Evaluating Vaccine Efficacy Against SARS-CoV-2 Infection

Although interim results from several large, placebo-controlled, phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic coronavirus disease 2019 (COVID-19), it is unknown how effective the vaccines are in preventing people from becoming asymptomatically infected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between 2 antibody or reverse-transcription polymerase chain reaction (RT-PCR) tests. Additional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment of participants or crossover of placebo recipients to the vaccine arm before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, staggered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies that mimic the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates.

Testing measurement equivalence of neurocognitive assessments across language in the Hispanic Community Health Study/Study of Latinos

OBJECTIVE

Neuropsychological instruments are often developed in English and translated to other languages to facilitate the clinical evaluation of diverse populations or to utilize in research environments. However, the psychometric equivalence of these assessments across language must be demonstrated before populations can validly be compared.

METHOD

To test this equivalence, we applied measurement invariance procedures to a subsample (N = 1,708) of the Hispanic Community Health Survey/Study of Latinos (HCHS/SOL) across English and Spanish versions of a neurocognitive battery. Using cardinality matching, 854 English-speaking and 854 Spanish-speaking subsamples were matched on age, education, sex, immigration status (U.S. born, including territories, or foreign-born), and Hispanic/Latino heritage background. Neurocognitive measures included the Six-Item Screener (SIS), Brief-Spanish English Verbal Learning Test (B-SEVLT), Word Fluency (WF), and Digit Symbol Substitution (DSS). Confirmatory factor analysis was utilized to test item-level invariance of the SIS, B-SEVLT, and WF, as well as factor-level invariance of a higher-order neurocognitive functioning latent variable.

RESULTS

One item of both the SIS and WF were more difficult in Spanish than English, as was the DSS test. After accounting for partial invariance, Spanish-speakers performed worse on each of the subtests and the second-order neurocognitive functioning latent variable.

CONCLUSIONS

We found some evidence of bias at both item and factor levels, contributing to the poorer neurocognitive performance of Spanish test-takers. While these results explain the underperformance of Spanish-speakers to some extent, more work is needed to determine whether such bias is reflective of true cognitive differences or additional variables unaccounted for in this study. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Socio-demographic and trauma-related predictors of PTSD within 8 weeks of a motor vehicle collision in the AURORA study

This is the initial report of results from the AURORA multisite longitudinal study of adverse post-traumatic neuropsychiatric sequelae (APNS) among participants seeking emergency department (ED) treatment in the aftermath of a traumatic life experience. We focus on n = 666 participants presenting to EDs following a motor vehicle collision (MVC) and examine associations of participant socio-demographic and participant-reported MVC characteristics with 8-week posttraumatic stress disorder (PTSD) adjusting for pre-MVC PTSD and mediated by peritraumatic symptoms and 2-week acute stress disorder (ASD). Peritraumatic Symptoms, ASD, and PTSD were assessed with self-report scales. Eight-week PTSD prevalence was relatively high (42.0%) and positively associated with participant sex (female), low socioeconomic status (education and income), and several self-report indicators of MVC severity. Most of these associations were entirely mediated by peritraumatic symptoms and, to a lesser degree, ASD, suggesting that the first 2 weeks after trauma may be a uniquely important time period for intervening to prevent and reduce risk of PTSD. This observation, coupled with substantial variation in the relative strength of mediating pathways across predictors, raises the possibility of diverse and potentially complex underlying biological and psychological processes that remain to be elucidated with more in-depth analyses of the rich and evolving AURORA data.

Prognostic neuroimaging biomarkers of trauma-related psychopathology: resting-state fMRI shortly after trauma predicts future PTSD and depression symptoms in the AURORA study

Neurobiological markers of future susceptibility to posttraumatic stress disorder (PTSD) may facilitate identification of vulnerable individuals in the early aftermath of trauma. Variability in resting-state networks (RSNs), patterns of intrinsic functional connectivity across the brain, has previously been linked to PTSD, and may thus be informative of PTSD susceptibility. The present data are part of an initial analysis from the AURORA study, a longitudinal, multisite study of adverse neuropsychiatric sequalae. Magnetic resonance imaging (MRI) data from 109 recently (i.e., ~2 weeks) traumatized individuals were collected and PTSD and depression symptoms were assessed at 3 months post trauma. We assessed commonly reported RSNs including the default mode network (DMN), central executive network (CEN), and salience network (SN). We also identified a proposed arousal network (AN) composed of a priori brain regions important for PTSD: the amygdala, hippocampus, mamillary bodies, midbrain, and pons. Primary analyses assessed whether variability in functional connectivity at the 2-week imaging timepoint predicted 3-month PTSD symptom severity. Left dorsolateral prefrontal cortex (DLPFC) to AN connectivity at 2 weeks post trauma was negatively related to 3-month PTSD symptoms. Further, right inferior temporal gyrus (ITG) to DMN connectivity was positively related to 3-month PTSD symptoms. Both DLPFC-AN and ITG-DMN connectivity also predicted depression symptoms at 3 months. Our results suggest that, following trauma exposure, acutely assessed variability in RSN connectivity was associated with PTSD symptom severity approximately two and a half months later. However, these patterns may reflect general susceptibility to posttraumatic dysfunction as the imaging patterns were not linked to specific disorder symptoms, at least in the subacute/early chronic phase. The present data suggest that assessment of RSNs in the early aftermath of trauma may be informative of susceptibility to posttraumatic dysfunction, with future work needed to understand neural markers of long-term (e.g., 12 months post trauma) dysfunction. Furthermore, these findings are consistent with neural models suggesting that decreased top-down cortico-limbic regulation and increased network-mediated fear generalization may contribute to ongoing dysfunction in the aftermath of trauma.

Subjective cognitive decline and objective cognition among diverse U.S. Hispanics/Latinos: Results from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA)

Introduction

Despite increased risk of cognitive decline in Hispanics/Latinos, research on early risk markers of Alzheimer's disease in this group is lacking. Subjective cognitive decline (SCD) may be an early risk marker of pathological aging. We investigated associations of SCD with objective cognition among a diverse sample of Hispanics/Latinos living in the United States.

