Obesity, Insulin Resistance, and Incident Small Vessel Disease on Magnetic Resonance Imaging
Atherosclerosis Risk in Communities Study
Background and Purpose—The term metabolic syndrome describes the clustering of risk factors found in many individuals with obesity. Because of their pathophysiology, we hypothesized that 2 features of metabolic syndrome, central obesity and insulin resistance (IR), would be associated with cerebrovascular changes on magnetic resonance imaging, and specifically with incident lacunar disease and not white matter hyperintensity (WMH) progression.
Methods—Risk factors were defined at study baseline in 934 participants in the Atherosclerosis Risk in Communities (ARIC) study, who completed 2 brain magnetic resonance imagings ≈10 years apart. WMH progression and incident lacunes between the 2 magnetic resonance imagings were determined. An IR score for each participant was created using principal component analysis of 11 risk factors, including (among others): insulin, homeostatic model assessment-IR, body mass index, and waist circumference. Metabolic syndrome (presence/absence), using standard clinical definitions, and IR score at the first magnetic resonance imaging, were independent variables, evaluated in multivariate logistic regression to determine odds of WMH progression (Q5 versus Q1–Q4) and incident lacunes.
Results—Metabolic syndrome (adjusted odds ratio, 1.98; 95% confidence interval, 1.28–3.05) and IR score (adjusted odds ratio per 1-SD increase, 1.33; 95% confidence interval, 1.05–1.68) were associated with incident lacunes but not with WMH progression. Insulin, homeostatic model assessment-IR, and body mass index were not associated with incident lacunes or WMH progression in separate models.
Conclusions—The IR score and central obesity are associated with incident lacunar disease but not WMH progression in individuals. Central obesity and IR may be important risk factors to target to prevent lacunar disease.
Brain small vessel disease, including white matter hyperintensities (WMHs) and lacunes, often leads to cognitive impairment and dementia.1 Because dementia and obesity are increasing in prevalence, it is important to understand their association. In obese patients, fat metabolism is dependent on insulin, which suppresses lipolysis in fat cells (adipocytes). In insulin resistance (IR), adipocytes fail to respond to the actions of insulin, resulting in lipolysis, which explains why patients with IR often have dyslipidemia.2 Although IR is associated with diabetes mellitus, not all people with IR develop diabetes mellitus. Metabolic syndrome (MetS) is a commonly used term used to describe the clustering of central obesity, hypertension, hyperglycemia, and dyslipidemia and attempts to capture associations with cardiovascular risk. This may be a hypothetical construct less relevant than its individual components, in part, because the individual risk factors may adequately explain disease end points.3,4
The mechanisms of small vessel disease may provide insight into risk factor associations. WMHs are thought to arise from chronic ischemia and blood–brain barrier breakdown,5–7 and abnormal venous pathology, known as venous collagenosis, may also be present in these lesions.6 Two distinct pathologies are described in lacunes8,9: (1) plaque-like accumulations with the vessel that may represent microatheromas and (2) fibrinoid necrosis of the blood vessel wall (lipohyalinosis). A previous Atherosclerosis Risk in Communities (ARIC) study determined that smaller lacunes (<7 mm) and larger lacunes (8–20 mm) have different risk factor profiles, possibly because of unique pathological mechanisms.10 We hypothesize that WMH are more similar to smaller lacune subtypes. Given the association with IR and dyslipidemia, we hypothesized that midlife central obesity and IR would be associated with incident lacunes, especially larger lesions (microatheromatous disease), but not with WMH progression.
The ARIC study was conducted at 4 sites, with 15 792 participants aged 45 to 64 years on initial recruitment in 1987 to 1989.11 Five main study visits occurred: visit 1 to 4 (each 3 years apart) and visit 5 in 2011 to 2013. At visit 3, participants from Forsyth County, NC and Jackson, MS (black subjects only) were invited for the first brain magnetic resonance imaging (MRI; 1993–1995).12 After 10 years, these participants were invited to participate in the Brain MRI ancillary study in 2004 to 2006.12 Of the 1134 participants who underwent both scans, 999 had interpretable MRIs at both visits. Other exclusions were as follows: 9 with a previous history of stroke, 3 for nonphysiological values of MRI measurements, and 53 missing MetS variables. The analytic population included 934 participants. All participants signed informed consent, and each institution’s institutional review board approved the study.
