Depression is a common mental health disorder that often starts during adolescence, with potentially important future consequences including ‘Not in Education, Employment or Training’ (NEET) status.

We took a structured life course modeling approach to examine how depressive symptoms during adolescence might be associated with later NEET status, using a high-quality longitudinal data resource. We considered four plausible life course models: (1) an early adolescent sensitive period model where depressive symptoms in early adolescence are more associated with later NEET status relative to exposure at other stages; (2) a mid adolescent sensitive period model where depressive symptoms during the transition from compulsory education to adult life might be more deleterious regarding NEET status; (3) a late adolescent sensitive period model, meaning that depressive symptoms around the time when most adults have completed their education and started their careers are the most strongly associated with NEET status; and (4) an accumulation of risk model which highlights the importance of chronicity of symptoms.

Our analysis sample included participants with full information on NEET status (N = 3951), and the results supported the accumulation of risk model, showing that the odds of NEET increase by 1.015 (95% CI 1.012–1.019) for an increase of 1 unit in depression at any age between 11 and 24 years.

Given the adverse implications of NEET status, our results emphasize the importance of supporting mental health during adolescence and early adulthood, as well as considering specific needs of young people with re-occurring depressed mood.

Chronic musculoskeletal pain is associated with neurobiological, physiological, and cellular measures. Importantly, we have previously demonstrated that a biobehavioral and psychosocial resilience index appears to have a protective relationship on the same biomarkers. Less is known regarding the relationships between chronic musculoskeletal pain, protective factors, and brain aging. This study investigates the relationships between clinical pain, a resilience index, and brain age. We hypothesized that higher reported chronic pain would correlate with older appearing brains, and the resilience index will attenuate the strength of the relationship between chronic pain and brain age.

Participants were drawn from an ongoing observational multisite study and included adults with chronic pain who also reported knee pain (N = 135; age = 58.3 ± 8.1; 64% female; 49% non-Hispanic Black, 51% non-Hispanic White; education Mdn = some college; income level Mdn = $30,000 - $40,000; MoCA M = 24.27 ± 3.49). Measures included the Graded Chronic Pain Scale (GCPS), characteristic pain intensity (CPI) and disability, total pain body sites; and a cognitive screening (MoCA). The resilience index consisted of validated biobehavioral (e.g., smoking, waist/hip ratio, and active coping) and psychosocial measures (e.g., optimism, positive affect, negative affect, perceived stress, and social support). T1-weighted MRI data were obtained. Surface area metrics were calculated in FreeSurfer using the Human Connectome Project's multi-modal cortical parcellation scheme. We calculated brain age in R using previously validated and trained machine learning models. Chronological age was subtracted from predicted brain age to generate a brain age gap (BAG). With higher scores of BAG indicating predicated age is older than chronological age. Three parallel hierarchical regression models (each containing one of three pain measures) with three blocks were performed to assess the relationships between chronic pain and the resilience index in relation to BAG, adjusting for covariates. For each model, Block 1 entered the covariates, Block 2 entered a pain score, and Block 3 entered the resilience index.

GCPS CPI (R2 change = .033, p = .027) and GCPS disability (R2 change = 0.038, p = 0.017) significantly predicted BAG beyond the effects of the covariates, but total pain sites (p = 0.865) did not. The resilience index was negatively correlated and a significant predictor of BAG in all three models (p < .05). With the resilience index added in Block 3, both GCPS CPI (p = .067) and GCPS disability (p = .066) measures were no longer significant in their respective models. Additionally, higher education/income (p = 0.016) and study site (p = 0.031) were also significant predictors of BAG.

In this sample, higher reported chronic pain correlated with older appearing brains, and higher resilience attenuated this relationship. The biobehavioral and psychosocial resilience index was associated with younger appearing brains. While our data is cross-sectional, findings are encouraging that interventions targeting both chronic pain and biobehavioral and psychosocial factors (e.g., coping strategies, positive and negative affect, smoking, and social support) might buffer brain aging. Future directions include assessing if chronic pain and resilience factors can predict brain aging over time.

Air pollution is linked to mortality and morbidity. Since humans spend nearly all their time indoors, improving indoor air quality (IAQ) is a compelling approach to mitigate air pollutant exposure. To assess interventions, relying on clinical outcomes may require prolonged follow-up, which hinders feasibility. Thus, identifying biomarkers that respond to changes in IAQ may be useful to assess the effectiveness of interventions.

