Older adults' coping strategies during the COVID-19 pandemic - a longitudinal mixed-methods study.

Introduction: Older age is a main risk factor for severe COVID-19. In 2020, a broad political debate was initiated as to what extent older adults need special protection and isolation to minimize their risk for SARS-CoV-2 infection. However, isolation might also have indirect negative psychological (e.g., loneliness, stress, fear, anxiety, depression) or physical (e.g., lack of exercise, missing medical visits) consequences depending on individual strategies and personality traits to cope longitudinally with this crisis. Methods: To examine the impact of individuals' coping with the pandemic on mental health, a large sample of 880 older adults of the prospective longitudinal cohort TREND study were surveyed six times about their individual coping strategies in the COVID-19 pandemic between May 2020 (05/2020: Mage = 72.1, SDage = 6.4, Range: 58-91 years) and November 2022 in an open response format. The relevant survey question was: "What was helpful for you to get through the last months despite the COVID-19 pandemic? E.g., phone calls, going for a walk, or others." Results and Discussion: In total, we obtained 4,561 records containing 20,578 text passages that were coded and assigned to 427 distinct categories on seven levels based on qualitative content analysis using MAXQDA. The results allow new insights into the impact of personal prerequisites (e.g., value beliefs, living conditions), the general evaluation of the pandemic (e.g., positive, irrelevant, stressful) as well as the applied coping strategies (e.g., cognitive, emotional- or problem-focused) to deal with the COVID-19 pandemic by using an adapted Lazarus stress model. Throughout the pandemic emotional-focused as well as problem-focused strategies were the main coping strategies, whereas general beliefs, general living conditions and the evaluation were mentioned less frequently.

Longitudinal dynamics of depression in risk groups of older individuals during the COVID-19 pandemic.

Background: Older individuals are most at risk of severe COVID-19 and particularly require protection causing (self)restriction of psychosocial interaction in daily living. So far, the impact of psychosocial withdrawal on mental health seems less pronounced in community-dwelling older individuals compared to younger individuals. However, dynamics and adverse long-term effects of the pandemic, such as increases in depression, are still mostly unclear, especially for vulnerable subgroups. Methods: Pre-pandemic and 3-, 8-, 14-, 20-month peri-pandemic data were analyzed in 877 older participants (age at 3-month peri-pandemic: mean ± SD: 72.3 ± 6.3, range: 58-91 years) of the observational prospective TREND study in Germany. Severity of depression (Beck's Depression Inventory-II scores) and key factors of (mental) health were investigated for cross-sectional associations using path modeling. Risk groups defined by resilience, loneliness, history of depression, stress, health status and fear of COVID-19 were investigated for differences in depression between timepoints. Findings: The early pandemic (3-month) severity of depression was most strongly associated with history of depression, stress and resilience. Overall increases in clinically relevant depression (mild-severe) from pre- to 3-month peri-pandemic were small (% with depression at pre-/3-month peri-pandemic: 8.3%/11.5%). Changes were most pronounced in risk groups with low resilience (27.2%/41.8%), loneliness (19.0%/28.9%), fear of COVID-19 (17.6%/31.4%), high stress (24.4%/34.2%), a history of depression (27.7%/36.9%), and low health status (21.8%/31.4%). Changes in depression were largely observed from pre- to 3-month and were sustained to the 20-month peri-pandemic timepoint, overall and in stratified risk groups defined by single and cumulative risk factors. Changes between timepoints were heterogenous as indicated by alluvial diagrams. Conclusion: Only specific risk groups of older individuals showed a large increase in depression during the COVID-19 pandemic. Since these increases occurred early in the pandemic and were sustained over 20 months, these vulnerable risk groups need to be prioritized for counselling and risk mitigation of depression.

A novel biomarker-based prognostic score in acute ischemic stroke: The CoRisk score.

OBJECTIVES: To derive and externally validate a copeptin-based parsimonious score to predict unfavorable outcome 3 months after an acute ischemic stroke (AIS). METHODS: The derivation cohort consisted of patients with AIS enrolled prospectively at the University Hospital Basel, Switzerland. The validation cohort was prospectively enrolled after the derivation cohort at the University Hospital of Bern and University Hospital Basel, Switzerland, as well as Frankfurt a.M., Germany. The score components were copeptin levels, age, NIH Stroke Scale, and recanalization therapy (CoRisk score). Copeptin levels were measured in plasma drawn within 24 hours of AIS and before any recanalization therapy. The primary outcome of disability and death at 3 months was defined as modified Rankin Scale score of 3 to 6. RESULTS: Overall, 1,102 patients were included in the analysis; the derivation cohort contributed 319 patients, and the validation cohort contributed 783. An unfavorable outcome was observed among 436 patients (40%). For the 3-month prediction of disability and death, the CoRisk score was well calibrated in the validation cohort, for which the area under the receiver operating characteristic curve was 0.819 (95% confidence interval [CI] 0.787-0.849). The calibrated CoRisk score correctly classified 75% of patients (95% CI 72-78). The net reclassification index between the calibrated CoRisk scores with and without copeptin was 46% (95% CI 32-60). CONCLUSIONS: The biomarker-based CoRisk score for the prediction of disability and death was externally validated, was well calibrated, and performed better than the same score without copeptin. CLINICALTRIALSGOV IDENTIFIER: NCT00390962 (derivation cohort) and NCT00878813 (validation cohort).

