To estimate the prevalence, types, and sociodemographic and biobehavioral correlates of antinuclear antibodies (ANAs) in the US.
To estimate the prevalence, types, and sociodemographic and biobehavioral correlates of antinuclear antibodies (ANAs) in the US.
We conducted a cross-sectional analysis of 4,754 individuals from the National Health and Nutrition Examination Survey 1999–2004. ANAs were assessed by indirect immunofluorescence. In ANA-positive individuals, cellular staining patterns were determined, and specific autoantibody reactivities were assessed by immunoprecipitation.
The ANA prevalence in the US population of individuals ages 12 years and older was 13.8% (95% confidence interval [95% CI] 12.2–15.5%). ANA prevalence increased with age (P = 0.01), and ANAs were more prevalent among females than males (17.8% versus 9.6%; P < 0.001), with the female-to-male ratio peaking at 40–49 years of age. ANA prevalence was modestly higher in African Americans compared with whites (age-adjusted prevalence odds ratio [POR] 1.30, 95% CI 1.00–1.70). Remarkably, ANAs were less common in overweight and obese individuals (age-adjusted POR 0.74) than in persons of normal weight. No significant associations of ANA with education, family income, alcohol use, smoking history, serum levels of cotinine, or C-reactive protein were observed. In ANA-positive individuals, nuclear patterns were seen in 84.6%, cytoplasmic patterns were seen in 21.8%, and nucleolar patterns were seen in 6.1%; the most common specific autoantibodies were anti-Ro (3.9%) and anti-Su (2.4%).
These findings suggest that more than 32 million persons in the US have ANAs, and that the prevalence is higher among females, older individuals, African Americans, and those with a normal body weight. These data will serve as a useful baseline for future investigations of predictors and changes in ANA prevalence over time.
Autoantibodies to cellular constituents are the serologic hallmarks of autoimmunity and are frequently seen in patients with systemic autoimmune diseases, including systemic lupus erythematosus (SLE), scleroderma, and polymyositis/dermatomyositis (1). They also are detected in patients with organ-specific autoimmune diseases, such as autoimmune thyroiditis and hepatitis (1), certain infections, and neoplasms (2), and in some individuals without diagnosed disease (2, 3). The most common autoantibodies are antinuclear antibodies (ANAs), which are traditionally assessed by indirect immunofluorescence and include antibodies to both nuclear and cytoplasmic cell components (4). The cellular staining patterns and specific autoantibodies detected in individuals with ANAs are clinically useful, because they are strongly associated with particular autoimmune diseases, such as the nucleolar staining patterns that are often seen in scleroderma and anti-Sm autoantibodies that are included in the SLE classification criteria (1).
A variety of methods have been used to estimate ANA prevalence in selected populations, including blood donors (5, 6), hospital workers (6, 7), healthy volunteers (3, 8), or residents in small towns (9, 10), leading to a wide range of prevalence estimates (from 1.1% to 20%), which are difficult to compare. Factors associated with ANA production are largely unknown, with the exception of some reports suggesting a higher prevalence of ANAs in females (8, 10–13) and older individuals (12, 14–16). A proportion of the ANA-positive population is thought to represent the preclinical stage of autoimmune diseases, based on observations that autoantibodies are usually produced prior to clinical manifestations of disease (17). Thus, defining the prevalence and types of ANAs, as well as characterizing factors associated with their production, may provide insight into the etiology of autoimmune diseases. Autoimmune diseases are thought to be increasing in frequency but are more difficult to characterize and study at the population level compared with ANAs (18).
Therefore, we evaluated serum samples from the US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2004, to estimate ANA prevalence, cellular patterns, and specific autoantibody reactivities and to identify sociodemographic and biobehavioral factors associated with their production. We specifically assessed selected systemic autoimmune disease risk factors, including smoking (19) and alcohol use (20). We also evaluated C-reactive protein levels (21) and obesity, the former being a marker and the latter an underlying cause of chronic inflammation (22) and a growing public health concern.
