A team led by Cecilia Sanjuan-Ortiz of King’s College London, together with colleagues spanning Spain and the United Kingdom, conducted a systematic review following a pre-registered protocol and PRISMA reporting standards. Their search swept across five scientific databases to identify longitudinal studies that followed individuals at clinical high risk and reported which baseline characteristics were associated with remission from the high-risk state. After independent screening and data extraction by four reviewers, the team settled on fifty-one studies encompassing 10,073 participants from fifteen countries. The participants were young, averaging 19.1 years at study level with a range from 13.8 to 28.4 years, and just under half were female. Follow-up periods stretched from as short as eight months to as long as six years, giving researchers a meaningful window into how these young people fared over time.

The headline statistic is striking: roughly one-third of individuals at clinical high risk achieve remission from their CHR-P state at follow-up. That figure reframes the entire clinical conversation. For decades, research in this field has fixated on transition to psychosis, treating the onset of a diagnosable disorder as the outcome that matters most. But the new synthesis, published in the journal Schizophrenia, argues that remission deserves equal billing. A young person whose attenuated symptoms fade and who returns to their previous level of functioning has, in a very real sense, dodged the clinical bullet, and understanding what makes that outcome possible could transform early intervention from a blunt instrument into a precision tool.

Methodologically, the review carries considerable weight. The authors assessed risk of bias using a modified Newcastle-Ottawa Scale, and the results were reassuring: the mean score across studies was 7.7, with 92.1 percent of the included studies rated as high quality. Because the studies differed so substantially in their designs, measures and follow-up windows, the team could not pool results statistically; instead they performed a narrative synthesis, weighing how consistently each candidate predictor appeared across independent cohorts. That consistency, rather than any single effect size, became the currency of the review. A factor that showed up again and again in different countries, different clinics and different decades carries more evidentiary weight than one reported once, however dramatically.

What emerged was a clear hierarchy of predictors. The most robustly replicated factor was baseline functioning: ten separate studies found that individuals who were doing better in their daily lives, at school, at work and in relationships, when they first presented were more likely to remit. Close behind came symptom severity. Seven studies replicated the finding that lower levels of attenuated positive symptoms at baseline predicted remission, while three studies pointed to the same relationship for negative symptoms, the flattening of emotion, motivation and speech that often proves more disabling than hallucinations or delusions. Two studies independently identified lower self-disturbance, the unsettling sense that one’s thoughts, body or sense of self are slipping out of one’s own ownership, as a predictor of recovery.

Cognition emerged as another pillar. Four studies replicated the association between higher baseline verbal learning and later remission, and two studies each supported verbal memory, working memory and visual memory as predictors. The pattern makes intuitive sense: cognitive reserves buffer the impact of emerging symptoms and support the problem-solving, planning and social navigation that recovery demands. But the review did not stop at psychology. Three studies found that a preserved mismatch negativity amplitude, an electrophysiological brain response that fires when the brain detects an unexpected deviation in a repetitive sound stream, predicted remission. The mismatch negativity has long been studied as a candidate biomarker in psychosis research, and its appearance here, in the context of recovery rather than deterioration, suggests it may index the integrity of the very neural prediction machinery that psychosis disrupts.

Perhaps the most futuristic finding concerns language. Two studies replicated the association between speech fluency and connectedness and remission, joining a growing literature in which computational analysis of what patients say, how coherent their narratives are, how semantically rich their word choices, how smoothly their sentences flow, provides a window into underlying neurocognitive function. The idea that a short recorded speech sample could help clinicians stratify risk is no longer science fiction; it is an active frontier, and this review gives it a foothold in the CHR-P field specifically. Alongside these replicated findings, the authors flagged emerging but less consistent evidence for peripheral neurochemical, genetic and psychosocial or environmental factors, domains that remain underexplored and underpowered in the existing literature.

The review is candid about its limits. Substantial heterogeneity across the fifty-one studies, in how remission was defined, how predictors were measured and how long participants were followed, prevented any meta-analytic pooling and limits direct comparability of results. The authors also note that potentially protective factors associated with favourable outcomes remain understudied relative to risk factors, a bias that mirrors the field’s historical preoccupation with deterioration. Most studies examined predictors of remission from the high-risk state itself, but the relationships between remission, transition to psychosis and long-term functional recovery are not always straightforward, and the review stops short of claiming that any single marker can be used clinically today.

