Search

Background: Cognitive and brain reserve aim to explain individual differences in susceptibility to dementia and may also affect the risk of late-life depressive events. We assessed whether higher cognitive and brain reserve are associated with lower risk of a late-life depressive event. Methods: This study included 4509 participants from the population-based Rotterdam Study (mean age: 63.4 ± 10.2 years, 55 % women) between 2005 and 2019. Participants completed cognitive testing and brain-MRI at baseline. Cognitive reserve was defined as the common variance across cognitive tests, while adjusting for demographic factors and brain MRI-markers. Brain reserve was defined as total brain volume divided by intracranial volume. Depressive events (depressive symptoms/depressive syndrome/major depressive disorder) were repeatedly measured (follow-up: 6.6 ± 3.9 years) with validated questionnaires, clinical interviews, and follow-up of medical records. Hazard ratios (HR) with 95 % confidence intervals (CI) were estimated using Cox-regressions. Results: Higher cognitive (HR: 0.91/SD, 95%CI: 0.84; 1.00) and brain reserve (HR: 0.88/SD, 95%CI: 0.77; 1.00) were associated with a lower risk of a depressive event after adjustment for baseline depressive symptoms. These associations attenuated when participants with clinically relevant depressive symptoms at baseline were excluded (HR: 0.95/SD, 95%CI: 0.86; 1.05, HR: 0.89/SD, 95%CI: 0.76; 1.03, respectively). Limitations: No data was available on depression in early-life and the number of participants with major depressive disorder was relatively low (n = 105). Conclusions: Higher cognitive and brain reserve may be protective factors for late-life depressive events, particularly in those who have experienced clinical relevant depressive symptoms before. Further research is needed to determine whether cognitive and brain reserve could be used as targets for the prevention of late-life depression.

Background: Cognitive reserve aims to explain individual differences in the susceptibility to the functional impact of dementia in the presence of equal amount of neuropathological damage. It is thought to be shaped by a combination of innate individual differences and lifetime exposures. Which determinants are associated with cognitive reserve remains unknown. Objective: The objective of this study was to investigate the associations of sociodemographic, lifestyle, physical, and psychosocial determinants with cognitive reserve, and potential sex differences. Methods: This cross-sectional study included 4,309 participants from the Rotterdam Study (mean age 63.9±10.7) between 2006–2016. Participants completed five cognitive tests and a brain MRI-scan. Cognitive reserve was defined as a latent variable that captures variance common across five cognitive tests, while adjusting for demographic and MRI-inferred neuropathological factors. The associations of potential determinants and cognitive reserve, adjusted for relevant confounders, were assessed with structural equation models. Results: Current smoking (adjusted mean difference: –0.31, 95%confidence interval –0.42; –0.19), diabetes mellitus (–0.25, –0.40; –0.10) and depressive symptoms (–0.07/SD, –0.12; –0.03) were associated with a lower cognitive reserve whereas alcohol use (0.07/SD, 0.03; 0.12) was associated with higher cognitive reserve. Only smoking was associated with cognitive reserve in both men and women. Employment, alcohol use, diabetes, history of cancer, COPD, and depressive symptoms were only associated with cognitive reserve in women. Conclusion: Our study found that current smoking, diabetes mellitus, and depressive symptoms were associated with a lower cognitive reserve, whereas more alcohol use was associated with a higher cognitive reserve, but with clear differences between men and women.

