Abstract
Background
Vascular brain injury (VBI) may be an under-recognised contributor to mobility impairment. We examined associations between MRI VBI biomarkers and impaired mobility.
Methods
We separately analysed Atherosclerosis Risk in Communities (ARIC) and UK Biobank (UKB) study cohorts. Inclusion criteria were no prevalent clinical stroke, and available brain MRI and balance and gait data. MRI VBI biomarkers were (ARIC: ventricular and white matter hyperintensity (WMH) volumes, non-lacunar and lacunar infarctions, microhaemorrhage; UKB: ventricular, brain and WMH volumes, fractional anisotropy (FA), mean diffusivity (MD), intracellular and isotropic free water volume fractions). Quantitative biomarkers were categorised into tertiles. Mobility impairment outcomes were imbalance and slow walk in ARIC and recent fall and slow walk in UKB. Adjusted multivariable logistic regression analyses were performed.
Results
We included 1626 ARIC (mean age 76.2 years; 23.4% imbalance, 25.0% slow walk) and 40 098 UKB (mean age 55 years; 15.8% falls, 2.8% slow walk) participants. In ARIC, imbalance associated with four of five VBI measures (all p values<0.05), most strongly with WMH (adjusted OR, aOR 1.64; 95% CI 1.18 to 2.29). Slow walk associated with four of five VBI measures, most strongly with WMH (aOR 2.32; 95% CI 1.66 to 3.24). In UKB, falls associated with all VBI measures except WMH, most strongly with FA (aOR 1.16; 95% CI 1.08 to 1.24). Slow walking associated with WMH, FA and MD, most strongly with FA (aOR 1.57; 95% CI 1.32 to 1.87).
Conclusions
VBI is associated with mobility impairment in community-dwelling, clinically stroke-free cohorts. Consequences of VBI may extend beyond clinically apparent stroke to include mobility.
Keywords: STROKE
WHAT IS ALREADY KNOWN ON THIS TOPIC
Impairment of mobility is associated with significant disability. Clinical stroke is a known cause of mobility impairment, but there is a paucity of evidence linking vascular brain injury (VBI) evident on brain MRI and mobility impairment in stroke-free individuals. Furthermore, no study to date has assessed this association while accounting for the contribution of disorders external to the central nervous system on mobility impairment.
WHAT THIS STUDY ADDS
In an analysis of two population-based cohorts of stroke-free individuals in the Atherosclerosis Risk in Communities and UK Biobank (UKB) Studies, MRI biomarkers of VBI were significantly associated with mobility impairment outcomes in both cohorts. In a subanalysis of the UKB cohort, this significant relationship between MRI biomarkers of VBI and mobility impairment remained even on adjusting for conditions external to the central nervous system that may contribute to impaired mobility.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Understanding the link between VBI and mobility impairment may have significant implications such as inclusion of mobility impairment as an outcome in trials of interventions aiming to modify vascular risk factors.
Introduction
Impaired mobility, characterised by imbalance, slow walk speed and falls, is common and disabling.1 Nearly, 20% of participants in the 2008 National Health Interview Survey reported imbalance, of whom 27% indicated that it prevented participation in activities including exercise and social events.2 Slow gait is frequent with a prevalence of 43% in individuals over age 65 and has been associated with difficulty in performing daily functions and fall risk.3 Preventing mobility impairment may preserve independence and social participation, and reduce falls, the leading cause of fatal and non-fatal injuries among older adults.4
Mobility impairment can result from disorders of the cerebral vasculature, vestibular system, visual system, spinal cord, peripheral nerves, muscles and joints. Often, the aetiological diagnosis of mobility impairment is uncertain because of no apparent or multiple potential aetiologies. Clinical stroke is a well-known cause of mobility impairment. Clinically inapparent vascular brain injury (VBI), defined by the presence of asymptomatic but radiographically evident infarcts and small vessel disease, also associated with disability. These forms of VBI are being established as contributors to cognitive impairment with comparable effects to clinical stroke on quality of life.5 Studies have demonstrated associations between biomarkers of VBI and measures of mobility impairment but have been limited by narrow ranges of neuroimaging biomarkers assessed in individual studies, evaluations in single cohorts, inclusion of participants with prior history of clinical stroke and not accounting for non-neurological contributors to mobility impairment.6 7
We aimed to evaluate the association between MRI biomarkers of VBI and mobility impairment in stroke-free participants of two large epidemiological studies: the Atherosclerosis Risk in Communities (ARIC) study and the UK Biobank (UKB) study. We hypothesise that MRI biomarkers of VBI are independently associated with mobility impairment among stroke-free adults.
Methods
Study design and participants
We separately performed cross-sectional analyses to evaluate the association between brain MRI markers of VBI and measures of mobility impairment in the ARIC and UKB cohorts.
ARIC is a population-based, prospective cohort study which recruited 15 792 participants ages 44–66 years from 1987 to 1989 to evaluate risk factors and clinical outcomes associated with atherosclerosis.8 9 Participants were recruited from four US communities (Washington County, Maryland; Jackson, Mississippi; Forsyth County, North Carolina and Minneapolis suburbs in Minnesota) and underwent interviews and clinical examinations during six in-person visits (visit 2 1990–1993, visit 3 1993–1995, visit 4 1996–1999, visit 5 2011–2013 and visit 6 2016–2017). A subset of ARIC participants were eligible for a visit 5 brain MRI in the ancillary ARIC-Neurocognitive Study if there was no contraindication and they met one of the following criteria: (1) had a prior study MRI from 2004 to 2006 (visit 3), (2) had either low cognitive scores or decline on longitudinally administered tests and (3) were from an age-stratified random sample of remaining participants.10 We included participants in the ARIC-Neurocognitive Study with (1) an in-person visit 5 examination, (2) a visit 5 brain MRI, (3) documented ability to ambulate and (4) availability of balance and 4 min walk speed data from visit 5. Patients with a history of stroke per self-report were excluded. The time from visit 5 to visit 5 MRI is not available.
UKB is a prospective cohort study which enrolled 502 480 individuals ages 40–69 in the UK from 2006 to 2010.11 12 Demographic, clinical and biological sample data were collected at baseline. A subset of participants underwent a brain MRI at an average of 4.15 (SD 0.91) years after recruitment. The average age of participants at the time of brain MRI was 61.72 years (SD 7.47 years). We included UKB participants who were (1) able to walk, (2) answered the query, ‘How would you describe your usual walking pace’ and (3) answered the query, ‘In the last year, have you had any falls?’. Participants were deemed unable to walk if the response to the query, ‘number of days walked per weekend’ was ‘unable to walk’.13 Participants with a history of clinical stroke, ascertained using self-reports or electronic health records (EHR), were excluded.
