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. Author manuscript; available in PMC: 2023 Jun 16.
Published in final edited form as: Brain Imaging Behav. 2021 Jul 16;16(1):130–140. doi: 10.1007/s11682-021-00483-y

Inflammatory Markers and Tract-Based Structural Connectomics in Older Adults with a Preliminary Exploration of Associations by Race

Elizabeth A Boots a,b, Liang Zhan c, Karla J Castellanos d, Lisa L Barnes b,e, Lisa Tussing-Humphreys f,g, Melissa Lamar a,b,e
PMCID: PMC10275616  NIHMSID: NIHMS1884946  PMID: 34272684

Abstract

Peripheral inflammation has been implicated in cognitive dysfunction and dementia. While studies outline the relationship between elevated inflammation and individual gray or white matter alterations; less work has examined inflammation as related to connectivity between gray and white matter, or variability in these associations by race. We examined the relationship between peripheral inflammation and tract-based structural connectomics in 74 non-demented participants (age=69.19±6.80 years; 53% female; 45% Black) who underwent fasting venipuncture and MRI. Serum was assayed for C-reactive protein, interleukin-6, and interleukin-1β. Graph theory analysis integrated T1-derived gray matter volumes and DTI-derived white matter tractography into connectivity matrices analyzed for local measures of nodal strength and efficiency in a priori regions of interest associated with cardiovascular disease risk factors and dementia. Linear regressions adjusting for relevant covariates showed associations between inflammatory markers and nodal strength in the isthmus, posterior and caudal anterior cingulate (p’s≤.042). Adding an inflammatory marker*race term showed race-modified associations between C-reactive protein and efficiency in the thalamus and amygdala, and nodal strength in the putamen (p’s≤.048), between interleukin-6 and efficiency in the pars triangularis and amygdala (p’s≤.024), and between interleukin-1β and nodal strength in the pars opercularis (p=.048). Higher levels of inflammation associated with lower efficiency and higher strength for White participants but higher efficiency and lower strength for Black participants. Results suggest inflammation is associated with tract-based structural connectomics in an older diverse cohort and that differential relationships may exist by race within prefrontal and limbic brain regions.

Keywords: inflammation, tract-based structural connectivity, graph theory, aging, race

Introduction

Higher levels of peripheral inflammation are directly associated with cardiovascular disease (CVD) risk factors (Golia et al., 2014), poorer cognitive health (Teunissen et al., 2003), and Alzheimer’s disease (AD; Engelhart et al., 2004) both cross-sectionally (Bettcher et al., 2012) and longitudinally (Tegeler et al., 2016). To date, attempts to understand the underlying neuroanatomy of these associations have focused on relationships between peripheral inflammation and either gray or white matter brain structures. Generally, these investigations have shown that higher levels of inflammation are associated with lower brain volumes (Hilal et al., 2018), although null findings do exist (Janowitz et al., 2019). For example, higher levels of interleukin-6 (IL-6) have been associated with lower hippocampal volumes in middle (Marsland, Gianaros, Abramowitch, Manuck, & Hariri, 2008) to later (Schmidt et al., 2016) life while higher levels of CRP have been associated with lower gray matter volumes more generally (Hilal et al., 2018; Janowitz et al., 2019). Higher levels of inflammatory markers including interleukin-1-beta (IL-1β) have also been associated with lower volumes of gray matter regions including inferior parietal and medial prefrontal cortices (Zhang et al., 2016). Other studies have shown positive associations between inflammation and white matter hyperintensity volumes (Hilal et al., 2018), and negative associations with diffusion tensor imaging (DTI)-derived white matter integrity within commissural and cortico-subcortical tracts of the frontal and temporal lobes (Bettcher et al., 2015; Miralbell et al., 2012; Wersching et al., 2010).

Despite this work, there is still limited understanding of how peripheral inflammation associates with connectivity characteristics between gray and white matter. Novel neuroimaging techniques utilizing graph theory allow for the examination of structural connectivity metrics that consider gray and white matter (Rubinov & Sporns, 2010). These techniques allow for a more integrated understanding of how gray matter ‘nodes’ and white matter ‘edges’ influence structural connectivity metrics including efficiency (how the brain exchanges information), and nodal strength (how strong connections are between brain regions). Employing graph theory-derived structural connectivity metrics to understand relationships between inflammation and gray and white matter may help determine how peripheral inflammation negatively influences the complex neuroanatomical networks key to cognitive health and AD in older adults.

