Psychiatric drugs, including antipsychotics and antidepressants, are widely prescribed, even in young and adolescent populations at early or subthreshold disease stages. However, their impact on brain structure remains elusive. Elucidating the relationship between psychotropic medication and structural brain changes could enhance the understanding of the potential benefits and risks associated with such treatment.

Investigation of the associations between psychiatric drug intake and longitudinal grey matter volume (GMV) changes in a transdiagnostic sample of young individuals at early stages of psychosis or depression using an unbiased data-driven approach.

The study sample comprised 247 participants (mean [SD] age = 25.06 [6.13] years, 50.61% male), consisting of young, minimally medicated individuals at clinical high-risk states for psychosis, individuals with recent-onset depression or psychosis, and healthy control individuals. Structural magnetic resonance imaging was used to obtain whole-brain voxel-wise GMV for all participants at two timepoints (mean [SD] time between scans = 11.15 [4.93] months). The multivariate sparse partial least squares (SPLS) algorithm (Monteiro et al. JNMEDT 2016; 271:182-194) was embedded in a nested cross-validation framework to identify parsimonious associations between the cumulative intake of psychiatric drugs, including commonly prescribed antipsychotics and antidepressants, and change in GMV between both timepoints, while additionally factoring in age, sex, and diagnosis. Furthermore, we correlated the retrieved SPLS results to personality domains (NEO-FFI) and childhood trauma (CTQ).

SPLS analysis revealed significant associations between the antipsychotic classes of benzamides, butyrophenones and thioxanthenes and longitudinal GMV decreases in cortical regions including the insula, posterior superior temporal sulcus as well as cingulate, postcentral, precentral, orbital and frontal gyri (Figure 1A-C). These brain regions corresponded most closely to the dorsal and ventral attention, somatomotor, salience and default network (Figure 1D). Furthermore, the medication signature was negatively associated with the personality domains extraversion, agreeableness and conscientiousness and positively associated with the CTQ domains emotional and physical neglect.

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Psychiatric drug intake over a period of one year was linked to distinct GMV reductions in key cortical hubs. These patterns were already visible in young individuals at early or subthreshold stages of mental illness and were further linked to childhood neglect and personality traits. Hence, a better and more in-depth understanding of the structural brain implications of medicating young and adolescent individuals might lead to more cautious, sustainable and targeted treatment strategies.

None Declared

The Stricker Learning Span (SLS) is a computer-adaptive word list memory test specifically designed for remote assessment and self-administration on a web-based multi-device platform (Mayo Test Drive). Given recent evidence suggesting the prominence of learning impairment in preclinical Alzheimer’s disease (AD), the SLS places greater emphasis on learning than delayed memory compared to traditional word list memory tests (see Stricker et al., Neuropsychology in press for review and test details). The primary study aim was to establish criterion validity of the SLS by comparing the ability of the remotely-administered SLS and inperson administered Rey Auditory Verbal Learning Test (AVLT) to differentiate biomarkerdefined groups in cognitively unimpaired (CU) individuals on the Alzheimer’s continuum.

Mayo Clinic Study of Aging CU participants (N=319; mean age=71, SD=11; mean education=16, SD=2; 47% female) completed a brief remote cognitive assessment (∼0.5 months from in-person visit). Brain amyloid and brain tau PET scans were available within 3 years. Overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (A+, n=110) or not (A-, n=209), and for 2) those with biological AD (A+T+, n=43) vs no evidence of AD pathology (A-T-, n=181). Primary neuropsychological outcome variables were sum of trials for both the SLS and AVLT. Secondary outcome variables examined comparability of learning (1-5 total) and delay performances. Linear model ANOVAs were used to investigate biomarker subgroup differences and Hedge’s G effect sizes were derived, with and without adjusting for demographic variables (age, education, sex).

