Research | Mount Sinai Today

Mount Sinai Researcher Launches Three Studies of Alzheimer’s Disease in Asian Americans

Updated on Oct 5, 2023 | Diversity and Inclusion, Featured, Psychiatry, Research

Clara Li, PhD, a clinical neuropsychologist and Associate Professor, Psychiatry, at the Icahn School of Medicine at Mount Sinai, has received new grants that will total more than $12 million from the National Institute on Aging (NIA), part of the National Institutes of Health (NIH). The funding will support three new projects that seek to improve the diagnosis and treatment of Alzheimer’s disease and Alzheimer’s disease-related dementias (AD/ADRD) in Asian Americans.

Asian Americans are historically under-represented in clinical research on AD/ADRD. As a result, many older adults with Asian ancestry do not receive adequate diagnosis and treatment for mild cognitive impairment (MCI) or AD/ADRD.

Clara Li, PhD

“Chinese is the third-most-spoken language in the United States after English and Spanish, yet we don’t have many of these tools available,” Dr. Li explains. She’s hoping to change that, with three new studies launched in 2023.

Adapting Assessments for Alzheimer’s: Chinese Translation and Cultural Adaptation

In one of the studies, a five-year effort, Dr. Li will develop assessment tools that are linguistically and culturally adapted for older adults who speak Cantonese or Mandarin, with the hope to extend it to other Asian languages in the future.

Researchers rely on assessment tools from the National Alzheimer’s Coordinating Center Uniform Data Set (NACC UDS) to identify research participants with cognitive impairment or AD/ADRD. But those tests were developed for English speakers and Western cultures.

“I’ve seen many Asian American patients who try to take the English tests because a Chinese version isn’t available, and the language is a barrier,” Dr. Li says. “Sometimes a test would suggest cognitive impairment, but when I would translate the test myself into Chinese, the patient would score in the normal range.”

Language isn’t the only barrier. Cultural differences also make the test confusing for many Asian American patients. When asked to identify an image of a witch on the standard test, for instance, some of Dr. Li’s patients said “janitor” or “cleaner”—a common error because witches aren’t typically depicted with brooms in Chinese culture.

The lack of adequate tests hampers diagnosis and treatment, and also affects research seeking to better understand AD/ADRD in Asian Americans.

“Because we can’t enroll patients unless they can take the tests in English, many are excluded from studies. As a result, Asian Americans make up less than 2 percent of the participants in U.S. clinical trials,” Dr. Li explains. “If we want to increase diversity in research, we need to adapt these materials for Chinese speakers and eventually other Asian languages.”

A Research Infrastructure for Alzheimer’s Disease in Asian Americans

In the second study, Dr. Li will develop a research infrastructure and tools for studying AD/ADRD in older Asian Americans. She and her colleagues will develop questionnaires to fully characterize Asian American participants, including social determinants of health and any environmental or lifestyle factors that could increase or decrease their risk of developing AD/ADRD.

This five-year study will also investigate blood samples from Asian American participants to determine whether there may be novel biomarkers in this population, and whether known biomarkers are relevant to people from Asian backgrounds.

“Amyloid and tau are well known as biomarkers associated with Alzheimer’s disease, but those biomarkers were developed primarily from Caucasian samples. Therefore, the generalization of these findings in Asian Americans is not always clear, including criteria for amyloid and tau burden to establish AD/ADRD risk,” she says. “There may be different thresholds for those biomarkers in different populations.”

Support for Mild Cognitive Impairment

Dr. Li’s third newly funded project is a two-year pilot clinical trial. She and her colleagues will adapt the Memory Support System (MSS) for use in Chinese Americans who speak Cantonese or Mandarin. The MSS is a memory calendar training program to help older adults with MCI organize and remember their daily activities. The system is a component of the Healthy Action to Benefit Independence & Thinking^Ò (HABIT) Program, an evidence-based intervention that provides lifestyle and behavioral treatments for older adults with MCI.

“I see patients with MCI who want to do something to prevent the development of dementia, but if they can’t speak fluent English, they aren’t able to participate in clinical trials,” Dr. Li says. “We hope that by adapting this program, we can offer Chinese American older adults with MCI an opportunity to participate in a trial that seeks to improve memory and function, as well as their mood and quality of life.”