Methods

SCD was measured with the Everyday Cognition Short Form (ECog‐12) and cognitive performance with a standardized battery in 6125 adults aged ≥ 50 years without mild cognitive impairment or dementia (x̄_age = 63.2 years, 54.5% women). Regression models interrogated associations of SCD with objective global, memory, and executive function scores.

Results

Higher SCD was associated with lower objective global (B = −0.16, SE = 0.01), memory (B = −0.13, SE = 0.02), and executive (B = −0.13, SE = 0.02, p's < .001) function composite scores in fully adjusted models.

Discussion

Self‐reported SCD, using the ECog‐12, may be an indicator of concurrent objective cognition in diverse middle‐aged and older community‐dwelling Hispanics/Latinos.

Evaluating Vaccine Efficacy Against SARS-CoV-2 Infection

Although interim results from several large placebo-controlled phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic COVID-19, it is unknown how effective the vaccines are in preventing people from becoming asymptomatically infected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against SARS-CoV-2 infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between two antibody or RT-PCR tests. Additional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment or crossover before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, staggered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies mimicking the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates.

SUMMARY

We show how to estimate potentially waning efficacy of COVID-19 vaccines against SARS-CoV-2 infection using blood or nasal samples collected periodically from clinical trials with staggered enrollment of participants and crossover of placebo recipients.

Inferring latent heterogeneity using many feature variables supervised by survival outcome

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.

Hearing Sensitivity, Cardiovascular Risk, and Neurocognitive Function: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Question

Is hearing impairment associated with cardiovascular disease risk and cognitive function among Hispanic or Latino participants?

Findings

In this cohort study of 9623 Hispanic/Latino adults, hearing impairment was associated with poorer cognitive performance on all tasks, and cardiovascular disease risk did not attenuate these relationships. Rather, hearing impairment modified the associations between cardiovascular disease risk and learning and memory; only among individuals with hearing impairment, being identified as having excessively high glucose was associated with poorer learning and memory relative to participants considered healthy individuals.

Meaning

Hearing impairment may exacerbate the associations between high glucose and poorer cognition, particularly for learning and memory among Hispanic or Latino persons.

Importance

Both cardiovascular disease risk and hearing impairment are associated with cognitive dysfunction. However, the combined influence of the 2 risk factors on cognition is not well characterized.

Objective

To examine associations between hearing impairment, cardiovascular disease risk, and cognitive function.

Design, Setting, and Participants

This population-based, prospective cohort, multisite cross-sectional analysis of baseline data collected between 2008 and 2011 as part of the Hispanic Community Health Study/Study of Latinos included 9623 Hispanic or Latino adults aged 45 to 74 years in New York, Chicago, Miami, and San Diego.

Exposures

Hearing impairment of at least mild severity was defined as the pure tone average of 500, 1000, 2000, and 4000 Hz greater than 25 dB hearing level (dB HL) in the better ear. Our measure of cardiovascular disease risk was a latent class variable derived from body mass index, ankle-brachial index, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, fasting blood glucose, and the Framingham Cardiovascular Risk score.

Main Outcomes and Measures

Results on Brief-Spanish English Verbal Learning Test (episodic learning and memory), and Word Fluency (verbal fluency), and Digit Symbol Subtest (processing speed/executive functioning), and a cognitive composite of the mentioned tests (overall cognition).

Results

Participants (N = 9180) were 54.4% female and age 56.5 years on average. Hearing impairment was associated with poorer performance on all cognitive measures (global cognition: unstandardized β, −0.11; 95% CI, −0.16 to 0.07). Cardiovascular grouping (healthy, typical, high cardiovascular disease risk, and hyperglycemia) did not attenuate the associations between hearing impairment and cognition (global cognition: unstandardized β, −0.11; 95% CI, −0.15 to −0.06). However, cardiovascular grouping interacted with hearing impairment such that hyperglycemia in the context of hearing impairment exacerbated poor performance on learning and memory tasks (F₃ = 3.70 and F₃ = 2.92, respectively).

Conclusions and Relevance

The findings of this cohort study suggest that hearing impairment increases the likelihood that individuals with excessively high glucose perform poorly on learning and memory tasks. Further research is needed to specify the mechanisms by which cardiovascular disease risk and hearing impairment are collectively associated with cognition.

Evaluating the Efficacy of Therapies in Patients With Coronavirus Disease 2019

There is a proliferation of clinical trials worldwide to find effective therapies for patients diagnosed with coronavirus disease 2019 (COVID-19). The endpoints that are currently used to evaluate the efficacy of therapeutic agents against COVID-19 are focused on clinical status at a particular day or on time to a specific change of clinical status. To provide a full picture of the clinical course of a patient and make complete use of available data, we consider the trajectory of clinical status over the entire follow-up period. We also show how to combine the evidence of treatment effects on the occurrences of various clinical events. We compare the proposed and existing endpoints through extensive simulation studies. Finally, we provide guidelines on establishing the benefits of treatments.

Prior sleep problems and adverse post-traumatic neuropsychiatric sequelae of motor vehicle collision in the AURORA study

STUDY OBJECTIVES

Many patients in Emergency Departments (EDs) after motor vehicle collisions (MVCs) develop post-traumatic stress disorder (PTSD) or major depressive episode (MDE). This report from the AURORA study focuses on associations of pre-MVC sleep problems with these outcomes 8 weeks after MVC mediated through peritraumatic distress and dissociation and 2-week outcomes.

METHODS

A total of 666 AURORA patients completed self-report assessments in the ED and at 2 and 8 weeks after MVC. Peritraumatic distress, peritraumatic dissociation, and pre-MVC sleep characteristics (insomnia, nightmares, daytime sleepiness, and sleep duration in the 30 days before the MVC, trait sleep stress reactivity) were assessed retrospectively in the ED. The survey assessed acute stress disorder (ASD) and MDE at 2 weeks and at 8 weeks assessed PTSD and MDE (past 30 days). Control variables included demographics, MVC characteristics, and retrospective reports about PTSD and MDE in the 30 days before the MVC.

