Serious Mental Illness at Young Age Can Have Lasting Impact on Future Employment

A new study has found that people who have been hospitalized due to a mental disorder before the age of 25 have considerably poorer prospects of finding a job, as well as poor education and low income.

Researchers at the University of Helsinki found that the employment rate was the lowest among individuals who were hospitalized for schizophrenia. Less than 10 percent were employed during the follow-up period of the study, researchers reported.

Additionally, less than half of the individuals hospitalized for mood disorders worked after the age of 25.

The earnings of people with serious mental disorders in their youth were quite low and did not improve later, the study found. More than half had no earnings over the follow-up period.

The study involved more than 2 million individuals living in Finland between 1988 and 2015, who were monitored between the ages of 25 and 52.

“People suffering from mental disorders drop out from the labor market for a wide range of reasons,” said Dr. Christian Hakulinen, a postdoctoral researcher from the University of Helsinki. “However, opportunities for contributing to professional life and acquiring an education should already be taken into consideration at the early stages of treating serious mental disorders, provided the patient’s condition allows it.”

The study was published in the Acta Psychiatrica Scandinavica journal.

Source: University of Helsinki

Uninsured Kids With Mental Health Emergencies Often Transferred to Another Hospital

Children without health insurance who present to the emergency department (ED) for mental health issues are more likely to be transferred to another hospital compared to kids with private insurance, according to a new study by researchers at the University of California (UC) Davis Children’s Hospital and the UC Davis Department of Psychiatry.

Previous research has shown a significant increase in the number of children and teens presenting to the ED for mental health issues. Between 2012 and 2016, hospital EDs saw a 55 percent jump in kids with mental health problems, according to findings presented at a meeting of the American Academy of Pediatrics in 2018. The increase is highest among minorities.

Transferring a child from one hospital to another creates additional burdens for the patient, family and health care system as a whole. It can add to overcrowding in busy emergency departments, higher costs of care and higher out-of-pocket costs for the family.

For the study, the researchers analyzed a national sample of 9,081 acute mental health events among children in EDs. They looked at the patient’s insurance coverage and a hospital’s decision to admit or transfer patients with a mental health disorder.

“We found that children without insurance are 3.3 times more likely to be transferred than those with private insurance,” said Jamie Kissee Mouzoon, research manager for the Pediatric Telemedicine Program at UC Davis Children’s Hospital and first author on the study.

“The rate was even higher for patients presenting with bipolar disorder, attention-deficit and conduct disorders and schizophrenia.”

The findings, published in the journal Pediatric Emergency Care, reveal gaps in providing equitable and quality care to pediatric patients with mental health emergencies based on their insurance coverage.

According to James Marcin, senior author on the study, there are regulations in place to prevent EDs from making treatment decisions based on the patients’ insurance. Transferring a patient for any other reason than clinical necessity should be avoided.

“Unfortunately, the financial incentives are sometimes hard to ignore and can be even unconscious,” said Marcin, who also is director for the UC Davis Center for Health and Technology and leads the telemedicine program at UC Davis Health.

“What we have found in this study is consistent with other research that demonstrates that patients without health insurance are more likely to get transferred from clinic to clinic or hospital to hospital.”

Marcin is currently looking into how telemedicine — video visits delivered to the children who seek care in remote EDs — might be a solution to the tendency to transfer the patient to another hospital.

Source: University of California – Davis Health

Precision Therapy Targeting Specific Gene Mutation Reduces Psychotic Symptoms

Breakthrough research has found that treatment of some forms of psychosis can be enhanced by tailoring an intervention to a specific genetic mutation.

The new study provides a proof-of-principle demonstration that treatments can be focused to a specific genotype, rather than diagnosis, to relieve symptoms. The findings also link an individual structural mutation to the underlying biology of psychosis and treatment response.

Nevertheless, genetic mutations that have large effects on psychiatric disease risk are rare, with some known to occur in only one or a few families. However, therapy directed at one mutation is described in the study led by Deborah L. Levy, PhD, McLean Hospital, an affiliate of Harvard Medical School. Study findings appear in the journal Biological Psychiatry.

