Because Drs. Katz and Yelin are Editors of Arthritis Care & Research, review of this article was handled by the Editor of Arthritis & Rheumatism.
Systemic Lupus Erythematosus
Obesity and its measurement in a community-based sample of women with systemic lupus erythematosus
Article first published online: 28 JAN 2011
Copyright © 2011 by the American College of Rheumatology
Arthritis Care & Research
Volume 63, Issue 2, pages 261–268, February 2011
How to Cite
Katz, P., Gregorich, S., Yazdany, J., Trupin, L., Julian, L., Yelin, E. and Criswell, L. A. (2011), Obesity and its measurement in a community-based sample of women with systemic lupus erythematosus. Arthritis Care Res, 63: 261–268. doi: 10.1002/acr.20343
Because Drs. Katz and Yelin are Editors of Arthritis Care & Research, review of this article was handled by the Editor of Arthritis & Rheumatism.
- Issue published online: 28 JAN 2011
- Article first published online: 28 JAN 2011
- Accepted manuscript online: 7 SEP 2010 01:06PM EST
- Manuscript Accepted: 25 AUG 2010
- Manuscript Received: 2 JUN 2010
- National Institute of Arthritis and Musculoskeletal and Skin Diseases. Grant Number: P60-AR-053308
- National Center for Research Resources
- Clinical and Translational Science Institute
- University of California, San Francisco. Grant Number: UL-1-RR024131
To determine the prevalence of obesity and evaluate how accurately standard anthropometric measures identify obesity among women with systemic lupus erythematosus (SLE).
Dual x-ray absorptiometry (DXA), height, weight, and waist and hip circumference measurements were collected from 145 women with SLE. Three anthropometric proxies of obesity (body mass index [BMI] ≥30 kg/m2, waist circumference [WC] ≥88 cm, and waist:hip ratio [WHR] ≥0.85) were compared with a DXA-based obesity criterion. Correspondence between measures was assessed with Cohen's kappa. Receiver operating characteristic curves determined optimal cut points for each anthropometric measure relative to DXA. Framingham cardiovascular risk scores were compared among women who were classified as not obese by both traditional and revised anthropometric definitions, obese by both definitions, and obese only by the revised definition.
Of the 145 women, 28%, 29%, 41%, and 50% were classified as obese by WC, BMI, WHR, and DXA, respectively. Correspondence between anthropometric and DXA-based measures was moderate. Women misclassified by anthropometric measures had less truncal fat and more appendicular lean and fat mass. Cut points were identified for anthropometric measures to better approximate DXA estimates of percent body fat: BMI ≥26.8 kg/m2, WC ≥84.75 cm, and WHR ≥0.80. Framingham risk scores were significantly higher in women classified as obese by either traditional or revised criteria.
A large percentage of this group of women with SLE was obese. Substantial portions of women were misclassified by anthropometric measures. Utility of revised cut points compared with traditional cut points in identifying risk of cardiovascular disease or disability remains to be examined in prospective studies, but results from the Framingham risk score analysis suggest that traditional cut points exclude a significant number of at-risk women with SLE.
Obesity is a growing public health concern and is associated with a variety of health problems such as increased risk of cardiovascular disease, osteoarthritis, and disability. Among the few studies that have examined obesity or body composition in systemic lupus erythematosus (SLE), rates of obesity appear to be higher than in the general population (1–4). Individuals with SLE have an elevated risk of both cardiovascular disease and disability, but the increase in risk for these conditions that may be conferred by obesity is not known.
