Detection
To increase access to the technology, Chiu and his colleagues founded Delve Bio, which is now the exclusive provider of the mNGS tool created at UCSF. In December, the company announced the commercial launch of Delve Detect, a metagenomic test for infectious diseases. According to its website, Delve Detect “offers genomic testing of cerebrospinal fluid (CSF) for more than 68,000 pathogens, with 48-hour turnaround time and metagenomics experts readily available to discuss results.”
“These findings support including mNGS as a core tool in the clinical workup for CNS [central nervous system] infections,” said Steve Miller, MD, PhD, UCSF volunteer clinical professor, laboratory medicine, and chief medical officer of Delve Bio in the UCSF news release. “mNGS offers the single most unbiased, complete and definitive tool for pathogen detection. Thanks to its ability to quickly diagnose an infection, mNGS helps guide management decisions and treatment for patients with meningitis and encephalitis, potentially reducing healthcare costs down the line.”
This mNGS test may prove to have the potential to greatly improve medical care for some infections and possibly expedite the detection of new viral threats. It is probable that clinical laboratories will soon be learning about and performing more tests of this nature in the future.
What researchers call “the largest proteomic study in the world” could lead to new clinical laboratory assays for determining genetic risk for multiple cancers
Examining blood proteins may be superior to clinical information in determining an individual’s risk for developing multiple diseases. That’s according to a new study conducted by researchers from the UK, America, and Germany who determined that measuring thousands of proteins from a single drop of blood can predict the onset of several illnesses.
The findings may provide clinical laboratories and physicians with new assays to more accurately predict an individual’s risk for more than 60 diseases.
“With data on genetic, imaging, lifestyle factors and health outcomes over many years, this will be the largest proteomic study in the world to be shared as a global scientific resource,” said Naomi Allen, MSc, DPhil, chief scientist at UK Biobank and professor of epidemiology, University of Oxford, in a UK Biobank news release. “These combined data could enable researchers to make novel scientific discoveries about how circulating proteins influence our health, and to better understand the link between genetics and human disease.”
“Measuring protein levels in the blood is crucial to understanding the link between genetic factors and the development of common life-threatening diseases,” said Naomi Allen, MSc, DPhil (above), chief scientist at UK Biobank and professor of epidemiology, University of Oxford, in a news release. With further study, this research could lead to new clinical laboratory assays that help physicians predict an individual’s risk for certain diseases including many forms of cancer. (Photo copyright: UK Biobank.)
Protein Signatures Outperform PSA Testing
To conduct their research, the team collected data from the UK Biobank Pharma Proteomics Project (UKB-PPP). This initiative is “one of the world’s largest studies of blood protein concentrations” and “aims to significantly enhance the field of ‘proteomics,’ enabling better understanding of disease processes and supporting innovative drug development,” according to the Biobank’s website.
The scientists analyzed the values of approximately 3,000 plasma proteins among 41,931 participants in the UKB-PPP. They examined the 10-year potential of developing certain diseases by measuring the plasma proteome and linking those observations to incident cases noted in electronic health records (EHRs).
The team specifically looked at the pathology types for several illnesses and utilized advanced techniques to identify a signature of proteins associated with those various diseases. They found their protein-based model exceeded traditional prediction methods when comparing the models with polygenic risk scores.
“We are therefore extremely excited about the opportunities that our protein signatures may have for earlier detection and ultimately improved prognosis for many diseases, including severe conditions such as Multiple myeloma and idiopathic pulmonary fibrosis,” she added. “We identified so many promising examples; the next step is to select high priority diseases and evaluate their proteomic prediction in a clinical setting.”
Identifying Individuals at High Risk for Certain Diseases
Of the thousands of known proteins in humans, the team focused on about 20 proteins found in blood. With as few as five proteins and as many as 20, they were able to do a risk assessment on 67 diseases, including:
The model could prove to be beneficial in the development of new therapies for certain diseases.
“A key challenge in drug development is the identification of patients most likely to benefit from new medicines. This work demonstrates the promise in the use of large-scale proteomic technologies to identify individuals at high risk across a wide range of diseases, and aligns with our approach to use tech to deepen our understanding of human biology and disease,” said Robert Scott, vice president and head of human genetics and genomics, GSK, and co-lead author of the study in the UCL news release.
“Further work will extend these insights and improve our understanding of how they are best applied to support improved success rates and increased efficiency in drug discovery and development,” he added.
“We are extremely excited about the opportunity to identify new markers for screening and diagnosis from the thousands of proteins circulating and now measurable in human blood,” said Claudia Langenberg, PhD, director of the Precision Healthcare University Research Institute (PHURI) at Queen Mary University of London and professor of computational medicine at the Berlin Institute of Health, in the UCL news release. “What we urgently need are proteomic studies of different populations to validate our findings, and effective tests that can measure disease relevant proteins according to clinical standards with affordable methods.”
More research and studies are needed before the protein-based model can be used to predict disease in clinical settings. However, the model could someday provide clinical laboratories, pathologists, and physicians with new assays that more accurately forecast an individual’s risk for certain illnesses.
