Search

Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Pregnancy complications are risk factors for cardiovascular disease (CVD). Little is known about the role of renal biomarkers measured shortly after delivery, individually or in combination with pregnancy complications, in predicting subsequent severe maternal CVD., Methods and Results: This study included 566 mothers of diverse races and ethnicities from the Boston Birth cohort, enrolled at delivery and followed prospectively. Plasma creatinine and CysC (cystatin C) were measured 1 to 3 days after delivery. CVD during follow-up was defined by physician diagnoses in electronic medical records. Associations of renal biomarkers and pregnancy complications with time-to-CVD events were assessed using Cox proportional hazards models. During an average of 10.3±3.2 years of follow-up, 30 mothers developed 1 or more CVDs. Only a modest association was observed between creatinine and risk of CVD. In comparison, we found that per 0.1 mg/L increase of CysC was associated with a hazard ratio (HR) of 1.2 (95% CI, 1.1-1.4) for CVD after adjusting for covariates. Compared with those without preeclampsia and with normal CysC level (≤75th percentile), mothers with preeclampsia and elevated CysC (>75th percentile) had the highest risk of CVD (HR, 4.6 [95% CI, 1.7-17.7]), whereas mothers with preeclampsia only or with elevated CysC only did not have significantly increased CVD risk. Similar synergistic effects for CVD were observed between CysC and preterm delivery., Conclusions: In this sample of US, traditionally underrepresented multiracial and multiethnic high-risk mothers, elevated maternal plasma CysC, independently and jointly with pregnancy complications, increased risk of CVD later in life. These findings warrant further investigation., Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03228875.

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications., (© 2022 The International Biometric Society.)

Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID-19 in-patient treatment trials, the outcomes of COVID-19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID-19 in-patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Rationale & Objective: Pregnancy complications are risk factors for cardiovascular diseases (CVD). Little is known about the role of renal biomarkers measured shortly after delivery, individually or in combination with pregnancy complications, in predicting subsequent severe maternal CVD., Methods: This study included 576 mothers of diverse ethnicities from the Boston Birth cohort, enrolled at delivery and followed prospectively. Plasma creatinine and cystatin C were measured 1-3 days after delivery. CVD during follow-up was defined by physician diagnoses in electronic medical records. Associations of renal biomarkers and pregnancy complications with time-to-CVD events were assessed using Cox proportional hazards models., Results: During an average of 10.3±3.2 years of follow-up, 34 mothers developed one or more CVD events. Although no significant associations were found between creatinine and risk of CVD, per unit increase of cystatin C (CysC) was associated with a hazard ratio (HR) of 5.21 (95%CI = 1.49-18.2) for CVD. A borderline significant interactive effect was observed between elevated CysC (≥75th percentile) and preeclampsia. Compared to those without preeclampsia and with normal CysC level (<75 th percentile), mothers with preeclampsia and elevated CysC had the highest risk of CVD (HR=3.8, 95%CI = 1.4-10.2), while mothers with preeclampsia only or with elevated CysC only did not have significantly increased CVD risk. Similar synergistic effects for CVD were observed between CysC and preterm delivery., Conclusions: In this sample of US, traditionally under-represented multi-ethnic high-risk mothers, elevated maternal plasma cystatin C and pregnancy complications synergistically increased risk of CVD later in life. These findings warrant further investigation., Clinical Perspectives: What is new?Maternal postpartum elevated levels of cystatin C are independently associated with higher risk of cardiovascular diseases (CVD) later in life.Maternal pregnancy complications coupled with postpartum elevated levels of cystatin C synergistically increased future risk of CVD.What are the clinical implications?These findings, if further confirmed, suggest that women with pregnancy complications and elevated postpartum cystatin C may be at particular high risk for CVD later in life compared to women without these risk factors.

Background: Racial and ethnic differences in survival after a first cancer are well established but have not been examined after a second primary cancer (SPC) despite the increasing incidence among survivors., Methods: We examined 39 029 female breast cancer survivors who developed an SPC between 2000 and 2014 in the Surveillance, Epidemiology, and End Results 18 database. Multivariable Cox proportional hazards regression for competing risks data was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for cancer and cardiovascular disease mortality after SPCs comparing Hispanic, Non-Hispanic Asian, and Non-Hispanic Black survivors with Non-Hispanic White survivors. Models were adjusted for sociodemographics, tumor characteristics, and treatments of the first and second cancer. Analyses were stratified by SPC type., Results: During 17 years of follow-up, there were 15 117 deaths after SPCs. The risk of cancer death was 12% higher among Non-Hispanic Black survivors (HR = 1.12, 95% CI = 1.05 to 1.19) and 8% higher among Hispanic survivors (HR = 1.08, 95% CI = 1.00 to 1.16) compared with Non-Hispanic White survivors. In subgroup analyses, the strongest associations were observed among Non-Hispanic Black survivors with a second breast or uterine cancer and among Hispanic survivors with a second breast cancer. Non-Hispanic Black survivors also experienced a 44% higher risk of cardiovascular disease death after SPC diagnosis than Non-Hispanic White survivors (HR = 1.44, 95% CI = 1.20 to 1.74)., Conclusions: Higher cancer mortality among Non-Hispanic Black and Hispanic survivors and higher cardiovascular mortality among Non-Hispanic Black survivors exist among women who survive a first breast cancer to develop an SPC. Studies focused on identifying the contributors to these disparities are needed to enable implementation of effective mitigation strategies., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) are altered many years before the onset of clinical symptoms of mild cognitive impairment (MCI). Incorporating clinical symptom onset time into biomarker modeling may enhance our understanding of changes preceding MCI., Objective: Using a new analytical approach, we examined patterns of biomarker change prior to MCI symptom onset among individuals who progressed from normal cognition to MCI, stratified based on the age of symptom onset. We also analyzed biomarker patterns of change among participants who remained cognitively normal, and examined potential modifiers of biomarker trajectories, including demographics and apolipoprotein E (APOE) status., Methods: Analyses included 93 participants who progressed from normal cognition to MCI and 186 participants who remained cognitively normal, over an average follow-up period of 16.2 years. CSF biomarkers, including Aβ42, Aβ40, total tau (t-tau), and phosphorylated tau181 (p-tau181), were measured using the fully automated Lumipulse assays., Results: Among participants who progressed to MCI, Aβ42/Aβ40 decreased, and t-tau and p-tau181 increased. For participants who did not progress to MCI, CSF biomarkers showed relatively stable patterns. In both progressors and non-progressors, APOE4 carriers showed lower Aβ 42/Aβ40 levels (compared to non-carriers) at each point of the mean curves. Among non-progressors, APOE4 carriers had higher levels of p-tau181, p-tau181/(Aβ 42/Aβ40), and t-tau/(Aβ 42/Aβ 40). Additionally, among those who did not progress, female sex was associated with higher levels of t-tau, p-tau181, t-tau/(Aβ 42/Aβ 40), and p-tau181/(Aβ 42/Aβ 40)., Conclusions: These findings suggest that this analytic approach may provide additional insights into biomarker changes during early phases of AD.