The aim of this study was to investigate whether peripheral inflammatory markers (CRP, IL-6, and IL-1β) associated with structural connectivity metrics of nodal strength and efficiency in older adults. Given that higher levels of inflammation have been linked to CVD risk factors (Golia et al., 2014) and AD (Swardfager et al., 2010), we focused our investigation on 23 regions of interest (ROIs) implicated in both age-related disease states (Lamar, Boots, Arfanakis, Barnes, & Schneider, 2020a, 2020b). We hypothesized that greater levels of CRP, IL-6, and IL-1β (separately) would be associated with lower nodal strength and efficiency in these ROIs in the full sample. Additionally, racial differences exist in inflammatory marker levels and CVD risk factors with Black adults showing higher levels of both compared to White adults (Howard et al., 2017; Nazmi & Victora, 2007); racial differences also exist in associations between inflammatory markers and brain volumes (Walker et al., 2017). Social determinants of health including systemic racism too long experienced in communities of color and their subsequent negative impact on health have been shown to underlie these racial differences (Bagby, Martin, Chung, & Rajapakse, 2019; Hill, Perez-Stable, Anderson, & Bernard, 2015; Kaplan & Bennett, 2003). When taken together with prior literature suggesting a differential association of inflammation on brain and behavior by race (Boots et al., 2020; Goldstein, Zhao, Steenland, & Levey, 2015; Walker et al., 2017; Windham et al., 2014), we also explored whether race was an effect modifier of associations between inflammatory markers and structural connectivity.

Methods

Study Design

Participants.

Participant data comes from a study of CVD risk factors and brain aging conducted at the University of Illinois at Chicago. Participant inclusion and exclusion criteria have been previously described (Boots et al., 2019; Bronas et al., 2019). Briefly, individuals underwent a telephone screen to determine initial study eligibility. This was followed by an in-person evaluation of final inclusion and exclusion criteria. For all inclusion/exclusion criteria, see Supplementary Material. Together, these procedures ensured the non-demented, non-depressed nature of our healthy older adults sample (n=121).

Given the current analyses focused on associations of peripheral inflammation with structural connectivity in self-identified non-Latino White and Black participants, we excluded 18 individuals who self-identified as Latino. Additionally, we excluded 18 participants that did not have at least 2 of 3 inflammatory markers and/or relevant covariate data. This left 85 participants in our analytic sample.

Inflammatory Markers.

Described elsewhere (Boots et al., 2020), CRP, IL-6, and IL-1β were quantified using fasting blood serum in all but one participant where plasma was used in the absence of serum. CRP was measured in duplicate (n=34) and singly (n=68) by quantitative sandwich enzyme linked immunoassay (ELISA; R&D Systems, Minneapolis, MN); mean intra-assay coefficient of variability (CV) was 4.5%. IL-6 was measured in duplicate by solid-phase ELISA; mean intra-assay CV was 5.5%. IL-6 was log-transformed to normalize the distribution. IL-1β was measured by quantitative sandwich ELISA. IL-1β was assayed singly in a restricted sample (n=74) due to manufacturer error. The 12 participants missing IL-1β did not differ from participants with IL-1β (data not shown).

Neuroimaging Acquisition.

Whole brain images were acquired on a GE MR750 Discovery 3T scanner (General Electric Health Care, Waukesha, WI) using an 8-channel head coil. T1-weighted images were acquired using a Brain Volume (BRAVO) imaging sequence (FOV: 22mm; voxel size=0.42×0.42×1.5mm3; 120 contiguous axial slices; TR/TE=1200ms/5.3ms; flip angle=13o). Multi-slice T2-weighted fluid-attenuated inversion recovery (FLAIR) images were acquired using a two-dimensional PROPELLER sequence to improve robustness against motion (FOV=22cm, voxel size=0.35×0.35×3.0mm3, 40 contiguous axial slices, TR/TI/TE=9500ms/2500ms/93.3ms, flip angle=142.35o). Diffusion images were acquired using 2-D spin-echo EPI sequence (FOV=20mm; voxel size=0.78×0.78×3.0mm3; TR/TE=5,525/93.5ms; flip angle=90o). Forty contiguous axial slices aligned to the AC-PC line were collected in 32 gradient directions with b=1400s/mm2 and 6 baseline (b0) images.