Alzheimer’s Disease Research at Mount Sinai

In addition to the three new studies Dr. Li has launched this year, she is leading two clinical trials at the Alzheimer’s Disease Research Center at Icahn Mount Sinai and is the site Principal Investigator for the Asian Cohort for Alzheimer’s Disease (ACAD) study, a multisite project to analyze genetic data to identify risk variants for Alzheimer’s disease in Asian Americans and Asian Canadians.

Through these projects, she hopes to improve research participation, diagnosis, and treatment related to patients of Asian ancestry—an effort that is long overdue, she says.

“There’s a lot of work that needs to be done. In addition to research inequities, there aren’t enough bilingual physicians outside the community, which often makes it difficult for Asian American older adults to receive integrated specialty care, leading to delayed diagnosis and treatment for AD/ADRD,” she adds.

Mount Sinai serves a diverse patient population and is committed to improving care by addressing bias and racism. Icahn Mount Sinai and Mount Sinai Health System created the Center for Asian Equity and Professional Development to address the equity and professional development challenges faced by Asian Americans and Pacific Islanders.

Learn more about the Alzheimer’s Disease Research Center

Mount Sinai Receives Five-Year Grant to Support First-of-Its-Kind Translational Science Program for Nurses

Oct 4, 2023 | Featured, Nursing, Research

Mount Sinai’s Center for Nursing Research and Innovation (CNRI) is developing a first-of-its-kind program that supports Doctor of Nursing Practice (DNP) students from underrepresented minority communities and disadvantaged backgrounds to become experts in translating research into clinical practice. The program’s development is being funded by a five-year grant from the National Heart, Lung and Blood Institute (NHLBI) of the National Institutes of Health.

“We are so excited to have achieved this significant milestone,” says Kimberly Souffront, PhD, RN, FNP-BC, Associate Director of CNRI. “This initiative is a significant step toward fostering diversity, equity, and inclusion in our research and health care communities. It not only creates opportunities for underrepresented DNP students but also underscores the vital role of diverse perspectives in advancing translational research and eliminating health disparities.”

The 12-week summer program, Translational Research and Implementation Science for Nurses (TRAIN) at Mount Sinai, will provide DNP students with impactful translational research mentorship within the clinical setting. TRAIN will deliver collaborative, multidisciplinary, multispecialty classroom education and hands-on research experiences mentored by experts in fields of health disparities, hypertension, and other clinical topics central to the NHLBI mission. Students who meet the criteria and are enrolled in any accredited DNP program are eligible to apply.

“DNP-prepared nurses from diverse backgrounds are uniquely and exceptionally qualified to lead translational research for advancing health equity,” says Bevin Cohen, PhD, MS, MPH, RN, CNRI Director.

The inaugural TRAIN program will run from Tuesday, May 28, through Friday, August 16, 2024, with participants devoting 30 hours per week to program activities. A generous stipend is provided to offset the financial impact of professional development in this critical field.

“Having nurses who are prepared to participate as full partners in the research enterprise is critically important,” says Lynne Richardson, MD, FACEP, Founding Co-Director of the Institute for Health Equity Research at Mount Sinai and Endowed Professor of Emergency Medicine and Health Equity Science, Icahn School of Medicine at Mount Sinai. “TRAIN will build the pool of doctoral nurses who are engaged in translational research and implementation science.”

Those interested in learning more about the TRAIN program can email questions to TRAIN@mountsinai.org.

Mount Sinai Researchers Publish First Genome-Wide Analysis of Binge Eating Disorder

Sep 22, 2023 | Featured, Psychiatry, Research

Binge eating disorder is the most common eating disorder in the United States, thought to affect as many as 3 percent of people during their lifetimes. Yet it remains poorly understood.

Now, researchers from the Icahn School of Medicine at Mount Sinai have made important progress with the first genome-wide analysis of binge eating disorder (BED). The study, published in Nature Genetics in August, identified genes that appear to be associated with BED risk. The study also found evidence that iron metabolism may play a role in the disease.

