Though PCR clinical laboratory testing is widely used, some scientists are concerned its specificity may limit the ability to identify all variants of bird flu in wastewater
Wastewater testing of infectious agents appears to be here to stay. At the same time, there are differences of opinion about which methodologies and clinical laboratory tests are best suited to screen for specific contagions in wastewater. One such contagion is avian influenza, the virus that causes bird flu.
Wastewater testing by public health officials became a valuable tool during the COVID-19 pandemic and has now become a common method for detecting other diseases as well. For example, earlier this year, scientists used wastewater testing to learn how the H5N1 variant of the bird flu virus was advancing among dairy herds across the country.
In late March, the bird flu was first detected in dairy cattle in Texas, prompting scientists to begin examining wastewater samples to track the virus. Some researchers, however, expressed concerns about the ability of sewage test assays to detect all variants of certain diseases.
“Right now we are using these sort of broad tests to test for influenza A viruses,” Denis Nash, PhD, Distinguished Professor of Epidemiology at City University of New York (CUNY) and Executive Director of CUNY’s Institute for Implementation Science in Population Health (SPH), told the Los Angeles Times. “It’s possible there are some locations around the country where the primers being used in these tests might not work for H5N1.” Clinical laboratory PCR genetic testing is most commonly used to screen for viruses in wastewater. (Photo copyright: CUNY SPH.)
Effectiveness of PCR Wastewater Testing
Polymerase chain reaction (PCR) tests are most commonly used to distinguish genetic material related to a specific illness such as the flu virus. For PCR tests to correctly identify a virus, the tests must be designed to look for a specific subtype. The two most prevalent human influenza A viruses are known as H1N1 (swine flu) and H3N2, which was responsible for the 1968 pandemic that killed a million people worldwide. The “H” stands for hemagglutinin and the “N” for neuraminidase.
Hemagglutinin is a glycoprotein that assists the virus to attach to and infect host cells. Neuraminidase is an enzyme found in many pathogenic or symbiotic microorganisms that separates the links between neuraminic acids in various molecules.
Avian flu is also an influenza A virus, but it has the subtype H5N1. Although human and bird flu viruses both contain the N1 signal, they do not share an H. Some scientists fear that—in cases where a PCR test only looks for H1 and H3 in wastewater—that test could miss the bird flu altogether.
“We don’t have any evidence of that. It does seem like we’re at a broad enough level that we don’t have any evidence that we would not pick up H5,” Jonathan Yoder, Deputy Director, Infectious Disease Readiness and Innovation at the US Centers for Disease Control and Prevention (CDC) told the Los Angeles Times.
The CDC asserts current genetic testing methods are standardized and will detect the bird flu. Yoder also affirmed the tests being used at all the testing sites are the same assay, based on information the CDC has published regarding testing for influenza A viruses.
Genetic Sequencing Finds H5N1 in Texas Wastewater
In an article published on the preprint server medRxiv titled, “Virome Sequencing Identifies H5N1 Avian Influenza in Wastewater from Nine Cities,” the authors wrote, “using an agnostic, hybrid-capture sequencing approach, we report the detection of H5N1 in wastewater in nine Texas cities, with a total catchment area population in the millions, over a two-month period from March 4th to April 25th, 2024.”
The authors added, “Although human to human transmission is rare, infection has been fatal in nearly half of patients who have contracted the virus in past outbreaks. The increasing presence of the virus in domesticated animals raises substantial concerns that viral adaptation to immunologically naïve humans may result in the next flu pandemic.”
“So, it’s not just targeting one virus—or one of several viruses—as one does with PCR testing,” Eric Boerwinkle, PhD, Dean of the UTHealth Houston School of Public Health told the LA Times. “We’re actually in a very complex mixture, which is wastewater, pulling down viruses and sequencing them. What’s critical here is it’s very specific to H5N1.”
Epidemiologist Blake Hanson, PhD, Assistant Professor, Department of Epidemiology, Human Genetics, and Environmental Sciences at the UT Health Houston Graduate School of Biomedical Science, agreed with Boerwinkle that though the PCR-based methodology is highly effective at detecting avian flu in wastewater samples, the testing can do more.
