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Perspectives on the design and methodology of periconceptional nutrient supplementation trials


Periconceptional supplementation could extend the period over which maternal and fetal nutrition is improved, but there are many challenges facing early-life intervention studies. Periconceptional trials differ from pregnancy supplementation trials, not only because of the very early or pre-gestational timing of nutrient exposure but also because they generate subsidiary information on participants who remain non-pregnant. The methodological challenges are more complex although, if well designed, they provide opportunities to evaluate concurrent hypotheses related to the health of non-pregnant women, especially nulliparous adolescents. This review examines the framework of published and ongoing randomised trial designs. Four cohorts typically arise from the periconceptional trial design — two of which are non-pregnant and two are pregnant — and this structure provides assessment options related to pre-pregnant, maternal, pregnancy and fetal outcomes. Conceptually the initial decision for single or micronutrient intervention is central — as is the choice of dosage and content — in order to establish a comparative framework across trials, improve standardisation, and facilitate interpretation of mechanistic hypotheses. Other trial features considered in the review include: measurement options for baseline and outcome assessments; adherence to long-term supplementation; sample size considerations in relation to duration of nutrient supplementation; cohort size for non-pregnant and pregnant cohorts as the latter is influenced by parity selection; integrating qualitative studies and data management issues. Emphasis is given to low resource settings where high infection rates and the possibility of nutrient-infection interactions may require appropriate safety monitoring. The focus is on pragmatic issues that may help investigators planning a periconceptional trial.

Peer Review reports


Trials of periconceptional folic acid supplementation to reduce neural tube defects [1, 2] and of iodised oil administered early in pregnancy to avoid cretinism [3] identified the major influence on clinical outcomes of maternal nutritional status during the first trimester of pregnancy. Since these early studies, many trials have been conducted, mostly with second and third trimester nutrient supplementation [4]. The majority have compared iron plus folic acid supplementation with interventions using variable compositions of micronutrients and vitamins. Meta-analyses and systematic reviews of clinical outcomes have reported varied results, from null effects on neonatal mortality [5] to improvement in general health indicators such as birth weight. Most showed no differences in the risk of pre-term birth, stillbirth, maternal or neonatal outcomes [6], with conflicting results for infant mortality reductions [7]. Null effects [8] or varied findings were reported for fetal growth restriction [8, 9]. Importantly, an increased risk of neonatal death was reported with multimicronutrient compared with iron/folate interventions after the first trimester of pregnancy [6, 10]. The evidence to support daily iron and folate supplementation in pregnancy is based mainly on its beneficial effects on maternal anaemia [11, 12], with micronutrients providing no additional benefit on third trimester maternal anaemia compared with iron-folic acid alone [9, 13].

None of the above trials started with mechanistic hypotheses. Improved understanding of nutritional intermediate pathways could explain differences between trials in fetal and pregnancy outcomes, but this requires integrated placental and biomarker studies and infection profiling from early in gestation [14]. Critical nutritional periods in early pregnancy include the pre-embryonic and embryonic developmental stages. If a nutrient exposure during these periods is associated either positively or negatively with the postulated clinical outcome, a causal pathway is implicated. Periconceptional trials re-focus attention on the fetal growth effects of placental vascularisation and function and the mechanisms determining fetal and placental phenotypes [15, 16]. Recently described effects of maternal overweight on cardiometabolic disease risk in the offspring on later adult disease indicate that early gestational nutritional influences can have lifelong effects [17]. Given that the periconceptional use of nutrition supplements has been assessed in few controlled trials [18], their mechanistic basis is even less evident.

In view of the potential importance of pre-pregnancy supplementation, this paper outlines several aspects related to the framework of periconceptional supplementation trials, including: underlying mechanistic hypotheses; single and multimicronutrient supplementation as alternative trial interventions; sample size considerations in relation to duration of nutrient supplementation, cohort size and composition; outcome measurement options and characteristics; design options, with comparison of published and current study designs, outcome assessments and supplement adherence; the role of qualitative studies within trial designs and data management options. Without improved insights into the nature and conduct of these studies there is a risk that they will yield confusing results. Statistical issues specific to data analysis are not included in the review, as these are trial specific.

The main emphasis is on developing countries where nutritional deficiencies are common and adolescents give birth while still growing [19]. Trying to improve nutrition before the first pregnancy is attractive, especially if this helps to optimise nutrient requirements throughout pregnancy.



We identified published studies using PubMed and Scopus search engines and the Cochrane Central Register of Controlled Trials (CENTRAL; The Cochrane Library). Search terms included micronutrient supplements, periconceptional period, placentation, embryogenesis, pregnancy outcomes, birth weight, growth restriction, iron, folic acid, and micronutrients. For identification of randomised controlled trials or prospective cohort studies we identified human studies on periconceptional interventions with nutrition supplements covering the period 1950 to July 2015 with no language restriction. Information from recent meta-analyses and systematic reviews addressing the periconceptional period, pre-conception, or pre-pregnancy nutrient supplement interventions was reviewed. Ongoing randomised trials with published or available methodologies but still awaiting trial closure were included. Observational studies were excluded, unless population based, as these may be confounded by baseline differences in the prevalence of one or more nutrient deficiencies. Figure 1 shows the PRISMA flow diagram, and Table 1 lists the study inclusion and exclusion parameters. Animal studies to identify biological mechanisms for nutritional regulation of fetal growth and gestational length were reviewed to identify mechanistic hypotheses.

Fig. 1
figure 1

PRISMA flow diagram

Table 1 Study inclusion and exclusion parameters

As definition of the periconceptional period varied across studies, here it has been defined as taking supplements before, or at the time of, the last menstrual period prior to conception, and up until the end of the first trimester. Overall quality of evidence for outcomes was not graded, as the purpose of this review was to assess methodological frameworks, and not the quality of the evidence for a particular outcome. For the majority of cohort studies published, grading schemes for quality of evidence had previously been applied [6].

Results and discussion

Trial structures and outcome assessments

Figure 2 illustrates a schema for 17 periconceptional studies identified for this review. Nine studies were double blind randomised trials [2028], two unblinded randomised trials [29, 30], and six community- or population-based unblinded prospective studies [29, 3134, 36]. Six randomised trials had two cohorts [20, 21, 24, 25, 30, 31], and five trials three or four cohorts [22, 23, 26, 28, 29]. Three included untreated controls [22, 23, 29], and four received placebo [20, 25, 27, 32] and the remainder a control intervention [21, 24, 26, 30]. All the community- or population-based studies defined two study arms [3337], with one using four study arms [38]. Five included untreated control groups [3438]. They covered 24 countries with the earliest randomised trial reported in 1981 [20]. The study sample sizes in Fig. 2 refer to numbers at enrolment. Studies with two intervention arms in single country locations are listed first, followed by trials with three or four intervention arms or with multiple country locations. Three were in sub-Saharan Africa [21, 29, 31], five in Europe [20, 22, 23, 30, 37], eight in Asia [2426, 28, 29, 3335], with single locations in Cuba [36], Algeria [38], Guatemala, Australia, Canada, Israel and Russia [23]. The minimum period of pre-pregnancy supplementation was 28 days or one month, and the maximum 3.5 years, with the majority providing pre-pregnancy supplementation for a period less than nine months. All studies supplemented through the first trimester; one randomised trial continued supplementation in one arm through later trimesters [29], and another trial continued supplementation after an intervening pregnancy [28].

Fig. 2
figure 2

Summary outlines of periconceptional nutrition supplement intervention studies. Description of intervention supplements uses investigators’ terminology. Multimicronutrient contents of intervention supplements summarised in Table 3. All intervention regimens are daily unless specified. Regimen details, blinding and duration are outlined in Table 8. Hb: haemoglobin; NTDs: neural tube defects; IDA: iron deficiency anaemia; PTB: pre-term birth; LBW: low birth weight; SB: stillbirth; HC: head circumference; Brackets: reference number

For non-pregnant cohorts in the randomised trials, that is, study subjects who did not become pregnant during the study period, six trials reported no outcome measurements and no end assessment survey [20, 22, 23, 25, 26, 30], five reported haemoglobin [24, 26, 27, 29, 30], three provided iron biomarker measurements [21, 26, 29], one measured anthropometry, morbidity, risk of malaria and lower genital tract infections, haemoglobin and iron biomarkers [21], and one measured maternal mortality [28]. All the community-/population-based studies had no end assessment for participants who remained non-pregnant, except one study which measured iron deficiency anaemia [33]. Those without a non-pregnant evaluation were primarily assessing neural tube defect prevention and did not anticipate side effects with the use of folate supplements. Four studies reported neural tube defects alone [20, 22, 34, 36], five reported birth weight measurement and neural tube defects or birth defects [20, 22, 34, 36, 37], twelve included birth weight outcomes [21, 25, 26, 2830, 3338] and variably measured gestational age [21, 26, 29, 35, 37], neonatal anthropometry [25, 29, 37], and maternal and neonatal iodine status [38]. Only two trials included tissue placental sampling [21, 29] or placental function [27, 29]. Consent was a single process except for one study which took consent at each key stage: enrolment, first pregnancy visit and for a follow-up infant survey [21].

