Jared Kaltwasser

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Investigators in Australia have designed a new method to evaluate whether oral appliances are likely to be effective in treating obstructive sleep apnea in individual patients.

The strategy uses clinical data and standard patient testing in order to create individualized virtual patient models that can predict the treatment’s success or failure.

The model was developed by Danny Eckert, Ph.D., of Flinders University, and colleagues. 

 Eckert and colleagues said oral appliances have a number of advantages over continuous positive airway pressure (CPAP) devices, namely, that are well-tolerated and less invasive than CPAP machines.

The catch? Oral appliances do not always work, and clinicians have little way of knowing which patients will benefit from them.

“A major clinical challenge with dental appliance or mandibular advancement therapy is the inability to accurately predict who will respond,” Eckert said, in a press release. “At present, it is a toss of the coin.”

Eckert and colleagues had previously developed a computer model designed to estimate pathophysiological endotypes of patients with obstructive sleep apnea. Having succeeded in that effort, they wondered whether the model might also lend insights into the likelihood of an oral appliance helping a particular patient.

They recruited 62 people with obstructive sleep apnea, performing an in-laboratory diagnostic exam and then oral appliance treatment efficacy polysomnography. The investigators culled seven 

 variables from that study and then paired that information with age and body mass index data. They alsoused a machine learning algorithm to predict therapy response. Forty-five of the patients were used to train the model. The remaining 17 patients were used as a validation cohort, with intervention success based on standard definitions of the apnea-hypopnea index.

Using a 10-fold cross-validation system, investigators found that the model was able to predict oral appliance responders versus nonresponders 91% of the time, on average. The authors used fewer than five apnea-hypopnea index events per hour as their definition of efficacy.

Eckert said a model like this has significant potential to help ensure that patients are given the most appropriate and most effective sleep therapy possible, without the usual trial-and-error approach.

“An accurate model that takes into account the different causes of sleep apnea to match therapy to each person should produce much better results from a dental appliance, particularly when CPAP or other treatment aren’t appropriate or preferred,” he said.

When investigators ran the model using different definitions of success (ranging from <5 to <20 apnea-hypopnea index events), the model’s success rate varied, ranging from 60% to 100%.

Eckert and colleagues said those accuracy ranges are promising. They said the next step is to try to test the model on larger data sets to see how well its accuracy holds up across a wider range of patients.

If the model proves successful, it could become a low-cost, low-burden method of optimizing patient care. Eckert said it is also a demonstration of the ways in which machine learning can be leveraged to make healthcare more efficient.

According to the American Sleep Association

, sleep apnea mouth guards can cost up to $2,000. CPAP devices have price tags in a similar range, though the highest-cost models can be around $3,000, 

Aside from cost differences, one reason oral appliances might be preferable is patient comfort. A

 found outcomes were similar in patients given CPAP machines and patients treated with mandibular advancement devices for obstructive sleep apnea; however, they said the difference may have been due to better rates of compliance among patients given the mandibular device compared to those prescribed CPAP treatments.

If left untreated, obstructive sleep apnea can increase a patient’s risk of obesity, diabetes, and heart disease, among other diseases.

Articles

The model could make it quicker and easier to decide whether patients need continuous positive airflow pressure machines or not.










Virtual Model Can Predict Success of Oral Appliance Intervention, Study Finds

Researchers may be on the cusp of better understanding one of the most elusive parts of the human brain, and their findings could have major implications for our understanding of sleep problems and related disorders.

Investigators from the Children’s Hospital of Philadelphia (CHOP) last month released the findings of a study of the genomic architecture of the hypothalamus. The results highlight potential target genes that appear to be associated with a number of functions, such as sleep cycles, body temperature, appetite, and thirst.

, Ph.D., the study’s senior author, told 

that the hypothalamus can be particularly difficult to study given that it is located in the center of the brain. As director of the Center for Spatial and Functional Genomics at CHOP, he and colleagues had been looking into body mass index and puberty, and in both cases their research pointed to the hypothalamus. The problem, though, was that very little was known about that region of the brain.

They found out that Rudolph L. Leibel, M.D., and his colleagues at Columbia University had been studying the hypothalamus using a model derived from embryonic stem cells (ESC).

