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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Objective

To examine relationships of obesity and physical inactivity to cognitive impairment in women with systemic lupus erythematosus (SLE).

Methods

Body composition was measured with dual x-ray absorptiometry (DXA) for 138 women with SLE. Obesity was defined by total percent body fat. Physical activity was ascertained with the self-reported International Physical Activity Questionnaire; inactivity was defined as expenditure of <600 metabolic equivalent minutes/week. Cognitive function was assessed with a 12-index neuropsychological battery. Impairment was defined as age-adjusted Z scores ≤1.5 SDs below the mean on 1 of 3 of tests completed. Scores were obtained for the total battery and for memory and executive function components. Multivariate analyses examined the relationship of obesity and physical activity, individually and combined, to cognitive impairment, controlling for education, race/ethnicity, disease activity, glucocorticoid use, and depression.

Results

Fifteen percent of subjects were cognitively impaired, 28% were physically inactive, and 50% were obese. Five percent of active women were impaired on the executive function battery compared to 23% of those who were inactive (P = 0.003). Obese women were more likely to be impaired on the total battery (6% versus 23%; P = 0.007) and on the executive function portion (2% versus 19%) than nonobese women. In multivariate analysis, both inactivity and DXA-defined obesity were significantly associated with impairment in executive function (inactivity: odds ratio [OR] 9.4, 95% confidence interval [95% CI] 1.7–52.8; obesity: OR 14.8, 95% CI 1.4–151.0).

Conclusion

Both obesity and inactivity were significantly and independently associated with impairment in cognitive function. If longitudinal studies show that physical inactivity and obesity are precursors to cognitive impairment, these may become important targets for intervention.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Cognitive dysfunction is common in systemic lupus erythematosus (SLE), with prevalence rates ranging from 20–80%, largely depending on methodologic and diagnostic practices (1–5). The nature of the cognitive deficits common in SLE includes difficulties in the domains of verbal and visual learning and memory, attention, concentration, visual–spatial functioning, and executive functioning (3, 6–8), yet the mechanisms leading to cognitive impairment are not well understood (9).

In the general population, there is an increasing appreciation of the roles of physical inactivity and obesity as important determinants of cognitive decline. A number of large-scale studies have observed relationships among low levels of physical activity and lower kilocalorie expenditures as predictors of cognitive dysfunction and longitudinal decline in cognitive function (10–12). Similarly, relationships have been observed between metrics of obesity and accelerated cognitive decline (13, 14). Further, there is increasing evidence that interventions targeting improved cardiorespiratory fitness and decreasing adiposity may buffer against cognitive decline (15).

While rates of obesity and physical inactivity appear to be elevated in SLE (16–23), neither of these factors has been examined in relation to cognitive impairment in SLE. Therefore, the objective of this analysis was to examine the relationship of both obesity and physical activity to cognitive impairment in women with SLE.

Significance & Innovations

  • Cognitive impairment is present in 20–80% of individuals with systemic lupus erythematosus (SLE).

  • Obesity and physical inactivity have been linked to cognitive impairment in aging populations.

  • Obesity and physical inactivity each appear to be present in a significant portion of individuals with SLE.

  • Both obesity and physical inactivity were independently associated with cognitive impairment among this sample of women and may offer targets for intervention.

SUBJECTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Subjects.

The sample for the present study was drawn from participants in the University of California, San Francisco (UCSF) Lupus Outcomes Study (LOS). Participants in the LOS had formerly participated in a study of genetic risk factors for SLE outcomes (24, 25); were recruited from both clinical and community-based sources, including UCSF-affiliated clinics (22%), non-UCSF rheumatology offices (11%), lupus support groups and conferences (26%), and newsletters, web sites, and other forms of publicity (41%); and participate in annual structured telephone interviews. SLE diagnoses using the American College of Rheumatology (ACR) criteria (26) were verified by medical record review. Additional details regarding the LOS are reported by Yelin et al (27). LOS participants who lived in the greater San Francisco Bay area were recruited for an in-person assessment in the UCSF Clinical and Translational Science Institute's Clinical Research Center (CRC) that included measurement of body composition and cognitive function. Exclusion criteria were non–English speaking, age <18 years, current daily oral prednisone dose of 50 mg or greater, current pregnancy, uncorrected vision problems that would interfere with reading ability, and joint replacement within 1 year.

