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For decades, psychiatry has used trial-and-error symptom-based diagnoses and treatments. Standardized diagnostic frameworks brought much-needed consistency to the field, but they also grouped diverse patients with different biology. Consequently, many people receive unsuitable treatments.
On this episode of Behind the Breakthroughs, Alto Neuroscience founder and CEO Amit Etkin, MD, PhD, discusses how precision medicine will change mental health care. Etkin explains how objective biological measures like cognitive testing, EEG brain activity, sleep and circadian rhythm monitoring, and advanced computational analysis can help identify patients who will benefit from specific therapies rather than just symptoms. Comparing psychiatry to precision oncology, he explains why it is at a turning point. Instead of finding a perfect biomarker, the field is developing practical, scalable tools to link brain function to targeted drug development.
Etkin shows from Alto’s clinical pipeline how matching therapies to biologically defined patient populations can improve outcomes and reduce psychiatric treatment uncertainty. We also examine the potential and limitations of genetics, multi-omics, wearables, and AI in precision psychiatry. Etkin explains why brain measurements may be more clinically useful than peripheral biomarkers and how AI can help find patterns in complex biological data. Finally, we discuss how precision psychiatry will become routine clinical practice, from regulatory acceptance and standardized data collection to the first biomarker-guided therapies. If successful, these advances could transform psychiatric disorder diagnosis, treatment, and understanding.
This interview has been edited for length and clarity.
IPM: What has been the key limiting factor to advancing precision psychiatry?
Etkin: There was a period of time in the 1940s, 1950s, and 1960s when psychiatry was beginning to develop. Our definition of diseases was bespoke to how you practiced them. They really made very little sense.
Amit Etkin, MD, PhD, co-founder and CEO of Alto Neuroscience [Alto Neuroscience]
The DSM did a great job of bringing everybody under the same diagnostic umbrella. We can talk about a common set of symptoms and everybody’s talking about them. Yes, we have wonky definitions of diseases, but at least we’re starting to use the same definition.
We are finding large, heterogeneous groups of people that fit into a category. If you examine the words used to describe some psychiatric labels from the past, they sound antiquated because they are linked to outdated concepts and are inconsistently defined. That was good. The problem is that we never transitioned beyond that.
I try to focus on a much simpler approach to what we’re doing. Instead of thinking about biomarkers, machine learning, and so forth, it’s about knowing what we are doing. If what you’re doing is developing a drug for a population with depression, it’s a massive category where some people just symptomatically might sleep more and others sleep less, and some eat more and others eat less. There seems like it’s a bit of a mess. What would you want to know? At a very basic level, that would simply allow you to know what you’re doing better. What would you like to measure?
There are any number of answers. We know that some patients with depression have cognitive problems and others don’t and that people with cognitive problems have worse outcomes regarding standards of care and treatment. We’ve known that for a long time. But why aren’t we doing that measurement systematically in every single drug trial or in clinical care? Because if we did that one little thing systematically, we would see that some treatments or people saw it one way and others saw it another way just by collecting that data more systematically. That’s what we had been doing in the lab: figuring out what kind of data to collect systematically.
That’s what led us to the form Alto, understanding that there are certain measures, like cognition and brain activity. We do this non-invasively with electroencephalogram (EEG) brainwave recordings and wearables to look at circadian rhythms that are physiologically and biologically meaningful, easily scalable, low cost to meet our health system’s needs, and, if done consistently, insightful for separating populations and identifying mechanisms to develop for them and for translating back to an animal model, which is not possible if you only look at depressive symptoms.
Do we know what we’re doing now? No. We’re really just beginning on this journey as a field. But I do think people now recognize that this is the direction of travel. You can see that more and more companies and academic research centers are emerging under this theme.
IPM: What are your thoughts on the use of genomics, multi-omics, or blood-based biomarkers of the central nervous system?
Etkin: The way I interpret that literature is that there’s smoke but not yet fire. The classical molecular marker is genetics. We get that on every patient, but we do not use stratification here, as genetics cannot achieve meaningful stratification in our populations. It’s predominantly common variance with each of them, or in combination, having a very rare large effect size variance, which is really not going to be clinically meaningful from a drug perspective because you’re treating one out of every 10,000 people or whatever the prevalence is.
I don’t know the best polygenic risk score for schizophrenia, determined from 100,000 people, which is probably the high watermark for psychiatric genetics and may explain 1–2% of the variance. You needed to explain at least 10% to stratify the population even a little bit. I don’t think genetics will ever get us there for that purpose.