Still, the practical message is clear and, in its way, radical. Remission in CHR-P is consistently associated with lower symptom severity at baseline, preserved functioning and better cognitive performance, with self-disturbance, linguistic features and electrophysiological markers emerging as promising supplementary signals. No single domain, demographic, clinical, cognitive, neurophysiological or linguistic, tells the whole story. The authors argue that translation into clinical practice will require integrating information across domains rather than relying on single-domain indicators, effectively calling for multivariable prediction models that weigh a young person’s functioning, symptom profile, cognitive test results and possibly their speech patterns together. For clinicians running early intervention services, that means the most informative assessment may be the broadest one. For the young people and families sitting in those assessment rooms, the review offers something rarer than a risk calculator: evidence that for a substantial minority, and possibly with the right support, a much larger share, the story does not end in psychosis at all. It ends in recovery, and science is finally learning how to see it coming.

Subject of Research: Predictors of remission in individuals at clinical high risk for psychosis

Article Title: Predictors of remission in individuals at clinical high-risk for psychosis: a systematic review

Article References: Sanjuan-Ortiz, C., Laherran-Cantera, N., Aymerich, C., Dully, J., Ahmed, L., Salazar de Pablo, G., & Cella, M. (2026). Predictors of remission in individuals at clinical high-risk for psychosis: a systematic review. Schizophrenia. https://doi.org/10.1038/s41537-026-00809-z

Image Credits: AI Generated

DOI: 10.1038/s41537-026-00809-z

Keywords: clinical high risk for psychosis, CHR-P, remission, psychosis, systematic review, attenuated psychotic symptoms, cognition, verbal learning, mismatch negativity, self-disturbance, speech analysis, early intervention

The study, published in the Journal of Translational Medicine, applied single-cell RNA sequencing to 55,648 individual cells drawn from 12 somatotroph pituitary neuroendocrine tumors, the benign but hormonally aggressive growths responsible for acromegaly. Rather than treating each tumor as a uniform mass of identical hormone-producing cells, the team dissected them cell by cell, cataloguing the transcriptional states that coexist within a single patient’s tumor. What emerged was a picture of structured heterogeneity: the tumor cells occupied a continuum of states the researchers labeled classic, intermediate, and neuronal-associated, all while retaining the molecular identity of their pituitary lineage. The tumors, in other words, are not blobs of sameness but layered ecosystems of related cell states arranged along a transcriptional gradient.

That finding alone would have been a useful contribution to the growing atlas of pituitary tumor biology. But the Yonsei team, led by neurosurgeons and endocrinologists from the Severance Hospital Pituitary Tumor Center, pushed the analysis one level higher. Using non-negative matrix factorization, a computational technique that decomposes complex expression matrices into additive components, they aggregated the single-cell data into patient-level profiles and asked whether the twelve tumors would sort themselves into meaningful groups. They did — and the dividing line was unexpected. The tumors separated cleanly into two clusters defined not by their cell-state composition, tumor size, or canonical oncogenic mutations, but by how strongly they expressed the gene encoding prolactin, a hormone classically associated with a different pituitary tumor type altogether.

The statistical robustness of this split is notable. Across 100 independent runs of the clustering algorithm, the two groups co-clustered with a mean within-group consensus of 0.925 and a between-group consensus of just 0.058, with an overall consensus silhouette width of 0.915 — figures that indicate the division is not an artifact of algorithmic noise. The team also ruled out obvious confounders: the correlation between prolactin expression and the proportion of classic tumor states was weak, and tumor size did not differ systematically between groups. One tumor showed an intermediate assignment, a reminder that biology rarely respects clean boundaries, but the core separation held firm.

Crucially, the prolactin signal was not confined to sequencing data. The researchers validated it at the protein level using immunohistochemistry on tumor sections, showing that tumors rich in PRL transcript were also rich in prolactin immunoreactivity. Even more striking, tumor prolactin expression correlated with patients’ preoperative serum prolactin levels — a routine blood test already measured in nearly every acromegaly workup. That correlation transforms an esoteric molecular observation into something clinically actionable: a biomarker that could, in principle, be read off a standard preoperative lab panel without any need for fresh tumor tissue or specialized sequencing.

To test whether the prolactin signature actually mattered for patients, the team turned to an independent clinical cohort of 188 individuals with acromegaly. Using logistic regression and stratified analyses, they asked whether prolactin-high tumor status predicted postoperative biochemical remission — the formal endpoint, determined by growth hormone and insulin-like growth factor 1 levels under oral glucose tolerance testing, that defines surgical success. It did. Patients whose tumors fell into the prolactin-high group were significantly more likely to fail to achieve remission after surgery. The association held even as the researchers accounted for other clinical variables, positioning prolactin as a candidate adjunctive risk marker alongside the anatomical and surgical predictors — tumor size, invasiveness, and extent of resection — that surgeons already weigh.

