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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: Gynecol Oncol. 2021 Jan 24;161(1):282–290. doi: 10.1016/j.ygyno.2021.01.011

Epidemiologic and biologic correlates of serum HE4 and CA125 in women from the National Health and Nutritional Survey (NHANES).

Daniel W Cramer 1,2,3, Allison F Vitonis 1, Naoko Sasamoto 1,2, Hidemi Yamamoto 4, Raina N Fichorova 2,4
PMCID: PMC7994188  NIHMSID: NIHMS1663664  PMID: 33504456

Abstract

Objectives:

In women with ovarian cancer, tumor features largely determine serum HE4 and CA125 levels; but non-tumor factors may also influence levels and be better understood by studying determinants in a well-characterized sample of women without cancer.

Methods:

Serum HE4 and CA125 were measured in 2302 women from the 2001–2002 cohort of the National Heath and Nutritional Survey (NHANES). Publicly-available data on this cohort included demographic/reproductive variables, blood counts, and measurements of C-reactive protein (CRP), total homocysteine (tHcy), cotinine, and creatinine which were examined as predictors of HE4 and CA125 using multivariate models and correlational analyses.

Results:

HE4 increased non-linearly by age and current smokers had higher HE4. CA125 was lower in postmenopausal women and non-whites and trended downward with increasing BMI. Current-users of oral contraceptives (OCs) had lower HE4 and CA125; and a downward trend for CA125 was seen with increasing OC use. Pregnant women had higher CA125 and nursing women higher HE4. HE4 and CA125 were positively correlated with neutrophils, monocytes, and the neutrophil-to-lymphocyte ratio and inversely correlated with lymphocytes and the lymphocyte-to-monocyte ratio. CRP was positively correlated with both HE4 and CA125 in postmenopausal women. Strong positive correlations existed for HE4 with both tHcy and creatinine.

Conclusions:

Serum levels of HE4 and CA125 are influenced by several hormonal or environmental stimuli which affect non-cancerous tissues which normally express HE4 or CA125. Cytokine co-expression in those tissues may, in turn, affect white cell counts and account for their correlation with HE4 or CA125 levels.

Keywords: HE4, CA125, NHANES, Epidemiologic Factors, Inflammatory Biomarkers

Introduction

Currently, the two best biomarkers for ovarian cancer are CA125 and HE4. CA125 was discovered in the early 1980’s; and subsequently identified as a member of the mucin (MUC) glycoprotein family (MUC16) [1, 2]. It was approved for use in monitoring ovarian cancer and tested as a screening biomarker in national trials, which failed to establish a benefit in early detection [3, 4]. HE4 was identified in 1991 expressed in the epididymis [5]. HE4 is a member of Whey Acidic Protein (WAP) domain family. Also called WFDC2, HE4 was subsequently found to be expressed elsewhere including the female reproductive tract [6]. A re-analysis of specimens from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) showed HE4 to be the second-best screening marker for ovarian cancer next to CA125 [7]. It was approved to monitor ovarian cancer and, as part of panels that also include CA125, for use in the differential diagnosis of pelvic masses [8, 9].

Tumor features that raise CA125 include; bi-laterality, non-mucinous histology, advanced stage, and presence of ascites. Other factors include; personal history of breast cancer, family history of breast or ovarian cancer, and greater parity. Lower CA125 is seen with greater BMI [10]. Differential white counts, concurrently measured, show inverse correlations of CA125 with lymphocytes and positive correlations with neutrophils and the neutrophil-to-lymphocyte ratio (NLR) [11]. In postmenopausal women without ovarian cancer, lower CA125 is associated with non-white race, greater BMI, current smoking, and earlier age at menopause, while higher levels are seen with personal or family history of breast cancer and greater parity [12–15]. HE4 is elevated in high grade serous and endometrioid ovarian cancer [6]. HE4 is known to increase with age, smoking, and declining renal function, [16, 17]; but, compared to CA125, less information is available on other predictors of HE4 in women with or without cancer. Correlations between differential white counts and HE4 have not been investigated.

Information from generally healthy women about how and which factors affect CA125 and HE4 is important since, presumably, serum levels reflect those from tissues that express them rather than from an occult tumor. In this study, we sought to add to our knowledge about predictors of CA125 in premenopausal women and HE4 in both pre-and postmenopausal women. Using archived sera from women who participated in the 2001–2002 National Health and Nutritional Study (NHANES), we measured HE4 and CA125 and identified their epidemiologic predictors as well as their correlations with other laboratory measurements in the NHANES cohort including: differential blood counts, creatinine, cotinine, and the inflammatory markers, C-reactive protein (CRP) and total homocysteine (tHcy).

Material and Methods

Study population

The NHANES program, within the U.S. Centers for Disease Control and Prevention, began in 1999 with 2-year data collection cycles. In each cycle, about 5,000 people are selected and enrolled using a complex, multistage probability sampling design to create a representative sample of the civilian, non-institutionalized US population. Participants are interviewed once and provide health-related data via questionnaires, physical exams, and laboratory assessments. To increase the precision of estimates for certain subpopulations, the NHANES design includes over-sampling of people age 60 years and older, African Americans, and Hispanics. Health interviews are conducted in participants’ homes and exams in mobile examination centers where biologic samples are also collected. [18] In this study, we used publicly-available de-identified data collected from women in the 2001 to 2002 cohort (n=11,039). We excluded men (n=5,331) and women less than 20 years old (n=2,833). Among the remaining women, we obtained sera from 2,522 to measure biomarkers including CA125 and HE4. We excluded those with outlying HE4 or CA125 values (n=7) and women missing reproductive-health questionnaires (n=213) for a final sample of 2302 women. The study was approved by the scientific and ethics panels of NHANES. Because the research involved specimens from women anonymous to us, it was deemed exempt by the Brigham and Women’s Hospital Human Research Committee.

