Category: 8. Health

 

Researchers led by Bruce Yanker, codirector of the Paul F. Glenn Center for Biology of Aging Research and a professor of genetics and neurology at Harvard Medical School, have found that lithium in the brain plays an early role in Alzheimer’s disease progression, and the disease can be reversed in mice by providing a different form of the metal (Nature 2025, DOI: 10.1038/s41586-025-09335-x).

The brain changes during the progression of Alzheimer’s: amyloid-β proteins clump together to form plaques, a protein called tau tangles together stopping neuron communication, and neurons die. All these changes lead to a brain that misfires, but the changes that start this cascade of events are still unclear.

Research has shown that heavy metals could be an early contributing factor. But understanding their impact has been hindered by analytical techniques that can’t detect small amounts in the brain.

In the new study, Yanker’s team used a highly sensitive mass spectrometry technique to detect metals at extremely low concentrations in samples taken from different parts of brains postmortem. They found lower lithium concentrations in the prefrontal cortex of human brains with Alzheimer’s or mild cognitive impairment, and lower levels of the metal correlated with worse cognitive function.

But the lithium hadn’t vanished from the body. Instead, it was being trapped by amyloid plaques.

“They showed a very striking sequestration of lithium by plaques,” says Li-Huei Tsai, director of the Picower Institute for Learning and Memory at the Massachusetts Institute of Technology, who was not involved in the study. “This likely represents a piece of [the Alzheimer’s] puzzle.”

To understand the role the metal has on brain function, the researchers turned to mice. The team gave healthy mice a lithium-deficient diet. Those on the meal plan had more plaque formation, faulty tau, and neuron inflammation. In every major brain cell type, genes important to neuronal function were also dysregulated, and the neurons themselves had less myelin, a critical component of brain signaling.

Since amyloid plaques sequester lithium, the team reasoned that administering more of the metal wouldn’t necessarily solve the problem. The team instead looked for a way to get the metal to the brain in a way that wouldn’t interact with the plaques.

Lithium orotate, a salt formed from lithium ions and orotic acid, evaded plaque capture, and in mice models with Alzheimer’s given lithium orotate at physiological concentrations, plaque formation decreased, gene expression returned to normal, and memory improved—even in animals in later stages of the disease.

The supplemented lithium orotate showed no signs of toxicity in the mice.

Most lithium research has focused on specific pathological and cellular responses, but “the most interesting and intriguing aspects of what we found is that lithium cuts across all these domains,” Yanker says. “It is a potential common mechanism for multisystem degeneration.”

It’s still too early to tell if the results will hold up in humans, and Yanker warns no one with Alzheimer’s should change their medications based on this work, yet he remains hopeful that it could be a new way to not only treat Alzheimer’s but mitigate some of the side effects of aging.

This article originally appeared on the Moran Eye Center blog. 

John A. Moran Eye Center researcher Adam Dubis, PhD, is part of a big leap forward for the use of artificial intelligence in eye care.

Cofounded by Dubis, German-based health tech company deepeye Medical GmbH has gained European regulatory approval for an AI algorithm that assists ophthalmologists treating so-called “wet” or neovascular age-related macular degeneration (nAMD).

The company’s Treatment Planning Support (deepeye® TPS) product is the first predictive AI tool for ophthalmology therapy management approved for clinical use in the Western world.

“This is an exciting milestone in the collective global effort to ethically source and provide physicians with data in a way that can advise treatment and improve care,” says Dubis, who is applying to have deepeye TPS tested at Moran Eye Center clinics later this year as a step toward FDA approval.

Working to personalize AMD treatments

AMD is a leading cause of blindness among people age 55 and older, and patients with the neovascular form experience abnormal growth of blood vessels in the eye that can leak and rupture. Specialists treat neovascular or “wet” AMD with injections of medication aimed to prevent this growth and use retinal imaging to evaluate progress.

But not all patients respond to the same treatment regimen equally; treatment must be personalized. If appointments are scheduled too far apart, or if patients feel they are coming in too frequently and miss some appointments or even drop out of treatment, they could lose vision.

To create deepeye® TPS, Dubis and colleagues used tens of thousands of 3D scans of the retina combined with medical records to analyze the progression of nAMD in correlation with treatment regimens. The resulting algorithm, he says, helps physicians make two decisions, acting like a second expert reader of the scans.

• Does the person need to receive the next treatment sooner than the last one, or can the time between appointments be extended?
• How many treatments will the patient need over the next 12 months?

“The idea is that it will allow patients to keep their vision as long as possible and ensure they are only spending time going to a clinic when they need to,” says Dubis. “For physicians, it optimizes clinic flow, provides reassurance for the treatment course, and possibly helps in patient education or justifying therapy switching.”

