Association Between Hypertensive Disorders of Pregnancy and Interval Neurocognitive Decline: An Analysis of the Hispanic Community Health Study/Study of Latinos.

OBJECTIVE: To evaluate whether hypertensive disorders of pregnancy, including gestational hypertension, preeclampsia, and eclampsia, are associated with cognitive decline later in life among U.S. Hispanic/Latina individuals. METHODS: The HCHS/SOL (Hispanic Community Health Study/Study of Latinos) is a prospective population-based study of Hispanic/Latino individuals aged 18-74 years from four U.S. communities. This analysis included parous individuals aged 45 years or older who participated in the HCHS/SOL clinic study visit 1 (2008-2011) neurocognitive assessment and subsequently completed a repeat neurocognitive assessment as part of the Study of Latinos-Investigation of Neurocognitive Aging ancillary study visit 2 (2015-2018). Hypertensive disorders of pregnancy were assessed retrospectively by self-report of any gestational hypertension, preeclampsia, or eclampsia. Cognitive functioning was measured at both study visits with the Brief Spanish-English Verbal Learning Test, Digit Symbol Substitution, and Word Fluency. A regression-based approach was used to define cognitive decline at visit 2 as a function of cognition at visit 1 after adjustment for age, education, and follow-up time. Linear regression models were used to determine whether hypertensive disorders of pregnancy or their component diagnoses were associated with standardized cognitive decline after adjustment for sociodemographic characteristics, clinical and behavioral risk factors, and follow-up time. RESULTS: Among 3,554 individuals included in analysis, the mean age was 56.2 years, and 467 of individuals (13.4%) reported at least one hypertensive disorder of pregnancy. Individuals with hypertensive disorders of pregnancy compared with those without were more likely to have higher mean systolic blood pressure, fasting glucose, and body mass index. After an average of 7 years of follow-up, in fully adjusted models, gestational hypertension was associated with a 0.17-SD relative decline in Digit Symbol Substitution scores (95% CI, -0.31 to -0.04) but not other cognitive domains (Brief Spanish-English Verbal Learning Test or Word Fluency). Neither preeclampsia nor eclampsia was associated with neurocognitive differences. CONCLUSION: The presence of preeclampsia or eclampsia was not associated with interval neurocognitive decline. In this cohort of U.S. Hispanic/Latina individuals, gestational hypertension alone was associated with decreased processing speed and executive functioning later in life.

Associations between avocado intake and measures of glucose and insulin homeostasis in Hispanic individuals with and without type 2 diabetes: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

BACKGROUND AND AIMS: To investigate associations between avocado intake and glycemia in adults with Hispanic/Latino ancestry. METHODS AND RESULTS: The associations of avocado intake with measures of insulin and glucose homeostasis were evaluated in a cross-sectional analysis of up to 14,591 Hispanic/Latino adults, using measures of: average glucose levels (hemoglobin A1c; HbA1c), fasting glucose and insulin, glucose and insulin levels after an oral glucose tolerance test (OGTT), and calculated measures of insulin resistance (HOMA-IR, and HOMA-%ß), and insulinogenic index. Associations were assessed using multivariable linear regression models, which controlled for sociodemographic factors and health behaviors, and which were stratified by dysglycemia status. In those with normoglycemia, avocado intake was associated with a higher insulinogenic index (ß = 0.17 ± 0.07, P = 0.02). In those with T2D (treated and untreated), avocado intake was associated with lower hemoglobin A1c (HbA1c; ß = -0.36 ± 0.21, P = 0.02), and lower fasting glucose (ß = -0.27 ± 0.12, P = 0.02). In the those with untreated T2D, avocado intake was additionally associated with HOMA-%ß (ß = 0.39 ± 0.19, P = 0.04), higher insulin values 2-h after an oral glucose load (ß = 0.62 ± 0.23, P = 0.01), and a higher insulinogenic index (ß = 0.42 ± 0.18, P = 0.02). No associations were observed in participants with prediabetes. CONCLUSIONS: We observed an association of avocado intake with better glucose/insulin homeostasis, especially in those with T2D.