Trial design

Figure 3 provides a structural outline for a periconceptional intervention trial using individual randomisation at enrolment to intervention or non-intervention arms. This template illustrates that four cohorts arise from a periconceptional design, two non-pregnant and two pregnant.

Fig. 2
figure 3


In terms of trial inclusion criteria, the pregnancy rate will be optimised if only women who have experienced menarche are selected. Exclusions at enrolment generally include currently pregnant women and those with significant illnesses. In low resource settings, moderately severe anaemia (Hb < 10 g/dl) may be a criterion for exclusion. Enrolment of anaemic women avoids selection bias but creates a heterogenous cohort, as those with nutritional anaemias may benefit more from supplementation than non-anaemic women, with different outcome effects. Women with anaemia observed at baseline would normally require treatment with iron and folate supplements. This might compromise study outcomes if large numbers had to be treated, especially in developing countries where two-thirds of women are typically anaemic. Treatment is sometimes reserved for severe anaemia, providing adequate follow-up of the less anaemic participants is possible. An alternative would be treating participants with clinical anaemia and without haemoglobin assessment. Sera collected at baseline could be stored for ethically approved retrospective measurement of nutritional biomarkers. Outcome assessments would then be based on differences in mean haemoglobin values at follow-up, precluding comparison of change from baseline haemoglobin concentration [39]. For the studies outlined in Fig. 2, eleven collected no baseline blood sample for haemoglobin [2123, 25, 28, 30, 32, 3437], and one indicated clinical screening for anaemia, with baseline sera storage, and exclusion only if hospital treatment was required [21]. Of the remaining studies, five measured baseline haemoglobin [24, 26, 29, 31, 33], two excluded participants if this was <7 g/dl [24, 26], one if <8 g/dl [29], one treated if <7 g/dl and re-recruited [31], and one did not describe study practice [33].

Comprehensive baseline assessment is central for assessing the magnitude of change in outcome measures [39]. Ideally this would include dietary studies, as the effect of the supplement has to be distinguished from a non-specific effect of diet [20]. Other confounding factors or biases such as maternal mental health or workload activities [26] may generate associations between exposure at baseline and the outcome and need to be pre-specified if anticipated, or indicated as post hoc exploratory analyses [40].

In terms of monitoring visits, Fig. 3 does not specify frequency for the non-pregnant and pregnant cohorts, as this is governed by the timing of supplement delivery (daily, weekly, monthly), method(s) of adherence assessment and the frequency of safety assessments. A weekly structure was employed in the PALUFER trial of weekly iron and folate supplementation because it addressed safety issues which required weekly follow-up visits [21]. The intensity of follow-up may itself influence adherence.

The non-pregnant cohort

In periconceptional studies, the trial design generally focusses on the pregnancy cohort and pregnancy-related primary outcomes. However, the evaluation of the non-pregnant cohort is important, as secondary outcomes, such as anaemia, specific nutritional deficiencies, or inflammatory or biomarker profiles can be measured and compared with the pregnant cohort. For studies including parous women, parity-related hypotheses can be evaluated such as effects of supplementation on fertility in different parity cohorts [41], growth in nulliparous adolescent women [19], or predictive value of nutritional parameters at baseline for outcomes at the end assessment [42]. This requires collecting relevant anthropometric and nutritional data.

Non-pregnant women may perceive less benefit from supplement use before pregnancy, and adherence levels in periconceptional trials may differ from pregnancy supplementation studies starting after the first trimester [24]. Follow-up of the non-pregnant cohort after discontinuation of supplementation may be indicated if latency in onset of adverse events might occur (for example, with delayed incidence of infection) or if pregnancy is probable soon after cessation. Monitoring unscheduled health visits is useful, as it may reflect adverse events or relate directly to trial outcomes.

Figure 3 includes an interim cross-sectional survey which can provide an interim safety analysis for trials with infection parameters as primary outcomes, or if there is a substantial concern about side effects. An interim survey needs to be carefully designed, especially if treatment is required which may influence outcomes. An interim survey may not be required if there is a long lag between recruitment and availability of the primary outcome at birth (for example, birth size) [29].

The pregnant cohort

Identification of conception can be based on a reported missed menstrual period with confirmation by a pregnancy test. Some pregnant participants might still be excluded, dependent on whether a minimal period of non-pregnant supplementation was pre-specified. Trial re-entry for participants with repeat pregnancy during the period of supplementation would normally be an exclusion criterion as the two pregnancies would not be independent, or if only nulliparous women were eligible.

In the majority of studies in this review, trial supplementation continued through early gestation to a scheduled first or early second trimester visit. For practical reasons this first scheduled study visit could correspond with the first visit for routine antenatal care when an ultrasound assessment can also confirm gestational age. This visit may be used as an end-point if parameters early in gestation are related to the primary outcome. After the first antenatal study assessment, and depending on study objectives, the intervention supplement may be stopped in order to allow the pregnancy cohorts in both arms to receive standard antenatal iron and folate supplementation, possibly in higher doses than in the intervention supplement, and following national guidelines. If study outcomes are measured at delivery, then re-consenting the individual to pregnancy follow-up should be considered as part of Good Clinical Practice (GCP), especially as there are two distinct cohorts (pregnant and non-pregnant) in the study design. The same applies to any further infant follow-up.

The frequency of study visits during pregnancy may vary according to biological or obstetric outcomes to be measured, although at least one further antenatal attendance prior to delivery would be appropriate for monitoring purposes. Assessment at 32–34 weeks gestation may be the most practical, as it provides near-delivery clinical indicators (for example, infection status, anaemia, blood pressure) which can be used as proxy measures if assessment at delivery may be missed, as is often the case with village deliveries in developing countries. Monitoring of unscheduled pregnancy visits may be required to pick up pregnancy complications or intervening treatments. Adherence to routine antenatal iron and folate supplements can be assessed from the antenatal card or using monthly tablet counts. Frequent monitoring may lead to study fatigue, which may be reduced by assistance with transport and free health care provision, providing these are not provided as active inducements.

At delivery, clinical outcomes are assessed as part of standard care, but because of the importance of placental assessment in periconceptional interventions, it is a priority to obtain adequate placental tissue, with chorioamniotic membrane samples. Processing for placental histology may have specific requirements, for example, malaria histopathology [43]. Placental weight should be measured after trimming of the cord and marginal membranes.

At delivery, duplicate anthropometric measurements reduce measurement error of infant length, which can be measured as crown-heel and crown-rump length, together with the baby’s head and abdominal circumferences. In addition to newborn examination, a clinical gestational age assessment is advantageous, especially if pregnancy ultrasound assessments are not routine. The time of cord clamping should be established, as this influences anaemia risk in infancy [44], although this might be difficult for babies delivered at home by traditional birth attendants. Post-partum outcomes would require a scheduled study visit in the first week, with an additional postnatal visit at 4–6 weeks.

The infant cohort

Only three of the seventeen periconceptional studies reviewed included infant follow-up assessments, two of which adopted a cohort design [23, 29], and one a cross-sectional survey assessment [21]. There is an issue about statistical power if these outcomes are being measured opportunistically, and in analysis, inadequate power reduces the impact of any negative findings. If powered, large cohorts may be required and multiple statistical comparisons will need to be addressed.

The importance of infant follow-up relates to predictive risk of infant micronutrient status on infection risk and assessment of potential benefits to the offspring [45]. The operational framework for infant follow-up should be established early in the study, as infants can be born within seven months of enrolment or sooner if participants are already pregnant at recruitment. Infant follow-up will require active household visits and/or linkage with routine postnatal care and child health care visits. Infant assessments at 6 weeks, 3, 6, and 9 months and at one year of age would be appropriate for screening for anaemia, vaccination status and infection profiles, with less frequent scheduling in the second year. The timing of these would vary according to the outcomes of interest, but an assessment survey at around 9–12 months of age would be suitable as infant haemoglobin would normally plateau by that time. This timing would facilitate initial developmental and auditory testing, infant feeding practices and assessment of core breast feeding indicators. An alternative, more pragmatic, but less informative approach for assessing infant outcomes would be to complete a single cross-sectional survey for all study infants after the last trial infant is born. The mean age of children in this survey would be expected to be comparable by trial arm, unless the intervention had influenced child survival.

