Effective anticancer treatments have dramatically improved the outcome of patients with cancer, but cardiac toxicity reduces their clinical efficacy in a non-negligible percentage of patients. Sacubitril/valsartan is a new paradigm in the treatment of chronic heart failure, with a reduced ejection fraction due to the enhancement of natriuretic peptides' properties when coupled with a blocking effect on the angiotensin II type 1 (AT1) receptors. As with other clinical conditions of heart failure with potentially reversible declines in cardiac function, a wearable cardioverter defibrillator (WCD) is a valid tool for protection against sudden death until recovery occurs. We report a case series of four patients with chemotherapy-related acute cardiac failure with severely reduced cardiac function. They were successfully treated with sacubitril/valsartan while being protected from malignant arrhythmias using a wearable cardioverter defibrillator until the recovery of cardiac function. Sacubitril/valsartan was confirmed to be effective in anthracycline-related cardiac toxicity and the wearable cardioverter defibrillator should be considered as a support tool even in the oncology patient. Improvements in anti-cancer global strategy have resulted in better outcomes for a large part of patients with cancer, with many of them experiencing remission or long-term survival. Cardiovascular disease is the second leading cause of death in cancer survivors, preceded only by the cancer-related mortality itself . SystoliCardiac magnetic resonance (CMR) represents the gold standard in cardiac function assessment and can be used for an early-stage screening of heart damage. Its role in tissue characterization can help eliminate other possible causes of myocardial dysfunction when cardiac toxicity is suspected. For this purpose, T1 and T2 mapping, in addition to late gadolinium enhancement (LGE), are very useful tools . We coll2), and with a regular clinical and echocardiographic follow-up. The patient was hospitalized in November 2019 for acute heart failure with reduced ejection fraction (EF 25%) 1 month after the end of chemotherapy. The event occurred 8 months after the treatment started and 5 months after crossing the threshold dose of 300 mg/m2. The patient started the anthracyclines therapy after a long period of hospitalization due to complications from abdominal surgery and a significant weight loss. No coronary lesions were detectable on coronary angiography. The CMR findings were an increased biventricular size and a severe biventricular dysfunction, with left systolic ejection fraction (LVEF) of 25% and right ventricle (RV) EF of 40%, increased native T1 signal  with a marked increase of extracellular volume (ECV) , but no LGE was found. Laboratory findings showed normal high-sensitive C-reactive protein (hs-CRP) levels and glomerular filtration rate. High-sensitive troponin T (hs-TnT) was elevated at admission time (190 pg/ml), with a slight decline during the in-hospital stay (162 pg/ml). The N-terminal prohormone of natriuretic peptide (NT-proBNP) levels was clearly elevated at baseline , but we observed a reduction during hospitalization to 7,200 pg/ml at discharge. During the follow-up, the NT-proBNP levels continued to decrease to 530 pg/ml within the year. Given the severe LV dysfunction, the patient was discharged with heart failure therapy  and a WCD. At the first follow-up visit 10 days later, ACE-inhibitor was replaced with S/V due to persistent systolic dysfunction. After 3 months, the LVEF increased from 25 to 40% with the reverse remodeling of ventricular size and WCD was discontinued. The patient compliance to WCD was high with a median of 22.4 h/day. No significant arrhythmias were detected. Echocardiography findings were confirmed at 6 months by CMR (LVEF 43% and RVEF 51%), and while the native myocardial T1 signal decreased, it was not yet normal . At the one-year follow-up visit, the LVEF had improved to 45%  plus cyclophosphamide followed by trastuzumab for 1 year, ending in 2007. In September 2020, the patient was hospitalized for acute heart failure, with EF at 30%, and a coronary computed tomography (CT) that documented the absence of coronary artery disease. The patient reported smoking habits and hypertension in her medical history. The Hs-CRP and hs-TnT values and the glomerular filtration rate were all within the normal range, while NT-proBNP levels were elevated at admission , but during hospitalization, they rapidly decreased to 863 pg/ml at discharge and normalized at 3 months follow-up. The CMR showed an LV dilatation with severe impairment of contractile function (LVEF 32%) and a mild RV failure (RVEF 48%); native myocardial T1 signal was increased  while LGE was absent. The patient was discharged with WCD and heart failure therapies including beta-blocker, anti-aldosterone therapy, and S/V that was later replaced by ramipril (2.5 mg/day) due to hypotension. After 3 months, the CMR highlighted a normalized left ventricular size and right ventricular function (RVEF 69%), left ventricle systolic function improvement (LVEF 48%), and a native myocardial T1 signal decreased to 1,000 ms with respect to baseline 1,040 ms with normal ECV (28%). No LGE was detectable, but septal edema was still present in the T2 mapping. The WCD was discontinued due to LVEF recovery . The patient presented with a reduced glomerular filtration rate and elevated hs-CRP levels (42 mg/L). The filtration rate remained unchanged during the in-hospital stay, while hs-CRP decreased to 18 mg/L. A high-sensitive troponin T was elevated at admission time (130 pg/ml) with a trend in reduction (90 pg/ml). The baseline NT-proBNP levels were clearly elevated , with a sharp reduction during hospitalization . A coronary CT  showed diffuse and severe coronary atherosclerosis without obstructive coronary disease. The patient was started on heart failure therapy with beta-blocker and S/V; after 2 weeks from therapy, introductory CMR was performed and has shown the normal biventricular dimensions and right ventricular function (RVEF 60%), left ventricular systolic function improvement (LVEF 45 vs. 25%), interventricular septal and lateral wall abnormal native T1 signal , and an increased ECV (32%) and a mid-wall septal LGE with a non-ischemic pattern. At the one-month follow-up, LVEF had further improved to 48% .2), followed by radiotherapy (dose 50Gy) in 2003. In January 2021, the patient reported dyspnea for mild effort. This patient also reported a history of smoking and hypertension. Chest X-Ray showed a pleural effusion, and the echocardiography documented a dilated left ventricle with severe systolic dysfunction (EF 25%) leading to hospitalization. The admission hs-CRP levels and hs-TnT were slightly elevated but remained in a stable trend during hospitalization. The filtration rate showed a baseline reduction that stayed unchanged during observation. As expected, the NT-proBNP levels were clearly elevated at baseline  but we observed a sharp and fast reduction . No coronary artery disease was found at coronary angiography. CMR confirmed an increased left ventricular size, severe biventricular systolic dysfunction (LVEF 27% and RVEF 29%), and no significant LGE. Heart failure therapy, with beta-blocker and anti-aldosterone therapy plus S/V, was started together with SGLT2 inhibitor empagliflozin for the previously undiagnosed diabetes. The patient was discharged with WCD. At the first follow-up visit 1 month later, LV size was normal, and the ejection fraction improved to 35%. At the three-month follow-up visits, LVEF had improved to 46%, and WCD was discontinued  is the gold standard for ventricular dimension and ejection fraction assessment. All our patients had biventricular dysfunction in the acute phase, but, while LV impairment was clearly detected by echocardiography, RV dysfunction was detected only by CMR. However, EF reduction is just a small part of cardio-toxic damage . Early dThe reference for non-invasive recognition of focal fibrosis areas is LGE, but a limitation of this technique is the low sensitivity for diffuse fibrosis that is more frequent in patients with cardiotoxicity, especially in anthracycline cardiomyopathy . The T1 Implantable cardioverter defibrillators (ICDs) are indicated for the primary prevention of sudden cardiac death (SCD) in patients with reduced LV function . A subset of patients with cancer is at risk for SCD due to a variety of cardiac causes, including chemotherapy-induced cardiomyopathy. The data regarding the risk of arrhythmic death in these patients are very limited, but a study on the use of WCD in patients suffering from anthracycline cardiotoxicity showed a risk of malignant arrhythmia at around 7% in 3 months, which is significantly higher than in the general population with heart failure . An indiChemotherapy-induced acute heart failure could represent a potentially life-threatening side effect of many oncology drugs. Besides representing a clinical emergency, it could also negatively affect long-term patient outcome from an oncological point of view. In fact, a steadily impaired EF could limit the anticancer therapeutic options. Prompt and steady recovery of cardiac function has been a 2-fold relevance. All available therapeutic strategies should be implemented to get this goal. The presented case series highlighted the positive role of the early use of S/V in LVEF recovery in this clinical setting. The WCD should be considered in oncology patients when recovery of cardiac function is expected. A prospective evaluation of a larger size of the S/V effect in an oncology population is needed to confirm its retrospective and positive results.