Genetic associations between sleep traits and cognitive ageing outcomes in the Hispanic Community Health Study/Study of Latinos.

BACKGROUND: Sleep phenotypes have been reported to be associated with cognitive ageing outcomes. However, there is limited research using genetic variants as proxies for sleep traits to study their associations. We estimated associations between Polygenic Risk Scores (PRSs) for sleep duration, insomnia, daytime sleepiness, and obstructive sleep apnoea (OSA) and measures of cogntive ageing in Hispanic/Latino adults. METHODS: We used summary statistics from published genome-wide association studies to construct PRSs representing the genetic basis of each sleep trait, then we studied the association of the PRSs of the sleep phenotypes with cognitive outcomes in the Hispanic Community Healthy Study/Study of Latinos. The primary model adjusted for age, sex, study centre, and measures of genetic ancestry. Associations are highlighted if their p-value <0.05. FINDINGS: Higher PRS for insomnia was associated with lower global cognitive function and higher risk of mild cognitive impairment (MCI) (OR = 1.20, 95% CI [1.06, 1.36]). Higher PRS for daytime sleepiness was also associated with increased MCI risk (OR = 1.14, 95% CI [1.02, 1.28]). Sleep duration PRS was associated with reduced MCI risk among short and normal sleepers, while among long sleepers it was associated with reduced global cognitive function and with increased MCI risk (OR = 1.40, 95% CI [1.10, 1.78]). Furthermore, adjustment of analyses for the measured sleep phenotypes and APOE-ε4 allele had minor effects on the PRS associations with the cognitive outcomes. INTERPRETATION: Genetic measures underlying insomnia, daytime sleepiness, and sleep duration are associated with MCI risk. Genetic and self-reported sleep duration interact in their effect on MCI. FUNDING: Described in Acknowledgments.

Association of neighborhood segregation with 6-year incidence of metabolic syndrome in the Hispanic community health study/study of Latinos.

PURPOSE: Examine the association between neighborhood segregation and 6-year incident metabolic syndrome (MetSyn) in the Hispanic Community Health Study/Study of Latinos. METHODS: Prospective cohort of adults residing in Miami, Chicago, the Bronx, and San Diego. The analytic sample included 6,710 participants who did not have MetSyn at baseline. The evenness and exposure dimensions of neighborhood segregation, based on the Gini and Isolation indices, respectively, were categorized into quintiles (Q). Racialized economic concentration was measured with the Index of Concentration at the Extremes (continuously and Q). RESULTS: Exposure, but not evenness, was associated with higher disease odds (Q1 (lower segregation) vs. Q4, OR = 1.53, 95% CI = 1.082.17; Q5, OR = 2.29, 95% CI = 1.493.52). Economic concentrationprivilege (continuous OR = 0.87, 95% CI = 0.770.98), racial concentrationracialized privilege (Q1 (greater concentration) vs. Q2 OR = 0.75, 95% CI = 0.541.04; Q3 OR = 0.68, 95% CI = 0.441.05; Q4 OR = 0.68, 95% CI = 0.451.01; Q5 OR = 0.64, 95% CI = 0.420.98)(continuous OR = 0.93, 95% CI = 0.821.04), and racialized economic concentrationprivilege (i.e., higher SES non-Hispanic White, continuous OR = 0.86, 95% CI = 0.760.98) were associated with lower disease odds. CONCLUSION: Hispanics/Latino adults residing in neighborhoods with high segregation had higher risk of incident MetSyn compared to those residing in neighborhoods with low segregation. Research is needed to identify the mechanisms that link segregation to poor metabolic health.

Plasma metabolites associated with cognitive function across race/ethnicities affirming the importance of healthy nutrition.