We conducted a narrative review by searching several databases to identify studies published over the last decade that measured the response of blood, urine, and/or salivary biomarkers to variations (natural and intervention-induced) of changes in indoor air pollutant exposure.

Numerous studies reported on associations between IAQ exposures and biomarkers with heterogeneity across study designs and methods. This review summarizes the responses of 113 biomarkers described in 30 articles. The biomarkers which most frequently responded to variations in indoor air pollutant exposures were high sensitivity C-reactive protein (hsCRP), von Willebrand Factor (vWF), 8-hydroxy-2′-deoxyguanosine (8-OHdG), and 1-hydroxypyrene (1-OHP).

This review will guide the selection of biomarkers for translational studies evaluating the impact of indoor air pollutants on human health.

Some patients with schizophrenia switch medications due to lack of efficacy or side effects; improvement in symptoms and side effects following a switch must be assessed.

In a 12-week, open-label, baseline-controlled, flexible dose switch study, adult outpatients with schizophrenia experiencing suboptimal efficacy or tolerability problems were switched from haloperidol (n=99), olanzapine (n=82), or risperidone (n=104) to ziprasidone (80¬–160 mg/d; dosed bid with food). The primary efficacy evaluation was the BPRS score at Week 12. Safety evaluations included change from baseline in movement disorders (SAS, BAS, AIMS), weight, prolactin, and fasting lipids levels. Statistical tests were 1-sided non-inferiority comparisons with correction for multiple comparisons (0.025/3 significance level), for the primary efficacy endpoint, or 2-sided (0.05 significance level), for secondary endpoints.

BPRS scores improved significantly compared with all 3 preswitch medications at Week 12. Mean change from baseline (SD) for patients switched from haloperidol, olanzapine, and risperidone was –11.3 (16.3), –6.3 (14.2), and –9.9 (13.2), respectively (p < 0.0001 vs baseline). Movement disorders, measured by SAS, BAS, and AIMS, improved significantly for subjects switched from haloperidol and risperidone. Change in weight (kg ± SD) from baseline was 0.4 ± 3.97, –2.0 ± 3.99 (p < 0.001), and –0.6 ± 3.21 for subjects switched from haloperidol, olanzapine, and risperidone, respectively.

Patients switched to ziprasidone demonstrated improvement in symptoms and movement disorders, with a weight neutral effect. Ziprasidone is an appropriate switch option for patients experiencing suboptimal efficacy or poor tolerability with their current treatment.

The diagnosis of anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis relies on the detection of NMDAR IgG autoantibodies in the serum or cerebrospinal fluid (CSF) of symptomatic patients. Commercial kits are available that allow NMDAR IgG autoantibodies to be measured in local laboratories. However, the performance of these tests outside of reference laboratories is unknown.

To report an unexpectedly low rate of NMDAR autoantibody detection in serum from patients with anti-NMDAR encephalitis tested using a commercially available diagnostic kit in an exemplar clinical laboratory.

Paired CSF and serum samples from seven patients with definite anti-NMDAR encephalitis were tested for NMDAR IgG autoantibodies using commercially available cell-based assays run according to manufacturer’s recommendations. Rates of autoantibody detection in serum tested at our center were compared with those derived from systematic review and meta-analyses incorporating studies published during or before March 2019.

NMDAR IgG autoantibodies were detected in the CSF of all patients tested at our clinical laboratory but not in paired serum samples. Rates of the detection were lower than those previously reported. A similar association was recognized through meta-analyses, with lower odds of NMDAR IgG autoantibody detection associated with serum testing performed in nonreference laboratories.

Commercial kits may yield lower-than-expected rates of NMDAR IgG autoantibody detection in serum when run in exemplar clinical (nonreference) laboratories. Additional studies are needed to decipher the factors that contribute to lower-than-expected rates of serum positivity. CSF testing is recommended in patients with suspected anti-NMDAR encephalitis.

Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9.

We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy.

16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (−0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01).

PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.

Different diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification.

To evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics.

Data collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.

A total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15–3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98–10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7–15) (OR = 0.96; 95% CI = 0.56–1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26–0.97).

The MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.

Drs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.