Ambient Air Pollution and Long-Term Trajectories of Episodic Memory Decline among Older Women in the WHIMS-ECHO Cohort
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 9 Linguagem: Inglês
10.1289/ehp7668
ISSN1552-9924
AutoresXinhui Wang, Diana Younan, Andrew J. Petkus, Daniel P. Beavers, Mark A. Espeland, Helena C. Chui, Susan M. Resnick, Margaret Gatz, Joel D. Kaufman, Gregory A. Wellenius, Eric A. Whitsel, JoAnn E. Manson, Jiu‐Chiuan Chen,
Tópico(s)Air Quality and Health Impacts
ResumoVol. 129, No. 9 ResearchOpen AccessAmbient Air Pollution and Long-Term Trajectories of Episodic Memory Decline among Older Women in the WHIMS-ECHO Cohort Xinhui Wang, Diana Younan, Andrew J. Petkus, Daniel P. Beavers, Mark A. Espeland, Helena C. Chui, Susan M. Resnick, Margaret Gatz, Joel D. Kaufman, Gregory A. Wellenius, Eric A. Whitsel, JoAnn E. Manson, and Jiu-Chiuan Chen Xinhui Wang Department of Neurology, University of Southern California, Los Angeles, California, USA Search for more papers by this author , Diana Younan Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA Search for more papers by this author , Andrew J. Petkus Department of Neurology, University of Southern California, Los Angeles, California, USA Search for more papers by this author , Daniel P. Beavers Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA Search for more papers by this author , Mark A. Espeland Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA Search for more papers by this author , Helena C. Chui Department of Neurology, University of Southern California, Los Angeles, California, USA Search for more papers by this author , Susan M. Resnick Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Department of Health and Human Services, Baltimore, Maryland, USA Search for more papers by this author , Margaret Gatz Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA Search for more papers by this author , Joel D. Kaufman Departments of Environmental & Occupational Health Sciences, Medicine (General Internal Medicine), and Epidemiology, University of Washington, Seattle, Washington, USA Search for more papers by this author , Gregory A. Wellenius Department of Environmental Health, Boston University, Boston, Massachusetts, USA Search for more papers by this author , Eric A. Whitsel Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA Search for more papers by this author , JoAnn E. Manson Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA Search for more papers by this author , and Jiu-Chiuan Chen Address correspondence to Jiu-Chiuan Chen, Departments of Population and Public Health Sciences & Neurology, University of Southern California, SSB 225P 2001 N. Soto St., Los Angeles, CA 90033 USA. Telephone: (323) 442-2949. Email: E-mail Address: [email protected] Department of Neurology, University of Southern California, Los Angeles, California, USA Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA Search for more papers by this author Published:13 September 2021CID: 097009https://doi.org/10.1289/EHP7668AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Episodic memory decline varies by age and underlying neuropathology. Whether ambient air pollution contributes to the heterogeneity of episodic memory decline in older populations remains unclear.Objectives:We estimated associations between air pollution exposures and episodic memory decline according to pollutant, exposure time window, age, and latent class subgroups defined by episodic memory trajectories.Methods:Participants were from the Women’s Health Initiative Memory Study–Epidemiology of Cognitive Health Outcomes. Older women (n=2,056; 74–92 years of age) completed annual (2008–2018) episodic memory assessments using the telephone-based California Verbal Learning Test (CVLT). We estimated 3-y average fine particulate matter [PM with an aerodynamic diameter of ≤2.5μm (PM2.5)] and nitrogen dioxide (NO2) exposures at baseline and 10 y earlier (recent and remote exposures, respectively), using regionalized national universal kriging. Separate latent class mixed models were used to estimate associations between interquartile range increases in exposures and CVLT trajectories in women ≤80 and >80 years of age, adjusting for covariates.Results:Two latent classes were identified for women ≤80 years of age (n=828), “slow-decliners” {slope=−0.12/y [95% confidence interval (CI): −0.23, −0.01] and “fast-decliners” [slope=−1.79/y (95% CI: −2.08, −1.50)]}. In the slow-decliner class, but not the fast-decliner class, PM2.5 exposures were associated with a greater decline in CVLT scores over time, with a stronger association for recent vs. remote exposures [−0.16/y (95% CI: −2.08, −0.03) per 2.88 μg/m3 and −0.11/y (95% CI: −0.22, 0.01) per 3.27 μg/m3, respectively]. Among women ≥80 years of age (n=1,128), the largest latent class comprised “steady-decliners” [slope=−1.35/y (95% CI: −1.53, −1.17)], whereas the second class, “cognitively resilient”, had no decline in CVLT on average. PM2.5 was not associated with episodic memory decline in either class. A 6.25-ppb increase in recent NO2 was associated with nonsignificant acceleration of episodic memory decline in the ≤80-y-old fast-decliner class [−0.21/y (95% CI: −0.45, 0.04)], and in the >80-y-old cognitively resilient class [−0.10/y (95% CI: −0.24, 0.03)] and steady-decliner class [−0.11/y (95% CI: −0.27, 0.05)]. Associations with recent NO2 exposure in women >80 years of age were stronger and statistically significant when 267 women with incident probable dementia were excluded [e.g., −0.12/y (95% CI: −0.22, −0.02) for the cognitively resilient class]. In contrast with changes in CVLT over time, there were no associations between exposures and CVLT scores during follow-up in any subgroup.Discussion:In a community-dwelling U.S. population of older women, associations between late-life exposure to ambient air pollution and episodic memory decline varied by age-related cognitive trajectories, exposure time windows, and pollutants. https://doi.org/10.1289/EHP7668IntroductionDecline in episodic memory (e.g., the ability to remember details from daily experience, as well as the spatial and temporal context of events) is commonly associated with normal cognitive aging (Tulving 2002), but more severe changes are the hallmark symptom of Alzheimer’s disease. Given that the field of Alzheimer’s disease has shifted its focus to the preclinical stage of the disease (Dubois et al. 2016), research attention has been placed on episodic memory as one of the most sensitive cognitive domains with early decline detectable in preclinical Alzheimer’s disease (Gallagher and Koh 2011). Episodic memory performance undergoes significant changes throughout the life span, following a curvilinear shape with rapid improvement during childhood, early decline beginning around middle age, and accelerated decline in very old age (>80 years of age) (Shing et al. 2010; Singer et al. 2003). However, there is considerable heterogeneity in episodic memory decline, with individual trajectories varying from average population trajectories in terms of both starting levels and rates of change (Olaya et al. 2017). This heterogeneity has been demonstrated in longitudinal studies in general populations that have identified from two to four distinct trajectories of episodic memory over time among older individuals (Lee et al. 2018; McFall et al. 2019; Olaya et al. 2017; Wilson et al. 2020; Zahodne et al. 2015), and there is evidence suggesting that heterogeneity in memory performance increases into very old age (Finkel and Reynolds 2014; Olaya et al. 2017). For instance, across old age, memory performance may remain relatively unchanged until very late in life, decline linearly over time, or decline rapidly with the acceleration becoming more evident in very old age (Ding et al. 2019; Small and Bäckman 2007). Studies using a data-driven approach to identify latent classes of cognitive trajectories have also shown that the influence of modifiable risk factors on cognitive change may differ across latent classes (Wu et al. 2020). Although twin studies suggest that late-life episodic memory performance is heritable, approximately 40–60% of the total variance has been attributed to environment factors (Finkel and McGue 1998; Giubilei et al. 2008; Swan et al. 1999). Previous studies have focused primarily on the social environment (Josefsson et al. 2012; McFall et al. 2019) while overlooking the influence of the physical environment.Data has emerged over the past decade supporting the detrimental effects of air pollution exposure on brain aging (The Lancet Neurology 2018). Longitudinal studies have shown that late-life exposures to ambient air pollution, especially fine particulate matter [PM with an aerodynamic diameter of ≤2.5μm (PM2.5)] and oxides of nitrogen [nitrogen oxide (NO) and nitrogen dioxide (NO2)], are associated with increased risk of dementia, including Alzheimer’s disease (Peters et al. 2019). However, published studies have reported mixed findings for associations between air pollution and episodic memory decline (Kulick et al. 2020a, 2020b; Oudin et al. 2017; Petkus et al. 2020; Tonne et al. 2014; Weuve et al. 2012; Wurth et al. 2018; Younan et al. 2020). These studies assumed a single common trajectory for changes in episodic memory performance over time, and only one (Kulick et al. 2020a) investigated whether associations varied by age. In addition, the majority of previous studies investigated recent exposures averaged over a few years prior to the neuropsychological assessment, and it remains unclear whether exposures that occurred earlier in time are associated with episodic memory decline in later life.To address these knowledge gaps, we conducted a longitudinal study to examine the association between long-term exposure to ambient air pollution and late-life episodic memory assessed annually (2008–2018) in a geographically diverse sample of community-dwelling older women. The aim of our study was to investigate whether long-term exposures were associated with changes in episodic memory and whether the putative exposure effects differed by sample age (≤80 vs. >80 years of age), exposure time window, or pollutants (PM2.5 vs. NO2).MethodsStudy SampleWe conducted a prospective cohort study on community-dwelling women enrolled in the Women’s Health Initiative Memory