INTRODUCTION: We studied the replication and generalization of previously identified metabolites potentially associated with global cognitive function in multiple race/ethnicities and assessed the contribution of diet to these associations. METHODS: We tested metabolite-cognitive function associations in U.S.A. Hispanic/Latino adults (n = 2222) from the Community Health Study/ Study of Latinos (HCHS/SOL) and in European (n = 1365) and African (n = 478) Americans from the Atherosclerosis Risk In Communities (ARIC) Study. We applied Mendelian Randomization (MR) analyses to assess causal associations between the metabolites and cognitive function and between Mediterranean diet and cognitive function. RESULTS: Six metabolites were consistently associated with lower global cognitive function across all studies. Of these, four were sugar-related (e.g., ribitol). MR analyses provided weak evidence for a potential causal effect of ribitol on cognitive function and bi-directional effects of cognitive performance on diet. DISCUSSION: Several diet-related metabolites were associated with global cognitive function across studies with different race/ethnicities. HIGHLIGHTS: Metabolites associated with cognitive function in Puerto Rican adults were recently identified. We demonstrate the generalizability of these associations across diverse race/ethnicities. Most identified metabolites are related to sugars. Mendelian Randomization (MR) provides weak evidence for a causal effect of ribitol on cognitive function. Beta-cryptoxanthin and other metabolites highlight the importance of a healthy diet.

Correcting conditional mean imputation for censored covariates and improving usability.

Missing data are often overcome using imputation, which leverages the entire dataset to replace missing values with informed placeholders. This method can be modified for censored data by also incorporating partial information from censored values. One such modification proposed by Atem et al. (2017, 2019a, 2019b) is conditional mean imputation where censored covariates are replaced by their conditional means given other fully observed information. These methods are robust to additional parametric assumptions on the censored covariate and utilize all available data, which is appealing. However, in implementing these methods, we discovered that these three articles provide nonequivalent formulas and, in fact, none is the correct formula for the conditional mean. Herein, we derive the correct form of the conditional mean and discuss the bias incurred when using the incorrect formulas. Furthermore, we note that even the correct formula can perform poorly for log hazard ratios far from 0${\mathbf {0}}$ . We also provide user-friendly R software, the imputeCensoRd package, to enable future researchers to tackle censored covariates correctly.

Robust methods to correct for measurement error when evaluating a surrogate marker.

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We investigate and propose robust methods to correct for the effect of measurement error when evaluating a surrogate marker using multiple estimators developed for parametric and nonparametric estimates of the proportion of treatment effect explained by the surrogate marker. In addition, we quantify the attenuation bias induced by measurement error and develop inference procedures to allow for variance and confidence interval estimation. Through a simulation study, we show that our proposed estimators correct for measurement error in the surrogate marker and that our inference procedures perform well in finite samples. We illustrate these methods by examining a potential surrogate marker that is measured with error, hemoglobin A1c, using data from the Diabetes Prevention Program clinical trial.

svReg: Structural varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups.

For Huntington disease, identification of brain regions related to motor impairment can be useful for developing interventions to alleviate the motor symptom, the major symptom of the disease. However, the effects from the brain regions to motor impairment may vary for different groups of patients. Hence, our interest is not only to identify the brain regions but also to understand how their effects on motor impairment differ by patient groups. This can be cast as a model selection problem for a varying-coefficient regression. However, this is challenging when there is a pre-specified group structure among variables. We propose a novel variable selection method for a varying-coefficient regression with such structured variables and provide a publicly available R package svreg for implementation of our method. Our method is empirically shown to select relevant variables consistently. Also, our method screens irrelevant variables better than existing methods. Hence, our method leads to a model with higher sensitivity, lower false discovery rate and higher prediction accuracy than the existing methods. Finally, we found that the effects from the brain regions to motor impairment differ by disease severity of the patients. To the best of our knowledge, our study is the first to identify such interaction effects between the disease severity and brain regions, which indicates the need for customized intervention by disease severity.

Estimating disease onset from change points of markers measured with error.