Introduction: We examined longitudinal cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarker changes among cognitively normal individuals with 10.7 years follow-up, on average., Methods: Analyses included 278 participants ( M age = 57.5 years); 94 have progressed from normal cognition to mild cognitive impairment (MCI). Amyloid beta (Aβ) 42 /Aβ 40 , phosphorylated tau 181 (p-tau 181 ), and total tau (t-tau) were measured using automated electrochemiluminescence assays., Results: Apolipoprotein E ( APOE ) ε4 carriers had lower baseline Aβ 42 /Aβ 40 , but longitudinal Aβ 42 /Aβ 40 decreases did not differ by APOE ε4 after accounting for Aβ 42 /Aβ 40 positivity. Lower baseline Aβ 42 /Aβ 40 was associated with greater increases in tau (more strongly in males), and APOE ε4 genotype was associated with greater tau increases after reaching Aβ 42 /Aβ 40 positivity. Participants who progressed to MCI had more abnormal biomarker levels and greater tau increases prior to MCI symptom onset. Biomarkers were more abnormal among older adults, but unrelated to sex or education., Discussion: Our results confirm accelerated biomarker changes during preclinical AD and highlight the important role of amyloid levels in tau accelerations., Competing Interests: CP, AS, JW, and MCW have no disclosures. Dr. Greenberg is Editor‐in‐Chief of the other open access journal of the Alzheimer's Association: Alzheimer's & Dementia—Translational Research and Clinical Interventions. Dr. Albert is an advisor to Eli Lilly. Dr. Moghekar receives research support to JHU (but no direct or indirect salary support) from Fujirebio Diagnostics to analyze CSF preanalytics., (© 2022 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Background and Objectives: This study aimed to examine whether baseline CSF measures of Alzheimer disease (AD)-related pathology are associated with the time to onset of mild cognitive impairment (MCI) and whether these associations differ by age, sex, Apolipoprotein E ( ApoE4 ) status, and proximal (≤7 years) vs distal (>7 years) time to symptom onset., Methods: Measures of amyloid (Aβ1-42 and Aβ1-40), phospho-tau (ptau181), and total tau (t-tau) were determined from CSF samples obtained at baseline from participants in an ongoing longitudinal project, known as the Biomarkers for Older Controls at Risk for Alzheimer Disease study (BIOCARD) study. The fully automated, Lumipulse G immunoassay was used to analyze the specimens. Cox regression models were used to examine the relationship of baseline biomarker levels with time to symptom onset of MCI and interactions with age, sex, and ApoE allelic status in subjects who progressed from normal cognition to MCI., Results: Analyses included 273 participants from the BIOCARD cohort, who were cognitively normal and predominantly middle-aged at baseline, and have been followed for an average of 16 years (max = 23.6). During follow-up, 94 progressed to MCI (median time to symptom onset = 6.9 years). In Cox regression models, elevated ptau181 and t-tau levels were associated with time to MCI symptom onset if it occurred within 7 years of baseline (HR 1.386 and 1.329; p = 0.009 and 0.017, respectively), while a lower Aβ42/Aβ40 ratio was associated with symptom onset if it occurred >7 years from baseline (HR 0.596, p = 0.003). There were also significant 3-way CSF × age × sex interactions for ptau181 and Aβ42/Aβ40, with follow-up analyses indicating that associations between these biomarkers and progression to MCI were stronger among men than among women, but this difference between sexes diminished with increasing age., Discussion: The lengthy follow-up of BIOCARD participants permitted an examination of time-varying associations between CSF AD biomarkers with MCI symptom onset and the influence of sex, baseline age, and ApoE4 genotype on these associations. These factors may inform clinical trial enrollment strategies, or trial duration and outcomes, which may use these measures as surrogate markers of treatment response., (© 2022 American Academy of Neurology.)

Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies to identify the relationship between covariates and disease outcome. A cross-sectional population is defined as a population of living individuals at the sampling or observational time. It is generally understood that binary disease outcome from cross-sectional data contains less information than longitudinally collected time-to-event data, but there is insufficient understanding as to whether bias can possibly exist in cross-sectional data and how the bias is related to the population risk of interest. Wang and Yang (2021) presented the complexity and bias in cross-sectional data with binary disease outcome with detailed analytical explorations into the data structure. As the distribution of the cross-sectional binary outcome is quite different from the population risk distribution, bias can arise when using cross-sectional data analysis to draw inference for population risk. In this paper we argue that the commonly adopted age-specific risk probability is biased for the estimation of population risk and propose an outcome reassignment approach which reassigns a portion of the observed binary outcome, 0 or 1, to the other disease category. A sign test and a semiparametric pseudo-likelihood method are developed for analyzing cross-sectional data using the OR approach. Simulations and an analysis based on Alzheimer's Disease data are presented to illustrate the proposed methods., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Limited information exists about survival outcomes after second primary cancers (SPCs) among breast cancer survivors. Studies suggest that mortality after certain SPCs may be higher than mortality after first primary cancers (FPCs) of the same type. A cohort study was conducted among 63,424 US women using the Surveillance, Epidemiology, and End Results 18 database (2000-2016) to compare mortality after a SPC among breast cancer survivors to mortality among women after a FPC using Cox proportional hazard regression. Propensity scores were used to match survivors with SPCs to women with FPCs 1:1 based on cancer type and prognostic factors. During a median follow-up of 42 months, 11,532 cancer deaths occurred after SPCs among survivors compared to 9305 deaths after FPCs. Cumulative cancer mortality was 44.7% for survivors with SPCs and 35.2% for women with FPCs. Survivors with SPCs had higher risk of cancer death (hazard ratio (HR): 1.27, 95% CI: 1.23-1.30) and death overall (HR: 1.18, 95% CI: 1.15-1.21) than women with FPCs. Increased risk of cancer death after SPCs compared to FPCs was observed for cancer in breast, lung, colon and/or rectum, uterus, lymphoma, melanoma, thyroid, and leukemia. Estrogen receptor status and treatment of the prior breast cancer as well as time between prior breast cancer and SPC significantly modified the mortality difference between women with SPC and FPC. A more tailored approach to early detection and treatment could improve outcomes from second cancer in breast cancer survivors., (© 2022. The Author(s).)

Objective: This study examined the association of lifetime experiences, measured by a cognitive reserve (CR) composite score composed of years of education, literacy, and vocabulary measures, to level and rate of change in white matter microstructure, as assessed by diffusion tensor imaging (DTI) measures. We also examined whether the relationship between the proxy CR composite score and white matter microstructure was modified by participant age, APOE -ε4 genetic status, and level of vascular risk., Methods: A sample of 192 non-demented ( n = 166 cognitively normal, n = 26 mild cognitive impairment) older adults [mean age = 70.17 (SD = 8.5) years] from the BIOCARD study underwent longitudinal DTI (mean follow-up = 2.5 years, max = 4.7 years). White matter microstructure was quantified by fractional anisotropy (FA) and radial diffusivity (RD) values in global white matter tracts and medial temporal lobe (MTL) white matter tracts., Results: Using longitudinal linear mixed effect models, we found that FA decreased over time and RD increased over time in both the global and MTL DTI composites, but the rate of change in these DTI measures was not related to level of CR. However, there were significant interactions between the CR composite score and age for global RD in the full sample, and for global FA, global RD, and MTL RD among those with normal cognition. These interactions indicated that among participants with a lower baseline age, higher CR composite scores were associated with higher FA and lower RD values, while among participants with higher age at baseline, higher CR composite scores were associated with lower FA and higher RD values. Furthermore, these relationships were not modified by APOE -ε4 genotype or level of vascular risk., Conclusion: The association between level of CR and DTI measures differs by age, suggesting a possible neuroprotective effect of CR among late middle-aged adults that shifts to a compensatory effect among older adults., Competing Interests: MA is an advisor to Eli Lily. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Brichko, Soldan, Zhu, Wang, Faria, Albert, Pettigrew and The BIOCARD Research Team.)