NHANES Covariates

Characteristics examined included age at testing, body mass index (BMI), smoking status, race/ethnicity, and marital status. Participants who reported not having smoking more than 100 cigarettes were classified as never smokers, those reporting smoking at the time of the NHANES interview were classified as current smokers, and all others were classified as former smokers. Reproductive characteristics included age at menarche, oral contraceptive (OC) use, parity, age at first birth, breastfeeding, hysterectomy, oophorectomy, age at menopause, and menopausal hormone use. Women who were ages 20–54 were ask if they had been diagnosed with endometriosis and fibroids; women who were ages 14–49 were asked if they had used powder in their genital area in the past month. For menopausal status, women were categorized as premenopausal if they reported regular (or normally irregular) periods in the past year or were less than age 50 and reported periods had stopped because of a hysterectomy without bilateral salpingo-oophorectomy (BSO). Otherwise they were categorized as postmenopausal. Age at natural menopause was defined as the age at last period for those who either never had a BSO or had a BSO after their periods stopped.

As part of the NHANES survey, an automated determination of complete blood count (CBC) was performed using the Coulter method. We accessed total white blood cells (WBC), lymphocytes, neutrophils, monocytes, hemoglobin, and platelets. Basophils and eosinophils were not included because of insufficient variability in their distributions. C-reactive protein (CRP) had been measured using Latex enhanced nephelometry on the Behring Nephelometer II Analyzer. Total plasma homocysteine (tHCY) was measured using high-performance liquid chromatography or commercial fluorescence polarization immunoassay on different-generation Abbott systems. Serum cotinine was measured by an isotope dilution-high performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry. Serum creatinine was measured using a modification of the Jaffè method. Further details on assays performed on the 2001–2002 cohort may be found on the NHANES website, https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/labmethods.aspx?BeginYear=2001.

Biomarker measurements

Serum CA125 and HE4 levels were measured at the Genital Tract Biology Laboratory (Brigham and Women’s Hospital, Boston, MA), using an electrochemiluminescence immunoassay platform (Meso Scale Discovery (MSD) (Gaithersburg, MD, USA) and available kits (CA125: catalog number K151WC, HE4: catalog number N45YA-1). It should be noted that the MSD assays are not equivalent to the commonly used clinical assays for CA125II and HE4. We have previously estimated the correlation between the CA125II assay values with the corresponding MSD assay values [19]. Both assay measurements were highly correlated with a Pearson correlation coefficient of 0.90 (95%CI: 0.88–0.91) overall and yielded a mean (and 95% confidence interval) of 12.8 (5.9–22.8) for CA125II, and a mean of 27.0 (9.2–56.4) for the MSD assay in postmenopausal women. We have not made a similar comparison between the MSD HE4 assay and the conventional HE4 assay. However, a large study [17] of HE4 levels (by the conventional assay) in healthy women suggested an arithmetic mean (and 95% reference interval) of 55.4 (26.8–118.9) pmol/L for premenopausal women and 67.6 (29–155.4) for postmenopausal women, compared to crude arithmetic means (95% reference intervals) of 14.9 (1.9–38.8) and 27.9 (4.7–92.4) for pre- and postmenopausal women in this study. This suggests a conversion ratio of 3.7 for pre- and 2.4 for postmenopausal women for translating the MSD assay to the standard clinical assay. Data on CA125 and HE4 measured in this study and additional details on laboratory methods are publicly available https://wwwn.cdc.gov/Nchs/Nhanes/search/datapage.aspx?comonent=laboratory&cyclebeginyear=2001.

Statistical analyses

We calculated the following ratios: platelet-neutrophil (PNR), platelet-lymphocyte (PLR), platelet-monocyte (PMR), neutrophil-lymphocyte (NLR), neutrophil-monocyte (NMR), and lymphocyte-monocyte (LMR). CA125, HE4, blood counts, blood count ratios, the inflammatory markers (CRP, homocysteine), creatinine, and cotinine were log transformed to achieve approximately normal distributions. CA125 and HE4 outliers were identified using the extreme studentized deviate many-outlier procedure [20] and excluded from the analysis (n=7). We used SAS survey procedures (SURVEYMEANS, SURVEYFREQ, and SURVEYREG) with the DOMAIN option for analyzing subpopulations and used sampling weights according to NHANES guidelines https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx. To assess associations between the biomarkers (CA125 and HE4) and demographic and reproductive characteristics, we used multivariable linear regression models to calculate least squares means (and 95% confidence intervals) within each level of the demographic/reproductive characteristic of interest. Means and confidence intervals were then exponentiated back to the original biomarker units. Regressions were run separately with log transformed CA125 and HE4 as the dependent variables. Models for demographic characteristics included age (and age-squared for HE4). Each model for reproductive characteristics included the reproductive characteristic of interest and potentially confounding variables. HE4 models included the following covariates: age (continuous), age-squared (continuous), and smoking status (indicators variables for never, former, and current). CA125 models included age (continuous), BMI (indicator variables for <18.5, 18.5–24.9, 25–29.9, 30–34.9, 35–39.9, and ≥40 kg/m2), and race/ethnicity (indicator variables for Mexican American, other Hispanic, non-Hispanic white, non-Hispanic Black, and other races). The adjusted least-squares means for HE4 and CA125 by reproductive events are reported for all women and separately for pre- and postmenopausal women. Trend tests for exposures in ordinal categories were calculated by modeling the median of each category as a continuous term. P-values for categorical and trend demographic and reproductive variables were calculated with Wald’s F test. To assess the associations between HE4 and CA125 and blood counts, blood count ratios, and inflammatory markers, we calculated partial correlations adjusted for potential confounders. As described above for the linear regression models, correlations with HE4 were adjusted for age, age-squared, and smoking status and correlations with CA125 were adjusted for age, BMI, and race/ethnicity. These partial correlations were calculated among all, pre-, and postmenopausal women. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).