Huntington disease is an autosomal dominant, neurodegenerative disease without clearly identified biomarkers for when motor-onset occurs. Current standards to determine motor-onset rely on a clinician's subjective judgment that a patient's extrapyramidal signs are unequivocally associated with Huntington disease. This subjectivity can lead to error which could be overcome using an objective, data-driven metric that determines motor-onset. Recent studies of motor-sign decline-the longitudinal degeneration of motor-ability in patients-have revealed that motor-onset is closely related to an inflection point in its longitudinal trajectory. We propose a nonlinear location-shift marker model that captures this motor-sign decline and assesses how its inflection point is linked to other markers of Huntington disease progression. We propose two estimating procedures to estimate this model and its inflection point: one is a parametric method using nonlinear mixed effects model and the other one is a multi-stage nonparametric approach, which we developed. In an empirical study, the parametric approach was sensitive to correct specification of the mean structure of the longitudinal data. In contrast, our multi-stage nonparametric procedure consistently produced unbiased estimates regardless of the true mean structure. Applying our multi-stage nonparametric estimator to Neurobiological Predictors of Huntington Disease, a large observational study of Huntington disease, leads to earlier prediction of motor-onset compared to the clinician's subjective judgment.

Dynamic landmark prediction for mixture data.

In kin-cohort studies, clinicians want to provide their patients with the most current cumulative risk of death arising from a rare deleterious mutation. Estimating the cumulative risk is difficult when the genetic mutation status is unknown and only estimated probabilities of a patient having the mutation are available. We estimate the cumulative risk for this scenario using a novel nonparametric estimator that incorporates covariate information and dynamic landmark prediction. Our estimator has improved prediction accuracy over existing estimators that ignore covariate information. It is built within a dynamic landmark prediction framework whereby we can obtain personalized dynamic predictions over time. Compared to current standards, a simple transformation of our estimator provides more efficient estimates of marginal distribution functions in settings where patient-specific predictions are not the main goal. We show our estimator is unbiased and has more predictive accuracy compared to methods that ignore covariate information and landmarking. Applying our method to a Huntington disease study of mortality, we develop dynamic survival prediction curves incorporating gender and familial genetic information.

A consistent estimator for logistic mixed effect models.

We propose a consistent and locally efficient estimator to estimate the model parameters for a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where in many cases we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial if we could have an estimator robust to such violations and then we could make better use of current data harvested using various valuable resources. Our method generalizes the framework presented in Garcia & Ma (2016) which also deals with a logistic mixed effect model but only considers a random intercept. A simulation study reveals that our proposed estimator remains consistent even when the independence and normality assumptions are violated. This contrasts from the traditional maximum likelihood estimator which is likely to be inconsistent when there is dependence between the covariates and random effects. Application of this work to a Huntington disease study reveals that disease diagnosis can be further improved using assessments of cognitive performance.

Analysis of repeated measures data in nutrition research.

Amino acid nutrition studies often involve repeated measures data. An example is that the concentrations of plasma citrulline in steers are repeatedly measured from the same animals. The standard repeated measures ANOVA method does not detect significant time changes in the concentrations of plasma citrulline within 6 hours after steers consumed rumen-protected citrulline, while a graphical analysis indicates that there exists a time effect. Here we describe three mixed model analyses that capture the time effect in a statistically significant way, while accounting for the correlations of measurements over time from the same steers. First, we allow flexible variance-covariance structures on our model. Second, we use baseline measurements as a covariate in our model. Third, we use percent-change from baseline as a data normalization method. In our data analysis, all these three approaches can lead to meaningful statistical results that oral administration of rumen-protected citrulline enhances the concentrations of plasma citrulline over time in ruminants. This supports the notion that rumen-protected citrulline can bypass the rumen to effectively enter the blood circulation.

Single Vehicle Logging-Related Traffic Crashes in Louisiana from 2010-2015.

