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Background: Depression has been associated with an increased risk of Alzheimer's disease (AD), but the biological mechanisms are still not fully understood. Plasma biomarkers, such as amyloid‐beta, tau, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL), have been associated with AD pathophysiology. However, the relationship with depression is unclear. Assessing these biomarkers in the blood allows for the opportunity to assess if AD pathology is related to depressive symptoms on a large‐scale. We hypothesized if depression is part of the AD process, then these markers will be associated with depressive symptoms. Method: A two‐stage IPD meta‐analysis was performed based on eight cohorts of individuals without dementia as part of the Netherlands Consortium of Dementia Cohorts (NCDC). We examined the cross‐sectional association between each plasma marker for AD (amyloid‐beta42/40 ratio, p‐tau181, NfL, and GFAP) with depressive symptoms (the GDS‐15, PHQ‐9, CES‐D, and SF‐36). Plasma markers were assessed using Single Molecular Array (Simoa; Quanterix) assays. Both plasma markers and depressive symptoms were standardized. We estimated the effect per plasma marker with depressive symptoms using linear regressions, correcting for age, sex, education, and APOE e4 allele presence, in each cohort. The effect estimates were entered into a random‐effects meta‐analysis. We also performed subgroup analyses assessing sex differences and between those with and without an APOE e4 allele. Result: This study involved 7210 participants with an age range of 38 to 102 years. Based on clinical cut‐offs per questionnaire, high depressive symptomology ranged from one to 22% per cohort. None of the plasma markers were associated with depressive symptoms in the meta‐analysis. However, subgroup analyses found an association with NfL and depressive symptoms in women (beta 0.07; 95% CI: 0.03‐0.10, p < 0.001) and in those with an APOE e4 allele (beta 0.11; 95% CI: 0.05‐0.17, p = 0.001). Conclusion: AD pathology did not show an overall relationship with depressive symptoms. However, in women and in those with a genetic risk for AD, NfL showed an association. As NfL is a marker of overall neurodegeneration, this pathology in plasma may be specific to depressive symptoms in certain subgroups. [ABSTRACT FROM AUTHOR]

Background: Biomarkers for Alzheimer's disease (AD), including amyloid‐beta (Abeta) and phosphorylated‐tau have recently been accurately detected in blood. Non‐specific biomarkers such as neurofilament light (NFL) and glial fibrillary acidic protein (GFAP) have been added as complementary biomarkers to fully investigate AD etiology in vivo. This has increased the possibility to examine relationships with other pathways to AD, such as white matter hyperintensities (WMH) and brain atrophy on MRI. Our aim was to explore the relationship between plasma AD biomarkers and MRI markers of cerebral small vessel disease and neurodegeneration in mid‐ and late life. Method: Data from 594 individuals (mean (SD) age: 64 (8) years; 17% female) were included from the SMART‐MR Study, a prospective cohort study of non‐demented individuals with a history of vascular disease from the UMC Utrecht in the Netherlands. MRI markers of CSVD included WMH, presence of infarcts, total brain volume (TBV), and hippocampal volume (HV) assessed on 1.5T MRI. AD plasma markers (Abeta1‐40, Abeta1‐42, pTau‐181, NFL, and GFAP) were assessed using Single Molecular Array (Simoa; Quanterix) assays. Linear regressions were performed for each plasma marker, as well as Abeta1‐42/Abeta1‐40 ratio, with age, sex, education, and intracranial volume, with WMH volume, TBV, and HV. Additionally, logistic regressions were performed for the presence of infarcts, including lacunar infarcts and cortical infarcts, corrected for age, sex, and education. Plasma AD levels were converted to z‐scores. P < 0.01 was considered statistically significant. Result: Higher NFL and pTau‐181 were associated with larger WMH volume (B NFL=0.17, 95% CI=0.06;0.29, p=0.003; B pTau‐181=0.16, 95% CI=0.06;0.26, p=0.001). Higher Abeta1‐40 (B=‐0.11, 95% CI=‐0.17;‐0.05, p<0.001) levels were associated with lower HV. Higher NFL (B=‐5.63, 95% CI=‐8.95;‐2.31, p<0.001) and Abeta1‐42 (B=‐3.61, 95% CI=‐6.26;‐0.96, p=0.008) were associated with lower TBV. Regarding infarcts, higher NFL was associated with any infarct (OR=1.42, 95% CI=1.13;1.78, p=0.003). Higher GFAP was marginally associated only with cortical infarcts (OR=1.45, 95% CI=1.09;1.92, p=0.01). Conclusion: Within non‐demented adults with a history vascular disease, plasma AD markers differentially mapped to MRI markers. NFL and pTau‐181 were associated with markers reflecting vascular damage, whereas amyloid markers were associated with atrophy in the TBV and HV. [ABSTRACT FROM AUTHOR]