Structural Connectivity Processing.

Structural connectivity networks were created using a pipeline described previously (Boots et al., 2019; Zhan et al., 2015). T1-weighted images were used to generate label maps for volumetric segmentation of 82 gray matter and subcortical regions as well as total intracranial volume (ICV) using FreeSurfer 6.0 software (Dale, Fischl, & Sereno, 1999; Destrieux, Fischl, Dale, & Halgren, 2010). For computation of probabilistic tractography (Zhan et al., 2015), diffusion images were corrected for eddy current distortions and head motion using the FSL (Smith et al., 2004) eddy-correct tool (Andersson & Sotiropoulos, 2016) and non-brain tissue was removed using BET (Smith, 2002). Diffusion tensors at each voxel were estimated using FSL dtifit for calculation of three principal eigenvectors, three eigenvalues, and fractional anisotropy (FA). Then, images were elastically registered to T1-weighted scans using Advanced Normalization Tools (Avants et al., 2014). FSL Bedpostx was used to model up to three fibers per voxel for crossing fibers (Behrens, Berg, Jbabdi, Rushworth, & Woolrich, 2007). To construct connectivity matrices, FSL Probtrackx (Behrens et al., 2007) was run on individual seed voxels with FA ≥ 0.2. One thousand iterations were run to ensure convergence of the Markov chains. Finally, a symmetric matrix was formed by detecting the number of fibers connecting all 82 volume pairs (no self-connections) determined from Probtrackx (Zhan et al., 2013) and normalized by adjusting for individual gray matter region volumes.

Structural Connectivity Analyses.

Resulting weighted and undirected matrices were analyzed in Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/) for measures of nodal strength – the sum of the weights of the paths connected to a node – and local efficiency – the average inverse path length across nodes for a given region (Rubinov & Sporns, 2010). We targeted nodal strength and local efficiency for 23 a priori ROIs implicated in both CVD risk factors and AD (Lamar et al., 2020a, 2020b). ROIs included bilateral superior frontal gyrus, inferior frontal gyrus (pars opercularis, pars triangularis, pars orbitalis), rostral and caudal middle frontal gyrus, caudal and rostral anterior, posterior, and isthmus cingulate cortex, entorhinal cortex, supramarginal gyrus, middle and inferior temporal gyrus, hippocampus, amygdala, superior and inferior parietal cortex, the basal ganglia (caudate, putamen, pallidum, and accumbens), and precuneus. We also included the thalamus given this region’s associations with inflammatory markers including IL-6 (Gu et al., 2017; Marsland et al., 2015) and its structural connectivity associations with cognition (Boots et al., 2019).

Neuroimaging Quality Control.

Four individuals did not complete neuroimaging, three had incidental findings, and four did not pass neuroimaging quality control. Thus, the final analytic sample included 74 participants (33 Black and 41 White adults).

Covariates.

In addition to ICV, a priori covariates included the Framingham Stroke Risk Profile 10-year risk of stroke (FSRP-10), body mass index (BMI), white matter hyperintensities (WMHs), and word reading as measured via estimated premorbid verbal IQ (pVIQ) from the Wechsler Test of Adult Reading (WTAR). In keeping with previous research (Boots et al., 2019; Jefferson et al., 2015), age and sex were not included as covariates as they are included in the FSRP-10. For covariate details, see Supplementary Material.

Statistical Analyses

Analyses were conducted in SPSS 27, with p≤.05 for significance. Due to outliers, IL-1β measures falling three standard deviations from the interquartile range were winsorized. Basic between-group differences were calculated utilizing independent-samples t-test for continuous variables and χ2 for categorical variables. To test our hypotheses, multivariate linear regression investigated the associations between IL-6, CRP, and IL-1β (separately), and structural connectivity metrics of nodal strength and efficiency in a priori ROIs in the full sample. Covariates included ICV, FSRP-10, BMI, WMHs, and pVIQ. These analyses were followed by a second series of analyses that additionally included a race*inflammatory marker interaction term.

