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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: Microvascular dysfunction may contribute to the development of various cerebral disorders, including cognitive dysfunction, dementia, and certain forms of depression. The suggested mechanism through which microvascular dysfunction contributes to these disorders is by disrupting white matter tracts and altering brain connectivity, but evidence is scarce. We investigated the association between multiple biomarkers of microvascular function and cerebral white matter connectivity. Method: We used cross‐sectional data from The Maastricht Study, a Dutch population‐based cohort (n = 4,326, mean±SD age 59.4±8.6 years, 49.7% women). Measures of microvascular function included urinary albumin excretion, central retinal arteriolar and venular calibers (CRAE and CRVE), a composite score of flicker light‐induced retinal arteriolar and venular dilation response, and a composite score of plasma biomarkers of endothelial dysfunction (soluble intercellular adhesion molecule‐1 (sICAM‐1), soluble vascular cell adhesion molecule‐1 (sVCAM‐1), soluble E‐selectin (sE‐selectin) and von Willebrand factor (vWF)). White matter connectivity was calculated from 3T diffusion MRI to quantify the number (average node degree) and organization (characteristic path length, global efficiency, clustering coefficient and local efficiency) of cerebral white matter connections. Result: A higher endothelial dysfunction composite score was associated with a longer characteristic path length (beta per SD: 0.066 (95% confidence interval: 0.017; 0.114)) after adjustment for sociodemographic, lifestyle and cardiovascular factors, but not with any of the other white matter connectivity measures. All other measures of microvascular dysfunction were not associated with any of the connectivity measures after adjustments. Conclusion: The present study suggests that microvascular dysfunction, as quantified by a set of various measures, is not consistently associated with cerebral white matter connectivity. Only a composite score of plasma biomarkers of endothelial dysfunction was associated with a longer characteristic path length, with a longer characteristic path length being indicative for a topographically less integrated and less efficient cerebral network. Future cross‐sectional and longitudinal studies are needed to further understand the role of microvascular dysfunction in the development of cerebral disorders. [ABSTRACT FROM AUTHOR]

Background: The need persists for sensitive, low‐cost, and high‐access cognitive markers to complement biomarker information in the earliest stages of Alzheimer's disease. Sensitive cognitive markers may be derived from information at the item level of neuropsychological tests. We investigated if item‐level metrics add information beyond total score in the relationship of semantic fluency—generating as many words as possible of a category in a given time frame—to cortical thinning across eight years in middle‐aged to older adults without dementia. Method: Participants were part of the SMART‐MR cohort (n = 251, age = 59.6±8.6, 19.5% women), designed to investigate brain changes in individuals with arterial disease, which puts them at high risk for dementia. Global cortical thickness was derived from MRI scans at two visits approximately eight years apart. Item‐level metrics were derived from linguistic databases: mean log lexical frequency (how often a word occurs in our language) and mean age of acquisition (AoA; at what age a word was learned) of words generated on a 2‐minute semantic (animal) fluency task (Fig 1). We calculated residualized item‐level scores representing the deviation in the item‐level metric from what is expected based on the total score to account for collinearity. Linear regression models—adjusted for age, sex/gender, education, and history of stroke—analyzed the relationship between total score or item‐level metrics as determinants and cortical thickness at follow‐up as the outcome, adjusting for baseline level. Result: Total score alone was not related to cortical thinning at follow‐up (β = ‐.023[‐.001;.000], p =.466). However, a higher‐than‐expected mean lexical frequency (β = ‐.065[‐.054;‐.002], p =.033) and lower‐than‐expected mean AoA (β =.066[.001;.019], p =.033) were related to more cortical thinning at follow‐up. An alternative