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17 January 2022

Sex-Specific Effects of Nutritional Supplements for Infants Born Early or Small: An Individual Participant Data Meta-Analysis (ESSENCE IPD-MA) II: Growth

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1
Liggins Institute, University of Auckland, Auckland 1023, New Zealand
2
NICU, Woman and Child Department, Ospedale Del Ponte, Insubria University, 21100 Varese, Italy
3
Department of Pediatrics, Faculty of Health Sciences, McMaster University, Hamilton, ON L8S 4L8, Canada
4
Donor Human Milk Bank Italian Association (AIBLUD), 20126 Milan, Italy
Nutrients2022, 14(2), 392;https://doi.org/10.3390/nu14020392
This article belongs to the Special Issue Feeding in Preterm Infants
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Abstract

Neonatal nutritional supplements may improve early growth for infants born small, but effects on long-term growth are unclear and may differ by sex. We assessed the effects of early macronutrient supplements on later growth. We searched databases and clinical trials registers from inception to April 2019. Participant-level data from randomised trials were included if the intention was to increase macronutrient intake to improve growth or development of infants born preterm or small-for-gestational-age. Co-primary outcomes were cognitive impairment and metabolic risk. Supplementation did not alter BMI in childhood (kg/m2: adjusted mean difference (aMD) −0.11[95% CI −0.47, 0.25], p = 0.54; 3 trials, n = 333). Supplementation increased length (cm: aMD 0.37[0.01, 0.72], p = 0.04; 18 trials, n = 2008) and bone mineral content (g: aMD 10.22[0.52, 19.92], p = 0.04; 6 trials, n = 313) in infancy, but not at older ages. There were no differences between supplemented and unsupplemented groups for other outcomes. In subgroup analysis, supplementation increased the height z-score in male toddlers (aMD 0.20[0.02, 0.37], p = 0.03; 10 trials, n = 595) but not in females, and no significant sex interaction was observed (p = 0.21). Macronutrient supplementation for infants born small may not alter BMI in childhood. Supplementation increased growth in infancy, but these effects did not persist in later life. The effects did not differ between boys and girls.

1. Introduction

The risk of poor growth, slow development and disability are increased in preterm and small-for-gestational-age (SGA) infants [1,2,3,4]. Providing preterm and SGA infants with more protein and energy during the first few weeks after birth may improve short-term growth and result in better developmental outcomes from infancy to adolescence [5,6,7,8,9,10]. However, in observational studies, rapid Body Mass Index (BMI) gain and linear growth in infancy are associated with better cognitive development, but at the expense of increased risk for adiposity, metabolic and cardiovascular disease in adulthood [11]. Further, bone mineral content is often lower in preterm than term infants and the rate of metabolic bone disease is inversely proportional to birthweight and gestational age [12,13,14]. In turn, metabolic bone disease during infancy may result in neonatal rickets, childhood fractures, and impaired growth [14].
Nutritional supplementation is widespread as standard practice for infants born preterm or SGA [8], but there has been limited evidence regarding potential long-term consequences. Further, different prenatal growth patterns between girls and boys may potentially result in sex-specific responses to nutritional supplements [15]. Previous randomised trials have found sex-specific effects of early macronutrient supplements [6,16], and a recent systematic review found enhanced nutrition may improve length in toddler boys but not girls [17]. However, although thousands of infants have participated in randomised trials of enhanced nutrition, few trials have analysed girls and boys separately, and the long-term and the sex-specific effects of supplementation remain unclear.
Therefore, we undertook an individual participant data (IPD) meta-analysis (MA) of trials reporting long-term outcomes after macronutrient supplements for infants born preterm or SGA.

2. Methods

The protocol of the ESSENCE (Sex-Specific Effects of Early Nutritional Supplements in Children Born Early or Small) IPD-MA was published [18]. The study followed the IPD-MA approach [19] registered prospectively in PROSPERO (CRD42017072683) and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Text S1).

2.1. Search Strategies

We searched Ovid MEDLINE, Embase, Cochrane Library Central Registry of Controlled Trials, and Cochrane Database of Systematic Reviews from inception to 1 April 2019. We also searched for eligible ongoing trials in Current Controlled Trials (www.controlled-trials.com), Clinical Trials (www.clinicaltrials.gov), the Australian and New Zealand Clinical Trials Registry (www.anzctr.org.au), and WHO ICTRP Search Portal (https://apps.who.int/trialsearch/) on 30 January 2021 (Search Table S10).