“We have the ability to look at the representation of the entire genome, not just a marker component of it. And so that has allowed us to look at H5N1, differentiate it from some of our seasonal fluids like H1N1 and H3N2,” Hanson told the LA Times. “It’s what gave us high confidence that it is entirely H5N1, whereas the other papers are using a part of the H5 gene as a marker for H5.”
Human or Animal Sources
Both Boerwinkle and Hanson are epidemiologists in the team studying wastewater samples for H5N1 in Texas. They are not sure where the virus originated but are fairly certain it did not come from humans.
“Texas is really a confluence of a couple of different flyways for migratory birds, and Texas is also an agricultural state, despite having quite large cities,” Boerwinkle noted. “It’s probably correct that if you had to put your dime and gamble what was happening, it’s probably coming from not just one source but from multiple sources. We have no reason to think that one source is more likely any one of those things.”
“Because we are looking at the entirety of the genome, when we look at the single human H5N1 case, the genomic sequence has a hallmark amino acid change, compared to all of the cattle from that same time point,” Hanson said. “We do not see that hallmark amino acid present in any of our sequencing data. And we’ve looked very carefully for that, which gives us some confidence that we’re not seeing human-human transmission.”
CDC Updates on Bird Flu
In its weekly updates on the bird flu situation, the CDC reported that 48 states have outbreaks in poultry and 14 states have avian flu outbreaks in dairy cows. More than 238 dairy herds have been affected and, as of September 20, over 100 million poultry have been affected by the disease.
In addition, the CDC monitored more than 4,900 people who came into contact with an infected animal. Though about 230 of those individuals have been tested for the disease, there have only been a total of 14 reported human cases in the US.
The CDC posts information specifically for laboratory workers, healthcare providers, and veterinarians on its website.
The CDC also states that the threat from avian flu to the general public is low. Individuals at an increased risk for infection include people who work around infected animals and those who consume products containing raw, unpasteurized cow’s milk.
Symptoms of H5N1 in humans may include fever or chills, cough, headaches, muscle or body aches, runny or stuffy nose, tiredness and shortness of breath. Symptoms typically surface two to eight days after exposure.
Scientists and researchers have been seeking a reliable clinical laboratory test for disease organisms in a fast, accurate, and cost-effective manner. Wastewater testing of infectious agents could fulfill those goals and appears to be a technology that will continue to be used for tracking disease.
Though not biomarkers per se, these scores for certain genetic traits may someday be used by clinical laboratories to identify individuals’ risk for specific diseases
Can polygenic risk scores (a number that denotes a person’s genetic predisposition for certain traits) do a better job at predicting the likelihood of developing specific diseases, perhaps even before the onset of symptoms? Researchers at the Broad Institute of MIT and Harvard (Broad Institute) believe so, and their study could have implications for clinical laboratories nationwide.
In cooperation with medical centers across the US, the scientists “optimized 10 polygenic scores for use in clinical research as part of a study on how to implement genetic risk prediction for patients,” according to a Broad Institute news release.
The research team “selected, optimized, and validated the tests for 10 common diseases [selected from a total of 23 conditions], including heart disease, breast cancer, and type 2 diabetes. They also calibrated the tests for use in people with non-European ancestries,” the news release notes.
As these markers for genetic risk become better understood they may work their way into clinical practice. This could mean clinical laboratories will have a role in sequencing patients’ DNA to provide physicians with information about the probability of a patient’s elevated genetic risk for certain conditions.
However, the effectiveness of polygenic risk scores has faced challenges among diverse populations, according to the news release, which also noted a need to appropriately guide clinicians in use of the scores.
“With this work, we’ve taken the first steps toward showing the potential strength and power of these scores across a diverse population,” said Niall Lennon, PhD (above), Chief Scientific Officer of Broad Clinical Labs. “We hope in the future this kind of information can be used in preventive medicine to help people take actions that lower their risk of disease.” Clinical laboratories may eventually be tasked with performing DNA sequencing to determine potential genetic risk for certain diseases. (Photo copyright: Broad Institute.)