ARIC and UKB datasets are publicly available by application request to respective study committees. This research has been conducted using the UK Biobank Resource under Application Number 58743.
Outcome measures
In ARIC, individual outcomes reflecting mobility impairment included (1) impaired balance and (2) slow walk speed. Balance was classified as impaired at visit 5 if a participant scored <2 on the five-level ordinal composite balance score extracted from the Short Physical Performance Battery or SPPB (range 0–4; 0 indicates poorest and 4 indicates best).14 The balance score was derived from observed performance on the during standing from a chair, tandem gait, semitandem gait and side-by-side stand tests.15 The 4 m walk time was averaged over two trials at visit 5 and the walk speed was calculated by dividing 4 m by the average walk time. Slow walk speed was then defined by the lowest quartile.
In UKB, the individual, self-reported outcomes studied were participant selections from a multiple choice list describing (1) at least one fall in the past year and (2) slow compared with normal or fast walk speed. In the absence of a balance variable in the UKB database, we studied recent falls given the association between fall risk and balance demonstrated in the literature.12–14
MRI Biomarkers of VBI in ARIC
In ARIC, 3 Tesla brain MRI scans with 3.3 mm slices were obtained at imaging centres near each study field centre and read centrally at the Mayo Clinic (Rochester, Minnesota).15 Sequences relevant to this study were MP RAGE, axial T2* GRE and axial fluid-attenuated inversion recovery (FLAIR) images.
We analysed five biomarkers of VBI in ARIC: (1) white matter hyperintensity (WMH) volume, (2) ventricular volume (VV), (3) any infarct, (4) lacunar infarct and (5) microbleed (CMB) presence. Previous research has demonstrated their importance to cerebrovascular health.16–18 Axial FLAIR images were centrally segmented by an automated algorithm into voxels to derive WMH volume measures and both ventricular and total intracranial volumes were calculated from preprocessed MP RAGE pulse sequence with Freesurfer.16 These measures are available as numerical fields in the ARIC dataset. We normalised WMH and VV for intracranial volume. The presence of infarct and type and presence of CMB were centrally read and available as categorical variables in the dataset. Radiographic lacunar infarcts were 3–20 mm in maximal dimension and located in a subcortical region while non-lacunar infarctions were infarcts that did not meet this criteria. CMBs were defined as homogeneous lesions of haemosiderin deposits <10 mm in diameter detected by trained imaging analysts and confirmed by a radiologist. MRI exposures of VBI were (1) top tertile of VV defined by the distribution of the full cohort, (2) top tertile of WMH volume in the full cohort, (3) prior non-lacunar infarct, (4) prior lacunar infarct and (5) CMB presence.
MRI biomarkers of VBI in UKB
In the UKB, brain MRI scans were acquired by 3T scanners (Siemens Skyra with a 32-channel RF receive head coil).13 UKB procedures for acquisition and quality check of imaging have been previously described.19 T1-weighted MRI with an MP RAGE sequences were obtained with 1 mm isotropic resolution and field-of-view 208×256×256. The resolution of the T2 FLAIR was 1.05×1×1 mm where the T2* values were approximated by magnitude images at two echo times. Diffusion tensor imaging (DTI) sequences were obtained using two b-values (b=1000, 2000 s/mm2) at isotropic resolution of 2 mm and multiband acceleration factor of 3. For each of the two shells, 50 diffusion-encoding directions were acquisitioned.
We analysed MRI biomarkers of VBI in UKB which were centrally quantified: (1) WMH volume, (2) VV, (3) brain volume, (4) fractional anisotropy (FA), (5) mean diffusivity (MD), (6) intracellular volume fraction (ICVF) (an index of white matter neurite density) and (7) isotropic or free water volume fraction (ISOVF). We normalised the non-DTI measures for intracranial volume. WMH volume was quantified using the Brain Intensity Abnormality Classification Algorithm, a part of the FMRIB Software library. We categorised exposure to each continuous non-DTI MRI biomarker by tertiles (top tertile for WMH, bottom tertile for brain volume and top tertile for VV).
DTI markers were explored in UKB because white matter structural integrity has been associated with vascular risk factors and risk of incident stroke and dementia.20 21 Studies suggest that alterations in white matter that appear normal by T2 FLAIR but abnormal by DTI represent an early phase of injury, while WMH represents a late phase arising from the same pathophysiologic mechanism.22 UKB dataset includes quantitative data from central image processing previously described.10 21 22 FSL software was employed to calculate diffusion tensor and scalar parameters with the b=1000 shell (50 directions) and DTIFIT (diffusion tensor imaging fit). Neurite orientation dispersion and density modelling were performed with the Accelerated Microstructure imaging via Convex Optimisation tool to produce voxel-based parameters of ICVF and IsoVF. FMRIB Software Library’s Diffusion Toolbox for diffusion modelling and tractography analysis was used to correct noise in DTI data from distortions, eddy currents and head movement.23 24 DTI maps generated were employed as inputs for tract-based spatial statistics processing.
We calculated mean DTI measures from each of 48 standard space tracts per participant.25 In accordance with a published methodology of analysing DTI measures in UKB,26 we performed principal component analyses on each DTI measure aggregated over 48 tracts using the ‘stats’ package in R. We classified participants into tertiles of FA, MD, ICVF and ISOVF by principal component 1. The top tertile for each sequence in the full cohort was designated as the exposure for each DTI marker.
Covariates
We evaluated variables that may confound associations between MRI biomarkers and mobility impairment: age, sex, race, hypertension (defined as a measured diastolic blood pressure >90 mm Hg or use of a medication for high blood pressure), use of anticholesterol medication, diabetes (defined by glucose value >140 mg/dL, non-fasting glucose value >200 mg/dL, use of medication for diabetes or self-reported diagnosis of diabetes), body mass index (BMI) (kg/m2) and current smoking.27 In UKB, models were further adjusted for comorbidities that impact organs external to the central nervous system (CNS) which may also contribute to mobility impairment. These conditions were identified using International Classification of Diseases or ICD-10 codes for diseases of the eye and adnexa (H00–H59), the inner ear (H80–H83), the peripheral nervous system (G50–G73), peripheral vasculature (I73), and musculoskeletal system and connective tissue (M00–M99).