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.Written informed consent was obtained from all participants for their participation in this study.MLC, KC, GS, JDM, AC, FS, and EV: study conception and case description. MLC, KC, GS, AC, GC, NM, CT, and IB: drafting of the manuscript or revising it critically for important intellectual content. All authors: final approval of the manuscript.This study received funding from Zoll  for the open access option only. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.This study received funding from Zoll  for open access option only. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The median eating window was 14 h 04 min [12 h 57 min–15 h 21 min], which was significantly shorter on the weekend compared to weekdays (p < 0.0001). Only 13.1% of participants had an eating window <12 h/d. Additionally, there was greater irregularity with the first eating occasion during the week when compared to the weekend (p = 0.0002). In conclusion, adults with overweight or obesity have prolonged eating windows (>14 h/d). Future trials should examine the contribution of a prolonged eating window on adiposity independent of energy intake.We aim to describe temporal eating patterns in a population of adults with overweight or obesity. In this cross-sectional analysis, data were combined from two separate pilot studies during which participants entered the timing of all eating occasions  for 10–14 days. Data were aggregated to determine total eating occasions, local time of the first and last eating occasions, eating window, eating midpoint, and within-person variability of eating patterns. Eating patterns were compared between sexes, as well as between weekday and weekends. Participants ( The age-adjusted prevalence of obesity in adults is over 40% of the US population . ObesityThe National Health and Nutrition Examination Survey 2009–2014) reveals that US adults report approximately five eating occasions per day, with breakfast occurring at approximately 08:00, dinner around 18:25 and the last eating occasion at 20:18  reveals ,4,7,8, wThe body’s endogenous circadian system orchestrates rhythms in metabolism over a 24-h period ,12,13. MWe used cross-sectional baseline data from two separate intervention pilot studies at Columbia University and New York University Langone Health. All data were collected prior to the beginning of each intervention. Ethical approvals for both studies were obtained from the respective institutional review boards of Columbia University and New York University Langone Health. All participants provided written informed consent.2, in possession of a smartphone, English-speaking, and living in the New York City geographical area. Exclusion criteria included current shift work or shift work within the last 6 months, planned travel across more than 1 time zone during the study period, significant organ-system dysfunction/disease , history of seizure disorder, recent bariatric surgery (<2 years), weight-loss medication and history of significant psychiatric disorder .The New York Time-Restricted EATing (NY-TREAT) pilot study  was designed to assess the effect of TRE in individuals with long eating windows (≥14 h/d). The first phase of this study consisted of an observational baseline cross-sectional 2-week continuous assessment of eating patterns via a smartphone app in order to identify individuals eating for ≥14 h/day. Inclusion criteria included adults ages 30–75 years, BMI 25–50 kg/mPotential adult participants were recruited from the New York City metropolitan area via advertisements in local communities, citywide wellness programs, New York Presbyterian Hospital, the Columbia University Medical Center RecruitMe website, and NIH’s Researchmatch. Potentially eligible participants were screened via phone to assess the self-reported timing of meals, timing of bed and wake times, and smartphone use. Those who met inclusion criteria were invited for an in-person visit at Columbia University during which they underwent medical history screenings, anthropometric measurements of body weight, height, waist circumference, and blood pressure, and completed a morning-eveningness questionnaire to assess chronotype . Partici2, pre-diabetic (HbA1c 5.7–6.4%) or early-stage type 2 diabetes (HbA1c <8% managed with lifestyle or lifestyle plus metformin), and with an estimated glomerular filtration rate >60 mL/min/1.73 m2. Participants were excluded from the ancillary study if they reported shift work or were unable to comply with logging self-reported meal times in the smartphone app. Additional information on exclusion criteria has been previously published  unless otherwise noted. The Shapiro-Wilk test was run to assess normality of continuous variables. Participant characteristics were compared between the two studies (TEP vs. NY-TREAT) using independent samples, 2-sided  h apart . Bias, lParticipant characteristics for the entire cohort and by study are shown in The duration of the observation period was 14 days for NY-TREAT and 10 days for TEP. There was no significant difference between any of the eating-pattern variables by study type . A totalEating-pattern variables were not significantly associated with body weight, BMI, waist circumference, hip circumference, or waist-to-hip ratio . There wQuantifying baseline temporal eating patterns is central to selection of individuals who may benefit most from TRE and to demonstrate the efficacy of chrononutrition-based interventions. The aim of our study was to describe temporal eating patterns in adults with overweight or obesity and to assess the within- and between-person variability in eating patterns in this population. Our main finding was that >52% of our participants had a long eating window of >14 h/d, with the longest eating window of 18 h 58 min. We found no significant association between eating window and anthropometric measures. Our data also showed that adults with overweight or obesity reported approximately four eating occasions/day, with the peak number of eating occasions occurring around 12:30, and had their first eating occasion at 09:40 and last at 19:40. Additionally, weekend eating patterns exhibited fewer logged eating occasions, a delay in first eating occasion, a shorter eating window, and less within-person variability in the first eating occasion when compared to that of weekday eating patterns.Our results show no association between eating window and anthropometric measures . Similar results were reported elsewhere in a sample of young adults with normal BMI . Our finA decrease in the eating window with TRE is effective at reducing body weight ,18,19. FThe median eating window in our study was >14 h/d, which is similar to that of prior reports on this population using a similar definition . Others Self-reported eating window maybe used as a screening tool for TRE interventions . However, self-reported eating occasions are prone to underreporting, and the association between self-reported and measured eating window using an app is unclear ,33. The Eating patterns are impacted by the day of the week, as evidenced by a shift in the sleep-wake cycle, resulting in circadian desynchrony  . In a crAdditionally, there was greater within-person variability of the first eating occasion on weekdays compared to weekends, despite a delay in the timing of the first eating occasion on weekends. Based on these patterns, it appears adults with obesity may skip or delay their first eating occasion to a greater degree during the week compared to the weekend but are rigid in the timing of their last eating occasion throughout the entire week. Greater within-person variability in first eating occasion may be due to other lifestyle factors, such as employment or family responsibilities . Future studies should assess the within-person variability of eating patterns over a longer period (>4 week) to further elucidate the relationship with biological outcomes .Strengths of the present study include recording of eating occasions for up to 14 consecutive days and the use of smartphone apps to track and quantify eating patterns; however, there were some limitations. While two different smartphone apps were utilized to collect eating-pattern data, there were no between-study differences in eating-pattern variables. The patient-facing data and information provided with smartphone apps may influence an individual’s ability to record an eating occasion, dependent on the individual’s technological literacy and experience. Some smartphone apps are easier to navigate and access embedded information with than others, thus reducing barriers to logging. Temporal eating patterns are defined not only by timing but also by distribution of energy intake, which we were unable to quantify in the TEP and NY-TREAT studies. Prior TRE studies assessing the impact of TRE on weight loss report a 20–30% reduction in self-reported energy intake accompanying a reduction in the eating window ,30. RelyIn summary, we found that the majority of adults with overweight or obesity have an eating window >14 h/d, with only 13.1% of participants with an eating window <12 h/d. Anthropometric measures  were not associated with eating window; however, future studies are required to confirm these findings using more precise measures of body composition in a larger, more diverse sample, including measures of energy intake. We confirm prior findings showing differences in eating patterns between weekdays and weekends; in particular, we found greater within-person variability for the first eating occasion on weekdays compared to weekends. Moreover, the timing of the last eating occasion was consistent throughout the week. We encourage future TRE or intermittent fasting interventions to use the 95% eating window to define an individual’s eating duration. The findings of this study, specifically long eating windows and differences in eating patterns between weekdays and weekends, highlight the importance of characterizing baseline eating patterns prior to initiating chrononutrition-based interventions.