“By applying machine learning to the study of binge eating disorder, we’ve gained important insights into this poorly understood condition, and a new tool for exploring other underdiagnosed diseases,” says Panos Roussos, MD, PhD, Professor of Psychiatry, and Genetics and Genomic Sciences at Icahn Mount Sinai and Director of the Center for Disease Neurogenomics, who is a co-author of the study. “By combining Neuroscience with genomics and big data analysis, we can discover more about how the brain works and ultimately prevent psychiatric disease.”

A Fresh Look at Binge Eating Disorder

Binge eating disorder has significant impacts on a person’s health and well-being. “It can cause substantial distress and impairment in quality of life,” says Trevor Griffen, MD, PhD, a psychiatrist and neuroscientist who collaborated on the recent study while he was a fellow in child and adolescent psychiatry at Mount Sinai. “BED often co-occurs with other psychiatric disorders, such as depression, ADHD, and substance use, and seems to be a nexus of metabolic dysfunction, with associations to conditions like diabetes and high blood pressure.”

Trevor Griffen, MD, PhD

Yet it took a long time for the scientific community to recognize BED as a distinct disorder. It was first included as a new diagnosis when the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) was published in 2014. As a result, the diagnosis is all but absent in the electronic health records and large biobanks that researchers tap into for large-scale genetic analyses. Luckily, the Mount Sinai team developed a workaround.

“A big part of this study was using machine learning to figure out the people most likely to have BED,” says lead author David Burstein, PhD, a data scientist at Mount Sinai who works in the labs of Dr. Roussos and study co-author Georgios Voloudakis, MD, PhD, Assistant Professor of Psychiatry, and Genetics and Genomic Sciences.

Using electronic health record data from more than 767,000 people through the Million Veterans Project, Dr. Burstein and his colleagues applied machine learning approaches to sift through medical diagnoses, prescription medicines, body mass index (BMI) data, and other factors, looking for patterns that would predict if a person had BED. Applying their model to smaller cohorts of people with diagnosed BED, they showed the approach could meaningfully predict the disorder, even in the absence of a formal diagnosis.

Genes Point to New Binge Eating Disorder Treatments

Applying the machine learning model to some 362,000 people for whom genetic information was available, the researchers zeroed in on several genetic loci that appear to be associated with BED risk. One of the genes implicated in the new study is MCHR2, which is associated with the regulation of appetite in the brain. Two others, LRP11 and APOE, have previously been shown to play a role in cholesterol metabolism.

David Burstein, PhD

Another gene identified in the study, HFE, is involved in iron metabolism. The identification of HFE aligns with recent research suggesting iron metabolism may have an important role in regulating overall metabolism, Dr. Griffen says. In particular, iron overload seems to be associated with binge eating, the team found. Interestingly, iron deficiency has been implicated in pica, a disorder that drives people to eat non-food items such as soil or hair.

“There have been hints that iron is a player in the eating disorder world,” Dr. Burstein says. “This new study is more evidence that the mineral deserves a closer look.”

The findings also point toward new directions for treating BED. So far, treatment has mostly focused on repurposing therapies used for other disorders, such as depression or ADHD.

“This study identifies genes and systems that could serve as potential targets for treatments that finally address the underlying biology of BED,” Dr. Griffen says. “It also continues to build evidence that there are biological and genetic drivers of binge eating behaviors. The more we get that message out there, the more we can decrease stigma associated with binge eating.”

A New Tool for Eating Disorder Research

Dr. Griffen is continuing to collaborate with Dr. Roussos and Dr. Voloudakis to expand on their findings, with plans to develop mouse models and dig deeper into the mechanisms. Ultimately, their goal is to develop new treatments that target the underlying biology of BED.

Meanwhile, the researchers are eager to apply their new computational approach to other diseases such as bulimia nervosa—another common eating disorder for which no genome-wide analysis has ever been done.

“Being able to infer a diagnosis from medical records is really significant, not only for BED but for other eating disorders, which are often extremely underdiagnosed” and therefore challenging to study using electronic health records, Dr. Burstein says.

The approach can also extend the science into populations that have been overlooked in past research. Most research on eating disorders has focused on white females. Using machine learning, researchers can more thoroughly study eating disorders in males and populations with other racial or ethnic backgrounds.