2.2. Criteria for Inclusion and Exclusion

We included published and unpublished randomised and quasi-randomised trials without restrictions on the date of publication or language. Eligible trials studied infants born preterm (<37 weeks’ gestation) or born SGA (birthweight <10th centile for gestational age), and the intervention was intended to increase the intake of one or more macronutrients (protein, carbohydrate, fat, energy content, or protein to energy ratio), with the primary aim of improving growth and development.
Interventions could be enteral or parenteral or a combination, commence at any time during the hospitalisation or after discharge from hospital, and must have been provided for a minimum duration of one week. Trials were eligible if they reported comparisons between unsupplemented nutrition and supplemented nutrition with parenteral supplements, human breast milk supplements, formula milk, or other macronutrients. Trials were excluded that examined the timing of the introduction of nutrition (early versus delayed feeding); that compared macronutrients of different composition (e.g., different types of lipids or proteins); whose outcomes focussed on gastrointestinal development rather than growth and development; and reporting on variations in the composition of micronutrients (including sodium, potassium, calcium, phosphorus, vitamins, other minerals, amino acids, fatty acids).
Outcome data required reporting beyond term equivalent age (>37 weeks’ postmenstrual age) or following discharge from hospital after birth. Outcomes were categorised and evaluated in infancy (≤1 years), toddlers (1 to 3 years), childhood (4 to 8 years), adolescence (9 to 18 years), and adulthood (>18 years).
The primary outcome was BMI in childhood (4 to 8 years).
Secondary outcomes were (1) Growth assessments: weight (raw data and z scores), length/height (raw data and z scores), head circumference (raw data and z scores), Ponderal Index, BMI, body composition (fat mass, fat-free mass, measured by bioimpedance or (Dual-energy X-ray absorptiometry) DXA, skinfold thickness, or other methods); (2) Bone development: bone mineral content, volumetric bone mineral density, bone fractures; (3) Nutrition: feeding tolerance; intake (protein, energy); appetite; breastfeeding and duration.

2.3. Quality Assessment

The methods specified in the Cochrane Handbook for Systematic Reviews of Interventions [20] were used to evaluate the quality of eligible trials: (1) random sequence generation (selection bias); (2) allocation concealment (selection bias); (3) blinding of participants, personnel, and outcome assessment (performance and detection bias); (4) incomplete outcome data (attrition bias); (5) selective reporting (reporting bias); (6) Other bias (checking for bias due to problems not covered by (1) to (5) above).

2.4. Data Synthesis and Statistical Analysis

Trialists provided de-identified data, which were recoded as required, verified, and checked for consistency with published data. Each final trial dataset was verified by the original trialists before analysis individually using IPD-MA pre-specified variables and outcomes, and the results were returned to the trialists for verification. The individual trial datasets were then combined for IPD meta-analysis. There was no imputation for missing data.
We used a one-stage approach to analyse each outcome so that the IPD from all eligible trials were included in a single random effects model. Binary outcomes were analysed using log-binomial regression models, and data were reported as Relative Risk (RR) with 95% CIs and associated 2-sided p-values. We changed the algorithm to encourage the models to converge where specified. Continuous data were analysed using linear regression models, and data were reported as mean differences (MD) with 95% CIs and associated 2-sided p-values. All models included adjustment for pre-specified confounders. The analyses of IPD were adjusted for sex, gestational age, and birth weight z-scores.
We explored the effects of infant sex by presenting data separately for each sex as pre-specified subgroups and by testing a treatment by identifying sex interaction terms within the model. Statistical analyses were performed using SAS (v.9.4, SAS Institute, Cary, NC, USA). We validated the one-stage model using a two-stage approach in RevMan 5.3 [21].

2.5. Planned Subgroup and Sensitivity Analyses

Where data were available, subgroup analyses were performed to explore whether the effect of supplements differed between subgroups with regard to sex, size of infant at birth (≤1 kg vs. >1 kg at birth), size for gestation at birth (≤10th centile vs. >10th centile), gestational age (≤28 completed weeks vs. 29 to 32 completed weeks vs. 33 to 36 completed weeks), timing of supplementation (in hospital vs. after discharge vs. both), type of supplement (protein vs. carbohydrate vs. fat vs. multicomponent and their interactions), primary feed (breastmilk vs. formula vs. parenteral combined with enteral feed), trial timing (conducted before or after 2000), and duration of supplement (1 to 2 weeks vs. 3 to 6 weeks vs. more than 7 weeks). We also tested for interactions between effects. No unplanned analyses were performed.
Sensitivity analyses were performed to assess whether the results were robust enough for trial design by excluding trials assessed as high risk of bias. Where trials were unable to contribute IPD, we assessed the robustness and exclusion of these trials by combining their aggregate data (AD) with the IPD. The analyses of combined IPD and AD were adjusted for gestational age.