analytic approach to capture change, using the difference in cortical thickness between visits without baseline adjustment, yielded similar results. Conclusion: Alternative item‐level metrics of semantic fluency, as opposed to the traditional total score, related to future neurodegeneration in high‐risk individuals without a dementia diagnosis. The results seem robust across two different item‐level metrics and two different analytic approaches. Valuable item‐level information could be leveraged for accurate identification of the earliest stages of clinical Alzheimer's disease, in addition to biomarker evidence, for intervention and timely diagnosis. [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]

Abstract: Background: Homocysteine may be a modifiable risk factor for cognitive decline and brain atrophy, particularly in older persons. We examined whether homocysteine increased the risk for cognitive decline and brain atrophy, and evaluated the modifying effect of age. Methods: Within the Second Manifestations of ARTerial disease-Magnetic Resonance study—a prospective cohort study among patients with atherosclerotic disease—longitudinal analyses were performed in 663 patients (mean age: 57 ± 9 years; follow-up: 3.9 ± 0.4 years). At baseline and follow-up, brain segmentation on magnetic resonance imaging was used to quantify relative (%) cortical, ventricular, and global brain volumes, and z-scores of memory and executive functioning were calculated. Linear regression analysis was used to estimate associations of homocysteine (per standard deviation increase) and hyperhomocysteinemia (HHCY) with brain volumes, memory, and executive functioning at follow-up, adjusted for baseline brain volume, memory, and executive functioning, respectively, and age, sex, and vascular risk factors. Furthermore, interaction terms between homocysteine and age (continuous) were added. Results: Significant interactions were observed between total plasma homocysteine (tHcy) and age with cortical, ventricular, and global brain volume (for all three measures: P < .05), and between HHCY and age with executive functioning (P = .04), and results were stratified by age. In patients aged ≥65 years, increasing tHcy level and HHCY were significantly associated with progression of ventricular enlargement (B = 0.07%, 95% confidence interval [CI]: 0.01% to 0.13% and B = 0.16%, 95% CI: 0.01% to 0.31%, respectively) and with a decline in executive function (B = −0.29, 95% CI: −0.54 to −0.04 and B = −0.84, 95% CI: −1.37 to −0.32, respectively). Conclusion: Elevated tHcy was related to progression of ventricular enlargement and increased the risk for a decline in executive functioning in older persons. [Copyright &y& Elsevier]

Background: The hippocampal formation is considered to be one of the first brain structures affected in Alzheimer's disease. High resolution brain MRI has made it possible to visualize the subfields of the hippocampal formation in greater detail. We estimated volumes of hippocampal subfields on 7T MRI in normal aging and early Alzheimer's disease. We hypothesized that the subfields were differentially affected in normal cognition, subjective memory impairment, Mild Cognitive Impairment (MCI) and Alzheimer's disease. Method: We included 336 participants (68±9 years; 35% women) from memory clinics and from existing cohorts (SMART‐MR study and PREDICT‐MR study), and participants without dementia and MCI recruited from medical files from general practices, all of whom had 7T brain MRI using the same scan protocol. Using ASHS, hippocampal subfields (entorhinal cortex, subiculum, CA1, CA2, CA3, CA4/dentate gyrus, and tail) were segmented. Participants were categorized as having normal cognition (n=175), subjective memory impairment (n=66), cognitive impairment or MCI (n=75), and Alzheimer's disease (n=20). General linear models with multivariate outcomes (Z‐scores of hippocampal subfield volumes) were estimated across cognitive groups, and adjusted for age, sex and intracranial volume. Result: In the total study sample, subfield volumes (left and right hemisphere summed) (mm3) were 817±167 for entorhinal cortex, 1143±181 for subiculum, 2923±382 for CA1, 117±23 for CA2, 195±47 for CA3, 1547±245 for CA4/DG, and 284±69 for the tail. Compared to all other groups, Alzheimer's disease patients had significantly smaller subfield volumes (p<0.05 for all subfields). Participants with