NHANES is a continuous survey of the health and nutritional status of the US civilian, noninstitutionalized population, conducted by the National Center for Health Statistics, Centers for Disease Control and Prevention (CDC). During the period under study, 5,000 persons of all ages were surveyed each year using a complex, multistage sampling strategy. Data are released in 2-year cycles, and our analyses combined data from the 1999–2000, 2001–2002, and 2003–2004 periods. The overall participation rates for the 1999–2000, 2001–2002, and 2003–2004 cycles were 76%, 80%, and 76%, respectively (http://www.cdc.gov/nchs/nhanes/response_rates_cps.htm). Sampling rates were adjusted to reduce potential bias resulting from differences among respondents and nonrespondents. The NHANES protocol was approved by a human subjects review board, and written informed consent was obtained from all participants.
For each cycle encompassing the 1999–2004 NHANES, a representative sample of participants ages 12 years and older was selected by NHANES staff for a substudy assessing serum levels of organochlorines (n = 7,106), a focus of our future investigations. Of these, 4,754 participants gave permission for sera storage and had samples available for analysis. There were no appreciable differences in demographic profiles between the larger substudy and our study sample (data not shown).
Serum samples were evaluated by standard immunofluorescence ANA testing, using commercial HEp-2 ANA slides (Inova Diagnostics) with 1:80 dilutions of sera, followed by staining with DyLight 488–conjugated donkey anti-human IgG (γ-chain specific) antibodies (Jackson ImmunoResearch) (23). Antinuclear, antinucleolar, and anticytoplasmic ANA patterns were identified using a standard classification method (2, 24), and the intensities of immunofluorescence staining were graded using a 0–4 scale based on comparisons with a standard reference gallery (see http://www.cdc.gov/nchs/nhanes/nhanes1999-2000/SSANA_A.htm). ANA staining intensities of 3 and 4 were defined as positive based on findings from commercial ANA reference laboratories after the concurrent evaluation of CDC reference sera and 200 unknowns in our sample (25).
Results for all ANA assays were collected and stored as digital images. All readings were confirmed independently by at least 2 experienced evaluators. In less than 5% of the cases, the 2 independent evaluators disagreed on the ANA reading. In these cases, the evaluators would discuss the discrepant classifications and come to a consensus after re-reviewing the data. When consensus was not reached between the initial 2 reviewers, a third reviewer adjudicated. If the quality of the ANA staining was questionable, a repeat of the staining and reading would be performed.
Immunofluorescent ANA-positive sera were tested by immunoprecipitation of 35S-methionine–labeled K562 cell extracts for determination of specific autoantibodies (26). Repeat testing of randomly selected samples showed >98% concordance in immunofluorescence intensity, identification of ANA patterns, and immunoprecipitation results.
Sociodemographic data (race/ethnicity, sex, age, education of the head of the household, and household income that was used to calculate the family income–to–poverty level ratio), smoking history, and alcohol consumption were based on self report (National Center for Health Statistics, National Health and Nutrition Examination Survey Questionnaires, datasets, and related documentation, Atlanta, Georgia: CDC; 2009; http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm).
Height and weight were measured, and body mass index (BMI) was calculated as weight (kilograms) divided by height (meters squared). For adults, BMI was categorized using standard cut points (underweight, <18.5 kg/m2; normal weight, 18.5 to <25 kg/m2; overweight, 25 to <30 kg/m2; obese, ≥30 kg/m2). For adolescents (ages 12–19 years), American Medical Association recommended guidelines (27) and corresponding sex-specific BMI percentiles for age growth were used.
