Plasma oxysterols are associated with serum lipids and dementia risk in older women.

INTRODUCTION: Apolipoprotein E4 (APOE4) carriers' tendency toward hypercholesterolemia may contribute to Alzheimer's disease (AD) risk through oxysterols, which traverse the blood-brain barrier. METHODS: Relationships between baseline plasma oxysterols, APOE status, serum lipids, and cognitive impairment risk were examined in 328 postmenopausal women from the Women's Health Initiative Memory Study. Women were followed for 25 years or until incident dementia or cognitive impairment. RESULTS: Levels of 24(S)-hydroxycholesterol (24-OHC), 27-hydroxycholesterol (27-OHC), and 24-OHC/27-OHC ratio did not differ by APOE status (p's > 0.05). Higher 24-OHC and 27-OHC were associated with higher total, low density lipoprotein (LDL), non-high density lipoprotein (HDL), remnant, LDL/HDL, and total/HDL cholesterol and triglycerides (p's < 0.05). Higher 24-OHC/27-OHC was associated with greater dementia risk (hazard ratio = 1.51, 95% confidence interval:1.02-2.22), which interaction analyses revealed as significant for APOE3 and APOE4+, but not APOE2+ carriers. DISCUSSION: Less favorable lipid profiles were associated with higher oxysterol levels. A higher ratio of 24-OHC/27-OHC may contribute to dementia risk in APOE3 and APOE4+ carriers.

Association of type 2 diabetes mellitus with dementia-related and non-dementia-related mortality among postmenopausal women: A secondary competing risks analysis of the women's health initiative.

INTRODUCTION: Alzheimer's disease (AD) and AD-related dementias (ADRD) are leading causes of death among older adults in the United States. Efforts to understand risk factors for prevention are needed. METHODS: Participants (n = 146,166) enrolled in the Women's Health Initiative without AD at baseline were included. Diabetes status was ascertained from self-reported questionnaires and deaths attributed to AD/ADRD from hospital, autopsy, and death records. Competing risk regression models were used to estimate the cause-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for the prospective association of type 2 diabetes mellitus (T2DM) with AD/ADRD and non-AD/ADRD mortality. RESULTS: There were 29,393 treated T2DM cases and 8628 AD/ADRD deaths during 21.6 (14.0-23.5) median (IQR) years of follow-up. Fully adjusted HRs (95% CIs) of the association with T2DM were 2.94 (2.76-3.12) for AD/ADRD and 2.65 (2.60-2.71) for the competing risk of non-AD/ADRD mortality. DISCUSSION: T2DM is associated with AD/ADRD and non-AD/ADRD mortality. HIGHLIGHTS: Type 2 diabetes mellitus is more strongly associated with Alzheimer's disease (AD)/AD and related dementias (ADRD) mortality compared to the competing risk of non-AD/ADRD mortality among postmenopausal women. This relationship was consistent for AD and ADRD, respectively. This association is strongest among participants without obesity or hypertension and with younger age at baseline, higher diet quality, higher physical activity, higher alcohol consumption, and older age at the time of diagnosis of type 2 diabetes mellitus.

20-year depressive symptoms, dementia, and structural neuropathology in older women.

INTRODUCTION: The course of depressive symptoms and dementia risk is unclear, as are potential structural neuropathological common causes. METHODS: Utilizing joint latent class mixture models, we identified longitudinal trajectories of annually assessed depressive symptoms and dementia risk over 21 years in 957 older women (baseline age 72.7 years old) from the Women's Health Initiative Memory Study. In a subsample of 569 women who underwent structural magnetic resonance imaging, we examined whether estimates of cerebrovascular disease and Alzheimer's disease (AD)-related neurodegeneration were associated with identified trajectories. RESULTS: Five trajectories of depressive symptoms and dementia risk were identified. Compared to women with minimal symptoms, women who reported mild and stable and emerging depressive symptoms were at the highest risk of developing dementia and had more cerebrovascular disease and AD-related neurodegeneration. DISCUSSION: There are heterogeneous profiles of depressive symptoms and dementia risk. Common neuropathological factors may contribute to both depression and dementia. Highlights The progression of depressive symptoms and concurrent dementia risk is heterogeneous. Emerging depressive symptoms may be a prodromal symptom of dementia. Cerebrovascular disease and AD are potentially shared neuropathological factors.