Three hundred twenty-five individuals appeared to be eligible for the CRC study and were asked to participate during one of their annual telephone interviews; 74 (22.8%) were ineligible (35 were actually outside the recruitment area, 25 were too ill, 9 had had recent surgery, 2 were pregnant, 2 had poor English skills, and 1 had severe cognitive problems and was unable to complete the telephone interview). Of the 251 eligible individuals, 84 (33.5%) declined participation. The most common reasons for declining were primarily related to transportation (n = 12) and scheduling difficulties (n = 39). One hundred sixty-three individuals completed study visits. Fifteen participants were excluded from the analysis because they did not complete either the cognitive testing or the body composition assessment. Of the remaining 148, 138 were women and were included in these analyses. (The 10 men were excluded because of known sex differences in body composition, and there were too few men for separate analysis.) Sociodemographic and health-related characteristics of the study sample are shown in Table 1. This study was approved by the UCSF Committee on Human Research.

Table 1. Sample characteristics (n = 138)*
 ValueCognitive function on total batteryP
Not impaired (n = 118 [85.5%])Impaired (n = 20 [14.5%])
  • *

    Values are the percentage (number) unless otherwise indicated. SLE = systemic lupus erythematosus; SLAQ = Systemic Lupus Activity Questionnaire.

  • Based on the total battery.

  • From t-tests/chi-square analyses comparing impaired vs. not impaired.

  • §

    Low education: fewer than 12 years of education.

  • Possible score range 0–44; higher scores reflect greater disease activity (49, 50).

Sociodemographic    
 Age, mean ± SD years47.8 ± 12.547.7 ± 12.548.6 ± 13.00.76
 White race65.9 (91)71.2 (84)35.0 (7)0.004
 Low education§13.0 (18)11.0 (13)25.0 (5)0.14
Health related    
 Duration of SLE, mean ± SD years15.6 ± 9.115.5 ± 8.715.0 ± 11.40.86
 SLAQ score, mean ± SD12.6 ± 7.512.1 ± 7.215.4 ± 8.60.07
 Current glucocorticoid use, mg/day    
  None62.3 (86)61.9 (73)65.0 (13)0.47
  1–4.55.1 (7)4.2 (5)10.0 (2) 
  5–920.3 (28)22.0 (26)10.0 (2) 
  ≥1012.3 (17)11.9 (14)15.0 (3) 
 Major depressive disorder19.7 (27)18.6 (22)25.0 (5)0.53
 Framingham Risk Score, mean ± SD7.8 ± 5.66.8 ± 6.68.6 ± 8.10.27
 History of stroke6.5 (9)5.1 (6)15.0 (3)0.53
 History of other neurologic disorder52.2 (72)51.7 (61)55.0 (11)0.81

Measures.

Cognitive function.

To determine the presence of neuropsychological impairment, a battery of 12 tests was modified from the ACR-recommended 1-hour battery for SLE, which has been determined previously to be reliable and valid in SLE (5, 28). The battery was divided into 2 broad indices of cognitive functioning: memory and executive functioning.

The verbal and nonverbal episodic memory index included the following 6 tests: 1) California Verbal Learning Test–II learning trials 1 through 5, short delay free recall, and long delay free recall (29) and 2) Rey Complex Figure Test copy trial, immediate delay, and long delay (30, 31).

