Background
DHA intake and status of US women during pregnancy and physiological importance
Effects of DHA and EPA supplementation on gestation duration, preterm birth, ePTB and VLBW
Importance of reducing ePTB and what the evidence suggests may be happening with high dose DHA supplementation
Mechanisms by which DHA might reduce ePTB
Rationale for DHA doses
Nutritional approach to reduce ePTB and VLBW
Research objectives
Data category | Information |
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Primary registration and trial identification number | ClinicalTrials.gov NCT02626299 |
Date of registration | December 8, 2015 |
Secondary identifying numbers | R01 HD083292; IND 129482; IRB STUDY00003455 |
Source of monetary or material support | National Institute of Child Health and Human Development |
Primary sponsor | University of Kansas Medical Center |
Secondary sponsor/Collaborators | University of Cincinnati |
Ohio State University | |
Nationwide Children’s Hospital | |
Contact for public queries | Beth Kerling, MS RD (ekerling@kumc.edu) |
Contact for scientific queries | Susan Carlson, PhD (scarlson@kumc.edu) |
Public title | Assessment of DHA On Reducing Early preterm Birth (ADORE) Trial |
Scientific title | Docosahexaenoic Acid Supplementation in Pregnancy to Reduce Early Preterm Birth |
Countries of recruitment | United States |
Health conditions or problem studied | Preterm birth |
Interventions | Active treatment: 1,000 mg DHA per day |
Standard of care: 200 mg DHA per day | |
Key inclusion and exclusion criteria | Ages eligible for study: ≥ 18 years |
Genders eligible for study: female | |
Accepts healthy volunteers: yes | |
Inclusion criteria: pregnant female ≥ 18 years; 12–20 weeks of gestation, agree to consume study capsules; available by telephone | |
Exclusion criteria: multiple gestation, unwilling to discontinue use of another prenatal supplement with DHA, allergy to any component of DHA product (including algae), soybean oil or corn oil | |
Study type | Interventional |
Allocation: randomized | |
Endpoint classification: safety/efficacy study | |
Intervention Model: parallel assignment | |
Masking: double blind | |
Primary purpose: prevention | |
Phase III | |
Date of first enrollment | June 8, 2016 |
Recruitment status | Active recruitment |
Primary outcome | Occurrence of early preterm birth |
Key secondary outcomes | Efficacy analysis for subset populations |
Effect of DHA on inflammation | |
Safety evaluation |
Methods/Design
Participants and eligibility criteria
Inclusion criteria | |
1. | Pregnant females 18.0 years and older 12 to 20 weeks of gestation at study entry |
2. | Agree to consume study capsules and a typical prenatal supplement of 200 mg DHA |
3. | Available by telephone |
4. | English or Spanish speaking |
Exclusion criteria | |
1. | Less than 18 years of age |
2. | Expecting multiple infants |
3. | Gestational age at baseline <12 weeks or >20 weeks |
4. | Unable or unwilling to agree to consume capsules until delivery |
5. | Unwilling to discontinue use of another prenatal supplement with DHA |
6. | Women with allergy to any component of DHA product (including algae), soybean oil or corn oil |
Recruitment and enrollment
Enrollment | Treatment | Delivery | Postpartum F/up | ||||
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Baseline Visit | Enroll F/up | Refill Contacts | Mid-Pregnancy | Pre-delivery | Hospital Visit | Delivery F/up | |
Timeline | 12 to <20 weeks GA | Up to14 days after enrollment | ~ every 6 weeks after supplement dispensed | 28–36 weeks GA | PRN as EDD nears | During hospital admission | 30 days after deliverya
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Informed Consent | ✓ | ||||||
Verify Inclusion/Exclusion criteria | ✓ | ||||||
Health History | ✓ | ✓ | |||||
Dietary Supplement Intake | ✓ | ✓ | |||||
DHA FFQ | ✓ | ||||||
DHQ-II FFQ | ✓ | ||||||
Measure Maternal Height | ✓ | ||||||
Maternal Blood Draw | ✓ | ✓ | |||||
Maternal Urine Sample | ✓ | ✓ 33–36 weeks GA | |||||
Randomization and dispense supplement bottles | ✓ | Start supplements day after enrollment Continue daily intake until delivery | |||||
Review of Maternal Medical Records | ✓ | ✓ | ✓ | ||||
Telephone Contact | ✓ | ✓ | ✓ | ✓ | ✓ | ||
Mail Investigational Supplement | ✓ | ||||||
Return Investigational Supplement | ✓ | ✓b
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Delivery Information (if needed, Blood Kits) | ✓ | ||||||
Review of Infant Medical Records | ✓ | ||||||
Cord Blood Collection | ✓ | ||||||
ROI forms | ✓ | ✓ | |||||
Participant Compensation | ✓ | ✓ | |||||
Adverse Events | ✓ | Record as they occur after enrollment
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Randomization and implementation
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❖ Subject demographics are entered
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❖ Subject is attached to study (at this point patient status could be screening or pre-screening)
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❖ Verification of inclusion and exclusion criteria and documentation of the informed consent are entered into the patient randomization form triggering automatic assignment of a patient study ID and randomization to the next particular arm from the allocation table
Placebo and DHA supplementation
Capsule records and accountability
Blood collection
Fatty acid analysis
sRAGE analysis
Cell free plasma RNAs
Dietary and nutrient intake
Urine collection
Data collection and integrity
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❖ Early preterm delivery (ePTB, <34 weeks gestation) based on ACOG guidelines
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❖ VLBW (<1500 g) and low birth weight (<2500 g) as recorded in hospital record
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❖ Participant DHA status (RBC-PL-DHA) at enrollment and birth; fetal DHA status at birth from cord blood
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❖ Gestational age (days) at delivery based on EDD in clinic record recorded following ultrasound on or before ~14 weeks gestation
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❖ Birth weight (g), length (cm) and head circumference (cm) at delivery as recorded in hospital record
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❖ Preterm birth (<37 weeks) based on ACOG guidelines
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❖ Extreme preterm birth (<33 weeks)
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❖ Pregnancy outcomes: gestational diabetes, pre-eclampsia, C-section, spontaneous or induced labor, occurrence and reason for non-routine hospitalization
Attrition
Study termination
Statistical issues
Bayesian adaptive design
Summary of the Bayesian adaptive design
Statistical model
Response adaptive randomization
Power, sample size, trial duration, and allocation
Scenario | %. | %. | Power | Mean | Mean trial | ||