Personalised indocyanine-guided lymphadenectomy for prostate cancer: a randomised clinical trial.

OBJECTIVES: To study the safety and efficacy of a personalised indocyanine-guided pelvic lymph node dissection (PLND) against extended PLND (ePLND) during radical prostatectomy (RP). PATIENTS AND METHODS: Patients who were candidates for RP and lymphadenectomy, with intermediate- or high-risk prostate cancer (PCa) according to the National Comprehensive Cancer Network guidelines, were enrolled in this randomised clinical trial. Randomisation was made 1:1 to indocyanine green (ICG)-PLND (only ICG-stained LNs) or ePLND (obturator fossa, external, internal, and common iliac and presacral LNs). The primary endpoint was the complication rate within 3 months after RP. Secondary endpoints included: rate of major complications (Clavien-Dindo Grade III-IV), time to drainage removal, length of stay, percentage of patients classified as pN1, number of LNs removed, number of metastatic LNs, rate of patients with undetectable prostate-specific antigen (PSA), biochemical recurrence (BCR)-free survival, and rate of patients with androgen-deprivation therapy at 24 months. RESULTS: A total of 108 patients were included with a median follow-up of 16 months. In all, 54 were randomised to ICG-PLND and 54 to ePLND. The postoperative complication rate was higher in the ePLND (70%) vs the ICG-PLND group (32%) (P < 0.001). Differences between major complications in both groups were not statically significant (P = 0.7). The pN1 detection rate was higher in the ICG-PLND group (28%) vs the ePLND group (22%); however, this difference was not statistically significant (P = 0.7). The rate of undetectable PSA at 12 months was 83% in the ICG-PLND vs 76% in the ePLND group, which was not statistically significant. Additionally, there were no statistically significant differences in BCR-free survival between groups at the end of the analysis. CONCLUSIONS: Personalised ICG-guided PLND is a promising technique to stage patients with intermediate- and high-risk PCa properly. It has shown a lower complication rate than ePLND with similar oncological outcomes at short-term follow-up.

The relationship between optimism, MCI, and dementia among postmenopausal women.

OBJECTIVES: The relationship between optimism and cognitive functioning is not fully understood. We examined the association of optimism with risk of mild cognitive impairment (MCI) and dementia in the Women's Health Initiative Memory Study (WHIMS). METHODS: Optimism was measured by the Life Orientation Test-Revised (LOT-R) total score, and optimism and pessimism subscales. A panel of experts adjudicated cognitive endpoints based on annual cognitive assessments. We used cox proportional hazard regression models to examine the association of LOT-R total score and optimism and pessimism sub-scores with MCI/dementia. We also examined the relationship between vascular disease, LOT-R total score, optimism and pessimism, and cognition. RESULTS: Mean age was 70.5 (SD = 3.9) years. The sample (N = 7249) was 87% white, and 29.8% of participants had < 12 years of education. Total LOT-R score (HR = 0.96, 95% CI: 0.94, 0.98, p < 0.001) was associated with lower risk of combined MCI or dementia. More pessimism (HR = 1.08, 95% CI: 1.05, 1.11, p < 0.0001) was associated with higher risk of MCI or dementia after adjustment for ethnicity, education, vascular disease, and depression. No significant relationships emerged from the optimism subscale. CONCLUSION: These data suggest that less pessimism, but not more optimism, was associated with a lower risk of MCI and dementia.

Associations of dietary cholesterol and fat, blood lipids, and risk for dementia in older women vary by APOE genotype.