Executive functioning broadly includes higher-order cognitive functions, including such domains as problem solving, behavioral inhibition, organization, task set shifting, reasoning, sequencing, and planning. The executive functioning index was comprised of the following 6 tests: 1) Controlled Oral Word Association Test total correct on phonemic fluency (letters) (32); 2) Delis-Kaplan Executive Function Test (DKEFS) design fluency test: shifting condition (33); 3) DKEFS color word inhibition test: inhibition condition (33); 4) DKEFS color word inhibition test: switching condition (33); 5) DKEFS card-sorting test: total correct sorts (set 1) (33); and 6) DKEFS trail-making test: shifting condition (33).

For each index, impairment was assigned if performance fell below −1.5 SDs of age-adjusted population normative data. Using a conventional approach, patients were classified as having neuropsychological impairment if they were impaired on at least one-third of the indices in each battery (total, memory, and executive function) (28). Women could be considered impaired on the total battery or on either the memory or executive function indices alone. It was possible for women to be impaired on either the memory or executive function index (e.g., impaired on 2 of the 6 memory tests) and not be impaired on the total battery (e.g., impairment on fewer than 4 of the total 12 tests).

Physical activity.

Physical activity was assessed by self-report with the long form of the International Physical Activity Questionnaire (IPAQ) (34, 35). The IPAQ assesses physical activity across 4 domains: leisure-time physical activity, domestic and yard activities, work-related physical activity, and transport-related physical activity. It has been used and validated in a number of populations (35–37). Scoring the IPAQ yields an estimate of energy expenditure in the past week. The scoring protocol provides a cut point by which individuals' energy expenditure can be categorized as low, moderate, or high. Individuals who expended fewer than 600 metabolic equivalent (MET) minutes per week, the definition for low physical activity, were classified as inactive for these analyses (34, 35). To simplify reporting, individuals who reported 600 MET minutes per week or more will be referred to as “active.”

Body composition.

A Lunar Prodigy dual x-ray absorptiometry (DXA) scan (GE Healthcare) was performed to assess body composition. DXA provides good estimates of the amount of body fat and muscle, and previously has been successfully used to assess body composition among individuals with SLE (19, 20, 38) and rheumatoid arthritis (39–42) and in studies of aging (43–45). DXA results provide a measure of total percent body fat, as well as regional distribution of fat and lean tissue. To define obesity from total percent body fat, we used the definitions suggested by Gallagher et al (46). These definitions are calculated separately for sex, age, and race groups. Body fat percentage criteria for obesity according to these definitions range from 38% for African American women ages 20–39 years to 43% for white women ages 60–79 years (46).

In addition to DXA assessments of body composition, for secondary analyses we also examined anthropometric measures of body composition because these measures can be more easily implemented in a clinical setting. We collected anthropometric measures often used as proxies of body composition: height and weight, to calculate body mass index (BMI), and waist circumference. Height was measured with a wall-mounted stadiometer. Weight was measured with subjects wearing light indoor clothing and no shoes. BMI was calculated as weight (kg) divided by height (m2). Obesity was defined from BMI using 2 criteria. The first criterion was BMI ≥30 kg/m2, the common BMI criterion for obesity (47). The second criterion was BMI ≥26.8 kg/m2, a revised obesity criterion recently proposed for women with SLE based on data regarding body composition from DXA analyses of a subset of these women (19). This revised criterion was found to correspond with DXA-defined obesity better than a BMI of ≥30 kg/m2. Waist circumference was measured at the midpoint between the lower border of the ribs and the iliac crest. Two measurements were taken and the average measure was used. Again, 2 criteria were used to define obesity. The first was waist circumference ≥88 cm, the common criterion for obesity (47). The second was waist circumference ≥84.5 cm, based on the correspondence of this measurement with DXA-defined obesity (19).

Other variables.

Sociodemographic characteristics (age, race/ethnicity, and education) were obtained from the baseline LOS telephone interview. Disease activity was assessed using the Systemic Lupus Activity Questionnaire (SLAQ), a validated self-report measure of disease activity in SLE (48, 49). Current oral glucocorticoid use and dose were obtained by self-report. The presence of major depressive disorder or any mood disorder was obtained from the Mini International Neuropsychiatric Interview, a brief structured interview for major axis I psychiatric disorders in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition and the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision that has high validation and reliability scores. Interviewers were trained and supervised by a licensed psychologist (LJ) (50, 51). In addition, we examined cardiovascular risk using the Framingham Risk Score for cardiovascular disease (52) and self-reported history of stroke and neurologic disorders.