Tobacco smoke exposure was assessed by self report and by using serum cotinine measurements, as previously described (28). Cotinine was categorized as not detectable (below the detection limit), second-hand smoke exposure (greater than or equal to the detection limit to 15 ng/ml), and active smoking (greater than 15 ng/ml). C-reactive protein was quantified by latex-enhanced nephelometry, and standard cut points were used to categorize low (<1 mg/liter), moderate (1–3 mg/liter), high (>3–10 mg/liter), and very high (>10 mg/liter) levels (29).
To account for the complex sampling design used in NHANES and to assure unbiased variance estimates, we used the appropriate SAS Survey procedures to estimate the prevalence of ANA positivity, 95% confidence intervals (95% CIs), and corresponding P values and adjusted prevalence odds ratios (PORs). For specific ANA patterns and autoantibodies, we report prevalences for the subgroup of ANA-positive participants, based on variance estimates from the entire sample. P values for trend were calculated with SUDAAN version 10.0.1 (Research Triangle Institute), and figures were generated with R program version 2.9.2 (R Foundation for Statistical Computing).
To account for sampling differences between the substudy assessing organochlorines and our study sample, we adjusted the 6-year weights (adjusting the NHANES 1999–2002 4-year weights by a factor of two-thirds and the NHANES 2003–2004 2-year weights by a factor of one-third; http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_2005.pdf) according to observed proportions of age, sex, and race/ethnicity. P values less than 0.05 were considered significant.
The overall prevalence of ANAs in the population was 13.8% (95% CI 12.2–15.5%). ANA prevalence generally increased with age (P = 0.01) and was significantly higher in women than men (17.8% versus 9.6%; P < 0.001) (Table 1). Based on these findings, we estimated that in the US, 32.3 million people (95% CI 28.5–36.1 million) (21.5 million females [95% CI 18.7–24.3 million] and 10.8 million males [95% CI 8.6–13.1 million]) had ANAs during the period 1999–2004. The ANA prevalence among the age groups 50–59 years and 70+ years was significantly higher than that in younger age groups (P < 0.03). ANA prevalence was modestly higher among non-Hispanic blacks than among other race/ethnic groups. ANA prevalence did not vary by education or by the family income–to–poverty level ratio. After adjustment for age, females had a 2-fold increased odds of ANA (POR 2.02, 95% CI 1.57–2.60). In additional analyses, including further adjustments for race, sex, alcohol intake, smoking, BMI, and C-reactive protein, PORs for all variables analyzed were virtually unchanged (data not shown).
|Characteristic||No.†||No. ANA positive†||% ANA positive (95% CI)||Age-adjusted POR (95% CI)|
|12–19||1,190||146||11.2 (7.8–14.6)||1.00 (reference)|
|20–29||686||90||13.1 (9.6–16.7)||1.20 (0.74–1.93)|
|30–39||642||93||13.4 (9.5–17.3)||1.23 (0.75–2.02)|
|40–49||581||66||11.5 (8.5–14.4)||1.03 (0.72–1.46)|
|50–59||478||87||17.4 (13.2–21.7)||1.68 (1.13–2.48)|
|60–69||525||68||13.8 (8.7–18.9)||1.27 (0.77–2.08)|
|70+||652||120||19.2 (15.0–23.4)||1.88 (1.17–3.02)|
|Male||2,285||244||9.6 (7.6–11.6)||1.00 (reference)|
|Female||2,469||426||17.8 (15.5–20.1)||2.02 (1.57–2.60)|
|Non-Hispanic white||2,118||293||13.7 (11.7–15.7)||1.00 (reference)|
|Non-Hispanic black||994||155||16.5 (13.5–19.4)||1.30 (1.00–1.70)|
|Mexican American||1,246||168||12.8 (10.3–15.3)||1.00 (0.78–1.29)|
|Other||396||54||12.8 (8.5–17.2)||0.96 (0.65–1.42)|
|0–8 years||697||106||13.6 (9.6–17.6)||1.00 (reference)|
|9–11 years||848||104||13.2 (10.5–15.9)||1.01 (0.65–1.56)|
|High school diploma/GED||1,068||141||13.1 (10.4–15.7)||1.02 (0.74–1.41)|
|Some college||1,152||171||14.7 (12.0–17.4)||1.19 (0.81–1.74)|
|College or postgraduate||815||112||13.0 (10.1–16.0)||1.01 (0.65–1.57)|
|Family income–to–poverty level ratio|
|At or above poverty||3,370||477||13.7 (11.9–15.4)||1.00 (reference)|
|Below poverty||982||125||13.9 (10.7–17.2)||1.08 (0.84–1.39)|
Although the overall prevalence of ANAs increased with age, the pattern was not linear (Figure 1). When we explored different age groupings and evaluated males and females and different ethnic groups separately (Figure 2), similar patterns were seen. The magnitude of the female-to-male PORs varied considerably across age groups (Figure 3). Female-versus-male differences were minimal among persons younger than age 30 years but increased among those 30–39 years of age (POR 2.45, 95% CI 1.29–4.66), peaking at ages 40–49 years (POR 3.57, 95% CI 2.02–6.32), and then declined in older age groups.
