Differential impacts of fat and muscle mass on cardiovascular and non-cardiovascular mortality in individuals with type 2 diabetes.

BACKGROUND: The distribution of fat and muscle mass in different regions of the body can reflect different pathways to mortality in individuals with diabetes. Therefore, we investigated the associations between whole-body and regional body fat and muscle mass with cardiovascular disease (CVD) and non-CVD mortality in type 2 diabetes (T2D). METHODS: Within the National Health and Nutrition Examination Survey 1999-2006, 1417 adults aged ≥50 years with T2D were selected. Dual-energy X-ray absorptiometry was used to derive whole-body, trunk, arm, and leg fat mass and muscle mass indices (FMI and MMI). Mortality data until 31 December 2019 were retrieved from the National Death Index. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated from Cox proportional hazard models. RESULTS: A total of 1417 participants were included in this study (weighted mean age [standard error]: 63.7 [0.3] years; 50.5% female). Over a median follow-up of 13.6 years, 797 deaths were recorded (371 CVD-related and 426 non-CVD deaths). Higher FMI in the arm was associated with increased risk of non-CVD mortality (fourth quartile [Q4] vs. first quartile [Q1]: HR 1.82 [95% CI 1.13-2.94]), whereas higher FMI in the trunk or leg was not significantly associated with CVD or non-CVD mortality. Conversely, higher arm MMI was associated with a lower risk of both CVD (Q4 vs. Q1: HR 0.51 [95% CI 0.33-0.81]) and non-CVD (Q4 vs. Q1: HR 0.56 [95% CI 0.33-0.94]) mortality. There was a significant interaction between smoking status and arm FMI on non-CVD mortality (P for interaction = 0.007). Higher arm FMI was associated with a higher risk of non-CVD mortality among current or former smokers (Q4 vs. Q1: HR 2.67 [95% CI 1.46-4.88]) but not non-smokers (Q4 vs. Q1: HR 0.85 [95% CI 0.49-1.47]). CONCLUSIONS: Fat mass and muscle mass, especially in the arm, are differently associated with CVD and non-CVD mortality in people with T2D. Our findings underscore the predictive value of body compositions in the arm in forecasting mortality among older adults with T2D.

High cognitive reserve attenuates the risk of dementia associated with cardiometabolic diseases.

BACKGROUND: Cardiometabolic diseases (CMDs) including type 2 diabetes, heart disease, and stroke have been linked to a higher risk of dementia. We examined whether high levels of cognitive reserve (CR) can attenuate the increased dementia risk and brain pathologies associated with CMDs. METHODS: Within the UK Biobank, 216,178 dementia-free participants aged ≥ 60 were followed for up to 15 years. Baseline CMDs and incident dementia were ascertained from medical records, medication use, and medical history. Latent class analysis was used to generate an indicator of CR (low, moderate, and high) based on education, occupational attainment, confiding in others, social contact, leisure activities, and television watching time. A subsample (n = 13,663) underwent brain MRI scans during follow-up. Volumes of total gray matter (GMV), hippocampus (HV), and white matter hyperintensities (WMHV) were ascertained, as well as mean diffusivity (MD) and fractional anisotropy (FA) in white matter tracts. RESULTS: At baseline, 43,402 (20.1%) participants had at least one CMD. Over a mean follow-up of 11.7 years, 6,600 (3.1%) developed dementia. The presence of CMDs was associated with 57% increased risk of dementia (HR 1.57 [95% CI 1.48, 1.67]). In joint effect analysis, the HRs of dementia for people with CMDs and moderate-to-high CR and low CR were 1.78 [1.66, 1.91] and 2.13 [1.97, 2.30]), respectively (reference: CMD-free, moderate-to-high CR). Dementia risk was 17% lower (HR 0.83 [0.77, 0.91], p < 0.001) among people with CMDs and moderate-to-high compared to low CR. On brain MRI, CMDs were associated with smaller GMV (ß -0.18 [-0.22, -0.13]) and HV (ß -0.13 [-0.18, -0.08]) as well as significantly larger WMHV (ß 0.06 [0.02, 0.11]) and MD (ß 0.08 [0.02, 0.13]). People with CMDs and moderate-to-high compared to low CR had significantly larger GMV and HV, but no differences in WMHV, MD, or FA. CONCLUSIONS: Among people with CMDs, having a higher level of CR was associated with lower dementia risk and larger gray matter and hippocampal volumes. The results highlight a mentally and socially active life as a modifiable factor that may support cognitive and brain health among people with CMDs.