“They were already working with a hypothalamic model derived from ESC cells,” Grant said. “And that's where we generated our 3D genomic maps, using the cells that they generated up at Colombia.”

Leibel is a co-author on the new study, which was 

The model made it possible for Grant and colleagues to better understand the genetic architecture of hypothalamic progenitor cells and the arcuate nucleus-like hypothalamic neurons. By looking at the various subtypes of neurons on the hypothalamus and using existing data from genome-wide association studies, Grant and colleagues were able to identify genes associated with particular traits that were regulated by the hypothalamus.

For example, the work confirmed that the BDNF gene in body mass index and obesity risk. They also identified PER2as playing a role in sleep regulation.

The science is early and investigational, Grant said. Its importance is in pointing investigators in meaningful directions that could eventually lead to better understandings of the pathogenesis of diseases and possibly future therapeutic targets.

In terms of sleep, Grant noted that there have been a number of studies highlighting the genetic roots of sleep problems. The model helped elucidate the genetic links to insomnia, as well as depression and bipolar disorder.

Adding to the intrigue, Grant noted that there’s significant literature linking sleep with body mass index. Yet, it is not clear whether sleep dysfunction is the first mover, leading to obesity, or whether a patient’s weight might affect his sleep. He said one active area of research is in Mendelian randomization, a method in which investigators use genetic variants to determine causes and effects.

“There's a lot of literature actually on Mendelian randomization on the relationships between sleep and various traits,” he said. “Some studies agree with each other; some disagree. So I think the jury's still out.”

Grant and colleagues said they are making their data from these studies publicly available in hopes that the wider scientific community can build upon their findings. He noted that over the past 15 years, there has been a tremendous amount of scientific work on genome-wide association studies. One of the outgrowths of that research, he said, has been the realization that identifying causal genes is more complicated than initially thought. This new research can help investigators identify the right genes to target.

“If you’ve got the right gene, and it’s therapeutically tractable, that could be exciting,” he said.

A new model of the hypothalamus can lead to potential new genetic targets for a number of disorders.

Experimental Research Uncovers Genetic Insights Into Sleep, Other Traits

A new report suggests metabolic profiling may be able to give clinicians a much clearer picture of individual cases of 5q spinal muscular atrophy (SMA), creating the potential for earlier, more targeted therapeutic intervention.

SMA is caused by loss of function or deletion of the 

gene with retained function of at least one copy of the 

 gene, explained corresponding author Andreas Ziegler, M.D., of Heidelberg University Hospital, in Germany, and colleagues.

New advances in the treatment of SMA have made it possible to significantly improve the trajectories of cases, particularly when children are diagnosed early. But newest therapies also come at a high cost and can be burdensome to administer.

that the current status quo involves making treatment decisions based primarily on the quantity of 

copy numbers. However, that method is imprecise.

“The biggest problem currently is to determine the optimal ‘window of opportunity’ for the different SMA treatments,” Ziegler said.

While gene therapy can have a meaningful impact on patients with SMA, investigators do not yet know if the benefits of the therapy are permanent.

“That means treating these children too early in a presymptomatic stage of the disease might be an over-treatment at their current stage of disease, making a later gene therapy impossible when they really need it (that means: one extreme is too early treatment),” he said.

In cases where an infant screening identifies a newborn as having a type of 

likely to have a later-in-childhood onset, clinicians have to make a difficult choice about when to actually start therapy.

“For these children we have also to focus on possible toxic effects of ‘too much SMA treatment’ with the result of toxic high levels of the SMN-protein,” he said. “Sometimes it would probably be less harmful to watch and wait and monitor the children closely.”

Noting that urinary metabolic profiling has successfully been used in other disorders of the central nervous system, Ziegler and colleagues set about to investigate whether the use of 1H-nuclear magnetic resonance (NMR)-based metabolomics might be able to identify reliable biomarkers of SMA diagnosis and disease prediction.

The investigators collected urine samples from 29 patients with SMA, all of whom were treatment naive. Of those 29, 5 were presymptomatic, and the remaining patients were split among the 3 types of SMA that appear in childhood. Samples were also collected from 18 patients with Duchenne muscular dystrophy and from 444 healthy controls.