Polygenic Scores Need to Reflect Diversity
“There have been a lot of ongoing conversations and debates about polygenic risk scores and their utility and applicability in the clinical setting,” said Niall Lennon, PhD, Chair and Chief Scientific Officer of Broad Clinical Labs and first author of the study, in the news release. However, he added, “It was important that we weren’t giving people results that they couldn’t do anything about.”
In the paper, Lennon and colleagues explained polygenic risk scores “aggregate the effects of many genetic risk variants” to identify a person’s genetic predisposition for a certain disease or phenotype.
“But their development and application to clinical care, particularly among ancestrally diverse individuals, present substantial challenges,” they noted. “Clinical use of polygenic risk scores may ultimately prevent disease or enable its detection at earlier, more treatable stages.”
The scientists set a research goal to “optimize polygenic risk scores for a diversity of people.”
While performing the polygenic risk score testing on participants, Broad Clinical Labs focused on 10 conditions—including cardiometabolic diseases and cancer—selected by the research team based on “polygenic risk score performance, medical actionability, and clinical utility,” the Nature Medicine paper explained.
For each condition, the researchers:
Identified “exact spots in the genome that they would analyze to calculate the risk score.”
Used information from the NIH’s All of Us Research Program to “create a model to calibrate a person’s polygenic risk score according to that individual’s genetic ancestry.”
The All of Us program, which aims to collect health information from one million US residents, has three times more people of non-European ancestry than other data sources developing genetic risk scores, HealthDay News reported.
20% of Study Participants Showed High Risk for Disease
To complete their studies, Broad Institute researchers processed a diverse group of eMERGE participants to determine their clinical polygenic risk scores for each of the 10 diseases between July 2022 and August 2023.
Listed below are all conditions studied, as well as the number of participants involved in each study and the number of people with scores indicating high risk of the disease, according to their published paper:
Over 500 people (about 20%) of the 2,500 participants, had high risk for at least one of the 10 targeted diseases, the study found.
Participants in the study self-reported their race/ancestry as follows, according to the paper:
White: 32.8%
Black: 32.8%
Hispanic: 25.4%
Asian: 5%
American Indian: 1.5%
Middle Eastern: 0.9%
No selection: 0.8%
“We can’t fix all biases in the risk scores, but we can make sure that if a person is in a high-risk group for a disease, they’ll get identified as high risk regardless of what their genetic ancestry is,” Lennon said.
Further Studies, Scoring Implications
With 10 tests in hand, Broad Clinical Labs plans to calculate risk scores for all 25,000 people in the eMERGE network. The researchers also aim to conduct follow-up studies to discover what role polygenic risk scores may play in patients’ overall healthcare.
“Ultimately, the network wants to know what it means for a person to receive information that says they’re at high risk for one of these diseases,” Lennon said.
The researchers’ findings about disease risk are likely also relevant to healthcare systems, which want care teams to make earlier, pre-symptomatic diagnosis to keep patients healthy.
Clinical laboratory leaders may want to follow Broad Clinical Labs’ studies as they perform the 10 genetic tests and capture information about what participants may be willing to do—based on risk scores—to lower their risk for deadly diseases.
Another report finds nearly half of all healthcare systems planning to opt out of Medicare Advantage plans because of issues caused by prior authorization requirements
Prior-authorization is common and neither healthcare providers (including clinical laboratories) nor Medicare Advantage (MA) health plans are happy with the basic process. Thus, labs—which often must get prior-authorization for molecular diagnostics and genetic tests—may learn from a recent KFF study of denial rates and successful appeals.
“While prior authorization has long been used to contain spending and prevent people from receiving unnecessary or low-value services, it also has been [the] subject of criticism that it may create barriers to receiving necessary care,” KFF, a health policy research organization, stated in a news release.