The mutation was a copy number variant (CNV) in which the two patients in the study had four, instead of the usual two, copies of the GLDC gene. The authors hypothesized that this mutation might reduce brain glycine, a key factor for proper glutamatergic functioning, which is disrupted in schizophrenia.

“The compelling aspect is that this CNV can be linked to pathophysiology, and, as the new study shows, to treatment,” said Dr. Levy.

The researchers assessed whether this CNV could help guide treatment decisions by targeting the mutation to normalize its effects, a “genotype first” approach.

“This approach contrasts with the standard clinical practice of treating individuals on the basis of clinical symptoms or diagnosis independent of specific genetic variants,” said Dr. Levy.

Agents to restore glutamate function, glycine or D-cycloserine were added to the patients’ standard medications and improved psychotic symptoms in both patients beyond their usual treatment regimens.

Each of the patients also saw some reductions in other symptoms, including mood symptoms and negative symptoms of schizophrenia, and improvements in emotional engagement and social interaction.

“It is important to note that the two subjects studied here bore little clinical resemblance, with distinctly different symptom burdens and highly dissimilar courses of illness,” noted first author J. Alexander Bodkin, MD, McLean Hospital. This suggests that response to the treatment arose from targeting a specific biological process rather than a clinical diagnosis.

“Most studies of rare structural variants will have very small sample sizes, complicating the usual approach to statistical analysis. Nevertheless, because the effects of a targeted treatment can be large, it is important to prioritize opportunities to study even small groups of patients who may benefit,” observed author Charity J. Morgan, University of Alabama.

“Psychiatry is in the very early days of precision medicine, i.e., the effort to match particular patients to the specific treatments that they need. In their article, Dr. Levy and her colleagues provide a wonderful example of this approach,” said John Krystal, MD, Editor of Biological Psychiatry.

“The substances that they administered, glycine and D-cycloserine, do not produce noticeable behavioral effects in healthy people or in patients with psychotic disorders. However, because these substances replaced a deficient co-factor involved in neural communication in these particular individuals, their administration alleviated mood and psychosis symptoms.

As in these cases, we expect psychiatry to develop more instances where specific treatments can be developed to meet the needs of particular groups of patients.”

Source: Elsevier

Some Schizophrenia Brains Show Abnormal Protein Buildup Similar to Alzheimer’s

In a new study, Johns Hopkins Medicine researchers unveiled new evidence showing that some schizophrenia brains are marked by a buildup of abnormal proteins similar to those found in the brains of people with neurodegenerative disorders such as Alzheimer’s or Huntington’s diseases.

The findings, published in the American Journal of Psychiatry, are based on brain tissue samples of deceased human donors (average age 49). The researchers analyzed 42 samples from schizophrenia patients as well as 41 brain samples from healthy controls. Around 75 percent of the brains came from men, and 80 percent were from white subjects.

Based on their experience with schizophrenia and neurodegenerative disorders, the research team wanted to determine if the features of schizophrenia brains could also be seen in the brains of patients with Alzheimer’s disease or other illnesses.

“The brain only has so many ways to handle abnormal proteins,” says Frederick Nucifora Jr., DO, PhD, MHS, the leader of the study and an assistant professor of psychiatry and behavioral sciences at the Johns Hopkins University School of Medicine.

“With schizophrenia, the end process is mental and behavioral, and doesn’t cause the pronounced physical neural cell death we see with neurodegenerative diseases, but there are clearly some overall biological similarities.”

In neurodegenerative disorders, certain abnormal proteins are churned out but don’t assemble into properly functioning molecules; instead they end up misfolded, clumping up and leading to disease.

For the study, the team broke open the cells from the brain tissue samples and analyzed their contents by looking at how much of the cell’s contents could be dissolved in a specific detergent. The more dissolved contents, the more “normal” or healthy the cell’s contents.

On the other hand, less dissolved cell contents indicated that the cell contains a high volume of abnormal, misfolded proteins, as found in other brain diseases.

The team discovered that slightly less than half (20) of the schizophrenia brains had a greater proportion of proteins that couldn’t be dissolved in detergent, compared to the amount found in the healthy samples.

