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. Author manuscript; available in PMC: 2020 Jun 9.
Published in final edited form as: Med Sci Sports Exerc. 2019 Aug;51(8):1613–1618. doi: 10.1249/MSS.0000000000001957

Sedentary Time and White Matter Hyperintensity Volume in Older Adults

Ulf G Bronas 1, Alana Steffen 2, Catherine Dion 3, Elizabeth A Boots 4, Konstantinos Arfanakis 5,6, David X Marquez 7, Melissa Lamar 4,6,8,9
PMCID: PMC7282192  NIHMSID: NIHMS1594129  PMID: 30817720

Abstract

Purpose

Cerebrovascular disease in the form of white matter hyperintensities (WMH) increases with age and is associated separately with sedentary time and reduced kidney function. A better understanding of the relationships among these variables would help clarify whether sedentary time should be considered more closely in older adults at particular levels of kidney function to reduce the risk of WMH.

Methods

We analyzed information from 94 healthy community-dwelling older adults to determine the association of sedentary time and WMH in non-demented, non-depressed older adults, and whether level of kidney function was an effect modifier of the relationship between sedentary time and WMH. Sedentary behavior was measured using the Sedentary Behavior Questionnaire. WMH was assessed using whole-brain 3T MRI T1- and T2-weighted images. Kidney function was calculated by the epi-CKD formula for estimated glomerular filtration rate (eGFR). Exposures or predictors were sedentary time, age, sex, education in years, Framingham stroke risk 10-year prediction score (FSRP-10), and eGFR. The analytical approach was multiple linear regression.

Results

Adjusting for age, sex, education in years, FSRP-10, greater sedentary time was associated with greater WMH but this effect was dependent on level of eGFR (sedentary time*eGFR interaction b=−0.0005, p=.022). At eGFR values of 69, 81, and 93 ml/min/per 1.73m2 (the 25th, 50th, and 75th percentiles), sedentary time b coefficients were b=0.021 (95% CI 0.011–0.031), b=0.015 (95% CI 0.008–0.022), and b=0.009 (95% CI 0.003–0.016). The effect weakened linearly as eGFR increased, with no significant association at eGFR ≥97 ml/min/per 1.73m2.

Conclusions

Findings suggest that sedentary time is associated with WMH in persons with an eGFR ≤96 ml/min/per 1.73m2 and that this association is stronger with lower levels of kidney function.

Keywords: Sedentary time, White matter hyperintensity, Renal function, eGFR

Introduction

Cerebrovascular disease in the form of white matter hyperintensities (WMH) as seen on T2 FLAIR magnetic resonance imaging (MRI) increase with age and are associated with cardiovascular disease risk factors (1–3). WMH represents small vessel disease pathology in the white matter of the brain and WMH are associated with cognitive decline, overt stroke, and incident dementia (4–8). Less studied, but equally important, WMH are also associated with reduced kidney function in healthy community dwelling older adults, as measured via estimated glomerular filtration rate (eGFR) (9–11). Reductions in eGFR may occur in conjunction with, or because of, uncontrolled cardiovascular disease risk factors, especially hypertension and diabetes. More than 35% of adults over age 60 have reduced kidney function (12). Moreover, a recent meta-analysis of 16 case-control studies showed that persons with an eGFR < 60 ml/min/per 1.73m2—the criteria for moderate or stage 3 chronic kidney disease (CKD)—have an increased incidence of silent cerebral infarction (odds ratio [OR] of 2.71), cerebral microbleeds (OR 2.70), white matter lesions (OR 2.03), and lacunar infarctions (OR 1.77) compared to persons with an eGFR > 60 ml/min/per 1.73m2 (10). Although reduced kidney function increases with age, as do cardiovascular disease risk factors and white matter damage (including WMH), very little work has been done investigating the relationship between levels of kidney function, WMH, and modifiable lifestyle factors while controlling for other more often studied age-related cardiovascular disease risk factors. Only by exploring multiple possible pathways that may be related to pathological brain aging we will be able to pinpoint intervention targets for specific groups of older adults.