Generating mechanistic hypotheses as a trial basis

Mechanisms can be considered in a general classification related to maternal, placental or fetal factors. These will interface, and underlying hypotheses should consider this interaction. Information from human, animal or laboratory studies should be considered. Table 2 summarises putative mechanisms using the above classification.

Table 2 Maternal, placental and fetal mechanistic hypotheses

General mechanisms

These may not account for compensatory growth mechanisms, such as the placenta adapting by up-regulating its transfer systems, and some changes may be irreversible, such as altered expression of transcription factors which in turn produce reduced cell content and enzyme actrivity [46]. Cell cycle regulation and cytoskeletal remodelling are critical processes in the nutritional programming of embryonic development [47], and rapid fetal growth may increase vulnerability to impaired nutrient supply [48]. Even a minor variation in maternal nutritional status is capable of producing important shifts in the fetal environment, as demonstrated in animal studies [49]. There is also evidence that nutrient status in the immediate pre-conception period may affect fertility [41, 50].

Specific mechanisms

The possibility of sex differences in studies concerned with birth size should be considered because of sex differentials in growth [51], sex-specific effects of first trimester progesterone levels on female birth weight [52], and sex-specific epigenetic effects [53]. A number of nutrient- and endocrine-related mechanisms are listed in Table 2, including interfacing hormone receptors on the placenta and fetal-hypothalamic axis [51, 5457]. Although all trials include surveillance for adverse events, there may be a basis for evaluating hypotheses related to adverse events [58]. An example would be when potential nutrient-infection interactions suggest negative outcomes for some participants. One periconceptional trial measured malaria risk in early gestation in participants randomised to receive weekly iron and folate supplements, or folate alone [21]. The hypothesis was that Plasmodium falciparum parasites utilise iron for growth [45, 59].

Selection of single versus multimicronutrient interventions

While there is consensus on use of folic acid in the periconceptional period for prevention of neural tube defects, opinions vary on whether to use single micronutrient supplementation, or composite multimicronutrients including the nutrient of interest [10]. In low resource settings the rationale for multimicronutrient supplementation is influenced by high population prevalence of chronic undernutrition in pregnancy, and a broad nutritional approach is pragmatic. In such settings a single micronutrient supplement may be less likely to improve placental function and fetal growth. Conversely, in areas where overnutrition is common, the developmental origins of health and disease hypothesis suggests that fetal exposure to additional glucose, fatty acids and micronutrients may increase fetal insulin secretion, influencing the development of the hypothalamic-endocrine system, which controls appetite [60, 61].

The rationale for the composition of mineral and vitamin multimicronutrient supplements is often unclear. Individual nutrient dosage is often based on dietary recommendations using physiological requirements, but there are substantive differences between trials. Table 3 illustrates this variation in composition for the seventeen reviewed studies. Five used folic acid alone, and were designed to assess efficacy for prevention of neural tube defects in women with a previously affected infant [20, 22, 23, 25, 36]. Different supplement multimicronutrient content, dosages, and consumption patterns, with or without food, could influence effects. Uniformity across future periconceptional trials would facilitate comparative analyses.

Table 3 Nutrient content of vitamins and micronutrients used in supplementation trials outlined in Fig. 2

Table 4 summarises considerations related to the selection of single or multimicronutrient interventions. In principle, this decision should relate to a mechanistic hypothesis, although in practice, holistic considerations may be prioritised, such as a high population prevalence of several micronutrient deficiencies. The selection of control groups can be problematic. Trial designs frequently use pregnant women who received iron and folic acid supplements as controls, although iron may be included in the composite supplement for the intervention group. Ethically this meets international recommendations for the prevention of pregnancy anaemia, but concomitantly restricts the facility to examine some nutrient-specific hypotheses. Supplements given to control, or placebo groups varied between those receiving two, three, five, or fewer micronutrients [6, 30].

Table 4 Factors related to use of a single or multimicronutrient periconceptional supplements

Cohort size and composition

Study sample size is governed by non-inferiority (or superiority) margins between trial arms, and estimates of numbers lost to follow-up, migrations, withdrawals, and deaths. These latter factors may be considerable for studies undertaken in low resource settings. The proportion of marriageable young women may affect the study sample, as marriage generally involves movement to a new home. Exclusion of participants who indicate an imminent change in residential location at enrolment is sometimes adopted, but such movements are not always predictable and the effect on loss to follow-up may be substantial. If the primary outcome is a pregnancy variable, and only a minority of the pre-pregnancy cohort subsequently conceive, the protective effect of randomisation against bias may be weakened.

A standard definition of fertility is the number of live births per 1,000 women aged 15–49 years in a given year (number of births per year/number of women 15–49 years). An estimate of the population fertility rate is essential in order to estimate accrual of pregnancies, the period of time to attain this, and the size of the residual non-pregnant cohort. In practice accurate fertility estimates are only available in areas where there has been a recent census, or if the study is undertaken in a demographic surveillance area. Approximate fertility estimates may be misleading and can jeopardise trial integrity if the approximation substantially overestimates the true rate.

As an example, Fig. 4 shows fertility rates, by five-year age groups and birth order, for Moroccan women using National Demographic Health Surveys (DHS) data [62]. Based on these fertility rates, parity and maternal age effects across groups would have an impact on cohort study design, because age-specific fertility depends on the proportion of the fertility distribution spent in each category. An average fertility rate would be confounded by residual parity and age effects. These may be considerable over a long period of trial supplementation. Using the DHS data plotted in Fig. 4, the estimated number of births by differential age and parity fertility rates (parities 1, 2, and 3), and for increasing population sample sizes, are shown in Table 5. This illustrates the substantial numerical variation between class groups, emphasising the importance of accurate demographic data for estimating the sample size. This will be dictated by whether study objectives relate to all parity groups or focus on a specific group such as nulliparae. An example was the PALUFER periconceptional trial, in which malaria in primigravidae early in pregnancy was the primary outcome, as malaria affects more primigravidae than multigravidae [21].

Fig. 2
figure 4


Table 5 Estimated annual numbers of births by differential age and parity (P1, P2, P3) fertility rates

If the study allows recruitment across parities, prior construction of a differential age-parity fertility rate table will be helpful in estimating the sample size. This can be done using DHS, recent census or local demographic surveillance data, thus avoiding inaccurate approximations that may require extension of the enrolment period. One periconceptional trial in this review was discontinued because of low fertility rates, combined with a fall in occurrence of the primary outcome of neural tube defects [22]. Secular declines in fertility, particularly in Asia, may occur between censuses and need to be allowed for. Sub-Saharan Africa is the only major region in the developing world that has not yet undergone a general decline in fertility.

Figure 5 shows the increase in the cohort sample size of pregnant women, and proportional decrease for the non-pregnant cohort, using data from Table 5. This is plotted for six-monthly sequential supplementation periods for a total of 18 months supplementation, and using an enrolment sample of 2,000 nulliparous women. It highlights a number of points: with higher fertility rates shorter periods of supplementation are required to reach an expected sample size; lower fertility requires longer supplementation periods for non-pregnant women; the non-pregnant cohort experiences longer periods of supplement exposure than the pregnant cohort for all fertility rates; if the duration of supplementation is an important variable (for example, for safety assessments, tolerability), then the sample size should be estimated according to this variable; there are programmatic implications, as the impact of different durations of supplement delivery relate to the fertility rate.

Fig. 2
figure 5


Outcome measurements

Assessment options will be dictated by study hypotheses, although a broad-based approach to profiling outcomes is useful. Table 6 summarises the range of parameters to be considered for non-pregnant and pregnant cohorts, for post-partum and infant assessments, and biochemical markers relevant for placental studies. These are grouped by categories related to study activities and potentially relevant mechanistic hypotheses. Relevant specimen sampling is described which covers major biological options to be considered. In view of epigenetic phenomena and the potential associations between maternal micronutrient status and genetic variants in metabolic enzymes affecting health, genotype profiling is included [63]. The seminal example is reduced efficacy of periconceptional folic acid supplementation with deficiency of folate metabolising enzymes [64], but a much broader approach assessing multiple enzyme loci is possible [65]. Ethical permission for collection and storage of blood spots and sera should be anticipated if the study aims do not relate to a primary genetic hypothesis, as it would permit post hoc genotype profiling. This is especially relevant if study protocols are complex, prolonged or costly, as repeat studies may not be feasible and future studies would be potentially biased by time-varying confounding.