These same 20 samples also showed elevated levels of a small protein ubiquitin that is a marker for protein aggregation in neurodegenerative disorders. Elevated levels of ubiquitin weren’t seen in the healthy brain tissue samples.

Importantly, the team wanted to confirm that the antipsychotic medications the patients were taking before they died didn’t cause the accumulation of abnormal proteins. To clarify this, they examined the proteins in the brains of rats treated with the antipsychotic drugs haloperidol or risperidone for 4.5 months compared to control rats treated with plain water.

The results reveal that treatment with antipsychotic medications didn’t cause an accumulation of undissolvable proteins or extra ubiquitin tags, suggesting that the disease — and not the medication — caused the abnormal protein build-up in some of the brains with schizophrenia.

Next, the researchers used mass spectroscopy to determine the identity of these undissolvable proteins. They found that many of these abnormal proteins were involved in nervous system development, specifically in generating new neurons and the connections that neurons use to communicate with one another.

Nucifora says the main finding of abnormal proteins in nervous system development is consistent with theories that trace schizophrenia’s origins to brain development and to problems with neural communication.

“Researchers have been so focused on the genetics of schizophrenia that they’ve not paid as much attention to what is going on at the protein level and especially the possibility of protein aggregation,” says Nucifora. “This may be a whole new way to look at the disorder and develop more effective therapies.”

Source: Johns Hopkins Medicine

Machine Learning Can Help Predict Psychosis Via Language Analysis

A new machine-learning method can predict with 93 percent accuracy whether a person at-risk for psychosis will go on to develop the disorder.

The method, developed by scientists at Emory University and Harvard University, discovered that higher than normal usage of words related to sound, combined with a higher rate of using words with similar meaning, meant that psychosis was likely on the horizon.

Even trained clinicians had not noticed how people at risk for psychosis use more words associated with sound than the average, although abnormal auditory perception is an early warning sign.

“Trying to hear these subtleties in conversations with people is like trying to see microscopic germs with your eyes,” says Neguine Rezaii, first author of the paper. “The automated technique we’ve developed is a really sensitive tool to detect these hidden patterns. It’s like a microscope for warning signs of psychosis.”

The onset of schizophrenia and other psychotic disorders typically occurs in the early 20s, with early warning signs — known as prodromal syndrome — beginning around age 17. Around 25 to 30 percent of young people with prodromal syndrome will eventually develop schizophrenia or another psychotic disorder.

Currently, there is no cure for psychosis. Through structured interviews and cognitive tests, trained clinicians can predict psychosis with about 80 percent accuracy in those with a prodromal syndrome.

Now, research with machine-learning, a form of artificial intelligence that can uncover hidden patterns, is one of the many ongoing efforts to streamline diagnostic methods, identify new variables, and improve the accuracy of predictions.

“It was previously known that subtle features of future psychosis are present in people’s language, but we’ve used machine learning to actually uncover hidden details about those features,” says senior author Phillip Wolff, a professor of psychology at Emory. Wolff’s lab focuses on language semantics and machine learning to predict decision-making and mental health.

For the study, the researchers first used machine learning to establish “norms” for conversational language. They fed a computer software program the online conversations of 30,000 users of Reddit, a social media platform where people have informal discussions about a range of topics.

The software program, known as Word2Vec, uses an algorithm to change individual words to vectors (a mathematical term referring to the position of one point in space relative to another). In other words, the program assigned each word to a location in a semantic space based on its meaning. Words with similar meanings were positioned closer together than those with very different meanings.

The Wolff lab also developed a computer program to perform “vector unpacking,” or analysis of the semantic density of word usage. Vector unpacking allowed the researchers to quantify how much information was packed into each sentence.

After generating a baseline of “normal” data, the researchers applied the same techniques to diagnostic interviews of 40 young people at high risk for psychosis. The automated analyses of the participant samples were then compared to the normal baseline sample.

The results showed that higher than normal usage of sound-related words, along with a higher rate of using words with similar meaning, meant that psychosis was likely to occur.