Results

Characteristics of the participants in the 2001–2002 cohort are shown in Table 1 with geometric means (and 95% confidence intervals) for HE4 and CA125. The average age of all participants was 46.1 (SE=0.52) years and 60.7% were premenopausal. HE4 increased with age with the rate of increase greater in older women, while CA125 was significantly lower in postmenopausal women and non-whites. CA125 decreased with increasing BMI. Current smokers had almost a 60% increase in HE4 compared to never or former smokers while CA125 was (non-significantly) lower. HE4 increased with more cigarettes smoked per day; and, among current smokers, HE4 was strongly correlated with cotinine, r = 0.28 (p<0.0001), while the correlation between cotinine and CA125 was null, r = −0.01 (p = 0.92). Thus, from Table 1, major determinants for HE4 are age and current smoking and, for CA125, age, race, and BMI. It should be noted that, for HE4, both age and age-squared were significant in the linear regression models suggesting the relationship between HE4 and age is better described as quadratic. Adjusting for these factors, the associations between reproductive variables and HE4 and CA125 are examined in Table 2. Age at menarche or ever-use of oral contraceptives (OCs) were not associated with either HE4 or CA125. Among premenopausal women, current OC use was associated with both lower HE4 and CA125 and longer duration of OC use was associated with lower CA125. Women who had ever had a child had higher CA125—more apparent in premenopausal women. No trend with number of livebirths was seen for HE4; but a non-significant trend for CA125 to increase with more children was seen. Women who were currently pregnant had higher CA125. A later age at first birth was associated with lower HE4 in all and premenopausal women. No significant differences in HE4 or CA125 were seen in women who had ever breastfed, but women who were currently breastfeeding had higher HE4. Premenopausal women who had a hysterectomy had lower CA125 but not HE4; and no association was seen with unilateral oophorectomy without hysterectomy. There was a non-significant trend for women with an early age at menopause to have lower CA125; and, for women using menopausal hormones, especially estrogen-only preparations, to have a lower HE4 (see Table 2 footnote). No clear associations with HE4 or CA125 were found for women who reported history of endometriosis or fibroids. The final entry shows that postmenopausal women who powdered their genital area had higher HE4. As noted in Methods, this question was addressed only to women age 14–49 years, hence the limited number of postmenopausal cases. In a footnote to Table 2, we point out that the mean (and 95% CI) for HE4 was also significantly greater among the 12 women 40 years or older who used powder compared to the 368 of the same age who had not; 28.0 (18.9–41.3) vs. 12.7 (11.4–14.1), (p=0.002).

Table 1.

Geometric mean (95% CI) HE4 and CA125 by demographic characteristics, NHANES 2001–02 (n=2302)

N (%)1 Mean HE4 (95%CI)2 Mean CA125 (95%CI)2
Age at blood draw (years)
 20–24 262 (11) 10.9 (9.6–12.3) 12.4 (11.5–13.3)
 25–34 431 (18) 10.7 (9.7–11.7) 13.2 (12.0–14.5)
 35–44 407 (21) 12.6 (11.5–13.7) 13.3 (12.4–14.3)
 45–54 344 (20) 14.5 (13.4–15.7) 11.8 (11.0–12.7)
 55–64 301 (12) 18.5 (16.9–20.2) 9.8 (9.0–10.6)
 65–74 268 (9) 22.7 (21.2–24.2) 9.8 (9.1–10.6)
 75+ 289 (8) 36.0 (32.9–39.5) 12.4 (11.2–13.6)
 p-trend <0.0001 0.0001
Menopausal status
 Premenopausal 1273 (61) 15.4 (14.4–16.4) 14.6 (13.4–16.0)
 Postmenopausal 1029 (39) 16.4 (15.4–17.5) 9.2 (8.4–10.0)
 p-value 0.22 <0.0001
Race
 Mexican American 475 (6) 14.8 (13.4–16.3) 11.9 (11.1–12.7)
 Other Hispanic 95 (6) 14.7 (11.7–18.4) 10.5 (8.7–12.7)
 Non-Hispanic white 1250 (73) 16.2 (15.5–16.9) 12.2 (11.7–12.7)
 Non-Hispanic Black 396 (10) 15.1 (13.8–16.7) 10.7 (9.8–11.6)
 Other race 86 (5) 15.4 (12.8–18.7) 11.2 (9.4–13.3)
 p-value 0.63 0.04
Ever married/living with partner
 No 304 (15) 16.0 (13.9–18.3) 11.3 (10.3–12.4)
 Yes 1996 (85) 15.8 (15.3–16.4) 12.0 (11.5–12.4)
 p-value 0.90 0.27
Body Mass Index (kg/m2)
 < 20 148 (8) 16.9 (14.4–19.8) 11.9 (10.5–13.5)
 20–24.9 599 (31) 16.3 (15.3–17.5) 12.3 (11.4–13.3)
 25–29.9 709 (28) 15.0 (13.8–16.3) 12.0 (11.3–12.6)
 30–34.9 412 (18) 15.0 (14.0–16.1) 11.9 (11.1–12.8)
 35+ 345 (15) 14.9 (13.6–16.3) 10.6 (9.7–11.5)
 p-trend 0.11 0.04
Smoking status
 Never 1396 (57) 14.2 (13.4–15.1) 11.9 (11.6–12.3)
 Former 482 (21) 14.8 (13.9–15.7) 12.5 (11.4–13.8)
 Current 420 (22) 23.0 (21.1–25.0) 11.1 (10.3–12.0)
 p-value <0.0001 0.14
Cigarettes per day among current smokers
 <6 135 (26) 15.4 (12.7–18.8) 11.1 (9.7–12.6)
 6–14 113 (25) 19.9 (18.2–21.7) 10.5 (9.2–12.1)
 ≥15 171 (48) 21.9 (19.6–24.5) 11.9 (10.8–13.1)
 p-trend  0.008  0.31
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1

Unweighted number and weighted percentage.