In most large-scale studies, obesity has been estimated from body mass index (BMI), which is calculated as body weight adjusted for height (kg/m2). For the general population, BMI is assumed to be an adequate proxy measure for body fatness, but for some groups its inaccuracy has been demonstrated. For example, in rheumatoid arthritis (RA), there is evidence that body weight or BMI may not accurately reflect the amount of body fat (5). The chronic inflammation seen in RA can affect body composition and metabolism, resulting in rheumatoid cachexia, or the loss of lean body mass, particularly in skeletal muscle (6, 7). Rheumatoid cachexia may occur with little or no weight loss, indicating that muscle mass is lost in conjunction with increased fat mass (7). In other words, an individual may have a BMI within a normal range, but have greater fat mass than suggested by the BMI. This is important because an overabundance of fat tissue may create unhealthy levels of hormones, proteins, and cytokines, producing inflammation that may elevate the risk of cardiovascular disease or other disease processes (8). Additionally, relatively low muscle mass may cause muscle weakness, leading to disability.
An additional limitation of BMI is that it does not provide an indication of fat distribution. Abdominal fat appears to be particularly relevant for cardiovascular and metabolic disease. Other anthropometric proxy measures of body composition or obesity, such as waist circumference (WC) and the ratio of WC to hip circumference, may provide better estimates of fat distribution and the risk of cardiovascular disease and diabetes mellitus, although limitations also exist with these measures (9).
Studies seeking a more accurate view of body composition often use whole dual x-ray absorptiometry (DXA) (1, 10–19), which provides good estimates of body fat and muscle. DXA is not feasible to use on a wide-scale basis, primarily because of the cost and lack of access to equipment (9), but results from a DXA study can provide an indication of the accuracy of proxy measures, such as BMI. Studies examining the accuracy of BMI, or other proxy measures of obesity, have not been conducted in SLE.
The goals of these analyses were: 1) to determine the proportion of a cohort of women with SLE who are obese using DXA and 3 commonly used anthropometric proxy measures (BMI, WC, and waist:hip ratio [WHR]), 2) to assess the correspondence of obesity determinations among the 4 methods, 3) to evaluate the accuracy of the standard obesity cut points of the anthropometric measures for women with SLE compared with a DXA determination of obesity, and 4) to examine cardiovascular risk relative to obesity classifications.
SUBJECTS AND METHODS
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 (20, 21) and 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%). SLE diagnoses have been verified by medical record review. Additional details regarding the LOS were reported by Yelin et al (22). 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. Exclusion criteria were non–English-speaking, age <18 years, current oral prednisone dose of ≥50 mg, current pregnancy, uncorrected vision problems that would interfere with reading ability, and joint replacement within 1 year.
A total of 325 individuals were asked to participate: 74 (22.8%) were ineligible (35 lived too far away, 25 were too ill, 9 had had recent surgery, 2 were pregnant, 2 had poor English skills, and 1 had cognitive problems). Of the 251 eligible individuals, 84 (33.5%) declined participation. Reasons for declining were primarily related to transportation (n = 12) and scheduling difficulties (n = 39). A total of 163 individuals completed study visits; DXAs were completed for 157 participants (145 women and 12 men). Only the 145 women are included in these analyses. Sociodemographic and health-related characteristics of the study sample are shown in Table 1. The study was approved by the UCSF Committee on Human Research.
|Mean ± SD||% (no.)|
|Age, years||47.9 ± 12.2|
|White, non-Hispanic||54.5 (79)|
|African American||14.5 (21)|
|Other, unknown||8.3 (12)|
|High school education or less||13.1 (19)|
|Less than poverty-level income||13.0 (18)|
|Disease duration, years||15.8 ± 9.2|
|Systemic Lupus Activity Questionnaire score||12.4 ± 7.0|
|Glucocorticoid dosage, mg/day|
Height was measured with a wall-mounted stadiometer (Perspective). Weight was measured with subjects wearing light indoor clothing and no shoes. BMI was calculated as weight (kg) divided by height (m2). Obesity by BMI was defined as BMI ≥30 kg/m2 (23). Waist and hip circumferences were measured with a nonstretch measuring tape that applies a consistent amount of tension to the tape (Gullick II Tape Measure, Country Technology). WC was measured at the midpoint between the lower border of the ribs and the iliac crest. Hip circumference was measured at the widest point over the buttocks. Two measurements were taken at each point, and the average measure was used. Women with a WC ≥88 cm were classified as obese (23). WHR was calculated by dividing the average WC by the average hip circumference. Women with a WHR ≥0.85 were classified as obese (23).