RESULTS

Prevalence estimates were 41.0% for 2-week ASD, 42.0% for 8-week PTSD, 30.5% for 2-week MDE, and 27.2% for 8-week MDE. Pre-MVC nightmares and sleep stress reactivity predicted 8-week PTSD (mediated through 2-week ASD) and MDE (mediated through the transition between 2-week and 8-week MDE). Pre-MVC insomnia predicted 8-week PTSD (mediated through 2-week ASD). Estimates of population attributable risk suggest that blocking effects of sleep disturbance might reduce prevalence of 8-week PTSD and MDE by as much as one-third.

CONCLUSIONS

Targeting disturbed sleep in the immediate aftermath of MVC might be one effective way of reducing MVC-related PTSD and MDE.

Evaluating the Long-Term Efficacy of COVID-19 Vaccines

Large-scale deployment of safe and durably effective vaccines can curtail the coronavirus disease-2019 (COVID-19) pandemic. However, the high vaccine efficacy (VE) reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about 2 months, and thus does not pertain to long-term efficacy. To evaluate the duration of protection while allowing trial participants timely access to efficacious vaccine, investigators can sequentially cross participants over from the placebo arm to the vaccine arm. Here, we show how to estimate potentially time-varying placebo-controlled VE in this type of staggered vaccination of participants. In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term VE.

Evaluating the Long-Term Efficacy of COVID-19 Vaccines

Large-scale deployment of safe and durably effective vaccines can curtail the COVID-19 pandemic. ^1-3 However, the high vaccine efficacy (VE) reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about two months ^4-5 and thus does not pertain to long-term efficacy. To evaluate the duration of protection while allowing trial participants timely access to efficacious vaccine, investigators can sequentially cross participants over from the placebo arm to the vaccine arm according to priority groups. Here, we show how to estimate potentially time-varying placebo-controlled VE in this type of staggered vaccination of participants. In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term VE.

Authors’ Information

Dan-Yu Lin, Ph.D., is Dennis Gillings Distinguished Professor of Biostatistics, and Donglin Zeng, Ph.D., is Professor of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7420, USA. Peter B. Gilbert, Ph.D., is Member, Vaccine and Infectious Disease Division, Fred Hutch, Seattle, WA 98109-1024, USA.

Summary

We show how to estimate the potentially waning long-term efficacy of COVID-19 vaccines using data from randomized, placebo-controlled clinical trials with staggered enrollment of participants and sequential crossover of placebo recipients.

Modeling Temporal Biomarkers With Semiparametric Nonlinear Dynamical Systems

Dynamical systems based on differential equations are useful for modeling the temporal evolution of biomarkers. These systems can characterize the temporal patterns of biomarkers and inform the detection of interactions among biomarkers. Existing statistical methods for dynamical systems mostly target single time-course data based on a linear model or generalized additive model. Hence, they cannot adequately capture the complex interactions among biomarkers; neither can they take into account the heterogeneity between systems or subjects. in this work, we propose a semiparametric dynamical system based on multi-index models for multiple subjects time-course data. Our model accounts for between-subject heterogeneity by introducing system-level or subject-level covariates to dynamic systems, and it allows for nonlinear relationship and interaction between the combined biomarkers and the temporal rate of each biomarker. For estimation and inference, we consider a two-step procedure based on integral equations from the proposed model. We propose an algorithm that iterates between the estimation of the link function through splines and the estimation of index parameters and that allows for regularization to achieve sparsity. We prove model identifiability and derive the asymptotic properties of the estimated model parameters. A benefit of our approach is to pool information from multiple subjects to identify the interaction among biomarkers. We apply the method to analyze electroencephalogram (EEG) data for patients affected by alcohol dependence. The results reveal new insight on patients' brain activities and demonstrate differential interaction patterns in patients compared to health control subjects.

APOE alleles' association with cognitive function differs across Hispanic/Latino groups and genetic ancestry in the study of Latinos-investigation of neurocognitive aging (HCHS/SOL)

INTRODUCTION

Apolipoprotein E (APOE) alleles are associated with cognitive decline, mild cognitive impairment (MCI), and Alzheimer's disease in Whites, but have weaker and inconsistent effects reported in Latinos. We hypothesized that this heterogeneity is due to ancestry-specific genetic effects.

METHODS

We investigated the associations of the APOE alleles with significant cognitive decline and MCI in 4183 Latinos, stratified by six Latino backgrounds, and explored whether the proportion of continental genetic ancestry (European, African, and Amerindian) modifies these associations.

RESULTS

APOE ε4 was associated with an increased risk of significant cognitive decline (odds ratio [OR] = 1.15, P-value = 0.03), with the strongest association in Cubans (OR = 1.46, P-value = 0.007). APOE-ε2 was associated with decreased risk of MCI (OR = 0.37, P-value = 0.04) in Puerto Ricans. Amerindian genetic ancestry was found to protect from the risk conferred by APOE ε4 on significant cognitive decline.

DISCUSSION

Results suggest that APOE alleles' effects on cognitive outcomes differ across six Latino backgrounds and are modified by continental genetic ancestry.

INTEGRATIVE NETWORK LEARNING FOR MULTI-MODALITY BIOMARKER DATA

The biomarker networks measured by different modalities of data (e.g., structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI)) may share the same true underlying biological model. In this work, we propose a node-wise biomarker graphical model to leverage the shared mechanism between multi-modality data to provide a more reliable estimation of the target modality network and account for the heterogeneity in networks due to differences between subjects and networks of external modality. Latent variables are introduced to represent the shared unobserved biological network and the information from the external modality is incorporated to model the distribution of the underlying biological network. We propose an efficient approximation to the posterior expectation of the latent variables that reduces computational cost by at least 50%. The performance of the proposed method is demonstrated by extensive simulation studies and an application to construct gray matter brain atrophy network of Huntington's disease by using sMRI data and DTI data. The identified network connections are more consistent with clinical literature and better improve prediction in follow-up clinical outcomes and separate subjects into clinically meaningful subgroups with different prognosis than alternative methods.