Findings may lead to new clinical laboratory biomarkers for predicting risk of developing MS and other autoimmune diseases
Scientists continue to find new clinical laboratory biomarkers to detect—and even predict risk of developing—specific chronic diseases. Now, in a recent study conducted at the University of California San Francisco (UCSF), researchers identified antibodies that develop in about 10% of Multiple Sclerosis (MS) patients’ years before the onset of symptoms. The researchers reported that of those who have these antibodies, 100% develop MS. Thus, this discovery could lead to new blood tests for screening MS patients and new ways to treat it and other autoimmune diseases as well.
The UCSF researchers determined that, “in about 10% [of] cases of multiple sclerosis, the body begins producing a distinctive set of antibodies against its own proteins years before symptoms emerge,” Yahoo Life reported, adding that “when [the patients] are tested at the time of their first disease flare, the antibodies show up in both their blood and cerebrospinal fluid.”
That MS is so challenging to diagnose in the first place makes this discovery even more profound. And knowing that 100% of a subset of MS patients who have these antibodies will develop MS makes the UCSF study findings quite important.
“This could be a useful tool to help triage and diagnose patients with otherwise nonspecific neurological symptoms and prioritize them for closer surveillance and possible treatment,” Colin Zamecnik, PhD, scientist and research fellow at UCSF, told Yahoo Life.
“From the largest cohort of blood samples on Earth, we obtained blood samples from MS patients years before their symptoms began and profiled antibodies against self-autoantibodies that are associated with multiple sclerosis diagnosis,” Colin Zamecnik, PhD (above), scientist and research fellow at UCSF, told Yahoo Life. “We found the first molecular marker of MS that appears up to five years before diagnosis in their blood.” These findings could lead to new clinical laboratory tests that determine risk for developing MS and other autoimmune diseases. (Photo copyright: LinkedIn.)
UCSF Study Details
According to the MS International Foundation Atlas of MS, there are currently about 2.9 million people living with MS worldwide, with about one million of them in the US. The disease is typically diagnosed in individuals 20 to 50 years old, mostly targeting those of Northern European descent, Yahoo Life reported.
To complete their study, the UCSF scientists used the Department of Defense Serum Repository (DoDSR), which is comprised of more than 10 million individuals, the researchers noted in their Nature Medicine paper.
From that group, the scientists identified 250 individuals who developed MS, spanning a period of five years prior to showing symptoms through one year after their symptoms first appeared, Medical News Today reported. These people were compared to 250 other individuals in the DoDSR who have no MS diagnosis but who all had similar serum collection dates, ages, race and ethnicities, and sex.
“The researchers validated the serum results against serum and cerebrospinal fluid results from an incident MS cohort at the University of California, San Francisco (ORIGINS) that enrolled patients at clinical onset. They used data from 103 patients from the UCSF ORIGINS study,” according to Medical News Today. “They carried out molecular profiling of autoantibodies and neuronal damage in samples from the 500 participants, measuring serum neurofilament light chain measurement (sNfL) to detect damage to nerve cells.
“The researchers tested the antibody patterns of both MS and control participants using whole-human proteomeseroreactivity which can detect autoimmune reactions in the serum and CSF,” Medical News Today noted.
Many who developed MS had an immunogenicity cluster (IC) of antibodies that “remained stable over time” and was not found in the control samples. The higher levels of sNfL in those with MS were discovered years prior to the first flare up, “indicating that damage to nerve cells begins a long time before symptom onset,” Medical News Today added.
“This signature is a starting point for further immunological characterization of this MS patient subset and may be clinically useful as an antigen-specific biomarker for high-risk patients with clinically or radiologically isolated neuroinflammatory syndromes,” the UCSF scientists wrote in Nature Medicine.
“We believe it’s possible that these patients are exhibiting cross reactive response to a prior infection, which agrees with much current work in the literature around multiple sclerosis disease progression,” Zamecnik told Yahoo Life.
It “validates and adds to prior evidence of neuro-axonal injury occurring in patients during the MS preclinical phase,” the researchers told Medical News Today.
Implications of UCSF’s Study
UCSF’s discovery is a prime example of technology that could soon work its way into clinical use once additional studies and research are done to support the findings.
The researchers believe their research could lead to a simple blood test for detecting MS years in advance and discussed how this could “give birth to new treatments and disease management opportunities,” Neuroscience News reported.
Current MS diagnosis requires a battery of tests, such as lumbar punctures for testing cerebrospinal fluid, magnetic resonance imaging (MRI) scans of the spinal cord and brain, and “tests to measure speed and accuracy of nervous system responses,” Medical News Today noted.
“Given its specificity for MS both before and after diagnosis, an autoantibody serology test against the MS1c peptides could be implemented in a surveillance setting for patients with high probability of developing MS, or crucially at a first clinically isolated neurologic episode,” the UCSF researchers told Medical News Today.