Study–Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO). WHIMS-ECHO was an extension of the WHIMS, a Women’s Health Initiative (WHI)–Hormone Therapy (HT) trials ancillary study designed to investigate the role of postmenopausal hormone therapy on the incidence of all-cause dementia (Shumaker et al. 1998). Women enrolled in the WHI-HT trials were recruited to participate in the WHIMS ancillary study if they were ≥65 years of age at WHIMS enrollment in 1995–1998. WHIMS participants completed annual cognitive assessments in the WHI-HT trial phase (which was terminated in 2002 or 2004, depending on the WHI trial arm) and posttrial extension phase (which continued through May 2008). Starting in September 2008, WHIMS participants who were still engaged in WHI follow-up were enrolled in WHIMS-ECHO if they provided informed consent to undergo annual telephone-based assessments of their cognitive function, allowed a friend or family member to be contacted, and had adequate hearing to complete telephone interviews (Espeland et al. 2017). From WHIMS-ECHO enrollment until June 2018, participants completed annual neuropsychological assessments through centralized telephone-administered cognitive interviews conducted by trained and certified staff.For this study, we excluded women who were classified as probable dementia cases at or before WHIMS-ECHO enrollment (n=82), women who did not complete any California Verbal Learning Test (CVLT) assessments of episodic memory, including women who were lost to follow-up before the CVLT was added to the annual assessment (n=523), those with missing air pollution data for one or both of the exposure time windows examined in the analysis (n=71), and those with missing data on key covariates at WHI inception (n=148), including education, employment status, alcohol intake, smoking, diabetes, high cholesterol, hypertension, cardiovascular disease (CVD), or neighborhood socioeconomic characteristics. The final analytic sample comprised 2,056 women, including 828 who were ≤80 years of age and 1,228 who were >80 years of age at WHIMS-ECHO enrollment (Figure 1A).Figure 1. (A) Flowchart of study population and (B) illustration of the study timeline. Exposure time windows in (B) were defined based on each participant’s WHIMS-ECHO enrollment date; the example shown is used for illustrative purposes. Note: CVLT, California Verbal Learning Test; WHI-CT (HT), Women’s Health Initiative-Clinical Trial (Hormone Therapy); WHIMS-ECHO, Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes.The institutional review board at the University of Southern California reviewed all study protocols. Written informed consent was obtained from all participants as part of the original WHIMS-ECHO study.Measures of Episodic MemoryEpisodic memory was assessed annually over the phone using a modified version of the CVLT (Delis et al. 1987). CVLT data collected through June 2018 were used in the present study. Participants were read a 16-item list of words from four semantically related categories. Each time the list was read the participant was instructed to immediately repeat back as many words as she could remember. We defined the episodic memory score as the total number of correct responses across three learning trials (0 to 48), with higher scores representing better performance. In contrast with the standard version of the CVLT that includes five learning trials, the modified version administered in WHIMS-ECHO was limited to three learning trials and did not include interference trials, short- or long-delayed free or cued recall, or word recognition.Air Pollution ExposuresData on participants’ residential addresses were collected at each WHI assessment and updated either during regular follow-up contacts at least semiannually or when participants alerted WHI staff about any change of address between regularly scheduled follow-ups from WHI clinical trial inception. The exact date of the change in residence was entered and used in analyses when available, otherwise the date when the change in residence was ascertained was used. The location of each residence was geocoded using standardized procedures (Whitsel et al. 2006). Annual mean concentrations of PM2.5 in micrograms per meter cubed and NO2 in parts per billion (a proxy measure for traffic-related air pollutants) were estimated at each participant’s address, using validated regionalized national universal kriging models with partial least squares regression of geographic covariates and U.S. Environmental Protection Agency monitoring data. More than 300 geographic covariates covering categories of population, land use, vegetative index, impervious surfaces, roadway, and proximity to features were used in the historical models for pre-1999 PM2.5 estimation or the national models for post-1999 PM2.5 estimation (Kim et al. 2017; Sampson et al. 2013). For NO2 estimation, satellite data and >400 geographic covariates covering proximity and buffer measures were used in models (Young et al. 2016). The average cross-validation R2 was 0.88 for PM2.5 and 0.85 for NO2 (Sampson et al. 2013; Young et al. 2016). We then used the annual estimates of each pollutant to calculate the “recent” 3-y average spanning the 