Statistical analysis
We compared characteristics of eligible participants in the ARIC and UKB datasets. Mann-Whitney U and χ2 tests were used as appropriate. We fit separate logistic regression models to examine the association between the presence of individual MRI biomarker of VBI and the outcomes in each cohort. We also adjusted for covariates that were significant in descriptive analysis at a prespecified p<0.05. Unadjusted associations were studied in all eligible participants. For the adjusted analyses, we excluded participants with any missing covariate. We adjusted for two sets of covariates in UKB. Similar to analyses in ARIC, we adjusted for demographics and clinical covariates. We then further adjusted for disorders of organs external to the CNS. We examined two-way interactions between each pairwise combination of MRI biomarkers as a product term in the adjusted logistic regression analyses for the individual outcomes. To evaluate the links between microstructural white matter changes and mobility impairment, we calculated the OR of DTI MRI biomarkers of VBI and each outcome among a subgroup of participants with the lowest tertile of WMH burden. Statistical analyses were performed in Stata V.17.1 (StataCorp) and R.
Results
ARIC cohort
There were 1626 eligible participants (table 1). Mean age was 76.2 years (SD 5.2 years). Of these participants, 381 (23.4%) had impaired balance and 404 (25.0%) had slow walk speed. Median SPPB score was 2 (IQR 1–2) among participants with impaired balance and 4 (IQR 4–4) among participants with preserved balance. Median 4 m walk speed was 0.64 m/s (IQR 0.54–0.70) among participants with slow walk speed and 0.94 m/s (IQR 0.85–1.06) among participants with normal walk speed. Participants with impaired balance were significantly older (79 vs 75 years, p<0.001) and more likely to be female, black, and have hypertension, diabetes, and elevated BMI. Participants with slow walk speed were significantly older (78 vs 75 years, p<0.001), female, black, and had hypertension, diabetes, and elevated BMI. Characteristics of participants who did and did not meet inclusion criteria are in online supplemental table 1. Excluded participants were younger and less likely to be black.
Table 1.
Characteristics of ARIC participants (N=1626) stratified by balance and walk speed at visit 5
| Characteristic | Full cohort (N=1626) | No imbalance (n=1245) |
Imbalance (n=381) |
P value* | Normal or fast walk speed (n=1222) |
Slow walk speed (n=404) |
P value† |
| Visit 5 demographic and clinical characteristics | |||||||
| Age (years) | 76.2±5.3 | 75.4±5.0 | 78.9±5.3 | <0.001 | 75.6±5.0 | 78.2±5.5 | <0.001 |
| Age groups | <0.001 | <0.001 | |||||
| 60–69 | 168 (10.3%) | 151 (12.1%) | 18 (4.7%) | 144 (11.8%) | 24 (5.9%) | ||
| 70–79 | 981 (60.3%) | 812 (65.2%) | 169 (44.4%) | 780 (63.8%) | 201 (49.8%) | ||
| 80–90 | 477 (29.4%) | 283 (22.7%) | 194 (50.9%) | 298 (24.4%) | 179 (44.3%) | ||
| Female sex | 942 (57.9%) | 698 (56.1%) | 140 (63.8%) | 0.008 | 667 (54.6%) | 275 (68.1%) | <0.001 |
| Black race | 419 (25.8%) | 301 (24.2%) | 118 (31.0%) | 0.008 | 268 (21.9%) | 151 (37.4%) | <0.001 |
| Hypertension | 1085 (67.6%) | 818 (65.7%) | 282 (74.0%) | 0.003 | 794 (65.0%) | 305 (75.6%) | <0.001 |
| Cholesterol medication use | 897 (55.3%) | 675 (54.2%) | 225 (59.1%) | 0.092 | 670 (54.8%) | 230 (57.0%) | 0.439 |
| Diabetes | 476 (29.6%) | 342 (27.5%) | 138 (36.2%) | 0.001 | 329 (26.9%) | 153 (37.8%) | <0.001 |
| Body mass index (kg/m2) | 28.4±5.4 | 28.2±5.1 | 29.1±6.3 | 0.004 | 28.1±4.9 | 29.6±6.7 | <0.001 |
| Current smoking | 83 (5.2%) | 780 (5.6%) | 14 (3.7%) | 0.150 | 55 (4.5%) | 29 (7.1%) | 0.048 |
| Visit 5 MRI Biomarkers | |||||||
| Ventricular volume (mL) | 34.5, 24.5–48.4 |
32.9, 23.6–46.9 |
38.8, 28.0–51.2 |
<0.001 | 33.4, 24.0–46.9 |
37.1, 27.2–51.0 |
<0.001 |
| White matter hyperintensity volume (mL) | 11.6, 6.4–21.6 |
10.7, 5.8–19.6 |
15.9, 8.8–30.5 |
<0.001 | 10.4, 5.8–19.5 |
15.8, 9.3–27.0 |
<0.001 |
| Presence of prior infarct | 398 (24.5%) | 279 (22.4%) | 119 (31.2%) | <0.001 | 267 (21.0%) | 142 (35.2%) | <0.001 |
| Prior lacunar infarct | 278 (17.1%) | 188 (15.1%) | 90 (23.6%) | <0.001 | 169 (13.8%) | 109 (27.0%) | <0.001 |
| Microhaemorrhage | 369 (22.9%) | 265 (21.3%) | 107 (28.0%) | 0.006 | 266 (21.8%) | 106 (26.3%) | 0.064 |
Values are n (percentage) for categorical variables or median (IQR) for continuous variables.
*P values comparing participants with and without imbalance.
†P values comparing participants with and without slow gait.
ARIC, Atherosclerosis Risk in Communities.
bmjno-2023-000501supp001.pdf (52.6KB, pdf)
The prevalence of each MRI biomarkers of VBI was significantly higher in patients with impaired versus preserved balance (top tertile of VV 40% vs 31% with respect to the full cohort; top tertile of WMH volume 45% vs 30%; prior infarct 31% vs 22%; prior lacunar infarct 24% vs 15%; CMB 28% vs 21%; all p values<0.05). The prevalence of each MRI biomarker of VBI except CMB was significantly higher among participants with slow versus normal or fast walk speed (top tertile of VV 39% vs 31% with respect to the full cohort; top tertile WMH volume 45% vs 29%; prior infarct 35% vs 21%; prior lacunar infarct 27% vs 14%; all p values<0.01).