Nearly all MA plan enrollees have to get prior authorization for high cost services such as inpatient stays, skilled nursing care, and chemotherapy. However, “some lawmakers and others have raised concerns that prior authorization requirements and processes, including the use of artificial intelligence to review requests, impose barriers and delays to receiving necessary care,” KFF reported.
“Insurers argue the process helps to manage unnecessary utilization and lower healthcare costs. But providers say prior authorization is time-consuming and delays care for patients,” Healthcare Dive reported.
“There are a ton of barriers with prior authorizations and referrals. And there’s been a really big delay in care—then we spend a lot of hours and dollars to get paid what our contracts say,” said Katie Kucera (above),Vice President and CFO, Carson Tahoe Health, Carson City, Nev., in a Becker’s Hospital CFO Report which shared the health system’s plan to end participation in UnitedHealthcare commercial and Medicare Advantage plans effective May 2025. Clinical laboratories may want to review how test denials by Medicare Advantage plans, and the time cost of the appeals process, affect the services they provide to their provider clients. (Photo copyright: Carson Tahoe Health.)
Key Findings of KFF Study
To complete its study, KFF analyzed “data submitted by Medicare Advantage insurers to CMS to examine the number of prior authorization requests, denials, and appeals for 2019 through 2022, as well as differences across Medicare Advantage insurers in 2022,” according to a KFF issue brief.
Here are key findings:
Requests for prior authorization jumped 24.3% to 46 million in 2022 from 37 million in 2019.
More than 90%, or 42.7 million requests, were approved in full.
About 7.4%, or 3.4 million, prior authorization requests were fully or partially denied by insurers in 2022, up from 5.8% in 2021, 5.6% in 2020, and 5.7% in 2019.
About 9.9% of denials were appealed in 2022, up from 7.5% in 2019, but less than 10.2% in 2020 and 10.6% in 2021.
More than 80% of appeals resulted in partial or full overturning of denials in the years studied. Still, “negative effects on a person’s health may have resulted from delay,” KFF pointed out.
KFF also found that requests for prior authorization differed among insurers. For example:
Humana experienced the most requests for prior authorization.
Among all MA plans, the share of patients who appealed denied requests was small. The low rate of appeals may reflect Medicare Advantage plan members’ uncertainty that they can question insurers’ decisions, KFF noted.
It’s a big market. Nevertheless, “between onerous authorization requirements and high denial rates, healthcare systems are frustrated with Medicare Advantage,” according to a Healthcare Financial Management Association (HFMA) survey of 135 health system Chief Financial Officers.
According to the CFOs surveyed, 19% of healthcare systems stopped accepting one or more Medicare Advantage plans in 2023, and 61% are planning or considering ending participation in one or more plans within two years.
“Nearly half of health systems are considering dropping Medicare Advantage plans,” Becker’s reported.
Federal lawmakers acted, introducing three bills to help improve timeliness, transparency, and criteria used in prior authorization decision making. Starting in 2023, KFF reported, the federal Centers for Medicare and Medicaid Services (CMS) published final rules on the bills:
Rule One (effective June 5, 2023), “clarifies the criteria that may be used by Medicare Advantage plans in establishing prior authorization policies and the duration for which a prior authorization is valid. Specifically, the rule states that prior authorization may only be used to confirm a diagnosis and/or ensure that the requested service is medically necessary and that private insurers must follow the same criteria used by traditional Medicare. That is, Medicare Advantage prior authorization requirements cannot result in coverage that is more restrictive than traditional Medicare.”
Rule Two (effective April 8, 2024), is “intended to improve the use of electronic prior authorization processes, as well as the timeliness and transparency of decisions, and applies to Medicare Advantage and certain other insurers. Specifically, it shortens the standard time frame for insurers to respond to prior authorization requests from 14 to seven calendar days starting in January 2026 and standardizes the electronic exchange of information by specifying the prior authorization information that must be included in application programming interfaces starting in January 2027.”
Rule Three (effective June 3, 2024), requires “Medicare Advantage plans to evaluate the effect of prior authorization policies on people with certain social risk factors starting with plan year 2025.”