2

Geometric least squares means calculated from linear regression models with either log transformed HE4 or CA125 as the dependent variable. Each model included the characteristic of interest and age (continuous). HE4 models also included age-squared. P-values from Wald’s F test.

Table 2.

Geometric mean (95% CI) HE4 and CA125 by reproductive characteristics and stratified by menopausal status, NHANES 2001–02 (n=2302)

All (n=2302)
 Premenopausal (n=1273)
 Postmenopausal (n=1029)
N (%)1 Mean HE4 (95%CI)2 Mean CA125 (95%CI)3 N (%)1 Mean HE4 (95%CI)2 Mean CA125 (95%CI)3 N (%)1 Mean HE4 (95%CI)2 Mean CA125 (95%CI)3
Age at menarche (years)
 ≤11 450 (21) 17.1 (16.1–18.2) 11.2 (10.1–12.5) 283 (23) 12.4 (11.4–13.5) 12.6 (11.5–13.8) 167 (18) 25.2 (22.9–27.7) 9.7 (7.9–11.8)
 12 585 (27) 16.3 (15.4–17.3) 10.9 (9.9–12.0) 332 (27) 11.5 (10.6–12.5) 12.3 (11.2–13.6) 253 (26) 25.7 (23.9–27.5) 9.5 (8.0–11.3)
 13 571 (27) 16.8 (15.7–17.9) 11.6 (10.6–12.8) 315 (27) 12.1 (11.0–13.2) 13.2 (11.7–15.0) 256 (26) 25.3 (23.9–26.8) 9.8 (8.9–10.9)
 14 281 (12) 17.1 (15.4–18.9) 10.8 (9.6–12.2) 146 (12) 12.4 (10.9–14.0) 12.1 (10.5–14.0) 135 (13) 25.2 (22.0–28.9) 9.4 (7.8–11.4)
 ≥15 325 (13) 17.4 (15.9–19.2) 11.0 (10.2–11.9) 144 (11) 12.9 (11.7–14.2) 11.9 (10.5–13.4) 181 (16) 25.6 (22.5–29.0) 10.0 (8.9–11.3)
 p-trend 0.55 0.80 0.48 0.41 0.97 0.76
Oral contraceptive use
 Never 995 (37) 17.7 (16.5–19.1) 11.2 (10.5–12.0) 376 (27) 12.6 (11.5–13.7) 12.7 (11.7–13.9) 619 (52) 26.8 (24.8–29.1) 9.6 (8.5–10.8)
 Ever 1274 (63) 16.5 (15.5–17.5) 11.0 (10.1–12.1) 867 (73) 12.0 (11.2–12.8) 12.4 (11.2–13.8) 407 (48) 24.0 (21.9–26.3) 9.8 (8.4–11.4)
 p-value 0.20 0.63 0.40 0.73 0.19 0.76
Duration of oral contraceptive use (months)
 Never users 998 (37) 17.7 (16.4–19.1) 11.2 (10.5–12.0) 379 (27) 12.6 (11.5–13.7) 12.7 (11.7–13.8) 619 (52) 26.9 (24.8–29.1) 9.6 (8.5–10.9)
 1–12 293 (12) 15.7 (14.1–17.4) 10.6 (9.6–11.7) 195 (14) 11.6 (10.5–12.7) 12.3 (10.6–14.4) 98 (10) 22.1 (18.5–26.4) 9.0 (7.4–11.0)
 13–47 302 (15) 16.3 (15.1–17.6) 12.0 (10.3–13.9) 210 (17) 11.7 (10.6–12.8) 13.9 (11.6–16.6) 92 (12) 24.5 (20.8–28.9) 10.4 (8.7–12.6)
 48–96 392 (20) 16.3 (14.7–18.0) 11.3 (10.4–12.2) 283 (25) 11.8 (10.5–13.1) 12.7 (11.7–13.8) 109 (14) 24.0 (21.5–26.7) 9.5 (8.0–11.3)
 > 96 275 (15) 17.6 (15.9–19.6) 10.3 (9.1–11.6) 175 (17) 13.0 (11.4–14.7) 10.5 (9.6–11.5) 100 (12) 25.3 (22.4–28.6) 10.4 (8.4–12.8)
 p-trend 0.71 0.10 0.44 0.0004 0.76 0.36
Currently using OCs
 No -- -- -- 1096 (84) 12.7 (12.0–13.3) 13.2 (12.2–14.1) -- -- --
 Yes -- -- -- 147 (16) 9.8 (8.4–11.3) 9.0 (8.2–9.9) -- -- --
 p-value -- -- 0.002 <0.0001 -- --
Parity
 Nulliparous 358 (20) 17.0 (15.3–19.0) 10.0 (9.0–11.2) 245 (26) 12.0 (10.4–13.8) 11.0 (9.9–12.3) 113 (11) 25.9 (21.4–31.5) 10.3 (8.3–12.7)
 Parous 1864 (80) 17.2 (16.7–17.8) 11.3 (10.5–12.1) 950 (74) 12.3 (11.8–12.9) 13.0 (12.1–14.1) 914 (89) 25.6 (24.7–26.6) 9.6 (8.6–10.8)
 p-value 0.81 0.04 0.69 0.01 0.91 0.49
Number of births among parous women
 1 385 (21) 16.9 (15.4–18.6) 11.0 (9.9–12.3) 272 (26) 11.3 (9.9–12.9) 13.9 (12.4–15.6) 113 (14) 26.5 (22.2–31.6) 8.7 (7.8–9.8)
 2 549 (35) 18.1 (17.0–19.3) 10.9 (10.2–11.7) 335 (41) 13.3 (12.2–14.5) 13.0 (11.9–14.3) 214 (27) 24.3 (22.4–26.4) 8.8 (8.0–9.6)
 3 440 (24) 17.8 (17.2–18.6) 11.3 (10.4–12.3) 224 (23) 13.3 (12.2–14.5) 12.7 (11.4–14.2) 216 (25) 24.2 (21.9–26.7) 10.2 (9.0–11.6)
 4+ 490 (21) 17.3 (16.5–18.3) 11.2 (9.9–12.5) 119 (11) 11.0 (9.6–12.5) 13.6 (11.4–16.2) 371 (34) 26.3 (24.5–28.1) 9.3 (8.3–10.4)
 p-trend 0.82 0.75 0.77 0.62 0.73 0.10