cognitive impairment or MCI had smaller CA1 and CA4/dentate gyrus (p<0.05), compared to normal cognition, but not other subfields. Participants with subjective memory impairment did not differ significantly from the group with normal cognition (Figure 1). Conclusion: This study suggests that in MCI the CA1 and CA4/dentate gyrus are primarily affected, and that all hippocampal subfields are progressively affected in Alzheimer's disease. No volume loss was observed in participants with subjective memory impairment, potentially due to inclusion of participants with etiologies other than Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) may be a marker of preclinical Alzheimer's disease (AD) in some individuals and is related to biomarkers of AD. We investigated the longitudinal associations of SCD and APOE genotype with incidence of dementia and AD. Method: Data are from 4,349 older adults (76±5 years, 59% women) without dementia who participated in the population‐based AGES‐Reykjavik Study. SCD was assessed with the question 'Has your memory gotten worse in the past 12 months'. A 1.5T brain MRI was performed in all participants. Five mutually exclusive groups were defined: 1) normal (i.e. no SCD, no MCI, no dementia)/APOE e4 negative (n=2039); 2) normal/APOE e4 positive (n=711); 3) SCD/APOE e4 negative (n=806); 4) SCD/APOE e4 positive (n=355); 5) MCI (regardless of APOE) (n=438). MCI and dementia, including AD, were assessed according to international criteria. We used Cox regression analyses adjusted for age, sex, and education to estimate the Hazard Ratios of each group with incident dementia, including AD and non‐AD dementia. Result: During on average 9 years of follow‐up, 843 persons developed dementia, including 397 with AD. Within the normal/APOE e4 negative group conversion rate to dementia was 13%; these rates were 20%, 20%, 30%, and 38% for groups 2 through 5, respectively. For AD, the conversion rates for groups 1 through 5 were 6%, 12%, 10%, 21%, and 23%. Cox regression analyses showed that compared to the normal/APOE e4 negative group, the normal/APOE e4 positive group had 2.48 times higher risk of AD (95% CI 1.84‐3.34), the SCD/APOE e4 negative group 1.78 times higher (1.31‐2.42), the SCD/APOE e4 positive group 5.23 times higher (3.83‐7.16), and the MCI group 4.43 times higher (3.24‐6.06) (Figure 1). Conclusion: Subjective cognitive decline and APOE e4 positivity each increased the risk of AD development, but the combination of a subjective cognitive symptom and a biomarker posed the strongest risk for AD, comparable to that of MCI. These findings may help to identify high‐risk groups in the population to target for preventive or therapeutic interventions. [ABSTRACT FROM AUTHOR]

Background: Multivariable prognostic models in the general population predict individual risk of developing Alzheimer's disease (AD) or all‐cause dementia, which can aid selection of high‐risk individuals for clinical trials and prevention. Development of new models flourished across the past decades. However, external validation for the majority of models is lacking. We aimed to evaluate external performance of previously published prediction models for AD or all‐cause dementia. Method: Based on multiple systematic literature reviews, we identified 12 eligible models for external validation in the AGES‐Reykjavik Study, a prospective population‐based cohort study (n=5343 without dementia at baseline; 76.6±5.7 years). Models were developed to predict either AD or all‐cause dementia up to 5, 6, or 10 years of follow‐up, and were based on logistic, Cox, or competing risk regression. Predictive performance was assessed with C‐statistics (discrimination) and calibration plots (observed vs. predicted probabilities). Result: During 45,021 and 40,917 person‐years of follow‐up, 1099 participants developed dementia and 492 participants developed AD, respectively. The mean observed risk of dementia was 5.1% [CI:4.5‐5.7] for 5 years, 10.1% [CI:9.2‐11.0] for 6 years, and 22.8% [CI:21.5‐24.1] for 10 years; the risk for AD was 2.0% [CI:1.6‐2.4], 5.6% [CI:4.9‐6.3], and 12.1% [11.0‐13.2], respectively. Table 1 presents the validated models and their C‐statistics. Models ranged from 2‐11 predictors; all models with a cognitive test obtained C‐statistics ≥0.75, all models without ≤0.73. Information to perform calibration was provided for 5 models, and all but one showed overestimation