KFF’s report details how prior authorization affects patient care and how healthcare providers struggle to get paid for services rendered by Medicare Advantage plans amid the rise of value-based reimbursements.
Clinical laboratory leaders may want to analyze their test denials and appeals rates as well and, in partnership with finance colleagues, consider whether to continue contracts with Medicare Advantage health plans.
Charges include $1.1 billion in alleged telemedicine and fraudulent clinical laboratory testing
Nearly 200 individuals in 25 states are facing charges for alleged participation in a variety of healthcare frauds, the US Department of Justice (DOJ) announced in a press release. This major enforcement action involves telemedicine and clinical laboratory testing as well as other healthcare schemes. In total, the DOJ is alleging the defendants are responsible for $2.75 billion in intended losses and $1.6 billion in actual losses.
The charges include:
$1.1 billion in alleged telemedicine and clinical laboratory fraud.
As part of the action, the government has seized more than $231 million in assets, including cash, luxury vehicles, and gold.
Monica Cooper, JD (above), a DOJ trial attorney and member of the Texas Strike Force, is one of two attorneys prosecuting the case against Harold Albert “Al” Knowles of Delray Beach, Fla., and Chantal Swart of Boca Raton, Fla., in the DOJ’s latest crackdown on healthcare fraud. Charges against Knowles and Swart include conspiracy to commit healthcare fraud, conspiracy to defraud the United States, and paying/receiving healthcare kickbacks in a $359 million scheme to bill Medicare for medically unnecessary genetic tests at two Houston clinical laboratories. (Photo copyright: US Department of Justice.)
Houston-Area Labs Charged in $359 Million Scheme
In one case, the government charged Florida residents Harold Albert “Al” Knowles and Chantal Swart in a $359 million scheme involving fraudulent Medicare billing for medically unnecessary genetic tests. Knowles owned two Houston-area labs—Bio Choice Laboratories, Inc. and Bios Scientific, LLC—while Swart ran a telemarketing operation. According to DOJ case summaries, the government alleges that Knowles paid kickbacks to Swart to obtain DNA samples and doctors’ orders for tests.
“Knowles, Swart, and others obtained access to tens of thousands of beneficiaries across the United States by targeting them with deceptive telemarketing campaigns,” the indictments allege. “Call center representatives—who were almost never medical professionals—often prompted beneficiaries to disclose their medical conditions and induced them to agree to genetic testing regardless of medical necessity.”
In addition, “Knowles, Swart, and others agreed that Swart and others would pay illegal kickbacks and bribes to purported telemedicine companies to obtain signed doctors’ orders for genetic testing after only a brief telemedicine visit,” the indictment stated. “Knowles and his co-conspirators knew that the purported telemedicine companies’ physicians were rarely, if ever, the beneficiaries’ treating physicians and rarely, if ever, used the genetic testing results in the beneficiaries’ treatment.”
Dallas-Area Labs Charged in $335 Million Scheme
In another case, the federal government charged that the owner of two Dallas-area clinical laboratories engaged in a $335 million Medicare billing scheme.
Keith Gray, owner of Axis Professional Labs, LLC and Kingdom Health Laboratory, LLC, “offered and paid kickbacks to marketers in exchange for their referral to Axis and Kingdom of Medicare beneficiaries’ DNA samples, personally identifiable information (including Medicare numbers), and signed doctors’ orders authorizing medically unnecessary cardio genetic testing,” the government alleged. “As part of the scheme, the marketers engaged other companies to solicit Medicare beneficiaries through telemarketing and to engage in ‘doctor chase,’ i.e., to obtain the identity of beneficiaries’ primary care physicians and pressure them to approve genetic testing orders for patients who purportedly had already been ‘qualified’ for the testing.”
Other Clinical Laboratory and Healthcare Fraud Cases
DOJ attorneys charged the owners of Innovative Genomics, a clinical laboratory in San Antonio, in a $65 million scheme to bill Medicare and the COVID-19 Uninsured Program for “medically unnecessary and otherwise non-reimbursable COVID-19 and genetic testing,” according to the indictment. Also charged were two patient recruiters who allegedly received kickbacks for referring patients.