Currently pregnant
 No -- -- -- 998 (93) 12.2 (11.5–12.9) 12.1 (11.3–12.9) -- -- --
 Yes -- -- -- 275 (7) 11.0 (8.8–13.8) 18.3 (15.2–22.1) -- -- --
 p-value -- -- 0.44 0.0004 -- --
Breastfeeding
 Never 1138 (54) 17.1 (16.3–18.0) 10.6 (10.0–11.3) 586 (53) 12.0 (11.1–13.0) 12.0 (11.1–13.0) 552 (55) 26.1 (25.0–27.2) 9.7 (8.7–10.7)
 Ever 1079 (46) 17.4 (16.5–18.3) 11.5 (10.5–12.7) 604 (47) 12.6 (11.9–13.3) 13.1 (11.9–14.3) 475 (45) 25.1 (23.5–26.9) 9.6 (8.4–11.1)
 p-value 0.61 0.07 0.25 0.12 0.42 0.92
Currently breastfeeding
 No -- -- -- 1223 (97) 12.0 (11.4–12.6) 12.6 (11.7–13.5) -- -- --
 Yes -- -- -- 50 (3) 15.5 (13.1–18.3) 12.5 (8.6–18.3) -- -- --
 p-value -- -- 0.008 0.99 -- --
Age at first live birth among parous women (years)
 <20 596 (29) 18.3 (17.2–19.4) 11.2 (10.4–12.2) 318 (30) 13.1 (11.9–14.3) 13.2 (11.8–14.8) 278 (28) 25.9 (23.9–28.2) 9.9 (9.0–10.9)
 20–24 741 (40) 17.8 (16.8–18.9) 11.0 (10.1–12.0) 353 (36) 12.8 (11.7–14.0) 13.2 (12.3–14.2) 388 (45) 25.3 (23.6–27.1) 9.4 (8.4–10.6)
 25–29 361 (22) 17.3 (16.0–18.6) 11.0 (9.9–12.2) 184 (22) 12.4 (11.4–13.4) 13.7 (12.4–15.0) 177 (21) 24.4 (21.7–27.5) 8.5 (7.5–9.7)
 30+ 162 (10) 15.1 (13.0–17.6) 11.4 (9.6–13.6) 95 (12) 10.1 (8.2–12.5) 12.7 (10.3–15.8) 67 (7) 24.0 (20.3–28.3) 10.1 (7.7–13.2)
 p-trend 0.006 0.96 0.002 0.89 0.33 0.41
Endometriosis4
 No 1310 (91) 12.4 (11.9–13.0) 12.1 (11.2–13.1) 1169 (93) 12.0 (11.4–12.6) 12.5 (11.6–13.5) 141 (78) 15.5 (13.6–17.5) 10.3 (8.5–12.4)
 Yes 102 (9) 14.1 (12.2–16.3) 12.1 (10.2–14.3) 65 (7) 14.0 (11.2–17.6) 13.9 (11.5–17.0) 37 (22) 16.1 (12.5–20.7) 10.6 (8.0–14.0)
 p-value 0.08 0.96 0.16 0.30 0.74 0.81
Fibroids4
 No 1246 (87) 12.6 (12.0–13.2) 12.2 (11.3–13.0) 1114 (89) 12.2 (11.6–12.8) 12.6 (11.7–13.5) 132 (74) 14.9 (13.2–17.0) 10.2 (8.5–12.3)
 Yes 165 (13) 12.3 (10.7–14.1) 11.9 (9.5–14.9) 120 (11) 11.1 (9.7–12.8) 12.4 (9.7–15.9) 45 (26) 17.5 (14.3–21.5) 11.3 (8.1–15.7)
 p-value 0.76 0.85 0.23 0.90 0.13 0.50
Hysterectomy and oophorectomy
 Neither 1554 (75) 17.6 (16.9–18.3) 11.9 (11.0–12.8) 1034 (91) 12.1 (11.5–12.8) 13.1 (12.2–13.9) 520 (52) 26.8 (25.4–28.2) 10.0 (8.6–11.7)
 Hysterectomy only 202 (9) 16.5 (14.7–18.5) 9.4 (8.2–10.9) 47 (5) 12.0 (10.5–13.8) 8.3 (6.4–10.8) 155 (14) 24.4 (21.4–27.9) 9.5 (8.1–11.1)
 Unilateral 38 (2) 17.4 (13.6–22.3) 12.8 (9.5–17.3) 18 (2) 13.5 (11.0–16.6) 16.3 (11.2–23.7) 20 (2) 21.4 (13.3–34.3) 8.7 (7.1–10.8)
 oophorectomy only
 Oophorectomy and 328 (14) 16.2 (14.5–18.0) 9.2 (8.3–10.3) 15 (2) 9.7 (6.5–14.5) 9.1 (6.3–13.2) 313 (32) 24.3 (22.4–26.3) 9.2 (8.4–10.0)
 hysterectomy
 p-value 0.48 <0.0001 0.47 0.009 0.35 0.37
Hormone replacement therapy
 Never -- -- -- -- -- -- 527 (45) 26.9 (25.4–28.6) 9.9 (8.6–11.3)
 Former -- -- -- -- -- -- 193 (21) 25.1 (22.6–27.8) 8.9 (7.7–10.3)
 Current5 -- -- -- -- -- -- 270 (34) 23.7 (21.8–25.8) 9.1 (8.1–10.1)
 p-value -- -- 0.08 0.26
Age at natural menopause (years)
 <46 -- -- -- -- -- -- 136 (22) 25.6 (22.8–28.7) 9.2 (7.5–11.3)
 46 to 49 -- -- -- -- -- -- 123 (24) 28.6 (25.7–31.9) 9.5 (7.6–11.8)
 50 to 52 -- -- -- -- -- -- 195 (35) 26.1 (23.7–28.7) 10.5 (8.9–12.4)
 52+ -- -- -- -- -- -- 116 (19) 28.5 (25.8–31.6) 10.3 (8.7–12.2)
 p-trend -- -- 0.28 0.11
Genital powder use6
 No 1208 (97) 11.9 (11.3–12.6) 12.6 (11.7–13.7) 1150 (97) 11.8 (11.2–12.5) 12.7 (11.7–13.8) 58 (92) 14.6 (11.1–19.1) 9.7 (8.4–11.3)
 Yes 45 (3) 13.1 (9.4–18.3) 11.1 (9.4–13.1) 40 (3) 11.9 (8.8–16.3) 11.4 (9.3–13.9) 5 (8) 36.5 (25.9–51.5) 7.7 (6.1–9.6)
 p-value 0.60 0.16 0.95 0.32 0.003 0.12
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1

