Cross-Platform Comparison of Highly Sensitive Immunoassays for Inflammatory Markers in a COVID-19 Cohort.

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing Ab pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on human serum samples from the National Institutes of Health IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. First, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Second, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Third, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

Cross-platform comparison of highly-sensitive immunoassays for inflammatory markers in a COVID-19 cohort 1.

A variety of commercial platforms are available for the simultaneous detection of multiple cytokines and associated proteins, often employing antibody pairs to capture and detect target proteins. In this study, we comprehensively evaluated the performance of three distinct platforms: the fluorescent bead-based Luminex assay, the proximity extension-based Olink assay, and a novel proximity ligation assay platform known as Alamar NULISAseq. These assessments were conducted on serum samples from the NIH IMPACC study, with a focus on three essential performance metrics: detectability, correlation, and differential expression. Our results reveal several key findings. Firstly, the Alamar platform demonstrated the highest overall detectability, followed by Olink and then Luminex. Secondly, the correlation of protein measurements between the Alamar and Olink platforms tended to be stronger than the correlation of either of these platforms with Luminex. Thirdly, we observed that detectability differences across the platforms often translated to differences in differential expression findings, although high detectability did not guarantee the ability to identify meaningful biological differences. Our study provides valuable insights into the comparative performance of these assays, enhancing our understanding of their strengths and limitations when assessing complex biological samples, as exemplified by the sera from this COVID-19 cohort.

NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing.

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range of the plasma proteome. Here we address these challenges with NUcleic acid Linked Immuno-Sandwich Assay (NULISA™), which improves the sensitivity of traditional proximity ligation assays by ~10,000-fold to attomolar level, by suppressing assay background via a dual capture and release mechanism built into oligonucleotide-conjugated antibodies. Highly multiplexed quantification of both low- and high-abundance proteins spanning a wide dynamic range is achieved by attenuating signals from abundant targets with unconjugated antibodies and next-generation sequencing of barcoded reporter DNA. A 200-plex NULISA containing 124 cytokines and chemokines and other proteins demonstrates superior sensitivity to a proximity extension assay in detecting biologically important low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA makes broad and in-depth proteomic analysis easily accessible for research and diagnostic applications.

Social problems and brain structure development following childhood mild traumatic brain injury.

Childhood mild traumatic brain injury (mTBI) is associated with elevated risk of developing social problems, which may be underpinned by changes in the structural developmental trajectory of the social brain, a network of cortical regions supporting social cognition and behavior. However, limited sample sizes and cross-sectional designs generally used in neuroimaging studies of pediatric TBI have prevented explorations of this hypothesis. This longitudinal retrospective study examined the development of parent-reported social problems and cortical thickness in social brain regions following childhood mTBI using data from the large population-based Adolescent Brain Cognitive Development (ABCD) Study. Two-group latent change score models revealed different developmental trajectories from ages 10-12 years in the level of social problems between children with (n = 345) and without (n = 7,089) mTBI. Children with mTBI showed higher, but non-clinical, levels of social problems than controls at age 10. Then, social problems decreased over 2 years, but still remained higher, but non-clinical, than in controls in which they stayed stable. Both groups showed similar decreases in social brain cortical thickness between ages 10 and 12 years. Further studies providing detailed information on the injury mechanism and acute symptoms are needed to better understand individual differences in social functioning and brain development in pediatric TBI.

No increase in inflammation in late-life major depression screened to exclude physical illness.