While controlling known cardiovascular disease risk factors (e.g., hypertension and diabetes) is an important intervention target in patients with reduced eGFR and has been shown to reduce WMH regardless of kidney function, lifestyle factors associated with WMH may provide additional methods for reducing risk in individuals at lower levels of kidney function.(13). Consistently, findings regarding a lack of physical activity and an increased time spent sedentary (defined as prolonged sitting), have become recognized as primary contributors to adverse cardiovascular health outcomes (hazard ratio of 1.8) (14), reduced kidney function (4), and brain structural alterations (15, 16) in the general population. It is therefore plausible that increased sedentary time may also be associated with higher levels of WMH; however, to our knowledge, this association remains to be elucidated. Furthermore, because increased sedentary time is associated with reduced eGFR, it is plausible that associations between WMH and sedentary time are modified by level of kidney function, i.e., glomerular filtration rate, as measured by eGFR (4). A better understanding of the relationships among sedentary time, WMH, and level of kidney function, as measured by eGFR, would help clarify whether sedentary time should be considered more closely in older adults at particular levels of kidney function in order to target as many pathways as possible to reduce the risk of WMH burden and its associated negative outcomes.

The purpose of this study was to investigate whether level of kidney function, as measured by eGFR, was an effect modifier of the relationship between sedentary time and WMH in non-demented, non-CKD older adults. We hypothesized that greater sedentary time would be associated with greater WMH volumes and that this relationship would differ depending on the level of kidney function as measured by eGFR. More specifically, individuals with lower levels of eGFR would have greater WMH volumes at higher levels of sedentary behavior.

Methods

Participants

Participants for the current study came from a larger study of healthy aging and cardiovascular disease risk factors conducted at the University of Illinois at Chicago (UIC). Individuals aged 60 or older were recruited via community outreach (e.g., advertisements and fliers) and word of mouth. The study was approved by the UIC Institutional Review Board (IRB) and conducted in accordance with the Declaration of Helsinki with written informed consent obtained on all participants.

Interested individuals underwent an initial telephone screen conducted in the participants’ language of choice (English or Spanish) to determine study eligibility. At this screen, exclusion criteria consisted of self-reported current or past history of neurological conditions including diagnosed dementia or mild cognitive impairment, Parkinson’s disease or any other movement disorder, stroke or seizure disorder; current or past history of Axis I or II disorders (e.g., depression or bipolar disorder); history of head injury or loss of consciousness; present or past history of substance abuse or dependence; self-reported psychotropic medication use; or contraindications for MRI including incompatible metallic implants, cardiac pacemaker/defibrillator, and/or claustrophobia. A history of stable (e.g., diabetes) or remitted (e.g., cancer) medical illness was not an exclusion criterion.

Following the telephone screen, eligible participants were scheduled for a more detailed evaluation, including cognitive (Mini-Mental State Examination, [MMSE]) (17) and affective (Structured Clinical Interview for DSM-IV-TR [SCID]) screens for final inclusion and exclusion determination (18). Screening measures were administered by a trained research assistant fluent in either English or Spanish and were followed by an evaluation by a board-eligible psychiatrist who completed the 17-item Hamilton Depression Rating Scale (HAM-D) (19). All raters were blind to telephone screen information.

Final inclusion criteria consisted of an absence of subjective memory complaints, an absence of psychiatric symptoms based on the SCID, a score ≤ 8 on the HAM-D and an MMSE score ≥ 24. In total, 121 individuals met final inclusion criteria for the larger parent study.

Sedentary Behavior Questionnaire

Sedentary behavior was assessed using the Sedentary Behavior Questionnaire (SBQ) (20) for English speakers and the SBQ-S for Spanish-speaking participants (21). These questionnaires measure the amount of time spent engaging in activities such as watching television or reading a book or magazine. Each activity was reported for the amount of time spent on a typical weekday as well as a weekend day. Sedentary behavior was calculated as (reported weekday hours*5) + (reported weekend day hours*2), for an approximate total hours per week of sedentary activity.