The U-CARE Heart trial was one of the first randomized controlled trials to evaluate the effect of internet-based cognitive behavioral therapy on self-reported symptoms of anxiety or depression for patients with a recent myocardial infarction. While the effects of internet-based cognitive behavioral therapy on Hospital Anxiety and Depression Scale (HADS) scores at 14 weeks postbaseline were not significant, in this study, we investigated possible long-term effects of treatment.The aim of this study was to evaluate the long-term effectiveness of internet-based cognitive behavioral therapy on self-reported symptoms of anxiety and depression in patients 12 months after a myocardial infarction and to explore subsequent occurrences of cardiovascular disease events.Shortly after acute myocardial infarction, 239 patients  reporting mild-to-moderate symptoms of anxiety or depression were randomized to 14 weeks of therapist-guided internet-based cognitive behavioral therapy (n=117) or treatment as usual (n=122). Data from national registries were used to explore group differences in clinical outcomes such as cardiovascular disease and cardiovascular-related mortality for a follow-up period of up to 5 years: group differences in HADS total score 1 year post–myocardial infarction, the primary outcome, was analyzed using multiple linear regression. Secondary outcomes, such as HADS anxiety and depression subscales and the Cardiac Anxiety Questionnaire total score (CAQ), which measures heart-focused anxiety, were analyzed in the same way. Multiple imputation was used to account for missing data, and a pooled treatment effect was estimated. Adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) for data pertaining to registry outcomes.β=–1.14, 95% CI –2.73 to 0.45, P=.16). CAQ was the only measure improved significantly by internet-based cognitive behavioral therapy when compared with treatment as usual  before adjusting for multiple comparisons. The composite outcome of nonfatal cardiovascular events and cardiovascular-related mortality did not differ between groups but was numerically higher in the internet-based cognitive behavioral therapy group, who were at slightly greater risk . Adjusting for previous myocardial infarction and diabetes attenuated this estimate .Both groups reported lower HADS total scores 1 year after myocardial infarction than those at baseline. HADS total scores were not significantly different between the treatment and control groups 1 year after myocardial infarction (Internet-based cognitive behavioral therapy was not superior in reducing self-reported symptoms of depression or anxiety compared to treatment as usual at the 1-year follow-up after myocardial infarction. A reduction in cardiac-related anxiety was observed but was not significant after adjusting for multiple comparisons. There was no difference in risk of cardiovascular events between the treatment groups. Low treatment adherence, which might have affected treatment engagement and outcomes, should be considered when interpreting these results.ClinicalTrials.gov NCT01504191; https://clinicaltrials.gov/ct2/show/NCT01504191RR2-10.1186/s13063-015-0689-y Myocardial infarction continues to be the leading cause of death worldwide . The risWeb-based interventions offer remote access to treatment which are sometimes otherwise inaccessible  and haveThe U-CARE heart trial  was, to The purpose of this follow-up study was thus to investigate whether patients with mild-to-moderate symptoms of anxiety or depression, self-reported within 2 months following myocardial infarction and treated with internet-based cognitive behavioral therapy, had (1) improvements in self-reported mental health at 12 months after the myocardial infarction, and (2) reduced risk for cardiovascular events including cardiovascular-related mortality, for a period of up to 5 years, compared to those receiving treatment as usual.This follow-up study (1) evaluated the effect of internet-based cognitive behavioral therapy on self-reported symptoms of anxiety and depression 12 months after the myocardial infarction, and (2) explored whether internet-based cognitive behavioral therapy had an effect on nonfatal cardiovascular events or cardiovascular-related mortality up to 5 years after study inclusion. The study design, procedure, intervention, and results of the posttreatment follow-up of the U-CARE Heart trial have been reported ,21.In brief, the U-CARE Heart trial was a multicenter study that recruited participants <75 years of age, reporting symptoms of anxiety or depression  within 3 months following their myocardial infarction. Myocardial infarction was defined according to International Statistical Classification of Disease Tenth Revision (ICD-10) code I21 and diagnosed by a cardiologist. Patients scheduled for coronary artery bypass surgery, with low adherence (such as missing appointments with the cardiac nurse or substance use), or expected to live <1 year, as judged on-site by the recruiting nurse, were not eligible. Patients who met eligibility criteria (n=239) were randomized to receive either internet-based cognitive behavioral therapy (n=117) or treatment as usual (n=122). This study received ethical approval by the regional ethics committee in Uppsala (2011/217) and is registered on ClinicalTrials.gov (NCT01504191).Screening for eligible participants took place across in Sweden from September 2013 to December 2016 in 25 cardiac clinics during routine visits between 1 and 8 weeks following their myocardial infarction. Consenting participants were sent an email and password login to a secure internet-based portal  where thTrial-specific outcomes were measured 14 weeks postbaseline, which corresponded to treatment completion for those in the internet-based cognitive behavioral therapy group, and again at follow-up 12 months after myocardial infarction. Participants in both the intervention and treatment-as-usual groups were sent automatic SMS texts and email reminders asking them to fill in the web-based questionnaires at each observation point and were subsequently reminded via telephone by one of the research staff  if initially unresponsive. The internet-based cognitive behavioral therapy intervention ,21 delivAll participants received standard protocol secondary prevention and cardiac rehabilitation offered by the regional health care system. This usually includes, but is not limited to, preventive medications, education about cardiovascular risk-factors, smoking cessation, organized and tailored physical exercise, and in some areas, access to counseling or psychosocial support.Sociodemographic data were self-reported at baseline. The primary outcome measure was HADS total score, measuring self-reported symptoms of both anxiety and depression . The HADS is a useful tool in the screening of depression in cardiac patients ,23, and Secondary outcome measures included the Cardiac Anxiety Questionnaire (CAQ) , which aData on cardiovascular events and mortality during follow-up were obtained from the Patient Registry and the Causes of Death Registry of the National Board of Health and Welfare in Sweden . ICD-10 Statistical analysis was planned in line with CONSORT  before treatment allocation was disclosed and before the trial database was locked.Main analysis investigated potential differences in HADS total scores 12 months post–myocardial infarction, with subsequent analyses of the HADS anxiety and HADS depression subscale scores. Secondary analyses focused on potential differences between groups in MADRS-S total score, CAQ total score, and BADS-SF total score.Linear modeling was used to estimate treatment effects. As with the posttreatment analysis , analyseTo account for missing values and analyze according to intention to treat, data were imputed by multiple imputation using chained equations and predictive mean matching . Variablβ with 95% confidence intervals. Paired t tests were conducted with outcomes pre- vs posttreatment to assess change over time across the full sample. Level of significance, α, for all separate comparisons was set to P<.05. For familywise testing of the multiple hypotheses, a Bonferroni-corrected P value=.008 (6 hypotheses) was used.HADS total score sensitivity analyses were conducted on observed data . Supplementary analyses of HADS total score were based on per protocol data, including only those in the model who had completed at least one homework assignment. Only the analyses according to intention to treat (with imputed data) were applied to the secondary outcomes. Effect estimates are reported as pooled adjusted point estimates Multivariable Cox regression analysis was performed to estimate adjusted hazard ratios (HR) with 95% confidence intervals with time (in days) to first cardiovascular event as the outcome event. The exposure was treatment group, with time under risk starting from the date of randomization during follow-up and censoring at the time of any cardiovascular event, end of follow-up , or the date of noncardiovascular death, whichever came first.The first participant was randomized in September 2013. The longest period of follow-up spanned 1898 days, and the shortest spanned 719 days; the mean follow-up period was 1139.3 days. Any incidence of cardiovascular death or cardiovascular disease event was used as the primary outcome for time-to-event analysis. There were no missing data for these outcomes. Adjustments for age and sex were performed for higher precision. Posthoc exploratory analyses were conducted with previous myocardial infarction and diabetes as additional covariates since previous medical history of these appeared to be more prevalent in the treatment group. All statistical analyses were performed using R statistical software .Of 239 patients who were enrolled, 24% (28 participants) from the internet-based cognitive behavioral therapy group and 8% (10 participants) from the treatment-as-usual group had missing data on the HADS questionnaire at the 12-month follow-up. In the internet-based cognitive behavioral therapy and treatment-as-usual groups 26% (31 participants) and 9% (11 participants), respectively, had missing data on MADRS-S; 26% (30 participants) and 8% (10 participants), respectively, had missing CAQ values; and 30% (35 participants) and 11% (13 participants) respectively, had missing data on the BADS-SF.Data were considered missing if (1) the entire questionnaire was left unanswered  or (2) items were skipped within a questionnaire that were required in order to compute a value for the total score .P=.57).Baseline characteristics are presented in All primary and secondary measures  at postt200t=13.57, P<.001). HADS total scores from baseline to the 12-month follow-up visit after myocardial infarction showed a consistent reduction across the total study sample . There was no effect of internet-based cognitive behavioral therapy on HADS depression  or anxiety  subscale scores.The main model showed no difference of internet-based cognitive behavioral therapy on HADS total scores at follow-up  for internet-based cognitive behavioral therapy; however, this was not significant after adjusting for multiple comparisons (Bonferroni-corrected level of