of predicted risk (Figure 1). Conclusion: The majority of models showed acceptable discrimination (C‐statistic >0.70), but poor calibration with systematic overestimation. These models may be acceptable for exclusion of dementia or AD (if the predicted risk is low, the observed risk is even lower), but lack the ability to accurately identify individuals at higher risk. The one model that calibrated well was developed with logistic regression, a technique that ignores observation times and censoring, which can introduce bias into the prediction process. Updating existing models or development and external validation of new models aimed at identifying high‐risk individuals using Cox regression or competing risk regression is therefore warranted. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline is increasingly being viewed as an early cognitive marker of preclinical Alzheimer's disease (AD). Studies have recommended including AD biomarkers to increase the specificity of subjective cognitive decline, because of its etiological heterogeneity. We studied the prevalence and burden of cerebral microbleeds in persons with normal cognition, subjective memory impairment, cognitive impairment, and a clinical diagnosis mild cognitive impairment (MCI) or AD. Methods: Data are from 386 participants (68±9 years, 30% women) included in the Medea‐7T cohort, a diverse cohort of persons with normal cognition, patients with a history of vascular disease, and memory clinic patients. A 7T brain MRI was performed in all participants. Four cognitive stages were defined: 1) normal cognition (n=196); 2) subjective memory impairment (n=78); 3) cognitive impairment (defined as <1.5 SD below age, sex, and education adjusted Z‐scores in any cognitive domain within the study sample) (n=57); 4) MCI/AD diagnosed according to international criteria (n=36). CMBs were visually rated on 7T MRI according to established criteria. Age‐ and sex‐adjusted log‐binomial regression was performed to examine the association between the cognitive stages and presence of CMBs (yes vs. no). Results: Overall microbleed prevalence was 61%, with estimates per cognitive stage: normal cognitive function 60%, SCD 73%, cognitive impairment 55%, MCI and AD 47% (Figure 1). Age‐ and sex‐adjusted log‐binomial regression showed that—compared with the normal group—differences in microbleed prevalence were not statistically significant. Nonetheless, SCD showed an increased risk of CMBs (RR=1.18; 95% CI, 0.99‐1.41; p=0.06), whereas MCI/AD showed a decreased risk (RR=0.68; 95% CI, 0.45‐1.02; p=0.06), both approaching statistical significance. Conclusion: In this study, cerebral microbleeds on 7T MRI were highly prevalent in older persons with normal cognition, subjective memory impairment, and MCI/AD. Differences observed between groups may reflect differences in underlying dominant etiology. [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensities (WMHs) are associated with cognitive decline and increased risk of dementia, including Alzheimer's disease. However, the association between WMH and cognitive functioning is weak, presumably due to heterogeneity of the underling WMH pathology. Recently, WMH shape has been related to the severity of the underlying pathology and, compared to volume, may provide a novel metric for WMH burden. We examined the relationship between WMH volume, shape and cognitive functioning in patients with arterial disease. We hypothesized that WMH shape was related to cognitive functioning, independent of WMH volume. Methods: In the SMART‐MR study, we performed cross‐sectional analyses in 563 patients (58±10 years) with available brain MRI and data on cognitive functioning. WMH volume and shape were automatically determined on MRI data. Linear regression analyses were used to estimate the association between Z‐scores of WMH volume, WMH shape features (concavity index, solidity, convexity, fractal dimension and eccentricity) and Z‐scores of cognitive functioning, adjusted for age, sex, education, and reading ability. Results: A larger WMH volume was associated with poorer executive functioning (b=0.09; 95% CI ‐0.17 to ‐0.01; p=0.02), but not memory functioning. A more complex shape of periventricular/confluent WMH was associated with poorer executive functioning (concavity index: [b=‐0.13; 95% CI ‐0.20 to ‐0.06; p <0.0001]; solidity: [b=0.09; 95% CI 0.02 to 0.17; p=0.014) and poorer memory scores (fractal dimension: b=‐0.10; 95% CI ‐0.18 to ‐0.02; p=0.02). Moreover, the association between WMH shape measured by the concavity index and executive functioning was independent of WMH volume (b=‐0.12; 95% CI ‐0.19 to ‐0.04; p=0.003). Figure 1 presents an example of two patients with similar WMH volume but a 2.7 standard deviation difference in concavity index. Conclusions: A more complex shape of WMH shape was associated with poorer cognitive functioning. Our results suggest that WMH shape contains additional information about WMH burden, not otherwise captured by WMH volume. [ABSTRACT FROM AUTHOR]