White Matter Hyperintensity Volumes

Image Acquisition

Whole-brain MRIs were acquired on a GE MR 750 Discovery 3T (General Electric Health Care, Waukesha, WI), using an 8-channel head coil. Participants were positioned supine on the scanner table with earplugs to improve patient comfort and foam pads to minimize head movement. High-resolution three-dimensional T1-weighted images were acquired using a brain volume (BRAVO) imaging sequence (field of view [FOV] = 22 cm; 120 contiguous axial slices; TR/TE = 1,200/5.3ms; flip angle = 13o; voxel size = 0.42×0.42×1.5 mm3). A set of multi-slice T2-weighted FLAIR images were acquired using a 2-D PROPELLER sequence to improve robustness against motion (FOV = 22 cm, voxel size = 0.35×0.35×3.0 mm3, 40 contiguous axial slices, TR/TI/TE = 9500 ms/2500 ms/93.3 ms, flip angle = 142.35°). Both sequences were acquired as part of a larger protocol.

Image Processing

Quantification of WMH volumes first involved registering the T1-weighted BRAVO data for each participant to the T2-weighted FLAIR data using affine registration (FLIRT, FMRIB, University of Oxford, UK) (22). Brains were extracted from the co-registered BRAVO and FLAIR image volumes (BET, FMRIB, University of Oxford, UK) (23). WMH volumes were then automatically segmented using a support vector machine classifier, considering both BRAVO and FLAIR information for each participant (24). WMH volumes were then divided by the participant’s corresponding intracranial volume (ICV), generated by FreeSurfer 6.0 (http://surfer.nmr.harvard.edu) to account for individual differences in head size that resulted in a measure of WMH volume as a percent of intracranial volume. These procedures have been described in more detail in other publications from our group (8).

Kidney Function and Cardiovascular Disease Risk Factors

Participants received a comprehensive history and physical by a registered nurse and an evaluation of cardiovascular disease risk factors by trained staff at UIC’s Clinical Research Center (CRC). This evaluation included height, weight, and waist circumference; a confirmed 12-hour fasting blood draw for other health-related variables such as glucose and hemoglobin A1C; and an electrocardiogram. It is important to note that no participant endorsed chronic kidney disease during their comprehensive history and physical.

Creatinine was obtained from the blood samples, and eGFR was calculated using the epi-CKD formula eGFR = 141 × min(SCr/κ, 1)α × max(SCr/κ, 1)−1.209 × 0.993 Age × 1.018 (if female) × 1.159 (if Black). For females, the following values were used for serum creatinine (SCr): sex = 1.018; alpha = −0.329; kappa = 0.7, and for males, the following values were used: sex = 1; alpha = −0.411; kappa = 0.9) (25). Additionally, we calculated the Framingham Stroke Risk-10-year Prediction Score (FSRP-10) (26) based on age, systolic blood pressure, antihypertensive medication use, diabetes mellitus, current cigarette smoking, cardiovascular disease, atrial fibrillation, and left ventricular hypertrophy.

Statistical Analysis

This study was a secondary data analysis of sedentary time, WMH volumes, and eGFR. For this analysis, descriptive statistics and Pearson correlations were determined for all variables of interest, including covariates. We then conducted a series of multivariable linear regression analyses to investigate the association of sedentary time with WMH volumes, first without adjustment (Model 1), then with adjustment for covariates (age, sex, education in years, FSRP-10, and eGFR; Model 2). We also assessed if the association of sedentary time with WMH volumes was moderated by kidney functioning using an interaction term (Model 3). The distribution of WMH volumes had a positive skew; therefore, we used robust standard errors obtained via a sandwich estimator because they are robust to violations of regression assumptions of homoscedasticity and normality (27) in addition to natural log transformation. All analyses used alpha = 0.05 as the threshold for statistical significance and were conducted using Stata, version 15.0.

Results

Of the 121 individuals included in the larger study, 94 participants had complete data on sedentary time, WMH volumes, and creatinine, and were included in the current analyses. Participants included in this study averaged 68.3 years of age, with approximately 15 years of education and were not significantly different from individuals not included in the current analyses (n = 27; data not shown). Women represented 51% of the current study sample and 46% of participants were Black. Details on these and other key participant characteristics may be found in Table 1.

Table 1.