To gain approval in Europe, deepeye® TPS was tested with an international group of over 300 patients (more than 2,000 visits). The physicians decided treatment plans independently based on scans, then consulted with deepeye® TPS after the fact to determine if their decisions aligned and if not, why.

When ophthalmologists participating in the study at Ludwig Maximilian University Eye Clinic in Munich recommended a treatment different from deepeye® TPS, they agreed with the recommended changes 56% of the time.

Moran Eye Center retina specialist and researcher Eileen Hwang, MD, PhD, is a highly trained vitreoretinal surgeon who regularly treats wet AMD patients. She says there is “definite potential” for improved accuracy as ophthalmologists must experiment to identify the optimal time interval between injections for each patient.

“Most physicians treating wet AMD are trying to personalize medicine by testing and seeing what works, but if we have some predictors, that could help us get there more quickly or more safely,” she says.

Hwang cautioned against viewing AI as a panacea.

“Nothing is going to be 100 percent,” she says. “We need to keep our minds open that even AI is a prediction that could be wrong, just like when a physician makes a decision to inject or not to inject a patient on a given day.”

Additional AI initiatives

The new deepeye® TPS is one of several AI-related projects for Dubis, who joined the Moran Eye Center in 2024 as a renowned expert in image and health data analysis who broadly consults across the image analysis and biopharmaceutical space. Dubis works with several international bodies on the evolution of policy and health technology regulation.

At the Moran Eye Center, his lab has created the Moran Phenotyping, Imaging and Advanced Technologies (PHIAT) database. This anonymized AI database can be queried by researchers to study myriad questions related to ophthalmology. For example, a researcher might query the database to predict which patients with diabetes are most at risk of getting diabetic eye disease, or to create an algorithm that helps doctors identify when one drug isn’t working and it’s time to try another.

Dubis emphasizes the gathering of ethically sourced data. In PHIAT, individual health records are de-identified, and the confidentiality of the data is strictly protected. Dubis explains that PHIAT is unique among academic medical centers.

“While other universities have limited databases open to research access, the data is preconfigured, whereas PHIAT is not,” says Dubis. “This allows for a much broader range of inquiry and investigation.”

Moran Eye Center CEO and Distinguished Professor and Chair of Ophthalmology Randall J Olson, MD, says the institution is excited to push the field forward in the areas of data and AI. Huntsman Cancer Center is already home to the Utah Population Database, one of the world’s richest sources of information on more than 11 million individuals that supports research on genetics, epidemiology, demography, and public health.

“Ophthalmology in particular is a field where we rely heavily on imaging of the eye, which has seen tremendous advances in the past decade,” explains Olson. “AI can assist with correlating these images to disease progression and to find those patterns that will better inform our treatment plans. Simply put, this is the future, and we are investing in it.”

Thinking about using a CPAP machine to quell sleep apnea?

It’s not necessarily a good idea for everyone, a new evidence review argues.

This common treatment for sleep apnea might increase some folks’ risk of heart attack, stroke and heart-related death, according to results published today in the European Heart Journal.

CPAP machines can dramatically lower a person’s heart risk if severe sleep apnea causes dramatic drops in blood oxygen levels or large surges in heart rate, researchers found by analyzing data from more than 3,500 participants in three major clinical trials.

But CPAP might escalate heart health risk among people with milder sleep apnea, particularly if the condition doesn’t cause them to feel sleepy during the daytime, results show.

“Our findings suggest that CPAP may offer long-term cardiovascular benefit in people with high-risk obstructive sleep apnea, but may have unintended harmful effects in those without high-risk OSA,” lead researcher Ali Azarbarzin, a sleep medicine investigator at Brigham and Women’s Hospital in Boston, said in a news release.

Sleep apnea occurs when the muscles in the back of the throat relax, causing a person’s airway to collapse.

These folks tend to snore, and if the airway collapses completely their breathing can start and stop throughout the night, causing them to repeatedly wake.

Continuous positive airway pressure machines blow air through a face mask as a person sleeps. The air pressure prevents the person’s airways from closing.

Sleep apnea has been previously linked to heart disease, researchers noted. It’s been reported to increase the risk of heart failure by 140%, stroke by 60% and heart disease by 30%.

But prior studies of CPAP treatment for sleep apnea have not shown a clear benefit to heart health from the devices, Azarbarzin said.

In an attempt to clarify CPAP’s heart benefits, Azarbarzin and colleagues pooled together data from three separate trials that tested CPAP for people with sleep apnea.

Results showed a clear benefit for people with high-risk sleep apnea that causes dramatic changes in blood oxygen and heart rate. Overall, these patients had a 17% lower risk of heart attack, stroke and heart-related death.

But in people without high-risk sleep apnea who don’t feel sleepy during the day, CPAP appeared to increase their risk of serious heart problems by 30%, researchers said.