3. Results

3.1. Search Results

We identified 7288 records after de-duplication. Title and abstract screening left 271 records for full-text screening. We excluded 62 records that did not meet the inclusion criteria (Table S11). The remaining 99 potentially eligible trials (209 records) were included, among which 44 trials had published post-discharge data, and 55 trials did not. We attempted to contact the authors of all 99 trials. The authors of 21 trials agreed to share their IPD. Of these, 19 trials with 2473 infants had post-discharge growth outcome data, and an additional 17 trials with 1808 infants for which IPD data were not available had published growth outcomes (Figure 1).
Figure 1. Flow diagram of included studies.
Details of the studies are described in Table 1.
Table 1. Included trials and their characteristics.
Included studies were of variable methodological quality (Table S1). Two studies [22,23] with IPD available were at high risk of selection bias because infants were quasi-randomised by birth date. We considered 32% of included trials at high risk of performance bias because of lack of blinding. For many studies, blinding was not possible, e.g., when the intervention was breastfeeding. Attrition bias was assessed for BMI (primary outcome). One study [24] was at high risk of attrition bias due to an imbalance in baseline characteristics between supplemented and unsupplemented groups in those who were followed up.

3.2. Primary Outcome-BMI in Childhood

In childhood, there was no significant difference in BMI between supplemented and unsupplemented groups in the analysis of IPD (kg/m2: aMD −0.11[95% CI −0.47, 0.25], p = 0.54; 3 trials, 333 children) or of combined IPD and AD (kg/m2: aMD −0.19[−0.45, 0.06], p = 0.14; 9 trials, 1319 children) (Figure 2).

3.3. Secondary Outcomes

There was no significant difference between supplemented and unsupplemented groups in BMI in the analysis of IPD or of combined IPD and AD (Figure 2 and Figure S1), or in BMI z-scores in the analysis of IPD alone, in infancy, toddlers, childhood, adolescence, or at >3 years (Figure S2).
There was no significant difference between supplemented and unsupplemented groups in BMI in the analysis of IPD or of combined IPD and AD (Figure 2 and Figure S1), or in BMI z-scores in the analysis of IPD alone, in infancy, toddlers, childhood, adolescence, or at >3 years (Figure S2).
In infancy, there was no significant difference in weight between supplemented and unsupplemented groups in the analysis of IPD (Figure S3). However, supplementation increased weight in infancy in the analysis of combined IPD and AD (Figure S4). There was no significant effect of supplementation on weight in the analysis of IPD or combined IPD and AD in toddlers, childhood, adolescence, or at >3 years (Figures S3 and S4), or on weight z-scores in infancy, toddlers, or childhood (Figure S5). Weight-for-age reference data are not available beyond age 10.
In infancy, supplementation increased length in the analysis of IPD (Figure S6) and of combined IPD and AD (Figure S7a). However, supplementation had no significant effect on height in the analysis of IPD or combined IPD and AD in toddlers, childhood, adolescence, or at >3 years (Figures S6 and S7), or on height z-scores in any age group (Figure S8). There was also no difference in weight for length z-scores in the analysis of IPD in infancy or toddlers (Figure S9).
In infancy, there was no significant difference in head circumference between supplemented and unsupplemented groups in the analysis of IPD (Figure S10), but in the combined IPD and AD analysis, supplementation increased head circumference (Figure S11). In the analysis of IPD and of combined IPD and AD, supplementation did not alter head circumference in toddlers or childhood (Figures S10 and S11) or head circumference z-scores in infancy or toddlers (Figure S12).
There was no significant difference between supplemented and unsupplemented groups in fat mass or fat mass index in the analysis of IPD or of combined IPD and AD in infancy, childhood, adolescence, and at >3 years (Figures S13 and S14), or in percent fat mass in the analysis of IPD in infancy, childhood, adolescence, and at >3 years or of combined IPD and AD in infancy (Figure S15). Similarly, there was no effect on lean mass or lean mass index in the analysis of IPD or of combined IPD and AD in infancy, childhood, adolescence, or at >3 years (Figures S16 and S17).
In infancy, supplementation increased bone mineral content (BMC) in the analysis of IPD and of combined IPD and AD. In childhood, there was no IPD for BMC, but no significant effect of supplementation on BMC in the analysis of AD. There was also no significant effect of supplementation on BMC in the analysis of IPD or combined IPD and AD in adolescence or at >3 years (Figure S18).
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Figure 2. Effect of macronutrient supplements on BMI. (a) IPD analysis of BMI. (b) Combined IPD and AD analysis of BMI in childhood and adolescence. The box size of point estimate is proportional to inverse variance data. For IPD analysis, p-value for heterogeneity in infancy = 0.43, in toddlers = 0.78, childhood = 0.39, in adolescence = 0.01, at >3 years = 0.006. Numbers in bold are overall effects.
Figure 2. Effect of macronutrient supplements on BMI. (a) IPD analysis of BMI. (b) Combined IPD and AD analysis of BMI in childhood and adolescence. The box size of point estimate is proportional to inverse variance data. For IPD analysis, p-value for heterogeneity in infancy = 0.43, in toddlers = 0.78, childhood = 0.39, in adolescence = 0.01, at >3 years = 0.006. Numbers in bold are overall effects.
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In infancy, there was no significant effect of supplementation on BMD (bone mineral density) in the analysis of IPD, but supplementation increased BMD in the analysis of combined IPD and AD. In childhood, there was no IPD for BMD, and no significant effect of supplementation on BMD in the analysis of AD. There was also no significant effect of supplementation on BMD in the analysis of IPD or of combined IPD and AD in adolescence or at >3 years (Figure S19).
There were no data for any outcomes >18 years.
An overview of the main findings of the meta-analyses are provided graphically (Figure 3 and Figure 4).
Figure 3. Summary of IPD analysis of macronutrient supplements on growth outcomes.
Figure 4. Summary of combined IPD and AD analysis of macronutrient supplements on growth outcomes.