[Background]: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., [Methods]: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., [Results]: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., [Conclusions]: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., This work was supported by the German Research Foundation (DFG Grant Nos. HA7070/2-2, HA7070/3, HA7070/4 [to TH]) and IZKF of the medical faculty of Münster (Grants No. Dan3/012/17 [to UD] and MzH 3/020/20 [to TH]), and NHMRC projects (Grant No. 1064643 [to BJH] and 1024570 [to CGD]). The CIAM study was supported by the University of Cape Town Research Committee, South African National Research Foundation, and the South African Medical Research Council (principal investigator [PI], Fleur M. Howells) and Grant No. R01MH117601 (to NJ). This work was funded by the German Research Foundation (Grant Nos. FOR2107 JA 1890/7-1 and FOR2107 JA 1890/7-2 [to AJ]) and Swinburne University scholarship/Australian Postgraduate Award (to JK). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to JK). This work was funded by the German Research Foundation (Grant Nos. FOR2107 KI588/14-1 and FOR2107 KI588/14-2 [to TK]). The St. Göran study was supported by grants from the Swedish Research Council (Grant No. 2018-02653 [to ML]), the Swedish foundation for Strategic Research (Grant No. KF10-0039 [to ML]), the Swedish Brain foundation (Grant No. FO2020-0261 [to ML]), and the Swedish Government under the LUA/ALF agreement (Grant Nos. ALF 20170019 and ALFGBG-716801 [to ML]). This work was supported by RFBR (Grant No. 20-013-00748 [to IL, AST]) and funded by the Health Research Board (Grant No. HRA_POR/2011/100 [to CM]). The Australian Schizophrenia Research Bank (ASRB) was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This work was supported by Deutsche Forschungsgemeinschaft (Grant Nos. DFG NE 2254/2-1, NE 2254/3-1, NE2254/4-1 [to IN]), the University Research Priority Program “Integrative Human Physiology” at the University of Zurich (to ROT), an NHMRC Senior Principal Research Fellowship (Grant No. 1105825 [to CPa]), an NHMRC L3 Investigator Grant (Grant No. 1196508 [to CPa]), and NHMRC Program Grant (Grant No. 1150083 [to CPa]). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (Agreement No. 798181 [to GP]). ASRB was supported by the NHMRC (Enabling Grant, ID 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. The Imaging Genetics in Psychosis study was funded by Project Grants from the NHMRC (Grant Nos. APP630471 and APP1081603 [to YQ]) and the Macquarie University’s Australian Research Council (ARC) Centre of Excellence in Cognition and its Disorders (Grant No. CE110001021 [to YQ]). This work was supported by the Spanish Ministry of Science, Innovation and Universities/Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI11/01766 and CPII19/00009 [to JR]), co-financed by European Regional Development Fund funds from the European Commission (“A Way of Making Europe”). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. SR was supported by an NHMRC Senior Fellowship (Grant No. GNT1154651). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to SR). This work was supported by NIHR; MRC (to KR), NIH (Grant Nos. MH-094268, MH-105660, and MH-107730 [to ASS]). The Neuroimaging of the Children's Attention Project cohort was funded by NHMRC, Australia (Grant No. 1065895). Earlier funding for the cohort as also provided by NHMRC (Grant No. 1008522) and a grant from the Collier Foundation. The ACPU cohort was funded by NHMRC, Australia (Project Grant Nos. 384419 and 569533 [to TS]). This work was supported by research grants from the National Healthcare Group, Singapore (Grant Nos. SIG/05004, SIG/05028, and SIG /1103), and the Singapore Bioimaging Consortium (RP C009/2006 [to KS]), and the Ministry of Health, Czech Republic - conceptual development of research organization (“Institute for Clinical and Experimental Medicine - IKEM, IN 00023001” [to ASk]). This study was supported by the Italian Ministry of Health (Grant No. RC/17-18-19-20-21/A [to GS]) and Ministry of Health of the Czech Republic (Grant No. NU20-04-00393 [to FS]). The Drakenstein Child Health Study (DCHS) cohort is funded by the Bill and Melinda Gates Foundation (Grant No. OPP 1017641) and the South African Medical Research Council. This DCHS