Sample characteristics

Variable (n = 94) N %
Sex
Male 46 48.9
Female 48 51.1
Race/Ethnicity
Non-Latinx White 40 42.6
Black 43 45.7
Latinx 11 11.7
Mean ±SD
Age (years) 68.3 6.7
Education (years) 14.8 3.3
Systolic BP (mm/Hg) 137.0 16.2
Diastolic BP (mm/Hg) 80.7 8.5
Creatinine (mg/dL) .94 .27
eGFR (ml/min/per 1.73m2) 80.3 17.3
 eGFR 27–<60 (n, %) 8 8.5
 eGFR 60–<90 (n, %) 54 57.5
 eGFR 90–116 (n, %) 32 34.0
Framingham stroke risk-10 10.2 7.7
WMH volume 0.23 0.30
Sedentary time (hours) 64.7 25.8
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eGFR = estimated glomerular filtration rate. Framingham stroke risk-10 = Framingham 10 year Stroke Risk Prediction, WMH volume=white matter hyperintensity volume in mm^3/intracranial volume*100.

Bivariate correlations between key participant characteristics and WMH volumes are shown in Table 2. WMH volumes (ln(mm^3/intracranial volume*100))were positively correlated with age, FSRP-10, and total sedentary time (p<0.01), and negatively correlated with eGFR (p=0.14). WMH volumes were not associated with any individual cardiovascular disease risk factors, glucose, or diabetes in our sample (all p>0.3). Independent sample t tests did not reveal differences in WMH volumes by sex (men = −1.99 ± 0.15; women = −2.01 ± 0.15; p = 0.93).

Table 2.

Correlational analyses (n=94)

Variable WMH Age eGFR FRSP-10 Sed-time Edu-years Glucose Cholest SBP DBP DM Smoking
WMH XX 0.40** −0.15 0.37** 0.31** −0.03 0.07 0.11 0.12 −0.07 −0.03 −0.08
Age XX −0.22* 0.57** −0.07 0.05 0.06 0.15 0.14 −0.21* 0.01 −0.22*
eGFR XX −0.21* 0.00 0.01 −0.23* 0.06 0.05 0.12 −0.17 −0.03
FRSP-10 XX 0.10 0.02 0.13 0.10 0.35** 0.13 0.10 0.03
Sed-time XX 0.07 0.07 0.25* 0.05 −0.02 −0.08 0.08
Edu-years XX −0.12 0.19 −0.01 0.10 −0.04 −0.06
Glucose XX 0.10 0.10 −0.01 0.46** −0.06
Cholesterol XX 0.09 0.08 −0.28** −0.07
SBP XX 0.64** −0.09 0.09
DBP XX −0.11 0.08
DM XX −0.09
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Pearson’s correlation.

*

p <.05

**

p <.01

cholesterol = fasting blood total cholesterol level; DBP = Diastolic blood pressure; DM = Diabetes mellitus type 2; Edu-years = years of education; eGFR = estimated glomerular filtration rate; FRSP-10 = Framingham 10 year Stroke Risk Prediction; Glucose = fasting blood glucose levels; SBP = systolic blood pressure; Sed-time = Sedentary time; Smoking = currently smoking; WMH = white matter hyperintensity volume

Sedentary Time and WMH

Unadjusted linear regression analysis (Model 1) revealed that greater sedentary time was associated with larger WMH volumes (sedentary time [unstandardized] b = 0.012, p = .002, 95% CI 0.004–0.020); these results were similar after adjusting for age, sex, education, FSRP-10, and eGFR (Model 2: sedentary time b = 0.013, p <.001, 95% CI 0.006–0.020). Thus, in fully adjusted models, for every additional 1 hour of sedentary time per week there was an additional 0.012 ln percentage of WMH volume, holding all other variables constant.