significance P<.008). Further details on outcomes are available in The CAQ was the only additional outcome to show a significant reduction at the 12-month follow-up  and per protocol analysis  showed no effect of internet-based cognitive behavioral therapy on HADS total score.Sensitivity analysis of observed data (P=.07). Posthoc analysis showed this estimate was attenuated when previous myocardial infarction and diabetes, as indicators of baseline health status, were entered in the model . Cardiovascular events included in the analysis are shown in Compared to those in the treatment-as-usual group, participants receiving internet-based cognitive behavioral therapy had a numerically but not significantly higher risk of experiencing a cardiovascular event . Of thePossible explanations to why there was no difference observed between the groups are low activity-levels among patients in the internet-based cognitive behavioral therapy arm and the low number of completed treatment modules. Just over half of those in the internet-based cognitive behavioral therapy group completed the first introductory module, and only 15% continued to work through any of the remaining 10 modules . InterviThe intervention design may have contributed to low adherence. Creating an internet-based cognitive behavioral therapy intervention is technically challenging and complex and must be made with the characteristics and demographics, such as age, of the population in mind. Despite excluding individuals not able or willing to use a computer, the internet, email, or a mobile device, participants still reported low computer literacy as a contributing factor to why they did not engage more with the intervention . A studyA well-discussed issue in intervention literature that applies to this study concerns how participants are recruited into a study that offers treatment. Patients were approached by hospital staff in participating hospitals and asked if they would be interested in participating rather than responding to an open-advertisement or actively seeking care themselves. Similar studies have also suggested that clinical recruitment might have contributed to low internet-based cognitive behavioral therapy adherence . It is lMore recent trials have had better success in maintaining participant retention, particularly when patients could self-identify as needing help with symptoms related to their coronary event and when there were fewer treatment modules to work through .P=.02). Interestingly, this parameter did not differ between groups at 14 weeks postbaseline [P value (P=.02) was not significant.Of note, secondary analyses revealed significant long-term improvement on the CAQ scale in the internet-based cognitive behavioral therapy group compared to that of the treatment-as-usual group (baseline . One posbaseline  and thatbaseline  has demoP=.07); this was a small study, with few events and lacking power for this exploratory analysis. Despite this, such an analysis is arguably justified as the knowledge gained thereof, and its potential benefits could contribute to the literature of internet-based cognitive behavioral therapy effects on cardiovascular events. Since we included a rather broad range of cardiovascular events, it is possible that those in the internet-based cognitive behavioral therapy group experienced more but milder incidences such as heart failure; the number of events were too few to adequately explore this, but upon counting the different event types in the 2 groups, no particular pattern emerged. It could also be that the treatment group was less healthy at baseline as randomization does not necessarily make groups similar. When adding indications of baseline health differences , the effect was attenuated. A similar recent study [The risk of cardiovascular events did not differ across groups at long-term follow-up. Unexpectedly, the hazard ratio indicated a slightly negative effect of the treatment. This effect was not significant  meant that patients with only mild symptoms were included, which could be considered a disadvantage—this may have contributed to the lack of difference in the primary outcome both immediately posttreatment and at the 1-year follow-up. Spontaneous improvement over time in anxiety and depression has been observed in patients with acute myocardial infarction ,43. At 2As previously reported , the HADOne challenge in this study, as is often the case in long-term follow-up studies, was the amount of missing data due to nonresponses. There was also more attrition in the internet-based cognitive behavioral therapy group than in the treatment-as-usual group at the 1-year follow-up. While this would need to be explored further to fully understand the reasons for the differences between the groups, one could speculate that it was partly because of the perceived burden by those in the internet-based cognitive behavioral therapy group of completing so many additional web-based forms and exercises, which was not experienced by those in the treatment-as-usual group. Alternatively, patients assigned to the internet-based cognitive behavioral therapy intervention could have been randomly more ill from the start, a factor which could contribute to more attrition and explain higher risk for cardiovascular events in this group.P<.02, which was not significant when corrected for multiple comparisons) toward improvement in the treatment group. This may indicate a differential effect of the internet-based cognitive behavioral therapy on cardiac anxiety that requires further research. The internet-based cognitive behavioral therapy was not associated with an increased time-to-event risk for cardiovascular events. The low adherence rates should be considered when interpreting these results.Therapist-guided internet-based cognitive behavioral therapy developed for patients with a recent myocardial infarction was not superior to treatment as usual at the 1-year follow-up in the U-CARE Heart trial; however, the CAQ scale did detect a possible trend (


However, sample mix-up is a pervasive problem in proteogenomics because of the complexity of sample processing. Here, we present a pipeline for Sample Matching in Proteogenomics (SMAP) to verify sample identity and ensure data integrity. SMAP infers sample-dependent protein-coding variants from quantitative mass spectrometry (MS), and aligns the MS-based proteomic samples with genomic samples by two discriminant scores. Theoretical analysis with simulated data indicates that SMAP is capable of uniquely matching proteomic and genomic samples when ≥20% genotypes of individual samples are available. When SMAP was applied to a large-scale dataset generated by the PsychENCODE BrainGVEX project, 54 samples (19%) were corrected. The correction was further confirmed by ribosome profiling and chromatin sequencing (ATAC-seq) data from the same set of samples. Our results demonstrate that SMAP is an effective tool for sample verification in a large-scale MS-based proteogenomics study. SMAP is publicly available at  Sample mix-up is a potential problem in large-scale omic studies due to the complexity of sample processing. Here, the authors present a pipeline for sample matching in proteogenomics to verify sample identity and ensure data integrity. These studies usually involve collecting single- or multi-layer omic data from a large number of subjects, followed by statistical significance tests. The power and effectiveness of the statistical tests rely on the accuracy of sample identity8. However, sample mix-up in large-scale multi-omics studies, as a result of specimen labeling errors, or data mismanagement, is a widespread problem9. Such error is often neglected, and it generally leads to irreproducible results, weakened statistical testing power, and false conclusions10. Therefore, to reduce the bias and increase the precision for subsequent analyses, verification of sample identity is one of the first steps in a large-scale omics project.With the development of high-throughput technologies in recent years, remarkable accomplishments have been made by well-conceived and large-scale projects carried out by large consortia, such as the Cancer Genome Atlas (TCGA) project11 that is complementary to other omics, such as genomics and transcriptomics. Due to recent technical advances, MS-based proteomics has undergone rapid development yielding numerous large-scale proteomic data5. Most of these data are generated by multiplexed isobaric labeling-based protein quantification methods, such as isobaric tags for relative or absolute quantitation (iTRAQ) and tandem mass tag (TMT). For example, the TMT-based strategy can now measure as many as 27 samples in one batch simultaneously12, and potentially allows for the measurement of 81 samples when combined with metabolic labeling by amino acids in cell culture (SILAC). These multiplexing strategies would further exacerbate the problem of sample mix-up, posing a significant challenge for sample verification and calibration.Mass spectrometry (MS)-based proteomics has emerged as an important molecular profiling technology13, correlation of mRNA and protein14, and correlation of variant fractions15. For example, we have also implemented a genotype-based method to address the sample mix-up problem from sequencing-based transcriptomic data16. Most of these methods exploit the genotypic information of a sample, which can be directly derived from sequencing data. Calling genotype from multiplexed isobaric labeling-based quantitative proteomic data remains a major challenge because multiple labeled samples are mixed during the experiment. While the proteogenomics approach has been widely used to detect variant peptides, it only calls variant peptides from label-free or isobaric labeled proteomic data19. As a result, there is no method available for verifying and calibrating sample identity in a multiplexed quantitative proteomic study.Several methods have been developed to verify sample identity in large-scale genomic and transcriptomic studies using genotype concordanceIn this study, we present a pipeline for Sample Matching in Proteogenomics (SMAP). SMAP first performs proteogenomics to detect variant peptides from multiplexed isobaric labeling-based quantitative proteomic data, and then infers genotypic information of each sample based on the expression level of the variant peptides. SMAP finally verifies and calibrates sample identity based on a combination of concordance and specificity scores using inferred genotypic information. The performance of SMAP is assessed by a simulation study and a large-scale proteomic dataset.19, which constructs a customized database using genomic variant files  or indirectly from RNAseq raw data in FASTQ format, in which JUMP performs preprocessing, tag generation, MS/MS pattern matching, and scoring as previously reported20. The identified variant peptides are further filtered with the target-decoy strategy to control the false discovery rate (FDR)22.SMAP consists of three main components Fig. : (i) ide2-scale, followed by a scale normalization using the following formula,SMAP infers sample genotypic information based on the relative expression level of variant peptides