Imbalance was significantly associated with four out of five MRI biomarkers of VBI (table 2). Adjusted ORs were 1.73 (95% CI 1.23 to 2.45) for top tertile of VV, 1.64 (95% CI 1.18 to 2.29) for top tertile of WMH and 1.46 (95% CI 1.07 to 1.99) for a radiographic lacunar infarct (table 2). The interaction of WMH tertile and CMB on the outcome of imbalance was statistically significant (p=0.03; online supplemental table 2). The remainder of the two-way interaction analyses of imbalance was not significant. Slow walk speed was significantly associated with four of five biomarkers of VBI (table 3). Adjusted ORs were 2.12 (95% CI 1.51 to 2.98) for top tertile of VV, 2.32 (95% CI 1.66 to 3.24) for top tertile of WMH and 2.17 (95% CI 1.61 to 2.93) for a radiographic lacunar infarct (table 3). There were no significant interactions between pairs of MRI biomarkers and slow walk speed (online supplemental table 2).
Table 2.
Associations between imbalance and MRI biomarkers of vascular brain injury in ARIC visit 5
| MRI biomarker | Category | Unadjusted OR for imbalance | 95% CI | P value* | Adjusted OR for imbalance† | 95% CI | P value‡ |
| Ventricular volume§ | Tertile 1 | Reference | – | – | – | – | – |
| Tertile 2 | 1.66 | 1.23 to 2.24 | 0.001 | 1.43 | 1.03 to 1.99 | 0.035 | |
| Tertile 3 | 1.92 | 1.43 to 2.57 | <0.001 | 1.73 | 1.23 to 2.45 | 0.002 | |
| White matter hyperintensity volume§ | Tertile 1 | Reference | – | – | – | – | – |
| Tertile 2 | 1.82 | 1.34 to 2.48 | <0.001 | 1.37 | 0.98 to 1.91 | 0.063 | |
| Tertile 3 | 2.66 | 1.98 to 3.59 | <0.001 | 1.64 | 1.18 to 2.29 | 0.004 | |
| Prior infarct | 1.57 | 1.22 to 2.03 | <0.001 | 1.37 | 1.04 to 1.81 | 0.027 | |
| Prior lacunar infarct | 1.74 | 1.31 to 2.31 | <0.001 | 1.46 | 1.07 to 1.99 | 0.016 | |
| Microhaemorrhage | 1.44 | 1.11 to 1.88 | 0.006 | 1.33 | 1.00 to 1.77 | 0.052 |
*P values comparing participants with and without imbalance.
†Adjusted for age, sex, race, hypertension, diabetes, body mass index at ARIC visit 5.
‡P values comparing participants with and without slow gait.
§Further adjusted for total brain volume in tertiles, n=1583.
ARIC, Atherosclerosis Risk in Communities.
Table 3.
Associations between slow walk speed and MRI biomarkers of vascular brain injury in ARIC visit 5
| MRI biomarker | Category | Unadjusted OR for slow walk speed | 95% CI | P value | Adjusted OR for slow walk speed | 95% CI | P value‡ |
| Ventricular volume§ | Tertile 1 | Reference | – | – | – | – | – |
| Tertile 2 | 1.50 | 1.12 to 1.99 | 0.006 | 1.53 | 1.11 to 2.12 | 0.010 | |
| Tertile 3 | 1.73 | 1.31 to 2.30 | <0.001 | 2.12 | 1.51 to 2.98 | <0.001 | |
| White matter hyperintensity volume§ | Tertile 1 | Reference | – | – | – | – | – |
| Tertile 2 | 2.24 | 1.65 to 3.05 | <0.001 | 1.83 | 1.31 to 2.56 | <0.001 | |
| Tertile 3 | 3.13 | 2.31 to 4.22 | <0.001 | 2.32 | 1.66 to 3.24 | <0.001 | |
| Prior infarct | 2.05 | 1.60 to 2.62 | <0.001 | 1.98 | 1.51 to 2.60 | <0.001 | |
| Prior lacunar infarct | 2.30 | 1.75 to 3.03 | <0.001 | 2.17 | 1.61 to 2.93 | <0.001 | |
| Microhaemorrhage | 1.28 | 0.99 to 1.66 | 0.064 | 1.19 | 0.89 to 1.59 | 0.242 |
‡ Adjusted for age, sex, race, hypertension, diabetes, and body mass index at ARIC Visit 5.
§ Further adjusted for total brain volume in tertiles, n=1,583.
ARIC, Atherosclerosis Risk in Communities.
UKB cohort
Of 500 271 UKB participants, 41 443 were enrolled in the MRI neuroimaging study, of whom 1345 were excluded due to prevalent stroke, resulting in a cohort of 40 098 eligible participants. Mean age of participants was 56.5 years (SD 8.10 years). There were 6316 participants (15.8%) who reported falling in the past year and 1112 participants (2.8%) with slow walk speed. Participants with recent fall were significantly more likely to be older, female and have hypertension, dyslipidaemia, diabetes, elevated BMI and disorders of organs external to the CNS (table 4). Participants with slow walk speed were significantly more likely to be older, female and have hypertension, dyslipidaemia, diabetes, elevated BMI and disorders of organs external to the CNS. Characteristics of all and only eligible UKB participants are presented in online supplemental table 3. Eligible participants were significantly younger and had less vascular risk factors compared with the full cohort.
Table 4.
Characteristics of UKB participants (N=40 098) stratified by recent fall history and walk speed
| Characteristics | No recent fall (n=33 782) |
Recent fall (n=6316) |
P value* | Normal/fast walk (n=38 986) |
Slow walk (n=1112) |
P value† |
| Age (years) | 54.77 (7.53) | 55.97 (7.40) | <0.001 | 54.92 (7.53) | 56.46 (7.10) | <0.001 |
| Female sex | 17 165 (50.8%) | 4113 (65.1%) | <0.001 | 20 592 (52.8%) | 686 (61.7%) | <0.001 |
| Black race | 220 (0.7%) | 35 (0.6%) | 0.416 | 245 (0.6%) | 10 (0.9%) | 0.342 |
| Hypertension | 6106 (18.1%) | 1372 (21.7%) | <0.001 | 7065 (18.1%) | 413 (37.1%) | <0.001 |
| Cholesterol medication use | 2798 (8.3%) | 615 (9.7%) | <0.001 | 3245 (8.3%) | 168 (15.1%) | <0.001 |
| Diabetes | 841 (2.5%) | 193 (3.1%) | 0.01 | 944 (2.4%) | 90 (8.1%) | <0.001 |
| Current smoking | 2057 (6.1%) | 403 (6.4%) | 0.391 | 2381 (6.1%) | 79 (7.1%) | 0.193 |
| BMI 25–30 kg/m2 | 14 471 (42.9%) | 2651 (42.1%) | <0.001 | 16 741 (43.0%) | 381 (34.4%) | <0.001 |
| BMI>30 kg/m2 | 5644 (16.7%) | 1355 (21.5%) | 6450 (16.6%) | 549 (49.5%) | ||
| Diseases of peripheral nervous system, eye and inner ear | 12 054 (35.7%) | 2488 (39.4%) | <0.001 | 14 057 (36.1%) | 485 (43.6%) | <0.001 |
| Peripheral vascular disease | 534 (1.6%) | 128 (2.0%) | 0.012 | 635 (1.6%) | 27 (2.4%) | 0.052 |
| Diseases of musculoskeletal system and connective tissue | 19 653 (58.2%) | 4102 (64.9%) | <0.001 | 22 919 (58.8%) | 836 (75.2%) | <0.001 |
Values are n (percentage) for categorical variables or median (IQR) for continuous variables.