Table 6 Assessment considerations in periconceptional supplementation trials

Randomised trials are the best design for testing intervention effectiveness, but more detailed laboratory science is required to explore possible causal mechanisms that can inform the development of interventions. Profiling a wider biochemical range of nutrients with maternal, neonatal and infant outcomes could help to identify additional interventions. Advances in metabolomics now make this possible [66], with screening of large sample numbers using mass spectrometry or nuclear magnetic resonance spectroscopy. Novel biomarkers may be assessed for predictive accuracy for fetal growth restriction and other pregnancy outcomes [67, 68]. For these reasons a storage sera set should be secured for future examination, and potentially to allow cross-cohort comparisons for replication across studies and use of triangulation methods [17].

Some of the general profiling options outlined in Table 6 may not be measured for pragmatic reasons, or because they do not relate to study objectives. In order to illustrate a specific trial outline, Table 7 shows the activities for the PALUFER trial of malaria risk prior to and during early pregnancy in nulliparous women receiving long-term weekly iron and folic acid supplementation [21]. This was a non-inferiority randomised controlled trial based on infection risk parameters, where the primary endpoint was malaria infection at the first scheduled antenatal visit. Malaria prevalence in the non-pregnant cohort was also assessed at an end assessment survey. The study used the structure outlined in Fig. 3, which provided a platform for a series of secondary analyses, for pregnant and non-pregnant cohorts. Risk of bacterial vaginosis, a major risk factor for chorioamnionitis, was also assessed in both cohorts [69]. Detailed baseline profiling allowed the predictive value of biomarkers to be assessed for subsequent health outcomes. The large size of the residual non-pregnant cohort provided adequate statistical power for secondary outcome assessments. In the PALUFER trial, participants were consented at enrolment, re-consented at first antenatal visit, and again for the infant cross-sectional survey, and this included permission for sample storage.

Table 7 Profile of PALUFER trial activities evaluating weekly iron supplementation [21]

Adherence assessment

Assessment of adherence to supplementation in the periconceptional trials shown in Fig. 2 was generally by tablet counts, and reported as the percentage compliance. This is probably not sufficient, as percentage compliance obtained by counting tablets can be calculated in different ways. Trial details are shown in Table 8 with assessment frequency varying from weekly to every three months. One of these trials reported an outcome assessment (change in haemoglobin) by quartiles of percent adherence [24]. The epidemiology of supplement intake patterns requires better description than total tablet counts, and current trials should be able to provide a more detailed analysis of the potential influence of adherence patterns on study outcomes [21]. With longer supplementation adherence may be discontiguous, with intake discontinued and re-commenced for variable monthly periods, resulting in intermittent patterns and variable adherence exposure at the time of conception, especially as seasonal factors may be influential. Percentage compliance does not capture this variation, as in longitudinal trials adherence is a rate rather than a period prevalence, and would be more precisely expressed as a rate in person weeks/months of follow-up. One study used directly observed intake [21], which is a definitive method of assessment, but even this does not secure intake if participants are absent for variable periods. Trials need to be adequately powered to allow for this influence, in particular with long periods of supplementation.

Table 8 Supplement compliance and uptake for studies outlined in Fig. 2

The adherence level is one criterion used to distinguish per protocol from intention-to-treat populations. Of the studies outlined in Fig. 2, all used an intention-to-treat analysis approach without attempting to define a theoretical per protocol population. This definition would require a percentage compliance cut-off, or particular compliance profiles to be selected, the biological basis of which may be uncertain. This is particularly challenging where participants have highly variable uptake patterns and excellent adherence is impossible to achieve. With improved biochemical measures of exposure it may be possible to develop alternative assessment methods.

There are reasons to believe that adherence to medication may differ between arms, for example, if side effects varied by trial arm, or if the control group knew they were taking placebo, and so would not be independent of randomisation. Strata generated by adherence may therefore compare dissimilar patients from each treatment arm. There are therefore some problems in interpreting analyses that include a stratified adherence variable. It is nevertheless important to describe the level of adherence by treatment arm in a non-inferiority trial, as non-adherence results in a bias towards the alternative hypothesis. With non-compliance in the intervention arm, an adherence-based analysis may provide meaningful information (causal effect estimate of the treatment effect) [70]. The effect of non-adherence should be considered in secondary analyses.

The role of qualitative studies

Only one trial in Burkina Faso included a qualitative component [21], in which focus group discussions with non-trial participants and field workers were conducted early in the study before supplementation had been established. The data drew attention to misconceptions about the purpose of supplementing unmarried, non-pregnant women in a setting in a developing country, which the team tried to address in subsequent community contacts (71). A high level of illiteracy in the rural study area compounded such misconceptions. Detailed interviews with participants who had varying levels of supplement adherence were also conducted at later stages, giving insights into unexpected factors that interfered with regular adherence and loss to follow-up, some of which could be addressed by the field team. Qualitative studies conducted in parallel with the main trial could assist in informing trial management, but may only be able to address a limited number of potential problems, especially in rural, more isolated communities in low resource settings. Examples include: falling adherence with longer supplementation periods; hidden pregnancies or abortions; adolescent pregnancies in unmarried women; miscarriages which may be interpreted as irregular menstruation, and their distinction from false positive pregnancy tests; misinformation on the nature of the intervention [71] or confusion with contraception; insufficient communication with local communities and partners or husbands. Some trials have enrolled only married participants, which may reduce occurrence of some of these factors, although in areas where unmarried adolescent pregnancy is common, this may result in selection bias. Protocol amendments may be required to address specific factors in order to ensure optimal trial participation, participant security, and community compliance. The Gambia trial did not integrate a qualitative component because it was a mechanistic study and also to avoid raising concerns about the nutritional supplement [72], and this restricted its ability to manage emerging issues. A study among non-participants illustrated extreme reluctance by women to disclose pregnancy in the first trimester.

The role of qualitative studies within trials has been diminished by views that such studies are relevant only for programmes, given that trials try to maximise adherence by intensive supervision and benefits (such as treatment and travel costs) that are not available routinely. Indeed, a community mobilisation and social marketing study to promote weekly pre-pregnancy iron and folic acid supplementation to married, nulliparous women in Vietnam reported that the purchase of supplements over four months fell from 92.5 % to 67.4 % [33]. In Burkina Faso the qualitative research has clearly shown that young women see little benefit from taking supplements and without free treatment, adherence would be much lower [21]. It would be better to anticipate such problems rather than have to initiate new studies to promote uptake following policy recommendations for supplementation.

Data management

The data management of periconceptional trials can incorporate three categories of participants: non-pregnant, pregnant, and child cohorts, as well as longitudinal and cross-sectional data sets. All data can be handled in an electronic case record form (CRF) developed in MACRO (Infermed©). This is a powerful, industry-standard electronic data capture solution, which supports trials from Phase I through IV. It is designed to increase compliance with the requirements of relevant regulatory bodies including the International Conference on Harmonisation (ICH) Good Clinical Practice, and Federal Drug Administration-CFR21Part11 compliance. It also allows exchange of an entire clinical study between two different and independent study software solutions, facilitating exchange of metadata [73]. Considering the sample size and the long weekly follow-up period, source data collection of the weekly household visits using hand-held portable devices is preferable to facilitate real-time data validation, to avoid duplicate data entry, and to simplify batch data entry of large inter-related data sets.

The complexity of work flow with periconceptional trials requires an ability to effectively track participant compliance with complex protocol schemas (such as intervention schedules, data collection requirements, parallel cohort tracking, monitoring of side effects, sequential delivery times and participant recruitment, and participant out migration) [74]. Participant attrition as a result of protocol noncompliance can result in increased costs, delayed completion times, biased data, and ambiguity over protocol violations. Validated methods for clinical trial participant tracking are required [75], which will enhance data safety and monitoring reporting.


There are many challenges facing early-life intervention studies, and trials in developing countries will have operational requirements which are context specific, and likely to be dependent on the literacy rate in the study population. This review has focussed on pragmatic issues that we hope can be drawn upon and expanded as experience grows with more completed studies. Periconceptional trials are distinguishable from pregnancy supplementation trials, not only because of the early gestational timing of nutrient exposure, but also because they generate a subsidiary trial in participants who remain non-pregnant. This provides an opportunity to evaluate concurrent hypotheses related to the health of women who may become pregnant. An expanded framework for conceptualisation, design, and implementation is required in order to reduce risks in achieving outputs, strengthen trial integrity, facilitate replication and data exchange and reduce costs. Basing trial design on a mechanistic hypothesis is an important pre-requisite which relates to the choice of a single or multiple micronutrient intervention. Micronutrient interventions are relevant across the entirety of a women’s reproductive period, even more so in young nulliparae and grand multiparae [76]. Longitudinal reference values for biochemical markers are required in uncomplicated pregnancies to facilitate interpretation [77]. Iron would be a requirement of most periconceptional supplements, especially in developing countries, as many women enter pregnancy with inadequate iron stores [78]. Early pregnancy iron deficiency has been associated with poorer [79], and iron supplementation with better birth weight outcomes [80, 81], although questions remain concerning iron-infection interactions which may have an impact on safety under specific infection exposures [14]. Folate is a requirement in view of strong evidence for periconceptional use preventing neural tube defects. The rationale for multimicronutrient dosage and content needs to be clearly outlined, in order to establish a comparative framework, improve standardisation, and facilitate interpretation of mechanistic hypotheses.