3.4. Subgroup Analysis

3.4.1. Sex of Infant

In childhood, there was no significant difference in BMI between supplemented and unsupplemented groups for boys or girls (Figure 5).
Figure 5. IPD analysis of BMI separated for boys and girls. p-value for heterogeneity for boys in childhood = 0.17. p-value for heterogeneity for girls in childhood = 0.74. Numbers in bold are overall effects.
In toddlers, supplemented boys had higher height z-scores than unsupplemented boys, but there were no effects of supplementation for girls and no significant interaction between effects (Table S2).

3.4.2. Size for Gestation of the Infant

The effects of supplementation on BMI in childhood were not different in SGA and AGA subgroups (Table S3). However, there was a significant interaction between size for gestation and the effects of supplements on height z-scores in childhood, with supplementation decreasing height z-scores in SGA but not AGA children. Supplementation also decreased weight z-scores in childhood and height z-scores at >3 years for children born SGA, but increased height z-scores in toddlers and increased lean mass, lean mass index, and BMC in infancy for children born AGA (no significant interaction).

3.4.3. Size of Infant at Birth

The effects of supplements on BMI in childhood were not different in those with birthweight ≤1 kg and >1 kg (Table S4). Supplemented children with birthweight >1 kg had greater height z-scores as toddlers than unsupplemented children, whereas supplemented children with birthweight ≤1 kg were shorter in childhood and presented lower height z-scores at >3 years, but none of the interactions were significant.

3.4.4. Gestational Age of Infant at Birth

There were significant interactions between supplementation and different gestational age subgroups for BMI and BMI z-scores in childhood and in toddlers and for weight for length z-scores in toddlers (Table S5). Supplemented children born very preterm (29 to 32 weeks) had lower BMI and BMI z-scores as toddlers and in childhood than unsupplemented children, but those born extremely or moderate to late preterm did not. Supplementation decreased weight z-scores and height z-scores in childhood for children moderate to late preterm and decreased weight z-scores in childhood and weight for length z-scores in toddlers for children born very preterm, but had no effect on weight and height z-scores for children born extremely preterm (no significant interaction terms).

3.4.5. Timing of Supplement

Supplementation did not alter BMI in childhood in the subgroups who received supplements in hospital or post-discharge (Table S6). However, children who received supplements in hospital, but not those who received supplements post-discharge, had lower BMI, BMI z-scores in adolescence and at >3 years, and lower weight, fat mass, fat mass index, lean mass index at >3 years, and percent fat mass in adolescence. Children who had received supplements post-discharge, but not those who received supplements in-hospital, had increased weight and length in infancy and height z-scores in toddlers (all interaction terms p < 0.05).