This cross-sectional study examined whether performance on the computerized Paired Associate Learning (PAL) task from the Cambridge Neuropsychological Test Automated Battery is associated with amyloid positivity as measured by Positron Emission Tomography, regional volume composites as measured by Magnetic Resonance Imaging, and cognitive impairment. Participants from the BIOCARD Study (N = 73, including 62 cognitively normal and 11 with mild cognitive impairment; M age = 70 years) completed the PAL task, a comprehensive clinical and neuropsychological assessment, and neuroimaging as part of their annual study visit. In linear regressions covarying age, sex, years of education and diagnosis, higher PAL error scores were associated with amyloid positivity but not with medial temporal or cortical volume composites. By comparison, standard neuropsychological measures of episodic memory and global cognition were unrelated to amyloid positivity, but better performance on the verbal episodic memory measures was associated with larger cortical volume composites. Participants with mild cognitive impairment demonstrated worse cognitive performance on all of the cognitive measures, including the PAL task. These findings suggest that this computerized visual paired associate learning task may be more sensitive to amyloid positivity than standard neuropsychological tests, and may therefore be a promising tool for detecting amyloid positivity in non-demented participants., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre-specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree-structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined., (© 2020 The International Biometric Society.)

Background: Neuropsychiatric symptoms (NPS) among cognitively normal older adults are increasingly recognized as risk factors for cognitive decline and impairment. However, the underlying mechanisms remain unclear., Objective: To examine whether biomarkers of Alzheimer's disease (amyloid burden) and cerebrovascular disease (white matter hyperintensity (WMH) volume) modify the association between NPS and cognitive decline among cognitively unimpaired older adults., Methods: Analyses included 193 cognitively unimpaired participants (M age = 70 years) from the BIOCARD study, including 148 with PET amyloid and WMH biomarker data. NPS were measured with Neuropsychiatric Inventory and Geriatric Depression Scale scores. Linear mixed effects models were used to examine the association between baseline NPS and longitudinal cognitive trajectories (M follow-up = 3.05 years), using separate models for global, episodic memory, and executive function cognitive composite scores. In a subset of individuals with biomarker data, we evaluated whether WMH or cortical amyloid burden modified the relationship between NPS and cognitive change (as indicated by the NPS×biomarker×time interactions)., Results: Higher baseline NPS were associated with lower executive function scores, but not a faster rate of decline in executive function. NPS symptoms were unrelated to the global or episodic memory composite scores, and there was little evidence of a relationship between NPS symptoms and cognitive change over time. The associations between NPS and cognitive decline did not differ by amyloid or WMH burden, and NPS were unrelated to amyloid and WMH burden., Conclusion: These results suggest that the effect of neuropsychiatric symptoms on executive dysfunction may occur through mechanisms outside of amyloid and cerebrovascular disease.

Background: Exposure to metals lead (Pb), mercury (Hg), and cadmium (Cd) and trace elements selenium (Se) and manganese (Mn) has been linked to the developmental origins of cardiometabolic diseases, but the mechanisms are not well-understood., Objective: Conduct a metabolome-wide association study to understand how in utero exposure to Pb, Hg, Cd, Se, and Mn affects the metabolic programming of fetuses., Methods: We used data from the Boston Birth Cohort, which enrolled mother-child pairs from Boston, MA. We measured metals and trace elements in maternal red blood cells (RBCs) collected 24-72 h after delivery, and metabolites in cord blood collected at birth. We used multivariable linear regression to examine associations of metals and trace elements with metabolites and Bonferroni correction to account for multiple comparisons. We assessed non-linear associations of metals and trace elements with metabolites using restricted cubic spline plots., Results: This analysis included 670 mother-child pairs (57% non-Hispanic Black and 24% Hispanic). After Bonferroni correction, there were 25 cord metabolites associated with at least one of the metals or trace elements. Pb was negatively associated with the xenobiotic piperine, Cd was positively associated with xenobiotics cotinine and hydroxycotinine, and Hg was associated with 8 lipid metabolites (in both directions). Se and Mn shared associations with 6 metabolites (in both directions), which mostly included nucleotides and amino acids; Se was additionally associated with 7 metabolites (mostly amino acids, nucleotides, and carnitines) and Mn was additionally associated with C36:4 hydroxy phosphatidylcholine. Restricted cubic spline plots showed that most associations were linear., Discussion: Maternal RBC metal and trace element concentrations were associated in a dose-dependent fashion with cord blood metabolites. What remains to be determined is whether these metals- and trace elements-associated changes in cord metabolites can influence a child's risk of cardiometabolic diseases., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study examines the relationship of engagement in different lifestyle activities to connectivity in large-scale functional brain networks, and whether network connectivity modifies cognitive decline, independent of brain amyloid levels. Participants (N = 153, mean age = 69 years, including N = 126 with amyloid imaging) were cognitively normal when they completed resting-state functional magnetic resonance imaging, a lifestyle activity questionnaire, and cognitive testing. They were followed with annual cognitive tests up to 5 years (mean = 3.3 years). Linear regressions showed positive relationships between cognitive activity engagement and connectivity within the dorsal attention network, and between physical activity levels and connectivity within the default-mode, limbic, and frontoparietal control networks, and global within-network connectivity. Additionally, higher cognitive and physical activity levels were independently associated with higher network modularity, a measure of functional network specialization. These associations were largely independent of APOE4 genotype, amyloid burden, global brain atrophy, vascular risk, and level of cognitive reserve. Moreover, higher connectivity in the dorsal attention, default-mode, and limbic networks, and greater global connectivity and modularity were associated with reduced cognitive decline, independent of APOE4 genotype and amyloid burden. These findings suggest that changes in functional brain connectivity may be one mechanism by which lifestyle activity engagement reduces cognitive decline., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Background: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission., Objective: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid&#95;trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization., Design: Retrospective observational cohort study., Settings: Five hospitals in Maryland and Washington, D.C., Patients: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease., Measurements: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization., Results: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively., Limitation: The SCARP tool was developed by using data from a single health system., Conclusion: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information., Primary Funding Source: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Background: In utero exposure to heavy metals lead (Pb), mercury (Hg), and cadmium (Cd) may be associated with higher childhood blood pressure (BP), whereas trace elements selenium (Se) and manganese (Mn) may have protective antioxidant effects that modify metal-BP associations., Objectives: We examined the individual and joint effects of in utero exposure to Pb, Hg, Cd, Se, and Mn on childhood BP., Methods: We used data from the Boston Birth Cohort (enrolled 2002-2013). We measured heavy metals and trace elements in maternal red blood cells collected 24-72 h after delivery. We calculated child BP percentile per the 2017 American Academy of Pediatrics Clinical Practice Guideline. We used linear regression models to estimate the association of each metal, and Bayesian kernel machine regression (BKMR) to examine metal coexposures, with child BP between 3 to 15 years of age., Results: Our analytic sample comprised 1,194 mother-infant pairs (61% non-Hispanic Black, 20% Hispanic). Hg and Pb were not associated with child systolic BP (SBP). Se and Mn were inversely associated with child SBP percentiles, which, on average, were 6.23 points lower with a doubling of Se (95% CI: - 11.51 , - 0.96 ) and 2.62 points lower with a doubling of Mn (95% CI: - 5.20 , - 0.04 ). BKMR models showed similar results. Although Cd was not associated with child SBP overall, the inverse association between Mn and child SBP was stronger at higher levels of Cd ( p -interaction = 0.04 ). Consistent with this finding, in utero exposure to cigarette smoke modified the Mn-child SBP association. Among children whose mothers smoked during pregnancy, a doubling of Mn was associated with a 10.09-point reduction in SBP percentile (95% CI: - 18.03 , - 2.15 ), compared with a 1.49-point reduction (95% CI: - 4.21 , 1.24) in children whose mothers did not smoke during pregnancy ( p -interaction = 0.08 )., Conclusion: Se and Mn concentrations in maternal red blood cells collected 24-72 h after delivery were associated with lower child SBP at 3 to 15 years of age. There was an interaction between Mn and Cd on child SBP, whereby the protective association of Mn on child SBP was stronger among mothers who had higher Cd. The association of Mn and child SBP was also modified by maternal cigarette smoking-a source of Cd-during pregnancy. Optimizing in utero Se levels, as well as Mn levels in women who had high Cd or smoked during pregnancy, may protect offspring from developing high BP during childhood. https://doi.org/10.1289/EHP8325.