identified in proteomic data with multiplexed isobaric labeling-based quantification methods Fig. . For eacSMAP then uses the proteogenomics approach to detect variant peptides Fig. , followeOnce the genotypic information of each sample is inferred, SMAP verifies and calibrates the sample identity using a combination of two scores: concordance score  and specificity score  were quantified by 29 batches of 11-plex TMT-based proteomic technology. In addition to proteomic data, the PsychENCODE BrainGVEX project also generated other omic data with matched samples, including 285 samples with low-depth whole-genome sequencing (WGS), 426 samples with RNA sequencing (RNA-Seq), 295 samples with assay for transposase-accessible chromatin using sequencing (ATAC-Seq), and 197 samples with ribosome sequencing (Ribo-Seq). Previous analysis using the DRAMS program has identified ~19% mislabeled samples in both WGS and ATAC-seq, ~25% in Ribo-seq, and ~3% in RNA-seq data23 and 17,844,001 theoretical peptides translated from variants generated by WGS and RNA-seq data. SMAP identified a total of 5,065 unique variant peptides  and a signal distribution   can be used as a cutoff. This is consistent with the result found when evaluating the impact of the minimal intensity on the number of genotypes and the Cscore  ratio Fig. . For a vty) Fig. . The resore Fig. . The anaore Fig. . Followiore Fig. .After optimizing the parameters, we used 7628 filtered SNPs to infer genotypic information for 288 biological samples and 31 controls in the PsychENCODE BrainGVEX proteomic data  showed the same calibration in samples from all three platforms: MS-based proteomic, ATAC-seq, and Ribo-seq. In addition, a total of 9 samples had the same calibration between proteomics and ATAC-seq, 5 between proteomic and Ribo-seq, and 6 proteomic-specific calibrations  were reciprocally swapped Fig. , with ab14, a program recently developed to correct sample mislabeling in omic data through correlating mRNA and protein expression levels. A key step in the COSMO is feature selection by selecting highly correlated gene and protein pairs. With the default correlation coefficient cutoff of 0.5 in COSMO, only a few features were selected in our data probably due to about 20% of the samples being mislabeled based on the correction made by SMAP. To obtain an optimal number of features, we examined correlation coefficients ranging from 0.2 to 0.55, and found that the correlation coefficient of 0.35 produced the best performance when considering the number of selected features and matched samples   samples, including 218 samples matched to its original ID, and 52 mislabeled samples but corrected to the same identity by both methods. In the remaining 18 (6%) samples that showed inconsistent correction between COSMO and SMAP Sample mix-up is a pervasive problem in large-scale omic studies. In this study, we described a method, SMAP, for validating and correcting samples that are used for a large-scale TMT-based quantitative proteomic study. By applying SMAP to proteomic data from 288 biological samples in the pyschENcode BrainGVEX project, we verified and calibrated ~18% mislabeled samples. Based on the simulation study, SMAP has proven capable of validating the sample identity of as few as 20% of reliable genotypes.19, there is no study to infer genotypic information for each sample in multiplexed isobaric quantitative proteomic data. It is the first time that SMAP determines genotypic information of each sample based on the expression level of the variant peptides. With advances in next-generation sequencing and high-resolution mass spectrometry technologies, omic data from the same set of biological samples are now routinely collected in a project. Appropriate data quality controls at each step are required to ensure high data integrity. Therefore, SMAP complements existing approaches for sample verification and calibration that have been developed for genomic and transcriptomic data.While the proteogenomics approach is increasingly used to detect protein-coding variants and mutant splicing sitesCorrection of sample mislabeling is paramount to all large-scale omic studies. The SMAP program centers on sample correction for proteomic data by leveraging genotypic data. This concept of using genotypic data for sample correction can be applied to other omic data. The genotypes of each sample can be readily extracted from all massive sequencing data, ranging from whole-genome DNA sequencing, epigenome , to the transcriptome (bulk RNA and single-cell RNAseq). However, inferring genotypes from MS data is a much greater challenge than from sequencing data as we need to convert MS signals into amino acids and then convert amino acids into genotypes. More importantly, we proposed a scoring scheme in which a combined score is generated from two scores: (i) Cscore, which is a measure of the goodness of fit of genotypes inferred from proteomic data to non-mutant genotypes; (ii) 24.Although SMAP can infer sample-level genotypic information for sample verification, the inference certainty was still imperfect. First, the expression level of a variant peptide is quantitative, unlike variants called from genomic and transcriptomic data generated by sequencing technologies. Although we proposed to use three quartiles to determine the genotypes, the genotypes inferred by SMAP show uncertainties. In the analysis of the PsychENCODE BrainGVEX proteomic data, we found many mismatched genotypes were in the boundary of each range. Second, the homozygous mutant genotype (BB) and heterozygous genotype (AB) cannot be distinguished solely depending on the expression level if only AB genotypes are present in the samples. To mitigate this issue, SMAP examines the number of genotypes in the original genotypic data , and 29 individuals with bipolar disorder (BD). The samples include 282 Caucasians, 1 Hispanic, 1 African American, 3 Asian American, and 1 unknown. The brain frontal cortex samples were subjected to 29 batches of deep proteome profiling using TMT LC/LC-MS/MS technology. In addition to the proteomic data that we generated, five other omic data are also available at the psychENCODE knowledge portal (https://psychencode.synapse.org/Explore/Studies/DetailsPage?studyName=BrainGVEX), including RNA-Seq data from 426 samples , assay for transposase-accessible chromatin using sequencing (ATAC-Seq) data from 295 samples , and ribosome sequencing (Ribo-Seq) data from 197 samples . The genotypes were generated by the combination of three platforms, including Affymetrix chip data, whole-genome sequencing, and RNA-seq data16.A total of 288 well-characterized postmortem human brain samples  from the Stanley Medical Research Institute and Banner Sun Health Research Institute were used for this study19. In JUMPg program, the variant peptides were identified by the JUMP search engine, which performs preprocessing, tag generation, MS/MS pattern matching, and scoring20. JUMP was used to search MS/MS raw data against a composite target/decoy database22 to evaluate the false discovery rate (FDR). In JUMPg, a two-stage FDR method is used: in the first stage, the MS/MS data are searched against a reference protein database, and the confidently identified spectra are removed; in the second stage, the remaining spectra are searched against the variant protein database, and the FDR for variant peptides is calculated based on the second stage search results. The target database contains both core protein sequences downloaded from UniProt23 and all theoretical variant peptides generated from all nonsynonymous protein-coding variants. To generate theoretical variant peptides, all variants in the genotypes were re-annotated using the genome annotation tool ANNOVAR26 based on the human reference genome . We used the UCSC gene annotation model. The decoy database was generated by reversing protein sequences in the target database. Major parameters included precursor and product ion mass tolerance (±8 ppm), full trypticity, static mass shift for the TMT tags (+229.16293) and carbamidomethyl modification of 57.02146 on cysteine, dynamic mass shift for Met oxidation (+15.99491), maximal missed cleavage (n = 2), and maximal modification sites (n = 3). To filter variant peptides, PSMs were first filtered by user-specified parameters , then by precursor ion mass accuracy. The resulting PSMs were further grouped by precursor ion charge state and tryptic ends and then filtered by matching scores (Jscore and ΔJscore) to achieve a peptide FDR < 5%. If one peptide could be generated from multiple homologous proteins, the peptide was assigned to the protein with the highest PSM based on the rule of parsimony.JUMPg program was used to identify variant peptides by taking MS-based proteomic data and a customized database curated with genomic variant data27: (1) extracting TMT reporter ion intensities of each PSM; (2) correcting the raw intensities based on the isotopic distribution of each labeling reagent ; (3) removing sample loading bias by normalization with the trimmed median intensity of all PSMs; (4) calculating the mean-centered intensities across samples (e.g. relative intensities between each sample and the mean), (5) summarizing relative intensities of proteins or variant peptides by averaging related PSMs. The quantification values of variant peptides with and without missing values were extracted for inferring genotypic information for each sample by SMAP.The analysis was performed in the following stepsWe generated a simulated dataset to test the performance of SMAP by evaluating how many genotypes identified from MS-based proteomic data are needed for validating and correcting the sample identity. We first randomly extracted a subset of a matrix with 500 genotypes and 420 samples. We then randomly selected a sample from the matrix and permutated the genotypic information with replacement sampling at a certain probability level Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