Where there’s smoke but not yet fire are immune measures. There have been many implications for different immune measures in psychiatric disorders, but every time it gets tested to see whether people with high inflammation respond to something that targets our process directly or indirectly, those studies never really work out.
Then there’s the even larger world of multi-omics, where you have a multiple testing issue and, fundamentally, the problem that what you’re sampling is very peripheral to the organ that matters. Where is the serotonin in the blood coming from? It’s mainly coming from platelets—it’s not coming from the brain. The bulk of serotonin in the body is in the gut. So you can measure… a ton of different proteins, different configurations, and modifications to those proteins probably have relatively little purchase on what is going on in the brain.
The simplest example is a protein called brain-derived neurotrophic factor (BDNF), which is a really important neuroplasticity protein in the brain. It’s also found in the blood, and people tried over and over and over again. You see some positive studies, but mainly studies that are negative and some that are just not published that come to the conclusion that there’s very little bearing of what you’re measuring peripherally to what’s going on centrally.
Measuring the brain directly with EEG or the output of specific brain circuits through behavioral tests is much more amenable and has better performance statistics and interpretability for gaining insights.
IPM: Has a specific layer or test modality enabled precision psychiatry programs for Alto Neuroscience, or are they ultimately based on aggregate measures?
Etkin: Less so in aggregate as measured together but each alone. We try not to combine everything into one model because it becomes very complicated, and we have already been told by the FDA in no uncertain terms that a multimodal biomarker is not something we will readily consider. To get a multimodal biomarker approved for some sort of use, you have to validate each and every component alone and their combination, which sounds like a headache. But I’m not sure you necessarily need to either.
What I would consider to be a win is getting a drug for the whole population with a marker that enriches finding ways to show additional value in a drug program through a biomarker perspective and, over time, an iteration. The field then transforms into one where oncology already exists, which means they expect you to know what you’re doing. You have to understand the population. You have to understand how your drug impacts the biology that defines the population. We’re not there yet.
But they weren’t there yet either, in the same kind of single stroke that we now envision. It was like the first precision therapeutics, like Herceptin, were approved well over a decade before the immuno-oncology (IO) revolution. That really brought precision oncology into maturity, as we understand it now. That history suggests we probably need our IO moment as an inflection point, but we are not yet ready for it.
We need that Herceptin moment first: start transitioning how people think and collect data and create a bit more of a common language across programs so that different drug makers and different academic labs aren’t collecting their own unique data sets that aren’t then harmonized across them. You can’t speak about a thing as an invariant measure of a process that doesn’t matter who is measuring; they get the same outcome.
The field has been somewhat resistant, probably for cultural reasons related to how people have historically operated, to a lot of data sharing and harmonizing of what we’re collecting, how we’re collecting it, and how we’re analyzing it. We’re just starting to really move in that direction. Those are all limits that gate the early-stage biomarker collection efforts.
IPM: Is there a future where someone walks in with a psychiatric condition and undergoes a battery of measurements that spits out a drug that has a high probability of being effective?
Etkin: I think that bar is a lot lower than that. It doesn’t have to be very effective—it just has to be more effective than chance. because that’s where we are. If I told you that instead of a 30% chance of remission with a drug, I could increase it to 40% or 45%, would that be helpful? That’s meaningful. You convert that to a number needed to treat it. For a clinician, this represents a significant effect, although it does not achieve perfect precision. All you need is something better than nothing, which is what we have. Of course, once you have something that’s better than nothing, now you have a new benchmark, and things will continue to improve, which is great. But the field’s got to start somewhere.
I don’t think it is that far away. I think in our efforts and the efforts of others in the field who have followed suit and taken a precision approach, something will work. When that changes, all of a sudden you can’t envision going back; it’s only going forward. That’ll be super exciting. It’s not like a “by the time I retire” kind of thing. It’s within the next five or a maximum of ten years that we will be at that inflection point.
IPM: Does precision psychiatry apply to the rest of neurology?
Etkin: If you anchored on the way I framed brain circuit function earlier. And what’s measurable is that there is no line between psychiatry and neurology. You have neurologists who are called “functional neurologists” or something in that vein, where they think about what I would call the “psychiatric aspects of neurology.”