Statistical analysis.

Bivariate differences in cognitive impairment were compared between women who were and were not physically inactive and were and were not obese using chi-square analyses. Multivariate logistic regression analyses were conducted to examine the independent contributions of physical inactivity and body composition to impairment on the total cognitive battery and on the memory and executive function components, adjusting for education, race/ethnicity, disease activity using the SLAQ score, glucocorticoid use, and depression. (Norms for calculating Z scores for cognitive tests were age adjusted, so age was not included in multivariate analyses.) In step 1 of the logistic regression analyses, models included inactivity only or obesity only, plus covariates (education, race/ethnicity, disease activity, oral glucocorticoid use, and depression). Step 2 of the logistic regression analysis included both inactivity and obesity, plus covariates. Analyses were also conducted examining the interaction of inactivity and obesity (using standard multiplicative interaction terms); no significant interaction effects were found and these analyses are not shown. Because some cell sizes were small (e.g., all but 1 of the women classified as impaired in executive function were obese), we repeated logistic regression analyses with exact logistic regression. As secondary analyses, we examined anthropometric measures of obesity using the same analysis strategy.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Twenty women (15%) were impaired on the total cognitive battery. There were no significant differences in age, education, duration of SLE, current glucocorticoid use, depression, cardiovascular risk as defined by the Framingham Risk Score, or self-reported history of stroke or other neurologic disorder between women who were and were not impaired on the total battery. Individuals who were impaired were less likely to be white (35% versus 71.2%; P = 0.004).

Memory impairment was more common than impairment in executive function (28% compared to 10%). Slightly more than one-quarter (n = 39 [28%]) of the women were physically inactive. Based on total percent body fat from DXA scans (46), 50% (n = 69) were obese.

Using the standard BMI criterion (≥30 kg/m2), 30% of the women were obese, and 42% were obese using the BMI ≥26.8 kg/m2 criterion. Using the standard waist circumference criterion, 41% were obese, whereas 48% were obese using the revised waist circumference criterion.

Bivariate associations with physical inactivity and obesity.

On the total cognitive battery, 11% of those who were physically active were impaired compared to 23% of those who were inactive (P = 0.10) (Table 2). Forty-one percent of those who were inactive were impaired on the memory component versus 23% of those who were active (P = 0.06), and 5% of those who were active were impaired on the executive function component compared to 23% of those who were inactive (P = 0.003).

Table 2. Bivariate analysis: associations of body composition and physical inactivity with cognitive impairment*
 Total battery (n = 20 [14.5%])Memory (n = 39 [28.3%])Executive function (n = 14 [10.1%])
  • *

    Values are the percentage (number) cognitively impaired. DXA = dual x-ray absorptiometry.

  • From chi-square analyses comparing physically inactive vs. active and obese vs. not obese.

Physical activity   
 Active (n = 99)11.1 (11)23.2 (23)5.1 (5)
 Inactive (n = 39)23.1 (9)41.0 (16)23.1 (9)
 P0.100.060.003
Obese by DXA (42)   
 No (n = 69)5.8 (4)21.7 (15)1.5 (1)
 Yes (n = 69)23.2 (16)34.8 (24)18.8 (13)
 P0.0070.130.001

Women who were obese by DXA standards were more likely to be impaired on the total battery (23% versus 6%; P = 0.007) and on the executive function portion of the battery (19% versus 2%; P = 0.001) than women who were not obese. Obesity was not significantly associated with impairment on the memory battery.

Viewed from a slightly different perspective, of the 20 women who were impaired on the total cognitive battery, 9 (45%) were inactive and 16 (80%) were obese. Among 39 women who were impaired on the memory battery, 16 (41%) were inactive and 24 (62%) were obese, and among 14 women who were impaired in executive function, 9 (64%) were inactive and 13 (93%) were obese.

