Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear.

Many studies have shown that patients with non-central nervous system (CNS) cancer can have brain abnormalities, such as reduced gray matter volume and cerebral microbleeds. These abnormalities can sometimes be present even before start of treatment, suggesting a potential detrimental effect of non-CNS cancer itself on the brain. In these previous studies, psychological factors associated with a cancer diagnosis and selection bias may have influenced results. To overcome these limitations, we investigated brain structure with magnetic resonance imaging (MRI) prior to cancer diagnosis.

Sleep apnea has been related to brain changes such as atrophy. However, which component of sleep apnea, the apnea-hypopnea index (AHI), nocturnal oxygen desaturation or arousals, can explain this association is unclear. In this large population-based study (n=681, mean age 62.1 years), we investigated the associations of AHI, nocturnal oxygen desaturation and arousals with global and regional gray matter and white matter volumes and with white matter lesion volumes. All participants underwent one night of polysomnography and MRI scanning of their brain. Gray matter, white matter and white matter lesion volumes adjusted for intracranial volume were studied as markers of brain atrophy. Nocturnal oxygen desaturation was related to whole brain white matter atrophy independent of covariates (multivariable adjusted B=-8.3, 95% CI=-16.7; -0.02). This association was most prominently reflected in the association between more oxygen desaturation and a smaller white matter parietal volume (B=-3.95 ml, 95% CI=-6.02; -1.88). Furthermore, oxygen desaturation was related to a smaller hippocampus (B=-0.22 ml, 95% CI=-0.42; -0.01). Although a higher AHI was related to smaller parietal gray (B=-0.05, 95% CI=-0.09; -0.004) and white matter (B=-0.06, 95% CI=-0.12; -0.10) volumes, these associations disappeared when adding oxygen desaturation to the model. We did not find a relation between arousals and gray and white matter brain atrophy and white matter lesion volumes. This suggests that oxygen desaturation mainly explains the association between sleep apnea and brain damage.

To determine the association between meditation and yoga practice, experienced stress, and amygdala and hippocampal volume in a large population-based study. This study was embedded within the population-based Rotterdam Study and included 3742 participants for cross-sectional association. Participants filled out a questionnaire assessing meditation practice, yoga practice, and experienced stress, and underwent a magnetic resonance scan of the brain. 2397 participants underwent multiple brain scans, and were assessed for structural change over time. Amygdala and hippocampal volumes were regions of interest, as these are structures that may be affected by meditation. Multivariable linear regression analysis and mixed linear models were performed adjusted for age, sex, educational level, intracranial volume, cardiovascular risk, anxiety, depression and stress. 15.7% of individuals participated in at least one form of practice. Those who performed meditation and yoga practices reported significantly more stress (mean difference 0.2 on a 1-5 scale, p < .001) and more depressive symptoms (mean difference 1.03 on CESD, p = .015). Partaking in meditation and yoga practices was associated with a significantly lower right amygdala volume (β = - 31.8 mm3, p = .005), and lower left hippocampus volume (β = - 75.3 mm3, p = .025). Repeated measurements using linear mixed models showed a significant effect over time on the right amygdala of practicing meditation and yoga (β = - 24.4 mm3, SE 11.3, p = .031). Partaking in meditation and yoga practice is associated with more experienced stress while it also helps cope with stress, and is associated with smaller right amygdala volume.

Cognitive and brain reserve refer to individual differences that allow some people to better withstand brain pathology than others. Although early life stress has been recognized as a risk factor for low reserve in late life, no research yet has studied this across midlife.

Conditions such as hyperglycemia and oxidative stress lead to the formation of advanced glycation end products (AGEs), which are harmful compounds that have been implicated in dementia. Within the Rotterdam Study, we measured skin AGEs as skin autofluorescence, reflecting long-term accumulation of AGEs, and determined their association with the risk of dementia and with brain magnetic resonance imaging (MRI) measures. Skin autofluorescence was measured between 2013 and 2016 in 2922 participants without dementia. Of these, 1504 also underwent brain MRI, on which measures of brain atrophy and cerebral small vessel disease were assessed. All participants were followed for the incidence of dementia until 2020. Of 2922 participants (mean age 72.6 years, 57% women), 123 developed dementia. Higher skin autofluorescence (per standard deviation) was associated with an increased risk of dementia (hazard ratio 1.21 [95% confidence interval 1.01-1.46]) and Alzheimer's disease (1.19 [0.97-1.47]), independently of age and other studied potential confounders. Stronger effects were seen in apolipoprotein E (APOE) ε4 carriers (1.34 [0.98-1.82]) and in participants with diabetes (1.35 [0.94-1.94]). Participants with higher skin autofluorescence levels also had smaller total brain volumes and smaller hippocampus volumes on MRI, and they had more often lacunes. These results suggest that AGEs may be involved in dementia pathophysiology.

Frontotemporal lobar degeneration is a neurodegenerative disease characterized by brain atrophy of the frontal and anterior temporal lobes. The associated frontotemporal dementia syndromes are clinically heterogeneous, and the pattern of affected cortical regions varies among subtypes. The TMEM106B rs1990622 polymorphism is associated with frontotemporal lobar degeneration, but little is known about how it affects the brain.