Results

Participant Characteristics

Participants were approximately 69 years of age, 53% female, and had 16 years of education. Screening measures were in the normal range, indicative of the non-demented, non-depressed nature of this sample (see Table 1). Black and White participants did not differ on demographic, screening, or covariate measures with the exception that Black participants had significantly higher FSRP-10 scores and significantly lower pVIQ scores derived from word reading when compared with White participants (see Table 1), which is consistent with the literature (Howard et al., 2017; Manly, Jacobs, Touradji, Small, & Stern, 2002; Sisco et al., 2015).

Table 1.

Characteristics of the full participant sample and stratified by race.

Overall Sample Non-Latino Black Participants Non-Latino White Participants p-value*
n 74 33 41
Demographics and Covariates
Age, years 69.19 ± 6.80 (60–89) 69.82 ± 6.70 (61–89) 68.68 ± 6.93 (60–85) .479
Female, % 52.7 57.6 48.8 .451
Education, degree years 15.64 ± 2.59 (10–22) 15.12 ± 2.55 (10–18) 16.05 ± 2.59 (12–22) .127
pVIQ derived from WTAR word reading 108.54 ± 11.15 (79–126) 100.33 ± 6.58 (89–110) 115.15 ± 9.60 (79–126) <.001
HAM-D (n=66) 1.05 ± 1.41 (0–7) 1.00 ± 1.75 (0–7) 1.08 ± 1.13 (0–4) .829
Mini Mental State Examination 28.95 ± 1.27 (25–30) 28.85 ± 1.30 (26–30) 29.02 ± 1.26 (25–30) .557
FSRP-10§ 6.68 ± 5.20 (1.08–26.28) 7.63 ± 5.69 (2.51–26.28) 5.92 ± 4.69 (1.08–17.49) .040
BMI 29.24 ± 6.27 (18.74–46.11) 30.21 ± 6.27 (19.86–46.11) 28.46 ± 6.23 (18.74–45.96) .235
WMH Volume, mm3§ 3759.21 ± 4756.53
(224.88 – 21205.40) 		3739.36 ± 4184.88 (265.32–18937.13) 3775.19 ± 5222.89 (224.88–21205.40) .403
ICV, mm3 1511034.54 ± 175291.57
(1120609.42 – 1904676.26) 		1470569.49 ± 152757.83 (1120609.42–1764418.87) 1543603.96 ± 187005.08 (1168488.28–1904676.26) .075
Inflammatory Markers
IL-6, pg/mL§ 3.97 ± 2.42 (0.87–10.33) 4.29 ± 2.23 (0.87–9.85) 3.71 ± 2.56 (0.91–10.33) .149
CRP, pg/mL 3.07 ± 1.72 (0.23–6.02) 3.38 ± 1.79 (0.23–6.02) 2.83 ± 1.65 (0.24–6.02) .170
IL-1β , pg/mL (n=65) 0.57 ± 0.12 (0.49–1.45) 0.56 ± 0.05 (0.50–0.68) 0.58 ± 0.15 (0.49–1.45) .819
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*

p-values for comparisons by race.

§

Raw values reported in Table 1 for ease of interpretation; log-transformed values used in analyses. Values are mean ± standard deviation (range) unless otherwise stated. pVIQ = predicted verbal intelligence quotient derived from WTAR=Wechsler Test of Adult Reading; HAM-D=Hamilton Depression Rating Scale score; FSRP-10=Framingham Stroke Risk Profile 10-year percent risk of stroke; BMI=body mass index; WMH=white matter hyperintensity; mm3=millimeters cubed; ICV=intracranial volume; IL-6=interleukin-6; CRP=C-reactive protein; IL-1β=interleukin 1-beta; pg/mL=picograms per milliliter.

Inflammatory Markers & Nodal Strength

Fully-adjusted linear regressions revealed significant inverse associations between inflammatory markers and nodal strength in the cingulate. Specifically, higher IL-6 was associated with lower nodal strength in the isthmus of the cingulate, β(SE)=−.093(.041), t=−2.279, p=.026, ηp2=.073; higher levels of CRP were associated with lower nodal strength in the posterior cingulate, β(SE)=−.011(.005), t=−2.069, p=.042, ηp2=.061; and higher IL-1β was associated with lower nodal strength in the caudal anterior cingulate, β(SE)=−.572(.250), t=−2.286, p=.026, ηp2=.084.