Unweighted number and weighted percentage

2

Geometric least squares means calculated from linear regression models with log transformed HE4 as the dependent variable. Each model included the characteristic of interest, age (continuous), age-squared (continuous), and smoking status (never, former, current). P-values from Wald’s F test.

3

Geometric least squares means calculated from linear regression models with log transformed CA125 as the dependent variable. Each model included the characteristic of interest, age (continuous), BMI (<18.5, 18.5–24.9, 25–29.9, 30–34.9, 35–39.9, and ≥40 kg/m2), and race/ethnicity (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic Black, and other races). P- values from Wald’s F test.

4

Information on endometriosis and fibroids was only collected on women age 20–54.

5

HE4 among current estrogen only HRT users, 23.5 (21.0–26.5) was borderline significantly lower than mean HE4 among never/former HRT users, 27.1 (25.5–28.7), p=0.05.

6

Genital powder use in the past month among women ages 20–49. When restricted to women age 40 years and older, the geometric mean (95% CI) HE4 among 12 powder users was 28.0 (18.9– 41.3) compared to 12.7 (11.4–14.1) among 368 non-users, p=0.002.

Partial correlations and p-values are shown in Table 3 for the blood count parameters, inflammatory markers, and creatinine. Focusing on correlations which had p-values <0.01, HE4 was positively correlated with neutrophils, monocytes, and NLR and inversely with LMR and PNR but only among postmenopausal women. For CA125, correlations were generally present in both pre- and postmenopausal women and included positive correlations with neutrophils, monocytes, and NLR and an inverse correlation with LMR. Among postmenopausal women, CA125 was also positively correlated with platelets. HE4 was strongly and positively correlated with serum creatinine especially in postmenopausal women in whom a correlation of 0.42 (p<0.0001) was seen. The final two entries in Table 3 show correlations between HE4 and CA125 and the two inflammatory markers, CRP and tHcy. Positive correlations were found with CRP for both HE4 and CA125 in postmenopausal women. For tHcy, positive correlations with HE4 were seen in both pre- and postmenopausal women with an r of 0.32 (p<0.0001) in the latter group. There was a positive correlation between HE4 and CA125 themselves in postmenopausal women, r=0.10 (p=0.002) and a strong correlation between tHcy and creatinine in all women, r=0.48 (p<0.0001).

Table 3.

Partial correlations between blood counts, blood count ratios, inflammatory markers, and creatinine and HE4 and CA125, stratified by menopausal status.

All Premenopausal Postmenopausal
HE41
r (p)2 		CA1251
r (p)3 		HE41
r (p)2 		CA1251
r (p)3 		HE41
r (p)2 		CA1251
r (p)3
Lymphocytes −0.05 (0.009) −0.04 (0.04) −0.04 (0.11) −0.04 (0.16) −0.07 (0.03) −0.01 (0.87)
Neutrophils 0.07 (0.001) 0.10 (<0.0001) 0.03 (0.37) 0.09 (0.001) 0.13 (<0.0001) 0.08 (0.008)
Monocytes 0.04 (0.03) 0.07 (0.001) −0.01 (0.73) 0.05 (0.07) 0.12 (0.0001) 0.06 (0.04)
Hemoglobin −0.05 (0.03) −0.07 (0.0009) −0.02 (0.50) −0.06 (0.02) −0.09 (0.006) 0.00 (0.97)
Platelets 0.01 (0.60) 0.05 (0.03) 0.00 (0.92) 0.01 (0.82) 0.02 (0.48) 0.10 (0.001)
PNR −0.06 (0.006) −0.07 (0.0006) −0.02 (0.40) −0.08 (0.006) −0.11 (0.0006) −0.02 (0.54)
PLR 0.05 (0.01) 0.06 (0.003) 0.04 (0.19) 0.04 (0.20) 0.07 (0.03) 0.06 (0.05)
PMR −0.03 (0.12) −0.03 (0.10) 0.01 (0.82) −0.04 (0.16) −0.09 (0.005) 0.01 (0.87)
NLR 0.09 (<0.0001) 0.12 (<0.0001) 0.05 (0.08) 0.10 (0.0002) 0.15 (<0.0001) 0.07 (0.02)
NMR 0.03 (0.12) 0.05 (0.02) 0.04 (0.21) 0.05 (0.08) 0.02 (0.43) 0.03 (0.39)
LMR −0.08 (<0.0001) −0.10 (<0.0001) −0.03 (0.37) −0.08 (0.008) −0.16 (<0.0001) −0.06 (0.06)
CRP (mg/dL) 0.03 (0.20) 0.07 (0.0003) −0.03 (0.26) 0.06 (0.02) 0.12 (<0.0001) 0.13 (<0.0001)
Homocysteine (umol/L)4 0.19 (<0.0001) −0.02 (0.25) 0.09 (0.0007) −0.09 (0.002) 0.32 (<0.0001) 0.06 (0.04)
Creatinine (mg/dL)4 0.25 (<0.0001) 0.02 (0.46) 0.12 (<0.0001) −0.05 (0.06) 0.42 (<0.0001) 0.08 (0.009)
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1

Partial correlation between HE4 and CA125 adjusted for age (continuous), age-squared (continuous), race/ethnicity (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic Black, and other races), BMI (<18.5, 18.5–24.9, 25–29.9, 30–34.9, 35–39.9, and ≥40 kg/m2), and smoking status (never, former, current): all r=0.02 (p=0.29); premenopausal r=−0.02 (p=0.37); postmenopausal r=0.10 (p=0.002).

2

Adjusted for age (continuous), age-squared (continuous), and smoking status (never, former, current)

3

Adjusted for age (continuous), race/ethnicity (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic Black, and other races), and BMI (<18.5, 18.5–24.9, 25–29.9, 30–34.9, 35–39.9, and ≥40 kg/m2)

4

Partial correlation between homocysteine and creatinine adjusted for age (continuous): all r=0.48 (p<0.0001); premenopausal r=0.48 (p<0.0001); postmenopausal r=0.49 (p<0.0001).

Discussion

We measured CA125 and HE4 in sera from generally healthy women who participated in the 2001 and 2002 NHANES cohort and correlated these with baseline demographic and reproductive variables, blood count data, and the inflammatory biomarkers, CRP and tHcy. In such women, biomarkers are likely to originate from the tissues that normally express them rather than from an occult tumor. Levels should reflect exogenous or endogenous factors raising or lowering tissue expression or other factors that may affect biomarker degradation or ability to be measured. In this Discussion, we place findings from our study in the context of an overview of published data on which demographic and reproductive variables affect CA125 and HE4 in healthy women (Table 4). We then review novel epidemiologic findings related to HE4 and CA125, as well as correlations with other measurements performed in the NHANES cohort.

Table 4.

Studies supporting effects of baseline and reproductive factors on HE4 and CA125 levels in healthy women.

FEATURE Findings HE4 REF Findings CA125 REF
Age Progressive increase with age [16, 17, 21, 22] Lower in younger and older women [24]
Body Mass Decreases with greater body mass with a similar trend seen here [16] Decreases with greater body mass as seen here [13]
Race No differences in limited data as seen here [26, 27] Lower in non-white as seen here [13, 14, 26]
Smoking Greater in current smokers as seen here [16, 21, 22, 25, 26] Lower in current smokers; higher in former smokers as trends seen here [13, 14, 25]
Age at Menarche Increased with later age not seen here [27] Increased with later age not seen here [14]
Current OC use Lower as seen here [28] Lower as seen here [29]
Duration OC use Lower with contraceptive use not seen here [23] Lower with longer OC use as seen here in premenopausal women [29]
Currently Pregnant Lower, not significant here [17] Higher as seen here [30]
Number of pregnancies Lower with increasing number in postmenopausal women not seen here [25] Higher with more pregnancies, similar to a non-significant trend seen here [15, 25]
Hysterectomy No apparent effect as seen here [25] Lower as seen here (only in pre-menopausal women) [13, 14, 25]
Genital Talc use Higher of borderline significance significant as seen here [27] Significantly higher not seen here [27]
Menopausal status Higher in PM women, likely due to age alone [21] Lower after menopause as seen here except in women 75 or older lower [25]
Age at menopause Lower with older age not seen here [25] Lower in women with early menopause [13, 14, 25]
HRT use No apparent effect while lower level seen here with estrogen-only ERT [25] Lower with HRT use not seen here [25]
C-Reactive Protein No data found No data found
Homocysteine No data found No data found
Creatinine Positively correlated [16] No data found
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Age affects both HE4 and CA125 but neither relationship is linear. HE4 levels increase with age [16, 17, 21, 22] with the level of increase greater as women age, suggesting a quadratic fit. Because HE4 is excreted through the kidneys, increasing levels of HE4 with age could be explained by kidney function since glomerular filtration rates (GFRs) clearly decline with age [23]. This relationship is supported by the strong positive correlation between creatinine, a crude measure of kidney function, and HE4 found here and observed by others [16]. CA125 may have an inverted U shape with lower levels in younger and older women [24] and clearly decreases after menopause [25]. CA125 and possibly HE4 also decrease with greater BMI [13, 16]; and CA125 is lower in non-whites, especially non-Hispanic Black women[13, 14]. In agreement with the literature [16, 21, 22, 25, 26], we found clear evidence that current smokers have higher HE4 which in our study correlated with cigarettes smoked per day and serum cotinine. This likely represents increased expression of HE4 in the upper-respiratory tract irritated by tobacco smoke [22]. Smoking does not raise CA125 levels; if anything, levels may be lower in current smokers and rebound in former smokers [13, 14].