The authors then used machine learning to analyze the samples to see if patterns could be identified that might match with phenotypic severity.

The analysis suggested a “metabolic fingerprint” that the investigators said might allow clinicians to distinguish children with SMA from healthy controls and from children with Duchenne muscular dystrophy. However, those “fingerprints” appear to be able to distinguish beyond a blanket SMA diagnosis.

“Importantly, profiles of affected SMA patients, in the majority of cases, displayed some correlation to phenotypic severity and might be able to reflect dynamic changes under therapy,” Ziegler and colleagues said.

They added that the analysis suggests that multiple regions of the metabolome appeared useful in stratifying patients, “emphasizing the concept of complex and multifactorial biochemical changes driving SMA phenotypes, as opposed to a single-biomarker-based approach to predict disease severity.”

The authors cautioned that they are still investigating these metabolic profiles, and in any case, the findings would need to be refined and tested in larger groups.

Still, they said, this early proof-of-concept study suggests it may soon be possible to use a non-invasive method to diagnose and classify SMA.

 that he envisions the new screening as a second-tier approach, following genetic testing.

“After having the first positive tier with a suspicion of SMA, NMR might be used as a second tier to determine the biochemical onset of the disease and stratify for SMA therapies,” he said.

In addition, he said the technology could be used as a monitoring tool to track the effectiveness of particular therapies in particular patients, though he said this is still a work in progress.

A new study suggests clinicians might be able to use metabolic “fingerprints” to better understand the likely severity of individual cases.

Metabolic Urine Analysis May Help Diagnose, Predict SMA

Associations between sleep and obesity have been well documented in adults and children, but a new study suggests sleep patterns at the earliest stages of life can set a child on a path toward obesity.

The new study comes from investigators at Brigham and Women’s Hospital and Massachusetts General Hospital, both in Boston.The scientists wanted to understand whether sleep trouble among infants had a similar effect on weight as sleep disorders in adults. To find out, they studied 298 full-term infants who participated in the longitudinal Rise & SHINE cohort study.

Each of the infants in the study wore a sleep monitor on their ankle, and their parents kept sleep journals. The ankle monitors were worn for 3 nights when infants were one month old and 3 more nights when the infants were 6 months old. Researchers tracked metrics such as nighttime sleep duration, waking after sleep onset, and number of waking bouts in excess of 5 minutes. They then compared those data to size measurements, using growth charts from the World Health Organization.

The investigators found that, for each extra hour of sleep an infant got, his or her odds of being overweight from 1 to 6 months old decreased by 26%. Similarly, as the number of nighttime wake-ups in excess of 5 minutes decreased, so did the odds of being overweight. The one exception was waking after sleep onset; changes in that metric did not appear to correlate with weight. The findings were 

“In this study, we found that not only shorter nighttime sleep, but more sleep awakenings, were associated with a higher likelihood of infants becoming overweight in the first six months of life,” said study co-author Susan Redline, MD, MPH, in a press release.

Though the study only dealt with infants, the authors said the data suggest that poor sleep early in life may contribute to obesity later in life. That conclusion aligns with a 2017 study that looked at links between childhood obesity and adult obesity. 

, investigators found that 2-year-olds who were obese had a 75% chance of being obese when they reached age 35. The risk of obesity at 35 appeared to increase throughout childhood, with obese 19-year-olds having an 88% chance of still being obese at age 35. The inverse was also true: children who were not obese at age 2 saw their risk of obesity at age 35 decrease through their childhood and teenaged years.

report by the T.H. Chan School of Public Health at Harvard

 found one-tenth of US healthcare costs were related to obesity in 2006, and estimated that by the year 2030, the US could spend $66 billion per year on obesity-related healthcare.

Redline and colleagues listed a number of limitations to their study. They said African-Americans and people of lower socioeconomic status were under-represented in their data set. They also noted that their study did not include potentially confounding factors, such as the duration of breastfeeding for the infants in the study.

Still, Redline said the data show how healthy sleep plays an important role in health, even in infancy.