3.4.6. Type of Supplement

Supplementation did not alter BMI in childhood in the subgroups who received protein and multicomponent fortification (Table S7). However, supplemented children who received additional protein, but not those who received multicomponent fortification, had lower BMI, BMI z-scores in adolescence and at >3 years, and lower weight, fat mass, fat mass index, lean mass index at >3 years, and percent fat mass in adolescence (all interaction terms p < 0.05). Supplemented children who received multicomponent fortification, but not those who received protein fortification, had increased length in infancy and height z-scores in toddlers, but there were no significant interactions.

3.4.7. Primary Feed

There were no differences in BMI in childhood between supplemented and unsupplemented groups for children whose primary feed was breastmilk, formula, or parenteral and enteral feeds (Table S8). However, there were significant interactions between supplements and different primary feed for height and height z-scores in toddlers. Supplementation increased height and height z-scores only for toddlers who received formula as primary feed, but not for those who received breast milk or parenteral and enteral as the primary feed (interaction terms p < 0.05). For children receiving formula as primary feed, supplementation also increased length and bone mineral content in infancy; for children receiving breast milk as primary feed, supplementation decreased head circumference and head circumference z-scores in toddlers, but these interaction terms were not significant. These effects were not due to differences between the primary feed groups in baseline macronutrient intakes or the quantity of the supplements (Text S2).

3.4.8. Different Trial Timing

There were no differences in BMI in childhood between supplemented and unsupplemented groups in the subgroups of trials conducted before or after 2000 (Table S9). In trials conducted after 2000, but not those conducted before, supplemented children had lower BMI and BMI z-scores than unsupplemented children in adolescence and at >3 years (all interaction terms p < 0.05). In trials conducted before 2000, supplementation increased weight, weight z-scores, length, head circumference, fat mass in infancy, fat mass index at >3 years, and BMC in infancy (all interaction terms p < 0.05); supplemented children also had increased lean mass and BMD in infancy, but these interaction terms were not significant. These effects were not due to changes over time in baseline macronutrient intake or quantity of the supplements (Text S2).