Both SLS and AVLT performances were worse in the biomarker positive relative to biomarker negative groups (unadjusted p’s<.05). Because biomarker positive groups were significantly older than biomarker negative groups, group differences were attenuated after adjusting for demographic variables, but SLS remained significant for A+ vs A- and for A+T+ vs A-T- comparisons (adjusted p’s<.05) and AVLT approached significance (p’s .05-.10). The effect sizes for the SLS were slightly better (qualitatively, no statistical comparison) for separating biomarker-defined CU groups in comparison to AVLT. For A+ vs A- and A+T+ vs A-T- comparisons, unadjusted effect sizes for SLS were -0.53 and -0.81 and for AVLT were -0.47 and -0.61, respectively; adjusted effect sizes for SLS were -0.25 and -0.42 and for AVLT were -0.19 and -0.26, respectively. In secondary analyses, learning and delay variables were similar in terms of ability to separate biomarker groups. For example, unadjusted effect sizes for SLS learning (-.80) was similar to SLS delay (.76), and AVLT learning (-.58) was similar to AVLT 30-minute delay (-.55) for the A+T+ vs AT- comparison.

Remotely administered SLS performed similarly to the in-person-administered AVLT in its ability to separate biomarker-defined groups in CU individuals, providing evidence of criterion validity. The SLS showed significantly worse performance in A+ and A+T+ groups (relative to A- and A-T-groups) in this CU sample after demographic adjustment, suggesting potential sensitivity to detecting transitional cognitive decline in preclinical AD. Measures emphasizing learning should be given equal consideration as measures of delayed memory in AD-focused studies, particularly in the preclinical phase.

Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web-based multi-device (smartphone, tablet, personal computer) platform optimized for remote self-administered cognitive assessment that includes a computer-adaptive word list memory test (Stricker Learning Span; SLS; Stricker et al., 2022; Stricker et al., in press) and a measure of processing speed (Symbols Test: Wilks et al., 2021). Study aims were to determine criterion validity of MTD by comparing the ability of the MTD raw composite and in-person administered cognitive measures to differentiate biomarkerdefined groups in cognitively unimpaired (CU) individuals on the Alzheimer’s continuum.

Mayo Clinic Study of Aging CU participants (N=319; mean age=71, SD=11, range=37-94; mean education=16, SD=2, range=6-20; 47% female) completed a brief remote cognitive assessment (∼0.5 months from in-person visit). Brain amyloid and brain tau PET scans were available within 3 years. Overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (A+, n=110) or not (A-, n=209), and for 2) those with biological AD (A+T+, n=43) or with no evidence of AD pathology (A-T-, n=181). Primary outcome variables were MTD raw composite (SLS sum of trials + an accuracy-weighted Symbols response time measure), Global-z (average of 9 in-person neuropsychological measures) and an in-person screening measure (Kokmen Short Test of Mental Status, STMS; which is like the MMSE). Linear model ANOVAs were used to investigate biomarker subgroup differences and Hedge’s G effect sizes were derived, with and without adjusting for demographic variables (age, education, sex).

Remotely administered MTD raw composite showed comparable to slightly larger effect sizes compared to Global-z. Unadjusted effect sizes for MTD raw composite for differentiating A+ vs. A- and A+T+ vs. A-T- groups, respectively, were -0.57 and -0.84 and effect sizes for Global-z were -0.54 and -0.73 (all p’s<.05). Because biomarker positive groups were significantly older than biomarker negative groups, group differences were attenuated after adjusting for demographic variables, but MTD raw composite remained significant for A+T+ vs A-T- (adjusted effect size -0.35, p=.007); Global-z did not reach significance for A+T+ vs A-T- (adjusted effect size -0.19, p=.08). Neither composite reached significance for adjusted analyses for the A+ vs A- comparison (MTD raw composite adjusted effect size= -.22, p=.06; Global-z adjusted effect size= -.08, p=.47). Results were the same for an alternative MTD composite using traditional z-score averaging methods, but the raw score method is preferred for comparability to other screening measures. The STMS screening measure did not differentiate biomarker groups in any analyses (unadjusted and adjusted p’s>.05; d’s -0.23 to 0.05).

Remotely administered MTD raw composite shows at least similar ability to separate biomarker-defined groups in CU individuals as a Global-z for person-administered measures within a neuropsychological battery, providing evidence of criterion validity. Both the MTD raw composite and Global-z showed greater ability to separate biomarker positive from negative CU groups compared to a typical screening measure (STMS) that was unable to differentiate these groups. MTD may be useful as a screening measure to aid early detection of Alzheimer’s pathological changes.