The recently proposed Cognitive Experiential Leadership Model (CELM) states that leaders’ preference for rational thinking and behavioral coping will be related to their level of transformational leadership. The CELM was based on research that principally used cross-sectional self-report methods. Study 1 compared both self-ratings and follower-ratings of leadership styles with leaders’ self-rated thinking styles in 160 leader-follower dyads. Study 2 compared both self-ratings and coworker-ratings of leadership styles with leaders’ self-rated thinking styles for 74 leaders rated by 607 coworkers. In both Studies, leaders’ rational thinking, imaginative thinking, and behavioral coping correlated positively with their self-rated transformational leadership. However, only behavioral coping, but not rational thinking, was correlated with follower-rated (FR) transformational leadership in Study 1, and thinking styles were unrelated to other-rated transformational leadership in Study 2. These results partly support and partly challenge the CELM. Practically, this study suggests that leadership may be improved by leaders developing their capacity for behavioral coping. Would anyone argue with the proposition that leaders who think better are likely to lead better? In order to be testable, however, this proposition requires a definition of good thinking and good leadership. Recently, transformational leadership , and underlies most behavior. Like other dual-process theories, according to CET rational thinking is slow, conscious, relatively affect-free, and more useful in novel situations. In contrast, the experiential system is defined as a broad intuition-based system that encapsulates emotion, concrete reasoning, generalization, spontaneity, and imagination  and Defensiveness (>1.5 SD above the mean) scales, as specified in the CTI Manual (N = 320).Each participant was issued with a unique code number that they entered when completing measures online. Leader and follower code numbers were retained by the researcher so that paired data could be matched. A total of 245 leaders and 239 followers, among whom there were 191 matched pairs completed online questionnaires. Thirty-one pairs were omitted from the sample because of incomplete responses (>5%) or for falling outside the acceptable bounds of the Constructive Thinking Inventory’s (CTI) Validity  was higher than the average age of their followers . Gender was similar in both the leader  and follower samples . Leaders had an average tenure of 5.75 years (SD = 6.37) in their current position. Followers had an average tenure of 3.19 years (SD = 3.73) in their current position. The participants were from a range of industries with the largest sub-groups being from retail, sales, and marketing (20.0%); healthcare (15.6%); engineering, mining, and construction (14.4%); and education (9.4%).Among the 160 dyads, the average age of leaders  and CTI, and they also completed the Multifactor Leadership Questionnaire ; followers completed the MLQ-5X (other report) and other measures that are not the focus of the present study . The experiential-thinking scale contains 30 items that represent three facets of experiential thinking: imagination, emotionality, and intuition . The imagination subscale measures respondents’ preference for visual stimuli and tendency to think visually . The emotionality subscale represents respondents’ emotional reactivity . The intuition subscale represents respondents’ tendency to base their decisions on how they feel . An overall experiential thinking scale is calculated by combining these subscales. All items were presented in five-point Likert format with response options ranging from 1  to 5 (Definitely True). The REIm’s scales had good Cronbach’s alpha internal consistency reliability coefficients except for emotionality  and behavioral coping . The emotional coping factor consists of four subscales: self-acceptance, absence of negative overgeneralization, non-sensitivity, and absence of dwelling. Behavioral coping consists of three subscales: positive thinking, action orientation, and conscientiousness. Facets of destructive thinking are represented on four scales: personal superstitious thinking , categorical thinking , esoteric thinking , and naïve optimism . The CTI also includes two lie scales: defensiveness  and Validity . As noted, participants scoring outside the acceptable bounds for Validity or Defensiveness were excluded from the sample. Items were in a five-point Likert format with scores ranging from 1  to 5 (Definitely True). The CTI scales had good Cronbach’s alpha internal consistentcy reliabilities , transactional , and passive-avoidant . Transactional leadership has two subscales (contingent-reward and management-by-exception-active). Passive-avoidant leadership has two subscales , we calculated Pearson’s correlations between thinking styles and both self-rated and other-rated leadership styles. In addition, where multiple thinking styles correlated with transformational leadership, we regressed transformational leadership on thinking styles in order to examine the unique contribution of thinking styles to transformational leadership.via a logarithm transformation. No other statistical assumption breaches were found, and, for ease of interpretation, descriptive statistics are reported for the untransformed data. Principal components analysis with direct oblimin rotation was used to statistically investigate potential common methods bias for the self-report measures. There was little evidence for common methods bias, with the largest single component only accounting for 21.57% of the variance  versions of the MLQ. For self-rated passive-avoidant leadership, the skew was corrected by removing three outliers, for follower-ratings, the skew was corrected variance . Descripr = 0.26, p = 0.001). However, self-rated and follower-rated transactional  and passive-avoidant leadership were not significantly correlated . Thus, there was a weak alignment between how followers evaluated their leaders’ styles and how leaders evaluated themselves.Pearson’s product movement correlations were calculated between leaders’ self-rated and follower-rated leadership styles in order to assess the level of agreement in their ratings of leadership styles. Self-rated and follower-rated transformational leadership correlated significantly but weakly . These analyses were supplemented with relative importance analysis, with relative weights calculated in order to determine the unique contributions of each thinking style variable as a predictor of leadership style . The resF = 15.48, p < 0.001] and 11.8% of the variance in follower-rated transformational leadership . Looking at the significance of the βs and relative weights in The regression analyses found that the thinking styles accounted for 48.2% of the variance in self-rated transformational leadership  but did not account for significant variance in other-rated transformational leadership . For self-rated transformational leadership, only behavioral coping was a significant predictor in the regression , and no thinking style variables significantly predicted other-rated transformational leadership.The regression analyses found that the thinking styles accounted for 30.2% of the variance in self-rated transformational leadership  but was approaching significance in Study 2 .Theoretically, the CELM expects that rational thinking will be connected with transformational leadership because rational thinking contributes to adaptation by leaders, and the selection of a transformational leadership style is an effective adaptation . The absIt is possible that a relationship exists between leader’s preference for rational thinking and the extent of their transformational leadership that other raters – especially followers – do not detect. Rational thinking, which engages a slower thinking system, may be associated with more deliberative and less action-focused decision-making that followers may perceive as evincing less transformational leadership. This possibility is in contrast to the relationship between leaders’ preference for behavioral coping and transformational leadership. Behavioral coping is a component of how constructively leaders’ experiential thinking is used . ExperieA limitation of the current studies was the low internal consistency reliabilities obtained for some measures that were of focal theoretical interest – emotionality (Study 1) and behavioral coping (Study 2). via coaching (e.g., The studies presented in this paper aimed to provide a more methodologically robust test of the CELM  than hasng e.g., . TherefoThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Murdoch University Human Research Ethics Committee. Written informed consent for participation was obtained for this study in accordance with the national legislation and the institutional requirements.GC conceptualized the studies, collected and analyzed the data, and wrote the manuscript. SW analyzed the multi-level data in Study 2, and reviewed and edited the manuscript. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Despite the widespread use of psychotropic medications in people with autism spectrum disorder (ASD), there is limited evidence to suggest that psychotropic medications including mood stabilisers are effective in individuals with ASD.To carry out a systematic review and meta-analysis of randomised controlled trials (RCTs) that assessed the effectiveness of mood stabilisers in people with ASD.We searched the following databases: Cochrane Library, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, DARE, and ClinicalTrials.gov. In addition, we hand-searched 12 relevant journals. We used the Cochrane Risk of Bias and Jadad scores to assess the quality of included RCTs. We carried out a meta-analysis using a random-effects model.We included eight RCTs  that included a total of 310 people with ASD, primarily children. Outcomes were based on core and associated ASD symptoms including irritability and aggression but not bipolar disorder. Only two small studies (25%) from the same group showed definite superiority over placebo and one over psychoeducation alone. Meta-analysis of pooled data on the Aberrant Behaviour Checklist-irritability, Clinical Global Impression Scale-improvement, and Overt Aggression Scale (OAS)/OAS-modified did not show any significant inter-group difference. The rates of adverse effects did not show any significant inter-group difference.Given the methodological flaws in the included studies and the contradictory findings, it is difficult to draw any definitive conclusion about the effectiveness of mood stabilisers to treat either ASD core symptoms or associated behaviours. Robust large-scale RCTs are needed in the future to address this issue.PROSPERO registration: CRD42021255467 on 18 May 2021. Relevant journals in the field of ASD , psychopharmacology , and IDD  were searched for relevant articles published between January 2000 and June 2021. Bibliographies of identified articles were also searched. The search terms used are described in Supplementary Appendix 1 available at https://doi.org/10.1192/bjo.2022.18.We followed PROSPERO guidelines and the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) checklist to develop our protocol and search strategy. English articles published between January 1985 and June 2021 were searched in the following databases: Cochrane Library, MEDLINE, Embase, CINAHL, PsycINFO, ERIC, DARE, and 17 All RCTs in ASD involving mood stabilisers were included. Two authors (M.R. and A.R.) were blind to each other's selection. Any discrepancy was resolved by discussion. The third author (S.D.) did not need to arbitrate because of the full consensus between the two authors (M.R. and A.R.) at the end.The search strategy for the definitive systematic review was finalised following a scoping search. After the initial systematic review search, articles on non-human subjects and duplicates were removed. The titles, abstracts and full articles were screened independently by two authors (M.R. and A.R.) to identify potential articles for inclusion, using pre-piloted eligibility criteria (Supplementary Appendix 2) designed as per Cochrane Library guidelines.All participants had a diagnosis of ASD, defined using standardised or unstandardised criteria, or based on a clinical assessment. No age limit was applied. Studies that