Association between Long-Term Air Pollution, Chronic Traffic Noise, and Resting-State Functional Connectivity in the 1000BRAINS Study

Background: Older adults show a high variability in cognitive performance that cannot be explained by aging alone. Although research has linked air pollution and noise to cognitive impairment and structural brain alterations, the potential impact of air pollution and noise on functional brain organization is unknown. Objective: This study examined the associations between long-term air pollution and traffic noise with measures of functional brain organization in older adults. We hypothesize that exposures to high air pollution and noise levels are associated with age-like changes in functional brain organization, shown by less segregated brain networks. Methods: Data from 574 participants (44.1% female, 56–85 years of age) in the German 1000BRAINS study (2011–2015) were analyzed. Exposure to particulate matter (PM10, PM2.5, and PM2.5 absorbance), accumulation mode particle number (PNAM), and nitrogen dioxide (NO2) was estimated applying land-use regression and chemistry transport models. Noise exposures were assessed as weighted 24-h (Lden) and nighttime (Lnight) means. Functional brain organization of seven established brain networks (visual, sensorimotor, dorsal and ventral attention, limbic, frontoparietal and default network) was assessed using resting-state functional brain imaging data. To assess functional brain organization, we determined the degree of segregation between networks by comparing the strength of functional connections within and between networks. We estimated associations between air pollution and noise exposure with network segregation, applying multiple linear regression models adjusted for age, sex, socioeconomic status, and lifestyle variables. Results: Overall, small associations of high exposures with lesser segregated networks were visible. For the sensorimotor networks, we observed small associations between high air pollution and noise and lower network segregation, which had a similar effect size as a 1-y increase in age [e.g., in sensorimotor network, −0.006 (95% CI: −0.021, 0.009) per 0.3 ×10−5/m increase in PM2.5 absorbance and −0.004 (95% CI: −0.006, −0.002) per 1-y age increase]. Conclusion: High exposure to air pollution and noise was associated with less segregated functional brain networks. https://doi.org/10.1289/EHP9737

: Comparison of sociodemographic and exposure characteristics for all participants that participated at the 10-year HNR follow-up examination (n=3,087), the 1000BRAINS participants included (n=574) in the analyses, and the 1000BRAINS participants excluded from current analyses (n=111), with variables collected at HNR baseline examination (2000)(2001)(2002)(2003), except for age at MRI.

Included (n=574)
Mean ± SD or Median [IQR] or n (%) Participants of 10 y-Examination (n=3,087) Mean ± SD or Median [IQR] or n (%)   Table S2: Estimates of the associations between an interquartile range increase in mean air pollution exposure or an 10 dB(A) increase in mean noise level and network segregation, intra-network FC and internetwork FC for seven established brain networks (default, dorsal and ventral attention, frontoparietal, limbic, sensorimotor, and visual) with increasing model adjustment.     a The Crude model with no adjustment. b Model1 with adjustment for sex and age at time of brain scan. c In addition to the variables in Model1, Model2 was adjusted for BMI, smoking status, physical activity, alcohol consumption and diet. While noise models were additionally adjusted for PM2.5abs, air pollution models were additionally adjusted for Lden. d In addition to the variables in Model2, Model3 was further adjusted for individual SES and neighborhood SES. Abbreviations: BMI, body mass index; dB(A), A-weighted decibels; Disttrafroad, distance from home address to the nearest high-traffic roads; FC, functional connectivity; fMRI, functional magnetic resonance imaging; IQR, interquartile range; Lden, outdoor 24-hour weighted noise; Lnight, outdoor nighttime noise; fMRI, functional magnetic resonance imaging; NO2, nitrogen dioxide; PM10, particulate matter with diameter ≤10 µm; PM2.5, particulate matter with diameter ≤2.5 µm; PM2.5abs, PM2.5 absorbance, soot; PNAM, accumulation mode particle number concentration; SES, socioeconomic status Table S3: Estimates of the associations between age and altered network segregation, intra-and internetwork FC in seven established networks. The models were adjusted for sex, body mass index, diet, physical activity, smoking status, cumulative smoking, environmental tobacco smoke exposure and alcohol consumption, individual and neighborhood SES. Estimates for a 1-year age increase are shown.  Table S4: Estimates of the associations between a 10 dB(A) increase in mean noise level with different thresholds and network segregation for seven established brain networks (default, dorsal and ventral attention, frontoparietal, limbic, sensorimotor, and visual). All models were adjusted for age at fMRI, sex, BMI, diet, physical activity, smoking status, cumulative smoking, environmental tobacco smoke exposure, alcohol consumption, individual SES, neighborhood SES and PM2.5 absorbance. Noise variables were partially bound continuous variables with a lower cut-off value of 50 dB ( Table S5: Estimates of the associations between a 10 dB(A) increase in mean indoor or outdoor noise level and network segregation for seven established brain networks (default, dorsal and ventral attention, frontoparietal, limbic, sensorimotor, and visual) in 522 participants. All models were adjusted for age at fMRI, sex, BMI, diet, physical activity, smoking status, cumulative smoking, environmental tobacco smoke exposure, alcohol consumption, individual SES, neighborhood SES and PM2.5 absorbance. Noise variables were partially bound continuous variables with a lower cut-off value of 50 dB ( Table S6: Estimates of the associations between an interquartile range increase in mean air pollution exposure or a 10 dB(A) increase in mean noise level and network segregation, intra-network FC and internetwork FC for seven established brain networks (default, dorsal and ventral attention, frontoparietal, limbic, sensorimotor, and visual) adjusted using inverse probability weights. Air pollution models were further adjusted for 24-h outdoor noise and noise models were adjusted for PM2.5 absorbance. Abbreviations: BMI, body mass index; dB(A), A-weighted decibels; Disttrafroad, distance from home address to the nearest high-traffic roads; FC, functional connectivity; fMRI, functional magnetic resonance imaging; IQR, interquartile range; Lden, outdoor 24-hour weighted noise; Lnight, outdoor nighttime noise; fMRI, functional magnetic resonance imaging; NO2, nitrogen dioxide; PM10, particulate matter with diameter ≤10 µm; PM2.5, particulate matter with diameter ≤2.5 µm; PM2.5abs, PM2.5 absorbance, soot; PNAM, accumulation mode particle number concentration Table S7: Estimates of the associations between air pollution, noise from 2006-2008 and altered network segregation, intra-network FC and internetwork FC in seven established networks (default, dorsal and ventral attention, frontoparietal, limbic, Sensorimotor, and visual) from 2011-2015. Models were adjusted for age at fMRI, sex, body mass index, diet, physical activity, smoking status, cumulative smoking, environmental tobacco smoke exposure and alcohol consumption, individual and neighborhood SES. Air pollution models were further adjusted for 24-h outdoor noise and noise models were adjusted for PM2.5 absorbance.   Figure S1: Timeline of exposure and outcome assessments: Marked fields display when the specific elements of the study were conducted.