Body composition and regional body fat distribution were assessed in the CRC using a Lunar Prodigy DXA system (GE Healthcare). The DXA technique is able to differentiate bone, muscle, and fat and calculates total body mass (kg), fat mass (gm), percent fat, and lean body mass (gm), as well as the regional distribution of these components (left arm, leg, and trunk; right arm, leg, and trunk; and total arm, leg, and trunk). DXA has been used extensively in determination of bone density, expanded for use in determination of soft tissue mass (24–26), and validated as a method of assessing body composition in both younger and older persons. It has good reported reproducibility and is sensitive to small changes in body composition (10). The precision errors ±1 SD for percent fat are 1.4% in soft tissue, 1.0 kg for fat mass, and 0.8 kg for lean tissue mass (25). DXA has previously been used to successfully assess body composition among individuals with RA (11, 16–18) and SLE (1, 19) and in studies of aging (10–12, 14).
There is no agreed-upon standard definition of obesity based on percent body fat (27). Although other definitions of obesity based on percent body fat have been suggested (28), we used the definitions suggested by Gallagher et al, which linked percent fat to the National Institutes of Health BMI guidelines (defining obesity as BMI ≥30 kg/m2) (29). In that study, the average percent body fat for individuals with BMI between 30 and 35 kg/m2 (obese but not morbidly obese) from 3 samples from the US, UK, and Japan was ascertained by DXA. Average body fat percentages for these obese individuals were calculated separately for sex, age, and race groups. We used those percentages as the criteria for defining obesity, based on the individual's age and race. Body fat percentage criteria ranged from 38% for African American women ages 20–39 years to 43% for white women ages 60–79 years.
Sociodemographic (e.g., age, race/ethnicity, education, income, and smoking status) characteristics 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 (30, 31). The SLAQ was taken from the LOS interview that most closely preceded the CRC visit. Glucocorticoid use was assessed at the time of the visit.
Additional data were collected at the clinic visit that permitted calculation of the Framingham cardiovascular risk score (32), including total cholesterol and high-density lipoprotein levels, blood pressure, treatment of hypertension, and presence of diabetes mellitus. Serum lipids were obtained through nonfasting blood draws. Although fasting measurements may be ideal, nonfasting measures of total cholesterol and high-density lipoprotein cholesterol have been found to very closely approximate fasting levels (33). Blood pressure was measured by registered nurses while subjects were in a seated position. Treatment of hypertension and presence of diabetes mellitus were ascertained by subject self-report.
Correspondence among obesity classifications was assessed using Cohen's kappa. Among women who were classified as obese by the DXA criteria, we compared regional body composition characteristics of those who were and were not correctly classified by anthropometric methods, including the relative distribution of fat tissue (trunk versus appendicular [arms and legs combined]).
Receiver operating characteristic (ROC) curves were calculated to determine the optimal cut points for each anthropometric measure, relative to the DXA-based classification. Two threshold selection methods were used: the Youden Index and a second technique that determines the proximity to perfect correspondence (referred to in this study as the Distance to Perfect Index) (34). Briefly, the Youden Index determines the maximum vertical distance from the ROC curve to the diagonal reference, or chance line, i.e., the “optimal” cut point corresponds to the point on the ROC curve farthest from the reference line, which has also been used as a measure of the accuracy of a diagnostic test in clinical epidemiology (35). Similarly, the Distance to Perfect Index selects the point on the ROC curve that is closest to the upper left-hand corner of the graph (0,1), which represents perfect classification (36) and thereby minimizes misclassification. We calculated the sensitivity, specificity, and positive and negative predictive value of each anthropometric measure using both the established and new cut points as compared with the DXA-based obesity classification.