The Stricker Learning Span (SLS) is a computer-adaptive digital word list memory test specifically designed for remote assessment and self-administration on a web-based multi-device platform (Mayo Test Drive). We aimed to establish criterion validity of the SLS by comparing its ability to differentiate biomarker-defined groups to the person-administered Rey’s Auditory Verbal Learning Test (AVLT).

Participants (N = 353; mean age = 71, SD = 11; 93% cognitively unimpaired [CU]) completed the AVLT during an in-person visit, the SLS remotely (within 3 months) and had brain amyloid and tau PET scans available (within 3 years). Overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (amyloid PET positive, A+, n = 125) or not (A-, n = 228), and those with biological AD (amyloid and tau PET positive, A+T+, n = 55) vs no evidence of AD pathology (A−T−, n = 195). Analyses were repeated among CU participants only.

The SLS and AVLT showed similar ability to differentiate biomarker-defined groups when comparing AUROCs (p’s > .05). In logistic regression models, SLS contributed significantly to predicting biomarker group beyond age, education, and sex, including when limited to CU participants. Medium (A− vs A+) to large (A−T− vs A+T+) unadjusted effect sizes were observed for both SLS and AVLT. Learning and delay variables were similar in terms of ability to separate biomarker groups.

Remotely administered SLS performed similarly to in-person-administered AVLT in its ability to separate biomarker-defined groups, providing evidence of criterion validity. Results suggest the SLS may be sensitive to detecting subtle objective cognitive decline in preclinical AD.

The coexistence of underweight (UW) and overweight (OW)/obese (OB) at the population level is known to affect iron deficiency (ID) anaemia (IDA), but how the weight status affects erythropoiesis during pregnancy is less clear at a population scale. This study investigated associations between the pre-pregnancy BMI (pBMI) and erythropoiesis-related nutritional deficiencies.

Anthropometry, blood biochemistry and 24-h dietary recall data were collected during prenatal care visits. The weight status was defined based on the pBMI. Mild nutrition deficiency-related erythropoiesis was defined if individuals had an ID, folate depletion or a vitamin B12 deficiency.

The Nationwide Nutrition and Health Survey in Taiwan (Pregnant NAHSIT 2017–2019).

We included 1456 women aged 20 to 45 years with singleton pregnancies.

Among these pregnant women, 9·6 % were UW, and 29·2 % were either OW (15·8 %) or OB (13·4 %). A U-shaped association between the pBMI and IDA was observed, with decreased odds (OR; 95 % CI) for OW subjects (0·6; 95 % CI (0·4, 0·9)) but increased odds for UW (1·2; 95 % CI (0·8, 2·0)) and OB subjects (1·2; 95 % CI (0·8, 1·8)). The pBMI was positively correlated with the prevalence of a mild nutritional deficiency. Compared to normal weight, OB pregnant women had 3·4-fold (3·4; 95 % CI (1·4, 8·1)) higher odds for multiple mild nutritional deficiencies, while UW individuals had lowest odds (0·3; 95 % CI (0·1, 1·2)). A dietary analysis showed negative relationships of pBMI with energy, carbohydrates, protein, Fe and folate intakes, but positive relationship with fat intakes.

The pre-pregnancy weight status can possibly serve as a good nutritional screening tool for preventing IDA during pregnancy.

The coronavirus disease 2019 (COVID-19) pandemic represents an unprecedented threat to mental health. Herein, we assessed the impact of COVID-19 on subthreshold depressive symptoms and identified potential mitigating factors.

Participants were from Depression Cohort in China (ChiCTR registry number 1900022145). Adults (n = 1722) with subthreshold depressive symptoms were enrolled between March and October 2019 in a 6-month, community-based interventional study that aimed to prevent clinical depression using psychoeducation. A total of 1506 participants completed the study in Shenzhen, China: 726 participants, who completed the study between March 2019 and January 2020 (i.e. before COVID-19), comprised the ‘wave 1’ group; 780 participants, who were enrolled before COVID-19 and completed the 6-month endpoint assessment during COVID-19, comprised ‘wave 2’. Symptoms of depression, anxiety and insomnia were assessed at baseline and endpoint (i.e. 6-month follow-up) using the Patient Health Questionnaire-9 (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7) and Insomnia Severity Index (ISI), respectively. Measures of resilience and regular exercise were assessed at baseline. We compared the mental health outcomes between wave 1 and wave 2 groups. We additionally investigated how mental health outcomes changed across disparate stages of the COVID-19 pandemic in China, i.e. peak (7–13 February), post-peak (14–27 February), remission plateau (28 February−present).