3-y time window prior to the WHIMS-ECHO enrollment date and the “remote” 3-y average exposure, which was lagged 10 y from the WHIMS-ECHO enrollment date, accounting for residential mobility (Figure 1B). The length of stay at each residential location within the 3-y time window was used as the weight in each calculation.Ascertainment of Probable DementiaIncident cases of probable dementia were determined using published WHIMS-ECHO protocols (WHI Memory Study 2020). Briefly, participants underwent an annual, validated telephone interview that comprised a neuropsychological battery, including the modified Telephone Interview for Cognitive Status (TICSm) and additional neuropsychological tests. If a woman scored high school but <4y of college, or ≥4y of college]; family income [<$9,999, $10,000–$34,999, $35,000–$49,999, $50,000–$74,999, ≥$75,000, or missing/unknown (as a separate category)]; employment status (currently working, not working, or retired), and lifestyle factors (smoking status [never, past or current smoker]; alcohol intake [nondrinker, past drinker, 4 episodes/wk)}. We also collected information on race/ethnicity, which was reported by participants in response to “How would you describe your racial or ethnic group? If you are of mixed blood, which group do you identify with most?,” with the following options for responding: “American Indian or Alaskan Native,” “Asian or Pacific Islander (ancestry is Chinese, Indo-Chinese, Korean, Japanese, Pacific Islander, Vietnamese),” “Black or African-American (not of Hispanic origin),” “Hispanic/Latino (ancestry is Mexican, Cuban, Puerto Rican, Central American, or South American),” “White (not of Hispanic origin),” and “Other.” Although we provide descriptive information according to the original response categories, it was necessary to aggregate women who self-classified as “American Indian or Alaskan Native” or “Asian or Pacific Islander” into the “Other” category because of the small numbers in these groups. Therefore, the race/ethnicity categories used in data analyses were “Black, non-Hispanic,” “Hispanic/Latino,” “White, non-Hispanic,” and “Other.” The “other” category also included women with missing data for race/ethnicity.Clinical characteristics were also ascertained (yes or no), including any postmenopausal hormone treatment and self-reported histories of CVD (defined as physician-diagnosed heart problems, problems with blood circulation, or blood clots), hypertension (defined as physician-diagnosed hypertension, not including high blood pressure during pregnancy), hypercholesterolemia (defined as physician-diagnosed high cholesterol requiring pills), and diabetes mellitus (defined as physician diagnosis plus oral medications or insulin therapy). Good reliability and validity of both the self-reported medical histories and the physical measures have been previously documented (Heckbert et al. 2004; Johnson-Kozlow et al. 2007; Margolis et al. 2008). Specifically, for cardiovascular events, there was substantial agreement between self-report and review by study physicians at clinical centers (kappa=0.64–0.85) (Heckbert et al. 2004). Self-reported prevalent diabetes was consistent with medication inventories in 77% and with fasting values of ≥126mg/dL in 75% of women (Margolis et al. 2008). In addition, a study investigating the psychometric properties of the physical activity measure of the WHI showed that it was highly correlated with accelerometer data (R=0.73, p<0.01) and the widely used 7-d Physical Activity Recall questionnaire (R=0.88, p 80 years of age) because of known heterogeneity in late-life episodic memory trajectories and the evidence of accelerated decline in very old age (Finkel and Reynolds 2014; Lee et al. 2018; McFall et al. 2019; Olaya et al. 2017; Wilson et al. 2020; Zahodne et al. 2015). Within each age group, we fitted latent class mixed models (LCMMs) (Proust-Lima et al. 2017) to identify groups of women with similar trajectories of episodic memory over time, where trajectories were characterized by a random intercept and the linear change of CVLT scores within each latent class. We determined the optimal number of latent classes using age-stratified models with follow-up time and age at WHIMS-ECHO enrollment as the only predictors, beginning with a one-class solution and sequentially increasing the number of classes until we identified the optimal set of latent classes for each age group on the basis of the Bayesian Information Criterion (the lower the better), the number of women in each class (at least 5% of the population), and interpretability of the identified trajectories, similar to our prior work (Petkus et al. 2019). Posterior probabilities were also evaluated to ensure that the average posterior probability for women assigned to a given latent class by the baseline LCMM was >70%. To avoid convergence at a local maximum, we used a grid of 10 random initial values and retained the estimates of the random initialization with the best log-likelihood.After determining the optimal number of latent classes within each age group, we used separate LCMMs to estimate either class-specific or global exposure effects of recent or remote PM2.5 