Effect Modification of eGFR

To determine whether lower kidney function modified the relationship between sedentary time and WMH volumes, we added the interaction of sedentary time and eGFR to the fully adjusted model that included age, sex, education, FSRP-10, and eGFR (Table 3). The interaction term was significant (Model 3: b = −0.0005, p = .022, 95% CI −0.0009 to −0.0001), indicating a diminished association of sedentary time and WMH volume for higher levels of eGFR. For ease of interpretation, we calculated the association of sedentary time and WMH volumes (unstandardized b coefficients and 95% CI) for every value in the range of observed eGFR values (27–116 ml/min/per 1.73m2; Figure 1). At eGFR of 27 ml/min/per 1.73m2 (the lowest eGFR level observed), the effect was strongest (b = 0.042, 95% CI 0.016 to 0.069), and weakened linearly with higher eGFR. Thus, at eGFR values of 69, 81, and 93 ml/min/per 1.73m2 (the 25th, 50th, and 75th percentiles in our sample), the sedentary time b coefficients were b = 0.021 (95% CI 0.011–0.031), b = 0.015 (95% CI 0.008–0.022), and b = 0.009 (95% CI 0.003–0.016), all statistically significant coefficients as estimated from our final model. In contrast, at an eGFR of 97 ml/min/per 1.73m2 or higher, the association of sedentary time and WMH volume was not statistically significant (b range: 0.007 to −0.002) based on confidence intervals that included 0.

Table 3.

Final Multiple Linear Regression Model (Model 3)

b Coefficient Robust SE P [95% Conf. Interval] Standardized Beta
Constant −9.07 1.600 <.001 −12.249 −5.890 .
eGFR 0.029 0.013 0.036 0.002 0.055 0.49
Sedentary-time 0.056 0.019 0.004 0.018 0.093 1.41
Interaction −0.0005 0.0002 0.022 −0.0009 −0.0001 −1.22
Age 0.060 0.015 <.001 0.030 0.089 0.39
Sex −0.187 0.177 0.294 −0.540 0.165 −0.09
Education −0.021 0.026 0.416 −0.072 0.030 −0.07
FSRP-10 0.009 0.014 0.510 −0.018 0.036 0.07
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95% Conf. Interval = 95% confidence interval; Age = age in years; eGFR = estimated glomerular filtration rate; FRSP-10 = Framingham 10 year stroke risk prediction; Interaction = Sedentary-time multiplied by eGFR; SE = standard error; Sedentary-time = total sedentary time per week in hours; Sex = female. Our final model accounted for 33% of the variance in ln WMH volume, of which 3.4% was due to the interaction of sedentary time and kidney function.

Figure 1.

Figure 1.

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Conditional effects of sedentary time per Week on WMH volume by eGFR with 95% confidence intervals. The 95% confidence intervals were also used to identify the values of eGFR where the association between sedentary time and WMH volumes included zero in the confidence interval estimate to determine the region of significance. WMH volume was positively associated with sedentary time, with the strongest associations observed at the lower levels of eGFR (the region of significance). At eGFR of 96 and higher, the association of WMH volumes and sedentary time was not statistically significant.

We also modeled WMH using the categorical version of eGFR presented in Table 1 (instead of continuous eGFR). Results revealed similar findings, i.e., however the interaction terms were not statistically significant (p=0.157). For example, the sedentary time slope estimate for category 2 of eGFR is 0.016 whereas the slope estimate was 0.018 based on manuscript model 3 when eGFR was set to the median value for individuals in category 2 (eGFR=75).

Discussion

This study reports, for the first time, that total sedentary time is positively associated with WMH volumes and that this association is stronger at lower levels of kidney function, as measured by eGFR. The finding of sedentary time being associated with WMH is consistent with and extends previous findings of Siddarth et al. (16), who reported that more sitting time, but not physical activity (measured via questionnaire), was negatively associated with other measures of brain integrity (i.e., less medial temporal lobe and associated subregional thickness). Further, findings from our study using a marker of white matter damage provided cross-sectional support for the longitudinal work by Arnadottir et al. (15), who reported an association between sedentary time and a 5-year decrease in overall white matter volume in older healthy adults. We also found that sedentary time was significantly associated with WMH only when eGFR was lower than 97 ml/min/per 1.73m2, which suggests an association of sedentary time and WMH in persons with reduced eGFR. The cut-points suggested in our study closely follow current guidelines, which consider the initial cut-off criteria for a significant reduction in eGFR to be < 90 ml/min/per 1.73m2 (28).