Definition of MetS and IR Score
MetS was defined at visit 1 by the presence of at least 3 of the following criteria: (1) waist circumference ≥102 cm in men or ≥ 88 cm in women, (2) high-density lipoprotein (HDL) <40 mg/dL in men and <50 mg/dL in women or on drug treatment, (3) elevated blood pressure ≥130 mm Hg systolic or ≥85 mm Hg diastolic or on drug treatment, (4) elevated triglycerides ≥150 mg/dL or on drug treatment, and (5) elevated fasting glucose ≥100 mg/dL or on treatment for diabetes mellitus.13
An IR score was defined to examine the clustering of risk factors using principal components analysis. This ad hoc construct was created to better capture the joint effects of central obesity and IR, and is specific to this population studied. Eleven metabolic factors were included: log-transformed values for insulin, homeostatic model assessment-IR (HOMA-IR) and triglycerides (to normalize the distributions), waist circumference, body mass index (BMI), waist:hip ratio, systolic blood pressure, diastolic blood pressure, fasting glucose, HDL, and low-density lipoprotein. Principal components analysis was computed using orthogonal rotation to look for linear combinations between the exposures to establish a new set of fewer variables comprised uncorrelated components. Components retained for the analysis had an Eigen value >2.0, leaving 1 component explaining 36.3% of the sample variance. This component was defined by correlated metabolic factors with a loading >0.30 that included: BMI, waist:hip ratio, waist circumference, log insulin, and log HOMA-IR (Table I in the online-only Data Supplement). Each participant was assigned an IR score based on these factor loadings.
Waist circumference was measured in centimeters at the level of the umbilicus. BMI was calculated in kg/m2. Fasting blood samples were collected and frozen at −70°C for storage and methods for serum triglycerides and HDL were described previously.14–16 Serum glucose was measured with the hexokinase method. Insulin was measured by radioimmunoassay (125-I Insulin 100 test kit; Cambridge Medical Diagnostics, Billerica, MA). The HOMA-IR was calculated using the formula: (glucose mg/dL×insulin μU/mL)/405 with glucose and insulin measured from visit 1.17
Magnetic Resonance Imaging
Eligibility and screening protocols for the MRI visits are described in detail elsewhere.18,19 Scans were completed on 1.5 Tesla machines, and spin-echo, spin-density/T2* weighted, and T1-weighted images were collected in 5-mm axial slices. A lacune was defined as 3 to 20 mm in size. Locations included were the basal ganglia, thalamus, brain stem, internal capsule, deep cerebellum, and subcortical white matter. Only nonhemorrhagic lesions hyperintense on proton density and T2-weighted images and hypointense on T1-weighted images were selected. The number of lacunes at the visit 3 MRI was subtracted from the number of Brain MRI lacunes and if this value was ≥1, the individual had an incident lacune. Lacunes were further subdivided by size into 2 groups: 3 to 7 mm and >7 to 20 mm, based on maximum anterior–posterior or right–left diameter.
WMH progression was determined with 2 methods. Using the Cardiovascular Health Study rating scale (0–9),20,21 periventricular and subcortical WMH were graded from visual comparison with 8 template images for both the MRI visits. The change in grade between visits was calculated. At the Brain MRI visit, an automated algorithm was used to segment WMH volume on the axial fluid–attenuated inversion recovery images, with manual editing to exclude infarcts and other lesions.22 Volumetric measurements were standardized to an intracranial volume of 1500 cm3. The actual data (visual grades and measured volumes) from the brain MRI visit were used to impute the estimated volumes at visit 3, WMH progression between the 2 MRIs was calculated using predicted volumes for visit 3 subtracted from measured volumes at the Brain MRI.23
Descriptive statistics were evaluated stratified by MetS status, using t tests for continuous variables and χ2 tests for categorical variables. Univariate and multivariate logistic regression was used to assess the odds of incident lacunes with each 1-SD increase in risk factors (insulin, HOMA-IR, BMI, and triglycerides). MetS (presence/absence) and each 1-SD increase in the IR score were used as composite measures of these risk factors. Outcomes were WMH progression (top quintile [Q5] versus Q1–Q4) and incident lacunes 3 to 20 mm, which were further subdivided by size. Because of the possibility of nondifferential misclassification of volumetric WMH progression using the prediction equation, and therefore under estimation of effect size, the analysis was also conducted using change in visual WMH grade.