included people with IDD were included if the participants also had a confirmed diagnosis of ASD. Only RCTs with more than ten participants were included.Mood stabilisers including lithium and anti-epileptic medications were included in the study. Other classes of psychotropic medications were excluded.Only RCTs that compared the effectiveness of mood stabiliser medications with a placebo or another form of intervention, e.g. another medication or a psychoeducation programme (PEP) were included. Non-RCT studies were excluded as they are likely to produce a bias.Any standardised validated outcome measures to assess core symptoms of ASD such as language and communication impairment and restrictive repetitive behaviours, any other associated behaviours such as agitation, aggression, irritability, hyperactivity, etc., and symptoms of any psychiatric disorder such as bipolar disorder were included.pro forma modified from a Cochrane template (Supplementary Appendix 3).18 Data extraction started on 12 July 2021. Mendeley19 was used to organise data from the included articles. A third author (S.D.) acted as an arbitrator if there was any major discrepancy in extracted data by R.L. and O.T. The quality of the included papers was assessed independently by R.L. and O.T. using the Cochrane risk-of-bias tool20 (Supplementary Appendix 4) and the Jadad score.21 Disagreements were resolved by discussion between the two authors with the third author (S.D.) acting as an arbitrator if necessary. Data on adverse effects were also extracted using the same data extraction pro forma.Data were independently extracted by two authors (R.L. and O.T.) using a data extraction 22 One author (B.L.) approached authors of five included studies asking for missing data, but as none of them responded, data from these RCTs on the Overt Aggression Scale (OAS)/OAS-modified (OAS-M) and Clinical Global Impression Scale-improvement (CGI-I) could not be pooled for meta-analysis.27Findings were presented using a narrative format . Where i28 was used to conduct the meta-analysis. Meta-analysis was carried out for RCTs that had the same outcome measures. Funnel plots and Egger's test29 were used to assess publication bias (Supplementary Appendix 5).RevMan version 5.3 software for Windows 1030 was used to assess the overall quality of the systematic review and meta-analysis. No ethical approval was necessary for this review.The quality of evidence was assessed across the risk-of-bias domains of consistency, directness, precision and publication bias. Any studies that were deemed to be of low quality were excluded from the review. A Measurement Tool to Assess Systematic Reviews 2 27 one each on lamotrigine,23 topiramate,31 and sodium valproate32 respectively; and two on levetiracetam.33The literature search produced 1571 results, from which 383 duplicates and 7 non-human studies were removed. A total of 1181 titles were screened, and 91 abstracts were selected for further screening. In the abstract screening, 74 articles were excluded and only 17 articles underwent full-text screening. Nine further articles were removed; reasons for removal can be found in 24 Almost all (n = 6) RCTs included children and adolescents only (age range from 3 to 18 years); two RCTs included children, adolescents and adults32 (age range from 20–40 years). Participants with IDD were included in four RCTs,32 but IQ data were not presented in the other four.33 Only one study32 recruited predominantly children with IDD (24 of the 30 participants) and reported data on the IDD participants separately. Most studies were parallel-design placebo-controlled double-blind RCTs, except for one which was single-blind and not placebo-controlled.33 One study was a non-inferiority study in which divalproex sodium was compared with risperidone rather than placebo,27 and one was an add-on study where either topiramate or placebo was added to risperidone.31 Five studies (63%)32 were from the USA and there was one each from China,33 Ukraine27 and Iran,31 respectively. Of the eight studies, four (50%) were part-funded by pharmaceutical companies;32 however, the source of funding was not mentioned in two studies (25%) and, therefore, sponsorship by pharmaceutical companies could not be ruled out.33 The remaining two studies (25%)31 were not funded by pharmaceutical companies.The studies included 310 participants in total, with 209 male and 62 female participants. One study did not specify the number of males and females in their study.27 and one each for levetiracetam26 and sodium valproate.32 The DSM-IV text revision (DSM-IV-TR) was used in two studies, involving divalproex sodium25 and topiramate,31 respectively. DSM-V criteria were used in one study involving levetiracetam.33 Furthermore, seven studies used the Autism Diagnostic Interview-Revised (ADI-R)  and five used the Autism Diagnostic Observation Schedule (ADOS)  to confirm the diagnosis of ASD.Different studies used different methods to diagnose ASD. The DSM-IV was used in four studies, two involving divalproex sodium,32 studies recruited people with IDD, but only one study on valproate provided separate data on participants with IDD .32 The type of scale used to assess IQ was not mentioned in one study.24 IQ was not specified in four studies,33 although two studies suggested that participants with IDD could be included if a diagnosis of ASD could be confirmed.31Fourwith IDD .32 Mean 34 followed by the CGI-I,35 and Children-Yale-Brown Obsessive Compulsive Scale (C-YBOCS)36 and OAS/OAS-M.38 ABC was used in five RCTs.31 CGI-I was used in four RCTs,32 while C-YBOCS26 and OAS-M were used in three RCTs,32 respectively. Other outcome measures used are shown in 39A variety of outcome measures were used in the included studies. The most common outcome measures used were the Aberrant Behaviour Checklist (ABC)/ABC-irritability (ABC-I),25 compared divalproex sodium with placebo, and one27 compared it with risperidone. In the first placebo-controlled study of 12 children and one adult (age 40 years), divalproex sodium was significantly better than placebo in improving repetitive behaviours as measured by C-YBOCS.24 In the second placebo-controlled trial by the same group on 27 children, a marginally statistically significant improvement was seen with divalproex sodium compared with placebo in reducing irritability when measured by the ABC scale, but no such significant reduction in irritability  was found when measured by OAS-M.25 This finding did not change when IQ difference was controlled for. The responder rate by CGI-I for irritability (62.5%) and general core autism symptoms domains  were higher in the divalproex sodium group, but only the former was significant. Responders had higher valproate blood levels compared with non-responders. There were also no significant inter-group differences between divalproex sodium and placebo groups on daily functioning as measured by VABS40 and on repetitive behaviours as measured by C-YBOCS.25 By contrast, the third study on divalproex sodium on 86 children found that risperidone was significantly better than divalproex sodium in reducing irritability, stereotypy, hyperactivity and non-compliant behaviours.27 Risperidone was also significantly better than divalproex sodium in improving irritability and aggression as measured by OAS-M and improving ASD symptoms as measured by CGI-I. However, the Jadad score for this study was poor.All three RCTs on divalproex sodium used a double-blind parallel design. Two studies41 Autism Behaviour Checklist (AUBC),42 and Pre-Linguistic ADOS (PL-ADOS).43 Lamotrigine, however, was marginally better than placebo at improving adaptive social behaviours as measured by VABS.23Only one study on 28 children compared lamotrigine with placebo and found no significant inter-group differences between the placebo and lamotrigine groups on ASD core symptoms and associated behaviours as measured by the ABC, Childhood Autism Rating Scale (CARS),33 whereas the other was a 10 week double-blind RCT which compared levetiracetam with placebo.26 In the 6 month RCT trial on 67 children, the combination of levetiracetam and PEP showed significantly better improvements in ASD core symptoms as measured by CARS and AUBC, and other behaviours including expressive language, receptive language and cognitive verbal/preverbal function as measured by the Psychoeducational Profile-third edition (PEP-3 Chinese version).44 The combination was also significantly better than PEP alone at controlling subclinical epileptiform discharge, which was present at baseline for all participants but absent during follow-up in 24 of the 32 participants (75%) in the intervention group compared with five of the 35 participants (14.3%) in the control group.33 In the other 10 week RCT trial of 20 children, no significant difference was found between levetiracetam and placebo in improving associated behaviours, particularly irritability, lethargy, stereotypy, hyperactivity and inappropriate speech according to parent-rated ABC scores. However, on teacher ratings, only the irritability subscale showed a significant difference, with the placebo significantly better than levetiracetam at improving irritability. No inter-group significant difference was seen for repetitive behaviours as measured by C-YBOCS or change in CGI-I.26Two RCTs investigated the efficacy of levetiracetam. One study was a 6 month physician-blinded RCT which compared a combination of levetiracetam and PEP with PEP alone,31 was included that compared topiramate add-on versus placebo add-on to risperidone in 40 children. Risperidone was titrated up to 2 mg/day for children weighing less than 40 kg and 3 mg/day for children weighing above 40 kg. The combination of topiramate and risperidone was significantly better than the combination of risperidone and placebo at reducing irritability, stereotypy and hyperactivity/noncompliant behaviours, but no significant inter-group differences were observed for inappropriate speech and lethargy/social withdrawal.Only one RCT32Only one RCT was included that compared sodium valproate with placebo in an 8 week parallel-design double-blind placebo-controlled trial on 30 children, adolescents and adults (up to age 20 years). No significant inter-group difference was reported in irritability as measured by ABC, aggressive behaviours as measured by OAS, or CGI-I or CGI-severity.31 and one study on sodium valproate.32 In one of the divalproex sodium trials,25 one person had clinically significant weight gain in the intervention group, but overall there was no significant inter-group difference in the rate of weight gain. In another study on levetiracetam,33 in the treatment group one child each reported irritability, fatigue and somnolence, and anorexia, respectively, which resolved after dose reduction or spontaneously. Only two participants dropped out owing to adverse events; one was in the divalproex sodium trial25 and the other was in the sodium valproate trial32  had a high risk for at least one item, of which one (12.5%) showed high risk for two items , the Egger's test of publication bias was not significant, suggesting no publication bias (P = 0.95). Eggers’ test for publication