Application of SWATH mass spectrometry in the identification of circulating proteins does not predict future weight gain in early psychosis.

Weight gain is a common consequence of treatment with antipsychotic drugs in early psychosis, leading to further morbidity and poor treatment adherence. Identifying tools that can predict weight change in early psychosis may contribute to better-individualised treatment and adherence. Recently we showed that proteomic profiling with sequential window acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry (MS) can identify individuals with pre-diabetes more likely to experience weight change in relation to lifestyle change. We investigated whether baseline proteomic profiles predicted weight change over time using data from the BeneMin clinical trial of the anti-inflammatory antibiotic, minocycline, versus placebo. Expression levels for 844 proteins were determined by SWATH proteomics in 83 people (60 men and 23 women). Hierarchical clustering analysis and principal component analysis of baseline proteomics data did not reveal distinct separation between the proteome profiles of participants in different weight change categories. However, individuals with the highest weight loss had higher Positive and Negative Syndrome Scale (PANSS) scores. Our findings imply that mode of treatment i.e. the pharmacological intervention for psychosis may be the determining factor in weight change after diagnosis, rather than predisposing proteomic dynamics.

Influences on the use of antidepressants in primary care: All England general practice-level analysis of demographic, practice-level and prescriber factors.

INTRODUCTION: General practice (GP) antidepressants (ADs) prescribing in England has almost doubled in the past decade: how does location, GP characteristics, and prescribing selection influence antidepressant prescribing rate (ADPR) and growth. METHODS: Stepwise multivariate regression analysis was applied to national public relevant data for each general practice to establish associations between these factors and ADPR. The regression coefficient was applied to the actual change in the number of different ADs and costs/dose to extrapolate the impact of these on growth. RESULTS: In 2017-2018, 2.1 billion doses of antidepressant were prescribed into a population of 52 million people in 6,146 larger practices. In the model, location demographics accounted for 62% of the variation in ADPR: including practice size and health raised this to 71%, and local prescribing behaviour to 80%. Practices using more different drugs and lower-cost/dose had higher ADPR. Extrapolation showed that 40% of growth in ADPR could be attributed to the historic changes in these factors. CONCLUSIONS: While practice location factors do impact on AD prescription rates, local long-term physical health condition prevalence and prescribing behaviours are almost as important. We hope that our findings can provide insights that are helpful to local clinical behaviour and medicines management.

Seasonal Variation in Antidepressant Prescribing: Year on Year Analysis for England.

OBJECTIVE: The number of prescriptions for antidepressants in England has almost doubled in the past decade. The objective of this study was to examine if this growth and seasonal variation in prescribing rates of different antidepressants by general practice are linked. METHOD: The number of prescriptions and quantity of antidepressants prescribed each month between January 1, 2014, and December 31, 2019 from the general practice prescribing data reports for each British National Formulary code and practice were analyzed. The data were aggregated to obtain the monthly total and analyzed to identify patterns by medication. RESULTS: : We included all oral antidepressant medications prescribed in England in 2019 for a total of 74 million prescriptions, which was equivalent to 2.36 billion defined daily doses. Monthly variation in daily prescribing percentage of the annual average over the year in total and antidepressant medications showed small but generally consistent seasonal variation, an average of up to 1.61% in total items and 1.47% in defined daily dose (highest prescribing in November/December). The seasonality varied by agents, with agents having highest seasonality also showing the most growth (r2 = 0.5, P = .002). This relation was strongest for mirtazapine and sertraline. There was a clear relation between average seasonality and year on year growth in antidepressant medication prescribing for 2016-2019 (r2 = .536, P = .002). CONCLUSIONS: The study confirmed seasonal variation in antidepressant prescribing with a peak in November/December. Growth in the antidepressant prescribing year on year was related to seasonality and requires further scrutiny in terms of understanding the factors that underlie the seasonal variation seen.

Quantifying the Impact of Patient-Practice Relationship Quality on the Levels of the Average Annual Antidepressant Practice Prescribing Rate in Primary Care in England.

OBJECTIVE: The National Health Service (NHS) in England makes data on demographics, prescribing, location, and specific conditions in general practice (GP) practices publicly available. The GP Patient Survey captures patients' views of their GP practice. The objective of this study was to determine how patient experience of a GP may relate to the volume of antidepressant prescribing at that practice. METHODS: We examined how antidepressant prescribing rates relate to specific NHS GP Patient Survey metrics. Postal questionnaires were sent out to 2.2 million adults registered with GP practices in England from January to March 2018. The national survey response rate was 34.1%. RESULTS: The average annual antidepressant practice prescribing rate (AAAPPR) was 0.11, with 90% of practices falling between 40% and 160% of this value. Practices with a higher overall experience rating prescribed more antidepressants. Practices more effective in empowering their patients, as assessed by "How confident are you that you can manage any issues arising from your condition (or conditions)," prescribed less antidepressants. The difference between the lowest and highest decile of prescribing for this response was over 10% and potentially modified by changing practice approach. CONCLUSIONS: There are opportunities to optimize antidepressant prescribing in GP practices. Antidepressants are a key facet of depression treatment. Our findings show that patient empowerment is a key modulator of antidepressant prescribing.

Service based comparison of group cognitive behavior therapy to waiting list control for chronic fatigue syndrome with regard to symptom reduction and positive psychological dimensions.

BACKGROUND: Although chronic fatigue syndrome (CFS) sometimes referred to as myalgic encephalomyelitis (ME) is a very challenging condition to treat, there is evidence that individual cognitive behavioral therapy (ICBT) can be effective for treatment and management of its symptoms. Furthermore, group cognitive behavioral therapy (GCBT) is emerging as promising treatment for the condition.The aim of the present study was to explore further the effectiveness of GCBT in a routine clinical setting and to investigate associated positive psychological effects related to GCBT. METHODS: In this pragmatic, non-randomized, controlled trial, 28 people acted as their own waiting list control by completing a range of measures 8 weeks prior to taking part in the GCBT. The intervention consisted of 8 consecutive weeks of 2.5-hour sessions. RESULTS: Repeated measures analysis of covariance revealed significant improvements in physical fatigue (F = 28.31, P < .01, effect size d = 0.52), mental fatigue (F = 7.72, P < .01, effect size d = 0.22), and depressive symptoms (Beck depression inventory-fast screen for medical individuals [BDI-FS]: F = 11.43, P < .01, effect size d = 0.30; hospital anxiety and depression scale [HADS-D]: F = 16.72, P < .01, effect size d = 0.38) compared with the waiting list. Improvements in quality of life (F = 7.56, P < .01, effect size d = 0.23), hope (F = 15.15, P < .01, effect size d = 0.36), and optimism (F = 8.17, P < .01, effect size d = 0.23) were also identified, but no change was reported for anxiety levels. Global outcome measures revealed that the majority of the individuals found the treatment beneficial and were satisfied with the results. CONCLUSION: GCBT is a beneficial and cost-effective treatment that individuals find amenable in routine clinical practice for CFS. Additionally we have described important effects emerged on positive psychological dimensions such as hope and optimism potentially enhancing the overall benefit.