For reproductive variables, a later age at menarche was associated with higher HE4 and CA125 levels [14, 27]; but neither association was seen here. We found that current OC use lowers both HE4 and CA125—observations having prior support [28, 29]. This may represent decreased expression of both markers in the endometrium which would otherwise be proliferating during a natural cycle. Similarly, two reports suggest that a longer duration of OC use is associated with lower HE4 [23] and CA125 [29] – only the latter observation confirmed here. Women who were currently pregnant had higher CA125 levels which is well established [30]. It is generally accepted that expression of CA125 by the decidua and endometrium are the source of elevated CA125 during pregnancy [21]. While the endometrium may also express HE4, increased GFR during pregnancy could explain the absence of any major effect (or even lowering) of HE4 [17]. We found that premenopausal women who ever had a child had higher CA125 but did not find that CA125 increases with number of children in postmenopausal women as reported in some studies[15, 25]. Confirming previous studies [13, 14, 25], we found lower CA125 in premenopausal women who had a hysterectomy. We found a trend for CA125 to be lower in women with an early age at menopause reported in other studies [13, 14, 25] but no clear association of age at menopause with HE4. An intriguing finding from a screening study of healthy postmenopausal women was that those using powder genitally had higher levels of both CA125 (p = 0.04) and HE4 (p = 0.06) [27]. We could not confirm the finding related to CA125, but we did see significantly higher HE4 levels in 12 genital powder users age 40 years and older, including 5 postmenopausal women. Although tenuous due to small numbers, the finding may have some biologic support. Parallel to that seen with smoking and the respiratory tract, talc may induce an inflammatory response in the genital tract leading to HE4 overexpression. Other causes of vaginal inflammation, like bacterial vaginosis, may also raise HE4, at least in vaginal secretions [31]. Talc can be found in the upper genital tract of women with ovarian cancer and identified as having the same physical properties as talc of the type women had used genitally[32].

Novel observations from our study related to HE4 include: lower HE4 in premenopausal women with a later age at first livebirth; higher HE4 levels in women who were breastfeeding; and lower HE4 in those using estrogen-only menopausal hormone preparations. The association with breastfeeding is compatible with the observation that HE4 is expressed in breast ducts [6]. Lower HE4 in women on estrogen-only hormonal therapy is important to confirm since this may impact on the interpretation of the HE4 level when it is used in the differential diagnosis of a pelvic mass.

Other novel findings from our study relate to the correlations described in Table 3 between HE4, CA125, and the blood count parameters available from the NHANES cohort. We found HE4 was positively correlated with neutrophils, monocytes, and NLR and inversely with LMR and PNR but only among postmenopausal women. For CA125, correlations were generally present in both pre- and postmenopausal women and included positive correlations with neutrophils, monocytes, and NLR and an inverse correlation with LMR. Among postmenopausal women, CA125 was also positively correlated with platelets. Notably, the same correlations between blood counts and CA125 have been described in women with ovarian cancer and go in the same direction[11]; but no similar data on HE4 is available. This suggests that at least CA125 (and likely HE4) affect peripheral blood count parameters regardless whether they are from a tumor or tissue subjected to hormonal or inflammatory stimuli that cause their secretion. Explanations are speculative but could include: neutrophilia, monocytosis, and lymphopenia induced by cytokines, such as TNF-alpha, IL-1, and MCP that are co-expressed with HE4 such as observed in smokers [33] or with CA125 in pelvic inflammatory disease [34].

Our study also examined correlations among HE4, CA125, CRP, and tHcy. Like CA125, HE4, and blood counts or ratios, elevated CRP has also been associated with poor prognosis for ovarian cancer [35], so it is not surprising that CRP was positively correlated with both HE4 and CA125, mainly among postmenopausal women. These correlations suggest that HE4 and CA125 should be included in the broad category of biomarkers of inflammation. We also found that HE4, but not CA125, was strongly correlated with tHcy. tHcy has been proposed as both a tumor marker as well as a risk factor for many cancers including ovarian [36]. tHcy is strongly correlated with age and creatinine [37], as is HE4, observed in this and another study [16]. Both tHcy and HE4 can be markers for not only cancer, but also cardiovascular or renal disease [38, 39]. This suggests a more fundamental association exists between tHcy and HE4 that could involve the important one-carbon pathway in disease pathogenesis. HE4 may need to be added to the long list of factors in that pathway including tHCy, methionine, cysteine, folates, folate receptors, the B-vitamins others, and multiple enzymes.

A major weakness of our study was that non-standard assays for HE4 and CA125 were used in order to accommodate the small volume of sera available (0.4ml, on average) and analytes proposed to be measured (at least 6). This prevents us from using our data to define normal ranges or cutpoints for clinical action; but does serve the important goal of defining key determinants of both biomarkers in largely healthy women which may need to be considered in clinical diagnositic algorihms using HE4 and CA125. Besides age and menopausal status already considered, non-white race for CA125 and current smoking for HE4 would likely increase the accuracy of these algorithms. An additional strength of this study is that we measured HE4 and CA125 in women from the 2001–2002 NHANES cohort, which provides generalizability through its sampling scheme and has well-annotated and extensive data on potential covariates. Although the availability of such data lends itself to the possibility of false discovery, we would point out that most of our findings have support, if not from general population data, at least from women with ovarian cancer. Perhaps the most important strength of our study is that our data on HE4 and CA125 are now publicly available to allow additional studies to be pursued, including those that might be relevant to the biologic explanations we have offered.

In conclusion, we have found that a range of demographic and reproductive factors, blood count parameters, and recognized inflammatory markers are associated with HE4 and CA125 in generally healthy women in similar ways as seen in women with ovarian cancer. This indicates more fundamental roles for HE4 and CA125 in health and disease. Explanations for these associations could include hormonal or environmental stimuli raising (or lowering) expression of HE4 or CA125 in normal tissues, co-expression of cytokines that affect blood count variables, and kidney function and one-carbon metabolism for HE4.

Highlights.

  • In archived sera, we measured HE4 and CA125 to correlate with features of 2302 women without cancer from a national survey.

  • Hormonal or environmental stimuli affecting tissues that normally express HE4 and CA125 can change their serum levels.

  • Cytokine co-expression may lead to neutrophilia, monocytosis, or lymphopenia and explain correlations with HE4 and CA125.

  • Suggesting roles as inflammatory biomarkers, HE4 and CA125 are positively correlated with CRP in postmenopausal women.

  • HE4 levels rise with creatinine showing renal function’s role and with tHcy suggesting a role in one-carbon metabolism.

Acknowledgments:

Funding:

Supported by a grant from the National Cancer Institute Grant number 5R35CA197605

Footnotes

Conflict of interest: Dr Cramer reports that he has served as a plaintiff’s expert in litigation related to ovarian cancer and talc.

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