ANAs were less common in overweight (POR 0.74, 95% CI 0.56–0.97) and obese (POR 0.74, 95% CI 0.59–0.94) individuals than in persons with normal weight (P for trend = 0.02), and these differences persisted after age adjustment (Table 2). When stratified by sex, PORs were slightly lower and no longer significant, with the exception of the comparison between obese women and women with a healthy weight (age-adjusted POR 0.73, 95% CI 0.55–0.97). To assess whether educational attainment, which is strongly associated with BMI, could be influencing these results, we further adjusted for education in the BMI model that also included age as a covariate. The resulting PORs were virtually identical, suggesting that educational attainment did not explain the observed association of BMI with ANAs.
|Characteristic||No.†||No. ANA positive†||% ANA positive (95% CI)||Age-adjusted POR (95% CI)|
|Healthy||1,757||264||15.5 (12.8–18.2)||1.00 (reference)|
|Underweight||79||17||16.5 (6.6–26.5)||1.11 (0.52–2.35)|
|Overweight||1,430||180||12.8 (10.3–15.3)||0.74 (0.56–0.97)|
|Obese||1,370||188||12.6 (10.5–14.7)||0.74 (0.59–0.94)|
|Tobacco smoke exposure§|
|Not detectable||1,246||191||14.5 (11.9–17.2)||1.00 (reference)|
|Second-hand smoke exposure||2,439||349||14.4 (12.2–16.5)||1.03 (0.81–1.31)|
|Active smoking||1,040||125||12.2 (9.3–15.1)||0.86 (0.64–1.16)|
|Never and past||1,142||153||12.7 (9.9–15.6)||1.00 (reference)|
|Current, light||1,473||232||15.9 (13.2–18.6)||1.41 (0.99–2.00)|
|Current, moderate/heavy||697||93||11.8 (8.7–14.9)||1.02 (0.73–1.41)|
|C-reactive protein, mg/liter|
|Low, <1||1,625||221||13.7 (10.8–16.6)||1.00 (reference)|
|Moderate, 1–3||1,505||217||14.0 (11.3–16.6)||0.92 (0.68–1.25)|
|High, >3–10||1,176||164||13.5 (11.2–15.8)||0.88 (0.64–1.19)|
|Very high, >10||448||68||14.9 (11.1–18.7)||0.98 (0.70–1.38)|
Alcohol consumption, smoking history, total pack-years of smoking (data not shown), current smoking status (based on serum cotinine), and C-reactive protein levels were not associated with ANAs.