Exploring Race as an Effect Moderator.

Linear regressions additionally including a race*inflammatory marker term resulted in a significant race*CRP interaction for nodal strength in the putamen, β(SE)=−.080(.037), t=−2.149, p=.035, ηp2=.066; specifically, higher levels of CRP were associated with lower strength in Black participants compared with White participants. A race*IL-1β interaction was significant for nodal strength in the pars opercularis, β(SE)=−.365(.180), t=−2.025, p=.048, ηp2=.068. Higher levels of IL-1β were negatively associated with strength in Black participants, and positively associated with strength in White participants. There were no significant race*IL-6 interactions (see Table 2; Figure 1).

Table 2.

Significant interactions between inflammatory markers and race on nodal strength in a priori regions of interest in the full sample.

Region of Interest β (SE) t-value p-value partial η2
CRP
Putamen
 FSRP-10 −.208 (.112) −1.859 .068 .050
 BMI −.014 (.006) −2.522 .014 .089
 pVIQ −.003 (.004) −.842 .403 .011
 WMH .243 (.077) 3.138 .003 .132
 ICV −2.12E−7 (1.87E−7) −1.130 .262 .019
 Race .275 (.149) 1.845 .070 .050
 CRP .021 (.027) .772 .443 .009
 CRP*Race Interaction −.080 (.037) −2.149 .035 .066
IL-1β (n=65)
Pars Opercularis
 FSRP-10 −.015 (.015) −.978 .332 .017
 BMI .0002 (.001) .239 .812 .001
 pVIQ −.001 (.001) −2.447 .018 .097
 WMH .009 (.011) .833 .408 .012
 ICV −3.00E−8 (2.65E−8) −1.132 .263 .022
 Race .164 (.101 1.622 .111 .045
 IL-1β .239 (.114) 2.094 .041 .073
 IL-1β*Race Interaction −.365 (.180) −2.025 .048 .068
IL-6 NS NS NS NS
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Note: Entries reflect full linear regression model interaction results for the interaction of inflammatory markers and race on nodal strength. FSRP-10 and WMH were log-transformed and IL-1β was winsorized for data normalization. pVIQ = predicted verbal intelligence quotient derived from the Wechsler Test of Adult Reading; HAM-D=Hamilton Depression Rating Scale score; FSRP-10=Framingham Stroke Risk Profile 10-year percent risk of stroke; BMI=body mass index; WMH=white matter hyperintensity; ICV=intracranial volume; IL-6 = Interleukin-6; CRP = C-Reactive Protein; IL-1β = Interleukin 1-beta; NS = not significant.

Figure 1.

Figure 1.

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Scatterplots depict interactions between inflammatory markers and race on measures of efficiency or nodal strength while adjusting for intracranial volume, Framingham Stroke Risk Profile 10-year percent risk of stroke, body mass index, white matter hyperintensity volume, and predicted verbal intelligence quotient derived from the Wechsler Test of Adult Reading. Panel A depicts associations between CRP or IL-1β on nodal strength metrics, where Black participants (blue) show negative associations between inflammation and nodal strength and White participants (red) show positive/stable associations. Panel B depicts associations between CRP or IL-6 on efficiency metrics, where Black participants (blue) show positive associations between inflammation and efficiency and White participants (red) show negative associations. Efficiency metrics were multiplied by a factor of 1000 for ease of interpretation. CRP = C-Reactive Protein; IL-6 = Interleukin-6; IL-1β = Interleukin 1-beta; pg/mL = picograms per milliliter.

Inflammatory Markers & Efficiency

Fully-adjusted linear regressions in the full sample did not reveal any significant associations between IL-6, CRP, or IL-1β and efficiency in any ROIs (data not shown).

Exploring Race as an Effect Moderator.

Linear regressions additionally including a race*inflammatory marker term resulted in significant race*CRP interactions for efficiency in the thalamus, β(SE)=.138(.067), t=2.059, p=.044, ηp2=.061, and amygdala, β(SE)=.532(.264), t=2.014, p=.048, ηp2=.069. Higher CRP was associated with higher efficiency in both ROIs for Black participants, but with lower efficiency in both ROIs for White participants. There were also significant race*IL-6 interactions for efficiency in the pars triangularis, β(SE)=1.929(.837), t=2.304, p=.024 ηp2=.076, amygdala, β(SE)=4.030(1.671), t=2.412, p=.019, ηp2=.082, and superior frontal gyrus, β(SE)=0.995(0.476), t=2.089, p=.041, ηp2=.063. Black participants showed positive associations between IL-6 and efficiency in the three ROIs and White participants showed negative associations between IL-6 and efficiency in the three ROIs. There were no significant race*IL-1β interactions (see Table 3; Figure 1).