“This is exciting work, with so many potential future directions,” Dr. Burstein says.

Could Personalized PSA Levels Enhance Prostate Cancer Screening?

Updated on Sep 14, 2023 | Featured, Research

The circular diagram displays which nearby gene or genes are connected to new variants linked with PSA levels. Kachuri, L., Hoffmann, T.J., Jiang, Y. et al.,Nature Medicine.

Prostate-specific antigen (PSA) screening has long been the gold standard for detecting prostate cancer at an early stage, aiming to improve treatment outcomes and increase survival rates.

But the screening tool is not without its controversies. PSA levels can be affected by various factors, resulting in false positives or negatives. This test may miss some cancers while detecting others that might not actually be clinically significant.

Therefore, there has been a growing interest in the field to find ways to enhance its effectiveness.

The answer might lie in every man’s genes.

In the June 1 online issue of Nature Medicine, a team of researchers from the Icahn School of Medicine at Mount Sinai and collaborators revealed that tailoring prostate cancer screening to individuals, in which PSA is adjusted for genetic factors, could offer a more viable approach to enhancing screening.

“While utilizing blood PSA levels for prostate cancer screening remains contentious due to issues like detecting cancer in men with low PSA and missing it in those with slightly elevated PSA, our multicenter study aimed to improve this approach by accounting for individual genetic variations in PSA levels,” says Robert J. Klein, PhD, Professor, Genetics and Genomic Sciences and a co-author of the paper.

Drawing from Dr. Klein’s earlier research highlighting the impact of individual genetics on PSA levels, this study aimed to determine whether adjusting for these genetic factors could enhance the accuracy of the screening tool.

“The goal is for this to pave the way for using genetically tailored PSA levels for prostate cancer screening, instead of using raw PSA levels,” says Robert J. Klein, PhD.

Using data from several cohorts of healthy men of diverse ancestries, the researchers measured PSA and genetic variants. Computational analyses linked each variant to PSA levels, and an algorithm constructed a genetic model for predicting PSA levels. The study assessed the effectiveness of PSA, either adjusted or not adjusted using this score, in predicting prostate cancer presence upon biopsy.

The researchers found that the score could predict nearly 10 percent of variation in PSA levels. However, it demonstrated more effectiveness in men of European descent than of East Asian or African heritage. Upon applying their scoring method to a dataset comprising men with and without confirmed prostate cancer through biopsy, the researchers estimated that about 30 percent of the men could have been spared from undergoing unnecessary biopsies.

“Our study shows how genetic variants can predict PSA levels and explores using this prediction in patient care. The goal is for this to pave the way for using genetically tailored PSA levels for prostate cancer screening, instead of using raw PSA levels,” says Dr. Klein.

Next, the researchers plan to perform a broader analysis involving more individuals of diverse ancestries, including through the BioMe® biobank at Mount Sinai. This step aims to confirm the applicability of the score across different populations.

Dr. Klein and his team are also exploring, as part of a separate undertaking, whether genetics could be used to better predict individuals at risk for prostate cancers that are potentially life-threatening.

Here’s What to Know About the First Approved Pill Treatment for Postpartum Depression

Updated on Aug 17, 2023 | Featured, Research, Your Health

On Friday, August 4, 2023, the U.S. Food and Drug Administration (FDA) approved Zurzuvae™ (zuranolone), developed by pharmaceutical firms Biogen and Sage Therapeutics, to treat postpartum depression. The treatment is a pill taken once daily for 14 days, and is the first oral treatment approved for this condition.

“We’re happy there’s attention for a disease that has not gotten much attention thus far,” says Veerle Bergink, MD, PhD, Director of the Women’s Mental Health Program, and Professor of Psychiatry, and Obstetrics, Gynecology and Reproductive Science at the Icahn School of Medicine at Mount Sinai. Zurzavae had received Fast Track and Priority Review designations from the FDA, deemed as having potential to address a serious unmet need.

Veerle Bergink, MD, PhD (left) and Kimberly Mangla, MD (right)

Postpartum depression occurs often enough in mothers, yet the public’s understanding of it remains limited, says Kimberly Mangla, MD, Clinical Director of the Women’s Mental Health Program at Icahn Mount Sinai. “I’m glad we have an additional, possibly effective treatment for patients, and hopefully it will raise conversations and awareness of postpartum depression resources and treatment options,” she adds.