Among the 670 NHANES participants who had ANAs, nuclear staining was seen in 84.6% (Table 3). Nuclear patterns were less common as age increased (P for trend = 0.02) and less common among those with 0–8 years of education (65.5%) compared with those in other educational categories (81–90%). The prevalence of nuclear patterns did not vary by sex, race/ethnicity, or the family income–to–poverty level ratio but did increase with increasing educational attainment (P for trend = 0.01).
|Characteristic||ANA pattern||Specific autoantibodies|
|All nuclear (n = 560)||Nucleolar (n = 43)||All cytoplasm (n = 152)||Ro/La/Su/U1 RNP (n = 46)||Ro (n = 23)||Su (n = 20)|
|Total (n = 670)||84.6 (81.1–88.2)||6.1 (3.6–8.6)||21.8 (18.3–25.4)||6.7 (4.3–9.1)||3.9 (1.9–5.9)||2.4 (1.0–3.8)|
|12–19||86.6 (78.7–94.5)||10.2 (1.7–18.6)†||18.1 (10.4–25.7)||2.0 (0.1–3.9)†||0.7 (0–1.5)†||1.3 (0–2.9)†|
|20–29||93.7 (88.6–98.8)||11.2 (3.2–19.2)†||16.0 (8.4–23.5)||3.7 (0–8.0)†||0||3.7 (0–8.0)†|
|30–39||85.3 (76.4–94.3)||1.1 (0–2.7)†||21.7 (11.3–32.0)||10.7 (2.0–19.4)†||7.6 (0.3–14.8)†||2.5 (0–6.5)†|
|40–49||86.5 (76.6–96.5)||6.9 (0–15.3)†||16.1 (6.1–26.1)†||3.1 (0–9.4)†||3.1 (0–9.4)†||0|
|50–59||77.9 (67.4–88.3)||5.1 (0–10.3)†||28.4 (18.0–38.7)||13.5 (4.7–22.3)†||6.4 (0.2–12.5)†||6.5 (0.8–12.3)†|
|60–69||81.7 (70.3–93.2)||4.3 (0–9.7)†||32.2 (19.8–44.5)||3.5 (0–8.4)†||2.6 (0–7.3)†||0.9 (0–2.4)†|
|70+||80.4 (72.7–88.1)||4.8 (0.5–9.1)†||22.9 (14.2–31.7)||6.3 (1.2–11.5)†||4.9 (0–10.0)†||0.2 (0–0.5)†|
|P for trend||0.02||0.15||0.04||0.05||0.03||0.33|
|Male||84.5 (77.9–91.1)||8.5 (2.6–14.3)†||22.8 (16.0–29.6)||2.4 (0–5.1)†||2.0 (0–4.6)†||0.1 (0–0.3)†|
|Female||84.7 (80.2–89.3)||4.9 (2.6–7.2)||21.3 (16.1–26.6)||8.8 (5.5–12.2)||4.9 (2.1–7.6)||3.6 (1.5–5.7)|
|Non-Hispanic white||86.3 (81.8–90.8)||4.9 (2.3–7.5)||20.2 (15.5–24.9)||5.4 (2.5–8.3)||4.1 (1.6–6.7)†||0.8 (0–1.9)†|
|Non-Hispanic black||79.5 (71.9–87.0)||6.2 (1.9–10.5)†||26.7 (18.6–34.7)||10.0 (4.0–16.0)||2.1 (0–5.1)†||5.8 (1.3–10.3)†|
|Mexican American||86.6 (80.2–93.0)||7.5 (1.7–13.3)†||20.3 (13.2–27.4)||9.6 (4.7–14.4)||2.9 (0–6.2)†||6.7 (2.5–10.9)†|
|Other||79.3 (65.7–92.9)||12.9 (1.0–24.8)†||27.3 (13.3–41.4)||8.6 (0.1–17.1)†||5.4 (0–12.2)†||5.8 (0–13.3)†|
|0–8 years||65.5 (52.0–79.0)||3.1 (0.1–6.1)†||46.4 (31.6–61.2)||8.4 (1.1–15.7)†||3.4 (0–7.2)†||5.3 (0–11.1)†|
|9–11 years||81.3 (72.8–89.7)||6.0 (0–12.2)†||24.4 (14.1–34.8)||9.2 (1.7–16.6)†||1.7 (0–4.1)†||5.8 (0–12.3)†|
|High school diploma/GED||89.3 (83.0–95.6)||5.8 (2.5–9.0)||18.9 (10.6–27.2)||5.7 (1.7–9.6)†||3.6 (0–7.3)†||1.7 (0–3.4)†|