To examine the potential usefulness of the revised cut points, we calculated Framingham cardiovascular disease risk scores (based on age, high-density lipoprotein cholesterol, total cholesterol, systolic blood pressure, smoking, and diabetes mellitus ) for 3 groups: women who would not be considered obese by traditional or revised definitions (BMI ≤26.7 kg/m2), women who would be considered obese by the revised definition but not the traditional definition (BMI 26.8–29.9 kg/m2), and women who would be considered obese by both definitions (BMI ≥30 kg/m2). Differences in the risk score among the 3 groups were tested with analysis of variance followed by post hoc means comparisons with Tukey's method. This analysis was performed for each of the 3 anthropometric measures.
The mean ± SD age of the 145 women in the analysis was 48 ± 12.2 years (Table 1). Approximately half (54.3%) were white non-Hispanic, 13.6% were African American, 12.9% were Asian, 10.7% were Hispanic, and 8.6% fell into another category. Thirteen percent had incomes below the poverty level. Four percent were current smokers, and one-third were former smokers. The mean ± SD duration of SLE was 15.8 ± 9.2 years, and the average SLAQ score was 12.4. Approximately half of the sample (48.2%) was taking oral corticosteroids at the time of assessment, with half of those taking less than 10 mg per day.
Prevalence of obesity.
There were substantial variations in the estimates of obesity prevalence (Table 2). Anthropometric measures yielded estimates of 28.3% for WHR, 29.2% for BMI, and 41.4% for WC. By contrast, the DXA estimate was higher, estimating that 49.7% were obese.
|Measure||Mean ± SD||Range||% (no.)|
|BMI, kg/m2||27.2 ± 6.8||17.6–45.4|
|Underweight (<18.5)||2.1 (3)|
|Normal (18.5 ≤ BMI <25)||45.8 (66)|
|Overweight (25 ≤ BMI <30)||22.9 (33)|
|Obese (≥30)||29.2 (42)|
|Waist circumference, cm||86.8 ± 17.1||62–143|
|Normal (<80)||44.8 (65)|
|Overweight (80–87)||13.8 (20)|
|Obese (≥88)||41.4 (60)|
|Waist:hip ratio||0.81 ± 0.08||0.65–1.15|
|Not obese (<0.85)||71.7 (104)|
|Obese (≥0.85)||28.3 (41)|
|Percent fat from dual x-ray absorptiometry||40.8 ± 9.3||19.5–58.9|
|Not obese||50.3 (73)|
|Obese (29)||49.7 (72)|
Correspondence among classifications.
The DXA classification corresponded fairly well with BMI and WC classifications (κ = 0.59 and 0.64, respectively). In contrast, the WHR estimate of obesity did not correspond well with the DXA-based measure (κ = 0.27) (Table 3).
|Body mass index, kg/m2||Waist circumference, cm||Waist:hip ratio|
|Not obese||Obese||Not obese||Obese||Not obese||Obese|
Among women who were classified as obese by DXA, examination of more detailed results from DXA revealed total and regional differences in body composition between those classified as obese versus not obese by an anthropometric method (Table 4). Women who were obese by DXA and not obese by BMI had significantly lower overall body fat, their trunk fat comprised a significantly lower proportion of their total body mass, and their appendicular lean mass comprised a significantly greater proportion of their total body mass. For example, women who were obese by DXA but not obese by BMI had a lower total percent body fat (45.7% versus 50.8%; P = < 0.0001), a lower proportion of total body mass as trunk fat (23.5% versus 27.2%; P < 0.0001), and a higher proportion of total body mass as appendicular lean mass (23.4% versus 21.2; P = 0.0002). A similar pattern was noted for women who were misclassified by WC. In addition, however, both appendicular lean and fat mass were greater than among the women correctly classified as obese. For women who were misclassified by WHR, trunk fat composed a significantly lower proportion of total mass, and appendicular fat a significantly higher proportion, but appendicular lean mass was not significantly different than for the women correctly classified as obese.
|DXA body composition characteristic||Classification by anthropometric measure||Anthropometric measure|
|Body mass index, kg/m2||Waist circumference, cm||Waist:hip ratio|
|Total body fat, %||Not obese||45.7||44.7||48.4|
|P||< 0.0001||< 0.0001||0.65|
|Trunk fat/total fat, %||Not obese||51.5||49.7||51.1|
|Trunk fat/total mass, %||Not obese||23.5||22.2||24.7|
|P||< 0.0001||< 0.0001||0.006|
|Appendicular fat/total fat, %||Not obese||45.6||47.3||46.4|
|Appendicular lean/total mass, %||Not obese||23.4||24.4||22.4|
Cut point analysis.
