Identifying patients with first-episode psychosis (FEP) who are unlikely to achieve early clinical recovery (ECR) is critical for personalised intervention and resource allocation. ECR – defined as the concurrent achievement of symptomatic and functional remission – represents a clinically meaningful outcome that captures both illness control and functional reintegration.

To develop and externally validate prediction models for ECR using clinical, cognitive and genetic data.

We analysed two large, independent Spanish cohorts: the primeros episodios psicóticos cohort (N = 335), for model development and internal validation, and the Programa Asistencial a las Fases Iniciales de Psicosis cohort (N = 668), for external validation. Forty-seven baseline clinical and cognitive variables and 87 polygenic risk scores (PRSs) were examined. Predictors were selected using penalised logistic regression. Logistic regression and three machine learning algorithms were compared for discrimination, calibration and clinical utility.

The best-performing model was a logistic regression using six routinely collected clinical and cognitive predictors (duration of untreated psychosis, days of treated psychosis, baseline functioning, insight, executive function and cognitive reserve), with an optimism-corrected area under the receiver operating characteristic curve of 0.73 in development and 0.63 in external validation. PRS models showed limited external generalisability and did not improve prediction. Machine learning algorithms offered no advantage over regression models.

A simple, interpretable logistic regression model based on routine clinical and cognitive variables can predict early recovery in FEP with acceptable generalisability. These findings support the use of transparent, clinically grounded models in early psychosis care and highlight the current limitations of genetic predictors for individualised treatment.

Non-suicidal self-injury (NSSI) is associated with mental disorders, yet work regarding the direction of this association is inconsistent. We examined the prevalence, comorbidity, time–order associations with mental disorders, and sex differences in sporadic and repetitive NSSI among emerging adults.

We used survey data from n = 72,288 first-year college students as part of the World Mental Health-International College Student Survey Initiative (WMH-ICS) to explore time–order associations between onset of NSSI and mental disorders, based on retrospective age-of-onset reports using discrete-time survival models. We distinguished between sporadic (1–5 lifetime episodes) and repetitive (≥6 lifetime episodes) NSSI in relation to DSM-5 mood, anxiety, and externalizing disorders.

We estimated a lifetime NSSI rate of 24.5%, with approximately half reporting sporadic NSSI and half repetitive NSSI. The time–order associations between onset of NSSI and mental disorders were bidirectional, but mental disorders were stronger predictors of the onset of NSSI (median RR = 1.94) than vice versa (median RR = 1.58). These associations were stronger among individuals engaging in repetitive rather than sporadic NSSI. While associations between NSSI and mental disorders generally did not differ by sex, repetitive NSSI was a stronger predictor for the onset of subsequent substance use disorders among females compared to males. Most mental disorders marginally increased the risk for persistent repetitive NSSI (median RR = 1.23).

Our findings offer unique insights into the temporal order between NSSI and mental disorders. Further work exploring the mechanism underlying these associations will pave the way for early identification and intervention of both NSSI and mental disorders.

To prospectively examine the extent to which mealtime habits influences the risk of weight gain and obesity in Mexican adults.

We performed a prospective cohort study. The Mealtime Habits Quality (MHQ) scale was used for assessing participants’ MHQ; the outcomes of interest were gain ≥5 % of body weight, developing overweight/obesity and abdominal obesity, after 7 years of follow-up. In order to estimate the independent effect of MHQ on anthropometric indicators, generalized linear models were computed to obtain adjusted relative risks (95 % CI).

The state of Morelos, Mexico.

Mexican adults (n 837) aged 18–70 years participating in a cohort study.

Compared with participants classified in the higher MHQ category, individuals in the middle and lower MHQ groups had a 4·1 (2·5, 6·7) and 6·2 (3·9, 9·7) fold greater risk of gain ≥5 % of body weight, respectively; 6·6 (2·8, 15·5) and 8·6 (3·7, 19·8) fold greater risk of becoming overweight/obese, respectively; and 3·8 (2·0, 7·3) and 5·3 (2·8, 9·8) fold greater risk of developing abdominal obesity, respectively.

This study provides evidence about the influence of a set of mealtime habits on obesity indicators, showing that greater adherence to unadvisable mealtime habits increases the risk of developing unhealthy anthropometric indicators. Since the meal is one of the most important sources of food intake, and consequently weight status, the MHQ scale can be a useful population tool to predict weight gain and obesity.