|Some college||84.3 (78.1–90.5)||7.0 (2.5–11.6)†||20.6 (14.0–27.3)||5.6 (0.7–10.5)†||5.3 (0.3–10.3)†||0.3 (0–0.8)†|
|College or postgraduate||89.7 (83.3–96.1)||6.3 (0.9–11.6)†||15.7 (8.5–22.9)||4.2 (0.4–8.1)†||3.2 (0–6.4)†||1.0 (0–3.1)†|
|P for trend||0.01||0.44||0.01||0.11||0.61||<0.001|
|Family income–to–poverty level ratio|
|At or above poverty||86.2 (82.0–90.5)||5.3 (2.7–7.9)||20.3 (15.7–24.8)||6.1 (3.5–8.7)||3.8 (1.6–6.0)||1.4 (0.4–2.4)†|
|Below poverty||82.6 (71.8–93.4)||7.8 (0.9–14.7)†||22.8 (11.2–34.3)||9.5 (0.9–18.0)†||4.6 (0–11.5)†||5.2 (0–10.5)†|
Nucleolar patterns were seen in 6.1% and cytoplasmic patterns in 21.8% of those with ANAs, and, as expected, some individuals had more than one ANA staining pattern (data not shown). Nucleolar patterns were not significantly associated with sociodemographic characteristics, but the prevalence of cytoplasmic patterns increased with age (P for trend = 0.04) and decreased with higher educational attainment (P for trend = 0.01). However, the associations of cytoplasmic patterns with age and education, while significant, were not linear.
Among individuals with ANAs, the most common autoantibodies identified by immunoprecipitation were anti-Ro autoantibodies (3.9%) and anti-Su autoantibodies (2.4%). The combined prevalence of the most common autoantibodies (anti-Ro, anti-La, anti-Su, and anti–U1 RNP), all of which are associated with multiple autoimmune diseases, was 6.7%. Furthermore, the presence of these combined autoantibodies was more common among females (8.8%) than males (2.4%).
Anti–Jo-1, anti–-PL-7, anti–-PL-12, anti-EJ, anti–signal recognition particle, anti-Ku, anti–PM-Scl, anti–Mi-2, anti–RNA polymerase I and III, and anti–U3 RNP autoantibodies were not observed in any individuals in our antinuclear antibody (ANA)–positive samples. Anti-Sm (n = 2), anti–topoisomerase I (n = 1), anti–U1 RNP (n = 7), anti-La (n = 7), anti–ribosomal P (n = 1), anti–replication protein A (n = 3), anti-OJ (n = 1), and anti–Nor 90 (n = 1) autoantibodies were rare in our sample. The estimated prevalence of anti-Ro, anti-La, anti-Su, and anti–U1 RNP autoantibodies in the general population are as follows (although the actual prevalence will likely be higher, because not all specific autoantibody–positive samples are classified as ANA positive by immunofluorescence): anti-Ro, 0.54% (female 0.86%, male 0.19%); anti-La, 0.21% (female 0.34%, male 0.08%); anti-Su, 0.33% (female 0.64%, male 0.01%); anti–U1 RNP, 0.12% (female 0.20%, male 0.03%).