A big part of Parkinson’s is cognitive impairment in a substantial portion of people, leading to dementia. Nothing to do with the movement disorder, but everything to do with the biology affecting different circuits. The right mood, in fact, is one of the earliest areas of perturbation in Parkinson’s that will then predict the development of the motor symptoms. Some people have perfectly well-controlled motor problems but have cognitive problems and mood problems that are even more prominent and lead to more, especially on the cognitive side, of their ultimate disabilities. Cognitive impairments are even a contraindication for deep brain stimulation.
Because of these interactions, all of these boundaries are artificial. It’s just a core engineering question of, can I know what I am measuring and what I am manipulating? There’s no reason we need to draw that line in an artificial way. It’s just about whether I can leverage the tools and the drugs in a useful way together.
ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.
Insomnia disorder (ID) is a common sleep–wake disorder characterized by persistent difficulty initiating or maintaining sleep, early-morning awakening, or non-restorative sleep, accompanied by daytime functional impairment. ID has traditionally been explained by the hyperarousal model, which emphasizes cognitive, emotional, cortical, neuroendocrine, and autonomic overactivation. However, this model alone does not fully account for the chronic persistence, relapse tendency, and multisystem associations of ID. Emerging evidence suggests that circadian rhythm disruption, impaired melatonin signaling, hypothalamic–pituitary–adrenal (HPA) axis activation, autonomic imbalance, and low-grade inflammation may also contribute to the development and maintenance of ID. Available evidence indicates that sleep disturbance is more consistently associated with selected inflammatory markers, particularly C-reactive protein (CRP) and interleukin-6 (IL-6), whereas findings for tumor necrosis factor-alpha (TNF-α) remain less consistent. The circadian system regulates sleep, endocrine function, metabolism, and immune-inflammatory activity through the suprachiasmatic nucleus, melatonin and cortisol rhythms, peripheral clock genes, and rhythmic immune-cell responses. Disruption of this temporal network may alter melatonin secretion, inflammatory rhythmicity, and stress-related neuroendocrine responses, thereby contributing to the persistence of insomnia symptoms. Compared with previous reviews that have separately discussed hyperarousal, circadian rhythm disruption, melatonin signaling, or sleep-related inflammation, this review integrates these processes into a circadian–immune perspective for understanding ID. We summarize alterations in sleep–wake rhythms, melatonin signaling, HPA-axis activity, autonomic regulation, and immune-inflammatory responses in ID, and discuss potential intervention strategies, including light management, melatonin and melatonin receptor agonists, cognitive behavioral therapy for insomnia (CBT-I), physical activity, time-restricted eating, and stress management. This review aims to provide a mechanistic basis for understanding the chronicity and heterogeneity of ID and for developing individualized intervention strategies.
A new genome-scale atlas is offering an unprecedented look at how individual genes shape the transcriptional landscape of human induced pluripotent stem cells (iPSCs). Published in Nature Biotechnology, the resource catalogs the effects of perturbing 11,692 expressed genes across more than 2.5 million single cells, creating a reference framework for understanding how pluripotent identity is maintained and regulated.
Human iPSCs can differentiate into virtually any cell type, yet the functions of most genes within this state remain poorly understood. Prashant Mali, PhD, senior author and professor of bioengineering at UC San Diego, said the team set out to fill that gap by systematically switching off genes one by one using CRISPR interference (CRISPRi) and measuring the resulting transcriptome-wide changes. “The result is a kind of reference atlas; it’s a way to look up what perturbing almost any gene does to a stem cell’s behavior, measured here as the impact on its whole transcriptome,” Mali said.
The dataset captures how gene perturbations cluster into shared molecular signatures, revealing functional relationships among protein complexes, metabolic pathways, and self-renewal genes. By correlating transcriptional phenotypes across thousands of perturbations, the researchers reconstructed a map of the pluripotent state that recapitulates known regulatory modules while surfacing previously unrecognized ones.
Exploring the atlas led the team to identify new regulators of stem cell biology. They uncovered ZBTB41 as a metabolic factor and RNF7 as a contributor to pluripotency regulation, validating both through metabolic tracing, immunofluorescence, and protein–protein interaction assays. The resource also enabled a genome-scale screen of A‑to‑I RNA editing modulators, revealing DBR1 as a potent regulator of adenosine-to-inosine conversion.
Co-first author Yesh Doctor, a bioengineering PhD student in Mali’s lab, described the atlas as a “hypothesis engine” for stem cell researchers. Instead of running thousands of perturbation experiments, scientists can now query the open-access map to identify candidate genes involved in differentiation, metabolism, or disease-relevant pathways. “Scientists can use it to look up the functions of genes and build hypotheses on them instead of having to run the experiments themselves,” Doctor said.
