Plant-based dietary patterns and the risk of dementia:a population-based study

Background: Plant-based dietary patterns are increasingly popular in western countries and are supported by many governments and health organisations for their potential beneficial role in the prevention of chronic diseases. Yet, the potential role of plant-based dietary patterns in the development of dementia remains unclear. Objective: To evaluate the association between plant-based dietary patterns and the risk of dementia. Methods: Dietary intake was measured at baseline in 9,543 dementia-free participants (mean age 64 years, birth years 1897–1960, 58% women) of the prospective population-based Rotterdam Study, using food frequency questionnaires. Based on these questionnaires, we calculated an overall plant-based dietary index (PDI), healthy PDI (hPDI) and unhealthy PDI (uPDI), with higher scores reflecting higher consumption of (any, healthy and unhealthy, respectively) plant-based foods and lower consumption of animal-based foods. We analysed the association of the PDIs with incident dementia using Cox proportional hazard models. Results: During a mean follow-up of 14.5 years, 1,472 participants developed dementia. Overall, the PDIs were not associated with the risk of dementia (hazard ratio [95% confidence interval] per 10-point increase: 0.99 [0.91–1.08] for PDI, 0.93 [0.86–1.01] for hPDI, 1.02 [0.94–1.10] for uPDI). However, among men and APOE ε4 carriers, a higher hPDI was linearly associated with a lower risk of dementia (0.86 [0.75–0.99] and 0.83 [0.73–0.95], respectively), while this association was U-shaped among APOE ε4 non-carriers (P value for non-linearity = 0.01). Conclusions:We found no strong evidence for an overall association between plant-based eating and the risk of dementia. Our findings in stratified analyses warranted further investigation.</p

Seasonality of cognitive function in the general population:the Rotterdam Study

Seasonal variation in cognitive function and underlying cerebral hemodynamics in humans has been suggested, but not consistently shown in previous studies. We assessed cognitive function in 10,276 participants from the population-based Rotterdam Study, aged 45 years and older without dementia, at baseline and at subsequent visits between 1999 and 2016. Seasonality of five cognitive test scores and of a summary measure of global cognition were determined, as well as of brain perfusion. Using linkage with medical records, we also examined whether a seasonal variation was present in clinical diagnoses of dementia. We found a seasonal variation of global cognition (0.05 standard deviations [95% confidence interval: 0.02–0.08]), the Stroop reading task, the Purdue Pegboard test, and of the delayed world learning test, with the best performance in summer months. In line with these findings, there were fewer dementia diagnoses of dementia in spring and summer than in winter and fall. We found no seasonal variation in brain perfusion. These findings support seasonality of cognition, albeit not explained by brain perfusion. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11357-021-00485-0

Visit-to-visit blood pressure variability and the risk of stroke in the Netherlands:A population-based cohort study

BACKGROUND: Apart from blood pressure level itself, variation in blood pressure has been implicated in the development of stroke in subgroups at high cardiovascular risk. We determined the association between visit-to-visit blood pressure variability and stroke risk in the general population, taking into account the size and direction of variation and several time intervals prior to stroke diagnosis. METHODS AND FINDINGS: From 1990 to 2016, we included 9,958 stroke-free participants of the population-based Rotterdam Study in the Netherlands. This is a prospective cohort study including participants aged 45 years and older. Systolic blood pressure (SBP) variability was calculated as absolute SBP difference divided by mean SBP over 2 sequential visits (median 4.6 years apart). Directional SBP variability was defined as SBP difference over 2 visits divided by mean SBP. Using time-varying Cox proportional hazards models adjusted for age, sex, mean SBP, and cardiovascular risk factors, hazard ratios (HRs) for stroke up to January 2016 were estimated per SD increase and in tertiles of variability. We also conducted analyses with 3-, 6-, and 9-year intervals between variability measurement and stroke assessment. These analyses were repeated for diastolic blood pressure (DBP). The mean age of the study population was 67.4 ± 8.2 years and 5,776 (58.0%) were women. During a median follow-up of 10.1 years, 971 (9.8%) participants had a stroke, including 641 ischemic, 89 hemorrhagic, and 241 unspecified strokes. SBP variability was associated with an increased risk of hemorrhagic stroke (HR per SD 1.27, 95% CI 1.05–1.54, p = 0.02) and unspecified stroke (HR per SD 1.21, 95% CI 1.09–1.34, p < 0.001). The associations were stronger for all stroke subtypes with longer time intervals; the HR for any stroke was 1.29 (95% CI 1.21–1.36, p < 0.001) at 3 years, 1.47 (95% CI 1.35–1.59, p < 0.001) at 6 years, and 1.38 (95%CI 1.24–1.51, p < 0.001) at 9 years. For DBP variability, we found an association with unspecified stroke risk. Both the rise and fall of SBP and the fall of DBP were associated with an increased risk for unspecified stroke. Limitations of the study include that, due to an average interval of 4 years between visits, our findings may not be generalizable to blood pressure variability over shorter periods. CONCLUSIONS: In this population-based study, we found that visit-to-visit blood pressure variation was associated with an increased risk of unspecified and hemorrhagic stroke, independent of direction of variation or mean blood pressure

Clinical Relevance of Cortical Cerebral Microinfarcts on 1.5T Magnetic Resonance Imaging in the Late-Adult Population