INTRODUCTION: Whether apolipoprotein E's (APOE's) involvement in lipid metabolism contributes to Alzheimer's disease (AD) risk remains unknown. METHODS: Incident probable dementia and cognitive impairment (probable dementia+mild cognitive impairment) were analyzed in relation to baseline serum lipids (total, low-density lipoprotein [LDL], high-density lipoprotein [HDL], non-HDL cholesterol, total-to-HDL, LDL-to-HDL, remnant cholesterol, and triglycerides) using Mendelian randomization in 5358 postmenopausal women from the Women's Health Initiative Memory Study. We also examined associations of baseline dietary cholesterol and fat with lipids based on APOE status. RESULTS: After an average of 11.13 years, less favorable lipid levels related to greater dementia and cognitive impairment risk. Dementia (odds ratio [OR] = 3.13; 95% confidence interval [CI]: 2.31 to 4.24) and cognitive impairment (OR = 2.38; 95% CI: 1.85 to 3.06) risk were greatest in relation to higher remnant cholesterol levels. Greater cholesterol consumption related to poorer lipids in APOE4+ compared to APOE3 carriers. DISCUSSION: APOE4+ carriers consuming more cholesterol had less favorable lipids, which were associated with greater dementia and cognitive impairment risk. HIGHLIGHTS: Less favorable serum lipids were associated with higher dementia incidence. Mendelian randomization findings suggest causality between lipids and dementia. Lipid levels in older women may be clinical indicators of dementia risk. APOE4 carriers had poorest lipid profiles in relation to cholesterol consumption. APOE risk for dementia may be modifiable through lipid management.

Blood-based bioenergetic profiling reveals differences in mitochondrial function associated with cognitive performance and Alzheimer's disease.

INTRODUCTION: Despite evidence for systemic mitochondrial dysfunction early in Alzheimer's disease (AD) pathogenesis, reliable approaches monitoring these key bioenergetic alterations are lacking. We used peripheral blood mononuclear cells (PBMCs) and platelets as reporters of mitochondrial function in the context of cognitive impairment and AD. METHODS: Mitochondrial function was analyzed using complementary respirometric approaches in intact and permeabilized cells from older adults with normal cognition, mild cognitive impairment (MCI), and dementia due to probable AD. Clinical outcomes included measures of cognitive function and brain morphology. RESULTS: PBMC and platelet bioenergetic parameters were lowest in dementia participants. MCI platelets exhibited higher maximal respiration than normocognitives. PBMC and platelet respiration positively associated with cognitive ability and hippocampal volume, and negatively associated with white matter hyperintensities. DISCUSSION: Our findings indicate blood-based bioenergetic profiling can be used as a minimally invasive approach for measuring systemic bioenergetic differences associated with dementia, and may be used to monitor bioenergetic changes associated with AD risk and progression. HIGHLIGHTS: Peripheral cell bioenergetic alterations accompanied cognitive decline in older adults with mild cognitive impairment (MCI) and Alzheimer's disease (AD) and related dementia (DEM). Peripheral blood mononuclear cells (PBMC) and platelet glucose-mediated respiration decreased in participants with dementia compared to normocognitive controls (NC). PBMC fatty-acid oxidation (FAO)-mediated respiration progressively declined in MCI and AD compared to NC participants, while platelet FAO-mediated respiration exhibited an inverse-Warburg effect in MCI compared to NC participants. Positive associations were observed between bioenergetics and Modified Preclinical Alzheimer's Cognitive Composite, and bioenergetics and hippocampal volume %, while a negative association was observed between bioenergetics and white matter hyperintensities. Systemic mitochondrial dysfunction is associated with cognitive decline.

Associations of Accelerometer-Measured Physical Activity and Sedentary Time With All-Cause Mortality by Genetic Predisposition for Longevity.

The goal of this study was to examine associations between accelerometer-measured physical activity (PA) and sedentary time (ST) with mortality by a genetic risk score (GRS) for longevity. Among 5,446 women, (mean [SD]: age, 78.2 [6.6] years), 1,022 deaths were observed during 33,350 person-years of follow-up. Using multivariable Cox proportional hazards models, higher light PA and moderate to vigorous PA were associated with lower mortality across all GRS for longevity categories (low/medium/high; all ptrend < .001). Higher ST was associated with higher mortality (ptrend across all GRS categories < .001). Interaction tests for PA and ST with the GRS were not statistically significant. Findings support the importance of higher PA and lower ST for reducing mortality risk in older women, regardless of genetic predisposition for longevity.