*P values comparing participants with and without recent fall.
†P values comparing participants with and without slow gait.
BMI, body mass index; UKB, UK Biobank.
The prevalence of each MRI biomarker of VBI was significantly higher among participants with versus without recent fall (top tertile of VV 35% vs 33% with respect to the full cohort; bottom tertile of brain volume 36% and 33%; top tertile of WMH volume 37% and 33%; top tertile of FA 38.3% vs 32.2%; top tertile of MD 37.2% vs 32.5%; top tertile of ICVF 37.0% vs 32.5%; top tertile of ISOVF 36.4% vs 32.6%; all p values<0.01; table 5).
Table 5.
MRI biomarkers of vascular brain injury of UKB participants stratified by recent fall history and walk speed
| MRI biomarker | No recent fall (n=33 782) |
Recent fall (n=6316) |
P value* | Normal/fast walk (n=38 986) | Slow walk (n=1112) |
P value† |
| Ventricular volume‡ (mL) | 42.91 (32.83, 56.68) |
43.96 (33.51, 57.38) |
0.001 | 43.03 (32.87, 56.74) |
44.93 (35.00, 59.95) |
<0.001 |
| Brain volume‡ (mL) | 1159.12 (1084.08, 1237.68) | 1131.13 (1060.21, 1210.16) |
<0.001 | 1155.84 (1080.97, 1234.55) | 1118.03 (1048.68, 1192.92) | <0.001 |
| White matter hyperintensity volume‡ (mL) | 3.64 (1.97, 7.33) |
4.17 (2.19, 8.59) |
<0.001 | 3.69 (1.98, 7.44) |
5.29 (2.74, 11.21) |
<0.001 |
*P values comparing participants with and without recent fall.
†P values comparing participants with and without slow gait.
‡Median and IQR.
UKB, UK Biobank.
The prevalence of each MRI biomarker of VBI was significantly higher among participants with slow versus normal or fast walk speed (top tertile of VV 37% and 33%; bottom tertile of brain volume 38% and 33%; top tertile of WMH volume 47% and 33%; top tertile of FA 46% vs 33%; top tertile of MD 41% vs 33%; top tertile of ICVF 40% vs 33%; top tertile of ISOVF 40% vs 33%; all p values<0.01). There were no significant interactions between pairs of MRI biomarkers and slow walk speed (online supplemental table 4).
VV (OR 1.11; 95% CI 1.02 to 1.19), brain volume (OR 1.10; 95% CI 1.02 to 1.19) and each of the DTI measures (FA OR 1.16; 95% CI 1.08 to 1.24) were significantly associated with recent fall in adjusted analyses (table 6). WMH volume (OR 1.48; 95% CI 1.24 to 1.76) and FA (OR 1.57; 95% CI 1.32 to 1.87) were significantly associated with slow walk speed. There were no significant interactions between pairs of MRI biomarkers and recent fall (online supplemental table 4).
Table 6.
Associations between recent fall and slow walk speed and MRI biomarkers of vascular brain injury in the UKB
| MRI biomarker (N=40 098) | Category | OR of recent fall* (95% CI), p value |
OR of recent fall† (95% CI), p value |
OR of slow walk speed* (95% CI), p value |
OR of slow walk speed† (95% CI), p value |
| Ventricular volume | Tertile 1 | Reference | Reference | Reference | Reference |
| Tertile 2 | 1.07 (1.00–1.15), 0.060 |
1.07 (1.00–1.15), 0.049 |
1.09 (0.92–1.28), 0.313 |
1.10 (0.93–1.29) 0.26 |
|
| Tertile 3 | 1.10 (1.02–1.19), 0.015 |
1.11 (1.02–1.19), 0.010 |
1.12 (0.94–1.33), 0.202 |
1.14 (0.96–1.36) 0.138 |
|
| Brain volume | Tertile 1 | Reference | Reference | Reference | Reference |
| Tertile 2 | 1.07 (1.00–1.15), 0.065 |
1.07 (1.00–1.15), 0.054 |
1.11 (0.94–1.30), 0.230 |
1.11 (0.94–1.31) 0.203 |
|
| Tertile 3 | 1.09 (1.01–1.19), 0.025 |
1.10 (1.02–1.19), 0.018 |
1.04 (0.87–1.24), 0.692 |
1.05 (0.88–1.26) 0.593 |
|
| WMH volume | Tertile 1 | Reference | Reference | Reference | Reference |
| Tertile 2 | 0.99 (0.93–1.07), 0.889 |
0.99 (0.93–1.07), 0.866 |
1.17 (0.98–1.39), 0.083 |
1.16 (0.98–1.38) 0.090 |
|
| Tertile 3 | 1.05 (0.97–1.13), 0.241 |
1.05 (0.97–1.13), 0.242 |
1.47 (1.24–1.76), <0.001 |
1.48 (1.24–1.76) <0.001 |
|
| FA PC1 | Tertile 1 | Reference | Reference | Reference | Reference |
| Tertile 2 | 1.03 (0.96–1.11), 0.487 |
1.03 (0.96–1.10), 0.530 |
1.31 (1.10–1.55), 0.002 |
1.30 (1.09–1.54) 0.003 |
|
| Tertile 3 | 1.17 (1.09–1.25), <0.001 |
1.16 (1.08–1.24), <0.001 |
1.58 (1.33–1.88), <0.001 |
1.57 (1.32–1.87) <0.001 |
|
| MD PC1 | Tertile 1 | Reference | Reference | Reference | Reference |
| Tertile 2 | 1.01 (0.95–1.09), 0.357 |
1.01 (0.94–1.09), 0.357 |
0.98 (0.84–1.15) 0.815 |
0.98 (0.84–1.15) 0.806 |
|
| Tertile 3 | 1.14 (1.07–1.23), <0.001 |
1.14 (1.06–1.23), <0.001 |
1.15 (0.98–1.34) 0.085 |
1.14 (0.98–1.34) 0.093 |
|
| ICVF PC1 | Tertile 1 | Reference | Reference | Reference | Reference |
| Tertile 2 | 1.03 (0.96–1.10), 0.357 |
1.02 (0.96–1.10), 0.357 |
0.98 (0.84–1.15) 0.815 |
0.98 (0.84–1.15) 0.806 |
|
| Tertile 3 | 1.14 (1.07–1.23), <0.001 | 1.14 (1.07–1.23), 0.001 |
1.15 (0.98–1.34) 0.085 |
1.14 (0.98–1.34) 0.093 |
|
| ISOVF PC1 | Tertile 1 | Reference | Reference | Reference | Reference |
| Tertile 2 | 1.09 (1.02–1.17), 0.015 |
1.09 (1.02–1.17), 0.015 |
1.24 (1.05–1.47) 0.010 |
1.39 (0.95–2.11) 0.105 |
|
| Tertile 3 | 1.13 (1.04–1.22), 0.002 |
1.13 (1.05–1.23), 0.002 |
1.35 (1.13–1.62) 0.001 |
1.36 (0.94–2.05) 0.119 |
*Adjusted for age, sex, race, hypertension, diabetes and body mass index.