Table 3.

Significant interactions between inflammatory markers and race on efficiency in a priori regions of interest in the full sample.

Region of Interest β (SE) t-value p-value partial η2
CRP
Amygdala
 FSRP-10 −1.097 (.792) −1.384 .171 .029
 BMI −.062 (.039) −1.586 .118 .037
 pVIQ −.012 (.027) −.433 .667 .003
 WMH .454 (.547) .831 .409 .011
 ICV −1.04E−6 (1.32E−6) −.784 .436 .009
 Race −.941 (1.055) −.892 .376 .012
 CRP −.413 (.192) −2.153 .035 .067
 CRP*Race Interaction .532 (.264) 2.014 .048 .059
Thalamus
 FSRP-10 −.047 (.200) −.234 .816 .001
 BMI −.021 (.010) −2.106 .039 .064
 pVIQ −.010 (.007) −1.441 .154 .031
 WMH .120 (.138) .866 .390 .011
 ICV −4.87E−8 (3.35E−7) .145 .885 <.001
 Race −.494 (.267) −1.851 .069 .050
 CRP −.060 (.049) −1.227 .224 .023
 CRP*Race Interaction .138 (.067) 2.059 .044 .061
IL-6
Amygdala
 FSRP-10 −1.174 (.784) −1.497 .139 .033
 BMI −.082 (.038) −2.156 .035 .067
 pVIQ −.009 (.027) −.333 .740 .002
 WMH .476 (.537) .887 .379 .012
 ICV −6.07E−7 (1.31E−6) −.463 .645 .003
 Race −1.371 (1.074) −1.276 .207 .024
 IL-6 −1.889 (1.045) −1.807 .075 .048
 IL-6*Race Interaction 4.030 (1.671) 2.412 .019 .082
Pars Triangularis
 FSRP-10 .004 (.393) .011 .992 <.001
 BMI −.035 (.019) −1.852 .068 .050
 pVIQ −.010 (.014) −.719 .475 .008
 WMH .649 (.269) 2.411 .019 .082
 ICV −7.66E−7 (6.57E−7) −1.166 .248 .020
 Race −1.445 (.538) −2.685 .009 .100
 IL-6 −.400 (.524) −.763 .448 .009
 IL-6*Race Interaction 1.929 (.837) 2.304 .024 .076
Superior Frontal Gyrus
 FSRP-10 −.028 (.224) −.126 .900 <.001
 BMI −.018 (.011) −1.641 .016 .040
 pVIQ −.009 (.008) −1.138 .259 .020
 WMH .205 (.153) 1.337 .186 .027
 ICV −5.923E−7 (3.740E−7) −1.584 .118 .037
 Race −.470 (.306) −1.534 .130 .035
 IL-6 −.528 (.298) −1.773 .081 .046
 IL-6*Race Interaction .995 (.476) 2.089 .041 .063
IL-1β NS NS NS NS
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Note: Entries reflect full linear regression model interaction results for the interaction of inflammatory markers and race on efficiency. FSRP-10, WMH, and IL-6 were log-transformed for data normalization. Efficiency metrics were multiplied by a factor of 1000 for ease of interpretation. pVIQ = predicted verbal intelligence quotient derived from the Wechsler Test of Adult Reading; HAM-D=Hamilton Depression Rating Scale score; FSRP-10=Framingham Stroke Risk Profile 10-year percent risk of stroke; BMI=body mass index; WMH=white matter hyperintensity; ICV=intracranial volume; IL-6 = Interleukin-6; CRP = C-Reactive Protein; IL-1β = Interleukin 1-beta; NS = not significant.