“For people with high-risk OSA, CPAP likely helps by preventing low oxygen levels and calming the overactive sympathetic nervous system during sleep,” Azarbarzin said. Both are linked to heart disease.

“But in people without these high-risk markers, who are already at very low cardiovascular risk, CPAP seems to have downsides,” Azarbarzin added. “While we don’t really know why, one possibility is that the pressure used in CPAP may stretch the lungs in a way that puts stress on the cardiovascular system. Another is that CPAP could disturb sleep for some people, and sleep disruption itself is a risk factor for cardiovascular problems.”

The results indicate the need for personalized treatment of sleep apnea, Azarbarzin said.

“Instead of treating everyone the same, we should consider whether someone has high-risk features,” Azarbarzin said. “These are the people who seem most likely to benefit from CPAP.”

However, more research is needed to better understand the potential risks posed by CPAP, Azarbarzin said.

“CPAP-related harm was seen only in non-sleepy patients with existing heart disease, according to the design of trials analyzed in this study,” Azarbarzin said. “Whether this applies to other patients remains unknown and needs further research.”

More information

Yale Medicine has more on sleep apnea.

Copyright © 2025 HealthDay. All rights reserved.

The time children and teens spend video gaming, scrolling through social media or watching TV could be putting their future heart health at risk, a new study says.

Each additional hour of screen time is associated with an increase in heart risk factors like blood pressure, cholesterol and blood sugar levels, researchers reported today in the Journal of the American Heart Association.

“It’s a small change per hour, but when screen time accumulates to three, five or even six hours a day, as we saw in many adolescents, that adds up,” lead investigator David Horner, a researcher at the University of Copenhagen in Denmark, said in a news release.

“Multiply that across a whole population of children, and you’re looking at a meaningful shift in early cardiometabolic risk that could carry into adulthood,” Horner added.

For the study, researchers pooled data from more than 1,000 participants in two Danish studies of childhood health.

Each child received a heart health risk score based on factors like waist size, blood pressure, “good” HDL cholesterol, triglycerides and blood sugar, researchers said. Parents reported on the kids’ screen time.

Every hour a child or teen spent glued to a screen caused those risk factors to tilt toward the bad, results showed.

A child’s sleep patterns contributed to this risk, researchers added.

Both shorter sleep duration and hitting the sack later intensified the relationship between screen time and heart health risk, results show. Kids and teens who had less sleep showed significantly higher risk associated with the same amount of screen time.

“About 12% of the association between screen time and cardiometabolic risk was mediated through shorter sleep duration,” Horner said. “These findings suggest that insufficient sleep may not only magnify the impact of screen time but could be a key pathway linking screen habits to early metabolic changes.”

An artificial intelligence analysis found that kids’ blood carried a set of markers — what researchers called a “screen-time fingerprint” — that could predict how much time they’d been spending with screens, researchers added.

“We also assessed whether screen time was linked to predicted cardiovascular risk in adulthood, finding a positive trend in childhood and a significant association in adolescence,” Horner said. “This suggests that screen-related metabolic changes may carry early signals of long-term heart health risk.”

Since this was an observational study, the research cannot prove a direct cause-and-effect relationship between screen time and heart health, researchers noted.

Nevertheless, pediatricians should consider a discussion of children’s screen habits during regular check-ups, Horner said.

The results also highlight the importance of good sleep to a child’s health, said Dr. Amanda Marma Perak, chair of the American Heart Association’s Young Hearts Cardiovascular Disease Prevention Committee. Perak, who was not involved in this research, reviewed the findings.

“If cutting back on screen time feels difficult, start by moving screen time earlier and focusing on getting into bed earlier and for longer,” said Perak, an assistant professor of pediatrics and preventive medicine at Northwestern University Feinberg School of Medicine in Chicago.

Parents also should be prepared to set a good example, she added in a news release.

“All of us use screens, so it’s important to guide kids, teens and young adults to healthy screen use in a way that grows with them,” Perak said. “As a parent, you can model healthy screen use — when to put it away, how to use it, how to avoid multitasking. And as kids get a little older, be more explicit, narrating why you put away your devices during dinner or other times together.”

It’s also important to teach kids how to entertain themselves without a screen, and to handle the discomfort that comes with boredom, Perak said.

“Boredom breeds brilliance and creativity, so don’t be bothered when your kids complain they’re bored,” Perak said. “Loneliness and discomfort will happen throughout life, so those are opportunities to support and mentor your kids in healthy ways to respond that don’t involve scrolling.”

More information

Johns Hopkins Medicine has more on the effects of screen time on children.

Copyright © 2025 HealthDay. All rights reserved.

The Trump administration is cancelling almost $500 million in contracts to develop mRNA vaccines to protect the nation against future viral threats. The move thrilled critics of the technology but horrified many public health and biosecurity experts.