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.

Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study.

INTRODUCTION: A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS: AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. RESULTS: Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. CONCLUSIONS: Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.

Bilateral Seminal Vesicle Invasion Is Not Associated with Worse Outcomes in Locally Advanced Prostate Carcinoma.

Background and Objectives: Patients with seminal vesicle invasion (SVI) are a highly heterogeneous group. Prognosis can be affected by many clinical and pathological characteristics. Our aim was to study whether bilateral SVI (bi-SVI) is associated with worse oncological outcomes. Materials and Methods: This is an observational retrospective study that included 146 pT3b patients treated with radical prostatectomy (RP). We compared the results between unilateral SVI (uni-SVI) and bi-SVI. The log-rank test and Kaplan-Meier curves were used to compare biochemical recurrence-free survival (BCR), metastasis-free survival (MFS), and additional treatment-free survival. Cox proportional hazard models were used to identify predictors of BCR-free survival, MFS, and additional treatment-free survival. Results: 34.93% of patients had bi-SVI. The median follow-up was 46.84 months. No significant differences were seen between the uni-SVI and bi-SVI groups. BCR-free survival at 5 years was 33.31% and 25.65% (p = 0.44) for uni-SVI and bi-SVI. MFS at 5 years was 86.03% vs. 75.63% (p = 0.1), and additional treatment-free survival was 36.85% vs. 21.93% (p = 0.09), respectively. In the multivariate analysis, PSA was related to the development of BCR [HR 1.34 (95%CI: 1.01-1.77); p = 0.03] and metastasis [HR 1.83 (95%CI: 1.13-2.98); p = 0.02]. BCR was also influenced by lymph node infiltration [HR 2.74 (95%CI: 1.41-5.32); p = 0.003]. Additional treatment was performed more frequently in patients with positive margins [HR: 3.50 (95%CI: 1.65-7.44); p = 0.001]. Conclusions: SVI invasion is an adverse pathology feature, with a widely variable prognosis. In our study, bilateral seminal vesicle invasion did not predict worse outcomes in pT3b patients despite being associated with more undifferentiated tumors.

Determining diagnosis date of diabetes using structured electronic health record (EHR) data: the SEARCH for diabetes in youth study.

BACKGROUND: Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. METHODS: A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children's hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children's Hospital, Cincinnati, OH, Seattle Children's Hospital, Seattle, WA, and Children's Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. RESULTS: Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. CONCLUSIONS: Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer's disease.

Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 µm (PM2.5) may increase the risk for Alzheimer's disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer's disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer's disease associated with exposure. Participants included older females (n = 998; aged 73-87) enrolled in both the Women's Health Initiative Study of Cognitive Aging and the Women's Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999-2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1-3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005-06; MRI-2: 2009-10). Subjects were assigned Alzheimer's disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer's disease), developed by supervised machine learning and validated with data from the Alzheimer's Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 µg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1-3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer's disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1-3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer's disease risk, independent of cerebrovascular damage.

Indocyanine green guidance improves the efficiency of extended pelvic lymph node dissection during laparoscopic radical prostatectomy.

OBJECTIVES: To evaluate whether indocyanine green guidance can improve the quality of extended pelvic lymph node dissection in patients undergoing radical prostatectomy. METHODS: A total of 214 patients underwent laparoscopic radical prostatectomy with indocyanine green-guided lymph node dissection plus extended pelvic lymph node dissection. These patients (group A) were matched 1:1 for clinical risk groups according to the National Comprehensive Cancer Network classification with patients who underwent the same procedure without fluorescence guidance (group B). Biochemical recurrence was defined as two consecutive prostate-specific antigen rises of at least 0.2 ng/mL. The Kaplan-Meier method and Cox regression models were used to identify predictors of biochemical recurrence. RESULTS: The median number of retrieved nodes was significantly higher in group A (22 vs 14, P < 0.001). The rate of lymph node metastases was higher in group A (65.9% vs 34.1%, P = 0.01). Increasing the yield of lymph node dissection was independently and negatively correlated with the biochemical recurrence risk in both overall and pN-positive patients (hazard ratio 0.97, P = 0.03; and hazard ratio 0.95, P = 0.02). The 5-year biochemical recurrence-free survival rates were (75.8% vs 65.9, P = 0.09) and (54.1% vs 24.9%, P = 0.023) for group A and group B in the overall cohort and pN-positive cohort, respectively. CONCLUSION: Indocyanine green-guided lymph node dissection plus extended pelvic lymph node dissection improves identification of lymphatic drainage, resulting in a higher number of lymph nodes and retrieved lymph node metastases, and allowing a more accurate local staging and a prolonged biochemical recurrence-free survival.

Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning.

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20-50 years), middle-aged (50-70 years], and older adults (70-89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20-89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost's models were high for sedentary (0.955-0.973), locomotion (0.942-0.964) and lifestyle (0.913-0.949) activity types with no apparent difference across age groups. Low (0.919-0.947), light (0.813-0.828) and moderate (0.846-0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835-1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.

The association between neighborhood socioeconomic status, cardiovascular and cerebrovascular risk factors, and cognitive decline in the Health and Retirement Study (HRS).

Background: A small but growing body of evidence supports a relationship between neighborhood socioeconomic status (NSES) and cognitive decline. Additional work is needed to characterize this relationship controlling for risk factors such as cardiovascular, cerebrovascular, and genetic risk factors.Methods: Cognitive decline was assessed in association with NSES, and cardiovascular and cerebrovascular risk factors (heart disease, diabetes, hypertension, and stroke) in 8,198 individuals from the 1992-2010 waves of the Health and Retirement Study (HRS). Latent class trajectory analysis determined the number of cognitive trajectory classes that best fit the data, and a multinomial logistic regression model in the latent class framework assessed the risk for cognitive classes conferred by NSES index score and heart disease, diabetes, hypertension, and stroke across three trajectory classes of cognitive function. The analyses controlled for genetic risk for cognitive decline (including APOE genotype) and demographic variables, including education.Results: The HRS sample was 57.6% female and 85.5% White, with a mean age of 67.5(3.5) years at baseline. The three-quadratic-class model best fit the data, where higher classes represented better cognitive function. Those with better cognitive function were mainly younger white females. Those in the highest quartile of NSES had 57% higher odds of being in the high cognitive function class. Heart disease, diabetes, hypertension, and stroke each increased the odds having of lower cognitive function.Conclusions: In examining the relationship of cognitive status with various variables, neighborhood socioeconomic status, cardiovascular risk, and cerebrovascular risk persisted across the cognitive trajectory classes.

Dementia outcomes after addition of proxy-based assessments for deceased or proxy-dependent participants.

OBJECTIVES: As people age and the incidence of dementia increases, studies of cognitive function continue to be of importance. Ascertaining cognitive data through different mechanisms is necessary to address missing data concerns. METHODS: The Dementia Questionnaire (DQ), which utilizes proxy-based assessments, is a potential tool to determine cognitive status in participants no longer being followed per traditional study protocol. The DQ is currently being used in the Supplemental Case Ascertainment Protocol (SCAP), which is being conducted in an ongoing study of postmenopausal women as part of the Women's Health Initiative Memory Study (WHIMS). RESULTS: Ninety-four percent of the 1260 SCAP participants were eligible because of being deceased. Those who are SCAP eligible were older, were less likely to be a minority, and were more likely to have hypertension, diabetes, and prior history of cardiovascular disease (CVD) as well as being a past or current smoker. SCAP added 109 cases of probable dementia to WHIMS. Risk factor relationships were modified upon inclusion of the SCAP cases including an attenuation of a hormone therapy effect and discovery of a hypertension effect. CONCLUSIONS: Augmenting clinic-based cases with proxy-based assessments is feasible and leads to increased incident cases of dementia. When planning future clinical trials, it may be of study benefit to include a protocol of proxy-based assessments, develop strong relationships with proxies early on in the study, and attempt to maintain this relationship throughout the lifespan of the trial.