Background: Sustained molecular detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in the upper respiratory tract (URT) in mild to moderate coronavirus disease 2019 (COVID-19) is common. We sought to identify host and immune determinants of prolonged SARS-CoV-2 RNA detection., Methods: Ninety-five symptomatic outpatients self-collected midturbinate nasal, oropharyngeal (OP), and gingival crevicular fluid (oral fluid) samples at home and in a research clinic a median of 6 times over 1-3 months. Samples were tested for viral RNA, virus culture, and SARS-CoV-2 and other human coronavirus antibodies, and associations were estimated using Cox proportional hazards models., Results: Viral RNA clearance, as measured by SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR), in 507 URT samples occurred a median (interquartile range) 33.5 (17-63.5) days post-symptom onset. Sixteen nasal-OP samples collected 2-11 days post-symptom onset were virus culture positive out of 183 RT-PCR-positive samples tested. All participants but 1 with positive virus culture were negative for concomitant oral fluid anti-SARS-CoV-2 antibodies. The mean time to first antibody detection in oral fluid was 8-13 days post-symptom onset. A longer time to first detection of oral fluid anti-SARS-CoV-2 S antibodies (adjusted hazard ratio [aHR], 0.96; 95% CI, 0.92-0.99; P = .020) and body mass index (BMI) ≥25 kg/m 2 (aHR, 0.37; 95% CI, 0.18-0.78; P = .009) were independently associated with a longer time to SARS-CoV-2 viral RNA clearance. Fever as 1 of first 3 COVID-19 symptoms correlated with shorter time to viral RNA clearance (aHR, 2.06; 95% CI, 1.02-4.18; P = .044)., Conclusions: We demonstrate that delayed rise of oral fluid SARS-CoV-2-specific antibodies, elevated BMI, and absence of early fever are independently associated with delayed URT viral RNA clearance., (© The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Background: Sustained molecular detection of SARS-CoV-2 RNA in the upper respiratory tract (URT) in mild to moderate COVID-19 is common. We sought to identify host and immune determinants of prolonged SARS-CoV-2 RNA detection., Methods: Ninety-five outpatients self-collected mid-turbinate nasal, oropharyngeal (OP), and gingival crevicular fluid (oral fluid) samples at home and in a research clinic a median of 6 times over 1-3 months. Samples were tested for viral RNA, virus culture, and SARS-CoV-2 and other human coronavirus antibodies, and associations were estimated using Cox proportional hazards models., Results: Viral RNA clearance, as measured by SARS-CoV-2 RT-PCR, in 507 URT samples occurred a median (IQR) 33.5 (17-63.5) days post-symptom onset. Sixteen nasal-OP samples collected 2-11 days post-symptom onset were virus culture positive out of 183 RT-PCR positive samples tested. All participants but one with positive virus culture were negative for concomitant oral fluid anti-SARS-CoV-2 antibodies. The mean time to first antibody detection in oral fluid was 8-13 days post-symptom onset. A longer time to first detection of oral fluid anti-SARS-CoV-2 S antibodies (aHR 0.96, 95% CI 0.92-0.99, p=0.020) and BMI ≥ 25kg/m 2 (aHR 0.37, 95% CI 0.18-0.78, p=0.009) were independently associated with a longer time to SARS-CoV-2 viral RNA clearance. Fever as one of first three COVID-19 symptoms correlated with shorter time to viral RNA clearance (aHR 2.06, 95% CI 1.02-4.18, p=0.044)., Conclusions: We demonstrate that delayed rise of oral fluid SARS-CoV-2-specific antibodies, elevated BMI, and absence of early fever are independently associated with delayed URT viral RNA clearance.

Importance: Clinical effectiveness data on remdesivir are urgently needed, especially among diverse populations and in combination with other therapies., Objective: To examine whether remdesivir administered with or without corticosteroids for treatment of coronavirus disease 2019 (COVID-19) is associated with more rapid clinical improvement in a racially/ethnically diverse population., Design, Setting, and Participants: This retrospective comparative effectiveness research study was conducted from March 4 to August 29, 2020, in a 5-hospital health system in the Baltimore, Maryland, and Washington, DC, area. Of 2483 individuals with confirmed severe acute respiratory syndrome coronavirus 2 infection assessed by polymerase chain reaction, those who received remdesivir were matched to infected individuals who did not receive remdesivir using time-invariant covariates (age, sex, race/ethnicity, Charlson Comorbidity Index, body mass index, and do-not-resuscitate or do-not-intubate orders) and time-dependent covariates (ratio of peripheral blood oxygen saturation to fraction of inspired oxygen, blood pressure, pulse, temperature, respiratory rate, C-reactive protein level, complete white blood cell count, lymphocyte count, albumin level, alanine aminotransferase level, glomerular filtration rate, dimerized plasmin fragment D [D-dimer] level, and oxygen device). An individual in the remdesivir group with k days of treatment was matched to a control patient who stayed in the hospital at least k days (5 days maximum) beyond the matching day., Exposures: Remdesivir treatment with or without corticosteroid administration., Main Outcomes and Measures: The primary outcome was rate of clinical improvement (hospital discharge or decrease of 2 points on the World Health Organization severity score), and the secondary outcome, mortality at 28 days. An additional outcome was clinical improvement and time to death associated with combined remdesivir and corticosteroid treatment., Results: Of 2483 consecutive admissions, 342 individuals received remdesivir, 184 of whom also received corticosteroids and 158 of whom received remdesivir alone. For these 342 patients, the median age was 60 years (interquartile range, 46-69 years), 189 (55.3%) were men, and 276 (80.7%) self-identified as non-White race/ethnicity. Remdesivir recipients had a shorter time to clinical improvement than matched controls without remdesivir treatment (median, 5.0 days [interquartile range, 4.0-8.0 days] vs 7.0 days [interquartile range, 4.0-10.0 days]; adjusted hazard ratio, 1.47 [95% CI, 1.22-1.79]). Remdesivir recipients had a 28-day mortality rate of 7.7% (22 deaths) compared with 14.0% (40 deaths) among matched controls, but this difference was not statistically significant in the time-to-death analysis (adjusted hazard ratio, 0.70; 95% CI, 0.38-1.28). The addition of corticosteroids to remdesivir was not associated with a reduced hazard of death at 28 days (adjusted hazard ratio, 1.94; 95% CI, 0.67-5.57)., Conclusions and Relevance: In this comparative effectiveness research study of adults hospitalized with COVID-19, receipt of remdesivir was associated with faster clinical improvement in a cohort of predominantly non-White patients. Remdesivir plus corticosteroid administration did not reduce the time to death compared with remdesivir administered alone.