Multivariate associations with physical inactivity and obesity.

Controlling for covariates in step 1 (education, race, SLAQ score, oral glucocorticoid use, and depression), physical inactivity was associated with a significantly elevated risk of age-adjusted impairment in executive function (odds ratio [OR] 9.0, 95% confidence interval [95% CI] 1.8–45.6) (Table 3). The odds of impairment on the total battery and the memory component were elevated, but did not reach statistical significance (total: OR 2.0, 95% CI 0.6–6.3; memory: OR 1.9, 95% CI 0.8–4.5) (Table 3). DXA-defined obesity was associated with both impairment on the total battery (OR 3.4, 95% CI 1.0–11.8) and impairment in executive function (OR 14.3, 95% CI 1.5–139.9), but not with memory impairment.

Table 3. Multivariate analysis: odds of cognitive impairment conferred by physical inactivity and obesity*
 TotalMemoryExecutive function
  • *

    Values are the odds ratio (95% confidence interval). DXA = dual x-ray absorptiometry.

  • Covariates: education, race, disease activity as measured by the Systemic Lupus Activity Questionnaire (49, 50), oral glucocorticoid use, and depression.

Step 1: inactivity and obesity in separate models, with covariates   
 Inactivity2.0 (0.6–6.3)1.9 (0.8–4.5)9.0 (1.8–45.6)
 Obese by DXA3.4 (1.0–11.8)1.5 (0.7–3.3)14.3 (1.5–139.9)
Step 2: combined inactivity + obesity, with covariates   
 Inactivity1.8 (0.6–5.9)1.8 (0.7–4.4)9.4 (1.7–52.8)
 Obese by DXA3.3 (0.9–11.4)1.4 (0.6–3.3)14.8 (1.4–151.0)

When physical inactivity and obesity were included in the same model (step 2), inactivity remained significantly associated with impairment on the executive function component (OR 9.4, 95% CI 1.7–52.8), but was not associated with impairment on the memory component or the total battery (Table 3). Obesity was also independently and significantly associated with impairment in executive function (OR 14.8, 95% CI 1.4–151.0), but not with impairment on the memory component or the total battery.

We repeated analyses using exact logistic regression because the CIs were wide due to the distribution of obesity and impairment. The point estimates obtained from these analyses were lower than those from the original analyses, and although the CIs became wider, the conclusions about statistical significance did not change. For example, the OR from the original logistic regression estimate of DXA obesity on cognitive function was 14.8 (95% CI 1.4–151.0); the exact logistic regression OR was 8.3 (95% CI 1.1–375.4). It is important to note that one reason for the wide CI was that only 1 woman with executive impairment was not obese, creating an unbalanced cell in the 2 − 2 distribution (Table 2).

Secondary analyses using anthropometric measures of obesity.

Women who were defined as obese by anthropometric methods had higher rates of impairment on the total cognitive battery, although the difference for the usual BMI definition of obesity (≥30 kg/m2) was not statistically significant (Table 4). Similar results were found for impairment in executive function. Anthropometric measures of obesity were not significantly associated with impairment on the memory battery.

Table 4. Bivariate analysis: associations of anthropometric measures of obesity with cognitive impairment*
 TotalMemoryExecutive function
  • *

    Values are the percentage (number) impaired. BMI = body mass index.

  • From chi-square analyses comparing physically inactive vs. active and obese vs. not obese.