A candidate gene study of risk for dementia in older, postmenopausal women: Results from the Women's Health Initiative Memory Study.

OBJECTIVE: While a number of single nucleotide polymorphisms (SNPs) associated with Alzheimer's disease (AD) or cognitive impairment have been identified, independent replications remain the only way to validate proposed signals. We investigated SNPs in candidate genes associated with either cognitive impairment or AD pathogenesis and their relationships with probable dementia (PD) in the Women's Health Initiative Memory Study (WHIMS). METHODS: We analyzed 96 SNPs across five genes (APOE/TOMM40, BDNF, COMT, SORL1, and KIBRA) in 2857 women (ages ≥65) from the WHIMS randomized trials of hormone therapy using a custom Illumina GoldenGate assay; 19% of the sample were MCI (N = 165) or PD (N = 387), and the remaining 81% were free of cognitive impairment. SNP associations were evaluated for PD in non-Hispanic whites adjusting for age and HT using logistic regression under an additive genetic model. RESULTS: One SNP (rs157582), located in the TOMM40 gene nearby APOE, was associated with the PD phenotype based on a P value accounting for multiple comparisons. An additional 12 SNPs were associated with the PD phenotype at P ≤ 0.05 (APOE: rs405509, rs439401; TOMM40: rs8106922, and KIBRA: rs4320284, rs11740112, rs10040267, rs13171394, rs6555802, rs2241368, rs244904, rs6555805, and rs10475878). Results of the sensitivity analyes excluding MCI were similar, with addition of COMT rs737865 and BDNF rs1491850 (P ≤ 0.05). CONCLUSIONS: Our results in older women provide supporting evidence that the APOE/TOMM40 genes confer dementia risk and extend these findings to COMT, BDNF, and KIBRA. Our findings may lead to a better understanding of the role these genes play in cognition and cognitive impairment.

Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases.

INTRODUCTION: The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a well-characterized imaging database of AD patients and cognitively normal subjects. We called this index AD Pattern Similarity (AD-PS) scores. To demonstrate the construct validity of the scores, we investigated their associations with several AD risk factors. The ADNI and WHIMS imaging databases were collected with different goals, populations and data acquisition protocols: it is important to demonstrate that the approach to estimating AD-PS scores can bridge these differences. METHODS: MRI data from both studies were processed using high-dimensional warping methods. High-dimensional classifiers were then estimated using the ADNI MRI data. Next, the classifiers were applied to baseline and follow-up WHIMS-MRI GM data to generate the GM AD-PS scores. To study the validity of the scores we investigated associations between GM AD-PS scores at baseline (Scan 1) and their longitudinal changes (Scan 2 -Scan 1) with: 1) age, cognitive scores, white matter small vessel ischemic disease (WM SVID) volume at baseline and 2) age, cognitive scores, WM SVID volume longitudinal changes respectively. In addition, we investigated their associations with time until classification of independently adjudicated status in WHIMS-MRI. RESULTS: Higher GM AD-PS scores from WHIMS-MRI baseline data were associated with older age, lower cognitive scores, and higher WM SVID volume. Longitudinal changes in GM AD-PS scores (Scan 2 - Scan 1) were also associated with age and changes in WM SVID volumes and cognitive test scores. Increases in the GM AD-PS scores predicted decreases in cognitive scores and increases in WM SVID volume. GM AD-PS scores and their longitudinal changes also were associated with time until classification of cognitive impairment. Finally, receiver operating characteristic curves showed that baseline GM AD-PS scores of cognitively normal participants carried information about future cognitive status determined during follow-up. DISCUSSION: We applied a high-dimensional machine learning approach to estimate a novel AD risk factor for WHIMS-MRI study participants using ADNI data. The GM AD-PS scores showed strong associations with incident cognitive impairment and cross-sectional and longitudinal associations with age, cognitive function, cognitive status and WM SVID volume lending support to the ongoing validation of the GM AD-PS score.

Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study.

BACKGROUND: The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression. METHODS AND FINDINGS: Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and "asymptomatic Alzheimer's disease" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-ß (Aß) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples. CONCLUSIONS: We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.