Learning latent heterogeneity for type 2 diabetes patients using longitudinal health markers in electronic health records

Electronic health records (EHRs) from type 2 diabetes (T2D) patients consist of longitudinally and sparsely measured health markers at clinical encounters. Our goal is to use such data to learn latent patterns that can inform patient's health status related to T2D while accounting for challenges in retrospectively collected EHRs. To handle challenges such as correlated longitudinal measurements, irregular and informative encounter times, and mixed marker types, we propose multivariate generalized linear models to learn latent patient subgroups. In our model, covariate effects were time-dependent and latent Gaussian processes were introduced to model between-marker correlations over time. Using inferred latent processes, we integrated the irregularly measured health markers of mixed types into composite scores and applied hierarchical clustering to learn latent subgroup structures among T2D patients. Application to an EHR dataset of T2D patients showed different trends of age, sex, and race effects on hypertension/high blood pressure, total cholesterol, glycated hemoglobin, high-density lipoprotein, and medications. The associations among these markers varied over time during the study window. Clustering results revealed four subgroups, each with distinct health status. The same patterns were further confirmed using new EHR records of the same cohort. We developed a novel latent model to integrate longitudinal health markers in EHRs and characterize patient latent heterogeneities. Analysis indicated that there were distinct subgroups of T2D patients, suggesting that effective healthcare managements for these patients should be performed separately for each subgroup.

Multi-Ancestry Genome-wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from 5 ancestry groups. In the combined meta-analyses of Stages 1 and 2, we identified 59 loci (p value <5e-8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A, PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5, CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations.

Central Obesity, Cardiometabolic Risk, and Cognitive Change in the Study of Latinos - Investigation of Neurocognitive Aging

BACKGROUND

The relationships between obesity and cognitive decline in aging are mixed and understudied among Hispanics/Latinos.

OBJECTIVE

To understand associations between central obesity, cognitive aging, and the role of concomitant cardiometabolic abnormalities among Hispanics/Latinos.

METHODS

Participants included 6,377 diverse Hispanics/Latinos enrolled in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and SOL-Investigation for Neurocognitive Aging (SOL-INCA). Participants were 45 years and older at the first cognitive testing session (Visit 1). Cognitive outcomes (z-score units) included global composite and domain specific (learning, memory, executive functioning, processing speed) measures at a second visit (SOL-INCA, on average, 7 years later), and 7-year change. We used survey linear regression to examine associations between central obesity (waist circumference≥88 cm and≥102 cm for women and men, respectively) and cognition. We also tested whether the relationships between obesity and cognition differed by cardiometabolic status (indication of/treatment for 2 + of the following: high triglycerides, hypertension, hyperglycemia, low high-density lipoprotein cholesterol).

RESULTS

Central obesity was largely unassociated with cognitive outcomes, adjusting for covariates. However, among individuals with central obesity, cardiometabolic abnormality was linked to poorer cognitive function at SOL-INCA (ΔGlobalCognition =-0.165, p < 0.001) and to more pronounced cognitive declines over the average 7 years (ΔGlobalCognition = -0.109, p < 0.05); this was consistent across cognitive domains.

CONCLUSION

Central obesity alone was not associated with cognitive function. However, presence of both central obesity and cardiometabolic abnormalities was robustly predictive of cognition and 7-year cognitive declines, suggesting that in combination these factors may alter the cognitive trajectories of middle-aged and older Hispanics/Latinos.

Integration of Small RNA and Transcriptome Sequencing Reveal the Roles of miR395 and ATP Sulfurylase in Developing Seeds of Chinese Kale

Seed development is closely related to plant production and reproduction, and MicroRNAs (miRNA) is widely involved in plant development including seed development. Chinese kale, as a Brassicaceae vegetable, mainly depends on seed for proper reproduction. In the present study, Chinese kale seed and silique at different stages were selected to establish small RNA (sRNA) libraries including silique wall sRNA libraries at torpedo-embryo stage (PC), silique wall sRNA libraries at cotyledonary-embryo stage (PD), seed sRNA libraries at torpedo-embryo stage (SC), and seed sRNA libraries at cotyledonary-embryo stage (SD). The results showed that miRNA expressed differentially in the seeds and corresponding siliques at different stages. To further clarify the functional mode of miRNA in the process of seed development, Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis was performed on target genes of the differentially expressed miRNAs, and these target genes were mainly enriched in plant hormone signal transduction, primary and secondary metabolic pathways. After joint analysis with the transcriptome change of the corresponding period, miR156-SPL10/SPL11, miR395-APS3, and miR397-LAC2/LAC11 modules were identified to be directly involved in the development of Chinese kale seeds. What's more, modified 5'RLM-RACE and Agrobacteria-mediated Chinese kale transient transformation suggest miR395b_2 is involved in sulfur metabolism during seed development by regulating its target gene APS3.

Long-Term Exposure to Ambient Air Pollution and Cognitive Function Among Hispanic/Latino Adults in San Diego, California

BACKGROUND

Hispanics/Latinos in the United States are more likely to live in neighborhoods with greater exposure to air pollution and are projected to have the largest increase in dementia among race/ethnic minority groups.

OBJECTIVE

We examined the associations of air pollution with performance on cognitive function tests in Hispanic/Latino adults.

METHODS

We used data from the San Diego site of the Hispanic Community Health Study/Study of Latinos, an ongoing cohort of Hispanics/Latinos. This analysis focused on individuals ≥45 years of age who completed a neurocognitive battery examining overall mental status, verbal learning, memory, verbal fluency, and executive function (n = 2,089). Air pollution (PM2.5 and O3) before study baseline was assigned to participants' zip code. Logistic and linear regression were used to estimate the associations of air pollution on overall mental status and domain-specific standardized test scores. Models accounted for complex survey design, demographic, and socioeconomic characteristics.

RESULTS

We found that for every 10μg/m3 increase in PM2.5, verbal fluency worsened (β: -0.21 [95%CI: -0.68, 0.25]). For every 10 ppb increase in O3, verbal fluency and executive function worsened (β: -0.19 [95%CI: -0.34, -0.03]; β: -0.01 [95%CI: -0.01, 0.09], respectively). We did not identify any detrimental effect of pollutants on other domains.

CONCLUSION

Although we found suggestions that air pollution may impact verbal fluency and executive function, we observed no consistent or precise evidence to suggest an adverse impact of air pollution on cognitive level among this cohort of Hispanic/Latino adults.

Actigraphic sleep patterns and cognitive decline in the Hispanic Community Health Study/Study of Latinos

INTRODUCTION

We determined if actigraphy-derived sleep patterns led to 7-year cognitive decline in middle-aged to older Hispanic/Latino adults.