contribution was made possible in part by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author (DJS). STRADL study was supported and funded by the Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (Grant No. 104036/Z/14/Z), and the Medical Research Council Mental Health Pathfinder Award “Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders” (MRC, Grant No. MC_PC_17209). Scottish Bipolar Family Study (SBFS) was supported by National Health Service Research Scotland, through the Scottish Mental Health Research Network (www.smhrn.org.uk), who provided assistance with subject recruitment and assessments. SBFS was conducted at the Brain Research Imaging Centre (http://www.bric.ed.ac.uk), which is supported by SINAPSE (Scottish Imaging Network, a Platform for Scientific Excellence, http://www.sinapse.ac.uk). Processing of the datasets used the resources provided by the Edinburgh Compute and Data Facility (http://www.ecdf.ed.ac.uk/) (ASt). TVR was supported by an NHMRC Early Career Fellowship (Grant No. GNT1088785). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to TVR). EV was supported by the Spanish Ministry of Science and Innovation (PI18/00805) integrated into the Plan Nacional de I+D+I and co-financed by the ISCIII-Subdirección General de Evaluación and the FEDER; the Instituto de Salud Carlos III; the CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental); by the CERCA Programme/Generalitat de Catalunya and the Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement (Grant No. 2017SGR1355). This study was also supported by the Departament de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca i Innovació en Salut (PERIS) 2016-2020 (Grant No. SLT006/17/00345) and the European Union Horizon 2020 research and innovation program (EU.3.1.1. Understanding health, wellbeing and disease: Grant Nos. 754907 and EU.3.1.3 [to EB]; Treating and managing disease: Grant No. 945151 [to EV]). This study was supported by the National Center for Complementary and Integrative Health (Grant Nos. R21AT009173 and R61AT009864 [to TTY]); by the National Center for Advancing Translational Sciences (CTSI), National Institutes of Health, through UCSF-CTSI (Grant No. UL1TR001872 [to TTY]); by the American Foundation for Suicide Prevention (Grant No. SRG-1-141-18 [to TTY]); by UCSF Research Evaluation and Allocation Committee (REAC) and J. Jacobson Fund (to TTY); by the NIMH (Grant No. R01MH085734 [to TTY]); and by the Brain and Behavior Research Foundation (formerly NARSAD) (to TTY). This work was supported by a personal Veni grant to MH from the Netherlands Organization for Scientific Research (NWO, Grant No. 91619115 [to MH]) and European Community’s Horizon 2020 Programme (H2020/2014 – 2020) (Grant Agreements Nos. 667302 [CoCA], 728018 [Eat2beNICE], and 847879 [PRIME] [to BF]). JBu has been supported by the EU-AIMS (European Autism Interventions) and AIMS-2-TRIALS programmes, which receive support from Innovative Medicines Initiative Joint Undertaking Grant Nos. 115300 and 777394, the resources of which are composed of financial contributions from the European Union’s FP7 and Horizon 2020 Programmes, and from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions, and AUTISM SPEAKS, Autistica and SFARI; and by the Horizon 2020–supported programme CANDY (Grant No. 847818 [to JBu]). This work is supported by Grant No. NIA T32AG058507 and NIH Grant No. U54EB020403 from the Big Data to Knowledge (BD2K) Program (to CRKC). ENIGMA MDD work is supported by NIH (Grant Nos. U54 EB020403 [to PT], R01 MH116147 [to PT], and R01 MH117601 [to NJ and LS]). LS was supported by an NHMRC Career Development Fellowship (Grant No. 1140764). This work was supported by the National Center for Research Resources at the NIH (Grant Nos. NIH 1 U24 RR021992 [Function Biomedical Informatics Research Network], NIH 1 U24 RR025736-01 [Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org]). TGMvE is supported by ENIGMA’s NIH BD2K initiative (Grant No. U54 EB020403), ENIGMA Sex Differences (Grant No. R01MH116147), and ENIGMA-COINSTAC: Advanced Worldwide Transdiagnostic Analysis of Valence System Brain Circuits (Grant No. R01MH121246). This work was supported by the NIH (Grant No. R01MH121246 [to JT, Calhoun, and TGMvE]). This work was supported in part by NIH (Grant No. U54 EB020403 [to PT]).