Other issues identified include: rationalisation of the duration of supplementation; problems with excluding anaemic participants at baseline; influence of follow-up intensity on intervention adherence; and out-migration, particularly in adolescent cohorts. Research and practice recommendations include: utilising accurate fertility data for both sample size and supplement duration estimations; prioritising placental tissue sampling; establishing appropriate safety assessments incorporating infection risk; inclusion of an infant follow-up; utilisation of electronic case record forms on MACRO (Infermed); and qualitative concurrent assessments prior to and during trial implementation. Qualitative assessments are not solely theoretical, as trial results will not appeal to policy makers if they are not implementable. This is a particular issue with pre-pregnancy studies and in first pregnancies, as women unfamiliar with routine care may fail to comply with supplementation. This may re-direct efforts towards food fortification, which are less realistic in some low resource settings. With more comprehensive assessments the complexity of the work flow and processes executed by the field clinical investigator should not be underestimated.

The heterogeneity of studies included in this review, which covers both randomised trials and prospective cohort studies, limited comparability. There was a predominance of studies examining NTD outcomes following folate supplementation, and considerable variation in duration of supplementation across studies. These differences related partly to assessment of alternative study outcomes, but most were not rationalised in physiological terms. While this lack of uniformity limited comparability, it facilitated identification of methodological issues relevant to future trial conduct.



arm circumference


alpha-1-acid glycoprotein, acyl glycol-protein




blood pressure


beta human chorionic gonadotrophin


body mass index


bacterial vaginosis






C-reactive protein


Demographic Health Surveys


deoxyribonucleic acid




femur length


Good Clinical Practice




head circumference


human immunodeficiency virus


hypothalamic-pituitary-adrenal axis






iron deficiency anaemia


intermittent preventive treatment with sulfadoxine-pyrimethamine




low birth weight


last menstrual period




loss to follow-up


mean corpuscular haemoglobin concentration


mean corpuscular volume






mid-upper arm circumference


National Institutes of Health (USA)


neural tube defects




P2, P3, parity one, two, or three


plasminogen activator inhibitor


plasma protein A


placental growth factor


pre-term birth


red blood cell


ribonucleic acid






sexually transmitted infection


serum transferrin receptor




uterine pulsatility index


uterine artery resistance indices


weekly iron and folic acid supplementation


  1. Smithells RW, Sheppard S, Schorah CJ, Seller MJ, Nevin NC, Harris R, et al. Possible prevention of neural-tube defects by periconceptional vitamin supplementation. Lancet. 1980;1:339–40.

    CAS  PubMed  Google Scholar 

  2. De-Regil LM,Fernandez-Gaxiola AC, Dowswell T, Pena-Rosas JP. Effects and safety of periconceptional folate supplementation for preventing birth defects. Cochrane Database of systematic Reviews 2010, Issue 10. [DOI:10.1002/14651858.CD007950.pub2]

  3. Pharoah POD, Buttfield IH, Hetzel BS. Neurological damage to the fetus resulting from severe iodine deficiency during pregnancy. Lancet. 1971;1:308–10.

    CAS  PubMed  Google Scholar 

  4. Cetin I, Berti C, Calabrese S. Role of micronutrients in the periconceptional period. Hum Reproduction Update. 2010;16:80–95.

    CAS  Google Scholar 

  5. Ronsmans C, Fisher DJ, Osmond C, Margetts BM. Fall CHD for the Maternal Micronutrient Supplementation Group (MMSSG). Multiple micronutrient supplementation during pregnancy in low-income countries: a meta-analysis of effects on stillbirths and on early and late neonatal mortality. Food Nutr Bull. 2009;30:S547–26.

    PubMed  Google Scholar 

  6. Ramakrishnan U, Grant FK, Goldenberg T, Bui V, Imdad A, Bhutta ZA. Effect of multiple micronutrient supplementation on pregnancy and infant outcomes: a systematic review. Paediatr Perinat Epidemiol. 2012;26 Suppl 1:153–67.

    PubMed  Google Scholar 

  7. Persson LA, Arifeen S, Ekström E-C, Rasmussen KM, Frongillo EA, Yunus M, et al. Effects of prenatal micronutrient and early food supplementation on maternal hemoglobin, birth weight, and infant mortality among children in Bangladesh: the MINIMat randomized trial. JAMA. 2012;307:2050–9.

    CAS  PubMed  Google Scholar 

  8. West Jr KP, Shamim AA, Mehra S, Labrique AB, Ali H, Shaikh S, et al. Effect of maternal multiple micronutrient vs iron-folic acid supplementation on infant mortality and adverse birth outcomes in rural Bangladesh: the JiVitA-3 randomized trial. JAMA. 2014;312:2649–58.

    PubMed  Google Scholar 

  9. Haider BA, Yakoob MY, Bhutta ZA. Effect of multiple micronutrient supplementation during pregnancy on maternal and birth outcomes. BMC Public Health. 2011;13 Suppl 3:S19.

    Google Scholar 

  10. Bhutta ZA, Imdad A, Ramakrishnan U, Martorell R. Is it time to replace iron folate supplements in pregnancy with multiple micronutrients? Paediatr Perinat Epidemiol. 2012;26 Suppl 1:27–35.

    PubMed  Google Scholar 

  11. Peña-Rosas JP, De-Regil LM, Dowswell T, Viteri FE. Daily oral iron supplementation during pregnancy. Cochrane Database Syst Rev. 2012;12:CD004736.

    PubMed  PubMed Central  Google Scholar 

  12. Peña-Rosas JP, De-Regil LM, Dowswell T, Viteri FE. Intermittent oral iron supplementation during pregnancy. Cochrane Database Syst Rev. 2012;7:CD009997.

    PubMed  PubMed Central  Google Scholar 

  13. Allen LH, Peerson JM. Impact of multiple micronutrient versus iron-folic acid supplements on maternal anemia and micronutrient status in pregnancy. Food Nut Bull. 2009;30:S527–32.

    Google Scholar 

  14. Brabin L, Brabin BJ, Gies S. Influence of iron status on risk of maternal or neonatal infection and on neonatal mortality with an emphasis on developing countries. Nutr Rev. 2013;71:528–40.

    PubMed  PubMed Central  Google Scholar 

  15. Khong TY. Placental vascular development and neonatal outcome. Semin Neonatol. 2004;9:255–63.

    PubMed  Google Scholar 

  16. Chaddha V, Viero S, Huppertz B, Kingdom J. Developmental biology of the placenta and the origins of placental insufficiency. Semin Fetal Neonatal Med. 2004;9:357–69.

    PubMed  Google Scholar 

  17. Lawlor DA. The Society for Social Medicine John Pemberton Lecture 2011. Developmental overnutrition—an old hypothesis with new importance? Int J Epidemiol. 2013;42:7–29.

    PubMed  Google Scholar 

  18. Ramakrishnan U, Grant F, Goldenberg T, Zongrone A, Martorell R. Effect of women’s nutrition before and during early pregnancy on maternal and infant outcomes: a systematic review. Paediatr Perinat Epidemiol. 2012;26 Suppl 1:285–301.

    PubMed  Google Scholar 

  19. Kalanda BF, Verhoeff FH, Brabin BJ. Chronic malnutrition in pregnant adolescents in rural Malawi: an anthropometric study. Acta Obstet Gynecol Scand. 2006;85:33–9.

    PubMed  Google Scholar 

  20. Laurence KM, James N, Miller MH, Tennant GB, Campbell H. Double-blind randomised controlled trial of folate treatment before conception to prevent recurrence of neural-tube defects. BMJ (Clinical Research Edition). 1981;282:1509–11.

    CAS  Google Scholar 

  21. The Lancet. Protocol summaries. Protocol 10PRT/6932: Malaria risk prior to and during early pregnancy in nulliparous women receiving long-term weekly iron and folic acid supplementation (WIFS): a non-inferiority randomised controlled trial [NCT 01210040]. Accessed 1 March 2015.