“Parents should consult their pediatricians on the best practices to promote healthy sleep, like keeping consistent sleep schedules, providing a dark and quiet space for sleeping, and avoiding having bottles in bed,” she said.

A new report suggests infants who do not get enough sleep can quickly become overweight.





Study: Poor Sleep Linked with Weight Gain, Even Among Infants

When her toddler was diagnosed with progeria in 1998, Leslie Gordon, M.D., Ph.D., faced three distinct challenges: Progeria was extremely rare. It was incurable. And most devastatingly, it was fatal.

Gordon’s son, Sam Berns, had appeared healthy at birth, but by the time he turned 2, it was clear that something was wrong. Caused by a mutation in the LMNA gene, progeria leads to rapid aging and physical changes, culminating in an early death. Not only was there no cure for progeria, but there was no treatment, no pipeline and no awareness. Nothing. “The story with rare diseases is a well-trodden one, and there was nothing out there for us,” she says.

Gordon, though, decided to fight. Her husband, her sister and she pooled their talents and set about ticking off a daunting to-do list: find children with progeria, create an international registry, launch a cell and tissue bank and raise money. The result was the Progeria Research Foundation. Sam died in 2014, but the foundation’s work continues. The FDA approved the first-ever therapy for patients with progeria, Zokinvy (lonafarnib), last year. Gordon says the approval is a breakthrough from not only a medical standpoint but also a psychological one. “It just puts us in a whole different place,” she says, “a whole different mindset.”

Yet an even more dramatic leap forward could be on the horizon. Two different research teams are working on a new strategy to fight progeria based on the idea that ribonucleic acid (RNA) can be leveraged or inhibited in ways to prevent or cure a host of diseases. The investigational therapies are part of a wave of new RNA agents that could one day reshape large portions of the therapeutic landscape. Tom Misteli, Ph.D., who directs the Center for Cancer Research at the National Cancer Institute, is working on one such investigational therapy, known as LB143. He notes that the DNA mutation underlying progeria causes defective processing of critical cellular RNA, leading to the creation of a mutant protein called progerin. By inhibiting production of the RNA coding for the defective protein, investigators hope to slow down the disease. In research findings published this past March, Misteli and colleagues showed that the strategy reduced progerin-producing RNA by 90% to 95%.

Development of another RNA-based product, led by current National Institutes of Health Director Francis S. Collins, M.D., Ph.D., is also showing early promise. In a mouse model of progeria, the agent increased survival by 60%.

RNA has long lived in the shadow of DNA, famous for its sinuous double helix shape and analogized to being the genetic blueprint of all life. RNA has a less elegant single strand and is frequently depicted as being the dutiful reader of DNA, the plodding translator of its complex instructions.

But some might say that RNA is having its day. The Pfizer and Moderna COVID-19 vaccines are messenger RNA vaccines, crafted to carry instructions for the making the telltale spike protein of the SARS-CoV-2 virus that trains the immune system to recognize and attack when it next encounters a SARS-CoV-2 infection. Traditional vaccines depended on growing pathogens in cell culture. Most flu shots are still contingent on growing influenza viruses in eggs. The stunning speed at which the Pfizer and Moderna vaccines were developed has been attributed to their dependence on RNA, which with today’s technologies is relatively easy to modify and keep stable.

Treatments involving RNA are taking off and filling the drug development pipeline. Some are scraps of DNA designed to keep messenger RNA from making proteins. Others operate on RNA processes so mutated portions don’t get expressed and eventually made into aberrant, disease-causing protein.

“Superficially, it’s a no-brainer. The DNA makes the RNA. The RNA makes the proteins,” Gordon says. If the proteins are the problem, disrupting that process could lead to a resolution — in theory. “Then, when you get into it, science is always more complicated,” she says.

Phillip D. Zamore, Ph.D., a pioneer in RNA therapies and chair of the RNA Therapeutics Institute at the University of Massachusetts, said RNA therapies represent a novel way of thinking about stopping diseases. Traditionally, small-molecule drugs target the enzymatic activity of proteins. “All of those are old-fashioned drugs, in the sense that they require searching through huge libraries of compounds to fortuitously find a lead compound that your medicinal chemists could turn into a drug,” he says.