†Adjusted for age, sex, race, hypertension, diabetes, body mass index, diseases of the peripheral nervous system, eye, and ear, peripheral vascular disease, diseases of the musculoskeletal system and connective tissue.
FA, fractional anisotropy; ICVF, intracellular volume fraction; IsoVF, isotropic volume fraction; MD, mean diffusivity; PC1, principal component 1; UKB, UK Biobank; WMH, white matter hyperintensity.
We evaluated the associations between the microstructural DTI markers of VBI and outcomes among participants without established macrostructural white matter disease. Among UKB participants in the lowest WMH tertile, the top tertile of the principal component of ICVF was significantly associated with recent fall (adjusted OR 1.17, p=0.015, online supplemental table 5). No other DTI markers associated with either outcome of interest.
Discussion
MRI biomarkers of VBI were common and associated with measures of mobility impairment, even after adjusting for demographics and clinical comorbidity, in stroke-free participants from two community-dwelling populations. Our results suggest that clinically unrecognised VBI confers an independent risk for mobility impairment.
Our study provides compelling evidence confirming the association between VBI and mobility impairment in two large, stroke-free cohorts. WMH burden has been most frequently studied24–30 and linked with mobility impairment. In the Cardiovascular Health Study, participants with worsening WMH lesions between serial MRI brain imaging over 5 years had a decline in gait speed.7 In the Leukoaraiosis AND DISability or LADIS study of 639 older participants, WMH severity was associated with gait speed and balance.6 Low FA was associated with gait function in 173 elderly individuals, in patients who fell among 94 individuals and with slow walk speed in 68 older adults.28 29 Our study confirms these results and extends the literature with findings of associations between additional MRI VBI biomarkers and measures of mobility impairment in two clinically stroke-free, community-based cohorts.
Our study uniquely demonstrates a link between MRI biomarkers of VBI and mobility impairment on adjustment for disorders of non-CNS organs. Mobility requires complex integration of sensory inputs from the body and its surroundings and appropriate motor responses for control of body movements.30 Balance is reliant on vision, coordination of vestibular and proprioception inputs and outputs, afferents to muscles, coordinated movement and reaction time. Reduced walk speed is associated with poor memory, executive function, processing speed and visuospatial ability.31 Disorders of non-CNS organs can disrupt these pathways required for maintaining mobility. One example of such a disorder is knee osteoarthritis. Furthermore, conditions external to the brain that impact mobility may also be associated with vascular risk factors, such as diabetic retinopathy and neuropathy. Our study uniquely isolates an independent association between VBI and mobility impairment adjusted for non-CNS disorders and vascular risk factors. Nevertheless, further study is necessary to definitively prove whether a true causal link exists between VBI and mobility impairment.
Among individuals without established white matter disease, microstructural white matter tract abnormalities were linked with recent fall. While WMH and lacunar infarcts are end-stage manifestations of ischaemic injury, DTI changes may represent microstructural damage even in ‘normal-appearing white matter’. MRI markers of early microstructural injury that correlate with mobility impairment indicate a potential window of opportunity in the absence of overt white matter disease markers. Since the compromise of the white matter susceptible to ischaemia may be mediated by cardiometabolic diseases,32 removal of a vascular insult may prevent the progression of microstructural white matter pathology and facilitate mobility preservation. Although WMH was not associated with recent fall in the UKB, mean volume of WMH was relatively low (5.2±6.8 mL), likely reflecting the younger UKB cohort. The older ARIC cohort had a larger WMH mean volume (17.3±17.0 mL), which was associated with imbalance and walking speed. Because FA is a biomarker of future WMH development, it follows that the positive association with FA in the UKB reflects a similar finding to that of the ARIC cohort, although at an earlier and potentially intervenable stage.
The association between mobility impairment and VBI may be relevant to clinical trial design. Trial endpoints representing brain health may be improved by inclusion of important clinical events with shared causes relevant to an intervention.33 Clinical stroke is included in the standard composite endpoint of major adverse cardiovascular events. The link between cognitive impairment and VBI is now also well established, with almost half of patients with Alzheimer’s disease noted to have concomitant cerebrovascular disease.34 About 5% of dementia is solely associated with cerebrovascular disease secondary to clinical stroke or WMH.35 36 Consequently, incident dementia is increasingly being incorporated as an outcome in cardiovascular trials.37 Mobility is critical for maintaining functional independence but is under-represented in trials.38 39 Mobility impairment can lead to decreased movement due to fear of falling, resulting in a sedentary lifestyle and a worsening vascular health profile. Our results demonstrating high prevalence of MRI biomarkers of VBI among individuals with mobility impairment and their relatedness support considering the inclusion of mobility impairment in composite outcomes that reflect brain health.
Our study had limitations. It was subject to constraints inherent in a cross-sectional design. There were differences in availability and ascertainment of mobility impairment outcomes and MRI biomarkers of VBI in the two cohorts. There may be cultural and recall biases in self-reporting walk speed and falls. The two cohorts were dissimilar in age. Nevertheless, the relationships between MRI biomarkers of VBI and mobility impairment measures held, likely reflecting the robustness of the association. The cohorts were assembled on the basis of self-report of no history of stroke. If individuals with prior stroke were included in the sample erroneously due to recall bias, this may result in overestimation of the prevalence of MRI biomarkers of VBI in the target population of individuals free of a clinical stroke, potentially resulting in risk estimates that are biased away from the null.