Using the DXA-defined obesity classification as the criterion, ROC analyses identified new cut points for each of the anthropometric measures. In each case, the cut point was lower than that commonly used, a higher proportion of the sample was classified as obese, and the correspondence between DXA-defined obesity and the anthropometric measure improved.
The revised BMI definitions of obesity were ≥26.4 kg/m2 and ≥26.8 kg/m2, derived from the Distance to Perfect and Youden methods, respectively (Table 5). For further consideration, we chose to use the higher of these 2 cut points, 26.8 kg/m2, since it was the more conservative (i.e., closer to the traditional cut point of 30 kg/m2). The revised BMI cut point produced a sensitivity of 0.80, a specificity of 0.95, and a correct classification of 87.5% of the sample. For WC, the revised obesity cut point was ≥84.75 cm, with a sensitivity of 0.85, specificity of 0.89, and correct classification of 86.8%. The revised criterion for WHR was ≥0.80, which provided a sensitivity of 0.69, specificity of 0.67, and correct classification of 68.1%.
|Body mass index, kg/m2||Waist, cm||Waist:hip ratio|
|Classified obese, %||29.2||44.4||42.2||41.4||47.9||28.3||51.4|
|Positive predictive value||1.00||0.92||0.93||0.88||0.88||0.73||0.68|
|Negative predictive value||0.72||0.85||0.83||0.78||0.85||0.60||0.69|
|Correctly classified, %||79.9||88.2||87.5||82.1||86.8||63.4||68.1|
Association with Framingham cardiovascular disease risk scores.
For each anthropometric measure, women were categorized as not obese by either the traditional or the revised criterion, obese by both criteria, or obese by only the revised criterion. In each case, the risk scores of the 2 obese groups were not significantly different from each other, and the risk score of the nonobese group was significantly lower than the scores of either obese group. For example, 56.7% (80 women) were classified as not obese by either criterion (BMI <26.8 kg/m2), and their mean ± SD risk score was 5.7 ± 6.6 (Figure 1). Forty-two women (29.8%) were classified as obese by both criteria (BMI ≥30 kg/m2), and their mean ± SD risk score was 9.1 ± 5.6. The remaining 19 women (13.5%) were classified as obese only by the revised criterion (BMI 26.8–29.9 kg/m2), and their mean ± SD risk score was 9.4 ± 8.1. The analysis of variance revealed a significant overall difference among the mean risk scores (P = 0.007). Post hoc means comparisons found a significant difference between the nonobese group and both of the obese groups.
A large proportion of this group of women with SLE was obese. Using the most common body composition measure (BMI), almost 30% were obese; using a more sensitive measure (DXA), one-half met the criterion for obesity. Substantial numbers of women were misclassified by the anthropometric measures. The majority of misclassifications were due to women who were obese by the DXA standard but did not meet the anthropometric criterion for obesity. Relatively few women were found to be obese by anthropometric methods and not obese by DXA. Women who were misclassified as not obese by anthropometric measures exhibited different patterns of fat distribution than those who were correctly classified, and they tended to have a lower proportion of their fat accumulation in the trunk and a greater proportion in their arms and/or legs.