This paper introduces two sets of measures as exploratory tools to study physical activity patterns: active-to-sedentary/sedentary-to-active rate function (ASRF/SARF) and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together to understand physical activity patterns. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). A two-level semiparametric regression model for ARF and the associated activity magnitude is developed under a unified framework using the marked point process formulation. The inactive and active states measured by accelerometers are treated as a 0-1 point process, and the activity magnitude measured at each active state is defined as a marked variable. The commonly encountered missing data problem due to device nonwear is referred to as "window censoring," which is handled by a proper estimation approach that adopts techniques from recurrent event data. Large sample properties of the estimator and comparison between two regression models as measurement frequency increases are studied. Simulation and NHANES data analysis results are presented. The statistical inference and analysis results suggest that ASRF/SARF and ARF/SRF provide useful analytical tools to practitioners for future research on wearable device data., (© 2020 The International Biometric Society.)

A cross sectional population is defined as a population of living individuals at the sampling or observational time. Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies for identifying how covariates correlate with disease occurrence. It is generally understood that cross-sectional binary outcome is not as informative as longitudinally collected time-to-event data, but there is insufficient understanding as to whether bias can possibly exist in cross-sectional data and how the bias is related to the population risk of interest. As the progression of a disease typically involves both time and disease status, we consider how the binary disease outcome from the cross-sectional population is connected to birth-illness-death process in the target population. We argue that the distribution of cross-sectional binary outcome is different from the risk distribution from the target population and that bias would typically arise when using cross-sectional data to draw inference for population risk. In general, the cross-sectional risk probability is determined jointly by the population risk probability and the ratio of duration of diseased state to the duration of disease-free state. Through explicit formulas we conclude that bias can almost never be avoided from cross-sectional data. We present age-specific risk probability (ARP) and argue that models based on ARP offers a compromised but still biased approach to understand the population risk. An analysis based on Alzheimer's disease data is presented to illustrate the ARP model and possible critiques for the analysis results., (© 2020 John Wiley & Sons Ltd.)

Background: Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts., Objective: To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19., Design: Retrospective cohort analysis., Setting: Five hospitals in the Maryland and Washington, DC, area., Patients: 832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020., Measurements: Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death., Results: Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively., Limitation: The study was done in a single health care system., Conclusion: A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions., Primary Funding Source: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Background: The need for diagnostic biomarkers of cognitive decline is particularly important among aging adults with Down syndrome (DS). Growing empirical support has identified the utility of plasma derived biomarkers among neurotypical adults with mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, the application of such biomarkers has been limited among the DS population., Objective: This study aimed to investigate the cross-sectional diagnostic performance of plasma neurofilament light chain (Nf-L) and total-tau, individually and in combination among a cohort of DS adults., Methods: Plasma samples were analyzed from n = 305 (n = 225 cognitively stable (CS); n = 44 MCI-DS; n = 36 DS-AD) participants enrolled in the Alzheimer's Biomarker Consortium -Down Syndrome., Results: In distinguishing DS-AD participants from CS, Nf-L alone produced an AUC of 90%, total-tau alone reached 74%, and combined reached an AUC of 86%. When age and gender were included, AUC increased to 93%. Higher values of Nf-L, total-tau, and age were all shown to be associated with increased risk for DS-AD. When distinguishing MCI-DS participants from CS, Nf-L alone produced an AUC of 65%, while total-tau alone reached 56%. A combined model with Nf-L, total-tau, age, and gender produced an AUC of 87%. Both higher values in age and total-tau were found to increase risk for MCI-DS; Nf-L levels were not associated with increased risk for MCI-DS., Conclusion: Advanced assay techniques make total-tau and particularly Nf-L useful biomarkers of both AD pathology and clinical status in DS and have the potential to serve as outcome measures in clinical trials for future disease-modifying drugs.

Objective: To determine whether vascular risk and Alzheimer disease (AD) biomarkers have independent or synergistic effects on cognitive decline and whether vascular risk is associated with the accumulation of AD pathology as measured by change in biomarkers over time., Methods: At baseline, participants (n = 168) were cognitively normal and primarily middle-aged (mean 56.4 years, SD 10.9 years) and had both vascular risk factor status and proximal CSF biomarkers available. Baseline vascular risk was quantified with a composite vascular risk score reflecting the presence or absence of hypertension, hypercholesterolemia, diabetes, current smoking, and obesity. CSF biomarkers of β-amyloid (Aβ) 1-42 , total tau (t-tau), and phosphorylated tau (p-tau) were used to create dichotomous high and low AD biomarker groups (based on Aβ 1-42 and tau). Linear mixed-effects models were used to examine change in a cognitive composite score (mean follow-up 13.9 years) and change in CSF biomarkers (mean follow-up 4.2 years)., Results: There was no evidence of a synergistic relationship between the vascular risk score and CSF AD biomarkers and cognitive decline. Instead, the vascular risk score (estimate -0.022, 95% confidence interval [CI] -0.043 to -0.002, p = 0.03) and AD biomarkers (estimate -0.060, 95% CI -0.096 to -0.024, p = 0.001) were independently and additively associated with cognitive decline. In addition, the vascular risk score was unrelated to levels of or rate of change in CSF Aβ 1-42 , t-tau, or p-tau., Conclusions: The results of this observational cohort study suggest that vascular risk and biomarkers of AD pathology, when measured in midlife, act along independent pathways and underscore the importance of accounting for multiple risk factors for identifying cognitively normal individuals at the greatest risk of cognitive decline., (© 2020 American Academy of Neurology.)

A time-dependent measure, termed the rate ratio, was proposed to assess the local dependence between two types of recurrent event processes in one-sample settings. However, the one-sample work does not consider modeling the dependence by covariates such as subject characteristics and treatments received. The focus of this paper is to understand how and in what magnitude the covariates influence the dependence strength for bivariate recurrent events. We propose the covariate-adjusted rate ratio, a measure of covariate-adjusted dependence. We propose a semiparametric regression model for jointly modeling the frequency and dependence of bivariate recurrent events: the first level is a proportional rates model for the marginal rates and the second level is a proportional rate ratio model for the dependence structure. We develop a pseudo-partial likelihood to estimate the parameters in the proportional rate ratio model. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. We illustrate the proposed models and methods using a soft tissue sarcoma study that examines the effects of initial treatments on the marginal frequencies of local/distant sarcoma recurrence and the dependence structure between the two types of cancer recurrence., (© 2020 The International Biometric Society.)

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration., (© 2019 The International Biometric Society.)

Introduction: Adults with Down syndrome (DS) older than 40 have Alzheimer's disease (AD) neuropathology and high risk for dementia, but little is known about the relationship of sex to AD risk in this population., Methods: Using nonparametric methods and Cox proportional hazards models we analyzed differences in incidence of dementia, by sex, presence of an apolipoprotein E ( APOE ) ε4 or ε2 allele, and dementia duration and decline in 246 adults over 40 with DS., Results: There was no significant sex difference in risk of AD or rate of cognitive decline. APOE ε4 allele significantly increased risk of AD irrespective of sex. No significant interactions were found between sex and APOE status on AD risk. Among those who died, dementia duration was significantly longer in women., Discussion: This study showed no effect of sex nor interaction between sex and APOE for risk of AD in adults with DS; however, women had longer dementia duration., (© 2020 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, Inc. on behalf of the Alzheimer's Association.)

Introduction: Adults with Down syndrome (DS) are at exceptionally high risk for Alzheimer's disease (AD), with virtually all individuals developing key neuropathological features by age 40. Identifying biomarkers of AD progression in DS can provide valuable insights into pathogenesis and suggest targets for disease modifying treatments., Methods: We describe the development of a multi-center, longitudinal study of biomarkers of AD in DS. The protocol includes longitudinal examination of clinical, cognitive, blood and cerebrospinal fluid-based biomarkers, magnetic resonance imaging and positron emission tomography measures (at 16-month intervals), as well as genetic modifiers of AD risk and progression., Results: Approximately 400 individuals will be enrolled in the study (more than 370 to date). The methodological approach from the administrative, clinical, neuroimaging, omics, neuropathology, and statistical cores is provided., Discussion: This represents the largest U.S.-based, multi-site, biomarker initiative of AD in DS. Findings can inform other multidisciplinary networks studying AD in the general population., Competing Interests: The authors have no conflicts of interest to declare., (© 2020 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.)