Depression is a common and debilitating disorder in the elderly. Late-life depression (LLD) has been associated with inflammation and elevated levels of proinflammatory cytokines including interleukin (IL)-1ß, tumor necrosis factor-alpha, and IL-6, but often depressed individuals have comorbid medical conditions that are associated with immune dysregulation. To determine whether depression has an association with inflammation independent of medical illness, 1120 adults were screened to identify individuals who had clinically significant depression but not medical conditions associated with systemic inflammation. In total, 66 patients with LLD screened to exclude medical conditions associated with inflammation were studied in detail along with 26 age-matched controls (HC). At baseline, circulating cytokines were low and similar in LLD and HC individuals. Furthermore, cytokines did not change significantly after treatment with either an antidepressant (escitalopram 20 mg/day) or an antidepressant plus a COX-2 inhibitor or placebo, even though depression scores improved in the non-placebo treatment arms. An analysis of cerebrospinal fluid in a subset of individuals for IL-1ß using an ultrasensitive digital enzyme-linked immunosorbent assay revealed low levels in both LLD and HC at baseline. Our results indicate that depression by itself does not result in systemic or intrathecal elevations in cytokines and that celecoxib does not appear to have an adjunctive antidepressant role in older patients who do not have medical reasons for having inflammation. The negative finding for increased inflammation and the lack of a treatment effect for celecoxib in this carefully screened depressed population taken together with multiple positive results for inflammation in previous studies that did not screen out physical illness support a precision medicine approach to the treatment of depression that takes the medical causes for inflammation into account.

Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment.

Resting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However, current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. In this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products (1) the estimated connectivity/symptom coefficients, (2) a vectorized e-field model for site/orientation, (3) rsFC matrix, scaled by a proportionality constant. In AM patients, our connectivity-based model predicted a significant decrease depression for sites near BA9, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. These results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact.

Mitigating site effects in covariance for machine learning in neuroimaging data.

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat-harmonized data retain accurate prediction of disease group.

A cross-national study of depression in preclinical dementia: A COSMIC collaboration study.

INTRODUCTION: Depression commonly accompanies Alzheimer's disease, but the nature of this association remains uncertain. METHODS: Longitudinal data from the COSMIC consortium were harmonized for eight population-based cohorts from four continents. Incident dementia was diagnosed in 646 participants, with a median follow-up time of 5.6 years to diagnosis. The association between years to dementia diagnosis and successive depressive states was assessed using a mixed effect logistic regression model. A generic inverse variance method was used to group study results, construct forest plots, and generate heterogeneity statistics. RESULTS: A common trajectory was observed showing an increase in the incidence of depression as the time to dementia diagnosis decreased despite cross-national variability in depression rates. DISCUSSION: The results support the hypothesis that depression occurring in the preclinical phases of dementia is more likely to be attributable to dementia-related brain changes than environment or reverse causality.

Effect of escitalopram dose and treatment duration on CSF Aß levels in healthy older adults: A controlled clinical trial.

OBJECTIVE: To determine whether treatment with escitalopram compared with placebo would lower CSF ß-amyloid 42 (Aß42) levels. RATIONALE: Serotonin signaling suppresses Aß42 in animal models of Alzheimer disease (AD) and young healthy humans. In a prospective study in older adults, we examined dose and treatment duration effects of escitalopram. METHODS: Using lumbar punctures to sample CSF levels before and after a course of escitalopram treatment, cognitively normal older adults (n = 114) were assigned to placebo, 20 mg escitalopram × 2 weeks, 20 mg escitalopram × 8 weeks, or 30 mg escitalopram × 8 weeks; CSF sampled pretreatment and posttreatment and within-subject percent change in Aß42 was used as the primary outcome in subsequent analyses. RESULTS: An overall 9.4% greater reduction in CSF Aß42 was found in escitalopram-treated compared with placebo-treated groups (p < 0.001, 95% confidence interval [CI] 4.9%-14.2%, d = 0.81). Positive baseline Aß status (CSF Aß42 levels <250 pg/mL) was associated with smaller Aß42 reduction (p = 0.006, 95% CI -16.7% to 0.5%, d = -0.52) compared with negative baseline amyloid status (CSF Aß42 levels >250 pg/mL). CONCLUSIONS: Short-term longitudinal doses of escitalopram decreased CSF Aß42 in cognitively normal older adults, the target group for AD prevention. CLINICALTRIALSGOV IDENTIFIER: NCT02161458. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for cognitively normal older adults, escitalopram decreases CSF Aß42.

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

Executive function predicts decline in mobility after a fall: The MYHAT study.