Next-generation RNA therapies create a new opportunity to disrupt the transfer of information within cells, thereby interfering with the process underlying the target disease. Misteli says RNA makes the best target within the DNA-to-RNA protein relay. He notes that genetic information is permanently encoded in DNA, but changing the underlying genetic code carries risk and could lead to unintended consequences. Another option is interfering with proteins, but that solution is not particularly efficient, he says. RNA, on the other hand, provides a meaningful target without the risks of DNA alteration. “Targeting RNA offers the benefit of eliminating the effect of a mutation close to the source yet without a permanent change to the hereditary information,” he says.

Zamore identifies two key turning points that helped transform RNA-targeting therapies from an idea into a reality. The first was the discovery that antisense oligonucleotides (also called antisense oligos) could interfere with RNA splicing, an intermediate step in the formation of messenger RNA. That led to the development of the first RNA therapy, Vitravene fomivirsen), which the FDA approved in 1998 as a treatment for patients with cytomegalovirus retinitis, a vision-threatening disease most commonly known as a complication of HIV/AIDS at the time. However, antisense oligos never worked all that well in suppressing messenger RNA expression, Zamore says, limiting their usefulness. I

nstead, one more step was necessary, the discovery that double-stranded small interfering RNA (siRNA) could be used to interfere with RNA or RNA interference (RNAi). The understanding of RNAi gives scientists the power to “silence” gene expression toward therapeutic ends, and siRNA is one type of RNA molecule used to achieve RNAi. “The real revolution was the discovery that you could make synthetic siRNAs and use them to destroy messenger RNA,” he says. The scientists credited with discovering RNA interference, Andrew Z. Fire, Ph.D., and Craig C. Mello, Ph.D., won the Nobel Prize in 2006.

A dozen years later, the FDA approved Onpattro (patisiran), an siRNA drug, as a treatment for patients with hereditary transthyretin-mediated amyloidosis. The drug was developed by Alnylam Pharmaceuticals. Zamore was a founder of the company, though he no longer works for or consults for Alnylam.

Some RNA therapies work further downstream. RNA aptamers are sticky pieces of RNA that cling to disease-causing proteins. One example is Macugen (pegaptanib), an anti-vascular endothelial growth factor drug used to treat patients with ocular vascular disease.

Although there was some early skepticism, plenty of people have been hard at work developing RNA drugs. A 2020 article in 

 counted more than 400 RNA therapeutics, including vaccines, in development pipelines. Most, though, are at early stages of development. Only 3% were in phase 3 trials, and only a handful were awaiting regulatory decisions.

Even as investigators have made progress, Misteli said two persistent problems have perplexed scientists: making sure RNA therapeutics are stable and finding effective means of delivering them to the right place. “The major obstacles at this point are delivery of the RNA agents as well as their stability in cells and tissues as they are relatively rapidly degraded,” he says.

But Zamore says the Pfizer and Moderna vaccines show that subcutaneous administration of RNA therapies can be successful. Earlier therapies, such as Onpattro, required administration by infusion. If the delivery question can be solved, and if the administration route can be simplified, Zamore says RNA therapies could match or even surpass small-molecule drugs, in part because RNA therapeutics’ effects are long lasting. Rather than hoping a patient remembers to take a small-molecule drug every day, physicians might someday be able to prescribe a once-yearly shot.

Zamore also believes RNA therapies will eventually compete with small-molecule drugs on price. The high price tags on the current crop of therapies for the most part are due to generating a return on investment rather than the cost of the goods, he says. As the investment pays off and the techniques for RNA therapies improve, the cost of developing the drugs will drop, Zamore predicts.

The potential of RNA therapies is more about platforms than specific proprietary products, in Zamore’s opinion. Rather than working on a single small molecule whose applicability may be limited to a single disease, RNA therapy platforms can be leveraged to rapidly develop products for any number of diseases, he says.