METHODS

We examined 1035 adults, 45 to 64 years of age, from the Hispanic Community Health Study/Study of Latinos. Participants had repeated measures of cognitive function 7 years apart, home sleep apnea studies, and 1 week of actigraphy. Survey linear regression evaluated prospective associations between sleep and cognitive change, adjusting for main covariates.

RESULTS

Longer sleep-onset latency was associated with declines in global cognitive function, verbal learning, and verbal memory. Longer sleep-onset latency was also cross-sectionally associated with verbal learning, verbal memory, and word fluency. Sleep fragmentation was not associated with cognitive change.

CONCLUSION

In a cohort of mostly middle-aged Hispanic/Latinos, actigraphy-derived sleep-onset latency predicted 7-year cognitive change. These findings may serve as targets for sleep interventions of cognitive decline.

Psychosocial Factors Associated with Cognitive Function Among Middle-Aged and Older Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos and its Sociocultural Ancillary Study

BACKGROUND

Evidence suggests that psychosocial factors are associated with cognitive health in older adults; however, associations of psychosocial factors with cognition remain largely unexamined in middle-aged and older Hispanics/Latinos.

OBJECTIVE

To examine the cross-sectional associations of psychosocial factors with cognitive function among middle-aged and older Hispanics/Latinos living in the US.

METHODS

Baseline (2008-2011) data from the Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study (n = 2,818; ages 45-74) were used to examine the associations of each psychosocial factor with global cognition (GC), verbal learning, verbal memory, verbal fluency, and processing speed independent of age, sex, education, Hispanic/Latino background, income, language, and depressive symptoms. Psychosocial variables included: intrapersonal factors (ethnic identity, optimism, and purpose in life), interpersonal factors (family cohesion, familism, social network embeddedness, and social support), and social stressors (perceived ethnic discrimination, loneliness, and subjective social status).

RESULTS

In fully-adjusted models, purpose in life and social support were each positively associated with all five cognitive variables. Loneliness was negatively associated with GC, verbal learning, memory, and processing speed. Ethnic identity was positively and familism negatively associated with GC, verbal fluency, and processing speed. Family cohesion was positively associated with verbal learning.

CONCLUSION

These findings extend previous evidence from older, largely non-Hispanic White cohorts to show that higher purpose in life and social support are also strongly associated with cognitive health among middle-aged and older Hispanics/Latinos. We also highlight that intrapersonal factors, interpersonal factors, and social stressors have differential relationships with individual cognitive tests.

Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments

For mental disorders, patients' underlying mental states are non-observed latent constructs which have to be inferred from observed multi-domain measurements such as diagnostic symptoms and patient functioning scores. Additionally, substantial heterogeneity in the disease diagnosis between patients needs to be addressed for optimizing individualized treatment policy in order to achieve precision medicine. To address these challenges, we propose an integrated learning framework that can simultaneously learn patients' underlying mental states and recommend optimal treatments for each individual. This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states). It allows incorporation of the multivariate pre- and post-treatment outcomes as well as biological measures while preserving the invariant structure for representing patients' latent mental states. A multi-layer neural network is used to allow complex treatment effect heterogeneity. Optimal treatment policy can be inferred for future patients by comparing their potential mental states under different treatments given the observed multi-domain pre-treatment measurements. Experiments on simulated data and a real-world clinical trial data show that the learned treatment polices compare favorably to alternative methods on heterogeneous treatment effects, and have broad utilities which lead to better patient outcomes on multiple domains.

On using electronic health records to improve optimal treatment rules in randomized trials

Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific characteristics in order to optimize patient outcomes. Data from randomized controlled trials (RCTs) are used to infer valid ITRs using statistical and machine learning methods. However, RCTs are usually conducted under specific inclusion/exclusion criteria, thus limiting their generalizability to a broader patient population in real-world practice settings. Because electronic health records (EHRs) document treatment prescriptions in the real world, transferring information in EHRs to RCTs, if done appropriately, could potentially improve the performance of ITRs, in terms of precision and generalizability. In this work, we propose a new domain adaptation method to learn ITRs by incorporating information from EHRs. Unless we assume that there is no unmeasured confounding in EHRs, we cannot directly learn the optimal ITR from the combined EHR and RCT data. Instead, we first pretrain "super" features from EHRs that summarize physician treatment decisions and patient observed benefits in the real world, as these are likely to be informative of the optimal ITRs. We then augment the feature space of the RCT and learn the optimal ITRs by stratifying by super features using subjects enrolled in RCT. We adopt Q-learning and a modified matched-learning algorithm for estimation. We present heuristic justification of our method and conduct simulation studies to demonstrate the performance of super features. Finally, we apply our method to transfer information learned from EHRs of patients with type 2 diabetes to learn individualized insulin therapies from RCT data.

High-Dimensional Precision Medicine From Patient-Derived Xenografts

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments. The optimal ITR is that which maximizes the mean of a clinical outcome in a population of interest. Patient-derived xenograft (PDX) studies permit the evaluation of multiple treatments within a single tumor, and thus are ideally suited for estimating optimal ITRs. PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments. Here we explore machine learning methods for estimating optimal ITRs from PDX data. We analyze data from a large PDX study to identify biomarkers that are informative for developing personalized treatment recommendations in multiple cancers. We estimate optimal ITRs using regression-based (Q-learning) and direct-search methods (outcome weighted learning). Finally, we implement a superlearner approach to combine multiple estimated ITRs and show that the resulting ITR performs better than any of the input ITRs, mitigating uncertainty regarding user choice. Our results indicate that PDX data are a valuable resource for developing individualized treatment strategies in oncology. Supplementary materials for this article are available online.

Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes

For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, in order to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning optimal individualized treatment rules (ITRs) by modeling patients' latent mental status. We first learn the multi-domain latent states at baseline from the observed symptoms under a restricted Boltzmann machine (RBM) model, through which patients' heterogeneous symptoms are represented using an economical number of latent variables and yet remains flexible. We then optimize a value function defined by the latent states after treatment by exploiting a transformation of the observed symptoms based on the RBM without modeling the relationship between the latent mental states before and after treatment. The optimal treatment rules are derived using a weighted large margin classifier. We derive the convergence rate of the proposed estimator under the latent models. Simulation studies are conducted to test the performance of the proposed method. Finally, we apply the developed method to real world studies and we demonstrate the utility and advantage of our method in tailoring treatments for patients with major depression, and identify patient subgroups informative for treatment recommendations.