Molecularly, the prolactin-high tumors turned out to be an enigma. Gene set enrichment analysis showed that they lacked enrichment of the canonical oncogenic pathways one might expect to find in a more aggressive tumor. Instead, the differences between prolactin-high and prolactin-low tumors played out at the level of individual genes, with a notable cluster of receptor-associated candidates — including EPHA7, a receptor tyrosine kinase gene — distinguishing the two groups. The authors suggest that these receptor-level differences may shape how tumor cells respond to their hormonal and microenvironmental milieu, though the functional biology remains to be worked out. The finding is intriguing precisely because it does not fit the standard playbook: the high-risk tumors are not more oncogenic in any obvious way, just transcriptionally different in ways that matter for hormone control.

The team took several steps to guard against wishful thinking. In an external cohort of 60 somatotroph tumors from a public dataset, a discovery-derived prolactin-associated signature score — deliberately constructed with prolactin itself and pituitary lineage genes excluded to avoid circularity — tracked strongly with tumor prolactin abundance, with a Spearman correlation of 0.64. As a negative control, the same signature applied to 45 nonfunctioning pituitary tumors from the same dataset showed no such association, confirming that the signal is specific to somatotroph biology rather than a generic pituitary artifact. A sensitivity analysis excluding the one ambiguously assigned tumor left the genome-wide fold-change estimates essentially unchanged, with a concordance of 0.98.

The translational stakes are real. Acromegaly is a rare disease, but its consequences are expensive and disabling, and the current decision tree after surgery is largely reactive: patients are monitored, and second-line treatments — repeat surgery, radiation, or medical therapy with somatostatin analogs, dopamine agonists, or the growth hormone receptor antagonist pegvisomant — are deployed only after remission fails to materialize. A preoperative biomarker that flags high-risk patients could shift that calculus, prompting earlier counseling, closer biochemical surveillance, or more aggressive multimodal treatment planning from the outset. The Yonsei group is already moving in this direction: according to the paper’s competing interests statement, the university is preparing a patent application covering the use of prolactin-associated features and selected receptor-associated markers, including EPHA7, for molecular and clinical characterization of these tumors.

Caveats remain, and the authors are careful to name them. The single-cell discovery cohort comprised only twelve tumors, and while the clinical validation cohort of 188 patients is substantial, the association between prolactin-high status and remission failure was established retrospectively. The researchers themselves state that preoperative prolactin may serve as a potential adjunctive biomarker for risk stratification but that prospective external validation is required before it enters routine practice. Whether the prolactin-high state reflects a distinct developmental origin of the tumor cells, an epigenetic program, or a response to microenvironmental cues is unknown, and the functional role of EPHA7 and the other receptor-associated genes is an open question. Still, the study exemplifies a productive pattern in modern tumor biology: single-cell atlases that do not merely describe heterogeneity but convert it into clinically legible categories. For a disease in which the difference between remission and chronic illness can hinge on molecular features invisible to the surgeon’s eye, a routine blood test that whispers a warning before the first incision is a prospect worth taking seriously.

Subject of Research: Single-cell transcriptomic stratification of somatotroph pituitary tumors by prolactin expression and postoperative remission risk

Article Title: Single-cell transcriptomics reveals prolactin-associated molecular stratification and remission risk in somatotroph PitNETs

Article References: Single-cell transcriptomics reveals prolactin-associated molecular stratification and remission risk in somatotroph PitNETs. (n.d.). https://doi.org/10.1186/s12967-026-09065-2

Image Credits: AI Generated

DOI: 10.1186/s12967-026-09065-2

Keywords: acromegaly, pituitary neuroendocrine tumor, single-cell RNA sequencing, prolactin, somatotroph tumor, transcriptomics, tumor heterogeneity, EPHA7, biomarker, remission, growth hormone, non-negative matrix factorization

The study, conducted at the University Medical Center Ho Chi Minh City and published in Discover Mental Health, followed patients who were either initiating or continuing antidepressant therapy. Participants were assessed at four time points: baseline, one month, three months, and six months. Depressive symptoms were measured with the Quick Inventory of Depressive Symptomatology–Self-Report, a sixteen-item instrument widely used in clinical research, while eating attitudes and behaviors were tracked using the Eating Attitudes Test-26, a validated screening tool that captures dieting behavior, food preoccupation, and oral control.

At the start of the study, the clinical picture was severe. More than 32 percent of participants scored above 15 on the QIDS-SR16, indicating severe or very severe depression. This is consistent with the reality that many patients reach psychiatric care only after symptoms have become disabling. Major depressive disorder has long been linked to disturbances in appetite and weight, with some patients losing interest in food entirely and others turning to eating as a coping mechanism. What has remained underexplored, the authors note, is how these psychological attitudes toward eating evolve once treatment begins and mood begins to stabilize.

The trajectory of depression over the six months was, by every standard metric, a success story. By the end of follow-up, 45.2 percent of participants had achieved remission, meaning their depressive symptoms had receded to clinically insignificant levels. For a condition that resists treatment in a large fraction of patients, this remission rate represents meaningful therapeutic progress and underscores the effectiveness of pharmacological care delivered in a structured outpatient setting.