Our analyses suggest new cut points for defining obesity among women with SLE: BMI ≥26.8 kg/m2, WC ≥84.75 cm, and a WHR ≥0.80. In each case, the revised cut points are substantially lower than those traditionally used. For example, the traditional BMI cut point for obesity is ≥30 kg/m2; the revised cut point is closer to the traditional cut point used to define overweight (25.0 kg/m2). The inaccuracy of BMI in identifying high adiposity in midrange BMI values has been noted previously. For example, a meta-analysis yielded a pooled sensitivity of BMI to identify excess body fat of 0.50, and a pooled specificity of 0.90, with considerable heterogeneity among studies (37). The BMI cut point we identified is similar to cut points derived in other studies (38, 39). For example, in a study using National Health and Nutrition Examination Survey (NHANES) data, a BMI of 25.5 kg/m2 was identified as the best cut point to identify high body fat (>35% for women). In our study and those cited above, results suggest that lean mass was relatively lower, and fat mass relatively higher than might be expected by BMI. Whether this is a general population trend or relevant specifically to SLE is not known. Likewise, whether disease factors (e.g., inflammation), treatment (e.g., glucocorticoid use), or behavior (e.g., low physical activity), or the combination of these, have a differential impact on the body composition, including infiltration of muscle with fat, of women with SLE is not known.
Sensitivity and specificity of BMI and WC to detect obesity using the revised cut points were high. The WHR, however, did not perform well, even when using an adjusted cut point, so this measure should be used with caution as a proxy for estimating obesity in women with SLE. Others have also found that WHR did not correspond with body fatness as well as BMI and WC (40).
There is a strong relationship between obesity, defined by BMI and WC, and cardiovascular morbidity (9, 23). However, the actual risk conferred by high BMI or WC is that of high adiposity. An examination of NHANES data found that among more than 6,000 women who had “normal” BMI scores, almost one-third had body fat >35% (41). Among this group of “normal weight obese” women, metabolic syndrome, dyslipidemia, and cardiovascular disease were each elevated. Because of the elevated rate of cardiovascular disease in SLE (42), this phenomenon might be expected to be even more prevalent. We found elevated cardiovascular risk scores among women meeting both the traditional and revised anthropometric criteria of obesity. Although the absolute 10-year risk of a cardiovascular event was relatively low for all groups, the risk of both obese groups (∼5.5% for the 2 BMI obese groups) was about 80% higher than that of the nonobese group (∼3%). Annual monitoring of BMI is recommended as a quality indicator to screen for cardiovascular risk (43), but a lower BMI cut point to define risk conferred by high adiposity may be appropriate for women with SLE to permit earlier and/or better identification of individuals at risk. In addition, based on findings from RA, in which women who had higher levels of appendicular fat had greater risk of disability, these new cut points may also be more useful in predicting development or progression of disability than the traditional ones.
This study included a relatively small number of women with SLE (n = 145), so larger studies may yield different results, as may studies that include subjects who are different from this cohort in racial/ethnic composition or disease severity. In addition, prospective studies are clearly needed to identify the value of the suggested revised cut points in terms of identifying both cardiovascular or disability risk. It is also possible that other analyses of body composition, such as studies of fat infiltration into muscle, may yield information regarding alterations of body composition among women with SLE that confer additional risk for poor health or functional outcomes (12, 44).
In conclusion, we suggest consideration of revised criteria to define obesity in women with SLE when using anthropometric methods. These revised criteria provide greater sensitivity to body fat and greater correspondence with DXA-defined obesity. Using these cut points, both BMI and WC provided robust proxies of DXA-defined obesity. WHR was less useful and, based on these data, would not be recommended as a proxy measure for obesity in women with SLE. Our results suggest that cardiovascular risk of women who meet the revised obesity criterion is equivalent to that of women who meet traditional anthropometric obesity criteria, and that the traditional criteria may underestimate obesity-related cardiovascular risk. The utility of the revised cut points compared with the traditional cut points in identifying risk of cardiovascular disease or disability remains to be examined in prospective studies.
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 submitted for publication. 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, Yazdany, Julian.
Acquisition of data. Katz, Yelin, Criswell.
Analysis and interpretation of data. Katz, Gregorich, Yazdany, Trupin, Julian, Yelin, Criswell.
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