Background: Early identification of dementia is crucial for prompt intervention and better outcomes for high‐risk individuals in the general population. The use of machine learning in dementia prediction has allowed for highly accurate models that could aid in early classification; however, they have focused on expensive predictors. Exploring predictors that are more accessible is crucial for the possible widespread use of machine learning models in clinical practice. Method: Data from 4,793 individuals without dementia or mild cognitive impairment at baseline were included from the population‐based AGES‐Reykjavik Study. Cognitive, biometric, and MRI assessments (total: 64 variables) were collected at baseline, with follow‐up of incident dementia diagnoses for a maximum of 12 years. Elastic net regression, random forest, support vector machine, Naïve Bayes, logistic regression, and elastic net Cox regression (for time‐to‐event analysis) were explored as possible algorithms. Model 1 was fit using all variables, model 2 after feature selection using the Boruta package, and model 3 without neuroimaging markers (clinically accessible model). Ten‐fold cross‐validation, repeated ten times, was implemented during training. Upsampling was used to account for imbalanced data. Tuning parameters were optimized for recalibration automatically using the caret package. Result: During training, the Model 2 elastic net regression had the highest AUC [0.78; 95% CI: 0.76‐0.80], sensitivity [72; 95% CI: 68‐75], and specificity [72; 95% CI: 70‐73]. For Model 3, excluding MRI markers, the AUC remained high [AUC 0.75; 95% CI: 0.73‐0.77]. Similar results were found in our test data for Model 3 [AUC 0.74; 95% CI: 0.71‐0.77]; thus, the risk of overfitting was low. Time‐to‐event analysis using elastic net Cox models showed similar discrimination [c‐statistic 0.79] during testing. The most important variables based on Boruta selection included the Activities of Daily Living, presence of APOE e4 allele, memory functioning, and sex. Conclusion: Supervised machine learning could be used to identify individuals at high‐risk for dementia in the general population using easily accessible variables. As dementia becomes a leading problem in developing countries, this clinically accessible model could be explored for use in these areas for better identification of individuals at risk in the community. Further external validation is needed. [ABSTRACT FROM AUTHOR]

Background: Specific subfields within the hippocampus have shown vulnerability to chronic stress, highlighting the importance of looking regionally within the hippocampus to understand the role of psychosocial risk factors in the development of neurodegenerative diseases. The current study had two aims: to perform a systematic review on psychosocial factors and hippocampal subfield volume and to explore the association of psychosocial factors with hippocampal volume, both total volume and among subfields, using high‐field 7T MRI. Method: Within the Memory Depression and Aging (Medea)‐7T study, 333 participants without dementia underwent 7T MRI and answered psychosocial questionnaires. Hippocampal subfields were automatically segmented from T2‐weighted images using ASHS software. Generalized linear models accounting for correlated outcomes were used to assess the association between subfields (i.e., entorhinal cortex, subiculum, Cornu Ammonis (CA)1, CA2, CA3, dentate gyrus, and tail) and each psychosocial factor (i.e., depressive symptoms, anxiety symptoms, childhood maltreatment, recent life events, and social support), adjusting for age, sex, and intracranial volume. Result: Neither depression nor anxiety was associated with specific hippocampal (sub)volumes. Recent life events were associated with greater total volume (B = 0.17, 95% CI: 0.07; 0.27). However, reporting childhood maltreatment was associated with smaller total hippocampal volume (B per standard deviation = ‐0.20, 95% CI: ‐0.31; ‐0.10). Among subfields, high social support was associated with greater volume in the CA3 (B = 0.47, 95% CI: 0.21; 0.73). Conclusion: This study suggests possible differential effects among hippocampal (sub)volumes and psychosocial factors, specifically reporting childhood maltreatment and social support. The systematic review highlighted inconsistencies in results, highlighting the need for future studies validating these results. [ABSTRACT FROM AUTHOR]

Background: The biological underpinnings of the relationship between depression and dementia are currently not completely understood. Previous research has highlighted the role of vascular, metabolic, inflammatory, and stress markers in the role of both depression and dementia. Allostatic load has been an all‐encompassing term to describe these biological factors when investigating subgroups in individuals based on health characteristics. The current study aimed to elucidate if depression subtypes exist based on modifiable allostatic load factors using latent class analysis (LCA), and whether these subtypes have differing risk on incident all‐cause dementia, Alzheimer's disease (AD), and non‐AD diagnosis using Cox regression analyses. Method: The study included 5343 individuals without dementia at baseline from the AGES‐Reykjavik Study. Vascular factors (systolic blood pressure and pulse pressure), cholesterol (high‐density lipoprotein and low‐density lipoprotein), metabolic factors (abdominal circumference, fasting glucose, and triglycerides), inflammation (c‐reactive protein), and stress factors (salivary morning and evening cortisol) were used to indicate allostatic load. Both allostatic load indicators and depressive symptoms (Geriatric Depression Scale‐15) were assessed at baseline. Incident dementia was assessed after 12‐years follow‐up. Allostatic load indicators as well as each item on the GDS‐15 were used in the LCA. Result: LCA revealed that allostatic load factors, more than the depression questionnaire, grouped the individuals in this cohort. We identified six classes, named based on their most identifying characteristics: 'Average' (34% prevalence), 'Cardiovascular + metabolic risk' (27% prevalence), 'Slim' (16% prevalence), 'Depressed' (10% prevalence), 'Hypertension' (10% prevalence), and 'Diabetic' (4% prevalence). Cox regression analyses found an increased risk for incident all‐cause dementia in the 'Slim', 'Depressed', and 'Diabetic' groups. The 'Slim' group had a specific risk for AD, and the 'Depressed' group for non‐AD dementias. Conclusion: The findings suggest subgroups exist in older individuals based on allostatic load factors, with differing risk for incident dementia. This highlights the importance of early interventions in diabetes and depression and in monitoring for dementia symptoms. [ABSTRACT FROM AUTHOR]