The federal Biomedical Advanced Research and Development Authority, or BARDA, which oversees the nation’s defenses against biological attacks, is terminating 22 contracts with university researchers and private companies to develop new uses for the mRNA technology, Health Secretary Robert F. Kennedy Jr. announced Tuesday.

The mRNA technology was used by the first Trump administration to create the most commonly used COVID-19 vaccines, which are widely considered a medical triumph that safely and effectively saved millions of lives. But vaccine mandates during the pandemic sowed fierce antipathy towards the technology, leading to widespread public opposition.

“Let me be absolutely clear: HHS supports safe, effective vaccines for every American who wants one,” Kennedy said in a video explaining the decision. “That’s why we’re moving beyond the limitations of mRNA vaccines for respiratory viruses and investing in better solutions.”

The announcement dismayed many who study infectious disease.

“This may be the most dangerous public health judgment that I’ve seen in my 50 years in this business,” says Michael Osterholm, who runs the Center for Infectious Disease Research and Policy at the University of Minnesota. “It is baseless and we will pay a tremendous price in terms of illnesses and deaths. I’m extremely worried about it.”

But the decision was welcomed by vaccine critics like the group Children’s Health Defense, which Kennedy himself founded.

“While we believe the mRNA vaccines should be taken off the market, the announcement is a positive move towards protecting public health,” said Mary Holland, the group’s president and CEO, in a statement.

Jennifer Nuzzo strongly disagrees. She runs the Brown University School of Public Health Pandemic Center, and says the move could erode preparedness for future pandemics.

“This is a profoundly disappointing development,” she says. “When there’s the next pandemic, we’re going to be caught flat-footed. It absolutely leaves the country vulnerable.”

Nuzzo and others aren’t just worried about the next pandemic. Many experts say mRNA vaccines would provide a crucial deterrent and powerful defense against bioterrorists.

“I think that it endangers the national security of the United States,” says Chris Meekins, a top biodefense official in the first Trump administration. “It could put the US at a strategic national security disadvantage and would be a significant threat to the national security of the United States.”

In announcing his decision, Kennedy claimed the COVID-19 vaccines were unsafe, ineffective, helped drive the evolution of the virus and could not keep up with new mutations.

“After reviewing the science and consulting top experts at NIH and FDA, HHS has determined that mRNA technology poses more risk than benefits against these respiratory viruses,” Kennedy said.

Many outside experts say Kennedy’s claims are wrong.

“His science is backwards, as it often is,” says Dr. Peter Hotez, the dean of the Baylor College of Medicine who runs the Texas Children’s Hospital Center for Vaccine Development. “This is a proven technology for emerging respiratory viruses or respiratory virus pandemics. It is extremely safe and has been incredibly effective.”

mRNA vaccines work by stimulating the immune system with a key protein from a virus. Kennedy says the federal government is instead investing in an alternative technology that uses whole killed viruses and can produce “natural immunity.”

While that technology has produced effective vaccines, it’s a much older approach that can have safety issues and is not nearly as nimble in responding to new threats, experts say.

“It is irresponsible to strip funding from future technologies with great potential and shift it towards outdated old fashioned technologies,” says Rick Bright, who ran BARDA during the first Trump administration. “We’re taking our country from 2025 back to 1940 and we all know that’s a recipe for disaster and failure.”

The mRNA technology is the only vaccine technology that can be developed quickly enough to respond swiftly to a new pathogenic threat, experts say.

“In an outbreak, when you are facing a rapidly spreading virus – whether it’s from nature or a nation-state adversary – speed is the name of the game,” Bright says.

The administration previously cancelled a $766 million contract with the vaccine company Moderna to develop an mRNA vaccine to protect people against flu strains with pandemic potential.

Many fear moves like this will continue to undermine public trust in vaccines generally and mRNA technology specifically, which is also showing promise for treating diseases, most notably cancer.

“The deleterious impact is not only in the contracts that they’re canceling but they’re trying to make the case to the public that mRNA technology doesn’t work very well and it’s unsafe,” Hotez says. “And that’s absolutely untrue.”

ABU DHABI, (UrduPoint / Pakistan Point News / WAM – 07th Aug, 2025) M42, a global health leader powered by technology, artificial intelligence (AI) and genomics, has unveiled landmark findings from a large-scale population study on AI-powered tuberculosis (TB) screening, published in the prestigious scientific journal npj Digital Medicine – Nature.

Conducted in collaboration with Capital Health Screening Centre (CHSC) in Abu Dhabi, part of the M42 group, the study is among the largest real-world clinical validations of an AI-driven healthcare solution to date, analyzing over one million chest X-rays (CXRs) to evaluate the efficacy and scalability of AI in TB screening.