4. Discussion

In this meta-analysis, we found no evidence that early macronutrient supplements had any effect on BMI in childhood. Early macronutrient supplementation did slightly increase growth in infancy, but none of these effects persisted into later life, and there were no significant sex differences.
Observational studies have reported that faster weight gain in early life is associated with higher fat mass in later life [25,26,27]. We used BMI in childhood as the primary outcome for this analysis, as it is the most widely available measure of adiposity and a predictor for overweight and obesity in adulthood [28,29]. We found that early supplements did not alter BMI or fat mass from infancy through to adolescence. Large numbers of infants were included in these analyses, and confidence intervals were narrow for infancy, although wider for >3 years. This is consistent with our previous systematic review [17] but contrasts with observational studies. Our findings suggest that early macronutrient supplements are very unlikely to increase later adiposity or risk of obesity.
In the IPD-MA, supplementation slightly increased length in infancy despite significant heterogeneity but had no effect on weight, head circumference, or lean mass in infancy. In the sensitivity analysis, which combined IPD and AD from around 800 additional children, supplementation increased weight, length, head circumference, and lean mass in infancy, but the size of these effects was small. Furthermore, in the analyses of both IPD and combined IPD and AD, weight, length, and head circumference z-scores were not altered by supplementation. The discrepancy between findings using absolute values and those using z-scores may be because of the different numbers of trials; 13–15 trials with AD reported absolute values, while only 2 reported z-scores. Anthropometric measurements may have different distributions for different populations, so z-scores are likely to provide a more reliable effect estimate. However, the findings of the effects of supplements on absolute measurements were based on large numbers of children and had narrow confidence intervals. We, therefore, conclude that supplementation does increase growth in infancy, but effects are small and do not persist after the first year. This contrasts with the findings of the systematic review of published randomized trials [17] that showed supplementation increased growth in toddlers. However, in this IPD-MA, we were able to separate infancy (≤1 year) and toddlers (1 to 3 years), whereas these age groups were combined in the previous review.
In the analysis of IPD and combined IPD and AD, we found that supplementation increased bone mineralisation in infants, suggesting that early macronutrient supplements for preterm and SGA infants can help compensate for the limited opportunity for accretion of bone minerals during the last trimester of pregnancy [6,7,8]. A deficit in mineralisation can increase the risk of childhood fractures and reduce peak bone mass, and potentially increase the risk of later osteoporosis [30,31]. Thus, nutritional supplements to achieve adequate early bone accretion may be of initial clinical benefit for infants born preterm or SGA, although these effects did not persist into later life.
Different growth patterns between girls and boys before birth may potentially determine sex-specific responses to early environmental perturbations, including nutrition [15]. As is well known, preterm boys are at greater risk of adverse health outcomes than preterm girls [32]. Sex-specific effects have also been reported in animal studies. For example, in sheep, prenatal testosterone treatment reduced the body weight and height of newborn sheep from both sexes, but only females exhibited catch-up growth during 2–4 months of postnatal life [33]. Therefore, we hypothesised that the effect of early macronutrient supplements might differ between girls and boys. However, we did not find any sex-specific effects of supplements on BMI in childhood. Supplementation did increase height z-scores in toddler boys but not girls, but the interaction terms were not statistically significant. Thus there is no evidence of widespread sex-specific effects of nutritional supplements in preterm and SGA infants, and therefore no evidence that neonatal nutritional supplements should differ for girls and boys.
In the subgroup analysis of children born SGA versus AGA, supplemented children born AGA had increased lean mass and lean mass index in infancy, although this did not persist in later life. However, supplemented children born SGA had lower weight z-scores, height z-scores in childhood, and height at >3 years. After birth, the generally accepted goal is to provide enough nutrients for children born SGA to achieve adequate postnatal growth [34]. However, our limited data suggest that early macronutrient supplementation may only benefit children born AGA, and may even result in impaired growth for children born SGA.
We also found that supplementation increased height z-scores for toddlers born >1 kg and decreased height in childhood and height z-scores at >3 years for children born ≤1 kg, although the interaction terms were not significant. Infants born preterm usually have low birthweight, and optimal nutrition plays a crucial role in supporting their growth to reduce morbidity in later life [8]. Furthermore, children with birth weight ≤1 kg usually have poorer growth than children with birth weight >1 kg [35,36]. We, therefore, hypothesised that children with birth weight ≤1 kg might be more likely to benefit from early nutritional supplements. However, our findings did not suggest this was the case.
We also hypothesised that growth outcomes might differ for children born at different gestational ages, with a greater effect of supplements in infants born at earlier gestations. We found that supplemented children born very preterm had lower BMI and BMI z-scores during toddlerhood and childhood and lower weight-for-length scores in toddlers than unsupplemented children, but these effects were not seen in children born extremely or moderate to late preterm, although the numbers of included children and trials are smallest in the moderate to late preterm group. The reasons for differences between gestational age at birth groups are not clear but might include greater neonatal illness in the smallest infants, thereby restricting growth in this group, and that weight gain in preterm infants is disproportionately fat [37].
Children who had received supplements post-discharge, but not those who received supplements in hospital, had greater weight, length in infancy, and height z-scores in toddlers, consistent with previous findings of a systematic review [17]. Children who had received supplements in hospital with additional protein also had lower BMI and measures of adiposity into adolescence. However, the separate effects of the timing and composition of the supplements could not be distinguished in our analysis, as some trials studied in-hospital supplements with protein as the primary supplement, whereas other trials studied post-discharge supplementation with multicomponent supplements. Nevertheless, this again indicates that early supplements do not increase later risk of becoming overweight/obesity, and in the specific situation of in-hospital supplements with protein, may actually reduce the risk.
We found supplements increased height in toddlers only if the primary feed was formula, but not if the primary feed was breastmilk or if supplements were provided as both parenteral and enteral feeds. Supplemented infants whose primary feed was formula also had greater length and BMC in infancy but lower head circumference and head circumference z-scores in toddlers, although none of these interaction terms were significant. We had hypothesised that infants whose primary feed was breastmilk would receive less baseline (unsupplemented) nutritional intake and may therefore show greater effects of supplementation. Consistent with this, estimated protein intake was lower if breastmilk was the primary feed, and supplements provided a much greater increase in protein, carbohydrate, and energy intakes than for infants who received formula as primary feed. Therefore, comparison of nutrition intakes cannot explain the effect of supplements in the formula group. Another possible explanation may be the effect of growth-regulating hormones and growth factors in breastmilk. One study found a positive correlation between insulin-like growth factor (IGF)-I and weight z-scores in healthy infants [38]; infants fed formula milk had higher IGF-I levels than those fed breastmilk [39]. Leptin, adiponectin, and cortisol in breast milk could also play roles in the short-term control of food intake and have long-term effects on energy balance and body weight regulation [40,41].