Reflector Localization of Breast Lesions and Parameters Associated with Positive Surgical Margins in Women Undergoing Breast Conservation Surgery

OBJECTIVE

To evaluate our experience with reflector localization of breast lesions and parameters influencing surgical margins in patients with a malignant diagnosis.

METHODS

A retrospective institution review board-approved review of our institutional database was performed for breast lesions preoperatively localized from September 1, 2016, through December 31, 2017. Wire localizations were excluded. From electronic medical records and imaging, the following data was recorded: breast density, lesion type and size, reflector placement modality and number placed, reflector distance from lesion and skin, excision of lesion and reflector, tissue volume, margin status, and final pathology. Statistical analysis was performed with a Fisher's exact test, Mann-Whitney test, and logistic regression. P < 0.05 was significant.

RESULTS

A total of 111 reflectors were deployed in the breasts of 103 women with 109 breast lesions. Ninety (81.1%) reflectors were placed under mammographic guidance and 21 (18.9%) under US. The lesions consisted of 68 (62.4%) masses, 17 (15.6%) calcifications, 2 (1.8%) architectural distortions, and 22 (20.2%) biopsy markers. Fourteen (21.2%) of 66 cases with a preoperative malignant diagnosis had a positive surgical margin. Final pathology, including 6 lesions upgraded to malignancy on excision, demonstrated 72 (66.0%) malignant, 22 (20.2%) high-risk, and 15 (13.8%) benign lesions. Univariate and multivariate analysis revealed no statistically significant parameters (lesion type or size, placement modality, reflector distance to skin or lesion, specimen radiography or pathology) were associated with a positive surgical margin.

CONCLUSION

Reflector localization is an alternative to wire localization of breast lesions. There were no lesion-specific or technical parameters affecting positive surgical margins.

Multicategory Classification via Forward-Backward Support Vector Machine

In this paper, we propose a new algorithm to extend support vector machine (SVM) for binary classification to multicategory classification. The proposed method is based on a sequential binary classification algorithm: we first classify a target class by excluding the possibility of labeling as any other classes using a forward step of sequential SVM; we then exclude the already classified classes and repeat the same procedure for the remaining classes in a backward step. The proposed algorithm relies on SVM for each binary classification and utilizes only feasible data in each step; therefore, the method guarantees convergence and entails light computational burden. We prove Fisher consistency of the proposed forward-backward-SVM (FB-SVM) and obtain a stochastic bound for the predicted misclassification rate. We conduct extensive simulations and analyze real-world data to demonstrate the superior performance of FB-SVM, for example, FB-SVM achieves a classification accuracy much higher than the current standard for predicting conversion from mild cognitive impairment to Alzheimer's disease.

Synthesizing independent stagewise trials for optimal dynamic treatment regimes

Dynamic treatment regimes (DTRs) adaptively prescribe treatments based on patients' intermediate responses and evolving health status over multiple treatment stages. Data from sequential multiple assignment randomization trials (SMARTs) are recommended to be used for learning DTRs. However, due to re-randomization of the same patients over multiple treatment stages and a prolonged follow-up period, SMARTs are often difficult to implement and costly to manage, and patient adherence is always a concern in practice. To lessen such practical challenges, we propose an alternative approach to learn optimal DTRs by synthesizing independent trials over different stages. Specifically, at each stage, data from a single randomized trial along with patients' natural medical history and health status in previous stages are used. We use a backward learning method to estimate optimal treatment decisions at a particular stage, where patients' future optimal outcome increments are estimated using data observed from independent trials with future stages' information. Under some conditions, we show that the proposed method yields consistent estimation of the optimal DTRs and we obtain the same learning rates as those from SMARTs. We conduct simulation studies to demonstrate the advantage of the proposed method. Finally, we learn optimal DTRs for treating major depressive disorder (MDD) by stagewise synthesis of two randomized trials. We perform a validation study on independent subjects and show that the synthesized DTRs lead to the greatest MDD symptom reduction compared to alternative methods.

[Time-series analysis on the relationship between ambient PM2.5 and daily outpatient visits due to allergic conjunctivitis among children in Shenzhen]

Objective: To explore the short-term effects of ambient PM_2.5 on the outpatient visits of allergic conjunctivitis among children in Shenzhen. Methods: It was a ecological study. Data on daily visits including date of visit, sex and age from children with allergic conjunctivitis were collected from Shenzhen Eye Hospital and Shenzhen Children's Hospital in 2018. Related data on air pollution (PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃) and meteorology (atmospheric pressure, temperature and relative humidity) were also collected. Pearson correlation analysis was used for normal distribution data and Spearman rank correlation analysis was used for non-normal distribution data. Generalized additive model was used to estimate the impact of PM_2.5 pollution on allergic conjunctivitis outpatients and the lagging effects. Results: In 2018, there were 16 133 allergic conjunctivitis outpatients in the two hospitals. The maximum age was 18 years and the minimum age was 2 months. Males accounted for 49.3%. The daily average concentration of PM_2.5 was 22 (15, 31) μg/m³. Changes of the concentration of PM_2.5 had a positive correlation with the amount of allergic conjunctivitis visits, and the Spearman correlation coefficient was 0.150 (P=0.004). The single pollutant model showed that the strongest effect appeared at 3 days (RR=1.111， 95%CI:1.071-1.152). A 10 μg/m³ increase of PM_2.5 would result in an excessive number of allergic conjunctivitis outpatients as much as 11.112% (95%CI：7.011%-15.212%). In the multiple air pollutants models, after the introduction of NO₂, O₃ and CO, the concentration of PM_2.5 showed an enhanced effect on the number of hospital visits due to allergic conjunctivitis on the same day, and the difference was statistically significant (P<0.05). Conclusion: Changes of the concentration of PM_{2.5 h}ad a positive correlation with daily outpatient visits of allergic conjunctivitis among children in Shenzhen. (Chin J Ophthalmol, 2020, 56: 608-614).