Models were built sequentially, with demographic values added first, and then history of coronary artery disease and hypertension. Hypertension was included in all models, except those in which it was a part of the variable definition (eg, MetS). Other covariates included age (years), sex, race (black and white), education, history of coronary artery disease, history of alcohol use, and history of tobacco use. Interaction terms were tested between MetS and sex, age and race, each, but were not included in the final model because of the lack of statistical significance. Because of the overlap between diabetes mellitus and IR, a sensitivity analysis was performed excluding all individuals with diabetes mellitus (n=73). A 2-sided P value of <0.05 was considered significant for all analyses.
The mean change in WMH was 5 cm3 (SD, 8.5) and the range of incident lacunes was 0 to 5, with 33% of those with a lacune having >1. At baseline (age, 45–64 years), participants with MetS did not differ significantly from those without MetS by age, race, or sex (Table 1).
MetS and Its Components, Incident Infarcts, and WMH Progression
Lipid markers were associated with all lacunes (triglycerides and inverse association with HDL) but not WMHs (Table II in the online-only Data Supplement; Table 2) in unadjusted and adjusted models. Systolic blood pressure was associated with WMH and incident lacunes in unadjusted models (Tables II and III in the online-only Data Supplement), but did not reach statistical significance for lacunes in adjusted models (Tables 2 and 3).
Mean WMH progression did not differ between those with and without MetS (Table 1). When using change in WMH grade instead of quantitative WMH progression, associations with MetS components remained null (Table IV in the online-only Data Supplement; Table 2). Persons with MetS had more incident lacunes than those without MetS (14.8% versus 8.5%; Table 1). This increase in lacunes in persons with MetS was predominantly because of an increase in infarcts >7 to 20 mm (8.6% versus 3.3%; Table 1). These associations remained significant after adjustments (Tables 2 and 3). A sensitivity analysis excluding diabetics revealed similar but attenuated associations, even though diabetes mellitus (as defined by fasting glucose ≥100) is part of the MetS definition (Tables V and VI in the online-only Data Supplement).
Central Obesity, Incident Infarcts, and WMH Progression
Increasing values of insulin, HOMA-IR, and BMI were not associated with WMH progression or all incident lacunes (Table 2). Each 1-SD increase in HOMA-IR and BMI was associated with increased odds of incident large lacunes in unadjusted models (Table III in the online-only Data Supplement), but this result was attenuated with adjustment (Table 3). Central obesity as is measured by waist circumference or waist:hip ratio was associated with incident lacunes, with a larger effect size for larger lacunes in unadjusted (Tables II and III in the online-only Data Supplement) and adjusted models (Tables 2 and 3).
A higher IR score represents increased values of each of the weighted measures of central obesity and IR in a participant. Unlike MetS, hypertension and hyperglycemia contribute to a small part of the weighting, with higher weights given to waist circumference, waist:hip ratio, HOMA-IR, insulin, and BMI (Table I in the online-only Data Supplement). Each 1-SD increase in the score was associated with increased odds of all incident lacunes, (adjusted odds ratio, 1.33; 95% confidence interval, 1.05–1.68; Table 2). Participants with a higher IR score had increased odds of having both larger and smaller lacunes, with increased effect size for larger lacunes (Figure; Table 3).
In this study, we found unique associations with metabolic factors and brain small vessel disease in a prospective community-based cohort. Serum insulin, HOMA-IR, and BMI were not independently associated with incident small vessel disease. An IR score, which may better capture their joint effects, and MetS were associated with all lacunes but not with WMH progression. Although the magnitude of the association for MetS was larger than that for the IR score, we hypothesized that because MetS includes hypertension, a well-known risk factor for lacunar disease,8 its association with lacune progression may be primarily driven by hypertension. Our results instead demonstrate that this difference may be partially driven by HDL or other unmeasured covariates. These results are compelling because they demonstrate different risk factor profiles for small vessel disease, which may have important implications for dementia and cognition in later life.1
MetS was previously shown in other cohorts to be associated with WMH cross-sectionally,24,25 and with changes in cerebral white matter microstructure, as measured by diffusion tensor imaging.26,27 These earlier studies did not, however, separate out the effect of hypertension between groups. In our study, we examined WMH progression, which is a more robust measurement of microvascular disease than single measurements of WMH, as it likely partially accounts for potential confounding by social and demographic factors (which would affect WMH but not necessarily its progression). Our results suggest that WMH progression is unrelated to central obesity. Whether the difference between our study and previous work is related to unmeasured confounders is unknown and suggests that this result requires confirmation in other populations.