Drs. Bergink and Mangla explain what postpartum depression is, and how Zurzuvae could potentially treat it.

What is postpartum depression?

Postpartum depression can appear similar to other forms of clinical depression, with symptoms that include general low mood, lack of enjoyment, low energy, and low motivation, says Dr. Mangla. But there are unique aspects, such as difficulty bonding with the baby.

Postpartum depression is also different from what is commonly called “baby blues,” which is a common phenomenon of feeling overwhelmed, tearful, or being “hormonal,” notes Dr. Mangla. Baby blues tend to go away after two weeks. “What would be alarming might be feelings of hopelessness, suicidality, or a complete disconnect from the baby that aren’t necessarily a component of baby blues—those are reasons to seek support for what might be postpartum depression,” Dr. Mangla says.

While regulatory or insurance entities might define postpartum depression as occurring within four weeks after delivery, experts in the field—clinicians and researchers—agree that onset can be highly variable, even up to 12 months after delivery, says Dr. Bergink.

“From a psychological or physiological point of view, we know that it could take half a year for a woman’s hormones and immune system to go back to normal,” says Dr. Bergink. “And we have heard women say it could take up to a year before they feel like the person they were before delivery, and psychologically used to the new state of being a mother.”

What is Zurzavae, and how does it work?

Many current antidepressants work by targeting the serotonin system, but this drug works by targeting the gamma-aminobutyric acid receptor GABA_A. While there are other drugs in this class of antidepressants, this is the first one approved for postpartum depression, says Dr. Bergink.

How common is postpartum depression?

One in Eight

or about 13 percent of women, have symptoms of depression after birth of baby.

>15 percent

of women in NYC experience depression symptoms after childbirth.

One in Five

women were not asked about depression during a prenatal visit.

>50 percent

of pregnant women with depression were not treated.

Source: Centers for Disease Control and Prevention

However, it is important to note that while this differs from serotonergic antidepressants, there have been no comparative studies done to demonstrate that Zurzavae is any better or worse than other antidepressant treatments out there, she points out. It is also unknown to what extent there is an antidepressant effect beyond the sedative effect, she adds.

What treatment options had been available for postpartum depression?

If the depression is not so severe, options include support therapy, such as cognitive behavioral therapy or psychotherapy, says Dr. Bergink. If it is more severe, then the doctor might consider using antidepressants, such as selective serotonin reuptake inhibitors (SSRIs).

How might Zurzavae differ from other antidepressants?

The way the drug has been marketed is that it works more rapidly than SSRIs, says Dr. Mangla. “Whether or not that’s true, and whether or not that benefit is sustained, we still have no idea,” she says, “but it would be wonderful to have a medication that starts working in three days instead of a few weeks.”

There are still some open questions clinicians might have with Zurzuvae at this point, notes Dr. Bergink. These include its effect on women who are breastfeeding, and whether the drug will keep depression away long beyond the study period, which was 45 days, she says.

What sources of support can mothers experiencing depression seek?

Generally, a mom experiencing depression symptoms should talk to anyone who is in her support system, says Dr. Mangla. This could include friends and family, but also a primary care doctor who might be able to make a referral to a general psychiatrist.

“Because the treatment of depression in postpartum is so similar to treatment of depression outside of postpartum, the disease is often well treated by general practitioners or general psychiatrists,” says Dr. Mangla.

Seeking help from social workers can be useful too. There are many ways mothers can access social workers, including through a local health institution, or even via online resources, such as Postpartum Support International, notes Dr. Mangla.

“Postpartum depression is a very treatable condition,” says Dr. Bergink. “We should do all we can to help mothers feel comfortable about reaching out for support.”

Learn more about women's mental health services and reproductive psychiatry

What has Zurzuvae shown in clinical trials?

Zurzuvae was approved based on data from two randomized, placebo-controlled trials in postpartum depression.