BACKGROUND: Evidence suggests that better cognitive functioning is associated with better mobility in older age. It is unknown whether older adults with better cognitive function are more resilient to mobility decline after a fall. METHODS: Participants from the Monongahela Youghiogheny Healthy Aging Team (MYHAT) study were followed annually for up to 9 years for incident falls. We examined one-year (mean 1.0 year, SD 0.1) change in mobility pre- to post-fall using the Timed Up and Go (TUG) in relation to pre-fall cognition (executive function, attention, memory, and visuospatial function) among incident fallers (n = 598, mean age 79.1, SD = 7.0). Linear regression models tested the association of cognition with change in TUG. Interaction terms were tested to explore if age, sex, body mass index, physical activity, depressive symptoms, or visual acuity modified the associations of cognition and mobility among fallers. The association between cognition and one-year change in TUG was also tested in a comparison sample of non-fallers (n = 442, mean age 76.3, SD = 7.2). RESULTS: Overall, mobility decline was greater in fallers compared to non-fallers. In fully-adjusted models, higher executive function, but not attention, memory, or visuospatial function, was associated with less decline in mobility among incident fallers. The effect was significantly stronger for those who were older, sedentary, and had lower body mass index. Higher scores in memory tests, but not in other domains, was associated with less mobility decline among non-fallers. CONCLUSIONS: Higher executive function may offer resilience to mobility decline after a fall, especially among older adults with other risk factors for mobility decline. Future studies should assess whether executive function may be a helpful risk index of fall-related physical functional decline in geriatric settings.

Aging, Diabetes, Obesity, and Cognitive Decline: A Population-Based Study.

BACKGROUND/OBJECTIVES: To investigate potential mechanisms underlying the well-established relationship of diabetes and obesity with cognitive decline, among older adults participating in a population-based study. DESIGN/SETTING: Ten-year population-based cohort study. PARTICIPANTS: A total of 478 individuals aged 65 years and older. MEASUREMENTS: We assayed fasting blood for markers of glycemia (glucose and hemoglobin A1c [HbA1c]), insulin resistance (IR) (insulin and homeostatic model assessment of IR), obesity (resistin, adiponectin, and glucagon-like peptide-1), and inflammation (C-reactive protein). We modeled these indices as predictors of the slope of decline in global cognition, adjusting for age, sex, education, APOE*4 genotype, depressive symptoms, waist-hip ratio (WHR), and systolic blood pressure, in multivariable regression analyses of the entire sample and stratified by sex-specific median WHR. We then conducted WHR-stratified machine-learning (Classification and Regression Tree [CART]) analyses of the same variables. RESULTS: In multivariable regression analyses, in the entire sample, HbA1c was significantly associated with cognitive decline. After stratifying by median WHR, HbA1c remained associated with cognitive decline in those with higher WHR. No metabolic indices were associated with cognitive decline in those with lower WHR. Cross-validated WHR-stratified CART analyses selected no predictors in participants older than 87 to 88 years. Faster cognitive decline was associated, in lower WHR participants younger than 87 years, with adiponectin of 11 or greater; and in higher WHR participants younger than 88 years, with HbA1c of 6.2% or greater. CONCLUSIONS: Our population-based data suggest that, in individuals younger than 88 years with central obesity, even modest degrees of hyperglycemia might independently predispose to faster cognitive decline. In contrast, among those younger than 87 years without central obesity, adiponectin may be a novel independent risk factor for cognitive decline. J Am Geriatr Soc 68:991-998, 2020.

Single-Voxel 1 H MR spectroscopy of cerebral nicotinamide adenine dinucleotide (NAD+ ) in humans at 7T using a 32-channel volume coil.

