| Literature DB >> 34959801 |
Christina J Valentine1, Aiman Q Khan2, Alexandra R Brown3, Scott A Sands4, Emily A Defranco5, Byron J Gajewski3, Susan E Carlson4, Kristina M Reber6, Lynette K Rogers2,7.
Abstract
Pregnancy and parturition involve extensive changes in the maternal immune system. In our randomized, multi-site, double-blind superiority trial using a Bayesian adaptive design, we demonstrated that 1000 mg/day of docosahexaenoic acid (DHA) was superior to 200 mg/day in preventing both early preterm birth (less than 34 weeks' gestation) and preterm birth (less than 37 weeks' gestation). The goal of this secondary study is to compare the effects of 1000 mg/day versus 200 mg/day on maternal inflammation, a possible mechanism by which DHA may prevent preterm birth. Maternal blood samples were collected at enrollment (12-20 weeks' gestation) and at delivery. Red blood cell DHA levels were measured by gas chromatography, and plasma concentrations of sRAGE, IL-6, IL-1β, TNFα, and INFγ were measured by ELISA. Data were analyzed for associations with the DHA dose, gestational age at birth, and preterm birth (<37 weeks). Higher baseline and lower delivery levels of maternal sRAGE were associated with a greater probability of longer gestation and delivery at term gestation. Higher-dose DHA supplementation increased the probability of a smaller decrease in delivery sRAGE levels. Higher IL-6 concentrations at delivery were associated with the probability of delivering after 37 weeks, and higher-dose DHA supplementation increased the probability of greater increases in IL-6 concentrations between enrollment and delivery. These data provide a proposed mechanistic explanation of how a higher dose of DHA during pregnancy provides immunomodulatory regulation in the initiation of parturition by influencing sRAGE and IL-6 levels, which may explain its ability to reduce the risk of preterm birth.Entities:
Keywords: Bayesian adaptive design; DHA; IL-6; pregnancy; preterm birth; sRAGE
Mesh:
Substances:
Year: 2021 PMID: 34959801 PMCID: PMC8703393 DOI: 10.3390/nu13124248
Source DB: PubMed Journal: Nutrients ISSN: 2072-6643 Impact factor: 6.706
Figure 1Study Design and Enrollment.
Descriptive Summary of Variables.
| 200 mg/day | 1000 mg/day | Total | |
|---|---|---|---|
| sRAGE [pg/mL], mean (SD) | |||
| Enrollment | 564.4 (286.4) | 520.6 (279.4) | 541.8 (283.5) |
| Delivery | 467.7 (277.3) | 443.1 (381.4) | 455.0 (335.1) |
| IL 6 [pg/mL], mean (SD) | |||
| Enrollment | 0.9 (1.0) | 1.3 (6.1) | 1.1 (4.4) |
| Delivery | 4.5 (7.5) | 7.7 (26.0) | 6.1 (19.4) |
| IL 1β [pg/mL], mean (SD) | |||
| Enrollment | 0.1 (0.2) | 0.1 (0.2) | 0.1 (0.2) |
| Delivery | 0.2 (0.8) | 0.2 (1.0) | 0.2 (0.9) |
| TNFα [pg/mL], mean (SD) | |||
| Enrollment | 1.7 (0.7) | 1.9 (2.1) | 1.8 (1.6) |
| Delivery | 2.1 (1.0) | 2.1 (1.0) | 2.1 (1.0) |
| INFγ [pg/mL], mean (SD) | |||
| Enrollment | 6.4 (30.8) | 5.1 (13.5) | 5.7 (23.5) |
| Delivery | 5.1 (22.7) | 4.4 (8.9) | 4.7 (17.0) |
| DHA in RBC fatty acids [%], mean (SD) | 6.5 (1.8) | 6.4 (1.8) | 6.5 (1.8) |
| Smoker (before or during pregnancy), yes | 105 (24.0) | 112 (24.1) | 217 (24.1) |
| Race, | |||
| Non-Hispanic Black | 104 (23.8) | 89 (19.1) | 193 (21.4) |
| Other | 333 (76.2) | 376 (80.9) | 709 (78.6) |
| History of Preeclampsia, | 33 (7.6) | 30 (6.5) | 63 (7.0) |
| BMI Group, | |||
| Obese | 138 (31.9) | 159 (34.7) | 297 (33.4) |
| Other (BMI < 30) | 294 (68.1) | 299 (65.3) | 593 (66.6) |
Posterior means (Bayesian credible interval) and Bayesian posterior probability for sRAGE concentrations and the continuous variable (gestation age at birth) or the binary variable (preterm birth). The means are slopes for gestation age at birth and log-odds ratios for preterm birth.