ANAs are the most commonly measured biomarkers for autoimmunity and are the easiest to assess at the population level. Estimating the prevalence and types of ANAs in the US is critical to understanding their etiology and changes over time. This study provides the first nationally representative estimates of the prevalence of ANAs according to sociodemographic groups. Our investigation also included a determination of the patterns of ANAs by standard immunofluorescence methods as well as the identification of specific autoantibodies by a reliable immunoprecipitation assay in ANA-positive sera. Our finding of an overall ANA prevalence of 13.8% at a 1:80 serum dilution level is similar to the prevalence observed in some small studies in selected healthy populations (3, 7, 8, 10); however, ANA prevalence in other studies at the same dilution level ranged from 1.1% to 20% (3, 5–8, 12, 13, 30). These differences likely relate to the different populations under study and variations in ANA assessments in different laboratories.
Our findings of higher ANA prevalences in females and older individuals are similar to several earlier reports (7, 10, 31). The reason for the female predominance in autoimmune diseases is not completely understood; nevertheless, the finding of a similar pattern of female dominance in ANA production suggests that hormonal or other factors in females play a role in this process (32, 33). Although some studies suggest that aging is associated with autoimmunity, and that the prevalence of ANAs is higher in the elderly (5, 14–16, 31, 34, 35), this trend was not apparent in other studies (3, 7, 9, 10, 36). Our finding of nonlinear variations in ANA prevalence among different age groups could be the result of the differential exposure to factors related to the development of ANAs in certain age groups, nonlinear intrinsic variations in the aging of the immune or endocrine systems, or sampling bias.
The variations in the female:male ratios of ANAs in different age groups in our study are similar to patterns seen in systemic autoimmune diseases, which are strongly associated with ANA production (10, 31). One report suggested a lack of sex effects on ANA prevalence in individuals younger than age 20 years (31), but the prevalence of many autoimmune diseases increases in females during the childbearing years. For example, a female:male ratio of ∼9:1 is seen in patients with SLE in whom disease onset occurred between 20 years and 40 years of age, but this ratio is only ∼2:1 in children with SLE (0–9 years old) and in patients with elderly-onset SLE (≥60 years old) (37, 38). The female:male ratio in young to middle-aged adults in whom rheumatoid arthritis (RA) develops is ∼4:1 but is only ∼1:1 in patients with elderly-onset RA (39), and similar patterns are reported in scleroderma (40).
The ANA prevalence in non-Hispanic blacks was modestly increased compared with that in non-Hispanic whites. This is consistent with the higher incidence of SLE (37, 38) and increased prevalence of certain lupus autoantibodies, such as anti–U1 RNP, anti-Sm, and anti-Ku autoantibodies, in non-Hispanic blacks (41).
The lower prevalence of ANAs in overweight and obese subjects (particularly in females) in the present study may be unexpected given the ability of adipose tissue to produce proinflammatory cytokines (22) and estrogens (42). Nonetheless, the effect of obesity on the immune system is complex, sometimes resulting in immunosuppression (43), and an inverse association of ANA frequency with obesity has been previously reported in women (44). Also, in a study of chronic obstructive pulmonary disease, ANAs were not associated with smoking, but their frequency was significantly higher in individuals with a low BMI (<22 kg/m2) compared with subjects with a normal or high BMI (45). Additional investigations are needed to understand the cause of a lower prevalence of autoantibodies in individuals with a high BMI. Despite some studies that suggest smoking as a risk factor for SLE, RA, and other autoimmune diseases (19), we did not observe any evidence suggesting an association or dose effect of current or past smoking with ANAs.
Although ANA pattern distributions among healthy individuals vary among studies, a nuclear pattern is usually the most commonly identified pattern, followed by cytoplasmic and nucleolar patterns (5, 7, 8), as was observed in the present study. The most commonly identified autoantibodies in subjects who were positive for ANAs were anti-Ro autoantibodies (3.9% among those with ANAs and 0.53% among persons in the US) and anti-Su autoantibodies (2.4% among those with ANAs and 0.33% among persons in the US). The prevalence estimate of anti-Ro autoantibodies in this study is similar to that in an investigation of 5,000 blood donors (0.44%) (5) but lower than that in a Japanese study (2.66% in 2,181 subjects) (10). Several investigations using reliable methods reported anti-Ro autoantibodies in 0.12–2% of blood donors or pregnant women (5, 46, 47), while other small studies using enzyme-linked immunosorbent assays showed an even higher percentage of anti-Ro positivity among healthy individuals (48, 49).