Beyond basic biology, the team sees the atlas as a foundation for computational modeling. The scale and consistency of the dataset make it well suited for training AI systems aimed at predicting genotype–phenotype relationships. “These comprehensive, genome-scale screens enable generation of reference maps that are not just invaluable for basic science discovery, but also an important resource for powering future computational and AI tools for genotype-phenotype prediction,” Mali said.
The open-access atlas is available here, providing a new reference point for understanding how genes shape human stem cell identity and offering a tool for virtual disease modeling and target discovery.
Background: Remote measurement technology (RMT) is increasingly used in health research to collect real-world data relevant to clinical states (eg, sleep, activity, and stress). Concerns exist about the impact of remote tracking via personal devices and wearables on individuals with or at risk of eating disorders (EDs) by promoting a focus on exercise, diet, and appearance. There is a lack of research applying RMT to EDs. Objective: This study aimed to explore how smartphone- and wearable-based RMTs influence eating-, exercise-, and weight-related experiences among individuals with a history of or at risk of EDs and to identify perceived benefits, harms, and recommendations for their use in this population. Methods: In total, 14 semistructured interviews were conducted with former participants of Remote Assessment of Disease and Relapse: Major Depressive Disorder, a 2-year digital health study tracking depression outcomes via RMTs. Participants were included in this follow-up if they had disclosed a history of a comorbid ED or were within the at-risk age range (18-30 years) for EDs during Remote Assessment of Disease and Relapse: Major Depressive Disorder and displayed subclinical ED symptoms (Eating Disorder Diagnostic Scale). Interviews explored the impact of app engagement and wearables (Fitbits) on food, activity, and weight-related behaviors and attitudes. Template analysis was adopted to capture themes guided by the focus on ED-relevant domains. Results: In total, 6 themes captured participants’ experiences with RMTs across clinical status and presentation. Participants broadly appreciated the convenience and reflective potential, while some described emotional strain linked to constant self-tracking. Health data impacted participants’ eating and exercise habits through a dynamic process from awareness to cognition to action, fostering healthy routines or obsessive patterns, depending on emotional state, ED presentation, and recovery stage. Self-tracking appeared to mirror illness stage, supporting ED recovery among those with greater distance from illness, but risking reinforcement of compulsive patterns among those with residual or emerging symptoms. Participants’ recommendations for future studies in EDs stressed balancing autonomy with safeguards for vulnerable individuals. Conclusions: These exploratory findings, drawn from individuals with lived ED experience and young people at subclinical risk, suggest that RMT use was shaped by recovery stage and contextual factors, rather than being inherently beneficial or harmful. While findings should not be interpreted as evidence of RMT safety or acceptability in ED cohorts broadly, they raise important questions about ethical RMT design, including the selection of wearables, access to data, and researcher communication with participants.
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This is the web edition of STAT’s AAIC in 30 newsletter. Sign up to get it in your inbox here.
Hi there from Day 2 of AAIC. There may be 10,200 people here at the Alzheimer’s Association International Conference — up 1,700 from last year — from 115 countries, as the association’s Maria Carrillo announced at Monday’s plenary session. But by far my favorite person is the woman I saw taking a nap on a bench in a corridor, her sunhat pulled over her eyes. You are an inspiration.
One housekeeping note: Due to the vagaries of newsletter scheduling, we won’t have an edition tomorrow, but will be back in your inbox with a wrap-up edition on Wednesday. Keep your eyes on statnews.com tomorrow though and expect some conference news around 9:15 a.m. Eastern.
Latin American study praised as ‘landmark’ effort
For a disease that’s proven as intractable to treat as Alzheimer’s, it’s been known for quite some time what factors can heighten one’s risk for dementia. Poor nutrition, sleep, and cardiovascular health; a lack of physical activity and social engagement; and lower education levels all increase the likelihood that a person will develop Alzheimer’s.
A study reported at last year’s AAIC put interventions designed to reduce risk to the test. The U.S. Pointer study found that an intensive, structured program that encouraged improved diet and exercise, cardiovascular health monitoring, and other steps helped protect cognitive function among older adults at risk of dementia to a greater extent than a lower-intensity program that participants navigated on their own, even as both groups saw benefits. The study underscored that healthy habits contribute to a healthy brain.