  22. Kirke PN, Daly LE, Elwood JH. A randomised trial of low dose folic acid to prevent neural tube defects. The Irish Vitamin Study Group. Arch Dis Child. 1992;67:1442–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. MRC Vitamin Study Research Group. Prevention of neural tube defects: results of the Medical Research Council Vitamin Study. Lancet. 1991;338:131–7.

    Google Scholar 

  24. Khambalia AZ, O’Connor DL, Macarthur C, Dupuis A, Zlotkin SH. Periconceptional iron supplementation does not reduce anemia or improve iron status among pregnant women in rural Bangladesh. Am J Clin Nutr. 2009;90:1295–302.

    CAS  PubMed  Google Scholar 

  25. Indian Council of Medical Research Collaborating Centres and Central Technical Co-ordinating Unit. Multicentric study of efficacy of periconceptional FA containing vitamin supplementation in prevention of open neural tube defects from India. Ind J Med Res. 2000;112:206–11.

    Google Scholar 

  26. Nguyen PH, Lowe AE, Martorell R, Nguyen H, Pham H, Nguyen S, et al. Rationale, design, methodology and sample characteristics for the Vietnam pre-conceptual micronutrient supplementation trial (PRECONCEPT): a randomized controlled study. Public Health. 2012;12:898.

    PubMed  PubMed Central  Google Scholar 

  27. Owens S, Gulati R, Fulford AJ, Sosseh F, Denison FC, Brabin BJ, et al. Periconceptional multiple-micronutrient supplementation and placental function in rural Gambian women: a double-blind, randomized, placebo-controlled trial. Am J Clin Nutr. 2015. doi:10.3945/ajcn.113.072413.

  28. West KP, Katz J, Khatry SK, LeClerq SC, Pradhan EK, Shrestha SR, et al. Double blind cluster randomised trial of low dose supplementation with vitamin A or β carotene on mortality related to pregnancy in Nepal. Br Med J. 1999;318:570–5.

    CAS  Google Scholar 

  29. Hambidge KM, Krebs NF, Westcott JE, Garces A, Goudar SS, Kodkany BS, et al. Preconception maternal nutrition: a multi-site randomized controlled trial. BMC Pregnancy Childbirth. 2014;14:11.

    Google Scholar 

  30. Czeizel AE, Dudas I. Prevention of the first occurrence of neural-tube defects by periconceptional vitamin supplementation. New Eng J Med. 1992;327:1832–5.

    CAS  PubMed  Google Scholar 

  31. Gulati R, Bailey R, Prentice AM, Brabin BJ, Owens S. Haematological effects of multimicronutrient supplementation in non-pregnant Gambian women. Eur J Clin Nutr. 2009;63:970–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Zheng X, Pei L, Chen G, Song X, Wu J, Ji Y. Periconceptional multivitamin supplementation containing folic acid and sex ratio at birth in a Chines population: a prospective cohort study. Paediatr Perinat Epidemiol. 2015;29:299–306.

    PubMed  Google Scholar 

  33. Berger J, Thanh HT, Cavalli-Sforza T, Smitasiri S, Khan NC, Milani S, et al. Community mobilization and social marketing to promote weekly iron-folic acid supplementation in women of reproductive age in Vietnam: impact on anemia and iron status. Nut Rev. 2005;63:S95–108.

    Google Scholar 

  34. Berry RJ, Li Z, Erickson JD, Li S, Moore CA, Wang H, et al. Prevention of neural-tube defects with folic acid in China. New Eng J Med. 1999;341:1485–90.

    CAS  PubMed  Google Scholar 

  35. Wang YF, Pei LJ, Song XM, Chen G, Zheng XY. Impact of periconceptional multi-micronutrient supplementation on gestation: a population-based study. Biomed Environ Sci. 2013;26:23–31.

    CAS  PubMed  Google Scholar 

  36. Vergel RG, Sanchez LR, Heredero BL, Rodriguez PL, Martinez AJ. Primary prevention of neural tube defects with folic acid supplementation: Cuban experience. Prenat Diagn. 1990;10:149–52.

    CAS  PubMed  Google Scholar 

  37. Timmermans S, Jaddoe VWV, Hofman A, Steegers-Theunissen RPM, Steegers EAP. Periconception folic acid supplementation, fetal growth and the risk of low birth weight and preterm birth: the Generation R Study. Br J Nut. 2009;102:777–85.

    CAS  Google Scholar 

  38. Chaouki ML, Benmiloud M. Article Title: Prevention of iodine deficiency disorders by oral administration of lipiodol during pregnancy. Eur J Endocrin. 1994;130:547–51.

    CAS  Google Scholar 

  39. Vickers AJ, Altman DG. Analysing controlled trials with baseline and follow-up measurements. BMJ. 2001;323:1123–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Davey Smith G, Leary S, Ness A, Lawlor DA. Challenges and novel approaches in the epidemiological study of early life influences on later disease. Adv Exp Med Biol. 2009;646:1–14.

    PubMed  Google Scholar 

  41. Buhling KJ, Grajecki D. The effect of micronutrient supplements on female fertility. Curr Opin Obstet Gynecol. 2013;25:173–80.

    PubMed  Google Scholar 

  42. Heerman WJ, Bian A, Shintani A, Barkin SL. Interaction between maternal prepregnancy body mass index and gestational weight gain shapes infant growth. Acad Pediatr. 2014;14:463–70.

    PubMed  PubMed Central  Google Scholar 

  43. Brabin BJ, Romagosa C, Abdelgalil S, Menéndez C, Verhoeff FH, McGready R, et al. The sick placenta-the role of malaria. Placenta. 2004;25:359–78.

    CAS  PubMed  Google Scholar 

  44. van Rheenen P. Less iron deficiency anaemia after delayed cord-clamping. Paediatr Int Child Health. 2013;33:57–8.

    PubMed  Google Scholar 

  45. Jonker FA, Calis JC, van Hensbroek MB, Phiri K, Geskus RB, Brabin BJ, et al. Iron status predicts malaria risk in Malawian preschool children. PLoS One. 2012;7(8):e42670. doi:10.1371/journal.pone.0042670.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. McArdle HJ, Gambling L, Kennedy C. Iron deficiency during pregnancy: the consequences for placental function and fetal outcome. Proc Nutr Soc. 2014;73:9–15.

    CAS  PubMed  Google Scholar 

  47. Swali A, McMullen S, Hayes H, Gambling L, McArdle HJ, Langley-Evans SC. Cell cycle regulation and cytoskeletal remodelling are critical processes in the nutritional programming of embryonic development. PLoS One. 2011;6:e23189.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. McArdle HJ, Ashworth CJ. Micronutrients in fetal growth and development. Br Med Bull. 1999;55:499–510.

    CAS  PubMed  Google Scholar 

  49. Langley-Evans SC. Nutritional programming of disease: unravelling the mechanism. J Anat. 2009;215:36–51.

    PubMed  Google Scholar 

  50. Cross JC, Mickelson L. Nutritional influences on implantation and placental development. Nutr Rev. 2006;64:S12–8. discussion S72-91.

    PubMed  Google Scholar 

  51. Lawlor DA, Owen CG, Davies AA, Whincup PH, Ebrahim S, Cook DG, et al. Sex differences in the association between birth weight and total cholesterol. A meta-analysis. Ann Epidemiol. 2006;16:19–25.

    PubMed  Google Scholar 

  52. Hartwig IR, Pincus MK, Diemert A, Hecher K, Arck PC. Sex-specific effect of first-trimester maternal progesterone on birthweight. Hum Reprod. 2013;28:77–86.

    CAS  PubMed  Google Scholar 

  53. Cooper WN, Khulan B, Owens S, Elks CE, Seidel V, Prentice AM, et al. DNA methylation profiling at imprinted loci after periconceptional micronutrient supplementation in humans: results of a pilot randomized controlled trial. FASEB J. 2012;26:1782–90.

    CAS  PubMed  Google Scholar 

  54. Connor KL, Challis JR, van Zijl P, Rumball CW, Alix S, Jaquiery AL, et al. Do alterations in placental 11beta-hydroxysteroid dehydrogenase (11betaHSD) activities explain differences in fetal hypothalamic-pituitary-adrenal (HPA) function following periconceptional undernutrition or twinning in sheep? Reprod Sci. 2009;16:1201–12.

    CAS  PubMed  Google Scholar 

  55. Catalano PM, Thomas AJ, Huston LP, Fung CM. Effect of maternal metabolism on fetal growth and body composition. Diabetes Care. 1998;21:B85–90.