Cognitive reserve, cortisol, and Alzheimer's disease biomarkers: A memory clinic study.

INTRODUCTION: Cognitive reserve might mitigate the risk of Alzheimer's dementia among memory clinic patients. No study has examined the potential modifying role of stress on this relation. METHODS: We examined cross-sectional associations of the cognitive reserve index (CRI; education, occupational complexity, physical and leisure activities, and social health) with cognitive performance and AD-related biomarkers among 113 memory clinic patients. The longitudinal association between CRI and cognition over a 3-year follow-up was assessed. We examined whether associations were influenced by perceived stress and five measures of diurnal salivary cortisol. RESULTS: Higher CRI scores were associated with better cognition. Adjusting for cortisol measures reduced the beneficial association of CRI on cognition. A higher CRI score was associated with better working memory in individuals with higher (favorable) cortisol AM/PM ratio, but not among individuals with low cortisol AM/PM ratio. No association was found between CRI and AD-related biomarkers. DISCUSSION: Physiological stress reduces the neurocognitive benefits of cognitive reserve among memory clinic patients. HIGHLIGHTS: Physiological stress may reduce the neurocognitive benefits accrued from cognitively stimulating and enriching life experiences (cognitive reserve [CR]) in memory clinic patients. Cortisol awakening response modified the relation between CR and P-tau181, a marker of Alzheimer's disease (AD). Effective stress management techniques for AD and related dementia prevention are warranted.

Social health and subsequent cognitive functioning in people aged 50 years and older: examining the mediating roles of depressive symptoms and inflammatory biomarkers in two European longitudinal studies.

BACKGROUND: Social health markers, including marital status, contact frequency, network size, and social support, have been shown to be associated with cognition. However, the mechanisms underlying these associations remain poorly understood. We investigated whether depressive symptoms and inflammation mediated associations between social health and subsequent cognition. METHODS: In the English Longitudinal Study of Ageing (ELSA), a nationally representative longitudinal study in England, UK, we sampled 7136 individuals aged 50 years or older living in private households without dementia at baseline or at the intermediate mediator assessment timepoint, who had recorded information on at least one social health marker and potential mediator. We used four-way decomposition to examine to what extent depressive symptoms, C-reactive protein, and fibrinogen mediated associations between social health and subsequent standardised cognition (verbal fluency and delayed and immediate recall), including cognitive change, with slopes derived from multilevel models (12-year slope). We examined whether findings were replicated in the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), a population-based longitudinal study in Sweden, in a sample of 2604 individuals aged 60 years or older living at home or in institutions in Kungsholmen (central Stockholm) without dementia at baseline or at the intermediate mediator assessment timepoint (6-year slope). Social health exposures were assessed at baseline, potential mediators were assessed at an intermediate timepoint (wave 2 in ELSA and 6-year follow-up in SNAC-K); cognitive outcomes were assessed at a single timepoint (wave 3 in ELSA and 12-year follow-up in SNAC-K), and cognitive change (between waves 3 and 9 in ELSA and between 6-year and 12-year follow-ups in SNAC-K). FINDINGS: The study sample included 7136 participants from ELSA, of whom 3962 (55·5%) were women and 6934 (97·2%) were White; the mean baseline age was 63·8 years (SD 9·4). Replication analyses included 2604 participants from SNAC-K, of whom 1604 (61·6%) were women (SNAC-K did not collect ethnicity data); the mean baseline age was 72·3 years (SD 10·1). In ELSA, we found indirect effects via depressive symptoms of network size, positive support, and less negative support on subsequent verbal fluency, and of positive support on subsequent immediate recall (pure indirect effect [PIE] 0·002 [95% CI 0·001-0·003]). Depressive symptoms also partially mediated associations between less negative support and slower decline in immediate recall (PIE 0·001 [0·000-0·002]) and in delayed recall (PIE 0·001 [0·000-0·002]), and between positive support and slower decline in immediate recall (PIE 0·001 [0·000-0·001]). We did not observe mediation by inflammatory biomarkers. Findings of mediation by depressive symptoms in the association between positive support and verbal fluency and between positive support and change in immediate recall were replicated in SNAC-K. INTERPRETATION: The findings of this study provide new insights into mechanisms linking social health with cognition, suggesting that associations between interactional aspects of social health, especially social support, and cognition are partly underpinned by depressive symptoms. FUNDING: EU Joint Programme-Neurodegenerative Disease Research (JPND) and Alzheimer's Society. TRANSLATION: For the Swedish translation of the abstract see Supplementary Materials section.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.