Treatment choices and neuropsychological symptoms of a large cohort of early MS.

OBJECTIVE: To assess clinical characteristics, distribution of disease-modifying treatments (DMTs), and neuropsychological symptoms in a large cohort of patients with early-stage MS. METHODS: The German National MS Cohort is a multicenter prospective longitudinal cohort study that has recruited DMT-naive patients with clinically isolated syndrome (CIS) and relapsing-remitting MS (RRMS) since 2010. We evaluated their baseline characteristics and the prevalence of neuropsychological symptoms. RESULTS: Of 1,124 patients, with a 2.2:1 female-to-male ratio and median age at onset of 31.71 years (interquartile range [IQR]: 26.06-40.33), 44.6% and 55.3% had CIS and RRMS, respectively. The median Expanded Disability Status Scale (EDSS) score at baseline was 1.5 (IQR: 1.0-2.0). A proportion of 67.8% of patients started DMT after a median time of 167.0 days (IQR 90.0-377.5) since the first manifestation. A total of 64.7% and 70.4% of the 762 patients receiving early DMT were classified as CIS and RRMS, respectively. Fatigue, depressive symptoms, and cognitive dysfunction were detected in 36.5%, 33.5%, and 14.7% of patients, respectively. CONCLUSION: Baseline characteristics of this large cohort of patients with early, untreated MS corroborated with other cohorts. Most patients received early DMT within the first year after disease onset, irrespective of a CIS or RRMS diagnosis. Despite the low EDSS score, neuropsychological symptoms affected a relevant proportion of patients.

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd.

Male-specific association between MT-ND4 11719 A/G polymorphism and ulcerative colitis: a mitochondria-wide genetic association study.

BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory disorder of still unknown pathogenesis. Increasing evidence indicates that alterations in mitochondrial respiration and thus adenosine triphosphate (ATP) production are involved. This may contribute to mucosal energy deficiency and subsequently intestinal barrier malfunction, which is accepted to be a major hallmark of UC. Genetic alterations of the mitochondrial genome are one cause of mitochondrial dysfunction. However, less is known about mitochondrial gene polymorphisms in UC. Therefore, we aimed at identifying genetic associations between mitochondrial polymorphisms and UC. METHODS: German UC cases (n = 1062) and German healthy controls (n = 3030) were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. The primary association analysis was to test for associations between mitochondrial single nucleotide polymorphisms (SNPs) and UC using Fisher's exact test in the total sample and stratified by sex. In addition, we tested for associations between mitochondrial haplogroups and UC and for interactions between the most promising mitochondrial SNPs and nuclear SNPs. An independent set of German subjects with 1625 UC cases and 3575 controls was used for replication. RESULTS: We identified a genetic association between the MT-ND4 11719 A/G polymorphism and UC in the subgroup of males (rs2853495; odds ratio, 1.40; 95 % confidence interval, 1.13 to 1.73; p = 0.002). This association was replicated in the second independent cohort. In the association analysis based on mitochondrial haplogroups the lowest p values were reached for haplogroups HV and T (p = 0.029 and 0.035). Haplogroup HV is determined by the mitochondrial 11719 A/G polymorphism. Accordingly, this association was only found in the subgroup of males (p = 0.009). CONCLUSIONS: For the first time, we observed an association between the MT-ND4 11719 A/G polymorphism and UC. The gene MT-ND4 encodes for a subunit of the mitochondrial electron transport chain complex I, which is pivotal for ATP production and might therefore contribute to mucosal energy deficiency. The male-specific association indicates differences between males and females concerning the impact of mitochondrial gene polymorphisms on the development of UC. Further investigations of the functional mechanism underlying this association and the relevance of the gender-specificity are highly warranted.

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation.

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis.

Calibrating random forests for probability estimation.

Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Successful Replication of GWAS Hits for Multiple Sclerosis in 10,000 Germans Using the Exome Array.

Genome-wide association studies (GWAS) successfully identified various chromosomal regions to be associated with multiple sclerosis (MS). The primary aim of this study was to replicate reported associations from GWAS using an exome array in a large German study. German MS cases (n = 4,476) and German controls (n = 5,714) were genotyped using the Illumina HumanExome v1-Chip. Genotype calling was performed with the Illumina Genome Studio(TM) Genotyping Module, followed by zCall. Single-nucleotide polymorphisms (SNPs) in seven regions outside the human leukocyte antigen (HLA) region showed genome-wide significant associations with MS (P values < 5 × 10(-8) ). These associations have been reported previously. In addition, SNPs in three previously reported regions outside the HLA region yielded P values < 10(-5) . The effect of nine SNPs in the HLA region remained (P < 10(-5) ) after adjustment for other significant SNPs in the HLA region. All of these findings have been reported before or are driven by known risk loci. In summary, findings from previous GWAS for MS could be successfully replicated. We conclude that the regions identified in previous GWAS are also associated in the German population. This reassures the need for detailed investigations of the functional mechanisms underlying the replicated associations.