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Weight-inducing psychotropic treatments are risk factors for age-related diseases such as cardiovascular disorders, which are associated with both inflammation and telomere length shortening. With a longitudinal design, the present study evaluates telomere length trajectories after 1 year of weight-inducing psychotropic medication, accounting for weight changes and the inflammatory biomarker high-sensitivity C-Reactive Protein (CRP). Among 200 patients, an overall median telomere shortening of -41.2 bp was observed (p = 0.014), which is comparable with the general populations yearly telomere attrition. Linear regression showed on average -93.1 and -58.9 bp of further telomere shortening per five units of BMI for BMI values < or ≥30 kg/m2, respectively (p = 0.003 and p = 0.009, respectively). Importantly, the overall telomere shortening was predicted to be increased four-fold among patients with low baseline weight (i.e., 50 kg) and with clinically relevant weight gain (≥ 7%) after 1 year of treatment (interaction term between relevant weight gain and baseline weight: +6.3 bp, p = 0.016). Patients with relevant weight gain showed greater CRP levels (+ 49%; p = 0.016), and a telomere shortening of -36.2 bp (p = 0.010) was estimated whenever CRP level doubled. Mendelian randomization using UKBiobank data showed a causal effect of BMI on telomere shortening, notably stronger among patients receiving weight-inducing psychotropic treatments (n = 9798) than among psychiatric patients without such drugs (n = 16228) and non-psychiatric controls (n = 252932) (beta: -0.37, -0.12, -0.06, respectively; p = 0.004, p

BackgroundHospital patient registries provide substantial longitudinal data sets describing the clinical and medical health statuses of inpatients and their pharmacological prescriptions. Despite the multiple advantages of routinely collecting multidimensional longitudinal data, those data sets are rarely suitable for advanced statistical analysis and they require customization and synthesis. ObjectiveThe aim of this study was to describe the methods used to transform and synthesize a raw, multidimensional, hospital patient registry data set into an exploitable database for the further investigation of risk profiles and predictive and survival health outcomes among polymorbid, polymedicated, older inpatients in relation to their medicine prescriptions at hospital discharge. MethodsA raw, multidimensional data set from a public hospital was extracted from the hospital registry in a CSV (.csv) file and imported into the R statistical package for cleaning, customization, and synthesis. Patients fulfilling the criteria for inclusion were home-dwelling, polymedicated, older adults with multiple chronic conditions aged ≥65 who became hospitalized. The patient data set covered 140 variables from 20,422 hospitalizations of polymedicated, home-dwelling older adults from 2015 to 2018. Each variable, according to type, was explored and computed to describe distributions, missing values, and associations. Different clustering methods, expert opinion, recoding, and missing-value techniques were used to customize and synthesize these multidimensional data sets. ResultsSociodemographic data showed no missing values. Average age, hospital length of stay, and frequency of hospitalization were computed. Discharge details were recoded and summarized. Clinical data were cleaned up and best practices for managing missing values were applied. Seven clusters of medical diagnoses, surgical interventions, somatic, cognitive, and medicines data were extracted using empirical and statistical best practices, with each presenting the health status of the patients included in it as accurately as possible. Medical, comorbidity, and drug data were recoded and summarized. ConclusionsA cleaner, better-structured data set was obtained, combining empirical and best-practice statistical approaches. The overall strategy delivered an exploitable, population-based database suitable for an advanced analysis of the descriptive, predictive, and survival statistics relating to polymedicated, home-dwelling older adults admitted as inpatients. More research is needed to develop best practices for customizing and synthesizing large, multidimensional, population-based registries. International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2019-030030

Background: In recent years, there has been a growing interest in the investigation of very old individuals. However, various challenges arise when collecting data from this age group. Given potential health and cognitive impairments and the difficulty of retrieving accurate self-reported data, involving individuals knowledgeable of the target person as proxy respondents are an invaluable solution. The primary goal of this study is to explore the association between proxy-centenarian relationship and item nonresponse, a pivotal data quality measure. Data and methods: We used data from the phone study conducted within SWISS100, a study on centenarians in Switzerland, focusing on a subsample of 94 centenarians and 75 proxy respondents. We compared characteristics of centenarians who provided self-reports with those interviewed through proxy respondents using descriptive analyses, and Firth's regression models to investigate the association of different types of proxies (children, other relatives, and non-relatives) with item nonresponse. Results: We noted differences between centenarians participating themselves in interviews and those represented by proxies, observing higher proportions of men and private home residents in the self-report group. There was a low prevalence of item nonresponse among proxy respondents. Proxy type significantly impacted item nonresponse, particularly with non-relatives showing notably higher unanswered question rates. A robust association between non-relatives as proxies and increased item nonresponse persisted even when controlling for potential confounding factors. Discussion and conclusions: This study enhances the understanding of data quality, particularly item nonresponse, and highlights the impact of the proxy-centenarian relationship when studying the oldest-old. The findings stress the importance of carefully choosing proxy respondents, preferably children, to improve data quality and inclusivity for those individuals hard to reach or to interview. Future research should investigate various data quality indicators and rely on larger samples to enhance the representation and methodological rigour in the study of the oldest-old. [ABSTRACT FROM AUTHOR]

Introduction: Major depressive disorder (MDD) exhibits heterogeneity in treatment response. Objective: This exploratory analysis aims to evaluate the differential changes in individual items of the MADRS between melancholic MDD (M-MDD) and unspecified MDD (U-MDD) following electroconvulsive therapy (ECT). Methods: The study included 23 patients with unipolar MDD who received ECT. Patients were classified as M-MDD or U-MDD according to DSM-5 criteria. MADRS scores were assessed at baseline and one-month post-ECT. Differences between subtypes were analyzed using the Wilcoxon test and multiple linear regression. Results: Among 23 participants receiving ECT for MDD, 10 had M-MDD and 13 had U-MDD. Baseline MADRS items showed significantly higher scores in the M-MDD group, except for reported sadness, suicidal ideation, and concentration difficulties. Total MADRS score reduction was significantly greater in the M-MDD group. This decline was especially pronounced in M-MDD patients for specific items, including apparent sadness, inability to feel, pessimistic thoughts, sleep disturbances, reduced appetite, and concentration difficulties, after adjusting for age and sex. Conclusion: MADRS score reductions were more substantial for M-MDD than U-MDD in both total and specific items following one month of ECT. Further research with larger samples is needed to clarify MADRS response differences after ECT between melancholic and unspecified depressive subtypes. [ABSTRACT FROM AUTHOR]