Background: Late‐life depression (LLD) is a major risk factor for dementia, including Alzheimer's disease. However, not every patient with LLD will develop dementia or show neurodegeneration. Since LLD is heterogeneous in symptom presentation subtypes may exist within LLD that have a higher risk of dementia than others. We aimed to 1) identify subtypes in LLD using latent class analyses (LCA), and 2) examine how these subtypes were related to dementia and MRI markers of neurodegenerative and vascular pathology over time. Method: For 2430 participants (75±6 years; 41% male) without dementia from the population‐based AGES‐Reykjavik Study the Geriatric Depression Scale (GDS‐15) was administered and 1.5T brain MRI was performed at baseline and follow‐up after 5 years. Dementia, including AD, was assessed according to international criteria during an average follow‐up of 9 years. We performed LCA with GDS‐15 items examining 1 to 7 classes. Cox regression analyses adjusted for age, sex, and education were used to estimate the Hazard Ratios of LLD subtype with incident dementia. ANCOVA was used to examine the difference in hippocampal and white matter lesion volume (WML) from baseline to follow‐up, adjusted for age, sex, education and ICV. Result: LCA with 5 classes rendered best fitting results: no depression (57%); apathy (31%); apathy with emptiness (2%), mild depression (8%) and severe depression (2%). Severe depression (HR=1.87; 95%CI: 1.2‐2.9), mild depression (HR=1.28; 95% CI:1.1‐1.6) and apathy (HR=1.39; 95% CI:1.2‐1.6) had increased risk of developing dementia compared to the no depression group (Figure 1). Figure 2 shows the hippocampal decline(Figure 2A) and increase in WML(Figure 2B) per LLD subgroup; compared to the no depression group the subtype with severe LLD showed most hippocampal decline (F(4,2429)= 2,28; p =0.04) and increase in WML (F(4,2429)= 2,72; p =0.02). Conclusion: This study shows that different subtypes of LLD can be identified. Severe depression as well as apathy were associated with increased risk of dementia, but only severe LLD was related to progression of markers of neurodegeneration and vascular pathology. [ABSTRACT FROM AUTHOR]

We have calculated the optical and electronic properties of several conjugated organic polymers: poly(p‐phenylene‐vinylene) (PPV) and its derivatives. Cyano substitutions on the phenylene ring: poly(2,5‐dicyano‐p‐phenylene‐vinylene) (2,5‐DCN‐PPV) and on the vinylene linkage: poly(p‐phenylene‐7(,8)‐(di)cyano‐vinylene) are considered. In addition, poly(quinoxaline‐vinylene) (PQV) is studied. The infinite isolated quasi‐1D chains are treated with periodic boundary conditions, using atomic basis sets. In a comparative study of PPV, some issues regarding the selection of the functionals and basis sets are discussed and excitation energies derived from time‐dependent and from ordinary methods are compared. It is concluded that for these polymers the calculations are informative at the B3LYP/6‐31G** density functional theory (DFT) level. The absolute values might change with improved methods, but the similarity of the polymers suggests that the relative characterization is adequate. Band structures are communicated along with characteristics of the highest occupied and the lowest unoccupied crystal orbitals (HOCO and LUCO). Electron affinities, ionization potentials, valence and conduction bandwidths, and effective masses at the bandgap are given. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2006 [ABSTRACT FROM AUTHOR]