bias could not be conducted for the OAS/OAS-M and CGI-I meta-analyses, as the meta-analyses included only two studies each. The funnel plots for the OAS/OAS-M and CGI-I meta-analyses were also difficult to interpret as only two studies were included. Therefore, large-scale RCTs on the use of mood stabilisers in ASD are needed in the future. The overall quality of the systematic review and the meta-analysis was high based on the AMSTAR 2 scale, showing only one non-critical weakness (Supplementary Appendix 6).The Cochrane risk-of-bias analysis showed that fivewo items . Six of 27 and one each on topiramate,31 sodium valproate32 and levetiracetam,26 respectively. However, as the topiramate study was an add-on study and the rest were on a single medication, the data from the former study were analysed separately in a subgroup analysis  were available from five of the eight RCTs (62.5%): two on divalproex sodium,analysis . For CGIP = 0.40). Only one study that used topiramate as an add-on therapy with risperidone was significant, whereas four studies comparing mood stabiliser monotherapy with placebo were not significant. However, heterogeneity was high (2I = 96%). A sensitivity analysis and removal of studies with a high risk of bias did not show any significant inter-group difference. The pooled data showed no statistically significant inter-group difference for either the OAS/OAS-M  or the CGI-I ; although the heterogeneity was low for OAS/OAS-M at 2I = 0%, it was high for CGI-I at 2I = 86% , including three on divalproex sodium, two on levetiracetam, and one each on lamotrigine, sodium valproate and topiramate (as an add-on to risperidone). This compares with Hirota and colleagues’ systematic review that included seven RCTs on anti-epileptic medications involving 117 children and adolescents, including two studies on divalproex sodium; one each on sodium valproate, lamotrigine and levetiracetam; and one each combining topiramate and risperidone, and sodium valproate and fluoxetine.25 that used a placebo-controlled parallel-design RCT, but the third was a non-inferiority comparison between divalproex and risperidone rather than placebo.27 The two studies on levetiracetam in our review both used a parallel design, but one was placebo-controlled and double-blind,26 and the other was a single-blind (physician-blind) comparison of combined levetiracetam and PEP with PEP alone.33 Single studies on lamotrigine23 and sodium valproate32 used a parallel-design placebo-controlled RCT, whereas the single study on topiramate was a comparison between two groups, both of which received risperidone, one with added topiramate and the other with added placebo.31Of the three studies included in our review on divalproex sodium, two were from the same group36 and language impairment and other associated behaviours such as irritability and aggression as measured by ABC-C and OAS/OAS-M.38Although our search terms included bipolar and other psychiatric disorders, and the review was on mood stabilisers, surprisingly we did not find any RCT on bipolar or any other psychiatric disorder. The included studies reported the effect of mood stabilisers primarily on ASD core symptoms such as repetitiveness as measured by CYBOCS32 which included participants between 6 and 20 years and another24 which included one adult (age 40 years) and 12 children.24 Some studies mentioned the IQ range of participants, but others did not. Even in those studies that included participants with IDD, no separate data were presented on participants with IDD, except in one study32 which included predominantly children with IDD. Most studies were from the USA and were part-funded by pharmaceutical companies, although the influence of pharmaceutical companies on the findings of these studies is not known.Although our search terms included studies on people with ASD of all ages, the search generated primarily studies involving children, except one study25 However, the third study found a significantly better outcome in the risperidone group compared with the divalproex group.27 There is some preliminary evidence to suggest that risperidone and to some extent aripiprazole may be effective in treating associated behaviours such as irritability and aggression in children with ASD.47 Of the two studies on levetiracetam included in our review, the combination of levetiracetam and PEP resulted in a significantly better outcome when compared with PEP alone, including improvements in cognition and also in subclinical electroencephalogram (EEG) abnormalities which were present at baseline in all participants.33Two studies on divalproex versus placebo showed a significantly better outcome in the treatment group compared with the placebo group on most outcome measures but not all.25 with a very small number of participants with EEG abnormalities, no study in our review reported the effect of the intervention on subclinical EEG abnormalities. However, the second study on levetiracetam showed no significant inter-group difference.26Apart from one study23 showed a marginally significant difference in improvement in adaptive social behaviours but did not show any inter-group difference for any of the other outcomes. The only placebo-controlled double-blind RCT included in our review on sodium valproate32 did not show any significant inter-group difference in either irritability or aggression as measured by OAS and ABC-I.The only placebo-controlled double-blind RCT included in our review on lamotrigine31 found equivocal results. The combination of risperidone and topiramate was significantly better than the combination of risperidone and placebo in reducing irritability, stereotypy and hyperactivity/noncompliance behaviours, but no inter-group difference was observed for inappropriate speech and lethargy/social withdrawal.The only placebo-controlled double-blind add-on RCT included in our review on topiramate33 showed a positive result when compared with PEP alone, one study on lamotrigine23 showed a marginal positive result (P = 0.045) when compared with placebo, and two studies on divalproex versus placebo by the same group25 showed a positive result. Although one of the studies on divalproex versus placebo showed a significant result according to the primary outcome measure, it failed to show any significant inter-group difference according to four secondary outcome measures.25 This was also the case for the lamotrigine study, which failed to show any significant inter-group difference according to five other outcome measures.23 The sample sizes were also very small in the two studies on divalproex versus placebo . Single placebo-controlled studies on valproate,32 and levetiracetam26 each showed negative results, and one comparison study of divalproex sodium with risperidone showed a negative finding for divalproex.27 The only study on topiramate showed an equivocal result.31Overall, one study on levetiracetamn = 2),27 sodium valproate (n = 1)32 and levetiracetam (n = 1)26 and one combination study with topiramate and risperidone31 for a meta-analysis. Similarly, data could be pooled for meta-analysis from two studies each using OAS/OAS-M and CGI-I data, respectively.32 None of the meta-analyses showed any statistically significant inter-group difference. Although many different adverse events were reported in the included studies in both the intervention and the control groups, only two studies32 showed a significant inter-group difference.It was possible to pool data on ABC-I scores from four placebo-controlled monotherapy studies on divalproex sodium  may still not have provided adequate power to detect a small effect size. However, very small effect sizes may not be clinically significant, as one has to weigh the adverse effects that will affect the person's quality of life against improvement in behaviour.The sample sizes in individual studies included in this review were small ;Also, two of the meta-analyses in our study showed high heterogeneity, and a sensitivity analysis did not change these findings. Apart from the two small placebo-controlled studies carried out by the same group on divalproex sodium, no study showed any significant inter-group difference.Another problem was the short follow-up period used in the included studies, which may not have allowed enough time to show an effect of the intervention. Although one study of a combination of levetiracetam and PEP followed up participants at 6 months, other studies used a much shorter follow-up period (8–18 weeks). However, the former study was not placebo-controlled or double-blind, and in the same study both groups showed improvement at follow-up.There was also a problem with the dosages and the blood levels of the medications used for dose titration in the included studies; these may not have been adequate to detect a small effect size. However, a balance has to be struck between efficacy and adverse effects in determining the correct dose.25 Similarly, in some studies, scoring by parents and teachers on the same scale showed contradictory results.26The use of different outcome measures also made interpretation of findings difficult; in particular, in some studies, different scales used to measure the same symptoms gave contradictory results.49One has to also consider the potential of strong placebo effects on most of the outcome measures, as shown in Luu and colleagues’ review of RCTs on one neurodevelopmental disorder.www.crd.york.ac.uk/prospero).Our systematic review used a very stringent methodology, including hand-searching of 12 relevant journals, and should have captured the most relevant RCTs. This is reflected in the full score in AMSTAR2 apart from one noncritical item for not including grey literature. We excluded grey literature as it was not possible to assess its quality using the Cochrane Risk of Bias instrument. It is still possible that relevant papers may have been missed; in addition, only English-language papers were included. Another problem is that studies with positive findings tend to find their way to publication more often than those with negative findings, creating a publication bias. However, Egger's test in our review did not show publication bias. Another strength of this review is that it was registered on PROSPERO so the study protocol can be accessed by anyone  of the eight RCTs. Although the meta-analysis of the OAS/OAS-M forest plot showed no heterogeneity, the forest plot involving data from ABC-I and CGI-I scores showed high heterogeneity (86–96%). Another major problem was the small sample sizes in most studies. For example, no study included more than 100 participants, and only two (25%) recruited more than 50 participants. This made the findings from most of these studies difficult to generalise. The quality of individual studies was poor. The Cochrane risk-of-bias analysis showed that five studies (63%) scored as high risk for at least one item, of which one (13%) showed high risk for two items. Six studies (75%) received a Jadad score of less than five, making the interpretation of findings difficult. Also, a number of studies were part-funded by pharmaceutical companies, although their influence on the study findings is unknown.