Discussion

This study demonstrated that in a sample of non-demented, non-depressed older Black and White adults, higher levels of peripheral inflammation were associated with lower nodal strength in cingulate regions. Additionally, higher inflammation was differentially associated with structural connectivity metrics of nodal strength and efficiency within select prefrontal and limbic regions on the basis of race. Specifically, higher levels of CRP and IL-1β were associated with lower nodal strength in the putamen and pars opercularis, respectively, for Black participants, but higher nodal strength, and only in the pars opercularis, for White participants. By contrast, higher CRP and IL-6 was associated with higher efficiency in the pars triangularis, amygdala, superior frontal gyrus, and thalamus for Black participants, but lower efficiency for White participants. Together, these findings indicate that inflammation associates with the strength of connections and the efficiency of information exchange in regions known to be vulnerable to both CVD risk factors and AD. Furthermore, findings point toward the importance of considering race or, more importantly, social determinants of health that may underlie racial differences, in associations between inflammation and tract-based structural connectivity.

While no other previous studies have, to our knowledge, examined relationships between peripheral inflammation and tract-based structural connectivity, our findings complement previous neuroimaging research. Specifically, prior DTI studies have shown that higher levels of peripheral inflammation are associated with lower white matter integrity in tracts connecting significant ROIs noted in our research, particularly the cingulum, uncinate fasciculus, and superior longitudinal fasciculus (Bettcher et al., 2015; Miralbell et al., 2012; Wersching et al., 2010). Additionally, work investigating gray matter morphology has revealed that higher CRP and IL-6 are associated with less gray matter volume in frontal and subcortical regions including the thalamus and basal ganglia (Gu et al., 2017; Marsland et al., 2015) as well as the anterior cingulate and prefrontal cortices (Zhang et al., 2016). Our work reflecting the structural connectivity implications of these previously reported relationships may provide an integrative framework for this body of research.

This study is also the first to report distinctions in patterns of structural connectivity associated with inflammation by race in older adults. Our findings of higher levels of inflammation associated with lower levels of nodal strength in Black compared to White adults suggests that previously noted racial differences in associations between peripheral inflammation and DTI-derived white matter integrity (Walker et al., 2017) may also be present in patterns of tract-based structural connectivity. Furthermore, the differential patterns of results for nodal strength versus efficiency by race suggest that the commonly held ‘deficit’ model of health disparities, where difficulties for underrepresented groups are highlighted rather than assets (Morgan & Ziglio, 2007), are not necessarily applicable. Instead, these results may point to a more fully encompassing risk and resilience model that varies by the connectome metric studied and the ROI interrogated. Additional work to test this assertion in a larger sample will be needed as will formal tests to determine if simultaneously high and low levels of nodal strength and/or efficiency within race reflect compensatory mechanisms as related to cognition (Barulli & Stern, 2013).

Unfortunately, the cross-sectional nature of this study does not allow for a true discussion of the mechanistic or directional links between peripheral inflammatory markers and connectivity of subcortical and prefrontal structures. However, evidence from prior literature may point toward directions for future work in this area. The peripheral inflammatory markers chosen for this study – IL-6, CRP, and IL-1β – increase with age due to a combination of greater oxidative stress, increased adipose tissue, and decreased sex hormone levels (Michaud et al., 2013; Singh & Newman, 2011), and have been consistently associated with cognitive dysfunction and AD (Engelhart et al., 2004; Teunissen et al., 2003). Previous work has shown that these peripheral inflammatory markers induce neuroinflammation (particularly with age) by signaling to the brain via activation of afferent nerves or microglia, and via transport across the blood-brain barrier (Holmes, 2013). A normal response to acutely high inflammation is to induce “sickness behavior” including lethargy, psychomotor slowing, and anhedonia (Holmes, 2013), thought to be mediated by neurotransmission of select brain structures. Specifically, pro-inflammatory cytokines target dopamine pathways of the basal ganglia to induce these behaviors (Felger & Miller, 2012), while also targeting dopamine pathways within the amygdala, dorsolateral prefrontal cortex, and cingulate for their roles in the emotional response of “sickness” (e.g., negative affect, withdrawal, anxiety; Miller, Haroon, Raison, & Felger, 2013). Chronically high inflammation can induce an exacerbating feedback loop ultimately leading to neurodegeneration (Heneka et al., 2015) of these “sickness behavior” regions (Kim & Won, 2017; Miller et al., 2013). Given that subcortical and, to a lesser extent, prefrontal regions are some of the first brain areas impacted by AD neuropathology (Braak & Braak, 1991; Dickerson et al., 2009), a potential mechanism whereby exacerbation of a normal inflammatory response for “sickness behavior” results in neurodegeneration may have negative implications for structural connectivity in these regions. Future longitudinal work is needed to assess the causal relationships of chronic inflammation and structural connectivity in aging.