Network
Abbreviations: HNR; Heinz-Nixdorf Recall, FU; Follow-up examination, CTM; Chemistry transport modeling, LUR; Land use regression modeling Figure S2: Directed Acyclic Graph: The graph displays the possible relationship between air pollution exposure (AP Exposure) and functional brain connectivity (Functional_Connectivity). An arrow between two variables represents a possible cause-effect relationship. Variable that are "upstream" from the exposure (air pollution) and outcome (functional connectivity) are colored in red. Variables in blue are "upstream" from the outcome (functional connectivity). Variables in grey are unobserved.

Figure S3: Categorization of Confidence Intervals for Summarization Plot: Confidence limits
resulting from the analysis of associations between air pollution, noise and FC Metrics (network segregation, intra-and internetwork FC) were categorized into ten categories. For this, the direction of the effect size and the quality of estimation (width of confidence interval) were considered. First, for each estimate, confidence intervals were divided into eight equally sized segments. Depending on the location of the zero value in segments, these eight segments plus two categories for confidence limits completely above or below the zero value, formed ten categories. Although, confidence limits in category eight display higher effect estimates than confidence limits in category nine, confidence limits in category eight are wider and display more uncertainty. Subsequently, each category was assigned a color and/or texture displaying possible decrease (confidence interval mostly below zero) to increase (confidence interval mostly above zero). Furthermore, to avoid graphically overestimating effects, confidence limits with effect estimates very close to zero (category 4 to 7) were grouped into one category (white).   Figure S6: Sensitivity Analyses -Noise Thresholds: Associations between a 10 dB(A) increase in mean noise level with different thresholds and network segregation for seven established brain networks (default, dorsal and ventral attention, frontoparietal, limbic, sensorimotor, and visual). All models were adjusted for age at fMRI, sex, BMI, diet, physical activity, smoking status, cumulative smoking, environmental tobacco smoke exposure, alcohol consumption, individual SES, neighborhood SES and PM2.5 absorbance. Noise variables were partially bound continuous variables with a lower cut-off value of 50 dB(A) and 45 dB(A) for Lden, 45 dB(A) and 35 dB(A) for Lnight with all noise values lower than the defined threshold being set to the threshold value.

Figure S4: Derivation of Study Population Flowchart
Abbreviations: BMI, body mass index; dB(A), A-weighted decibels; FC, functional connectivity; fMRI, functional magnetic resonance imaging; Lden50, outdoor 24-hour weighted noise with threshold of 50 dB(A); Lden45, outdoor 24-hour weighted noise with threshold of 45 dB(A); Lnight45 outdoor nighttime noise with threshold of 45 dB(A); Lnight35 outdoor nighttime noise with threshold of 35 dB(A); PM2.5abs, PM2.5 absorbance, soot; SES, socioeconomic status Figure S7: Sensitivity Analyses -Indoor vs. Outdoor Noise: Associations between a 10 dB(A) increase in mean indoor or outdoor noise level with different thresholds and network segregation for seven established brain networks (default, dorsal and ventral attention, frontoparietal, limbic, sensorimotor, and visual) in 522 participants. All models were adjusted for age at fMRI, sex, BMI, diet, physical activity, smoking status, cumulative smoking, environmental tobacco smoke exposure, alcohol consumption, individual SES, neighborhood SES and PM2.5 absorbance. Noise variables were partially bound continuous variables with a lower cut-off value of 50 dB(A) for outdoor Lden, 45 dB(A) for Lnight and indoor Lden, 20 dB(A) and 10 dB(A) for Lnight with all noise values lower than the defined threshold being set to the threshold value.
Air pollution models were further adjusted for 24-h outdoor noise and noise models were adjusted for PM2.5 absorbance. Estimates are shown for an IQR increase of 2.0 μg/m 3 for PM10, of 1.4 μg/m 3 for PM2.5, of 0.3 10 -5 /m for PM2.5abs, of 613 n/mL for PMAM and of 5.2 μg/m 3 for NO2.