In trials conducted before or in 2000, supplementation increased growth in infancy, but in trials conducted after 2000, supplementation decreased BMI, BMI z-scores, and weight in adolescence and at >3 years. We had expected that nutrition intakes might differ over time, with later studies potentially reporting higher macronutrient intakes in the unsupplemented groups, and hence smaller effects of additional supplements. However, we found no significant differences between the two time epochs in estimated mean baseline intakes nor in the additional macronutrients provided by supplements, although the mean protein intake was slightly higher and had greater variability in the later epoch. The populations of infants included in trials did also not appear to have changed, with similar inclusion criteria used in trials conducted before and after 2000. Another possible reason for the different effects in different epochs may be that the outcomes may be changing in the overall population over this period. For example, average height [42] and the incidence of overweight/obesity [43] have both increased in many populations over the period during which these trials were conducted, although it is not clear how this might alter the effect of early nutritional supplements on these outcomes.
Previously published Cochrane reviews [10,44,45] have investigated the effects of macronutrient supplements for preterm infants only and reported growth outcomes up to toddler age. One systematic review of published studies [17] explored the long-term effects on growth but was limited by high heterogeneity, possibly because of the wide range of ages studied (3–24 months). Therefore, we reported early growth outcomes separately for infancy (≤12 months) and in toddlers (1–3 years) in this IPD-MA. Further, by using participant-level rather than trial-level data in meta-analyses, we were able to explore the heterogeneity by conducting several different subgroup analyses.
Our IPD-MA has some limitations. We identified 42 eligible trials with published post-discharge growth outcomes. However, due to local ethics restrictions, missing datasets or investigators declining to share data, 25 of these trials could not be included. To address this issue, we conducted sensitivity analyses combining both IPD and AD. Most of these analyses gave results consistent with the analysis of IPD only, except for the growth outcomes in infancy, where supplementation appeared to improve early growth in infancy only in the analyses of combined IPD and AD. Furthermore, a post-hoc calculation showed that the analysis of IPD alone (338 children) had sufficient power to detect a 0.55 difference in BMI in childhood with 80% power and alpha of 0.05, 5% type I error. Therefore, it is unlikely that clinically important effects of supplementation were missed. Nevertheless, not all trials were able to provide data for all pre-specified subgroups, which limited the power to be able to detect overall or subgroup differences for some important outcomes. Additionally, a large number of subgroups, multiple outcomes, and time points were analysed, which increases the chance of type 1 error [46], and the subgroup findings should be interpreted with caution as hypothesis-generating. In addition, the included studies were of different types of macronutrient supplements given at different times for different durations. Although we followed the strategies suggested by the Cochrane handbook to address heterogeneity [20], significant unexplained heterogeneity remained for some outcomes. The duration of supplements and actual nutrient intakes for individual participants were not available for most trials, so the subgroup analyses of the duration, timing, and type of supplement were based only on the trial-level information and are therefore imprecise.
We also noticed significant heterogeneity when combining individual results for some outcomes, and this was not all explained in the subgroup analyses. As we were not able to calculate I-squared values, it is hard to quantify the inconsistency across studies. However, statistical heterogeneity is inevitable in a meta-analysis [47], especially when there are many trials included, as the test for heterogeneity has high power to detect a small amount of inconsistency that may be clinically unimportant [20].
Previous observational studies [25,26,27] have reported that early rapid growth is associated with later obesity. However, this IPD-MA found that early macronutrient supplements slightly improve growth in infancy but do not affect childhood adiposity. This apparent discrepancy may be because this IPD-MA focused on the effects of nutrition rather than growth, and the effects of supplementation on early growth may be too early or too small to cause later adverse effects. Alternatively, previous observations may reflect the relationship between other regulators of growth, including genetic and environmental influences, and later adiposity, rather than the effects of nutrition itself.
We conclude that early macronutrient supplements do not alter BMI in childhood for infants born small. Supplements improve growth in infancy, including bone mineralisation, but the effects are small and do not persist beyond the first year. Hypothesis-generating subgroup analyses suggest that in-hospital supplementation with protein may actually reduce later BMI, and that children born SGA are least likely to benefit from macronutrient supplements. There is little evidence of sex-specific effects. The current widespread practice of macronutrient supplementation for preterm and SGA infants is likely to have small effects on short-term growth but is unlikely to increase adiposity in childhood.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14020392/s1, Text S1. PRISMA-IPD Checklist of items to include when reporting a systematic review and meta-analysis of individual participant data (IPD); Text S2. Comparison of macronutrient intakes; Text S3. References of Table 1; Figure S1. Combined IPD and AD analysis of BMI; table Table S2. IPD analysis of BMI z-scores; Figure S3. IPD analysis of weight; Figure S4. Combined IPD and AD analysis of weight; Figure S5. Forest plot of the effect of macronutrient supplementation on weight z-scores; Figure S6. IPD analysis of length/height; Figure S7. Combined IPD and AD analysis of length/height; Figure S8. Forest plot of the effect of macronutrient supplementation on length/height z-scores; Figure S9. IPD analysis of weight for length z-scores; Figure S10. IPD analysis of head circumference; Figure S11. Combined IPD and AD analysis of head circumference; Figure S12. Forest plot of the effect of macronutrient supplementation on head circumference z-scores; Figure S13. Forest plot of the effect of macronutrient supplementation on fat mass; Figure S14. Forest plot of the effect of macronutrient supplementation on fat mass index; Figure S15. Forest plot of the effect of macronutrient supplementation on percent fat mass; Figure S16. Forest plot of the effect of macronutrient supplementation on lean mass; Figure S17. Forest plot of the effect of macronutrient supplementation on lean mass index; Figure S18. Forest plot of the effect of macronutrient supplementation on bone mineral content; Figure S19. Forest plot of the effect of macronutrient supplementation on bone mineral density; Table S1. Risk of bias within studies; Table S2. Subgroup analyses of infant sex; Table S3. Subgroup analyses of size for the gestation of the infant; Table S4. Subgroup analyses of the size of an infant at birth; Table S5. Subgroup analyses of the gestational age of the infant at birth; Table S6. Subgroup analyses of the timing of supplements; Table S7. Subgroup analyses of type of supplement; Table S8. Subgroup analyses of primary milk feed; Table S9. Subgroup analyses of different epochs; Table S10. Search strategies.