Thyroid hormone (TH) is crucial during neurodevelopment, but high levels of TH have been linked to neurodegenerative disorders. No data on the association of thyroid function with brain imaging in the general population are available. We therefore investigated the association of thyroid-stimulating hormone and free thyroxine (FT4) with magnetic resonance imaging (MRI)-derived total intracranial volume, brain tissue volumes, and diffusion tensor imaging measures of white matter microstructure in 4683 dementia- and stroke-free participants (mean age 60.2, range 45.6-89.9 years). Higher FT4 levels were associated with larger total intracranial volumes (β = 6.73 mL, 95% confidence interval = 2.94-9.80). Higher FT4 levels were also associated with larger total brain and white matter volumes in younger individuals, but with smaller total brain and white matter volume in older individuals (p-interaction 0.02). There was a similar interaction by age for the association of FT4 with mean diffusivity on diffusion tensor imaging (p-interaction 0.026). These results are in line with differential effects of TH during neurodevelopmental and neurodegenerative processes and can improve the understanding of the role of thyroid function in neurodegenerative disorders.

Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH).

In a population-based study of 1,912 community-dwelling persons of 45 years and older, we investigated the relation between age and fine motor skills using the Archimedes spiral-drawing test. Also, we studied the effect of brain volume on fine motor skills.

Cortical thickness is considered a potentially relevant marker for neurodegenerative diseases. However, the relationship of demographic and vascular risk factors with cortical thickness remains unclear. In a population-based sample of 1022 non-demented elderly persons (mean age 68.4±7.3 years), we examined aging effects on global and lobar cortical thickness and the relationship with demographic variables and cardiovascular risk factors. We used a validated model-based approach to calculate mean cortical thickness (μm) in brain MR-images. We found that women had a significant thicker cortex than men (p<0.01). Further, with increasing age, cortical thickness decreased (approximately 0.2% per year), with the largest age effects for the occipital and temporal lobes, and the decrease in the frontal lobe being more apparent in men than in women (p-interaction<0.001). Additionally, higher education, higher diastolic blood pressure and larger intra-cranial volume were related to a larger cortical thickness, whilst diabetes mellitus and higher HDL cholesterol levels were related to a thinner cortex.

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a "normal" burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease. In this work, we propose and evaluate an automated method to quantify PVS in the midbrain, hippocampi, basal ganglia and centrum semiovale. We also compare associations between (earlier established) determinants of PVS and visual PVS scores versus the automated PVS scores, to verify whether automated PVS scores could replace visual scoring of PVS in epidemiological and clinical studies. Our approach is a deep learning algorithm based on convolutional neural network regression, and is contingent on successful brain structure segmentation. In our work we used FreeSurfer segmentations. We trained and validated our method on T2-contrast MR images acquired from 2115 subjects participating in a population-based study. These scans were visually scored by an expert rater, who counted the number of PVS in each brain region. Agreement between visual and automated scores was found to be excellent for all four regions, with intraclass correlation coefficients (ICCs) between 0.75 and 0.88. These values were higher than the inter-observer agreement of visual scoring (ICCs between 0.62 and 0.80). Scan-rescan reproducibility was high (ICCs between 0.82 and 0.93). The association between 20 determinants of PVS, including aging, and the automated scores were similar to those between the same 20 determinants of PVS and visual scores. We conclude that this method may replace visual scoring and facilitate large epidemiological and clinical studies of PVS.

Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, "bi-modal comparison plots" show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using "worm plots". Group differences in connections are examined with an existing visualization, the "connectogram". These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the "Statistical Analysis of Minimum cost path based Structural Connectivity" method and the average fractional anisotropy along the fiber. The functional measures were Pearson's correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.

Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ4 non-carriers and carriers were found to be significantly different from one another (p<0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOEϵ4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p<0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOEϵ4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials.

Imaging plays an essential role in research on neurological diseases in the elderly. The Rotterdam Scan Study was initiated as part of the ongoing Rotterdam Study with the aim to elucidate the causes of neurological disease by performing imaging of the brain in a prospective population-based setting. Initially, in 1995 and 1999, random subsamples of participants from the Rotterdam Study underwent neuroimaging, whereas from 2005 onwards MRI has been implemented into the core protocol of the Rotterdam Study. In this paper, we discuss the background and rationale of the Rotterdam Scan Study. Moreover, we describe the imaging protocol, image post-processing techniques, and the main findings to date. Finally, we provide recommendations for future research, which will also be topics of investigation in the Rotterdam Scan Study.

Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.

The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus. In 3,686 unrelated individuals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent sample of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures.

To assesses whether automated brain image analysis with quantification of structural brain changes improves diagnostic accuracy in a memory clinic setting.

DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data.

The spatial distribution of atrophy in neurodegenerative diseases suggests that brain connectivity mediates disease propagation. Different descriptors of the connectivity graph potentially relate to different underlying mechanisms of propagation. Previous approaches for evaluating the influence of connectivity on neurodegeneration consider each descriptor in isolation and match predictions against late-stage atrophy patterns. We introduce the notion of a topological profile - a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease. By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels. Experimental results comparing topological profiles for Alzheimer's disease, multiple sclerosis and normal ageing show that topological profiles explain the observed data better than single descriptors. Within each condition, most individual profiles cluster around the cohort-level profile, and individuals whose profiles align more closely with other cohort-level profiles show features of that cohort. The cohort-level profiles suggest new insights into the biological mechanisms underlying pathology propagation in each disease.