    PubMed  Google Scholar 

  56. Fowden AL, Forhead AJ, Sferruzzi-Perri AN, Burton GJ, Vaughan OR. Endocrine regulation of placental phenotype. Placenta. 2014: S0143-4004(14)00873

  57. Nelson SM, Mathews P, Poston L. Maternal metabolism and obesity: modifiable determinants of pregnancy outcome. Hum Reprod Update. 2010;16:255–75.

    PubMed  Google Scholar 

  58. Czeizel AE, Dudas I, Fritz G, Técsöi A, Hanck A, Kunovits G. The effect of periconceptional multivitamin-mineral supplementation on vertigo, nausea and vomiting in the first trimester of pregnancy. Arch Gynecol Obstet. 1992;251:181–5.

    CAS  PubMed  Google Scholar 

  59. Raiten D, Namaste S, Brabin BJ. Eds. Considerations for the Safe and Effective Use of Iron Interventions in Areas of Malaria Burden: Full Technical Report. National Institute for Child health and Human Development, 2009. Accessed 1 March 2015.

  60. Alfaradhi MZ, Ozanne SE. Developmental programming in response to maternal overnutrition. Front Genet. 2011;2:27. doi:10.3389/fgene.2011.00027.

    PubMed  PubMed Central  Google Scholar 

  61. Taylor PD, Poston L. Developmental programming of obesity in mammals. Exp Physiol. 2007;92:287–98.

    CAS  PubMed  Google Scholar 

  62. Caselli G, Vallin J, Wunch G. Demography: Analysis and synthesis. A treatise in population. London: Academic Press; 2006.

    Google Scholar 

  63. Mistry HD, Gill CA, Kurlak LO, Seed PT, Hesketh JE, Méplan C, et al. SCOPE Consortium, Association between maternal micronutrient status, oxidative stress, and common genetic variants in antioxidant enzymes at 15 weeks׳ gestation in nulliparous women who subsequently develop preeclampsia. Free Radic Biol Med. 2015;78:147–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Eskes TKAB. Homocysteine and human reproduction. In: Carmel R, Jacobsen DW, editors. Homocysteine in health and disease. Cambridge: University Press; 2001.

    Google Scholar 

  65. Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytikainen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012;44:269–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Soininen P, Kangas AJ, Wurtz P, Tukiainen T, Tynkkynen T, Laatikainen R, et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst. 2009;134:1781–5.

    CAS  PubMed  Google Scholar 

  67. Conde-Agudelo A, Papageorghiou AT, Kennedy SH, Villar J. Novel biomarkers for the prediction of the spontaneous preterm birth phenotype: a systematic review and meta-analysis. Bri J Obs Gyaen. 2011;118:1042–54.

    CAS  Google Scholar 

  68. Conde-Agudelo A, Papageorghiou AT, Kennedy SH, Villar J. Novel biomarkers for predicting intrauterine growth restriction: a systematic review and meta-analysis. Bri J Obs Gyaen. 2013;120:681–94.

    CAS  Google Scholar 

  69. Garland SM, Ní Chuileannáin F, Satzke C, Robins-Browne R. Mechanisms, organisms and markers of infection in pregnancy. J Reprod Immunol. 2002;57:169–83.

    CAS  PubMed  Google Scholar 

  70. Shrier I, Steele RJ, Verhagen E, Herbert R, Riddell CA, Kaufman JS. Beyond intention to treat: what is the right question? Clin Trials. 2014;11(1):28–37. doi:10.1177/1740774513504151.

    PubMed  Google Scholar 

  71. Compaore A, Gies S, Brabin B, Tinto H, Brabin L. There is iron and iron…” Burkinabè women’s perceptions of iron supplementation: a qualitative study. Matern Child Health J. 2014;18:1976–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Stokes E, Dumbaya I, Owens S, Brabin L. The right to remain silent; a qualitative study of the medical and social ramifications of pregnancy disclosure for Gambian women. Bri J Obs Gyaen. 2008;115:1641–7.

    CAS  Google Scholar 

  73. Kuchinke W, Wiegelmann S, Verplancke P, Ohmann C. Extended cooperation in clinical studies through exchange of CDISC metadata between different study software solutions. Methods Inf Med. 2006;45:441–6.

    CAS  PubMed  Google Scholar 

  74. Chow SC, Liu JP. Design and analysis of clinical trials: concepts and methodologies. 2nd ed. Hoboken: Wiley; 2003.

    Google Scholar 

  75. Payne PRO, Embi PJ, Johnson SB, Mendonca E, Starren J. Improving clinical trial participant tracking tools using knowledge-anchored design methodologies. Appl Clin Inform. 2010;12:177–96.

    Google Scholar 

  76. Kozuki N, Lee AC, Silveira MF, Sania A, Vogel JP, Adair L, et al. Child Health Epidemiology Reference Group Small-for-Gestational-Age-Preterm Birth Working Group. The associations of parity and maternal age with small-for-gestational-age, preterm, and neonatal and infant mortality: a meta-analysis. BMC Public Health. 2013;13 Suppl 3:S2.

    PubMed  PubMed Central  Google Scholar 

  77. Wortelboer EJ, Koster MP, Kuc S, Eijkemans MJ, Bilardo CM, Schielen PC, et al. Longitudinal trends in fetoplacental biochemical markers, uterine artery pulsatility index and maternal blood pressure during the first trimester of pregnancy. Ultrasound Obstet Gynecol. 2011;38:383–8.

    CAS  PubMed  Google Scholar 

  78. Beard JL. Effectiveness and strategies of iron supplementation during pregnancy. Am J Clin Nutr. 2000;71:1288S–94.

    CAS  PubMed  Google Scholar 

  79. Ronnenberg AG, Wood RJ, Wang X, Xing H, Chen C, Chen D, et al. Preconception hemoglobin and ferritin concentrations are associated with pregnancy outcome in a prospective cohort of Chinese women. J Nutr. 2004;134:2586–91.

    CAS  PubMed  Google Scholar 

  80. Cogswell ME, Parvanta I, Ickes L, Yip R, Brittenham GM. Iron supplementation during pregnancy, anemia, and birth weight: a randomized controlled trial. Am J Clin Nutr. 2003;78:773–81.

    CAS  PubMed  Google Scholar 

  81. Siega-Riz AM, Hartzema AG, Turnbull C, Thorp J, McDonald T, Cogswell ME. The effects of prophylactic iron given in prenatal supplements on iron status and birth outcomes: a randomized controlled trial. Am J Obs Gynec. 2006;194:512–9.

    CAS  Google Scholar 

  82. Wu G, Imhoff-Kunsch B, Girard AW. Biological mechanisms for nutritional regulation of maternal health and fetal development. Paediatr Perinat Epidemiol. 2012;26 Suppl 1:4–26.

    PubMed  Google Scholar 

  83. Lawlor DA, Relton C, Satter N, Nelson SM. Maternal adiposity - a determinant of perinatal and offspring outcomes?  Nat Rev Endocrinol. 2012;11: 679-688.

  84. Poston L, Igosheva N, Mistry HD, Seed PT, Shennan AH, Rana S, et al. Role of oxidative stress and antioxidant supplementation in pregnancy disorders. Am J Clin Nutr. 2011;94:1980S–5.

    CAS  PubMed  Google Scholar 

  85. Kweider N, Huppertz B, Wruck CJ, Beckmann R, Rath W, Pufe T, et al. A role for Nrf2 in redox signalling of the invasive extravillous trophoblast in severe early onset IUGR associated with preeclampsia. PLoS One. 2012;7:e47055.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Brik M, Antonio P, Perales-Puchalt A, Diago V, Perales A. Cervical interleukin-6 as a predictive test for preterm delivery in symptomatic women: preliminary results. Eur J Obstet Gynecol Reprod Biol. 2011;155:14–8.

    CAS  PubMed  Google Scholar 

  87. Maitre L, Fthenou E, Athersuch T, Coen M, Toledano MB, Holmes E, et al. Urinary metabolic profiles in early pregnancy are associated with preterm birth and fetal growth restriction in the Rhea mother-child cohort study. BMC Med. 2014;12:110.

    PubMed  PubMed Central  Google Scholar 

  88. Hindmarsh PC, Geary MP, Rodeck CH, Jackson MR, Kingdom JC. Effect of early maternal iron stores on placental weight and structure. Lancet. 2000;26(356):719–23.

    Google Scholar 

  89. Cetin I, Alvino G. Intrauterine growth restriction: implications for placental metabolism and transport. A review. Placenta. 2009;30(Suppl A):S77–82.