The UCSF discovery is another example of nascent technology that could work its way into clinical use after more research and studies. Microbiologists, clinical laboratories, and physicians tasked with diagnosing MS and other autoimmune diseases should find the novel biomarkers the researchers identified most interesting, as well as what changed with science and technology that enabled researchers to identify these biomarkers for development.
Researchers intend their new AI image retrieval tool to help pathologists locate similar case images to reference for diagnostics, research, and education
Researchers at Stanford University turned to an unusual source—the X social media platform (formerly known as Twitter)—to train an artificial intelligence (AI) system that can look at clinical laboratory pathology images and then retrieve similar images from a database. This is an indication that pathologists are increasingly collecting and storing images of representative cases in their social media accounts. They then consult those libraries when working on new cases that have unusual or unfamiliar features.
The Stanford Medicine scientists trained their AI system—known as Pathology Language and Image Pretraining (PLIP)—on the OpenPath pathology dataset, which contains more than 200,000 images paired with natural language descriptions. The researchers collected most of the data by retrieving tweets in which pathologists posted images accompanied by comments.
“It might be surprising to some folks that there is actually a lot of high-quality medical knowledge that is shared on Twitter,” said researcher James Zou, PhD, Assistant Professor of Biomedical Data Science and senior author of the study, in a Stanford Medicine SCOPE blog post, which added that “the social media platform has become a popular forum for pathologists to share interesting images—so much so that the community has widely adopted a set of 32 hashtags to identify subspecialties.”
“It’s a very active community, which is why we were able to curate hundreds of thousands of these high-quality pathology discussions from Twitter,” Zou said.
“The main application is to help human pathologists look for similar cases to reference,” James Zou, PhD (above), Assistant Professor of Biomedical Data Science, senior author of the study, and his colleagues wrote in Nature Medicine. “Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing, and education.” Leveraging pathologists’ use of social media to store case images for future reference has worked out well for the Stanford Medicine study. (Photo copyright: Stanford University.)
Retrieving Pathology Images from Tweets
“The lack of annotated publicly-available medical images is a major barrier for innovations,” the researchers wrote in Nature Medicine. “At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter.”
In this case, the goal “is to train a model that can understand both the visual image and the text description,” Zou said in the SCOPE blog post.
“Pathology is perhaps even more suited to Twitter than many other medical fields because for most pathologists, the bulk of our daily work revolves around the interpretation of images for the diagnosis of human disease,” wrote Jerad M. Gardner, MD, a dermatopathologist and section head of bone/soft tissue pathology at Geisinger Medical Center in Danville, Pa., in a blog post about the Pathology Hashtag Ontology project. “Twitter allows us to easily share images of amazing cases with one another, and we can also discuss new controversies, share links to the most cutting edge literature, and interact with and promote the cause of our pathology professional organizations.”
The researchers used the 32 subspecialty hashtags to retrieve English-language tweets posted from 2006 to 2022. Images in the tweets were “typically high-resolution views of cells or tissues stained with dye,” according to the SCOPE blog post.
The researchers collected a total of 232,067 tweets and 243,375 image-text pairs across the 32 subspecialties, they reported. They augmented this with 88,250 replies that received the highest number of likes and had at least one keyword from the ICD-11 codebook. The SCOPE blog post noted that the rankings by “likes” enabled the researchers to screen for high-quality replies.
They then refined the dataset by removing duplicates, retweets, non-pathology images, and tweets marked by Twitter as being “sensitive.” They also removed tweets containing question marks, as this was an indicator that the practitioner was asking a question about an image rather than providing a description, the researchers wrote in Nature Medicine.
They cleaned the text by removing hashtags, Twitter handles, HTML tags, emojis, and links to websites, the researchers noted.
The final OpenPath dataset included:
116,504 image-text pairs from Twitter posts,
59,869 from replies, and
32,041 image-text pairs scraped from the internet or obtained from the LAION dataset.
The latter is an open-source database from Germany that can be used to train text-to-image AI software such as Stable Diffusion.
Training the PLIP AI Platform
Once they had the dataset, the next step was to train the PLIP AI model. This required a technique known as contrastive learning, the researchers wrote, in which the AI learns to associate features from the images with portions of the text.
As explained in Baeldung, an online technology publication, contrastive learning is based on the idea that “it is easier for someone with no prior knowledge, like a kid, to learn new things by contrasting between similar and dissimilar things instead of learning to recognize them one by one.”
“The power of such a model is that we don’t tell it specifically what features to look for. It’s learning the relevant features by itself,” Zou said in the SCOPE blog post.
The resulting AI PLIP tool will enable “a clinician to input a new image or text description to search for similar annotated images in the database—a sort of Google Image search customized for pathologists,” SCOPE explained.
“Maybe a pathologist is looking at something that’s a bit unusual or ambiguous,” Zou told SCOPE. “They could use PLIP to retrieve similar images, then reference those cases to help them make their diagnoses.”
The Stanford University researchers continue to collect pathology images from X. “The more data you have, the more it will improve,” Zou said.
Pathologists will want to keep an eye on the Stanford Medicine research team’s progress. The PLIP AI tool may be a boon to diagnostics and improve patient outcomes and care.