Quantum molecular similarity (QMS), for which the first measures were introduced by Carbó in the early 1980s, has been shown to be an important concept when comparing properties and reactivity of different, albeit mostly related, molecules. In this article we investigate how various subfields of Density Functional Theory (DFT) contribute in their own way to the evaluation and the understanding of QMS, reflecting the combined interest of our group in both domains in recent years. The contribution of computational DFT is easy to pinpoint, as DFT can now be used in the evaluation of electron densities of ever-increasing quality. When establishing a link between the fundamental(s) of DFT and QMS the role of the shape function turns out to be predominant and an extension of Mezey's Holographic Electron Density Theorem for p(r) to the shape function σ(r) is presented. On this basis, similarity in shape is the fundamental issue to be looked at, both globally and locally. As an application, global and local similarity measures were evaluated using the Hirshfeld partitioning technique for the two enantiomers of CHFClBr, giving numerical evidence for the Holographic Electron Density Theorem. As an application of conceptual DFT, similarity indices constructed via DFT based local reactivity descriptors (e.g., local softness) are used to probe the ‘similarity of reactivity’ of a series of peptide isosteres. The use of the autocorrelation function for condensed indices turns out to be a valuable technique to circumvent the orientation-translation dependence of similarity indices. [ABSTRACT FROM AUTHOR]

Bioelectrical impedance spectroscopy (BIS) can be a valuable tool in assessing changes in body composition. Although the validity of BIS in healthy subjects is relatively good, in patients considerably larger measurement errors have been reported. In this article the clinical usefulness of BIS in assessing nutritional status of one case study will be discussed. Interpretation of the predictions of BIS in this unstable patient was difficult. This is in agreement with the consensus that BIS does not give accurate prediction of body composition in individual patients. It is recommended that validation studies of BIS should focus on clinical aspects which can influence BIS measurements. [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]

Background: Worse semantic than letter fluency performance is a clinical marker of Alzheimer's disease (AD), but the longitudinal course of performance on these tasks in pre‐dementia stages remains undefined. This study investigated how many years before clinical AD the performance on the two verbal fluency tasks starts to diverge, and if this process develops similarly across race/ethnicity groups. Method: To estimate the trajectories and inflection point of verbal fluency performance in years prior to AD diagnosis, we performed piece‐wise linear mixed effects models in a diverse sample of 569 individuals (mean age = 78.5) from a community‐based cohort who were cognitively normal at baseline but developed dementia across 10 years of follow‐up (up to 5 visits). Performance was standardized on 569 age, sex/gender, education, and race/ethnicity matched controls who remained cognitively healthy during follow‐up (i.e., robust norms approach). Models were adjusted for recruitment wave and demographic factors. Result: AIC model comparison of spline‐fit revealed that prior to AD diagnosis, performance on both fluency tasks started to decline more rapidly 3.6 years before diagnosis (slope within later timeframe: semantic: B=‐1.34 [‐1.52, ‐1.16], p<.001; letter: B=‐.56 [‐.70, ‐.42], p<.001). Point‐in‐time performance on semantic fluency became worse than letter fluency as of approximately 2.8 years before AD diagnosis due to the disproportionally fast decline of semantic fluency. Stratified models showed that the inflection point for Whites was earlier than for Blacks and Hispanics, but that the rate of decline and flip in performance between the two tasks was similar across race/ethnicity. Conclusion: These results show how the conventional clinical index of worse semantic than letter fluency develops over time in the years before AD diagnosis. This study highlights the importance of serial neuropsychological assessments in detecting high‐risk individuals by showing that the differential rate of decline in semantic versus letter fluency is a sensitive preclinical AD‐marker, equivalent across race/ethnicity. [ABSTRACT FROM AUTHOR]