Objective: Examine whether cognitive reserve moderates the association of 1) vascular risk factors and 2) white matter hyperintensity burden with risk of clinical progression and longitudinal cognitive decline., Methods: BIOCARD Study participants were cognitively normal and primarily middle-aged (M = 57 years) at baseline and have been followed with annual cognitive and clinical assessments (M = 13 years). Baseline cognitive reserve was indexed with a composite score combining education with reading and vocabulary scores. Baseline vascular risk (N = 229) was assessed with a composite risk score reflecting five vascular risk factors. Baseline white matter hyperintensity load (N = 271) was measured with FLAIR magnetic resonance imaging. Cox regression models assessed risk of progression from normal cognition to onset of clinical symptoms of Mild Cognitive Impairment. Longitudinal mixed effects models measured the relationship of these variables to cognitive decline, using a global composite score, and executive function and episodic memory sub-scores., Results: Both vascular risk and white matter hyperintensities were associated with cognitive decline, particularly in executive function. Higher vascular risk, but not white matter hyperintensity burden, was associated with an increased risk of progression to Mild Cognitive Impairment. Higher cognitive reserve was associated with a reduced risk of symptom onset and higher levels of baseline cognition but did not modify the associations between the vascular risk score and white matter hyperintensities with clinical progression or cognitive decline., Interpretation: Although cognitive reserve has protective effects on clinical and cognitive outcomes, it does not mitigate the negative impact of vascular risk and small vessel cerebrovascular disease on these same outcomes., (© 2020 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Introduction: Previously generated serum and plasma proteomic profiles were examined among adults with Down syndrome (DS) to determine whether these profiles could discriminate those with mild cognitive impairment (MCI-DS) and Alzheimer's disease (DS-AD) from those cognitively stable (CS)., Methods: Data were analyzed on n = 305 (n = 225 CS; n = 44 MCI-DS; n = 36 DS-AD) enrolled in the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS)., Results: Distinguishing MCI-DS from CS, the serum profile produced an area under the curve (AUC) = 0.95 (sensitivity [SN] = 0.91; specificity [SP] = 0.99) and an AUC = 0.98 (SN = 0.96; SP = 0.97) for plasma when using an optimized cut-off score. Distinguishing DS-AD from CS, the serum profile produced an AUC = 0.93 (SN = 0.81; SP = 0.99) and an AUC = 0.95 (SN = 0.86; SP = 1.0) for plasma when using an optimized cut-off score. AUC remained unchanged to slightly improved when age and sex were included. Eotaxin3, interleukin (IL)-10, C-reactive protein, IL-18, serum amyloid A , and FABP3 correlated fractions at r 2  > = 0.90., Discussion: Proteomic profiles showed excellent detection accuracy for MCI-DS and DS-AD., Competing Interests: SEO has patents and patents pending regarding blood‐biomarkers for precision medicine in neurodegenerative diseases and served on an Advisory Board to Roche Diagnostics. No other authors have conflicts of interest to disclose., (© 2020 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.)

We examined whether cognitive reserve (CR) impacts level of, or rate of change in, biomarkers of Alzheimer's disease (AD) and small-vessel cerebrovascular disease in >250 individuals who were cognitively normal and middle-aged and older at the baseline. The four primary biomarker categories commonly examined in studies of AD were measured longitudinally: cerebrospinal fluid measures of amyloid (A) and tau (T); cerebrospinal fluid and neuroimaging measures of neuronal injury (N); and neuroimaging measures of white matter hyperintensities (WMHs) to assess cerebrovascular pathology (V). CR was indexed by a composite score including years of education, reading, and vocabulary test performance. Higher CR was associated with lower levels of WMHs, particularly among those who subsequently progressed from normal cognition to MCI. CR was not associated with WMH trajectories. In addition, CR was not associated with either levels of, or rate of change in, A/T/N biomarkers. This may suggest that higher CR is associated with lifestyle factors that reduce levels of cerebrovascular disease, allowing individuals with higher CR to better tolerate other types of pathology., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: Recent studies suggest that white matter hyperintensities (WMH) on MRI, which primarily reflect small vessel cerebrovascular disease, may play a role in the evolution of Alzheimer disease (AD). In a longitudinal study, we investigated whether WMH promote the progression of AD pathology, or alter the association between AD pathology and risk of progression from normal cognition to mild cognitive impairment (MCI)., Methods: Two sets of analyses were conducted. The relationship between whole brain WMH load, based on fluid-attenuated inversion recovery MRI, obtained in initially cognitively normal participants (n = 274) and time to onset of symptoms of MCI (n = 60) was examined using Cox regression models. In a subset of the participants with both MRI and CSF data (n = 204), the interaction of WMH load and CSF AD biomarkers was also evaluated., Results: Baseline WMH load interacted with CSF total tau (t-tau) with respect to symptom onset, but not with CSF β-amyloid 1-42 or phosphorylated tau (p-tau) 181. WMH volume was associated with time to symptom onset of MCI among individuals with low t-tau (hazard ratio [HR] 1.35, confidence interval [CI] 1.06-1.73, p = 0.013), but not those with high t-tau (HR 0.86, CI 0.56-1.32, p = 0.47). The rate of change in the CSF biomarkers over time was not associated with the rate of change in WMH volumes., Conclusion: These results suggest that WMH primarily affect the risk of progression when CSF measures of neurodegeneration or neuronal injury (as reflected by t-tau) are low. However, CSF biomarkers of amyloid and p-tau and WMH appear to have largely independent and nonsynergistic effects on the risk of progression to MCI., (© 2019 American Academy of Neurology.)

Background: Low-dose mercury (Hg) exposure has been associated with cardiovascular diseases, diabetes, and obesity in adults, but it is unknown the metabolic consequence of in utero Hg exposure. This study aimed to investigate the association between in utero Hg exposure and child overweight or obesity (OWO) and to explore if adequate maternal folate can mitigate Hg toxicity., Methods: This prospective study included 1442 mother-child pairs recruited at birth and followed up to age 15 years. Maternal Hg in red blood cells and plasma folate levels were measured in samples collected 1-3 days after delivery (a proxy for third trimester exposure). Adequate folate was defined as plasma folate ≥ 20.4 nmol/L. Childhood OWO was defined as body mass index ≥ 85% percentile for age and sex., Results: The median (interquartile range) of maternal Hg levels were 2.11 (1.04-3.70) μg/L. Geometric mean (95% CI) of maternal folate levels were 31.1 (30.1-32.1) nmol/L. Maternal Hg levels were positively associated with child OWO from age 2-15 years, independent of maternal pre-pregnancy OWO, diabetes, and other covariates. The relative risk (RR = 1.24, 95% CI 1.05-1.47) of child OWO associated with the highest quartile of Hg exposure was 24% higher than those with the lowest quartile. Maternal pre-pregnancy OWO and/or diabetes additively enhanced Hg toxicity. The highest risk of child OWO was found among children of OWO and diabetic mothers in the top Hg quartile (RR = 2.06; 95% CI 1.56-2.71) compared to their counterparts. Furthermore, adequate maternal folate status mitigated Hg toxicity. Given top quartile Hg exposure, adequate maternal folate was associated with a 34% reduction in child OWO risk (RR = 0.66, 95% CI 0.51-0.85) as compared with insufficient maternal folate. There was a suggestive interaction between maternal Hg and folate levels on child OWO risk (p for interaction = 0.086)., Conclusions: In this US urban, multi-ethnic population, elevated in utero Hg exposure was associated with a higher risk of OWO in childhood, and such risk was enhanced by maternal OWO and/or diabetes and reduced by adequate maternal folate. These findings underscore the need to screen for Hg and to optimize maternal folate status, especially among mothers with OWO and/or diabetes.