OBJECTIVES: According to the Centers for Disease Control and Prevention (CDC), highway transportation crashes are the number one cause of fatal occupational injuries in the United States. The rate of fatal crashes in logging far exceeds the average annual rate for all sectors combined, yet few studies examine logging-related transportation crashes, and little is known about factors influencing the frequency of these crashes. The purpose of this study was to identify factors associated with fatal and nonfatal injuries among drivers involved in a single vehicle logging-related crash in Louisiana. METHODS: All crashes involving a single logging vehicle from 2010 to 2015 were extracted from a dataset provided by the Louisiana Department of Transportation and Development. Descriptive statistics were computed to characterize crashes by person, vehicle, and environmental factors. A multiple logistic regression model was constructed to identify variables associated with driver injury (fatal and non-fatal). RESULTS: There were 361 crashes involving a single logging vehicle from 2010 to 2015 in Louisiana. Variables associated with driver injury included no seat belt use (OR = 3.23; 95% CI = 1.47-7.10), a violation issued for careless operation of the vehicle (OR = 3.23; 95% CI = 1.40-7.46), a harmful event classified as cargo or equipment loss or shift (OR = 2.47; 95% CI = 1.27-4.82), and a harmful event classified as the vehicle running off the road to the left (OR = 2.29; 95% CI = 1.12-4.70). CONCLUSION: Injury prevention efforts in the logging industry in Louisiana, including commercial vehicle licensing procedures, could benefit from additional driver training to improve crash avoidance skills and careless driving, seat belt use, and methods for securing loads.

Time-varying proportional odds model for mega-analysis of clustered event times.

Mega-analysis, or the meta-analysis of individual data, enables pooling and comparing multiple studies to enhance estimation and power. A challenge in mega-analysis is estimating the distribution for clustered, potentially censored event times where the dependency structure can introduce bias if ignored. We propose a new proportional odds model with unknown, time-varying coefficients, and random effects. The model directly captures event dependencies, handles censoring using pseudo-values, and permits a simple estimation by transforming the model into an easily estimable additive logistic mixed effect model. Our method consistently estimates the distribution for clustered event times even under covariate-dependent censoring. Applied to three observational studies of Huntington's disease, our method provides, for the first time in the literature, evidence of similar conclusions about motor and cognitive impairments in all studies despite different recruitment criteria.

Disease Progression in Huntington Disease: An Analysis of Multiple Longitudinal Outcomes.

BACKGROUND: Critical to discovering targeted therapies for Huntington disease (HD) are validated methods that more precisely predict when clinical outcomes occur for different patient profiles. OBJECTIVE: To more precisely predict the probability of when motor diagnosis (diagnostic confidence level 4) on the Unified Huntington's Disease Rating Scale (UHDRS), cognitive impairment (two or more neuropsychological scores on the UHDRS were 1.5 standard deviations below normative means) and Stage II Total Functional Capacity (TFC) first occur by accounting for dependencies between these outcomes. METHODS: Adult premanifest participants with ≥36 CAG repeats were selected from multi-center, longitudinal, observational studies: Prospective Huntington At Risk Observational Study (PHAROS, n = 346), Neurobiological Predictors of Huntington Disease (PREDICT, n = 909); and Cooperative Huntington Observational Research Trial (COHORT, n = 430). Probabilities were estimated for each study, and pooled using the Joint Progression of Risk Assessment Tool (JPRAT) which accounts for dependencies between outcomes. RESULTS: All studies had similar probabilities of when motor diagnosis, cognitive impairment, and Stage II TFC first occurred. Probability estimates from JPRAT were 43% less variable than from models that ignored dependencies between outcomes. The probability of experiencing motor-diagnosis, cognitive impairment, and Stage II TFC within 5 years was 10%, 18%, and 7%, respectively for 45-year-olds with 42 CAG repeats, and was 4%, 10% and 5%, respectively, for 40 year olds with 42 CAG repeats. CONCLUSIONS: Improved predictions from JPRAT may benefit treatment studies of rare diseases and is an alternative to composite outcomes when the objective is interpreting individual outcomes within the same model.

Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models.

We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance-covariance. We construct root-n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or model error heteroskedasticity. Instead, we form the estimator under possibly incorrect working distribution models for the model error, error-prone covariate, or both. Empirical results demonstrate robustness to different incorrect working models in homoscedastic and heteroskedastic models with error-prone covariates.