The study assessed AI Radiology in Screening TB (AIRIS-TB), M42’s cutting-edge AI model engineered to streamline routine tuberculosis screenings, allowing radiologists to focus on more complex or urgent cases. AIRIS-TB demonstrated exceptional performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 98.5% – a clear indicator of its high efficiency in triaging CXRs for TB.

With an estimated 10.8 million people falling ill from TB and a total of 1.25 million people dying from it in 2023, according to the World Health Organisation (WHO), and recent reports of TB returning to being the world’s leading cause of death from a single infectious agent, M42’s AIRIS-TB demonstrates the potential of AI solutions delivering real-world impact. AIRIS-TB reported an unprecedented 0% false-negative rate for tuberculosis-specific cases across the full dataset – an achievement that reinforces its safety and reliability as a workflow automation tool. In practical application, the model has the potential to safely automate up to 80% of routine CXR assessments, directly alleviating radiologist workload pressures, minimising the risk of human error, and delivering significant cost efficiencies in high-throughput low-prevalence settings. Presently, CXR review remains labor-intensive and prone to oversight and error, potentially leading to missed or delayed diagnoses, with a prior study stating a 26.6% increase in missed findings when radiologists double their annotation speed, and a rise in errors after 9 hours into their shift.

Importantly, AIRIS-TB delivered consistently strong performance across a wide range of demographic groups, including variations in gender, age, HIV status, income levels, and a diverse population covering six WHO regions, highlighting the model’s robustness, fairness and generalisability across diverse global populations.

These results underscore AIRIS-TB’s potential to significantly enhance clinical workflows and drive earlier, more equitable screening of TB in high-volume programmes worldwide.

Dimitris Moulavasilis, Group CEO of M42, said, “This landmark study marks a pivotal moment in the potential power of AI in the global fight against tuberculosis. Our AIRIS-TB model stands as a compelling testament to the unmatched accuracy, safety and scalability that AI can deliver, particularly in resource-limited settings where there is a shortage of radiologists and the need to tackle TB is greatest. In regions with a high prevalence of TB, we now have a scalable technological solution that can bridge the divide, expand radiologist capacity and help save lives. These results signal the transformative role AI can play in reshaping global public health and redefining how healthcare is diagnosed, delivered and experienced worldwide.”

Dr. Laila Abdel Wareth, CEO of Capital Health Screening Centre, added, “The outcomes of this study reaffirm that AI models like AIRIS-TB can not only match – but safely surpass – human-level accuracy and efficiency in clinical practice. By automating high-volume, routine screenings with precision, we are equipping radiologists to concentrate on complex and high-risk cases, unlocking greater diagnostic capacity. This shift holds immense potential to elevate patient outcomes, streamline healthcare delivery and bolster public health infrastructure on a global scale.”

The study underwent rigorous peer review and ethical oversight by the Department of Health – Abu Dhabi, ensuring transparency, accountability, and the highest standards of clinical integrity. Its publication in a leading scientific journal reinforces M42’s position as a global pioneer in AI-led health solutions and solidifies the UAE’s growing prominence as a global, data-driven hub for cutting-edge medical innovation and technology. The full study is accessible through npj Digital Medicine.

TAIPEI, Taiwan — An outbreak of the chikungunya virus in China has prompted authorities to take preventive measures from mosquito nets and clouds of disinfectant, threatening fines for people who fail to disperse standing water and even deploying drones to hunt down insect breeding grounds.

More than 7,000 cases of the disease have been reported as of Wednesday, focused largely on the manufacturing hub of Foshan near Hong Kong. Numbers of new cases appear to be dropping slowly, according to authorities.

Chikungunya is spread by mosquitoes and causes fever and joint pain, similar to dengue fever, with the young, older people and those with pre-existing medical conditions most at risk.

Chinese state television has shown workers spraying clouds of disinfectant around city streets, residential areas, construction sites and other areas where people may come into contact with virus-bearing mosquitos that are born in standing water.

Workers sprayed some places before entering office buildings, a throwback to China’s controversial hardline tactics used to battle the COVID-19 virus.

People who do not empty bottles, flower pots or other outdoor receptacles can be subject to fines of up to 10,000 yuan ($1,400) and have their electricity cut off.

The U.S. has issued a travel advisory telling citizens not to visit China’s Guangdong province, the location of Donguan and several other business hubs, along with countries such as Bolivia and island nations in the Indian Ocean. Brazil is among the othe rcountries hit hard by the virus.

Heavy rains and high temperatures have worsened the crisis in China, which is generally common in tropical areas but came on unusually strong this year.

China has become adept at coercive measures that many nations consider over-the-top since the deadly 2003 SARS outbreak. This time, patients are being forced to stay in hospital in Foshan for a minimum of one week and authorities briefly enforced a two-week home quarantine, which was dropped since the disease cannot be transmitted between people.

Reports also have emerged of attempts to stop the virus spread with fish that eat mosquito larvae and even larger mosquitos to eat the insects carrying the virus.