Richard Abrazi of New York City was charged in a $60 million Medicare billing scheme. Abrazi owned two clinical laboratories: Enigma Management Corp. and Up Services Inc. Both operated as Alliance Laboratories.
“Abrazi and others engaged in a scheme to pay and receive kickbacks and bribes in exchange for laboratory tests, including genetic tests, that Enigma and Up billed to Medicare,” the indictment alleges. “Abrazi and others also allegedly paid and received kickbacks and bribes in exchange for arranging for the ordering of medically unnecessary genetic tests that were ineligible for Medicare reimbursement.”
The DOJ charged Brian Cotugno, of Auburn, Ga., and James Matthew Thorton “Bo” Potter, of Santa Rosa Beach, Fla., in a $20 million Medicare billing scheme. Cotugno, the indictment alleges, sold Medicare Beneficiary Identification Numbers (BINs) to two Alabama laboratories co-owned by Potter.
“The BINs were used to bill Medicare tens of millions of dollars for OTC COVID-19 test kits, many of which had not been requested by the beneficiaries,” the government alleged.
These are only a few of the recent cases the DOJ brought against defendants nationwide for healthcare, telemedicine, and clinical laboratory fraud. Both Dark Daily and our sister publication The Dark Report have covered these ongoing investigations for years. And we will continue to do so because it’s important that lab managers and pathology group leaders are aware of the lengths to which the DOJ is pursuing bad actors in healthcare.
Study findings could lead to new clinical laboratory diagnostics that give pathologists a more detailed understanding about certain types of cancer
New studies proving artificial intelligence (AI) can be used effectively in clinical laboratory diagnostics and personalized healthcare continue to emerge. Scientists in the UK recently trained an AI model using machine learning and deep learning to enable earlier, more accurate detection of 13 different types of cancer.
DNA stores genetic information in sequences of four nucleotide bases: A (adenine), T (thymine), G (guanine) and C (cytosine). These bases can be modified through DNA methylation. There are millions of DNA methylation markers in every single cell, and they change in the early stages of cancer development.
One common characteristic of many cancers is an epigenetic phenomenon called aberrant DNA methylation. Modifications in DNA can influence gene expression and are observable in cancer cells. A methylation profile can differentiate tumor types and subtypes and changes in the process often come before malignancy appears. This renders methylation very useful in catching cancers while in the early stages.
However, deciphering slight changes in methylation patterns can be extremely difficult. According to the scientists, “identifying the specific DNA methylation signatures indicative of different cancer types is akin to searching for a needle in a haystack.”
Nevertheless, the researchers believe identifying these changes could become a useful biomarker for early detection of cancers, which is why they built their AI models.
“Computational methods such as this model, through better training on more varied data and rigorous testing in the clinic, will eventually provide AI models that can help doctors with early detection and screening of cancers,” said Shamith Samarajiwa, PhD (above), Senior Lecturer and Group Leader, Computational Biology and Genomic Data Science, Imperial College London, in a news release. “This will provide better patient outcomes.” With additional research, clinical laboratories and pathologists may soon have new cancer diagnostics based on these AI models. (Photo copyright: University of Cambridge.)
The researchers then used a combination of machine learning and deep learning techniques to train an AI algorithm to examine DNA methylation patterns of the collected data. The algorithm identified and differentiated specific cancer types, including breast, liver, lung and prostate, from non-cancerous tissue with a 98.2% accuracy rate. The team evaluated their AI model by comparing the results to independent research.
In their Biology Methods and Protocols paper, the authors noted that their model does require further training and testing and stressed that “the important aspect of this study was the use of an explainable and interpretable core AI model.” They also claim their model could help medical professionals understand “the underlying mechanisms that contribute to the development of cancer.”
Using AI to Lower Cancer Rates Worldwide
According to the Centers for Disease Control and Prevention (CDC), cancer ranks as the second leading cause of death in the United States with 608,371 deaths reported in 2022. The leading cause of death in the US is heart disease with 702,880 deaths reported in the same year.