Nine-Year Ethanol Intake Trajectories and Their Association With 15-Year Cognitive Decline Among Black and White Adults

Faster rates of age-related cognitive decline might result in early onset of cognitive impairment and dementia. The relationship between ethanol intake and cognitive decline, although studied extensively, remains poorly understood. Previous studies used single measurements of ethanol, and few were conducted in diverse populations. We assessed the association of 9-year trajectories of ethanol intake (1987-1998) with 15-year rate of decline in cognitive performance from mid- to late life (1996-2013) among 2,169 Black and 8,707 White participants of the US Atherosclerosis Risk in Communities study using multivariable linear regression models. We hypothesized that stable, low to moderate drinking would be associated with lesser 15-year cognitive decline, and stable, heavy drinking with greater 15-year cognitive decline. Stable, low to moderate drinking (for Blacks, adjusted mean difference (MD) = 0.03 (95% confidence interval (CI): -0.13, 0.19); for Whites, adjusted MD = 0.02 (95% CI: -0.05, 0.08)) and stable, heavy drinking (for Blacks, adjusted MD = 0.08 (95% CI: -0.34, 0.50); for Whites, adjusted MD = -0.03 (95% CI: -0.18, 0.11)) in midlife compared with stable never-drinking were not associated with 15-year decline in general cognitive function from mid- to late life. No association was observed for the stable former and "mostly" drinking trajectories with 15-year cognitive decline. Stable low, low to moderate, and stable heavy drinking in midlife are not associated with lesser and greater cognitive decline, respectively, from mid- to late life among Black and White adults.

A general framework for integrative analysis of incomplete multiomics data

There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative omics measurements, such as metabolite levels and protein expressions, are subject to detection limits in that the measurements below (or above) certain thresholds are not detectable. In this article, we propose a rigorous and powerful approach to handle missing values and detection limits in integrative analysis of multiomics data. We relate quantitative omics variables to genetic variants and other variables through linear regression models and relate phenotypes to quantitative omics variables and other variables through generalized linear models. We derive the joint-likelihood for the two sets of models by allowing arbitrary patterns of missing values and detection limits for quantitative omics variables. We carry out maximum-likelihood estimation through computationally fast and stable algorithms. The resulting estimators are approximately unbiased and statistically efficient. An application to a major study on chronic obstructive lung disease yielded new biological insights.

Genome-wide association study of cognitive function in diverse Hispanics/Latinos: results from the Hispanic Community Health Study/Study of Latinos

Cognitive function such as reasoning, attention, memory, and language is strongly correlated with brain aging. Compared to non-Hispanic whites, Hispanics/Latinos have a higher risk of cognitive impairment and dementia. The genetic determinants of cognitive function have not been widely explored in this diverse and admixed population. We conducted a genome-wide association analysis of cognitive function in up to 7600 middle aged and older Hispanics/Latinos (mean = 55 years) from the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Four cognitive measures were examined: the Brief Spanish English Verbal Learning Test (B-SEVLT), the Word Fluency Test (WFT), the Digit Symbol Substitution Test (DSST), the Six-Item Screener (SIS). Four novel loci were identified: one for B-SEVLT at 4p14, two for WFT at 3p14.1 and 6p21.32, and one for DSST at 10p13. These loci implicate genes highly expressed in brain and previously connected to neurological diseases (UBE2K, FRMD4B, the HLA gene complex). By applying tissue-specific gene expression prediction models to our genotype data, additional genes highly expressed in brain showed suggestive associations with cognitive measures possibly indicating novel biological mechanisms, including IFT122 in the hippocampus for SIS, SNX31 in the basal ganglia for B-SEVLT, RPS6KB2 in the frontal cortex for WFT, and CSPG5 in the hypothalamus for DSST. These findings provide new information about the genetic determinants of cognitive function in this unique population. In addition, we derived a measure of general cognitive function based on these cognitive tests and generated genome-wide association summary results, providing a resource to the research community for comparison, replication, and meta-analysis in future genetic studies in Hispanics/Latinos.

MicroRNA-374b accelerates the development of lung cancer through downregulating PTEN expression via activating PI3K/Akt pathway

OBJECTIVE

To elucidate whether microRNA-374b could participate in the development of lung cancer (LC) through downregulating PTEN (gene of phosphate and tensin homolog deleted on chromosome ten) expression via activating PI3K/Akt pathway.

PATIENTS AND METHODS

Expression levels of microRNA-374b and PTEN in LC tissues and adjacent normal tissues were detected by quantitative Real Time-Polymerase Chain Reaction (qRT-PCR). Moreover, the expression level of microRNA-374b in LC cell lines was detected as well. The microRNA-374b inhibitor was constructed and transfected to downregulate microRNA-374b expression in A549 and H358 cells. The regulatory effects of microRNA-374b on migratory and proliferative capacities of LC cells were explored by wound healing and cell counting kit-8 (CCK-8) assay, respectively. After co-transfection of microRNA-374b inhibitor and si-PTEN in LC cells, expression levels of PTEN/PI3K/Akt were determined by qRT-PCR and Western blot.

RESULTS

QRT-PCR results showed that microRNA-374b expression was higher, while PTEN expression was lower in LC tissues than adjacent tissues. Identically, microRNA-374b was also highly expressed in LC cell lines. PTEN expression was negatively correlated with microRNA-374b expression in LC. The downregulation of microRNA-374b in A549 and H358 cells inhibited their migratory and proliferative potentials. Subsequently, we verified that microRNA-374b could bind to PTEN through dual-luciferase reporter gene assay. MicroRNA-374b could inhibit PTEN expression and activate the PI3K/Akt pathway. Furthermore, PTEN knockdown enhanced migratory and proliferative abilities of LC cells, which were attenuated by co-transfection of microRNA-374b inhibitor.

CONCLUSIONS

MicroRNA-374b promotes the development of LC by downregulating PTEN expression through activating PI3K/Akt pathway.