Background and Purpose: Cortical cerebral microinfarcts (CMIs) have been linked with dementia and impaired cognition in cross-sectional studies. However, the clinical relevance of CMIs in a large population-based setting is lacking. We examine the association of cortical CMIs detected on 1.5T magnetic resonance imaging with cardiovascular risk factors, cerebrovascular disease, and brain tissue volumes. We further explore the association between cortical CMIs with cognitive decline and risk of stroke, dementia, and mortality in the general population. Methods: Two thousand one hundred fifty-six participants (age: 75.7±5.9 years, women: 55.6%) with clinical history and baseline magnetic resonance imaging (January 2009-December 2013) were included from the Rotterdam Study. Cortical CMIs were graded based on a previously validated method. Markers of cerebrovascular disease and brain tissue volumes were assessed on magnetic resonance imaging. Cognition was assessed using a detailed neuropsychological test at baseline and at 5 years of follow-up. Data on incident stroke, dementia, and mortality were included until January 2016. Results: Two hundred twenty-seven individuals (10.5%) had ≥1 cortical CMIs. The major risk factors of cortical CMIs were male sex, current smoking, history of heart disease, and stroke. Furthermore, presence of cortical CMIs was associated with infarcts and smaller brain volume. Persons with cortical CMIs showed cognitive decline in Stroop tests (color-naming and interference subtasks; β for color-naming, 0.18 [95% CI, 0.04-0.33], P interaction ≤0.001 and β for interference subtask, 1.74, [95% CI, 0.66-2.82], P interaction ≤0.001). During a mean follow-up of 5.2 years, 73 (4.3%) individuals developed incident stroke, 95 (5.1%) incident dementia, and 399 (19.2%) died. People with cortical CMIs were at an increased risk of stroke (hazard ratio, 1.18 [95% CI, 1.09-1.28]) and mortality (hazard ratio, 1.09 [95% CI, 1.00-1.19]). Conclusions: Cortical CMIs are highly prevalent in a population-based setting and are associated with cardiovascular disease, cognitive decline, and increased risk of stroke and mortality. Future investigations will have to show whether cortical CMIs are a useful biomarker to intervene upon to reduce the burden of stroke.</p

Dietary nitrate intake in relation to the risk of dementia and imaging markers of vascular brain health:a population-based study

Background: Nitric oxide is a free radical that can be produced from dietary nitrate and positively affects cardiovascular health. With cardiovascular health playing an important role in the etiology of dementia, we hypothesized a link between dietary nitrate intake and the risk of dementia. Objectives: This study aimed to find the association of total, vegetable, and nonvegetable dietary nitrate intake with the risk of dementia and imaging markers of vascular brain health, such as total brain volume, global cerebral perfusion, white matter hyperintensity volume, microbleeds, and lacunar infarcts. Methods: Between 1990 and 2009, dietary intake was assessed using food-frequency questionnaires in 9543 dementia-free participants (mean age, 64 y; 58% female) from the prospective population-based Rotterdam Study. Participants were followed up for incidence dementia until January 2020. We used Cox models to determine the association between dietary nitrate intake and incident dementia. Using linear mixed models and logistic regression models, we assessed the association of dietary nitrate intake with changes in imaging markers across 3 consecutive examination rounds (mean interval between images 4.6 y). Results: Participants median dietary nitrate consumption was 85 mg/d (interquartile range, 55 mg/d), derived on average for 81% from vegetable sources. During a mean follow-up of 14.5 y, 1472 participants developed dementia. A higher intake of total and vegetable dietary nitrate was associated with a lower risk of dementia per 50-mg/d increase [hazard ratio (HR): 0.92; 95% confidence interval (CI): 0.87, 0.98; and HR: 0.92; 95% CI: 0.86, 0.97, respectively] but not with changes in neuroimaging markers. No association between nonvegetable dietary nitrate intake and the risk of dementia (HR: 1.15; 95% CI: 0.64, 2.07) or changes in neuroimaging markers were observed. Conclusions: A higher dietary nitrate intake from vegetable sources was associated with a lower risk of dementia. We found no evidence that this association was driven by vascular brain health.</p

Lung function impairment and the risk of incident dementia : the Rotterdam study

Background: The etiology of dementia may partly be underpinned by impaired lung function via systemic inflammation and hypoxia. Objective: To prospectively examine the association between chronic obstructive pulmonary disease (COPD) and subclinical impairments in lung function and the risk of dementia. Methods: In the Rotterdam Study, we assessed the risk of incident dementia in participants with Preserved Ratio Impaired Spirometry (PRISm; FEV1/FVC≥0.7, FEV1 <  80%) and in participants with COPD (FEV1/FVC <  0.7) compared to those with normal spirometry (controls; FEV1/FVC≥0.7, FEV1≥80%). Hazard ratios (HRs) with 95%confidence intervals (CI) for dementia were adjusted for age, sex, education attainment, smoking status, systolic blood pressure, body mass index, triglycerides, comorbidities and Apolipoprotein E (APOE) genotype. Results: Of 4,765 participants, 110 (2.3%) developed dementia after 3.3 years. Compared to controls, participants with PRISm, but not COPD, had an increased risk for all-type dementia (adjusted HRPRISm 2.70; 95%CI, 1.53-4.75; adjusted HRCOPD 1.03; 95%CI, 0.61-1.74). These findings were primarily driven by men and smokers. Similarly, participants with FVC%predicted values in the lowest quartile compared to those in the highest quartile were at increased risk of all-type dementia (adjusted HR 2.28; 95%CI, 1.31-3.98), as well as Alzheimer's disease (AD; adjusted HR 2.13; 95%CI, 1.13-4.02). Conclusion: Participants with PRISm or a low FVC%predicted lung function were at increased risk of dementia, compared to those with normal spirometry or a higher FVC%predicted, respectively. Further research is needed to elucidate whether this association is causal and how PRISm might contribute to dementia pathogenesis

The interaction of cognitive and brain reserve with frailty in the association with mortality : an observational cohort study

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. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Transfer learning improves supervised image segmentation across imaging protocols

The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%