Strengths of this study include the novel application of tract-based structural connectomics to understand peripheral inflammation as related to connectivity in older Black and White adults. Further, while our study did not include social determinants of health that may underlie our exploratory associations of race as an effect modifier, we discuss the importance of their inclusion in future work and are actively pursuing this in our own work. We included covariates with strong associations with inflammation (BMI) and/or structural dysconnectivity (WMHs). We utilized probabilistic tractography given its more accurate representation of white matter tracts (Zhan et al., 2015). Furthermore, our participants were non-demented older adults, lending credence to the utility of tract-based structural connectomics for a more nuanced understanding of how inflammation may impact complex neuroanatomical networks in older adults prior to overt cognitive impairment.

This study is not without limitations and results should be interpreted with caution. The cross-sectional nature of this work prohibits causal determinations. Additionally, there may be a loss of contextual nuance when utilizing interaction terms in health disparities research, particularly if racial differences in predictor and/or outcome variables are present (Ward et al., 2019); however, this was not the case in our study. While we did not note significant racial differences in WMHs seen in some (Nyquist et al., 2014), but not all (Liu et al., 2015), prior studies, our simple WMH comparison was unadjusted (as it was not the focus of our work) in contrast to prior studies that controlled for key confounders including age. Also in contrast to existing work in larger cohorts, we did not note a main effect of race in our models as related to inflammatory markers. The size of our sample stratified by race (33 Black participants and 41 White participants) may have affected our power to detect such group differences. While blood draws were standardized and completed in the morning, participants were not asked about recent illnesses that may impact inflammation and the range of our inflammatory markers may have been limited by the healthy (non-depressed) nature of our sample. Lastly, and as noted previously, not all inflammatory marker samples were assayed in duplicate, which may have introduced error into results for those assays.

Conclusions

Our study provides insight into the relationship between inflammation and efficiency and nodal strength generally, and provides preliminary evidence regarding associations for Black versus White adults. Our findings noted that inflammatory markers are associated with the strength of connections in the cingulate, and highlight the importance of utilizing novel neuroimaging techniques to detect these associations in healthy older adults. Additionally, while our findings suggest that differential patterns of nodal strength and efficiency may exist for Black and White participants, more work is needed assessing contributing factors to these contrasting results including race-conscious psychosocial stressors of discrimination (Bagby et al., 2019; Glymour & Manly, 2008). Lastly, while we suggest an exacerbated feedback loop leading to neurodegeneration of “sickness behavior” brain regions should be the focus of future research to understand a possible underlying mechanism of our reported associations between inflammation and tract-based structural connectomics, we readily admit that this is only one of several possibilities researchers should consider. For example, circulating inflammatory markers are also influenced by a range of peripheral conditions, may be a downstream of central nervous system injury, or might not reflect the central nervous system immune response (Kempuraj et al., 2017; Waldburger & Firestein, 2010). Thus, we believe our study serves as a foundation from which to replicate and extend work with tract-based structural connectomics as related to inflammation and aging in Black and White older adults and provides a springboard for mechanistic exploration of these important relationships.

Supplementary Material

Supplementary Material

Acknowledgements

The authors would like to thank the participants of this study and all prior research assistants for their help collecting data.

Funding Sources.

Research was supported by the National Institute on Aging at the National Institutes of Health (K01 AG040192, R21 AG048176). Ms. Boots was further supported by F31 AG064829; Dr. Barnes was further supported by R01 AG056405.

Compliance with Ethical Standards:

The study was approved by the University of Illinois at Chicago Institutional Review Board (IRB) in accordance with the Declaration of Helsinki with written informed consent for participation and publication obtained from all participants; it was also approved by the Rush University Medical Center IRB with data use agreements in place prior to analysis.

Footnotes

Declarations

Availability of Data and Material: Research data is confidential given that we do not have participant approval to share data with outside investigators.

Code Availability: Available upon request.

Conflicts of Interest: None of the authors have a conflict of interest to declare.

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