Author Contributions

J.E.H. conceptualised and designed the study, coordinated and supervised data collection and data analyses, reviewed and revised the manuscript. L.L. conceptualised and designed the study, collected the data, carried out the initial analyses, drafted the initial manuscript, and revised the manuscript. G.D.G. conceptualised and designed the study, coordinated and supervised data collection and data analyses, and reviewed and revised the manuscript. C.A.C. conceptualised and designed the study, coordinated and supervised data collection, and reviewed and revised the manuscript. F.H.B. conceptualised and designed the study, coordinated and supervised data collection, and reviewed and revised the manuscript. ESSENCE-IPD Trialist Group provided the individual trial data, coordinated data collection, and provided critical revision of the manuscript for important intellectual content: M.A., S.A.A., A.B., N.D.E., F.L.F., C.F., M.L.G., H.G.K.K., W.K., I.L., C.M., K.M., E.N., J.-C.P., N.R., P.R., K.S., M.J.T., F.M.T., C.L.W., G.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The ESSENCE-IPD project is supported by the Health Research Council (HRC) of New Zealand (16/605). The funder/sponsor did not participate in the work.

Institutional Review Board Statement

This IPD-MA is approved by the University of Auckland Human Participants Ethics Committee (reference number: 019874). The contributing individual studies had approval from relevant local ethics committees.

Data Availability Statement

The de-identified participant data analysed for the ESSENCE IPD-MA project remain the property of the ESSENCE-IPD Trialist Group. Researchers should contact the original trial investigator directly for access to these data. The data dictionary, statistical analysis plan and metadata for this IPD-MA are available at doi:10.17608/k6.auckland.18729068.v1, accessed on 30 January 2021. Researchers are able to use this information and the provided contact address (researchhub@auckland.ac.nz) to request further information through the Data Access Committee of the Liggins Institute.

Acknowledgments

We would like to acknowledge Alan Lucas, Mary Fewtrell, and Atul Singhal for assisting with individual trial data and interpreting the findings, Julie Brown for help with developing the search strategies and study selection, Robyn May for help with establishing contacts with the ESSENCE IPD-MA collaborators, Mariana Muelbert and Laura Galante for help with translating the ESSENCE IPD-MA documents, and Gordon Liu for help with assessing the quality of the included studies.

Conflicts of Interest

The authors declare no conflict of interest.

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