It is difficult to compare these data, however, due to variations in the sensitivities and specificities among the assays used (2). Because not all anti-Ro and anti-Su autoantibodies show strong immunofluorescence (2, 23, 50), some sera containing these autoantibodies may not have met the threshold for immunoprecipitation testing in our study. Thus, the actual prevalence of these autoantibodies in the general population is likely higher than our estimate. The prevalence of autoantibodies associated with multiple systemic autoimmune diseases did not increase with age, which is consistent with a study of anti-Ro autoantibodies in female blood donors (5).
In contrast to autoantibodies that are associated with multiple systemic autoimmune diseases, such as anti-Ro and anti-Su autoantibodies (2, 23, 50), autoantibodies that are highly specific for certain diseases or disease phenotypes (e.g., anti-Sm, anti–topoisomerase I, anti–RNA polymerase I/III, and anti–Jo-1 autoantibodies) (1, 2) were rarely if ever seen in this study, supporting their disease specificity and rarity.
Our study has limitations. First, the institutionalized US population (e.g., nursing home residents) was not sampled by NHANES, and this may have led to an underestimate of ANA prevalence, especially in the elderly. Also, small sample sizes of certain subgroups may have limited our power to detect differences in ANA prevalence for some factors. Furthermore, not all types of autoantibodies were assessed by our testing, and there are other potential causes of autoantibody production in addition to autoimmune diseases, including certain infections, cancers, and drugs (2). Due to limitations inherent in the NHANES data collection methodology, including cross-sectional sampling, we could not identify which ANAs might be persistent versus transient, and we were unable to assess associations with specific autoimmune or other diseases. Finally, the prevalence of specific autoantibodies with less intense immunofluorescence may be underestimated, because only ANA-positive samples as determined by immunofluorescence were tested by immunoprecipitation.
These findings demonstrate a high prevalence of ANAs in the US, especially among females and older individuals. With the aging of the population, the number of individuals with ANAs will likely increase beyond our estimate of 32 million. These first population-based estimates of ANA prevalence as determined by indirect immunofluorescence, including their cellular staining patterns and specific autoantibody reactivities, resolve the uncertainties related to other published estimates from selected populations. These findings should be kept in mind by physicians when assessing ANA results and will serve as a useful baseline for future investigations of changes in ANA prevalence over time and the factors associated with their development.
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. Miller 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. Satoh, E. K. L. Chan, Parks, Jusko, Walker, Germolec, Whitt, Crockett, Birnbaum, Zeldin, Miller.
Acquisition of data. Satoh, E. K. L. Chan, Ho, Rose, Pauley, J. Y. F. Chan, Ross.
Analysis and interpretation of data. Satoh, E. K. L. Chan, Ho, Rose, Parks, Cohn, Jusko, Walker, Germolec, Whitt, Crockett, Pauley, J. Y. F. Chan, Ross, Birnbaum, Zeldin, Miller.
We gratefully acknowledge the important administrative contributions of Renee Jaramillo (SRA International, Inc.) as well as the statistical programming efforts and analytical insight of Dr. Zhanna Andrushchenko (SRA International, Inc). We also wish to thank Dr. Geraldine McQuillan of the CDC for administrative assistance and useful discussions, and Drs. Dale Sandler and Paivi Salo, National Institute of Environmental Health Sciences, and Dr. Mark Gourley, National Institute of Arthritis, Musculoskeletal and Skin Diseases, for their critical comments on the manuscript. We also thank the NHANES organizers and participants, without whom this study would not have been possible.