In biomedical studies involving survival data, the observation of failure times is sometimes accompanied by a variable which describes the type of failure event (Kalbeisch and Prentice, 2002). This paper considers two specific challenges which are encountered in the joint analysis of failure time and failure type. First, because the observation of failure times is subject to left truncation, the sampling bias extends to the failure type which is associated with the failure time. An analytical challenge is to deal with such sampling bias. Second, in case that the joint distribution of failure time and failure type is allowed to have a temporal trend, it is of interest to estimate the joint distribution of failure time and failure type nonparametrically. This paper develops statistical approaches to address these two analytical challenges on the basis of prevalent survival data. The proposed approaches are examined through simulation studies and illustrated by using a real data set., (© 2019 International Biometric Society.)

ABSTRACTObjective:There is increasing evidence of an association between depressive symptoms and mild cognitive impairment (MCI) in cross-sectional studies, but the longitudinal association between depressive symptoms and risk of MCI onset is less clear. The authors investigated whether baseline symptom severity of depression was predictive of time to onset of symptoms of MCI., Method: These analyses included 300 participants from the BIOCARD study, a cohort of individuals who were cognitively normal at baseline (mean age = 57.4 years) and followed for up to 20 years (mean follow-up = 2.5 years). Depression symptom severity was measured using the Hamilton Depression Scale (HAM-D). The authors assessed the association between dichotomous and continuous HAM-D and time to onset of MCI within 7 years versus after 7 years from baseline (reflecting the mean time from baseline to onset of clinical symptoms in the cohort) using Cox regression models adjusted for gender, age, and education., Results: At baseline, subjects had a mean HAM-D score of 2.2 (SD = 2.8). Higher baseline HAM-D scores were associated with an increased risk of progression from normal cognition to clinical symptom onset ≤ 7 years from baseline (p = 0.043), but not with progression &gt; 7 years from baseline (p = 0.194). These findings remained significant after adjustment for baseline cognition., Conclusions: These results suggest that low levels of depressive symptoms may be predictive of clinical symptom onset within approximately 7 years among cognitively normal individuals and may be useful in identifying persons at risk for MCI due to Alzheimer's disease.

Background: Few studies have examined the relationship between lifestyle activity engagement and cognitive trajectories among individuals who were cognitively normal at baseline., Objective: To examine the relationship of current engagement in lifestyle activities to previous cognitive performance among individuals who were cognitively normal at baseline, and whether this relationship differed for individuals who subsequently developed mild cognitive impairment (MCI), or by APOE-4 genotype, age, and level of cognitive reserve., Methods: Participants (N=189) were primarily middle-aged (M=56.6 y) at baseline and have been prospectively followed with annual assessments (M follow-up=14.3 y). Engagement in physical, cognitive, and social activities was measured by the CHAMPS activity questionnaire. Longitudinal cognitive performance was measured by a global composite score., Results: Among individuals who progressed to MCI (n=27), higher lifestyle activity engagement was associated with less decline in prior cognitive performance. In contrast, among individuals who remained cognitively normal, lifestyle activity engagement was not associated with prior cognitive trajectories. These effects were largely independent of APOE-4 genotype, age, and cognitive reserve., Conclusions: Greater engagement in lifestyle activities may modify the rate of cognitive decline among those who develop symptoms of MCI, but these findings need to be confirmed in prospective studies.

Recent advances of wearable computing technology have allowed continuous health monitoring in large observational studies and clinical trials. Examples of data collected by wearable devices include minute-by-minute physical activity proxies measured by accelerometers or heart rate. The analysis of data generated by wearable devices has so far been quite limited to crude summaries, for example, the mean activity count over the day. To better utilize the full data and account for the dynamics of activity level in the time domain, we introduce a two-stage regression model for the minute-by-minute physical activity proxy data. The model allows for both time-varying parameters and time-invariant parameters, which helps capture both the transition dynamics between active/inactive periods (Stage 1) and the activity intensity dynamics during active periods (Stage 2). The approach extends methods developed for zero-inflated Poisson data to account for the high-dimensionality and time-dependence of the high density data generated by wearable devices. Methods are motivated by and applied to the Baltimore Longitudinal Study of Aging., (© 2017, The International Biometric Society.)

Recent evidence indicates that measures from cerebrospinal fluid, MRI scans and cognitive testing obtained from cognitively normal individuals can be used to predict likelihood of progression to mild cognitive impairment several years later, for groups of individuals. However, it remains unclear whether these measures are useful for predicting likelihood of progression for an individual. The increasing focus on early intervention in clinical trials for Alzheimer's disease emphasizes the importance of improving the ability to identify which cognitively normal individuals are more likely to progress over time, thus allowing researchers to efficiently screen participants, as well as determine the efficacy of any treatment intervention. The goal of this study was to determine which measures, obtained when individuals were cognitively normal, predict on an individual basis, the onset of clinical symptoms associated with a diagnosis of mild cognitive impairment due to Alzheimer's disease. Cognitively normal participants (n = 224, mean baseline age = 57 years) were evaluated with a range of measures, including: cerebrospinal fluid amyloid-β and phosphorylated-tau, hippocampal and entorhinal cortex volume, cognitive tests scores and APOE genotype. They were then followed to determine which individuals developed mild cognitive impairment over time (mean follow-up = 11 years). The primary outcome was progression from normal cognition to the onset of clinical symptoms of mild cognitive impairment due to Alzheimer's disease at 5 years post-baseline. Time-dependent receiver operating characteristic analyses examined the sensitivity and specificity of individual measures, and combinations of measures, as predictors of the outcome. Six measures, in combination, were the most parsimonious predictors of transition to mild cognitive impairment 5 years after baseline (area under the curve = 0.85; sensitivity = 0.80, specificity = 0.75). The addition of variables from each domain significantly improved the accuracy of prediction. The incremental accuracy of prediction achieved by adding individual measures or sets of measures successively to one another was also examined, as might be done when enrolling individuals in a clinical trial. The results indicate that biomarkers obtained when individuals are cognitively normal can be used to predict which individuals are likely to develop clinical symptoms at 5 years post-baseline. As a number of the measures included in the study could also be used as subject selection criteria in a clinical trial, the findings also provide information about measures that would be useful for screening in a clinical trial aimed at individuals with preclinical Alzheimer's disease.

In the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. In some settings, exacerbation processes are observed from an onset time until death; to account for the relationship between the survival and alternating event processes, nonparametric approaches are developed for estimating exacerbation process over lifetime. By understanding the population dynamics and within-process structure, the paper provide a new and general way to study alternating event processes.