| Posterior Mean | Bayesian Posterior Probability | |
|---|---|---|
|
| ||
| Treatment | 0.23 (0.03, 0.42) | 0.99 |
| Enrollment sRAGE [pg/mL] | 0.0006 (0.0002, 0.0011) | 0.996 |
| Delivery sRAGE [pg/mL] | −0.0008 (−0.0012, −0.0004) | 0.00 |
|
| ||
| Maternal Race | −0.44 (−0.70, −0.19) | 0.0003 |
| Pre-pregnancy BMI | −0.50 (−0.73, −0.27) | 0.00 |
| History of preeclampsia | −1.17 (−1.56, −0.78) | 0.00 |
| DHA at Enrollment [%] | 0.08 (0.02, 0.13) | 0.996 |
| Smoker (before or during | −0.21 (−0.44, 0.03) | 0.04 |
|
| ||
| Treatment, | 0.59 (0.33, 0.96) | 0.98 |
| Enrollment sRAGE [pg/mL] | 0.999 (0.988, 1.0) | 0.92 |
| Delivery sRAGE [pg/mL] | 1.001 (1.0, 1.002) | 0.002 |
|
| ||
| Maternal Race (non-Hispanic Black vs. other) | 1.77 (0.95, 3.03) | 0.04 |
| Pre-pregnancy BMI | 1.60 (0.88, 2.71) | 0.07 |
| History of preeclampsia | 4.15 (1.91, 7.64) | 0.0003 |
| DHA at Enrollment [%] | 0.84 (0.70, 0.99) | 0.98 |
| Smoker (before or during | 1.40 (0.75, 2.34) | 0.16 |
At least 5000 burn-in and 40,000 Markov chain draws were performed. DHA, % total fatty acids; sRAGE pg/mL. * Modeling the probability of experiencing a preterm (<37 week) birth. # Mean (SD) gestation age at birth for all samples analyzed, 38.8 (1.6) weeks and % preterm birth (9.6%).
Posterior means (Bayesian credible interval) and Bayesian posterior probability for cytokine concentrations and the continuous variable (gestation age at birth) or the binary variable (preterm birth). The means are slopes for gestation age at birth and log-odds ratios for preterm birth. For brevity, the maternal confounder parameter estimates are not shown.
| Posterior Mean | Bayesian Posterior Probability | |
|---|---|---|
|
| ||
| Enrollment IL-6 [pg/mL] | 0.003 (−0.022, 0.028) | 0.58 |
| Delivery IL-6 [pg/mL] | −0.0005 (−0.0063, 0.0055) | 0.44 |
| Enrollment IL-1β [pg/mL] | −0.18 (−0.61, 0.25) | 0.20 |
| Delivery IL-1β [pg/mL] | −0.03 (−0.14, 0.07) | 0.26 |
| Enrollment TNFα [pg/mL] | 0.02 (−0.04, 0.09) | 0.76 |
| Delivery TNFα [pg/mL] | −0.08 (−0.19, 0.02) | 0.07 |
| Enrollment INFγ [pg/mL] | −0.001 (−0.005, 0.003) | 0.27 |
| Delivery INFγ [pg/mL] | −0.005 (−0.010, 0.001) | 0.06 |
|
| ||
| Enrollment IL-6 [pg/mL] | 0.89 (0.67, 1.04) | 0.87 |
| Delivery IL-6 [pg/mL] | 0.99 (0.95, 1.01) | 0.85 |
| Enrollment IL-1β [pg/mL] | 1.53 (0.56, 3.00) | 0.20 |
| Delivery IL-1β [pg/mL] | 0.91 (0.60, 1.16) | 0.73 |
| Enrollment TNFα [pg/mL] | 0.73 (0.45, 1.04) | 0.95 |
| Delivery TNFα [pg/mL] | 1.33 (1.00, 1.74) | 0.03 |
| Enrollment INFγ [pg/mL] | 0.99 (0.95, 1.01) | 0.83 |
| Delivery INFγ [pg/mL] | 0.996 (0.972, 1.010) | 0.62 |
At least 5000 burn-in and 40,000 Markov chain draws were performed. All models included treatment, race, BMI group, history of preeclampsia, DHA at enrollment, and smoking history. The estimates were similar, and conclusions did not change from the sRAGE analysis above. * Modeling the probability of experiencing a preterm (<37 week) birth.
Change in sRAGE/cytokine levels between enrollment and delivery, posterior means of the change (Bayesian credible intervals), and Bayesian posterior probability with treatment group (alone) as a predictor.
| Observed Difference | Posterior Mean | Bayesian Posterior | |||
|---|---|---|---|---|---|
| 200 mg | 1000 mg | 200 mg | 1000 mg | ||
| sRAGE | −96.76 | −77.49 | −96.94 | −77.55 | 0.84 |
| IL-6 | 3.66 | 6.36 | 3.65 | 6.36 | 0.99 |
| IL-1β | 0.15 | 0.18 | 0.15 | 0.18 | 0.70 |
| TNFα | 0.39 | 0.28 | 0.39 | 0.28 | 0.16 |
| INFγ | −1.32 | −0.66 | −1.34 | −0.67 | 0.64 |
At least 5000 burn-in and 40,000 Markov chain draws were performed.