As lacunar stroke has different histopathology than WMH, it is not surprising that there are differential risk factor associations with these disease subtypes. The MetS components hypertension, diabetes mellitus, triglycerides, and HDL are associated with incident lacunes in other work.28 A previous ARIC study reported that lacunes ≤7 mm were associated with diabetes mellitus and hemoglobin A1C, and larger lacunes were associated with elevated low-density lipoprotein. This study brought to the forefront that descriptions of 2 lacune subtypes, by Fisher et al,9 seem to withstand the epidemiological evidence. This study did not focus on hyperglycemia, and we in fact demonstrated that in patients without diabetes mellitus, IR trended to increase odds of lacunes. It may be that patients who have abnormally glycosylated end products, as are present in diabetes mellitus, may have more lipohyalinosis, whereas abnormal central obesity, resulting in IR, predisposes to lacunes via a distinct mechanism. This theory requires further investigation. Also requiring investigation is whether targeting the metabolic profile of obesity and IR might be particularly important to prevent incident lacunes (especially the larger subtype).
A limitation of the analysis is that we were unable to approximate how changes in metabolic factors through life affect brain small vessel disease, in that those with earlier exposures might have more disease than those who developed risk factors later. Another limitation is potential selection bias affecting subjects who received 2 MRIs, who may have been healthier than those who did not return for a second MRI. In addition, we did not obtain direct volumetric measurements from the visit 3 MRI, however, we also used WMH grade to compare to imputed WMH progression and found similar results. The relative health and age of the population (often before the onset of dementia) limit analysis of the extremes of disease.
In addition, although the recent STandards for ReportIng Vascular changes on nEuroimaging (STRIVE) consortium recommended a definition of lacunes as 3 to 15 mm in subcortical locations; we used 3 to 20 mm as a size cutoff based on previous work in ARIC to allow comparability.29 Although neurologists generally attribute stroke mechanism to stroke location, there is often overlap between mechanisms at different locations (hypoperfusion, embolism, and lipohyalinosis). As this is an observational study, there may be unmeasured confounders, such as socioeconomic indicators or medication adherence that contribute to the observed associations. In addition, we only evaluated whether each participant had ≥1 of a given type of infarct, so there is no dose–response effect, according to the number of lacunes. This could lead to less precision in risk factor differentiation. Finally, some participants had >1 type of incident infarct, which could lead to misclassification bias.
The strength of our study is the large prospective design and rigorously measured confounders in participants with good rates of follow-up. We think that incident imaging findings are more robust than cross-sectional data. Our exposures were measured at baseline, with outcomes measured ≤17 years later because midlife risk factors have been shown to have greater associations with later-life disease outcomes.30
Our results lend further support that brain small vessel disease is not homogeneous, and that unique risk factors profiles exist for WMH and lacunar disease. The IR score is associated with silent lacunes, but not WMH, which may be a result of differences in pathology. This composite score focusing on central obesity and IR may better capture the effects of adiposity, as compared with BMI, which did not have the same associations. Future studies are warranted to evaluate whether interventions to treat central adiposity reduce silent MRI markers of brain disease.
We thank the staff and participants of the Atherosclerosis Risk in Communities study for their important contributions.
Sources of Funding
The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Dr Schneider was supported by National Institutes of Health/National Heart, Lung, and Blood Institute training grant T32HL007024.
Dr Jack serves as a consultant for Eli Lilly and receives funding from the National Institutes of Health (NIH) and the Alexander Family Alzheimer Disease Research Professorship of the Mayo Clinic. Dr Knopman serves as Deputy Editor for Neurology and serves on a Data Safety Monitoring Board for Lundbeck Pharmaceuticals and for the Dominantly Inherited Alzheimer Network (DIAN) study; he is an investigator in clinical trials sponsored by TauRX Pharmaceuticals, Lilly Pharmaceuticals, and the Alzheimer Disease Cooperative Study; and he receives research support from the NIH.
Presented in part at the American Neurological Association meeting, New Orleans, LA, October 13–16, 2013.
The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.115.010060/-/DC1.
- Received May 13, 2015.
- Revision received August 5, 2015.
- Accepted August 26, 2015.
- © 2015 American Heart Association, Inc.
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