Meetings have been held and protocols adopted at the national level in a sign of China’s determination to eliminate the outbreak and avoid public and international criticism.

In an era of increasingly available disease-modifying therapies (DMTs) for various types of dementia, MR and CT imaging continue to predominate for diagnosis — although blood tests remain the most commonly-used method, researchers have reported.

On the other hand, PET imaging and cerebrospinal fluid (CSF) biomarker testing are infrequently applied, despite their diagnostic value — a trend that could impede early treatment of dementia, according to a team led by Jessie Yan, PhD, of Roche Diagnostics in Santa Clara, CA. The group’s findings were published on August 4 in Alzheimer’s and Dementia.

“Our study results suggest that, during the six years preceding the arrival of DMTs [2015 to 2020], despite the modest increasing trends, blood tests, MRI, and CT were the predominant diagnostic tests, while the use of CSF and PET was very infrequent,” the team wrote.

Current diagnosis of Alzheimer’s disease or dementia is mainly based on clinical signs and symptoms, and many individuals are diagnosed when their disease is advanced. MR and CT imaging are used to identify structural and functional changes in the brain, but despite their prevalent use, don’t always confirm an Alzheimer’s disease diagnosis, according to the team. Biomarker tests, such as those performed with PET imaging or CSF analysis, increase diagnostic accuracy. Yet in the end, it’s not clear which types of tests for dementia are being used in “real-world” settings, the researchers noted, writing that “a gap persists in understanding the broader use of diagnostic tools for dementia on a national scale.”

Yan and colleagues sought to address this knowledge gap in their study, which included Medicare fee-for-service data from 2015 to 2020 from 653,420 patients aged 67 or older. Of these patients, 9.1% had mild cognitive impairment, 30.3% had Alzheimer’s disease, and 60.6% had other dementias.

The group reported that while blood tests remain the primary method of diagnosing dementia at 71.9%, 53.9% of patients underwent neuroimaging. In last place was cerebrospinal fluid (CSF) testing, at 2.2%.

Imaging tests to diagnose dementia did increase slightly through the study time period, the researchers found.

Trends in use of imaging tests for diagnosing dementia within 1 year before diagnosis date
Type of imaging	2015	2020
Any	48.7%	55.5%
MRI	13.6%	20.4%
CT	43.6%	46.1%
PET	0.4%	0.8%

The lesser use of CSF and PET imaging to diagnose dementia may be because the invasiveness of lumbar puncture and the cost of PET imaging contribute, according to the authors. However, “with the advent of DMTs, the use of CSF biomarker and PET tests is expected to increase due to the confirmation requirement of the presence of [beta-amyloid] pathology prior to DMT initiation,” they added.

“Despite the modest increasing trends, substantial improvements are needed in the use of confirmatory tests, especially PET and CSF, which will be necessary for increased use of new DMTs,” Yan and colleagues concluded.

The complete study can be found here. 

Disclosure: Five of the eight study authors are employees of and shareholders in F. Hoffmann-La Roche; two receive consulting fees from Roche Diagnostics International.

Study population

The UKB (www.ukbiobank.ac.uk) was a large-scale longitudinal cohort study which recorded the baseline phenotypic and genetic data of over 500,000 participants of 22 assessment centers in England, Wales, and Scotland from 2006 to 2010 [14]. Participants were followed up until the earliest occurrence of either first diagnosis, death, loss to follow-up, or the final date with available data. Participants with dementia and major neuropsychiatric diseases at baseline were excluded from the present study. The UKB received approval from the North West Multicenter Research Ethics Committee, and all participants provided written informed consent.

To validate the findings from UKB and to strengthen the causal relationships, we further conducted data analyses of participants who were free of dementia and major neuropsychiatric diseases from the ADNI database (adni.loni.usc.edu). The multicenter ADNI was established to evaluate clinical, imaging, genetic, and biochemical biomarkers of AD, with participants aged 55 to 90 years recruited from the United States and Canada. Each ADNI participant underwent systemic neuropsychological examinations, as well as neurological and physical assessments at baseline and at annual follow-up. The ADNI received approval from the institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their guardians in accordance with the Declaration of Helsinki.