Here are the efficacy and safety highlights:

Both studies achieved their primary endpoint: a significant mean reduction from baseline in the Hamilton Rating Scale for Depression (HAMD-17) total score, a 17-item questionnaire on depression symptoms compared to placebo.
In one study, Zurzuvae achieved a significant reduction in depressive symptoms as early as day three.
Most common side effects of patients on Zurzuvae included drowsiness, dizziness, diarrhea, fatigue, and urinary tract infection.
The FDA has included a warning on Zurzuvae’s label that instructs health care providers to advise patients that the drug causes driving impairment due to sedative effects, and patients should not engage in activities that require mental alertness until at least 12 hours after the 14-day treatment.

AI Spotlight: Mapping Out Links Between Drugs and Birth Defects

Aug 9, 2023 | Featured, Research

Avi Ma’ayan, PhD, Director of the Mount Sinai Center for Bioinformatics at the Icahn School of Medicine at Mount Sinai

Birth defects can be linked to many factors—genetic, environmental, even pure chance. Characterizing the links of any factor to congenital abnormalities is a daunting task, given the vastness of the problem.

In the face of this challenge, a team of researchers at the Icahn School of Medicine at Mount Sinai tapped artificial intelligence (AI) methods to shed light on associations between existing medications and their potential to induce specific birth abnormalities.

“We wanted to improve our understanding of reproductive health and fetal development, and importantly, warn about the potential of new drugs to cause birth defects before these drugs are widely marketed and distributed,” says Avi Ma’ayan, PhD, Professor of Pharmacological Sciences and Director of the Mount Sinai Center for Bioinformatics at Icahn Mount Sinai.

The team developed a knowledge graph—a descriptive model that maps out the relationships between entities and concepts—called ReproTox-KG to integrate data about small-molecule drugs, birth defects, and genes. In addition to constructing the knowledge graph, the team also used machine learning, specifically semi-supervised learning, to illuminate unexplored links between some drugs and birth defects.

Here’s how ReproTox-KG works as a knowledge graph to predict birth defects.

The study examined more than 30,000 preclinical small-molecule drugs for their potential to cross the placenta and induce birth defects, and identified more than 500 “cliques”—interlinked clusters between birth defects, genes, and drugs—that can be used to explain molecular mechanisms for drug-induced birth defects. Findings were published in Communications Medicine on July 17, and the platform has been made available on a web-based user interface.

In this Q&A, Dr. Ma’ayan, senior author of the paper, discusses ReproTox-KG and its potential impacts.

What was the motivation for your study?

The motivation for the study was to find a use case that combines several datasets produced by National Institutes of Health (NIH) Common Fund programs to demonstrate how integrating data from these resources can lead to synergistic discoveries, particularly in the context of reproductive health.

The study identifies some relationships between approved drugs and birth defects to identify existing drugs that are currently not classified as harmful but which may pose risks to the development of a fetus. It also provides a new global framework to assess potential toxicity for new drugs and explain the biological mechanisms by which some drugs known to cause birth defects may operate.

What are the implications?

Identifying the causes of birth defects is complicated and difficult. But we hope that through complex data analysis integrating evidence from multiple sources, we can improve our understanding of reproductive health and fetal development, and also warn about the potential of new drugs to cause birth defects before these drugs are widely marketed and distributed.

What are the limitations of the study?

We have not yet experimentally validated any of the predictions. There are currently no considerations of tissue and cell type, and the knowledge graph representation omits some detail from the original datasets for the sake of standardization. The website that supports the study may not be appealing to a large audience.

How might these findings be put to use?

Regulatory agencies such as the U.S. Environmental Protection Agency or the Food and Drug Administration may use the approach to evaluate the risk of new drug or other chemical applications. Manufacturers of drugs, cosmetics, supplements, and foods may consider the approach to evaluate the compounds they include in products.

What is your plan for following up on this study?

We plan to use a similar graph-based approach for other projects focusing on the relationship between genes, drugs, and diseases. We also aim to use the processed dataset as training materials for courses and workshops on bioinformatics analysis. Additionally, we plan to extend the study to consider more complex data, such as gene expression from specific tissues and cell types collected at multiple stages of development.

Learn more about how Mount Sinai researchers and clinicians are leveraging machine learning to improve patient lives

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