Statistical modeling of Huntington disease onset.

Huntington disease (HD) is caused by a CAG trinucleotide expansion in the huntingtin gene. We now have the power to predict age-at-onset from subject-specific features like motor and neuroimaging measures. In clinical trials, properly modeling onset age is important, because it improves power calculations and directs clinicians to recruit subjects with certain features. The history of modeling onset, from simple linear and logistic regression to advanced survival models, is discussed. We highlight their advantages and disadvantages, emphasizing the methodological challenges when genetic mutation status is unavailable. We also discuss the potential bias and higher variability incurred from the uncertainty associated with subjective definitions for onset. Methods to adjust for the uncertainty in survival models are still in their infancy, but would be beneficial for HD and neurodegenerative diseases with long prodromal periods like Alzheimer's and Parkinson's disease.

Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington's Disease as a Model.

Understanding the overall progression of neurodegenerative diseases is critical to the timing of therapeutic interventions and design of effective clinical trials. Disease progression can be assessed with longitudinal study designs in which outcomes are measured repeatedly over time and are assessed with respect to risk factors, either measured repeatedly or at baseline. Longitudinal data allows researchers to assess temporal disease aspects, but the analysis is complicated by complex correlation structures, irregularly spaced visits, missing data, and mixtures of time-varying and static covariate effects. We review modern statistical methods designed for these challenges. Among all methods, the mixed effect model most flexibly accommodates the challenges and is preferred by the FDA for observational and clinical studies. Examples from Huntington's disease studies are used for clarification, but the methods apply to neurodegenerative diseases in general, particularly as the identification of prodromal forms of neurodegenerative disease through sensitive biomarkers is increasing.

ROBUST MIXED EFFECTS MODEL FOR CLUSTERED FAILURE TIME DATA: APPLICATION TO HUNTINGTON'S DISEASE EVENT MEASURES.

An important goal in clinical and statistical research is properly modeling the distribution for clustered failure times which have a natural intraclass dependency and are subject to censoring. We handle these challenges with a novel approach that does not impose restrictive modeling or distributional assumptions. Using a logit transformation, we relate the distribution for clustered failure times to covariates and a random, subject-specific effect. The covariates are modeled with unknown functional forms, and the random effect may depend on the covariates and have an unknown and unspecified distribution. We introduce pseudovalues to handle censoring and splines for functional covariate effects, and frame the problem into fitting an additive logistic mixed effects model. Unlike existing approaches for fitting such models, we develop semiparametric techniques that estimate the functional model parameters without specifying or estimating the random effect distribution. We show both theoretically and empirically that the resulting estimators are consistent for any choice of random effect distribution and any dependency structure between the random effect and covariates. Last, we illustrate the method's utility in an application to a Huntington's disease study where our method provides new insights into differences between motor and cognitive impairment event times in at-risk subjects.

COX REGRESSION WITH EXCLUSION FREQUENCY-BASED WEIGHTS TO IDENTIFY NEUROIMAGING MARKERS RELEVANT TO HUNTINGTON'S DISEASE ONSET.

Biomedical studies of neuroimaging and genomics collect large amounts of data on a small subset of subjects so as to not miss informative predictors. An important goal is identifying those predictors that provide better visualization of the data and that could serve as cost-effective measures for future clinical trials. Identifying such predictors is challenging, however, when the predictors are naturally interrelated and the response is a failure time prone to censoring. We propose to handle these challenges with a novel variable selection technique. Our approach casts the problem into several smaller dimensional settings and extracts from this intermediary step the relative importance of each predictor through data-driven weights called exclusion frequencies. The exclusion frequencies are used as weights in a weighted Lasso, and results yield low false discovery rates and a high geometric mean of sensitivity and specificity. We illustrate the method's advantages over existing ones in an extensive simulation study, and use the method to identify relevant neuroimaging markers associated with Huntington's disease onset.