While beyond the scope of this cross-sectional study, underlying mechanisms to explain these associations may be found in the literature. It is plausible that more time spent sedentary impairs the cerebral vascular function, increasing inflammation and oxidative stress, and leading to a reduction in angiogenesis, neurogenesis, and/or synaptogenesis (29). Additionally, sedentary time is positively associated with incidence of hypertension and impaired insulin sensitivity in some (30) but not this study. This association could increase the risk of diabetes, vascular calcification, and (by extension) the amount of WMH in affected individuals. While cardiovascular disease risk factors are known to be exacerbated when renal function is reduced (28), and stroke risk was higher with lower eGFR in our sample, possibly explaining the association of WMH with eGFR and sedentary time found in this study, we cannot rule out alternative explanations. For example, given the cross-sectional nature of this study, it is possible that individuals with lower eGFR (and higher stroke risk) have comorbid diseases not captured by this study that may make them inherently more sedentary putting them at risk for brain (i.e., WMH) and kidney (e.g., CKD) dysfunction.

Sedentary time is increasingly considered a significant risk factor for chronic disease and morbidity (14, 31). Given that physical activity levels are known to be significantly lower in older persons with reduced eGFR, and that sedentary time is associate with lower eGFR (4, 32), this suggests that these individuals are likely more sedentary compared to age- and sex-matched persons with normal eGFR. Such a profile of reduced physical activity may predispose older persons with reduced eGFR to the negative effects of prolonged sedentary time. It is plausible that replacing sedentary time with physical activity is required to slow or even reduce WMH burden, but it is also possible that frequent bouts of breaking up prolonged sedentary time (i.e., sedentary breaks) is just as important. The concept of increasing breaks from sedentary behavior has been proposed to reduce the risk of chronic diseases (30). Future intervention studies are needed to investigate the possible benefits of increasing breaks from sedentary behavior on WMH burden in participants with reduced eGFR.

Strengths of this study include a diverse cohort of older adults that included equal numbers of men and women. Further, this was a generally healthy sample that allowed for an investigation of levels of kidney function as it relates to sedentary time and WMH volumes. By adjusting for key characteristics known to impact our variables of interest, including age, sex, and cardiovascular disease risk factors, we were able to determine that the relationship between sedentary behavior and WMH volumes in older adults was modified by level of kidney function. This study, however, is cross-sectional; as such, it does not allow for causative effects to be determined. An additional limitation of our work is that the sedentary behavior questionnaire used to determine sedentary time was based on self-report; furthermore, we did not measure sedentary behavior with a device, e.g., an accelerometer. Future studies should include such instrument based measures and daily diaries. Finally, the statistical model is an imperfect representation of reality; its precision and accuracy are affected by sample size and sampling variability.

Conclusion

Based on our findings, we conclude that sedentary time is related to WMH volumes in persons with an eGFR ≤ 96 ml/min/per 1.73m2 and that this association becomes stronger with lower levels of kidney function (as measured by eGFR). Although the clinical implications of our work are currently not known, with replication results may begin to suggest that sedentary time should be considered more closely in older adults at particular levels of kidney function in order to target as many pathways as possible to reduce the risk of WMH burden and its associated negative outcomes.

Acknowledgements

The authors would like to thank the participants of this study. Additionally, we would like to thank our community partners including the Healthy City Collaborative program, the Austin Health Center on the Westside of Chicago, and the Ventanilla De Salud program and staff at the Mexican Consulate. We thank Kevin Grandfield, Publication Manager of the UIC Department of Biobehavioral Health Science, for editorial assistance.

We declare that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation, and statement that results of the present study do not constitute endorsement by ACSM.

Conflicts of Interest and Source of Funding

This work was supported by the National Institutes of Health, National Institute on Aging: K01 AG040192 (ML) and R21 AG048176 (ML). Additionally, aspects of this project were supported by the National Center for Advancing Translational Sciences and National Center for Research Resources, National Institutes of Health, through Grants UL1TR002003 and 1S10RR028898.

Footnotes

The authors have no conflicts of interest to report.

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