PURPOSE: Reliable monitoring of tissue nicotinamide adenine dinucleotide (NAD+ ) concentration may provide insights on its roles in normal and pathological aging. In the present study, we report a 1 H MRS pulse sequence for the in vivo, localized 1 H MRS detection of NAD+ from the human brain. METHODS: Studies were carried out on a 7T Siemens MRI scanner using a 32-channel product volume coil. The pulse sequence consisted of a spectrally selective low bandwidth E-BURP-1 90° pulse. PRESS localization was achieved using optimized Shinnar-Le Roux 180° pulses and overlapping gradients were used to minimize the TE. The reproducibility of NAD+ quantification was measured in 11 healthy volunteers. The association of cerebral NAD+ with age was assessed in 16 healthy subjects 26-78 years old. RESULTS: Spectra acquired from a voxel placed in subjects' occipital lobe consisted of downfield peaks from the H2 , H4 , and H6 protons of the nicotinamide moiety of NAD+ between 8.9-9.35 ppm. The mean ± SD within-session and between-session coefficients of variation were found to be 6.14 ± 2.03% and 6.09 ± 3.20%, respectively. In healthy volunteers, an age-dependent decline of the NAD+ levels in the brain was also observed (ß = -1.24 µM/y, SE = 0.21, P < 0.001). CONCLUSION: We demonstrated the feasibility and robustness of a newly developed 1 H MRS technique to measure localized cerebral NAD+ at 7T MRI using a commercially available RF head coil. This technique may be further applied to detect and quantify NAD+ from different regions of the brain as well as from other tissues.

Investigation of antihypertensive class, dementia, and cognitive decline: A meta-analysis.

OBJECTIVE: High blood pressure is one of the main modifiable risk factors for dementia. However, there is conflicting evidence regarding the best antihypertensive class for optimizing cognition. Our objective was to determine whether any particular antihypertensive class was associated with a reduced risk of cognitive decline or dementia using comprehensive meta-analysis including reanalysis of original participant data. METHODS: To identify suitable studies, MEDLINE, Embase, and PsycINFO and preexisting study consortia were searched from inception to December 2017. Authors of prospective longitudinal human studies or trials of antihypertensives were contacted for data sharing and collaboration. Outcome measures were incident dementia or incident cognitive decline (classified using the reliable change index method). Data were separated into mid and late-life (>65 years) and each antihypertensive class was compared to no treatment and to treatment with other antihypertensives. Meta-analysis was used to synthesize data. RESULTS: Over 50,000 participants from 27 studies were included. Among those aged >65 years, with the exception of diuretics, we found no relationship by class with incident cognitive decline or dementia. Diuretic use was suggestive of benefit in some analyses but results were not consistent across follow-up time, comparator group, and outcome. Limited data precluded meaningful analyses in those ≤65 years of age. CONCLUSION: Our findings, drawn from the current evidence base, support clinical freedom in the selection of antihypertensive regimens to achieve blood pressure goals. CLINICAL TRIALS REGISTRATION: The review was registered with the international prospective register of systematic reviews (PROSPERO), registration number CRD42016045454.

Incorporating prior information with fused sparse group lasso: Application to prediction of clinical measures from neuroimages.

Predicting clinical variables from whole-brain neuroimages is a high-dimensional problem that can potentially benefit from feature selection or extraction. Penalized regression is a popular embedded feature selection method for high-dimensional data. For neuroimaging applications, spatial regularization using the ℓ1 or ℓ2 norm of the image gradient has shown good performance, yielding smooth solutions in spatially contiguous brain regions. Enormous resources have been devoted to establishing structural and functional brain connectivity networks that can be used to define spatially distributed yet related groups of voxels. We propose using the fused sparse group lasso (FSGL) penalty to encourage structured, sparse, and interpretable solutions by incorporating prior information about spatial and group structure among voxels. We present optimization steps for FSGL penalized regression using the alternating direction method of multipliers algorithm. With simulation studies and in application to real functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange, we demonstrate conditions under which fusion and group penalty terms together outperform either of them alone.

Population-based genome-wide association study of cognitive decline in older adults free of dementia: identification of a novel locus for the attention domain.