Assessments of sleep behavior and classification of sleep quality

In UKB cohort, we utilized an algorithm derived from self-reported sleep quality data, initially introduced in 2020 [11], due to the lack of specialized sleep questionnaires during the baseline survey. This algorithm was used to create a SQI with five sleep-related items, including snoring, chronotype, daytime sleepiness, sleep duration, and insomnia [11], demonstrating its effectiveness as an alternative method for assessing the sleep quality. To further enhance the evaluation of sleep quality in the UKB, we incorporated six self-reported sleep behaviors: items previously used in the 5-item sleep score, along with the “difficulties in getting up in the morning” trait, which has been linked to reduced health-span [15] and accelerated biological aging [12]. This additional component was included to provide a more comprehensive measure of sleep quality, reflecting a broader range of behaviors that are predictive of long-term health outcomes. Low-risk sleep behaviors were defined as follows: early chronotype (‘morning’ or ‘more morning than evening’), sleeping 7–8 h per day, reporting never or rarely experiencing insomnia symptoms, no self-reported snoring, no frequent daytime sleepiness (‘never/rarely’ or ‘sometimes’), and finding it easy to get up in the morning (‘fairly easy’ or ‘very easy’). Each sleep component was scored as 0 if the participant was classified as low risk for that factor, and 1 if considered high risk. The scores for all components were then summed to generate a SQI ranging from 0 (best) to 6 (worst), with higher scores reflecting poorer sleep quality. We then classified the overall sleep patterns as ‘good sleep quality’ (SQI = 0–1, as reference), ‘general sleep quality’ (SQI = 2–3), and ‘poor sleep quality’ (SQI = 4–6) based on the continuous term of SQI.

The SQI in ADNI was calculated based on longitudinal data of sleep item in the Neuropsychiatric Inventory (NPI) scale. The informants of participants were asked “does the participant have difficulty falling asleep, awaken you during the night, rise too early in the morning, or sleep excessively during the day?”; “if yes, rate the severity: 1—mild (noticeable, but not a significant change).; 2—moderate (significant, but not a dramatic change).; 3—severe (very marked or prominent)”. Participants were interviewed annually for these sleep-related questions. Those with less than 1-year follow-up sleep data were excluded. Longitudinal data of sleep were extracted for further analyses only if they were in parallel with longitudinal measurements of cognitive or biomarker. The “exposure (answering yes to the question) intensity” [ratio] was calculated by dividing “exposure” times by total interview times. The SQI was calculated by multiplying ratio with the average severity score. The participants were further categorized into three groups according to tertiles of SQI: good sleep (0 ≤ SQI ≤ 0.1, as reference), general sleep (0.1 < SQI ≤ 0.5), and poor sleep quality (SQI > 0.5).

Cognitive measures

In UKB, five cognitive domains were assessed at baseline, including visuospatial memory (VM), measured by the average number of incorrect matched pairs; processing speed, indicated by mean reaction time (RT); prospective memory (PM), based on the total number of times an intention was forgotten in the PM task; fluid intelligence (FI), reflected by total scores on a set of cognitive tasks; and working memory (WM), assessed by the total number of digits correctly recalled. Higher scores in VM, RT, and PM indicated poorer cognitive performance, while higher scores in FI and WM reflected better performance. In ADNI, global cognition was evaluated by the Alzheimer’s Disease Assessment Scale (ADAS). Cognitive domains were assessed by administering the neuropsychological battery which included components indicative of memory function (MEM) and executive function (EF) [16]. Cognitive assessments in ADNI were carried out at both baseline and follow-up.

Hippocampal volume measurement

Hippocampal volumes in both UKB and ADNI were derived from magnetic resonance imaging (MRI), of which the T1-weighted and T2-FLAIR structural images were obtained in a straight sagittal orientation and underwent central preprocessing to derive the hippocampal volume. Image processing details in UKB and ADNI could be found elsewhere [17, 18]. In the UKB cohort, hippocampal volume represents the absolute volume at a single time point. In the ADNI cohort, hippocampal volume is measured at each visit, and the analysis is based on the annual rate of change in hippocampal volume derived from longitudinal MRI scans.

AD diagnoses

AD diagnose in UKB was defined as code 331.0 in ICD-9 and codes F00 and G30 in ICD-10 over a follow-up period from 2007 to 2020, using data from hospital inpatient records, death certificates, primary care records, and self-reports. In ADNI, AD was diagnosed according to the National Institute of Neurological Disorders and Stroke–Alzheimer Disease and Related Disorders criteria [19], whereas mild cognitive impairment was diagnosed according to the Mayo Clinic criteria [20].

Blood proteomics

For each participant in the UKB, blood samples were drawn into EDTA tubes and immediately centrifuged at 2,500 g for 10 min at 4 °C to separate the plasma. The plasma supernatant was then divided into aliquots and promptly stored at − 80 °C until further analysis. Using dual barcoded antibody technology on the Olink platform, the UKB conducted multiplexed proteomic assays on approximately 55,000 plasma samples, primarily collected at baseline. This approach produced specific and semiquantitative data for 2,923 protein assays in multiplex. Of the 2,923 protein assays available, we excluded proteins with more than 20% missing values (n = 12), resulting in a total of 2,911 protein assays retained for analyses.