Background: Accurate identification of the preclinical phase of Alzheimer's disease (AD) is important for intervention and timely diagnosis. Some researchers suggest that this phase is only detectable using biomarkers, but these are expensive and/or invasive. Because not all biomarker‐positive individuals develop AD‐dementia and cognitive impairment is at the core of this diagnosis, there is a critical need for sensitive, low‐cost, and high‐access cognitive markers. This study investigated how novel item‐level metrics of semantic fluency—naming as many animals in one minute—uniquely relate to 1) memory decline and 2) mild cognitive impairment (MCI). Method: To investigate the effect of traditional and novel metrics of semantic fluency on both outcomes in a diverse community‐based cohort followed up to 24 years (mean age=75), we used 1) growth curve models of memory decline (n=534, modeled up to 4 visits), and 2) Cox regression models of time to incident MCI (n=349, free of MCI at baseline and >1 visit). Each word's lexical frequency value—how often a word occurs in daily language—was derived from SUBTLEXus. Models with total number score were compared to two novel metrics: ratio of mean frequency:number and mean frequency of the lowest 10 frequency words. Covariates included age, sex/gender, education, race/ethnicity, recruitment wave, and all other neuropsychological scores. Results: Both novel metrics of ratio (standardized B=‐.049, p=.003) and lowest 10 (B=‐.024, p=.003) predicted rate of memory decline over and above other neuropsychological tasks, while total number did not (B=.015, p=.055). The novel metrics were also predictive of time to incident MCI over and above other neuropsychological tasks (HR ratio =1.43, HR lowest 10=1.54, both p<.05), while total number was not (HR=.74, p=.141). Conclusion: Novel item‐level metrics of semantic fluency predicted rate of memory decline and incident MCI over and above other neuropsychological tasks, while the traditional fluency score did not. Item‐level data hold a wealth of information that could detect cognitive symptoms in the preclinical phase of dementia beyond our current neuropsychological measures. Future research should investigate more item‐level data to identify optimal metrics that can be used as clinical tools and aid selection of high‐risk individuals for intervention. [ABSTRACT FROM AUTHOR]

Background: Histopathological studies suggest that microinfarcts in the deep gray matter are associated with ante‐mortem cognitive impairment, however in vivo data on subcortical microinfarcts are lacking. We examined the association of microinfarcts in the deep gray matter on 7T MRI with vascular risk factors, MRI markers of cerebrovascular disease and cognitive functioning in patients with a history of arterial disease. Method: Within the SMART‐MR study, 213 patients (68 ± 8 years) had risk factor assessment, a 7T and 1.5T brain MRI, and cognitive examination. Microinfarcts in the deep gray matter on 7T MRI were defined as lesions ≤4 mm. Regression models were used to examine the age‐adjusted associations between risk factors, MRI markers and presence of microinfarcts in the deep gray matter. Cognitive function was summarized as composite and domain‐specific z‐scores. Result: A total of 47 microinfarcts were found in 28 patients (13%). Older age, history of stroke, hypertension and intima‐media thickness were significantly associated with microinfarcts. On 1.5T MRI, cerebellar infarcts (RR = 2.75, 95% CI: 1.42 ‐ 5.33), lacunes in the white (RR = 3.28, 95% CI: 3.28 ‐ 6.04) and deep gray matter (RR = 3.06, 95% CI: 1.75 ‐ 5.35) were associated with microinfarcts, and on 7T MRI cortical microinfarcts (RR = 2.33, 95% CI: 1.32 ‐ 4.13). Microinfarcts were also associated with poorer global cognitive functioning (mean difference in global z‐score between patients with multiple microinfarcts vs. none = ‐0.97, 95% CI: ‐1.66 to ‐0.28, p = 0.006) and across all cognitive domains, independent of age, education level and number of infarcts ≥5 mm in the deep gray matter. Conclusion: Microinfarcts in the deep gray matter on 7T MRI were associated with MRI markers of small and large vessel disease and worse cognitive functioning. These findings suggest that microinfarcts in the deep gray matter represent a novel imaging marker of vascular brain injury. [ABSTRACT FROM AUTHOR]

Presents a case of dehydration and renal insufficiency during rapid opioid detoxification without general anesthesia. Laboratory profile of the patient; Administration of opioid antagonist naltrexone; Presence of withdrawal symptoms.