Globally cancer diagnoses and death rates are even more alarming. World Health Organization (WHO) data shows an estimated 20 million new cancer cases worldwide in 2022, with 9.7 million persons perishing from various cancers that year.
The UK researchers are hopeful their new AI model will help lower those numbers. They state in their paper that “most cancers are treatable and curable if detected early enough.”
More research and studies are needed to confirm the results of this study, but it appears to be a very promising line of exploration and development of using AI to detect, identify, and diagnose cancer earlier. This type of probing could provide pathologists with improved tools for determining the presence of cancer and lead to better patient outcomes.
Researchers have been exploring the role metabolites play in the development of disease for some time. Alzheimer’s is a progressive, degenerative brain disease typically linked to age, family history, and deposits of certain proteins in the brain that cause the brain to shrink and brain cells to eventually die. Alzheimer’s is the most common form of dementia, accounting for an estimated 60% to 80% of all dementia cases. It has no cure or proven method of prevention, according to the Alzheimer’s Association.
There are nearly seven million people living with Alzheimer’s in the US and 55 million people worldwide live with it or other forms of dementia. Patients are usually over the age of 65, but it can present in younger patients as well.
“Gut metabolites are the key to many physiological processes in our bodies, and for every key there is a lock for human health and disease,” said Feixiong Cheng, PhD (above), founding director of the Cleveland Clinic Genome Center, in a news release. “The problem is that we have tens of thousands of receptors and thousands of metabolites in our system, so manually figuring out which key goes into which lock has been slow and costly. That’s why we decided to use AI.” Findings from the study could lead to new clinical laboratory biomarkers for dementia screening tests. (Photo copyright: Cleveland Clinic Lerner Research Institute.)
Changes to Gut Bacteria
Metabolites are substances released by bacteria when the body breaks down food, drugs, chemicals, or its own tissue, such as fat or muscle. They fuel cellular processes within the body that may be either helpful or harmful to an individual’s health.
The Cleveland Clinic researchers believe that preventing detrimental interactions between metabolites and cells could aid in disease prevention. Previous studies have shown that Alzheimer’s patients do experience changes in their gut bacteria as the disease progresses.
To complete their study, the scientists used AI and machine learning (ML) to analyze more than 1.09 million potential metabolite-receptor pairs to determine the likelihood of developing Alzheimer’s.
They then examined genetic and proteomic data from Alzheimer’s disease studies and looked at different receptor protein structures and metabolite shapes to determine how different metabolites can affect brain cells. The researchers identified significant interactions between the gut and the brain.
They discovered that the metabolite agmatine was most likely to interact with a receptor known as CA3R in Alzheimer’s patients. Agmatine is believed to protect brain cells from inflammation and damage. They found that when Alzheimer’s-affected neurons are treated with agmatine, CA3R levels reduce. Levels of phosphorylated tau proteins, a biomarker for Alzheimer’s disease, lowered as well.
The researchers also studied a metabolite called phenethylamine. They found that it too could significantly alter the levels of phosphorylated tau proteins, a result they believe could be beneficial to Alzheimer’s patients.
New Therapies for Alzheimer’s, Other Diseases
One of the most compelling results observed in the study was the identification of specific G-protein-coupled receptors (GPCRs) that interact with metabolites present in the gut microbiome. By focusing on orphan GPCRs, the researchers determined that certain metabolites could activate those receptors, which could help generate new therapies for Alzheimer’s.
“We specifically focused on Alzheimer’s disease, but metabolite-receptor interactions play a role in almost every disease that involves gut microbes,” said Feixiong Cheng, PhD, founding director of the Cleveland Clinic Genome Center in the news release. “We hope that our methods can provide a framework to progress the entire field of metabolite-associated diseases and human health.”
The team plans to use AI technology to further develop and study the interactions between genetic and environmental factors on human health and disease progression. More research and studies are needed, but results of the Cleveland Clinic study suggest new biomarkers for targeted therapies and clinical laboratory tests for dementia diseases may soon follow.