    PubMed  Google Scholar 

  90. Kadyrov M, Kosanke G, Kingdom J, Kaufmann P. Increased fetoplacental angiogenesis during first trimester in anaemic women. Lancet. 1998;352:1747–9.

    CAS  PubMed  Google Scholar 

  91. Bazer FW, Kim J, Song G, Ka H, Tekwe CD, Wu G. Select nutrients, progesterone, and interferon tau affect conceptus metabolism and development. Ann N Y Acad Sci. 2012;1271:88–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Cha J, Sun X, Dey SK. Mechanisms of implantation: strategies for successful pregnancy. Nature Med. 2012;18:1754–67.

    CAS  PubMed  Google Scholar 

  93. Redmer DA, Wallace JM, Reynolds LP. Effect of nutrient intake during pregnancy on fetal and placental growth and vascular development. Domest Anim Endocrinol. 2004;27:199–217.

    CAS  PubMed  Google Scholar 

  94. Chappell LC, Seed PT, Briley AL, Kelly FJ, Lee R, Hunt BJ, et al. Effect of antioxidants on the occurrence of pre-eclampsia in women at increased risk: a randomised trial. Lancet. 1999;354:810–6.

    CAS  PubMed  Google Scholar 

  95. Magnusardottir AR, Steingrimsdottir L, Thorgeirsdottir H, Hauksson A, Skuladottir GV. Red blood cell n-3 polyunsaturated fatty acids in first trimester of pregnancy are inversely associated with placental weight. Acta Obstet Gynecol Scand. 2009;88:91–7.

    CAS  PubMed  Google Scholar 

  96. Wang X, Mendelsohn L, Rogers H, Leitman S, Raghavachari N, Yang Y, Yau YY, et al. Heme-bound iron activates placenta factor in erythroid cells via erythroid Kruppel-like factor. Blood 2014;124:946-954.

  97. Brabin BJ, Fletcher KA, Brown N. Do disturbances within the folate pathway contribute to low birth weight in malaria? Trends Parasitol. 2003;19:39–43.

    CAS  PubMed  Google Scholar 

  98. Morken NH, Källen K, Jacobsson B. Fetal growth and onset of delivery: a nationwide population-based study of preterm infants. Am J Obstet Gynecol. 2006;195:154–61.

    PubMed  Google Scholar 

  99. Miller HC, Jekel JF. Epidemiology of spontaneous premature rupture of membranes: factors in pre-term births. Yale J Biol Med. 1989;62:241–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Hodgetts V, Morris R, Francis A, Gardosi J, Ismail K. Effectiveness of folic acid supplementation in pregnancy on reducing the risk of small-for-gestational age neonates: a population study, systematic review and meta-analysis. Bri J Obs Gyaen. 2015;122:478–90.

    CAS  Google Scholar 

  101. Azzi S, Sas TC, Koudou Y, Le Bouc Y, Souberbielle JC, Dargent-Molina P, et al. Degree of methylation of ZAC1 (PLAGL1) is associated with prenatal and post-natal growth in healthy infants of the EDEN mother child cohort. Epigenetics. 2014;9:338–45.

    CAS  PubMed  Google Scholar 

  102. Zhou SJ, Anderson AJ, Gibson RA, Makrides M. Effect of iodine supplementation in pregnancy on child development and other clinical outcomes: a systematic review of randomized controlled trials. Am J Clin Nutr. 2013;98:1241–54.

    CAS  PubMed  Google Scholar 

  103. Korevaar TI, Steegers EA, Schalekamp-Timmermans S, Ligthart S, de Rijke YB, et al. Soluble Flt1 and placental growth factor are novel determinants of newborn thyroid (dys)function: the generation R study. J Clin Endocrinol Metab. 2014;99:E1627–34.

    CAS  PubMed  Google Scholar 

  104. Jackson AA. Nutrients, growth, and the development of programmed metabolic function. Adv Exp Med Biol. 2000;478:41–55.

    CAS  PubMed  Google Scholar 

  105. Godfrey KM, Barker DJ. Fetal nutrition and adult disease. Am J Clin Nutr. 2000;71:1344S–52.

    CAS  PubMed  Google Scholar 

  106. Cupedo T, Nagasawa M, Weijer K, Blom B, Spits H. Development and activation of regulatory T cells in the human fetus. Eur J Immunol. 2005;35:383–90.

    CAS  PubMed  Google Scholar 

  107. Gambling L, Danzeisen R, Fosset C, Andersen HS, Dunford S, Srai SK, et al. Iron and copper interactions in development and the effect on pregnancy outcome. J Nutr. 2003;133 Suppl 1:1554S–6.

    CAS  PubMed  Google Scholar 

  108. Tang R, Tang IC, Henry A, Welsh A. Limited evidence for calcium supplementation in preeclampsia prevention: a meta-analysis and systematic review. Hypertens Pregnancy. 201534:181-203.

  109. Strickland KC, Krupenko NI, Krupenko SA. Molecular mechanisms underlying the potentially adverse effects of folate. Clin Chem Lab Med. 2013;51:607–16.

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Berti C, Biesalski HK, Gärtner R, Lapillonne A, Pietrzik K, Poston L, et al. Micronutrients in pregnancy: current knowledge and unresolved questions. Clin Nutr. 2011;30:689–701.

    CAS  PubMed  Google Scholar 

  111. Imdad A, Bhutta ZA. Effect of balanced protein energy supplementation during pregnancy on birth outcomes. BMC Public Health. 2011;11 Suppl 3:S17. doi:10.1186/1471-2458-11-S3-S17.

    PubMed  PubMed Central  Google Scholar 

  112. Lynch SR. The potential impact of iron supplementation during adolescence on iron status in pregnancy. J Nutr. 2000;130:448S–51.

    CAS  PubMed  Google Scholar 

  113. Mihaila C, Schramm J, Strathmann FG, Lee DL, Gelein RM, Luebke AE, et al. Identifying a window of vulnerability during fetal development in a maternal iron restriction model. PLoS One. 2011;6(3):e17483.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Groenen PM, van Rooij IA, Peer PG, Ocké MC, Zielhuis GA, Steegers-Theunissen RP. Low maternal dietary intakes of iron, magnesium, and niacin are associated with spina bifida in the offspring. RP J Nutr. 2004;134:1516–22.

    CAS  Google Scholar 

  115. Lönnerdal B. Effects of maternal dietary intake on human milk composition. J Nutr. 1986;116:499–513.

    PubMed  Google Scholar 

  116. Kambe T, Weaver BP, Andrews GK. The genetics of essential metal homeostasis during development. Genesis. 2008;46:214–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  117. McPartlin J, Halligan A, Scott JM, Darling M, Weir DG. Accelerated folate breakdown in pregnancy. Lancet. 1993;341:148–9.

    CAS  PubMed  Google Scholar 

  118. Catov JM, Bodnar LM, Olsen J, Olsen S, Nohr EA. Periconceptional multivitamin use and risk of preterm or small-for-gestational-age births in the Danish National Birth Cohort. Am J Clin Nutr. 2011;94:906–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Timmermans S, Jaddoe VW, Silva LM, Hofman A, Raat H, Steegers-Theunissen RP, et al. Folic acid is positively associated with uteroplacental vascular resistance: the Generation R study. Nutr Metab Cardiovasc Dis. 2011;21:54–61.

    CAS  PubMed  Google Scholar 

  120. Goldberg MA, Dunning SP, Bunn HF. Regulation of the erythropoietin gene: evidence that the oxygen sensor is a heme protein. Science. 1988;242:1412–5.

    CAS  PubMed  Google Scholar 

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We wish to acknowledge the financial support of the National Institutes of Child Health and Development/Gates Foundation, and the NIH Office of Dietary Supplements for support for the periconceptional trial ‘Malaria risk prior to and during early pregnancy in nulliparous women receiving long-term weekly iron and folic acid supplementation (WIFS): a non-inferiority randomised controlled trial’ (PALUFER) (NICHD grant number NIH-1U01HD061234-01A1). We are grateful to Raffaella Ravinetto and Celine Schurmans of the Clinical Trials Unit, Institute of Tropical Medicine, Antwerp and Isidore Traore for their efforts on trial monitoring activities.

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Correspondence to Bernard J. Brabin.

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The authors declare that they have no competing interests.

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BB conceived the paper. BB, LB, SG and SO contributed to the design and analysis. BB and LB wrote the paper. YC, UD’A and HT contributed to the analysis and interpretation of the data. All authors read and approved the final manuscript.

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Brabin, B.J., Gies, S., Owens, S. et al. Perspectives on the design and methodology of periconceptional nutrient supplementation trials. Trials 17, 58 (2016).

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