Sparse Nonparametric Regression With Regularized Tensor Product Kernel

With growing interest to use black-box machine learning for complex data with many feature variables, it is critical to obtain a prediction model that only depends on a small set of features to maximize generalizability. Therefore, feature selection remains to be an important and challenging problem in modern applications. Most of existing methods for feature selection are based on either parametric or semiparametric models, so the resulting performance can severely suffer from model misspecification when high-order nonlinear interactions among the features are present. A very limited number of approaches for nonparametric feature selection were proposed, but they are computationally intensive and may not even converge. In this paper, we propose a novel and computationally efficient approach for nonparametric feature selection in regression field based on a tensor-product kernel function over the feature space. The importance of each feature is governed by a parameter in the kernel function which can be efficiently computed iteratively from a modified alternating direction method of multipliers (ADMM) algorithm. We prove the oracle selection property of the proposed method. Finally, we demonstrate the superior performance of our approach compared to existing methods via simulation studies and application to the prediction of Alzheimer's disease.

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2-3 weeks after the outbreak). A fast rate of decline in Rt was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, Rt significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.

Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers

Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available at every observed event time point. Lever-aging all available, infrequently measured time-varying biomarkers to improve prognostic model of event occurrence is an important and challenging problem. In this paper, we propose a kernel-smoothing based approach to borrow information across subjects to remedy infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation, and an efficient augmented penalization minimization algorithm related to the alternating direction method of multipliers (ADMM) is adopted for computation. Under some regularity conditions to carefully control approximation bias and stochastic variability, we show that even in the presence of ultra-high dimensionality, the proposed method selects important biomarkers with high probability. Through extensive simulation studies, we demonstrate superior performance in terms of estimation and selection performance compared to alternative methods. Finally, we apply the proposed method to analyze a recently completed real world study to model time to disease conversion using longitudinal, whole brain structural magnetic resonance imaging (MRI) biomarkers, and show a substantial improvement in performance over current standards including using baseline measures only.

Associations between perceived neighborhood environment and cognitive function among middle-aged and older women and men: Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study

PURPOSE

To examine cross-sectional associations between perceived neighborhood environment and cognitive function among middle-aged and older Hispanic/Latino women and men.

METHODS

Data from the Hispanic Community Health Study/Study of Latinos (2008-2011) and its Sociocultural Ancillary Study (2009-2010) were used. Participants were Hispanic/Latino women (n = 1812) and men (n = 1034) aged 45-74 years. Survey-weighted linear regression models were used to examine associations between self-reported perceived neighborhood environment (i.e., neighborhood social cohesion and problems categorized as quintiles, and neighborhood safety from crime categorized as low, medium, or high) with cognitive function (i.e., global cognition, verbal learning, memory, verbal fluency, and processing speed scores) in women and men. Final model adjusted for age, Hispanic/Latino background, language, field site, household income, education, years lived in neighborhood, and depressive symptoms.

RESULTS

Women in the lowest quintile of perceived neighborhood problems (vs. highest quintile) had higher global cognition (β 0.48, 95% CI 0.03, 0.94, p trend 0.229) and memory scores (0.60, 95% CI 0.11, 1.09, p trend: 0.060). Women in the highest quintile of perceived neighborhood social cohesion (vs. lowest quintile) had lower global cognition (β - 0.56, 95% CI - 1.02, - 0.09, p trend 0.004), verbal learning (B - 1.01, 95% CI - 2.00, - 0.03, p trend 0.015), verbal fluency (B - 2.00, 95% CI - 3.83, - 0.16, p trend 0.006), and processing speed (B - 2.11, 95% CI - 3.87, - 0.36, p trend 0.009). There was no association between perceived neighborhood safety from crime and cognition among women, or between any perceived neighborhood environment measure and cognition among men.

CONCLUSIONS

Middle-aged and older Hispanic/Latina women living in neighborhoods with the lowest perceived problems had higher global cognition and memory. Women living in neighborhoods with the highest perceived social cohesion had lower global cognition, verbal learning, verbal fluency, and processing speed.

1144 Actigraphy-defined Sleep And Neurocognitive Decline In Middle-age Hispanic/Latino Adults

Introduction

Few studies have evaluated objective sleep measures and longitudinal neurocognitive decline, particularly in middle-age or Hispanic/Latino adults. We evaluated prospective associations between actigraphy-defined sleep and 7-year neurocognitive change among Hispanic/Latino adults. We hypothesized that sleep duration would be associated with neurocognitive decline.

Methods

We analyzed data from 1,036 adults 45-64 years of age from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a multi-center prospective cohort study of diverse community-dwelling Hispanic/Latino adults. At Visit 1 (2008-2011), participants underwent neurocognitive assessments, 7-days of actigraphy, home sleep testing, and sleep questionnaires (including the Insomnia Severity Index). Seven years later, participants repeated neurocognitive assessments. The neurocognitive battery included the Six-Item Screener, Brief Spanish-English Verbal Learning Test, phonemic word fluency test, and Digit Symbol Subtest. Survey linear regression was used to evaluate prospective associations between actigraphy-defined or self-reported sleep variables and neurocognitive change. Final models adjusted for objectively-defined variables (age, body-mass index, Field Center, and time between neurocognitive assessments), and self-reported variables (sex, education, Hispanic/Latino background, alcohol consumption, physical activity, heart failure, cerebrovascular events, depression and anxiety symptoms, and antidepressant use).

Results

At Visit 1, the sample was 55% female and mean age was 54.9±2.2 years. The mean sleep duration was 402.6±27.6 minutes, mean sleep-onset latency was 11.3±9.7 minutes, mean number of days with naps of ≥ 15 minutes duration was 1.1±0.7, and mean sleep-time per nap was 51±14.1 minutes. Increased sleep-onset latency was associated with 7-year declines in global neurocognitive function (β=-0.0026, p&lt;0.01), verbal learning (β=-0.0028, p&lt;0.001) and verbal memory (β=-0.036, p&lt;0.05). Increased sleep-time per nap predicted better verbal memory (β=0.0038, p&lt;0.05). In contrast, sleep duration, sleep fragmentation, and self-reported sleep measures were not associated with neurocognitive change.

Conclusion

Among middle-age adults, sleep-onset latency and nap duration were associated with neurocognitive change. These findings may serve as targets for intervention of neurocognitive decline.

Support

This work is supported by the National Institute on Aging: R01AG048642, RF1AG054548, R01AG061022, R21AG056952, and R21HL140437 (AR).