Strengths of the study include the simplicity of using just two variables — both of which have a strong theoretical foundation — the replication of the results in a holdout dataset, and the high accuracy of its predictions, at above 90 percent.

“In the clinical realm, we often lack precision,” Rezaii says. “We need more quantified, objective ways to measure subtle variables, such as those hidden within language usage.”

Rezaii and Wolff are now gathering larger data sets and testing the application of their methods on a variety of neuropsychiatric diseases, including dementia.

“This research is interesting not just for its potential to reveal more about mental illness, but for understanding how the mind works — how it puts ideas together,” Wolff says. “Machine learning technology is advancing so rapidly that it’s giving us tools to data mine the human mind.”

Co-author Elaine Walker, Emory professor of psychology and neuroscience, says “If we can identify individuals who are at risk earlier and use preventive interventions, we might be able to reverse the deficits.”

The findings are published in the journal npj Schizophrenia.

Source: Emory Health Sciences

Social Media Data Used to ID Mental Health Conditions and Diabetes

A new study suggests mining data from social media sites may help professionals identify and manage a variety of health conditions, including diabetes, anxiety, depression and psychosis.

Researchers from Penn Medicine and Stony Brook University analyzed Facebook posts and believe that the language in posts could be indicators of disease. Moreover, if an individual provides consent, the posts could be monitored just like physical symptoms.

The study appears in PLOS ONE.

“This work is early, but our hope is that the insights gleaned from these posts could be used to better inform patients and providers about their health,” said lead author Raina Merchant, MD, MS, the director of Penn Medicine’s Center for Digital Health and an associate professor of Emergency Medicine.

“As social media posts are often about someone’s lifestyle choices and experiences or how they’re feeling, this information could provide additional information about disease management and exacerbation.”

Using an automated data collection technique, the researchers analyzed the entire Facebook post history of nearly 1,000 patients who agreed to have their electronic medical record data linked to their profiles.

The researchers then built three models to analyze their predictive power for the patients: one model only analyzing the Facebook post language, another that used demographics such as age and sex, and the last that combined the two datasets.

Looking into 21 different conditions, researchers found that all 21 were predictable from Facebook alone. In fact, 10 of the conditions were better predicted through the Facebook data than demographic information.

Some of the Facebook data that was found to be more predictive than demographic data seemed intuitive. For example, “drink” and “bottle” were shown to be more predictive of alcohol abuse.

However, others weren’t as easy. For example, the people who most often mentioned religious language like “God” or “pray” in their posts were 15 times more likely to have diabetes than those who used these terms the least. Additionally, words expressing hostility — like “dumb” and some expletives– served as indicators of drug abuse and psychoses.

“Our digital language captures powerful aspects of our lives that are likely quite different from what is captured through traditional medical data,” said the study’s senior author Andrew Schwartz, PhD.

“Many studies have now shown a link between language patterns and specific disease, such as language predictive of depression or language that gives insights into whether someone is living with cancer. However, by looking across many medical conditions, we get a view of how conditions relate to each other, which can enable new applications of AI for medicine.”

Last year, many members of this research team were able to show that analysis of Facebook posts could predict a diagnosis of depression as much as three months earlier than a diagnosis in the clinic.

This work builds on that study and shows that there may be potential for developing an opt-in system for patients that could analyze their social media posts and provide extra information for clinicians to refine care delivery. Merchant said that it’s tough to predict how widespread such a system would be, but it “could be valuable” for patients who use social media frequently.

“For instance, if someone is trying to lose weight and needs help understanding their food choices and exercise regimens, having a healthcare provider review their social media record might give them more insight into their usual patterns in order to help improve them,” Merchant said.

Later this year, Merchant will conduct a large trial in which patients will be asked to directly share social media content with their health care provider. This will provide a look into whether managing this data and applying it is feasible, as well as how many patients would actually agree to their accounts being used to supplement active care.

“One challenge with this is that there is so much data and we, as providers, aren’t trained to interpret it ourselves — or make clinical decisions based on it,” Merchant explained. “To address this, we will explore how to condense and summarize social media data.”

Source: University of Pennsylvania School of Medicine