The risk of diabetes and HbA1c deterioration during antipsychotic drug treatment: A Danish two-cohort study among patients with first-episode schizophrenia.

BACKGROUND: Antipsychotics increase the risk of developing diabetes, but clinical trials are not generalizable with short follow-up, while observational studies often lack important information, particularly hemoglobin A1c (HbA1c). METHODS: We followed two Danish cohorts with schizophrenia. First, using Danish nationwide registers, we identified all individuals diagnosed with first-episode schizophrenia (FES) between 1999 and 2019 (n = 31,856). Exposure was a redeemed prescription for an antipsychotic, and the outcome was diabetes, defined via hospital-based diagnosis and redeemed prescriptions for glucose-lowering drugs. Adjusted Cox regression calculated hazard rate ratios (HRR). Second, using data from the Central Denmark Region, we identified all individuals diagnosed with FES from October 2016 to September 2022 (n = 2671). Using a within-subject design, we analyzed the change in HbA1c during the 2 years after initiation of specific antipsychotics compared to the 2 years before. RESULTS: In the nationwide cohort, 2543 (8.0%) individuals developed diabetes (incidence rate = 9.39 [95% CI = 9.03-9.76] per 1000 person-years). Antipsychotics, compared to periods without, were associated with an increased risk of developing diabetes (HRR = 2.04, 95% CI = 1.75-2.38). We found a dose-response association, particularly for second-generation antipsychotics, and different risk rates for specific antipsychotics. In the Central Denmark Region cohort, a total of 9.2% developed diabetes but mean HbA1c levels remained stable at 37 mmol/mol during the 2 years after initiation of antipsychotic medication. CONCLUSION: This comprehensive real-world two-cohort study emphasizes that diabetes affects almost 10% of patients with FES. Antipsychotics increase this risk, while HbA1c deterioration requires longer treatment. These findings are important for clinicians and young patients with FES.

Lexical stability of psychiatric clinical notes from electronic health records over a decade.

OBJECTIVE: Natural language processing (NLP) methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice - as well as the systems and databases in which clinical notes are recorded and stored - change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models. Despite its importance, the stability of clinical notes over time has rarely been tested. METHODS: The lexical stability of clinical notes from the Psychiatric Services of the Central Denmark Region in the period from January 1, 2011, to November 22, 2021 (a total of 14,811,551 clinical notes describing 129,570 patients) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics. RESULTS: We find lexical stability of the clinical notes over time, with minor deviations during the COVID-19 pandemic. Out of 2988 data points, 17 possible changepoints (corresponding to 0.6%) were detected. The majority of these were related to the discontinuation of a specific note type. CONCLUSION: We find lexical and syntactic stability of clinical notes from psychiatric services over time, which bodes well for the use of NLP for predictive modelling in clinical psychiatry.

Monitoring of COVID-19 pandemic-related psychopathology using machine learning.

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright 'COVID-19-related psychopathology'. Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.

The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders.

BACKGROUND: The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years. METHODS: PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011, to October 28, 2020 (n = 119 291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests, etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation. DISCUSSIONS: We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative data set. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.

Splice cassette II of Na+,HCO3(-) cotransporter NBCn1 (slc4a7) interacts with calcineurin A: implications for transporter activity and intracellular pH control during rat artery contractions.

Activation of Na(+),HCO3(-) cotransport in vascular smooth muscle cells (VSMCs) contributes to intracellular pH (pH(i)) control during artery contraction, but the signaling pathways involved have been unknown. We investigated whether physical and functional interactions between the Na(+),HCO3(-) cotransporter NBCn1 (slc4a7) and the Ca(2+)/calmodulin-activated serine/threonine phosphatase calcineurin exist and play a role for pHi control in VSMCs. Using a yeast two-hybrid screen, we found that splice cassette II from the N terminus of NBCn1 interacts with calcineurin Aß. When cassette II was truncated or mutated to disrupt the putative calcineurin binding motif PTVVIH, the interaction was abolished. Native NBCn1 and calcineurin Aß co-immunoprecipitated from A7r5 rat VSMCs. A peptide (acetyl-DDIPTVVIH-amide), which mimics the putative calcineurin binding motif, inhibited the co-immunoprecipitation whereas a mutated peptide (acetyl-DDIATAVAA-amide) did not. Na(+),HCO3(-) cotransport activity was investigated in VSMCs of mesenteric arteries after an NH4(+) prepulse. During depolarization with 50 mM extracellular K(+) to raise intracellular [Ca(2+)], Na(+),HCO3(-) cotransport activity was inhibited 20-30% by calcineurin inhibitors (FK506 and cyclosporine A). FK506 did not affect Na(+),HCO3(-) cotransport activity in VSMCs when cytosolic [Ca(2+)] was lowered by buffering, nor did it disrupt binding between NBCn1 and calcineurin Aß. FK506 augmented the intracellular acidification of VSMCs during norepinephrine-induced artery contractions. No physical or functional interactions between calcineurin Aß and the Na(+)/H(+) exchanger NHE1 were observed in VSMCs. In conclusion, we demonstrate a physical interaction between calcineurin Aß and cassette II of NBCn1. Intracellular Ca(2+) activates Na(+),HCO3(-) cotransport activity in VSMCs in a calcineurin-dependent manner which is important for protection against intracellular acidification.