BMI ≥30 kg/m2   
 No (n = 96 [70.1%])10.4 (10)24.0 (23)7.3 (7)
 Yes (n = 41 [29.9%])24.4 (10)36.6 (15)17.1 (7)
 P0.060.150.12
BMI ≥26.8 kg/m2 (19)   
 No (n = 79 [57.7%])8.9 (7)21.5 (17)3.8 (3)
 Yes (n = 58 [42.3%])22.4 (13)36.2 (21)19.0 (11)
 P0.030.080.008
Waist circumference ≥88 cm   
 No (n = 81 [59.1%])8.6 (7)23.5 (19)3.7 (3)
 Yes (n = 56 [40.9%])23.2 (13)35.7 (20)19.6 (11)
 P0.030.130.004
Waist circumference ≥84.5 cm (19)   
 No (n = 72 [52.5%])6.9 (5)23.6 (17)2.8 (2)
 Yes (n = 65 [47.5%])23.1 (15)33.9 (22)18.5 (12)
 P0.010.190.004

Controlling for covariates in step 1, BMI ≥26.8 kg/m2, waist circumference ≥88 cm, and waist circumference ≥84.5 cm were each associated with a higher likelihood of impairment in executive function, although only waist circumference ≥84.5 reached statistical significance (OR 5.5, 95% CI 1.0–31.0) (Table 5). When both inactivity and obesity were included in the same model (step 2), inactivity remained significantly associated with impairment on the executive function component. BMI ≥26.8 kg/m2, waist circumference ≥88 cm, and waist circumference ≥84.5 cm were each associated with a higher likelihood of impairment in executive function, although none of the CIs excluded a relationship (Table 5).

Table 5. Multivariate analysis: odds of cognitive impairment conferred by physical inactivity and anthropometric measures of obesity*
 TotalMemoryExecutive function
  • *

    Values are the odds ratio (95% confidence interval). BMI = body mass index.

  • Covariates: education, race, disease activity as measured by the Systemic Lupus Activity Questionnaire (49, 50), oral glucocorticoid use, and depression.

Step 1: obesity measures in separate models, with covariates   
 Model 1: BMI ≥30 kg/m21.6 (0.5–5.0)1.2 (0.5–2.9)1.0 (0.2–4.0)
 Model 2: BMI ≥26.8 kg/m21.5 (0.5–4.8)1.4 (0.6–3.3)3.3 (0.7–15.0)
 Model 3: waist circumference ≥88 cm1.9 (0.6–6.1)1.2 (0.5–2.8)4.2 (0.9–20.1)
 Model 4: waist circumference ≥84.5 cm2.7 (0.8–8.9)1.2 (0.5–2.7)5.5 (1.0–31.0)
Step 2: combined inactivity + obesity measures, with covariates   
 Model 1: inactivity1.9 (0.6–6.1)1.9 (0.8–4.7)9.1 (1.8–46.7)
  BMI ≥30 kg/m21.5 (0.5–4.9)1.1 (0.5–2.8)0.8 (0.2–3.7)
 Model 2: inactivity1.9 (0.6–6.0)1.8 (0.7–4.5)8.1 (1.6–41.8)
  BMI ≥26.8 kg/m21.4 (0.5–4.5)1.3 (0.6–3.2)2.7 (0.6–13.1)
 Model 3: inactivity1.8 (0.6–6.0)1.9 (0.8–4.6)7.9 (1.5–41.0)
  Waist circumference ≥88 cm1.8 (0.6–5.7)1.1 (0.5–2.6)3.2 (0.7–16.0)
 Model 4: inactivity1.8 (0.5–5.8)1.9 (0.8–4.6)8.4 (1.6–44.3)
  Waist circumference ≥84.5 cm2.6 (0.8–8.4)1.1 (0.5–2.6)4.8 (0.8–27.7)

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

In this study of women with SLE, both physical inactivity and obesity defined by DXA were significantly associated with impairment in executive function. The findings from this study are comparable to large-scale studies in older adults (10, 11). In the general population, decreased physical activity and obesity have each been observed to be associated with executive dysfunction (14, 53, 54). To date, however, these links have been investigated only minimally in rheumatic diseases (55).

Because inactivity and obesity are closely related, it was important to examine the independent contributions of each. When we did so, we found that obesity as defined by DXA and inactivity had independent associations with impaired executive function. ORs for DXA-defined obesity and physical inactivity did not change substantively in the model including both variables, suggesting that inactivity and obesity had independent effects and did not modify the effects of each other.