To identify novel loci that affect cognitive decline in older adults free of dementia, we conducted genome-wide and gene-based meta-analyses on longitudinal slopes of 5 cognitive domains (memory, executive function, language, attention/processing speed, and visuospatial ability) derived from 2 population-based cohorts. For decline over time in each cognitive domain, we normalized intraindividual slopes within each cohort, accounting for baseline age, sex, and years of education. Normalized slope for each domain was used in cohort-specific genome-wide analyses after including top principal components as covariates followed by genome-wide and gene-based meta-analyses. Both analyses revealed a novel WDFY2 locus at genome-wide (p = 3.37E-08) and gene-wide (p = 7.10E-07) significance levels for the attention/processing speed domain. In the GTEx eQTL analysis, genome-wide significant single-nucleotide polymorphism was associated with RNA expression levels of WDFY2 in several brain regions: cerebellar hemisphere (p = 1.07E-04), cerebellum (p = 6.92E-04), hippocampus (p = 2.18E-03) and cortex (p = 2.29E-02), and in whole blood (p = 4.41E-05). Our results suggest that WDFY2 genetic variation may affect individual differences in decline over time on tests of attention/processing speed.

Does a cognitive stress test predict progression from mild cognitive impairment to dementia equally well in clinical versus population-based settings?

ABSTRACTBackground:Evidence suggests that semantic interference may be a sensitive indicator of early dementia. We examined the utility of the Semantic Interference Test (SIT), a cognitive stress memory paradigm which taps proactive and retroactive semantic interference, for predicting progression from mild cognitive impairment (MCI) to dementia in both a clinical and a population-based sample. METHODS: Participants with MCI in the clinical (n = 184) and population-based (n = 435) samples were followed for up to four years. We employed receiver operating characteristic (ROC) methods to establish optimal thresholds for four different SIT indices. Threshold performance was compared in the two samples using logistic and Cox proportional hazard regression models. RESULTS: Within four years, 42 (22.8%) MCI individuals in the clinical sample and 45 (10.3%) individuals in the population-based sample progressed to dementia. Overall classification accuracy of SIT thresholds ranged from 61.4% to 84.8%. Different subtests of the SIT had slightly different performance characteristics in the two samples. However, regression models showed that thresholds established in the clinical sample performed similarly in the population sample before and after adjusting for demographics and other baseline neuropsychological test scores. CONCLUSIONS: Despite differences in demographic composition and progression rates, baseline SIT scores predicted progression from MCI to dementia similarly in both samples. Thresholds that best predicted progression were slightly below thresholds established for distinguishing between amnestic MCI and cognitively normal subjects in clinical practice. This confirms the utility of the SIT in both clinical and population-based samples and establishes thresholds most predictive of progression of individuals with MCI.

Dual Trajectories of Depression and Cognition: A Longitudinal Population-Based Study.

OBJECTIVE: To examine the relationships over time between dual trajectories of depressive symptoms and several cognitive domains. METHODS: In a 5-year longitudinal study, 1,978 randomly selected individuals aged 65+ years at recruitment were assessed annually. Repeated measures were of depressive symptoms on the modified Center for Epidemiologic Studies Depression Scale and composite scores in the cognitive domains of attention, executive function, memory, language, and visuospatial function. Latent class trajectories were identified for depression and for each cognitive domain and their associations investigated using dual trajectory modeling. Cognitive trajectories with z scores below -1 were designated as persistently low. RESULTS: Five depressive symptom trajectories were observed: rarely depressed (60.5%); low-grade, decreasing symptoms (18.5%); low-grade, increasing symptoms (9.6%); moderate-grade symptoms (7.4%); and consistent higher-grade symptoms (4.0%). For each cognitive domain six trajectories were observed. The rarely depressed and low-grade decreasing symptom groups were the least likely to have persistently low cognition. The symptom trajectory most strongly associated with persistently low functioning in each domain was not the higher-grade group but rather the low-grade increasing group in the case of attention and the moderate-grade trajectory in the other four domains. CONCLUSION: Consistently higher-grade depressive symptoms are less strongly associated with poor cognitive functioning than with either moderate- or low-grade increasing depressive symptom trajectories, over time and across different domains. Examining both depression and cognition longitudinally allows heterogeneity of both to be addressed, revealing latent groups with potential diagnostic and prognostic implications.