Brain pathology develops at different rates between individuals with similar burden of risk factors, possibly explained by brain resistance. We examined if education contributes to brain resistance by studying its influence on the association between vascular risk factors and brain pathology. In 4111 stroke-free and dementia-free community-dwelling participants (62.9 ± 10.7 years), we explored the association between vascular risk factors (hypertension and the Framingham Stroke Risk Profile [FRSP]) and imaging markers of brain pathology (markers of cerebral small vessel disease and brain volumetry), stratified by educational attainment level. Associations of hypertension and FSRP with markers of brain pathology were not significantly different between levels of educational attainment. Certain associations appeared weaker in those with higher compared to lower educational attainment, particularly for white matter hyperintensities (WMH). Supplementary residual analyses showed significant associations between higher educational attainment and stronger resistance to WMH among others. Our results suggest a role for educational attainment in resistance to vascular brain pathology. Yet, further research is needed to better characterize determinants of brain resistance.

Summary Background A higher cognitive reserve and brain reserve could decrease mortality risk, but the interaction of these factors with general age-related loss of physical fitness (eg, frailty) remains unclear with regards to mortality. We investigated the associations of cognitive and brain reserve with mortality and the interaction of cognitive and brain reserve with frailty within these associations. Methods Within the observational population-based cohort of the Rotterdam Study, we included participants who visited the research centre for a cognitive assessment between March 2, 2009, and March 1, 2012. Participants with an incomplete assessment of cognition, no data on education attainment, no MRI or an MRI of insufficient quality, three or more missing frailty criteria, or a dementia diagnosis were excluded. Participants were followed up until their death or May 1, 2019. Cognitive reserve was defined as a latent variable that captures variance across five cognitive tests. Brain reserve was defined as the proportion of healthy-appearing brain volume relative to total intracranial volume measured with 1·5 Tesla MRI. Frailty was defined according to Fried's frailty phenotype; participants meeting at least one of the five criteria were considered frail. Hazard ratios (HRs) for associations of cognitive reserve, brain reserve, frailty, and reserve–frailty interactions with the risk of mortality were estimated using Cox regression models. Findings 2878 individuals in the Rotterdam Study who visited the research centre for a cognitive assessment were considered eligible. 1388 individuals were excluded due to incomplete or missing data or a dementia diagnosis. 1490 participants with valid information on cognitive reserve, brain reserve, and frailty were included (mean age 74·3 years [SD 5·5]; 815 [55%] female participants). 810 (54%) participants were classified as frail. A higher cognitive reserve (HR 0·87 per SD, 95% CI 0·76–0·99, p=0·036) and a higher brain reserve (0·85 per SD, 0·72–1·00, p=0·048) were associated with a lower risk of mortality, after adjusting for sex, age, educational level, body-mass index, smoking status, and number of comorbidities. The association between cognitive reserve and mortality was more pronounced (0·77 per SD, 0·66–0·90, p=0·0012) when the cognitive reserve–frailty interaction (p=0·0078) was included, indicating that higher cognitive reserve is related to lower mortality in individuals with frailty. The brain reserve–frailty interaction was non-significant. Interpretation Higher cognitive reserve and higher brain reserve were associated with a lower mortality risk. Additionally, cognitive reserve and frailty interact in the association with mortality, such that higher cognitive reserve is particularly associated with lower mortality in frail participants. Funding Netherlands Organization for Health Research and Development and EU Horizon 2020 research programme.