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Finish | Finish | Subjects | % Group 1 | % Group 2 | (Weeks) | ||
Early | Late | ||||||
#1. very likely (4 vs 1%)a
| 82% | 8% | 90% | 938 | 40% | 60% | 184 |
#2. likely (3 vs 0.5%) | 84% | 7% | 91% | 934 | 41% | 59% | 184 |
#3. unlikely (3 vs 1%) | 52% | 11% | 63% | 1046 | 44% | 56% | 204 |
#4. very unlikely (3 vs 2%) | 23% | 4% | 27% | 1142 | 46% | 54% | 221 |
#5. no difference (3 vs 3%) | 5% | 0% | 5% | 1188 | 51% | 49% | 231 |
Primary and secondary pregnancy efficacy analysis (specific Aim 1)
Pregnancy efficacy analysis according to intent-to-treat principles
Pregnancy efficacy analysis controlled for potential predictor variables
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❖ A final exploratory analysis investigates the impact of capsule intake on outcome as mediated by maternal RBC-PL DHA. Using a reasonable set of predictor variables from the regressions above, we will run two sets of regressions for each outcome variable. This will allow maternal RBC DHA to be a mediator. First we regress the RBC-PL DHA level on all appropriate predictor variables (as above). Then we will run a regression of outcome variables on RBC-PL DHA level and all other appropriate predictor variables (as above but with RBC DHA added). In this way, we are running a path analytic model where we can obtain direct effects of variables and indirect effects of variables through mediator plasma level.
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❖ We will investigate local model adequacy in all regression analyses by exploring standardized residuals and leverage points via Cook’s distance. Possible co-linearity among predictor variables will be examined with Pearson’s correlation coefficient and variance inflation factors (VIF). Scatter plots and histograms will also be used to investigate the adequacy of the model assumptions.
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❖ Of substantive interest in the regression analysis is that race is one of the predictor variables. Since we anticipate 22.5% of the subjects to be Black American of African descent, we can test whether efficacy of pregnancy outcomes are different for Black American women of African descent relative to other races.
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❖ For regression analysis purposes, the pregnancy outcomes are separated into two classes of variables, either continuous or dichotomous. For the continuous variables, Bayesian regression based on the normal distribution will be utilized. For the dichotomous variables, logistic regression will be utilized.
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❖ For all outcomes, we set the DHA dose as a predictor variable and then fit all possible subsets of the other predictor variables to explore, for the particular pregnancy outcome, which model is the best. We will utilize a global fit index called Deviance Information Criteria (DIC) to determine which variables to keep in the final model. The DIC is very general and can be used for normal and logistic regression analyses.
Potential predictor variables for regression analysis
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❖ DHA dose (capsules taken multiplied by the DHA in the type of capsule consumed)
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❖ maternal RBC-PL DHA level at enrollment and delivery (g DHA per 100 g total fatty acids) by chromatographic analysis
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❖ umbilical cord RBC DHA by chromatographic analysis
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❖ estimated DHA intake at enrollment from DHQ-II and frequency/amount of consumption of food and supplement sources containing DHA
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❖ estimated DHA intake at enrollment from DHQ-II and frequency/amount of consumption of food and supplement sources containing DHA
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❖ intake of other nutrients or foods, e.g., macronutrient quantity or quality, micronutrient quantity at enrollment
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❖ tobacco exposure prior to and during pregnancy by subject report
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❖ alcohol intake prior to and during pregnancy by subject report defined as standard drinks/day (Nutrition Educators of Health Professionals Teaching Tool)
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❖ measurement of endocrine disrupting chemicals (EDCs) in urine at 12–20 weeks and 33–36 weeks of gestation
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❖ potential exposure to EDCs in environmental by questionnaire at 12–20 weeks and 22–26 weeks of gestation
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❖ marital/relationship status, by subject report
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❖ household income by subject report
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❖ insurance type (private, public, uninsured) by review of clinic/hospital record
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❖ maternal and paternal education by subject report
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❖ maternal age at enrollment (years) from DOB listed in clinic/hospital record
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❖ maternal and paternal race/ethnicity from clinic record or subject report
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❖ fetal sex
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❖ BMI calculated from the measured prenatal clinic weight record and measured height during the first prenatal visit, or if missing, the self-reported pre-pregnancy weight
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❖ gestational weight gain (last clinic visit in pounds minus 1st measured weight or pre-pregnancy weight)
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❖ gestational age at enrollment (days) calculated from EDD (based on ACOG guidelines)
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❖ reproductive history
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❖ characteristics of previous pregnancies (early preterm birth, pre-eclampsia, gestational diabetes)
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❖blood pressure throughout pregnancy
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❖ iron status/hemoglobin at enrollment and mid-pregnancy
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❖ cervical length between 18 and 22 weeks of gestation if reported
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❖ estimated blood loss at delivery by estimate of the deliverer
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❖ infant APGAR scores
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❖ meconium in amniotic fluid
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❖ evidence of illicit drug use from clinic/hospital record