That’s a particularly good situation for people like Gordon who have devoted themselves to fighting rare diseases. It means she does not need to rely on persuading a drug company to spend billions of dollars working on progeria, a disease that affects just 1 in 20 million children. Instead, her cause can benefit from RNA therapy research aimed at entirely different disease categories. Gordon says she’s willing to talk and work with anyone who has insights that might help develop new therapies for progeria. “I just want to cure this thing,” she says.

Jared Kaltwasser is a freelance writer in Iowa.

The Pfizer and Moderna COVID-19 vaccines introduced much of the world to ribonucleic acid (RNA), which has long played second fiddle to DNA. But in addition to the messenger RNA vaccines, treatments targeting and using RNA in a variety of ways are filling the pipeline and exciting researchers and drug developers. 

The RNA Moment: Once Second to DNA are Treatments Now Filling the Drug Pipeline

A large-scale study that leveraged wrist-worn health trackers suggests sleep problems are the norm, not the exception, among people with psychiatric disorders.

, offers a comprehensive look at links between sleep and mental health, clarifying sleep’s role as both a symptom of, and a modifiable risk factor for, psychiatric disease.

Corresponding author Shreejoy Tripathy, PhD, of the University of Toronto, and colleagues, noted that links between sleep and psychiatry are well-documented. However, studies into the topic are generally subject to significant limitations. Measures of sleep quality are typically self-reported and therefore highly subjective, and studies using objective measures like polysomnography are necessarily limited in size.

In order to achieve a higher scale, Tripathy and colleagues decided to use objective sleep data gathered by wrist-based accelerometry, and then cross-reference that sleep data with diagnostic records to see whether links could be found between sleep and the likelihood of a mental health disorder diagnosis at any point in life.

, a database that is tracking the health of a half-million patients. Of those, more than 100,000 patients wore wrist-based accelerometers for a week, providing Tripathy and colleagues with a trove of sleep data.

After exclusions, including for those whose wristband use was inconsistent or whose week coincided with daylight savings time shifts, the authors based their analysis on 89,205 participants. Patients were scored based on 10 sleep measures, and then those data were compared with diagnoses for 4 psychiatric disorders: major depressive disorder, anxiety, bipolar/mania, and schizophrenia.

The investigators found that sleep problems were ubiquitous among patients with mental health diagnoses, and sleep quality tended to be more closely associated with psychiatric disorders than sleep duration.

Michael Wainberg, PhD, the study’s first author, told 

 that the consistency of the findings was a surprise. The data showed that each mental health diagnosis was associated with an average of 8.5 of the 10 sleep measures.

“We observed sleep pattern changes for all four categories of diagnoses we looked at… and for nearly every sleep parameter we looked at (bedtime, wake-up time, sleep duration, number of nighttime awakenings, and so on),” he said. However, Wainberg added that the data also had other surprises. For instance, while sleep problems were consistently correlated with psychiatric disorders, the effect sizes of the associations were generally modest.

While the study confirms links between sleep problems and the likelihood of a mental health disorder, it also shows the benefits—and limitations—of accelerometry in tracking sleep.

“Wrist accelerometers only measure wrist movement, and wrist movement is an imperfect proxy for sleep,” he said. “In particular, accelerometers tend to have trouble distinguishing sleep from the period immediately before falling asleep, when the person is already fairly motionless.”

The devices can also have trouble noticing when a person wakes up briefly but does not move his or her wrist.

“That said, accelerometry is generally considered fairly accurate for sleep measurement, and it’s much more versatile than polysomnography, which requires attaching multiple electrodes to your body and often spending the night in a sleep clinic,” he said.

 that he thinks such devices can play an important role in the way physicians and patients track and improve sleep.

“But I think this is likely some way off into the future, as both physicians and patients learn about the strengths and limitations of these devices, given their real-world usage in the home,” he said.

Tripathy noted that the wearable he uses, called a Whoop, “also offers a handy tool for exporting summaries of my sleep data to my clinician… though I’m not quite sure they’d know what to do with this information!”

Tripathy said the study shows sleep problems should be part of the patient/physician conversation regardless of which psychiatric conditions a patient presents with, though he said the accuracy of sleep trackers would need to be studied more closely in a well-designed clinical trial.

The study leveraged the power of wristband accelerometry to reveal links between sleep and psychiatry.