While obesity defined by DXA was associated with cognitive impairment, obesity defined by BMI and by the standard waist circumference measurement was not associated with impairment after adjusting for covariates. However, waist circumference ≥84.5 cm, a revised obesity criterion defined through our prior work (19), was significantly associated with impairment in executive function. This finding suggests that abdominal fat may be more of a critical component of overall body fat, and suggests a simple clinical measure that may identify risk for cognitive impairment.

A variety of mechanistic causes have been proposed for SLE-related cognitive impairment, including both inflammatory and vascular pathways. Inflammatory markers have been linked to cognitive impairment in the general population (21, 56–64). Adipose tissue is a known source of proinflammatory cytokines, including tumor necrosis factor α (TNFα) and interleukin-6 (IL-6) (65), and regular physical activity appears to decrease levels of inflammatory markers, including C-reactive protein, IL-6, and TNFα (66–70). In addition, both sedentary behavior and obesity are established risk factors for cerebrovascular and cardiovascular disease, which in turn can influence cognitive dysfunction. Based on our results, future studies evaluating the roles of both inflammatory biomarkers and cardiovascular factors in cognitive decline in SLE appear to be warranted.

This study has limitations that should be considered. First, it is cross-sectional. While our hypothesis is that obesity and physical inactivity may lead to cognitive impairment, it is possible that individuals who are cognitively impaired become less active, and subsequently obese, as a result of their impairment. Mechanisms producing cognitive impairment in SLE are not well defined, so longitudinal studies are needed to try to identify the causal relationships. Although we controlled for disease activity using the SLAQ and current glucocorticoid use, more active or severe disease not accounted for by the SLAQ or long-term glucocorticoid use may result in cognitive impairment or changes in body composition. Although we used a well-studied and well-validated measure of physical activity, physical activity was self-reported, and individuals who are impaired may be particularly inaccurate reporters. We do not have data to suggest the direction of a bias in such reporting, if one exists. Women with more severe SLE and, perhaps more importantly, women with more severe cognitive problems may not have participated because of their inability to travel to the study site or even participate in the telephone interviews, but this differential participation should serve to bias our results toward null findings. In addition, we did not include men in this study. While we have male participants, they are represented at approximately the same rate at which SLE occurs in men (female:male ratio 9:1). This number of men was too small for meaningful analysis, so we cannot generalize results to men with SLE. All of our participants were English speaking. It is also possible that, while our cohort is composed of 34% racial/ethnic minorities, there are additional racial or ethnic groups to which these results may not be generalizable. Nonetheless, the measure we used to estimate physical activity has been used internationally and validated in a wide variety of languages, and the cognitive function measures have undergone extensive testing and validation. Finally, we caution that our sample size was relatively small and some of the estimates lack precision (i.e., had wide CIs). The most obvious example of the lack of precision was the estimate for the odds of impairment in executive function attributable to DXA-defined obesity, due to the finding that all but 1 individual who exhibited executive dysfunction were obese. Because of this, we encourage future studies to replicate and extend these findings.

Strengths of the study include the comprehensive cognitive function battery that was administered to a large group of women with confirmed SLE. The use of DXA to determine obesity is also a strength because, as previously noted, BMI is a relatively inaccurate representation of body composition in this group of women with SLE (19).

The results of this analysis may have important clinical implications. Both physical inactivity and obesity are modifiable states. If future longitudinal analyses show that physical inactivity and/or obesity are precursors to cognitive impairment, these may become important targets for intervention to improve cognitive outcomes in SLE.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Katz had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Katz, Julian.

Acquisition of data. Katz, Trupin, Yelin, Criswell.

Analysis and interpretation of data. Katz, Julian, Tonner, Yazdany, Trupin, Yelin.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

We gratefully acknowledge Sandi Kaplan, BA, Holly Wing, MA, and Rachel Diskin, BS, who conducted the study assessments, as well as Stephanie Rush, BA, who prepared the data for analysis.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES