Abstract
Objective
Maternal prepregnancy obesity is associated with offspring obesity. Underlying mechanisms may involve a maternal-obesity-mediated pro-inflammatory state during pregnancy. Maternal C-reactive protein (CRP)-level during pregnancy is a biomarker of low-grade systemic inflammation.
Methods
Among 1116 mother-child pairs, we examined associations of maternal second trimester CRP-plasma-level, measured by high-sensitivity-CRP-arrays, with mid-childhood DXA fat-mass-index (FMI), trunk-fat-mass-index (trunkFMI), fat-free-mass-index (FFMI), and early- and mid-childhood BMI-z and waist circumference (WC). Main analyses were adjusted for maternal socio-demographic and lifestyle-related characteristics, gestational age at blood draw, child’s age, sex.
Results
Higher maternal CRP-level was associated with higher mid-childhood FMI and trunkFMI (adjusted difference: 0.15 kg/m2 [95%CI: 0.01, 0.29] [p-value=0.04] and 0.06 kg/m2 [95%CI: 0.00, 0.12] [p-value=0.06], per SD increment in maternal CRP, respectively), but not FFMI. Higher maternal CRP-level was associated with higher early- and mid-childhood BMI-z and WC in the basic models [p-value<0.05], but these associations attenuated after adjustment for maternal characteristics (adjusted difference in early- and mid-childhood BMI-z and WC: 0.05 [95%CI: −0.03, 0.13] [p-value=0.20], 0.10 cm [95%CI: −0.17, 0.37] [p-value=0.46], 0.07 [95%CI:−0.01, 0.14] [p-value=0.09], 0.34 cm [95%CI: −0.25, 0.94] [p-value=0.26], per SD increment in maternal CRP, respectively).
Conclusions
Higher second trimester maternal CRP-level was associated with higher mid-childhood overall and central adiposity.
Keywords: childhood obesity, cohort study, CRP, inflammation, pregnancy
INTRODUCTION
Maternal obesity during pregnancy is associated with an increased risk of obesity and an adverse cardio-metabolic risk profile in childhood and adulthood (1). The mechanisms underlying these associations remain unclear, but might involve obesity-mediated inflammatory mechanisms, which may adversely influence fetal development (2). In non-pregnant women, obesity is associated with low-grade systemic inflammation and oxidative stress (3, 4). Similar associations are present among pregnant women (5). Pregnancy itself also involves a state of mild maternal systemic inflammation, with the placenta producing a range of immunomodulatory hormones and cytokines (6). Maternal obesity-mediated inflammation and placental-mediated inflammation may interact with each other, creating an abnormal environment for fetal development (7, 8, 9). In line with this hypothesis, animal studies show that maternal inflammation and oxidative stress during pregnancy, due to a high-fat maternal diet or maternal obesity, are associated with increased adiposity levels and adverse cardio-metabolic outcomes in offspring (10, 11).
In humans, few studies have examined the influence of inflammatory mechanisms on long-term cardio-metabolic health outcomes in offspring. A study among 18 mother-offspring pairs showed that maternal IL-6-level correlated with neonatal fat mass (12). However, a recent study among 439 mother-offspring pairs that examined the associations of maternal third trimester CRP, TNF-α, IL-6, and IL-1β with offspring body mass index (BMI), waist circumference, blood pressure and several metabolic measures at the age of 20 years, observed no associations (13). Given the rise in maternal prepregnancy obesity, it is important from both an etiological perspective and for the potential development of preventive strategies, to obtain further insight into the potential role of obesity-mediated inflammatory mechanisms during pregnancy on long-term cardio-metabolic health of offspring. Maternal CRP-levels during pregnancy, which production is stimulated by pro-inflammatory cytokines, can be used as a biomarker of low-grade systemic inflammation(14).
Therefore, in a longitudinal cohort study among 1116 mothers and their children, we examined the associations of maternal second trimester plasma level of CRP with offspring total body and truncal fat mass measured by DXA, BMI and waist circumference in early- and mid-childhood.
SUBJECTS AND METHODS
Study design
This study was embedded in Project Viva, an ongoing prospective pre-birth cohort study in which pregnant women were recruited at their initial prenatal visit from Atrius Health, a multispecialty group practice in eastern Massachusetts, between April 1999 and July 2002(15, 16). All mothers gave written informed consent, and institutional review boards of participating institutions approved the study. All procedures were in accordance with the ethics standards established by the Declaration of Helsinki(17).
In total, 2128 mothers gave birth to singleton live-born children, of whom 1614 (75%) provided a mid-pregnancy blood sample for analysis (18). Of the N=1614 mothers and their children, we included 1116 mothers and their children who had an early or mid-childhood in-person visit. Compared with 1116 included participants, the 1012 excluded mothers had a lower education level (41.9% v. 29.4% less than a college degree), included fewer whites (58.8% v. 73.4%), and had a higher mean prepregnancy BMI (mean (SD): 25.2 kg/m2 (6.0) v. 24.6 kg/m2 (5.2)).
Maternal CRP-level
We collected blood samples in the second trimester of pregnancy, processed them within 24 hours, and stored at them at -80°C until analyses. We assessed the concentration of maternal CRP-level in plasma samples with a validated high-sensitivity immunoturbidimetric assay on the Hitachi 911 analyzer (Roche Diagnostics, Indianapolis, Indiana) by using reagents and calibrators from Denka Seiken (Niigata, Japan)(19).
Childhood body mass index and body fat distribution
During in-person visits in early- (3–5 y) and mid- (7–10 y) childhood, trained research assistants measured children’s weights (TBF-300A; Tanita, Arlington Heights, IL) and heights (calibrated stadiometer; Shorr Productions, Olney, MD). We calculated age- and sex-specific BMI percentiles and z-scores using U.S. national reference data (20). To measure waist circumference, we used the method used in the US National Health and Nutrition Examination Survey (NHANES)(21). We measured waist circumference just above the right iliac crest at the mid-axillary line to the nearest 0.1 cm using a Hoechstmass measuring tape (Hoechstmass Balzer, Sulzbach, Germany). Research assistants followed standardized techniques and participated in biannual in-service training to ensure measurement validity (IJ Shorr; Shorr Productions). Inter- and intra-rater errors for these measurements were within published reference ranges for all measurements (21).
During the mid-childhood visit, trained research assistants administered whole-body DXA scans with Hologic model Discovery A (Hologic, Bedford, MA), which they checked for quality control on visit days. We used Hologic software version 12.6 for scan analysis. A single trained research assistant checked all scans for positioning, movement and artifacts, and defined body regions for analysis. Intrarater reliability was high (r = 0.99). As total body fat mass and fat-free mass are strongly dependent on body size, we calculated the DXA fat mass index and DXA fat-free mass index to take into account childhood current height using the following formula: [total DXA fat mass or fat free mass in kg]/(height in meters)2] (22) We observed similar results when we used total body fat mass and fat free mass in kg (results not shown). We also calculated the DXA truncal fat mass index, a measure of central adiposity.
Covariates
Using a combination of questionnaires and interviews during pregnancy, we obtained information about maternal age, race/ethnicity, educational level, parity and smoking during pregnancy. Participants completed semiquantitative food frequency questionnaires (SFFQ) at study enrolment, from which we obtained information about total calorie intake.
Mothers reported their prepregnancy weight and height. We collected information from prenatal medical records on infant birth weight and delivery date. We derived gestational age from the last menstrual period or from the second trimester ultrasound if the two estimates differed by >10 days. Based on U.S. national natality data, we determined sex-specific birth weight for gestational age z scores(23). As described in detail previously, to identify mothers who developed gestational hypertension or preeclampsia, we reviewed outpatient charts for blood pressure and urine protein results, and additionally reviewed inpatient hospital charts only for women who had a diagnosis or discharge code indicating preeclampsia or gestational hypertension and who did not already meet criteria for the same diagnosis based upon our review of outpatient charts (24).
We obtained data on gestational diabetes (GDM) from the clinical laboratory and diagnosis records, which we described in detail previously (25). Briefly, obstetric clinicians routinely screened all women for GDM at 26–28 weeks of gestation with a nonfasting oral glucose challenge test (GCT), in which venous blood was sampled 1-h after a 50-g oral glucose load. If the blood glucose exceeded 140 mg/dL, the clinician referred the woman for a fasting 3-h 100-g oral glucose tolerance test (OGTT). We categorized women into 4 categories: GDM when women had two or more abnormal values on the OGTT; impaired glucose tolerance when women had one abnormal value on the OGTT; women with an abnormal GCT but a normal OGTT; and women with a normal glucose tolerance (25).
Statistical analysis
First, we explored bivariate associations of maternal and fetal characteristics with second trimester maternal CRP-level using linear regression models. Second, we examined the associations of maternal CRP-level with early-childhood and mid-childhood adiposity outcomes using linear regression models. We constructed 4 different models to examine these associations: 1) a basic model adjusted for gestational age at maternal CRP measurement, child sex and age at outcome measurement; 2) a confounder model, which was additionally adjusted for maternal age, pre-pregnancy BMI, race/ethnicity, educational level, parity, smoking during pregnancy and total calorie intake during pregnancy; 3) mediator models, which additionally included maternal pregnancy complications and birth characteristics as potential intermediates; 4) a fully adjusted model including all covariates. Similar results were found when we used maternal BMI at CRP measurement in the second trimester instead of maternal prepregnancy BMI (results not shown). Additional adjustment for maternal total gestational weight gain only marginally changed the observed effect estimates (results not shown). For all analyses, we constructed internal z-scores of maternal CRP to analyse the continuous associations with early- and mid-childhood adiposity outcomes. We examined potential interactions between maternal CRP-level and offspring sex, but since no significant interactions were present, no further stratified analyses were performed. Sensitivity analyses were performed among women with a normal glucose tolerance status only.
Missing data of variables were imputed using multiple imputations. We generated 50 imputed data sets, and results were computed by appropriately combining these results. We used all 2128 Project Viva subjects in the imputation process, but the analysis sample included only the 1116 participants with maternal mid-pregnancy blood available and early- or mid-childhood in-person visits. For early-childhood outcomes, we included 1043 participants with an early-childhood in-person visit and for mid-childhood outcomes, we included 874 participants with a mid-childhood in-person visit. As compared to the complete case analysis, the effect estimates only changed slightly after using multiple imputations to deal with the missing values (results not shown). We conducted all of the analyses using SAS version 9.3 (SAS Institute, Inc, Cary, North Carolina).
RESULTS
Maternal and childhood characteristics and CRP-level
Characteristics of the included mothers and children are given in Table 1. Median (IQR) maternal level of CRP was 1.2 mg/l (0.6, 2.1). Mean (SD) BMI-z was 0.43 (1.04) and WC 51.3 cm (3.5) in early-childhood. Mean (SD) mid-childhood FMI was 4.3 kg/m2 (1.8) , trunkFMI 1.4 kg/m2 (0.8), FFMI 13.0 kg/m2 (1.4), BMI-z 0.35 (0.98) and WC 59.6 cm (7.7).
TABLE 1.
Characteristics of mothers and their children in Project Viva (N=1116)
| Characteristic | Total group | CRP<1.2 mg/L | CRP=>1.2 mg/L |
|---|---|---|---|
| Maternal characteristics | |||
| Age, mean (SD), years | 32.4 (5.0) | 32.4 (5.0) | 32.4 (5.0) |
| Gestational age at intake, mean (SD), wks | 10.4 (2.5) | 10.5 (2.5) | 10.4 (2.4) |
| Height, mean (SD), m | 1.7 (0.1) | 1.7 (0.1) | 1.7 (0.1) |
| Prepregnancy weight, mean (SD), kg | 67.4 (15.3) | 63.6 (12.2) | 71.1 (17.1) |
| Prepregnancy Body Mass Index, mean (SD), kg/m2 | 24.6 (5.2) | 23.3 (4.0) | 26.0 (5.9) |
| Education, at least college graduate, N (%) | 788 (70.6%) | 413 (74.7%) | 375 (66.6%) |
| Ethnicity, White, N (%) | 819 (73.4%) | 415 (75.1%) | 404 (71.8%) |
| Parity, Nulliparous, N (%) | 531 (47.6%) | 266 (48.2%) | 265 (47.0%) |
| Smoking during pregnancy, N (%) | |||
| Never | 770 (69.0%) | 385 (69.6%) | 385 (68.3%) |
| Former | 228 (20.4%) | 119 (21.5%) | 109 (19.4%) |
| Continued | 118 (10.6%) | 49 (8.9%) | 69 (12.3%) |
| Total energy intake during pregnancy, kcal/day, | 2126 (602) | 2117 (576) | 2135 (626) |
| Maternal inflammatory biomarkers | |||
| Gestational age at measurement, median (IQR), wks | 28.0 (27.1–28.6) | 28.0 (27.1–28.6) | 28.0 (27.1–28.6) |
| CRP, median (IQR range), mg/L | 1.2 (0.6–2.1) | 0.6 (0.3–0.8) | 2.1 (1.6–2.9) |
| Maternal pregnancy complications | |||
| Gestational hypertensive disorder, N (%) | |||
| Normal | 994 (89.0%) | 508 (91.9%) | 486 (86.2%) |
| Chronic hypertension | 15 (1.4%) | 4 (0.8%) | 11 (2.0%) |
| Gestational hypertension | 71 (6.4%) | 26 (4.7%) | 45 (8.0%) |
| Preeclampsia | 36 (3.2%) | 14 (2.6%) | 22 (3.8%) |
| Gestational diabetes, No (%) | |||
| Normal | 932 (83.5%) | 471 (85.3%) | 461 (81.8%) |
| Failed GCT normal OGTT | 101 (9.1%) | 54 (9.9%) | 47 (8.4%) |
| Impaired glucose tolerance | 32 (2.9%) | 11 (1.9%) | 21 (3.7%) |
| Gestational diabetes | 50 (4.5%) | 16 (2.9%) | 34 (6.1%) |
| Birth characteristics | |||
| Females, N. (%) | 538 (48.2%) | 264 (47.8%) | 274 (48.6%) |
| Gestational age at birth, median (IQR range), weeks | 39.7 (38.9–40.6) | 39.7 (38.9–40.6) | 39.7 (38.7–40.6) |
| Birth weight, mean (SD), g | 3507 (537) | 3483 (512) | 3530 (559) |
| Birth weight for gestational age, mean (SD), z-score | 0.22 (0.96) | 0.17 (0.93) | 0.27 (0.98) |
| Early childhood characteristics | |||
| Age at follow up, median (IQR range), yr | 3.2 (3.1–3.3) | 3.1 (3.1–3.3) | 3.2 (3.1–3.3) |
| Body mass index, mean (SD), kg/m2 | 16.5 (1.5) | 16.3 (1.3) | 16.7 (1.6) |
| Body mass index, mean (SD), z-score | 0.43 (1.04) | 0.30 (0.97) | 0.57 (1.08) |
| Waist circumference, mean (SD), cm | 51.3 (3.5) | 51.0 (3.1) | 51.6 (3.9) |
| Mid-childhood characteristics | |||
| Age at follow up, median (IQR range), yr | 7.7 (7.3–8.3) | 7.7 (7.3–8.2) | 7.7 (7.3–8.3) |
| Total fat mass index, mean (SD), kg/m2 | 4.3 (1.8) | 4.0 (1.5) | 4.6 (2.0) |
| Fat free mass index, mean (SD), kg/m2 | 13.0 (1.4) | 12.8 (1.3) | 13.1 (1.4) |
| Truncal fat mass index, mean (SD), kg/m2 | 1.4 (0.8) | 1.3 (0.7) | 1.5 (0.9) |
| Body mass index, mean (SD), kg/m2 | 17.1 (2.9) | 16.6 (2.4) | 17.5 (3.2) |
| Body mass index, mean (SD), z-score | 0.35 (0.98) | 0.20 (0.94) | 0.49 (0.99) |
| Waist circumference, mean (SD), cm | 59.6 (7.7) | 58.6 (6.8) | 60.5 (8.3) |
Values represent means (SD), median (IQR range) or number of subjects (%).
Bivariate associations between maternal and fetal characteristics and second trimester maternal CRP-level are given in Table 2. Second trimester maternal CRP-level was higher among overweight and obese women and among women with gestational diabetes (difference in maternal CRP-level for overweight and obese women and women with gestational diabetes: 0.51 mg/L [95% CI: 0.29, 0.73], 1.02 mg/L [95% CI: 0.78, 1.27] and 0.55 mg/L [95% CI: 0.07, 1.02], as compared to normal weight women and women without gestational diabetes, respectively). No consistent associations of other maternal and fetal characteristics with maternal second trimester CRP-level were present.
TABLE 2.
Bivariate associations of maternal characteristics and pregnancy outcomes with maternal CRP level in the second trimester of pregnancy (N=1116)
| Maternal characteristics | Difference in CRP Levels (mg/L) (95%CI) |
|---|---|
| Maternal age | |
| <25 years | 0.06 (−0.28, 0.40) |
| 25–29.9 years | −0.07 (−0.31, 0.18) |
| 30–34.9 years | 0.0 (ref) |
| ≥35 years | −0.10 (−0.31, 0.10) |
| Body mass index | |
| <18.5 kg/m2 | −0.17 (−0.64, 0.30) |
| 18.5–24.9 kg/m2 | 0.0 (ref) |
| 25–29.9 kg/m2 | 0.51 (0.29, 0.73) |
| ≥30 kg/m2 | 1.02 (0.78, 1.27) |
| Parity | |
| Nulliparous | 0.0 (ref) |
| Multiparous | 0.09 (−0.08, 0.27) |
| Ethnicity | |
| White | 0.0 (ref) |
| Black | 0.36 (0.10, 0.63) |
| Hispanic | −0.02 (−0.42, 0.38) |
| Asian | −0.54 (−0.94,−0.14) |
| Other | 0.17 (−0.33, 0.67) |
| Educational level | |
| Less than college | 0.0 (ref) |
| College graduate | −0.29 (−0.47,−0.10) |
| Smoking habits | |
| No | 0.0 (ref) |
| Former smoking | −0.10 (−0.33, 0.12) |
| Smoking during pregnancy | 0.22 (−0.07, 0.52) |
| Dietary intake | |
| <1599 kcal | 0.04 (−0.2, 0.3) |
| 1600–1999 kcal | −0.02 (−0.3, 0.2) |
| 2000–2399 kcal | 0.0 (ref) |
| ≥2400 kcal | 0.06 (−0.2, 0.3) |
| Gestational hypertensive disorder | |
| Normal | 0.0 (ref) |
| Chronic hypertension | 0.31 (−0.51, 1.13) |
| Gestational hypertension | 0.21 (−0.15, 0.57) |
| Pre-eclampsia | 0.17 (−0.38, 0.71) |
| Glucose status | |
| Normal | 0.0 (ref) |
| Failed GCT normal OGTT | −0.07 (−0.38, 0.25) |
| Impaired glucose tolerance | 0.61 (0.00, 1.22) |
| Gestational diabetes | 0.55 (0.07, 1.02) |
| Birth characteristics | |
| Child sex | |
| Male | 0.0 (ref) |
| Female | −0.03 (−0.21, 0.15) |
| Gestational age adjusted size at birth | |
| SGA | −0.23 (−0.59, 0.12) |
| AGA | 0.0 (ref) |
| LGA | 0.22 (−0.02, 0.47) |
Values are effect estimates based on bivariate linear regression models (95% confidence interval). The effect estimates represent the difference in maternal second trimester CRP-level, as compared to reference group.
Maternal inflammatory markers and early- and mid-childhood body composition
Table 3 shows the associations of maternal second trimester CRP-level with detailed mid-childhood body fat mass measures measured by DXA, and the role of potential confounders and intermediates. A higher maternal second trimester CRP-level was associated with a higher childhood fat mass index and trunk fat mass index in the basic model (difference in FMI and trunkFMI: 0.30 kg/m2 [95% CI: 0.16, 0.44] and 0.12 kg/m2 [95% CI: 0.06, 0.18], per SD increment in maternal CRP-level, respectively). Additional adjustment for maternal socio-demographic and lifestyle-related characteristics attenuated these associations to 0.15 kg/m2 [95% CI: 0.01, 0.29] and 0.06 kg/m2 [95% CI: 0.00, 0.12], respectively. Adjustment for maternal pregnancy complications and birth characteristics did not further attenuate these associations. A higher maternal second trimester CRP-level was also associated with a higher mid-childhood fat free mass index in the basic model (difference in FFMI: 0.13 kg/m2 [95% CI: 0.03, 0.24] per SD increment in maternal CRP-level, respectively), but this association was fully explained by maternal socio-demographic and lifestyle-related characteristics (difference in FFMI: 0.01 kg/m2 [95% CI: −0.10, 0.12] per SD increment in maternal CRP-level, respectively).
TABLE 3.
Maternal second trimester CRP-level and mid-childhood body fat measures (N=874)
| Total fat mass index Difference in kg/m2 (95%CI) | P-Value | Truncal fat mass index Difference in kg/m2 (95%CI) | P-Value | Fat free mass index Difference in kg/m2 (95%CI) | P-Value | |
|---|---|---|---|---|---|---|
| Effect per SD increment in maternal CRP | ||||||
|
| ||||||
| Basic model1 | 0.30 (0.16, 0.44) | <0.001 | 0.12 (0.06, 0.18) | <0.001 | 0.13 (0.03, 0.24) | 0.01 |
| Confounder model2 | 0.15 (0.01, 0.29) | 0.04 | 0.06 (0.00, 0.12) | 0.06 | 0.01 (−0.10, 0.12) | 0.90 |
| Mediator models3 | ||||||
| Pregnancy complications | 0.15 (0.01, 0.29) | 0.04 | 0.06 (0.00, 0.12) | 0.07 | 0.01 (−0.09, 0.12) | 0.80 |
| Birth characteristics | 0.15 (0.01, 0.29) | 0.04 | 0.06 (0.00, 0.12) | 0.06 | 0.00 (−0.10, 0.11) | 0.96 |
| Fully adjusted model4 | 0.15 (0.01, 0.29) | 0.04 | 0.06 (0.00, 0.12) | 0.07 | 0.01 (−0.10, 0.12) | 0.85 |
Values are regression coefficients (95% confidence interval) that reflect the difference in mid-childhood body fat distribution outcomes per SDS change in maternal second trimester inflammatory marker.
Basic model is adjusted for gestational age at CRP measurement, child sex and age at outcome measurement.
Confounder model includes maternal age, prepregnancy BMI, race/ethnicity, educational level, parity, smoking during pregnancy, total calorie intake during pregnancy;
Intermediate models are confounder models additionally adjusted for each potential intermediate. Pregnancy complications included gestational hypertensive disorders (categorical variable: normal, chronic hypertension, gestational hypertension and preeclampsia) and gestational diabetes (categorical variable: normal, failed GCT normal OGTT, impaired glucose tolerance and gestational diabetes). Birth characteristics included gestational age at birth (continuous) and birth weight z-score (continuous)
Fully adjusted model includes all potential confounders and intermediates.
Table 4 shows the associations of maternal second trimester CRP-level with early- and mid-childhood BMI-z and waist circumference. In the basic model, higher maternal second trimester plasma CRP-level was associated with higher early- and mid-childhood BMI-z and waist circumference (difference in early-childhood and mid-childhood BMI-z and WC: 0.12 [95% CI: 0.05, 0.20], 0.25 cm [95% CI: 0.00, 0.50] and 0.16 [95% CI:0.08, 0.23], 1.02 cm [95% CI: 0.44, 1.60], per SD increment in maternal CRP-level, respectively).
TABLE 4.
Maternal second trimester CRP-level and childhood body mass index and waist circumference (N=1043)
| Early-childhood (N=1043) | Mid-childhood (N=874) | |||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| Body mass index Difference in z-score (95% CI) | P-Value | Waist circumference Difference in cm (95% CI) | P-Value | Body mass index Difference in z-score (95% CI) | P-Value | Waist circumference Difference in cm (95% CI) | P-Value | |
| Effect per SD increment in maternal CRP | ||||||||
|
| ||||||||
| Basic model1 | 0.12 (0.05, 0.20) | <0.001 | 0.25 (0.00, 0.50) | 0.05 | 0.16 (0.08, 0.23) | <0.001 | 1.02 (0.44, 1.60) | <0.001 |
| Confounder model2 | 0.05 (−0.03, 0.13) | 0.20 | 0.10 (−0.17, 0.37) | 0.46 | 0.07 (−0.01, 0.14) | 0.09 | 0.34 (−0.25, 0.94) | 0.26 |
| Mediator models3 | ||||||||
| Pregnancy complications | 0.06 (−0.02, 0.14) | 0.17 | 0.11 (−0.16, 0.38) | 0.43 | 0.07 (−0.01, 0.14) | 0.09 | 0.33 (−0.27, 0.92) | 0.28 |
| Birth characteristics | 0.04 (−0.04, 0.12) | 0.30 | 0.05 (−0.21, 0.32) | 0.69 | 0.06 (−0.01, 0.14) | 0.10 | 0.31 (−0.28, 0.91) | 0.30 |
| Fully adjusted model4 | 0.05 (−0.03, 0.13) | 0.26 | 0.06 (−0.20, 0.33) | 0.65 | 0.06 (−0.01, 0.14) | 0.10 | 0.30 (−0.29, 0.90) | 0.31 |
Values are regression coefficients (95% confidence interval) that reflect the difference in early- and mid-childhood BMI-z and waist circumference per SDS change in maternal second trimester inflammatory marker.
Basic model is adjusted for gestational age at CRP measurement, child sex and age at outcome measurement.
Confounder model additionally includes maternal age, prepregnancy BMI, race/ethnicity, educational level, parity, smoking during pregnancy, total calorie intake during pregnancy.
Intermediate models are confounder models additionally adjusted for each potential intermediate. Pregnancy complications included gestational hypertensive disorders (categorical variable: normal, chronic hypertension, gestational hypertension and preeclampsia) and gestational diabetes (categorical variable: normal, failed GCT normal OGTT, impaired glucose tolerance and gestational diabetes). Birth characteristics included gestational age at birth (continuous) and birth weight z-score (continuous).
Fully adjusted model includes all potential confounders and intermediates.
Additional adjustment for maternal socio-demographic and lifestyle-related characteristics attenuated these associations by approximately 58%. The largest reduction in the effect estimates was due to adjustment for maternal prepregnancy BMI. Thus after adjustment, positive associations of maternal CRP-level with early- and mid-childhood BMI-z and waist circumference were present (difference in early-childhood and mid-childhood BMI-z and WC: (0.05 [95% CI: −0.03, 0.13], 0.10 cm [95% CI: −0.17, 0.37] and 0.07 [95% CI:−0.01, 0.14], 0.34 cm [95% CI:−0.25, 0.94], per SD increment in maternal CRP-level, respectively), although these associations did not reach statistical significance. In the intermediate and full models, we found that the associations of maternal second trimester CRP-level with early- and mid-childhood BMI-z and waist circumference were not attenuated by maternal pregnancy complications or birth characteristics. We observed similar associations when we restricted the analyses to women with a normal glucose tolerance status only (results not shown).
DISCUSSION
In this cohort study of pregnant women and their children, we observed that a higher maternal second trimester plasma CRP-level, a marker of maternal low-grade systemic inflammation, was associated with a higher risk of childhood overall adiposity and central adiposity.
Interpretation of main findings
Maternal prepregnancy obesity is associated with increased risks of obesity and adverse cardio-metabolic outcomes in the offspring (1). The mechanisms underlying these associations remain unclear, but may involve an increased maternal obesity mediated pro-inflammatory state during pregnancy (2). In non-pregnant state, obesity is associated with an increased inflammatory response, which is characterized by abnormal production of adipokine and activation of pro-inflammatory signalling pathways (26). This leads to altered plasma levels of multiple inflammatory markers, such as CRP, TNF- à and IL-6 among obese individuals (26). This increased inflammatory state related to obesity may play a role in the development of atherosclerosis, diabetes and cardiovascular disease (27, 28, 29). During pregnancy, maternal obesity probably also leads to an increased pro-inflammatory state, which may adversely affect placental and fetal development, and long-term offspring cardio-metabolic health outcomes(5, 7, 8, 9).
We examined the associations of maternal CRP-level, measured in second trimester of pregnancy, with detailed offspring adiposity measures. CRP is a non-specific marker of low-grade systemic inflammation and a downstream marker of pro-inflammatory cytokines, as production of CRP is stimulated by pro-inflammatory cytokines, such as IL-6 and TNF- à (14). In addition, CRP itself has pro-inflammatory properties and adversely affects endothelial function(14). A Mendelian randomization study, a study design that uses a genetic variant robustly associated with the exposure of interest and not affected by confounding as an instrumental variable, among 21_836 participants showed a causal association of increased BMI with higher CRP levels(30). Among pregnant women, similar findings are reported. A cross-sectional study among 80 pregnant women showed that maternal obesity was associated with increased CRP-levels in the second trimester of pregnancy(31). A longitudinal study performed by Friis et al. among 240 pregnant women showed that maternal obesity was associated with increased CRP-levels during the first half of pregnancy, but differences in CRP-levels between maternal BMI-categories were no longer present towards the end of pregnancy(32). In line with these previous findings, we observed that maternal prepregnancy overweight and obesity were associated with a higher plasma level of maternal second trimester CRP.
Increased maternal CRP-levels during pregnancy are associated with an increased risk of adverse birth outcomes. Previously, we have shown among a nested-case control study within Project Viva, that a higher maternal CRP-level in the second trimester of pregnancy was associated with an increased risk of preterm delivery(19). A study among 6061 pregnant women showed that a higher level of maternal CRP was associated with fetal growth restriction(33). Both preterm birth and fetal growth restriction are risk factors for the development of increased fat mass levels in later life (34). Few studies have examined the associations of maternal CRP-level or other inflammatory markers during pregnancy with long-term detailed adiposity outcomes in the offspring. A study among 71 singleton white women with normal glucose tolerance showed no correlations of maternal IL-6 , IL-1b and TNF- à levels, measured at 28 weeks and 37 weeks of gestation, with fetal adiposity measures or birth weight (35). A study among 18 multi-ethnic women performed in the US showed that higher maternal IL-6, measured at delivery, correlated with higher neonatal fat mass (12). In a recent study among 439 Danish mother-offspring pairs, Danielsen et al did not find associations of maternal third trimester CRP, TNF-à, IL-6, and IL-1b with offspring BMI, waist circumference, blood pressure and metabolic measures at the age of 20 years (13).
We observed that a higher maternal CRP-level in the second trimester was associated with increased total body fat mass and truncal fat mass in the offspring. These associations were independent of maternal prepregnancy BMI and not explained by the development of maternal pregnancy complications, including gestational diabetes and gestational hypertensive disorders, or gestational age and weight at birth. A higher second trimester maternal CRP-level was also associated with slightly higher early- and mid-childhood BMI and waist circumference, but these associations attenuated after adjustment for maternal socio-demographic and lifestyle-related characteristics, especially maternal prepregnancy BMI. Differences between our findings and those of Danielsen et al. may be due to differences in timing of maternal inflammatory marker measurement (13). In early pregnancy, an increased maternal inflammatory state may be more strongly present than later in pregnancy, as later pregnancy is typically characterized by a physiological anti-inflammatory state. Accordingly, average CRP-levels tend to be higher in the second trimester of pregnancy than in the third trimester(36). Friis et al. also showed no differences in CRP-level between maternal BMI-categories towards the end of pregnancy (32). Also, differences in study populations and body composition measurements may partly explain these different findings, as we observed the strongest associations with detailed childhood fat mass measures.
The observed effect estimates for the associations of maternal second trimester CRP-level with childhood overall and central adiposity levels were in the order of a 1/10 SD difference in childhood adiposity levels per SD increment in maternal CRP. Thus, these differences are relatively small, compared to previous reported effect estimates for associations of maternal prepregnancy BMI with offspring adiposity outcomes (1). However, our findings are of importance on a population level and from an etiological perspective, as they provide a potential underlying causal mechanism by which maternal prepregnancy obesity may influence adiposity levels in offspring. The potential mechanisms that explain how a maternal pro-inflammatory state during pregnancy may lead to higher adiposity levels in the offspring are not known. Maternal inflammatory status may affect placental vascular function and inflammatory processes in the placenta, which may lead to suboptimal placental development and alterations in placental structure and function (7, 9, 37). Also, epigenetic influences on placental genes induced by an increased maternal inflammatory state may lead to a placental lipotoxic environment and increased placental inflammation, which negatively affects placental function (8, 38, 39). Suboptimal placental development and function may affect fetal nutrient supply and lead to fetal developmental adaptations in early life, which may predispose to a higher risk of obesity in later life (34). Increased maternal and placental inflammatory processes may also lead to inflammation-related maternal insulin resistance, which may subsequently lead to increased fetal adiposity levels (26, 40).
Methodological considerations
Strengths of this study were the prospective data collection from early pregnancy onwards and the large sample size. We had detailed childhood anthropometric measurements available. Follow-up data were available for only a subgroup of our study population. Mothers with offspring follow-up data available were more likely to be higher educated and to have a higher socio-economic status. This selection towards a more affluent population may affect the generalizability of our results. We measured maternal CRP-levels in blood samples that were processed within 24 hours after collection. Since the estimated half-life of CRP is approximately 19 hours, this may have caused some potential degradation of maternal CRP, which may have led to an underestimation of the observed associations. Maternal CRP-level was measured only once during pregnancy. Using repeated maternal CRP measurements throughout pregnancy is of interest as this could account for within-person variability and allows for assessment of change of maternal CRP-levels during pregnancy in relation to offspring outcomes. CRP is a non-specific marker of maternal low-grade systemic inflammation. To obtain further insight in the observed associations, it is of interest to assess the associations of more detailed maternal inflammatory markers, such as TNF-à, with offspring outcomes. We had detailed information about a large number of maternal socio-demographic and lifestyle-related confounding factors available in this study. However, because of the observational design, residual confounding due to unmeasured characteristics might still be an issue. In addition, information on some covariates was self-reported which may have resulted in underreporting of adverse lifestyle related characteristics.
Conclusion
Higher second trimester maternal plasma level of CRP, a non-specific marker of inflammation which is produced by the liver, was associated with higher childhood overall and central adiposity. Our findings are mainly of interest from an etiological perspective, as the effect size of the observed associations was relatively small. Further studies are needed to explore whether an increased obesity-mediated inflammatory state during pregnancy forms part of the causal mechanism that underlies the associations of maternal obesity during pregnancy with adverse cardio-metabolic outcomes in the offspring.
What is already known about this subject?
Maternal prepregnancy obesity is an important risk factor for obesity in the offspring
An increased maternal obesity mediated pro-inflammatory state during pregnancy may be part of the mechanism underlying this association
Maternal obesity is associated with a higher maternal CRP-level during pregnancy, a non-specific circulating marker of systemic inflammation produced by the liver
What does your study add?
Maternal second trimester plasma level of CRP is associated with higher mid-childhood total body fat mass and abdominal fat mass in the offspring
These associations were not explained by maternal body mass index, gestational diabetes, gestational hypertensive disorders or birth characteristics
Acknowledgments
SOURCES OF FUNDING
This research was supported by grants from the National Institutes of Health (R37HD034568, K24 HD069408, R01 HL 64925, R01HL 75504, and P30 DK092924). RG received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013), project EarlyNutrition under grant agreement No 289346.
We thank the staff and participants of Project Viva.
Footnotes
Disclosure: The authors declare no conflict of interest
References
- 1.Drake AJ, Reynolds RM. Impact of maternal obesity on offspring obesity and cardiometabolic disease risk. Reproduction. 2010;140:387–398. doi: 10.1530/REP-10-0077. [DOI] [PubMed] [Google Scholar]
- 2.Denison FC, Roberts KA, Barr SM, Norman JE. Obesity, pregnancy, inflammation, and vascular function. Reproduction. 2010;140:373–385. doi: 10.1530/REP-10-0074. [DOI] [PubMed] [Google Scholar]
- 3.Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. Jama. 1999;282:2131–2135. doi: 10.1001/jama.282.22.2131. [DOI] [PubMed] [Google Scholar]
- 4.Davi G, Guagnano MT, Ciabattoni G, Basili S, Falco A, Marinopiccoli M, et al. Platelet activation in obese women: role of inflammation and oxidant stress. Jama. 2002;288:2008–2014. doi: 10.1001/jama.288.16.2008. [DOI] [PubMed] [Google Scholar]
- 5.Ramsay JE, Ferrell WR, Crawford L, Wallace AM, Greer IA, Sattar N. Maternal obesity is associated with dysregulation of metabolic, vascular, and inflammatory pathways. The Journal of clinical endocrinology and metabolism. 2002;87:4231–4237. doi: 10.1210/jc.2002-020311. [DOI] [PubMed] [Google Scholar]
- 6.Rusterholz C, Hahn S, Holzgreve W. Role of placentally produced inflammatory and regulatory cytokines in pregnancy and the etiology of preeclampsia. Seminars in immunopathology. 2007;29:151–162. doi: 10.1007/s00281-007-0071-6. [DOI] [PubMed] [Google Scholar]
- 7.Challier JC, Basu S, Bintein T, Minium J, Hotmire K, Catalano PM, et al. Obesity in pregnancy stimulates macrophage accumulation and inflammation in the placenta. Placenta. 2008;29:274–281. doi: 10.1016/j.placenta.2007.12.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Radaelli T, Varastehpour A, Catalano P, Hauguel-de Mouzon S. Gestational diabetes induces placental genes for chronic stress and inflammatory pathways. Diabetes. 2003;52:2951–2958. doi: 10.2337/diabetes.52.12.2951. [DOI] [PubMed] [Google Scholar]
- 9.Stewart FM, Freeman DJ, Ramsay JE, Greer IA, Caslake M, Ferrell WR. Longitudinal assessment of maternal endothelial function and markers of inflammation and placental function throughout pregnancy in lean and obese mothers. The Journal of clinical endocrinology and metabolism. 2007;92:969–975. doi: 10.1210/jc.2006-2083. [DOI] [PubMed] [Google Scholar]
- 10.Sen S, Simmons RA. Maternal antioxidant supplementation prevents adiposity in the offspring of Western diet-fed rats. Diabetes. 2010;59(12):3058–65. doi: 10.2337/db10-0301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Dahlgren J, Nilsson C, Jennische E, Ho HP, Eriksson E, Niklasson A, et al. Prenatal cytokine exposure results in obesity and gender-specific programming. American journal of physiology Endocrinology and metabolism. 2001;281:E326–334. doi: 10.1152/ajpendo.2001.281.2.E326. [DOI] [PubMed] [Google Scholar]
- 12.Radaelli T, Uvena-Celebrezze J, Minium J, Huston-Presley L, Catalano P, Hauguel-de Mouzon S. Maternal interleukin-6: marker of fetal growth and adiposity. Journal of the Society for Gynecologic Investigation. 2006;13:53–57. doi: 10.1016/j.jsgi.2005.10.003. [DOI] [PubMed] [Google Scholar]
- 13.Danielsen I, Granstrom C, Rytter D, Halldorsson TI, Bech BH, Henriksen TB, et al. Subclinical inflammation during third trimester of pregnancy was not associated with markers of the metabolic syndrome in young adult offspring. Obesity (Silver Spring) 2014;22:1351–8. doi: 10.1002/oby.20650. [DOI] [PubMed] [Google Scholar]
- 14.Yeh ET. CRP as a mediator of disease. Circulation. 2004;109:II11–14. doi: 10.1161/01.CIR.0000129507.12719.80. [DOI] [PubMed] [Google Scholar]
- 15.Gillman MW, Rich-Edwards JW, Rifas-Shiman SL, Lieberman ES, Kleinman KP, Lipshultz SE. Maternal age and other predictors of newborn blood pressure. J Pediatr. 2004;144:240–245. doi: 10.1016/j.jpeds.2003.10.064. [DOI] [PubMed] [Google Scholar]
- 16.Oken E, Baccarelli AA, Gold DR, Kleinman KP, Litonjua AA, De Meo D, et al. Cohort Profile: Project Viva. International journal of epidemiology. 2015;44:37–48. doi: 10.1093/ije/dyu008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.World Medical Association I. Declaration of Helsinki. Ethical principles for medical research involving human subjects. Journal of the Indian Medical Association. 2009;107:403–405. [PubMed] [Google Scholar]