Conclusion
Clinically unrecognised cerebrovascular disease detected on brain MRI is independently associated with mobility impairment. Along with clinical stroke and cognitive impairment, incorporation of mobility impairment in composite, patient-centred outcomes related to brain health may capture a broader spectrum of clinically meaningful disability due to cerebrovascular injury.
Footnotes
Twitter: @RichaSharmaMD
Contributors: RS, WNK and AdH conceived and designed the study. CR, RS and AdH performed statistical analyses. SP, RF, HK, GJF, KNS and WNK. contributed to the writing of the manuscript and provided critical feedback. WNK contributed to oversight of the study. RS is the guarantor of this study.
Funding: RS reports National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS) funding (K23NS121634). AdH reports NIH/NINDS funding (K23NS105924). GJF is supported by the NIH (K76AG059992, R03NS112859 and P30AG021342), the American Heart Association (18IDDG34280056) and the Neurocritical Care Society Research Fellowship. KNS by NIH-NINDS U01NS106513, R01NS11072, R01NR018335, R03NS112859, U24NS107215, U24NS107136 and American Heart Association 17CSA33550004.
Competing interests: RS reports grants from NIH Clinical Centre. AdH has received investigator-initiated clinical research funding from Regeneron, AMGEN and AMAG pharmaceuticals, and has equity in TitinKM and Certus. HK reports receiving personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, F-Prime, Tesseract/4Catalyst, Martin/Baughman Law Firm, Arnold and Porter Law Firm, and Siegfried and Jensen Law Firm; being a cofounder of HugoHealth, a personal health information platform; being a cofounder of Refractor Health, an Enterprise Health Care artificial intelligence-augmented data management company; having contracts with the Centers for Medicare & Medicaid Services Association through Yale New Haven Hospital, to develop and maintain performance measures that are publicly reported; and receiving grants from Johnson & Johnson outside the submitted work. GJF reports grants from the Neurocritical Care Society Research Fellowship. KNS reports compensation from CSL Behring for consultant services; compensation from Sense for data and safety monitoring services; compensation from Cerevasc for consultant services; compensation from Rhaeos for consultant services; compensation from Certus for consultant services; service as President for Advanced Innovation in Medicine; a patent pending for Stroke wearables licensed to Alva Health. The other authors report no conflicts.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Data availability statement
Data are available in a public, open access repository. What the data are—Atherosclerosis Risk in Communities Study (ARIC). Apply for access at: https://biolincc.nhlbi.nih.gov/login/?next=/requests/type/aric. The repository where they are held—NHLBI Biologic Specimen and Data Repository (BioLINCC). Any conditions of reuse (eg, licence, embargo, copyright)—BioLINCC RegistrationWhat the data are—UK Biobank Database. Apply for access at: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. The repository where they are held—UK Biobank DatabaseAnd any conditions of reuse (eg, licence, embargo, copyright)—UK Biobank Registration.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and both ARIC and UK Biobank are deidentified, publicly available datasets. ARIC was approved by institutional review boards at each participating site: University of North Carolina at Chapel Hill IRB(96-0467), Wake Forest University IRB(#BG86-0155), Johns Hopkins University IRB (H.34.99.07.02.A1), University of Minnesota IRB and University of Mississippi Medical Center IRB (#1985-0122). UKB received ethical approval from the North West Multi-centre Research Ethics Committee (reference number 16/NW/0274). Participants gave informed consent to participate in the study before taking part.
References
- 1.Cummings SR, Studenski S, Ferrucci L. A diagnosis of dismobility--giving mobility clinical visibility: a mobility working group recommendation. JAMA 2014;311:2061–2. 10.1001/jama.2014.3033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Lin HW, Bhattacharyya N. Balance disorders in the elderly: epidemiology and functional impact. Laryngoscope 2012;122:1858–61. 10.1002/lary.23376 [DOI] [PubMed] [Google Scholar]
- 3.Castell M-V, Sánchez M, Julián R, et al. Frailty prevalence and slow walking speed in persons age 65 and older: implications for primary care. BMC Fam Pract 2013;14:86. 10.1186/1471-2296-14-86 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Web-based injury Statistics query and reporting system (WISQARS). n.d. Available: https://www.cdc.gov/injury/wisqars/index.html
- 5.The SPRINT MIND Investigators for the SPRINT Research Group, Williamson JD, Pajewski NM, et al. Effect of intensive vs standard blood pressure control on probable dementia: a randomized clinical trial. JAMA 2019;321:553. 10.1001/jama.2018.21442 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Baezner H, Blahak C, Poggesi A, et al. Association of gait and balance disorders with age-related white matter changes: the LADIS study. Neurology 2008;70:935–42. 10.1212/01.wnl.0000305959.46197.e6 [DOI] [PubMed] [Google Scholar]
- 7.Rosano C, Brach J, Longstreth WT, et al. Quantitative measures of gait characteristics indicate prevalence of underlying subclinical structural brain abnormalities in high-functioning older adults. Neuroepidemiology 2006;26:52–60. 10.1159/000089240 [DOI] [PubMed] [Google Scholar]
- 8.The ARIC investigators . The atherosclerosis risk in communities (ARIC) study: design and objectives. The ARIC investigators. Am J Epidemiol 1989;129:687–702. 10.1093/oxfordjournals.aje.a115184 [DOI] [PubMed] [Google Scholar]
- 9.Wright JD, Folsom AR, Corseh J, et al. n.d. Data from: Atherosclerosis risk in communities study. Available: http://biolincc.nhlbi.nih.gov/studies/aric/
- 10.Knopman DS, Griswold ME, Lirette ST, et al. Vascular imaging abnormalities and cognition: mediation by cortical volume in nondemented individuals: aatherosclerosis risk in communities-neurocognitive study. Stroke 2015;46:433–40. 10.1161/STROKEAHA.114.007847 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12:e1001779. 10.1371/journal.pmed.1001779 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.The UK Biobank board of trustees [UK Biobank]. n.d. Available: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access