COVID-19 increased the risk for three mental outcomes: (1) depression (odds ratio [OR] = 1.30, 95% confidence interval [CI]: 1.04–1.62); (2) anxiety (OR = 1.47, 95% CI: 1.16–1.88) and (3) insomnia (OR = 1.37, 95% CI: 1.07–1.77). The highest proportion of probable depression and anxiety was observed post-peak, with 52.9% and 41.4%, respectively. Greater baseline resilience scores had a protective effect on the three main outcomes (depression: OR = 0.26, 95% CI: 0.19–0.37; anxiety: OR = 1.22, 95% CI: 0.14–0.33 and insomnia: OR = 0.18, 95% CI: 0.11–0.28). Furthermore, regular physical activity mitigated the risk for depression (OR = 0.79, 95% CI: 0.79–0.99).

The COVID-19 pandemic exerted a highly significant and negative impact on symptoms of depression, anxiety and insomnia. Mental health outcomes fluctuated as a function of the duration of the pandemic and were alleviated to some extent with the observed decline in community-based transmission. Augmenting resiliency and regular exercise provide an opportunity to mitigate the risk for mental health symptoms during this severe public health crisis.

Previous studies have shown that African American youth are over-represented in the Criminal Justice System (CJS). Substance use problems are common among those with CJS involvement. However, less is known regarding racial disparities, among youth with CJS involvement, in receiving substance use treatment services.

To examine racial disparities with regard to receiving treatment services for substance use related problems, among youth with (CJS) involvement.

Data were obtained from the 2006–2008 United States National Survey on Drug Use and Health (NSDUH) in USA. Among White and African American adolescents (Ages 12–17) with recent CJS involvement and who met criteria for alcohol or illicit drug abuse or dependence (N = 602), racial differences in receiving treatment services for substance use problems were examined. Multiple logistic regression analyses were performed to identify predictors of service access among the adolescents, to see if the racial disparity could be explained by individual-level, family-level, and criminal justice system involvement factors.

While 31.2% of White adolescent substance abusers with CJS involvement had received treatment for substance use related problems, only 11.6% of their African American counterparts had received such treatment (P = 0.0005). Multiple logistic regression analyses showed that access to treatment services can be predicted by substance use related delinquent behaviors, but that racial disparities in treatment still exist after adjusting for these factors (AOR = 0.24, 95%CI = (0.09,0.59), P = 0.0027).

There is an urgent need to reduce racial disparities in receiving substance use treatment among U.S. youth with CJS involvement.

The aim of this study is to explore the different patterns of self-management among people with schizophrenia, in order to help professionals provide more effective support tailored to different characteristics and needs of clients.

Totally 214 clients with schizophrenia living in 8 communities of Beijing, China were assessed, using Chinese version of Schizophrenia Self-Management Instrument Scale (SSMIS). Cluster analysis was performed to categorize clients based on their scores in 6 self-management areas.

K-means cluster analysis revealed four different self-management patterns. 'Self-confident and Autonomous' (n=63), clients actively participated in self-management and had a certain skills. 'Overconfident' (n=94), these clients had high self-efficacy and did well in medication compliance, while not in other areas. In their eyes, this was enough and they were experienced enough. 'Passive and Compliant' (n=51), clients often relied on their caregivers to manage the disease, yet did worse either in dimensions of symptom management or using health resource and support. 'No involvement' (n=6), clients' self-efficacy was low. They rarely participated in self-management and did worse in every self-management areas. Four pattern clients had significant differences in educational level, work status and financial burden (P<0.05).

There are subgroups of schizophrenic people with unique multidimensional patterns of self-management behaviors. This may help health professionals provide more customized support to improve clients' health status.