Background Patients with an aneurysmal subarachnoid hemorrhage (aSAH) and World Federation of Neurosurgical Societies (WFNS) grade I on admission are generally considered to have a good clinical outcome. Objective The objective of this study was to assess the actual clinical outcome of WFNS grade I aSAH patients, and to determine which factors are associated with unfavourable outcome. Methods For this prospective cohort study, 132 consecutive patients (age 18 years or older) with a WFNS grade I aSAH admitted to our hospital between December 2011 and January 2016 were eligible. Clinical outcome was measured using the modified Rankin Scale (mRS) at 6-month follow-up. Unfavorable outcome was defined as an mRS score of 3–6. Univariable analyses were performed using logistic regression models. Results Of 116 patients, only 5 patients (4%) had an mRS score of 0 and most (65%) had an mRS score of 2. Twenty-five patients (22%) had an unfavorable outcome. Nine (8%) patients died, of whom 4 died during admission. Factors associated with unfavorable outcome were age (per increasing decade: odds ratio [OR]. 1.78; 95% confidence interval [CI], 1.16–2.72), delayed cerebral ischemia (OR, 4.32; 95% CI, 1.63–11.44), pneumonia (OR, 10.75; 95% CI, 1.94–59.46) and meningitis (OR, 28.47; 95% CI, 1.42–571.15). Conclusions Despite their neurologically optimal clinical condition on admission, 1 in 5 patients with WFNS grade I aSAH has an unfavorable clinical outcome or is dead at 6-month follow-up. Additional multivariable analysis in larger patient cohorts is necessary to identify the extent to which preventable complications contribute to unfavorable outcomes in these patients.

Objectives: Loss of muscle mass has been associated with poor survival in several surgical patient populations, including those with an abdominal aortic aneurysm (AAA). We wanted to replicate these findings and assess the association between psoas muscle area (PMA) and survival in patients with an asymptomatic AAA. Methods: Patients with an asymptomatic infrarenal AAA who underwent computed tomography (CT) scanning between January 1, 2007, and December 31, 2013, were included in this single-centre retrospective cohort study. PMA was measured with thresholding on an axial image at the centre level of the third lumbar vertebra. The lowest tertile of PMA in all patients was used as a cutoff value for a low PMA. Then, in separate analyses for conservatively and surgically managed patients, survival was estimated with the Kaplane-Meier method. Differences in survival between patients with and without a low PMA were tested with the log-rank test. Results: Of 228 patients, 104 were managed conservatively and 124 underwent AAA repair. Seventy-seven patients (62%) had an endovascular repair. In these 228 patients, the median PMA was 16.83 cm(2), while the cutoff value for low PMA was 14.56 cm(2). Patients who were managed conservatively were more often classified as having low PMA (45/104, 43%, vs. 31/124, 25%; p = .004) and were significantly older (mean 73.4 +/- 49.05 years vs. 69.03 +/- 7.46 years; p

Objectives: Patients with rheumatoid arthritis (RA) have a high risk of cardiovascular disease (CVD). Recent national and international guidelines suggest strict treatment of CVD risk factors in RA. The aim of this study was to evaluate the self-reported adherence to CV prevention strategies in patients with RA.Method: RA patients visiting an outpatient clinic for strict CVD risk management received a validated questionnaire to evaluate adherence to CV prevention strategies. Strict treatment targets were defined and lifestyle recommendations were given following a prespecified protocol. CVD risk was assessed using the SCORE algorithm.Results: In total, 111 questionnaires were returned (response rate of 82%). A high 10-year CVD risk ( 20%) was present in 53%, but only 3% thought they had an increased CVD risk. A total of 53% of patients reported that they follow the doctors' suggestions exactly' and 75% reported finding it easy to follow the suggestions'. Of the 69% of patients who were prescribed lipid- and/or blood pressure-lowering drugs, 90% reported taking all prescribed tablets. The advice to follow a diet was given to 42%, of whom 68% said they followed the advised diet. Physical exercise was advised to 67%, of whom 62% said they performed specific physical exercise on at least 3 days a week. The adherence to lifestyle recommendations was not significantly different across the CVD risk groups.Conclusions: RA patients tend to underestimate their CVD risk. The self-reported adherence of RA patients to CVD risk management was high concerning pharmaceutical interventions and moderate in the case of lifestyle interventions.