We examined if baseline levels of cognitive reserve (CR) and of Alzheimer's disease (AD) biomarkers modify the rate of change in cognition among individuals with normal cognition at baseline (n = 303, mean baseline age = 57 years, mean follow-up = 12 years); 66 participants subsequently developed mild cognitive impairment (MCI) or dementia due to AD. CR was indexed by years of education, reading, and vocabulary measures. AD biomarkers were measured with a composite score composed of measures of amyloid, phosphorylated tau, and neurodegeneration. Higher CR scores were associated with better cognitive performance but did not modify the rate of change in cognition among those who remained cognitively normal, nor among those who progressed to MCI before symptom onset, independent of baseline biomarker levels. However, higher CR scores were associated with faster cognitive decline after symptom onset of MCI. These results suggest that the mechanism by which CR mediates the relationship between pathology and cognitive function is by delaying the onset of symptoms rather than reducing the rate of cognitive decline., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Recurrent events together with longitudinal measurements are commonly observed in follow-up studies where the observation is terminated by censoring or a primary failure event. In this article, we developed a joint model where the dependence of longitudinal measurements, recurrent event process and time to failure event is modeled through rescaling the time index. The general idea is that the trajectories of all biology processes of subjects in the survivors' population are elongated or shortened by the rate identified from a model for the failure event. To avoid making disputing assumptions on recurrent events or biomarkers after the failure event (such as death), the model is constructed on the basis of survivors' population. The model also possesses a specific feature that, by aligning failure events as time origins, the backward-in-time model of recurrent events and longitudinal measurements shares the same parameter values with the forward time model. The statistical properties, simulation studies and real data examples are conducted. The proposed method can be generalized to analyze left-truncated data., (© 2017, The International Biometric Society.)

This study examined whether longitudinal MRI trajectories in medial temporal lobe (MTL) brain regions differed among groups of cognitively normal individuals defined by their cerebrospinal fluid (CSF) levels when they were first enrolled ( N  = 207; mean clinical follow-up = 13.3 years (max = 20 years), mean MRI follow-up = 2.4 years (max = 8 years)). We first compared atrophy rates among groups defined by CSF amyloid and phosphorylated-tau (p-tau) vs. CSF amyloid and total tau (t-tau). We also examined whether, in the presence of amyloid or tau/p-tau, the atrophy rates differed based on whether the subjects ultimately progressed to a diagnosis of mild cognitive impairment (MCI), as well as whether apolipoprotein ε4 (Apoε4) status had an impact on the longitudinal MRI trajectories. The primary finding was that when the groups were defined using CSF amyloid and p-tau, individuals with low levels of CSF amyloid and high levels of CSF p-tau (referred to as Stage 2) showed a significantly greater rate of atrophy in a composite measure of MTL volumes compared to groups defined by evidence of abnormal CSF levels in only one of the brain proteins (but not both), or no evidence of CSF abnormality. In contrast, there were no differences in rate of MTL atrophy when the groups were defined by levels of CSF amyloid and t-tau (instead of p-tau). Additionally, the rate of MTL atrophy did not differ between subjects who progressed to MCI at follow-up vs. those who remained cognitively normal when CSF levels of amyloid, t-tau, or p-tau were covaried. Lastly, the presence of an APOE ε4 genotype did not modulate the degree of MTL atrophy once baseline levels of CSF amyloid, p-tau or t-tau were accounted for. These results suggest that abnormal levels of CSF amyloid and CSF p-tau (but not t-tau) maximize the likelihood of observing significant MTL atrophy over time among individuals with normal cognition at baseline, and emphasize the importance of differentiating biomarkers that primarily reflect neurofibrillary tangle pathology (CSF p-tau) compared with biomarkers of neuronal injury (CSF t-tau).

In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient's condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study.

This study examined whether cognitive reserve (CR) alters the relationship between magnetic resonance imaging (MRI) measures of cortical thickness and risk of progression from normal cognition to the onset of clinical symptoms associated with mild cognitive impairment (MCI). The analyses included 232 participants from the BIOCARD study. Participants were cognitively normal and largely middle aged (M age = 56.5) at their baseline MRI scan. After an average of 11.8 years of longitudinal follow-up, 48 have developed clinical symptoms of MCI or dementia (M time from baseline to clinical symptom onset = 7.0 years). Mean thickness was measured over eight 'AD vulnerable' cortical regions, and cognitive reserve was indexed by a composite score consisting of years of education, reading, and vocabulary measures. Using Cox regression models, CR and cortical thickness were each independently associated with risk of clinical symptom onset within 7 years of baseline, suggesting that the neuronal injury occurring proximal to symptom onset has a direct association with clinical outcomes, regardless of CR. In contrast, there was a significant interaction between CR and mean cortical thickness for risk of progression more than 7 years from baseline, suggesting that individuals with high CR are better able to compensate for cortical thinning that is beginning to occur at the very earliest phase of AD.

Background: Changes in neuropsychological testing, neuroimaging, and cerebrospinal fluid may precede mild cognitive impairment (MCI). However, these markers are not routinely performed in outpatient clinical visits., Objective: To evaluate whether a simple clinical index, consisting of questions given to patients and their informants, could predict the onset of symptoms of MCI among cognitively normal individuals., Materials and Methods: Two hundred twenty-two participants in the BIOCARD study received a detailed history, physical examination, and neuropsychological testing annually. An index was calculated by including questions about memory problems, depression, age, education, history of cerebrovascular disease risk factors, and brain injury, family history of dementia, and the Mini-Mental State examination score. Cox regression analyses were used to determine if this index score was related to diagnosis of MCI., Results: The BIOCARD Index score mean for individuals who progressed to MCI was 20.3 (SD=2.9), whereas the score for individuals who remained normal was 24.8 (SD=2.3) (P<0.001) [hazard ratio, SE for subsequent diagnosis of MCI=0.75 (0.67 to 0.84); P<0.001]., Conclusions: Lower BIOCARD Index score predicted symptoms of MCI several years before the MCI diagnosis. The BIOCARD Index can be easily used in clinics to identify cognitively normal older individuals who are at risk for deterioration.

In follow-up studies, utility marker measurements are usually collected upon the occurrence of recurrent events until a terminal event such as death takes place. In this article, we define the recurrent marker process to characterize utility accumulation over time. For example, with medical cost and repeated hospitalizations being treated as marker and recurrent events respectively, the recurrent marker process is the trajectory of cumulative cost, which stops to increase after death. In many applications, competing risks arise as subjects are at risk of more than one mutually exclusive terminal event, such as death from different causes, and modeling the recurrent marker process for each failure type is often of interest. However, censoring creates challenges in the methodological development, because for censored subjects, both failure type and recurrent marker process after censoring are unobserved. To circumvent this problem, we propose a nonparametric framework for recurrent marker process with competing terminal events. In the presence of competing risks, we start with an estimator by using marker information from uncensored subjects. As a result, the estimator can be inefficient under heavy censoring. To improve efficiency, we propose a second estimator by combining the first estimator with auxiliary information from the estimate under non-competing risks model. The large sample properties and optimality of the second estimator is established. Simulation studies and an application to the SEER-Medicare linked data are presented to illustrate the proposed methods. Supplemental materials are available online.

Benefit-risk assessment is a crucial step in medical decision process. In many biomedical studies, both longitudinal marker measurements and time to a terminal event serve as important endpoints for benefit-risk assessment. The effect of an intervention or a treatment on the longitudinal marker process, however, can be in conflict with its effect on the time to the terminal event. Thus, questions arise on how to evaluate treatment effects based on the two endpoints, for the purpose of deciding on which treatment is most likely to benefit the patients. In this article, we present a unified framework for benefit-risk assessment using the observed longitudinal markers and time to event data. We propose a cumulative weighted marker process to synthesize information from the two endpoints, and use its mean function at a prespecified time point as a benefit-risk summary measure. We consider nonparametric estimation of the summary measure under two scenarios: (i) the longitudinal marker is measured intermittently during the study period, and (ii) the value of the longitudinal marker is observed throughout the entire follow-up period. The large-sample properties of the estimators are derived and compared. Simulation studies and data examples exhibit that the proposed methods are easy to implement and reliable for practical use. Supplemental materials for this article are available online.