- 18.Rich-Edwards JW, Mohllajee AP, Kleinman K, Hacker MR, Majzoub J, Wright RJ, et al. Elevated midpregnancy corticotropin-releasing hormone is associated with prenatal, but not postpartum, maternal depression. J Clin Endocrinol Metab. 2008;93:1946–1951. doi: 10.1210/jc.2007-2535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Pitiphat W, Gillman MW, Joshipura KJ, Williams PL, Douglass CW, Rich-Edwards JW. Plasma C-reactive protein in early pregnancy and preterm delivery. American journal of epidemiology. 2005;162:1108–1113. doi: 10.1093/aje/kwi323. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kuczmarski RJ, Ogden CL, Guo SS, Grummer-Strawn LM, Flegal KM, Mei Z, et al. 2000 CDC Growth Charts for the United States: methods and development. Vital and health statistics Series 11, Data from the national health survey. 2002:1–190. [PubMed] [Google Scholar]
- 21.Boeke CE, Oken E, Kleinman KP, Rifas-Shiman SL, Taveras EM, Gillman MW. Correlations among adiposity measures in school-aged children. BMC pediatrics. 2013;13:99. doi: 10.1186/1471-2431-13-99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Wells JC. A critique of the expression of paediatric body composition data. Arch Dis Child. 2001;85(1):67–72. doi: 10.1136/adc.85.1.67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Oken E, Kleinman KP, Rich-Edwards J, Gillman MW. A nearly continuous measure of birth weight for gestational age using a United States national reference. BMC pediatrics. 2003;3:6. doi: 10.1186/1471-2431-3-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Oken E, Ning Y, Rifas-Shiman SL, Rich-Edwards JW, Olsen SF, Gillman MW. Diet during pregnancy and risk of preeclampsia or gestational hypertension. Annals of epidemiology. 2007;17:663–668. doi: 10.1016/j.annepidem.2007.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Regnault N, Gillman MW, Rifas-Shiman SL, Eggleston E, Oken E. Sex-specific associations of gestational glucose tolerance with childhood body composition. Diabetes care. 2013;36:3045–3053. doi: 10.2337/dc13-0333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Bastard JP, Maachi M, Lagathu C, Kim MJ, Caron M, Vidal H, et al. Recent advances in the relationship between obesity, inflammation, and insulin resistance. European cytokine network. 2006;17:4–12. [PubMed] [Google Scholar]
- 27.Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. 2002;105:1135–1143. doi: 10.1161/hc0902.104353. [DOI] [PubMed] [Google Scholar]
- 28.Calle MC, Fernandez ML. Inflammation and type 2 diabetes. Diabetes & metabolism. 2012;38:183–191. doi: 10.1016/j.diabet.2011.11.006. [DOI] [PubMed] [Google Scholar]
- 29.Emerging Risk Factors C. Kaptoge S, Di Angelantonio E, Pennells L, Wood AM, White IR, et al. C-reactive protein, fibrinogen, and cardiovascular disease prediction. The New England journal of medicine. 2012;367:1310–1320. doi: 10.1056/NEJMoa1107477. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Timpson NJ, Nordestgaard BG, Harbord RM, Zacho J, Frayling TM, Tybjaerg-Hansen A, et al. C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization. Int J Obes (Lond) 2011;35:300–308. doi: 10.1038/ijo.2010.137. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Madan JC, Davis JM, Craig WY, Collins M, Allan W, Quinn R, et al. Maternal obesity and markers of inflammation in pregnancy. Cytokine. 2009;47:61–64. doi: 10.1016/j.cyto.2009.05.004. [DOI] [PubMed] [Google Scholar]
- 32.Friis CM, Paasche Roland MC, Godang K, Ueland T, Tanbo T, Bollerslev J, et al. Adiposity-related inflammation: effects of pregnancy. Obesity (Silver Spring) 2013;21:E124–130. doi: 10.1002/oby.20120. [DOI] [PubMed] [Google Scholar]
- 33.Ernst GD, de Jonge LL, Hofman A, Lindemans J, Russcher H, Steegers EA, et al. C-reactive protein levels in early pregnancy, fetal growth patterns, and the risk for neonatal complications: the Generation R Study. American journal of obstetrics and gynecology. 2011;205:132 e131–112. doi: 10.1016/j.ajog.2011.03.049. [DOI] [PubMed] [Google Scholar]
- 34.Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. The New England journal of medicine. 2008;359:61–73. doi: 10.1056/NEJMra0708473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Farah N, Hogan AE, O'Connor N, Kennelly MM, O'Shea D, Turner MJ. Correlation between maternal inflammatory markers and fetomaternal adiposity. Cytokine. 2012;60:96–99. doi: 10.1016/j.cyto.2012.05.024. [DOI] [PubMed] [Google Scholar]
- 36.Abbassi-Ghanavati M, Greer LG, Cunningham FG. Pregnancy and laboratory studies: a reference table for clinicians. Obstet Gynecol. 2009;114:1326–1331. doi: 10.1097/AOG.0b013e3181c2bde8. [DOI] [PubMed] [Google Scholar]
- 37.Roberts KA, Riley SC, Reynolds RM, Barr S, Evans M, Statham A, et al. Placental structure and inflammation in pregnancies associated with obesity. Placenta. 2011;32:247–254. doi: 10.1016/j.placenta.2010.12.023. [DOI] [PubMed] [Google Scholar]
- 38.Saben J, Lindsey F, Zhong Y, Thakali K, Badger TM, Andres A, et al. Maternal obesity is associated with a lipotoxic placental environment. Placenta. 2014;35:171–177. doi: 10.1016/j.placenta.2014.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Zhu MJ, Du M, Nathanielsz PW, Ford SP. Maternal obesity up-regulates inflammatory signaling pathways and enhances cytokine expression in the mid-gestation sheep placenta. Placenta. 2010;31:387–391. doi: 10.1016/j.placenta.2010.02.002. [DOI] [PubMed] [Google Scholar]
- 40.Retnakaran R, Hanley AJ, Raif N, Connelly PW, Sermer M, Zinman B. C-reactive protein and gestational diabetes: the central role of maternal obesity. J Clin Endocrinol Metab. 2003;88:3507–3512. doi: 10.1210/jc.2003-030186. [DOI] [PubMed] [Google Scholar]