The eating data told a strikingly different story. The mean EAT-26 total score rose from 4.7 at baseline to 17.0 at six months, a mean increase of 12.3 points that was highly statistically significant. All three subscales of the instrument—Dieting, Bulimia and Food Preoccupation, and Oral Control—showed significant progressive increases over the same period. Perhaps most alarming from a screening standpoint, the proportion of patients crossing the conventional clinical threshold of an EAT-26 score of 20 or higher grew from just 1.6 percent at baseline to 39.6 percent at six months. In other words, roughly two in five patients who began treatment with unremarkable eating attitudes ended the study period scoring in a range typically associated with elevated risk of disordered eating.

To understand what was driving this shift, the researchers turned to generalized structural equation modeling, a statistical framework capable of handling multivariate longitudinal associations while accounting for the complexity of repeated measurements. Two factors emerged with particular force. Patients who reported increased appetite or weight gain during treatment scored substantially higher on the EAT-26, with a beta coefficient of 3.2, while those who achieved clinical remission of their depression scored significantly lower, with a beta of minus 6.1. Both associations were highly significant, and together they sketch a nuanced picture: recovery itself appears protective, but the metabolic and appetite side effects that often accompany antidepressant medications may push patients toward dieting behaviors, food preoccupation, and restrictive attitudes even as their mood improves.

This finding carries particular weight given the pharmacology of modern antidepressants. Selective serotonin reuptake inhibitors, serotonin–norepinephrine reuptake inhibitors, and noradrenergic and specific serotonergic antidepressants all interact with neural circuits that regulate both mood and appetite. Some agents are well known to stimulate appetite and promote weight gain, effects that patients frequently report as distressing. The new study suggests these physical changes are not merely cosmetic concerns but may translate into measurable psychological distress centered on food, body, and control, even in patients whose depression is otherwise responding well to treatment.

The authors argue that the divergence between mood improvement and worsening eating attitudes exposes a blind spot in routine psychiatric monitoring. Clinical follow-up for depression typically focuses on mood, sleep, energy, and suicidal ideation, while eating attitudes are rarely assessed unless a patient volunteers concerns. Because the conventional EAT-26 cutoff of 20 would have flagged only the extreme end of the distribution, the researchers emphasize that continuous measures, rather than binary clinical thresholds, are essential for detecting the gradual erosion of healthy eating attitudes that their data reveal. A patient scoring 17 would escape screening under current practice, yet sits within a population whose mean scores more than tripled over six months.

The study’s setting adds an important dimension to its significance. Research on eating psychopathology in depression has been concentrated in high-income Western countries, and data from Southeast Asian populations remain scarce. Cultural factors shape both the expression of depression and attitudes toward food, body size, and dieting, and treatment guidelines imported from other contexts may not transfer cleanly. By demonstrating that the mood-eating divergence emerges in a Vietnamese outpatient cohort, the study suggests the phenomenon is not an artifact of Western clinical populations but a potentially general feature of antidepressant treatment that deserves attention across diverse health systems.

For clinicians, the practical message is straightforward: recovery from depression should be monitored with a wider lens. Patients whose mood is improving but who report new appetite changes, weight gain, or growing preoccupation with food and dieting may be developing disordered eating attitudes that warrant intervention before they harden into clinical eating disorders. For researchers, the findings open a clear agenda for future work, including studies that disentangle the effects of specific antidepressant classes, examine whether the trajectory reverses with longer follow-up, and test whether structured nutritional and psychological support during treatment can prevent the rise in disordered eating attitudes. What this study makes unmistakably clear is that healing the mind and healing the relationship with food are not always the same journey, and that treating depression successfully means watching both.

Subject of Research: Longitudinal changes in disordered eating attitudes and behaviors during antidepressant treatment for major depressive disorder

Article Title: Changes in depressive symptoms and disordered eating attitudes and behaviors during antidepressant treatment among Vietnamese patients with major depressive disorder

Article References: Bui, M. X., Ngo, L. T., Huynh, N., Le, P. T. V., Vuu, L. T. M., Nguyen, N. T. Y., & Nguyen, P. D. (2026). Changes in depressive symptoms and disordered eating attitudes and behaviors during antidepressant treatment among Vietnamese patients with major depressive disorder. Discover Mental Health. https://doi.org/10.1007/s44192-026-00598-y

Image Credits: AI Generated

DOI: 10.1007/s44192-026-00598-y

Keywords: major depressive disorder, antidepressants, disordered eating, EAT-26, QIDS-SR16, appetite changes, weight gain, Vietnam, longitudinal study, psychiatry, mental health, remission