- 13.Miller KL, Alfaro-Almagro F, Bangerter NK, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 2016;19:1523–36. 10.1038/nn.4393 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Martinez-Amezcua P, Powell D, Kuo P-L, et al. Association of age-related hearing impairment with physical functioning among community-dwelling older adults in the US. JAMA Netw Open 2021;4:e2113742. 10.1001/jamanetworkopen.2021.13742 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Guralnik JM, Ferrucci L, Simonsick EM, et al. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med 1995;332:556–61. 10.1056/NEJM199503023320902 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Geerlings MI, Appelman APA, Vincken KL, et al. Brain volumes and cerebrovascular lesions on MRI in patients with atherosclerotic disease. The SMART-MR study. Atherosclerosis 2010;210:130–6. 10.1016/j.atherosclerosis.2009.10.039 [DOI] [PubMed] [Google Scholar]
- 17.Enzinger C, Fazekas F, Matthews PM, et al. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. Neurology 2005;64:1704–11. 10.1212/01.WNL.0000161871.83614.BB [DOI] [PubMed] [Google Scholar]
- 18.Jochemsen HM, Muller M, Visseren FL, et al. Blood pressure and progression of brain atrophy: the SMART-MR study. JAMA Neurol 2013;70:1046–53. 10.1001/jamaneurol.2013.217 [DOI] [PubMed] [Google Scholar]
- 19.Alfaro-Almagro F, Jenkinson M, Bangerter NK, et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK biobank. Neuroimage 2018;166:400–24. 10.1016/j.neuroimage.2017.10.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Alves GS, Sudo FK, Alves CE de O, et al. Diffusion tensor imaging studies in vascular disease: a review of the literature. Dement Neuropsychol 2012;6:158–63. 10.1590/S1980-57642012DN06030008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Suzuki H, Gao H, Bai W, et al. Abnormal brain white matter microstructure is associated with both pre-hypertension and hypertension. PLoS One 2017;12:e0187600. 10.1371/journal.pone.0187600 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zhang H, Schneider T, Wheeler-Kingshott CA, et al. NODDI: practical in vivo Neurite orientation dispersion and density imaging of the human brain. NeuroImage 2012;61:1000–16. 10.1016/j.neuroimage.2012.03.072 [DOI] [PubMed] [Google Scholar]
- 23.Behrens TEJ, Woolrich MW, Jenkinson M, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003;50:1077–88. 10.1002/mrm.10609 [DOI] [PubMed] [Google Scholar]
- 24.Jbabdi S, Sotiropoulos SN, Savio AM, et al. Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems. Magn Reson Med 2012;68:1846–55. 10.1002/mrm.24204 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Wakana S, Jiang H, Nagae-Poetscher LM, et al. Fiber tract-based atlas of human white matter anatomy. Radiology 2004;230:77–87. 10.1148/radiol.2301021640 [DOI] [PubMed] [Google Scholar]
- 26.Rutten-Jacobs LCA, Tozer DJ, Duering M, et al. Genetic study of white matter integrity in UK Biobank (N=8448) and the overlap with stroke, depression, and dementia. Stroke 2018;49:1340–7. 10.1161/STROKEAHA.118.020811 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Kuipers S, Biessels GJ, Greving JP, et al. Sex and cardiovascular function in relation to vascular brain injury in patients with cognitive complaints. J Alzheimers Dis 2021;84:261–71. 10.3233/JAD-210360 [DOI] [PubMed] [Google Scholar]
- 28.Bhadelia RA, Price LL, Tedesco KL, et al. Diffusion tensor imaging, white matter lesions, the corpus callosum, and gait in the elderly. Stroke 2009;40:3816–20. 10.1161/STROKEAHA.109.564765 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Poole VN, Wooten T, Iloputaife I, et al. Compromised prefrontal structure and function are associated with slower walking in older adults. Neuroimage Clin 2018;20:620–6. 10.1016/j.nicl.2018.08.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Sturnieks DL, St George R, Lord SR. Balance disorders in the elderly. Neurophysiol Clin 2008;38:467–78. 10.1016/j.neucli.2008.09.001 [DOI] [PubMed] [Google Scholar]
- 31.Callisaya ML, Blizzard L, McGinley JL, et al. Risk of falls in older people during fast-walking--the TASCOG study. Gait Posture 2012;36:510–5. 10.1016/j.gaitpost.2012.05.003 [DOI] [PubMed] [Google Scholar]
- 32.Tamura Y, Shimoji K, Ishikawa J, et al. Subclinical atherosclerosis, vascular risk factors, and white matter alterations in diffusion tensor imaging findings of older adults with cardiometabolic diseases. Front Aging Neurosci 2021;13:712385. 10.3389/fnagi.2021.712385 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Stolker JM, Spertus JA, Cohen DJ, et al. Rethinking composite end points in clinical trials: insights from patients and trialists. Circulation 2014;130:1254–61. 10.1161/CIRCULATIONAHA.113.006588 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Arvanitakis Z, Shah RC, Bennett DA. Diagnosis and management of dementia: review. JAMA 2019;322:1589–99. 10.1001/jama.2019.4782 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Koton S, Pike JR, Johansen M, et al. Association of ischemic stroke incidence, severity, and recurrence with dementia in the atherosclerosis risk in communities cohort study. JAMA Neurol 2022;79:271–80. 10.1001/jamaneurol.2021.5080 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Smith EE, Egorova S, Blacker D, et al. Magnetic resonance imaging white matter hyperintensities and brain volume in the prediction of mild cognitive impairment and dementia. Arch Neurol 2008;65:94–100. 10.1001/archneurol.2007.23 [DOI] [PubMed] [Google Scholar]
- 37.McNeil JJ, Woods RL, Nelson MR, et al. Effect of aspirin on disability-free survival in the healthy elderly. N Engl J Med 2018;379:1499–508. 10.1056/NEJMoa1800722 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Krishnan S, Pappadis MR, Weller SC, et al. Patient-centered mobility outcome preferences according to individuals with stroke and caregivers: a qualitative analysis. Disabil Rehabil 2018;40:1401–9. 10.1080/09638288.2017.1297855 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Poranen-Clark T, von Bonsdorff MB, Rantakokko M, et al. The temporal association between executive function and life-space mobility in old age. J Gerontol A Biol Sci Med Sci 2018;73:835–9. 10.1093/gerona/glx217 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
bmjno-2023-000501supp001.pdf (52.6KB, pdf)
Data Availability Statement
Data are available in a public, open access repository. What the data are—Atherosclerosis Risk in Communities Study (ARIC). Apply for access at: https://biolincc.nhlbi.nih.gov/login/?next=/requests/type/aric. The repository where they are held—NHLBI Biologic Specimen and Data Repository (BioLINCC). Any conditions of reuse (eg, licence, embargo, copyright)—BioLINCC RegistrationWhat the data are—UK Biobank Database. Apply for access at: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. The repository where they are held—UK Biobank DatabaseAnd any conditions of reuse (eg, licence, embargo, copyright)—UK Biobank Registration.