exposures on linear changes in episodic memory over time, and we used similar models to estimate associations with recent or remote NO2. The LCMM with global exposure effects assumed a common association across latent classes, allowing us to explore heterogeneity in associations between age groups, exposure time windows, and pollutants without regard to latent class. In the data-driven LCMM, the number of latent classes was held constant according to our initial analysis, but the posterior probability of each class for a given woman, and thus each woman’s specific class assignment, could vary when air pollution exposures and additional predictors or covariates were added to the model. Two sets of covariates were considered in the models. Our base model included age at WHIMS-ECHO enrollment, follow-up time, interaction of age with follow-up time, and time-varying propensity score. Our fully adjusted model contained a full set of covariates with additional covariates including geographic region, race/ethnicity, education, income, employment status, neighborhood SES, lifestyle factors (smoking, drinking, and physical activities), and clinical characteristics (hormone treatment, CVD risk factors, and CVD histories). Except for the time-varying propensity scores, all covariates in the primary fully adjusted model were classified at WHI inception. These two sets of covariates were chosen in order to evaluate if there were significant associations with minimal covariates adjusted and whether the estimated associations were robust after further adjusting for known potential confounders. In these models, the parameter of interest is the interaction of the exposure with follow-up time. The age-equivalent effect for the association between each exposure and change in CVLT scores within the same class was calculated as βexposure×time/βage×time, where βexposure×time and βage×time were the parameters estimated for the interaction of the exposure with follow-up time or the interaction of age with follow-up time, respectively. Finally, in addition to estimating associations between air pollution exposures and changes in episodic memory over time, we estimated average associations with episodic memory scores modeled as repeated outcomes during follow-up using the same models, but without interaction terms for exposure with follow-up time.The time-varying propensity score approach to adjust for selective attrition due to loss to follow-up.To account for selective attrition during the WHIMS-ECHO follow-up, models also included time-varying propensity scores (Robins et al. 2000; Wyss et al. 2020), which were generated using a two-stage modeling approach for each woman at each year of follow-up. We calculated the probability of having the observed exposures over different follow-up intervals and included this periodically updated probability as a time-varying covariate in the LCMM to control for potential bias due to differential attrition. The procedure included four steps. First, we divided follow-up time into intervals based on time since WHIMS-ECHO enrollment (<2, 2–<3, 3–<4, 4–<5, 5–<6, 6–<7, 7–<8, and ≥8y since enrollment) so that data from women with cognitive function measurements in the same time interval could be grouped together. In the second step, we constructed linear regression models with air pollution exposure as the continuous outcome and used forward selection to assess each of the 15 covariates included in the fully adjusted LCMM and to identify statistically significant predictors of exposure using a significance level of 0.05/15 to account for multiple tests. Our final model included race/ethnicity and geographic region as independent variables and air pollution (At) as the dependent variable for each follow-up time interval t (t=1,2,…,8), as shown below: At=α+β1×race+β2×region+ε, where ε∼N(0,σ2).We used the parameters (α̂, β̂1, β̂2, and σ̂) to estimate the probability of having the observed exposure ai for each subject i by the normal density (Robins et al. 2000), as shown below: f(ai|racei,regioni)=12πσ̂2e−[ai−(α̂+β̂1racei+β̂2regioni)]2/2σ̂2.This is the preliminary propensity score (PSt) for each individual with a visit in time interval t. In the third step, we improved the propensity scores by adding an interaction term between preliminary propensity scores for different time intervals to the linear regression model as a surrogate for possible covariate interactions that may impact the prediction model, as shown below: At=α′+β′1×race+β′2×region+β′3×PSt−1×PSt+1+ε′, where ε′∼N(0,σ′2)where PSt−1 and PSt+1 represent the preliminary propensity scores for the closest time intervals that an individual had a visit in before and after time interval t, respectively. The updated PSt′ was then calculated as follows: PS′t=f(ai|racei,regioni, PSt−1,PSt+1)=12πσ′̂2e−[ai−(α′̂+β′1̂racei+β′2̂regioni+β′3̂PSt−1×PSt+1)]22σ′̂2.In the last step, we included the updated propensity score, or the preliminary version if it was unable to be updated, in the LCMM as a time-varying covariate to control for differential attrition in all analyses. Models without adjustment for propensity scores were a
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