Covariate assessments

We adjusted for the covariates, including age, sex, education, and APOE ε4 carrier status, smoking status, anxiety, depression, obesity, diabetes, hypertension, hyperlipidemia, stroke, and sleep medications, to lower potential confounding bias in both cohorts. APOE ε4 carrier status (rs7412 and rs429358) were determined by genetic information. Sociodemographic and behavioral confounders were collected by the questionnaire and information on comorbidities were ascertained based on self-reported information and medical records. Participants were dichotomized according to their self-reports on whether they took any sleep medication (including benzodiazepines, Z-drugs, and melatonin).

Statistical analyses

Baseline characteristics were summarized as mean and standard deviation (SD) for continuous variables, and as counts with percentages for categorical variables. We used χ² tests for categorical variables and the t-test or Mann–Whitney U test for continuous variables to assess intergroup differences in baseline characteristics. The values of dependent variables with skewed distribution in linear regression models were normalized using log-transformation method. The values greater or smaller than five fold SD from the mean value were considered as extreme outliers. We removed extreme outliers of hippocampal volume and then performed standardization by generating z-scores.

First, linear regressions were used to explore the cross-sectional relationships of SQI (the independent variable) with cognitive performance (dependent variables) in UKB cohort. Interaction effect between SQI and APOE ε4 status on cognition was tested and stratified analyses were further performed. Besides, the effects of SQI and its interaction with APOE ε4 status on longitudinal cognitive changes were examined using linear mixed-effects models in ADNI cohort. The linear mixed effects models were utilized due to their ability to manage unbalanced and censored data, as well as incorporate time as a continuous variable [21]. The overall significance of the three-way interaction term was evaluated using a likelihood ratio test, which compared the full model to a nested model without the three-way interaction term. Based on the APOE ε4 status and sleep quality, we further categorized the population as “good sleep and non-APOE ε4 carrier”, “good sleep and APOE ε4 carrier”, “poor sleep and non-APOE ε4 carrier”, “poor sleep and APOE ε4 carrier”. We fitted a linear mixed-effects model against the cognitive performance to further depict the combined genetic and sleep-related effects on cognition, in which Wald tests were adopted to compare the slope of each group. Next, we used similar approaches to test the association of sleep quality and its interaction by APOE ε4 with hippocampal volume (Fig. 1).

Then, the associations between sleep quality and incident probable AD in both UKB and ADNI cohorts were depicted using the time-dependent Cox proportional hazard models, in which results would be described as hazard ratios (HR) and 95% confidence intervals (95%CI). Cox proportional hazard models were conducted in longitudinal data when researchers focused on the time until a specific event [22]. Right censoring would be applied to account for scenarios where a subject does not develop AD before their last recorded observation or exits the study before its conclusion. The additive and multiplicative interactive effects of SQI and APOE ε4 status on the risk of incident AD were tested. Stratified analyses were performed to explore whether good sleep could attenuate APOE ε4-related AD risk. The proportional hazards assumption was checked using Schoenfeld’s global test to ensure that the proportional hazards assumption was not violated (P > 0.05). (Fig. 1).

Finally, comprehensive proteomic analyses combined with bioinformatic analyses were conducted to explore the biological mechanisms of poor sleep affecting AD risk. Cox proportional hazard models (for AD risk) and multiple logistic regression models (for poor versus good sleep) were employed to identify proteins associated with both sleep quality and incident AD risk in the total sample. Similar analyses were conducted among APOE ε4 carriers and among age- and sex-matched APOE ε4 non-carriers, respectively, to investigate the biological mechanisms underlying the interaction between sleep and APOE ε4 in AD development. Bonferroni corrections were applied to define the significance cutoff (P < 1.72 × 10^–5, number of proteins tested = 2,911). After screening out the proteins which were consistently positive or negative associated with incident AD and poor sleep, functional enrichment analyses were performed using the STRING database (http://string-db.org). (Fig. 1).

Considering that some discrepancies have been observed in how women report their sleep quality compared to men, we conducted sex-stratified analyses as a secondary analysis following the principal analysis described above. Specifically, we repeated all key models-including linear regressions, linear mixed-effects models, and Cox proportional hazard models-separately in female and male subsamples. This additional analysis provides a more nuanced understanding of how sleep quality may differently affect cognitive decline, hippocampal atrophy, and AD risk across genders.

All the models were calculated with adjustments for covariates, including age, sex, education, and APOE ε4 status (model 1). As for ADNI cohort, we additionally adjusted for cognitive status (mild cognitive impairment = 1, normal cognition = 0) and follow-up times (practice effect) before AD diagnosis (model 1). Sensitivity analyses were conducted by adding sleep medications and history of sleep apnea, hypertension, diabetes, stroke, smoking, hyperlipidemia, depression, anxiety, and obesity to the covariates in Model 1 (Model 2). A two-sided P value of < 0.05 was deemed significant unless otherwise specified. Statistical analyses and figure preparation were conducted using R software version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).