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Early neurological delopment, growth and nutrition in very preterm infants
Maas, Y.G.H.
Publication date
1999
Link to publication
Citation for published version (APA):
Maas, Y. G. H. (1999). Early neurological delopment, growth and nutrition in very preterm
infants.
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CHAPTER 3
Development of macronutrient composition of very preterm
human milk
Yolanda G.H. Maas, Jeanet Gerritsen, Augustinus A.M. Hart, Mijna Hadders-Algra, Jan M. Ruijter, Pieter Tamminga, Majid Mirmiran and Henk Spekreijse
3.1 Abstract 3.2 Introduction 3.3 Materials and methods
3.3.1 Milk donors
3.3.2 Collection of milk samples 3.3.3 Chemical analysis 3.3.4 Statistical analysis 3.4 Results 3.4.1 24-h milk volume 3.4.2 Total nitrogen 3.4.3 Fat 3.4.4 Lactose 3.4.5 Carbohydrate 3.4.6 Energy content 3.5 Discussion 3.5.1 Introduction 3.5.2 Statistical analysis 3.5.3 Effect of gestational age 3.5.4 Effect of postnatal age 3.5.5 Effect of postmenstrual age
3.6 References
45
Development of macronutrient composition of very preterm
human milk
Yolanda G.H. Maas', Jeanet Gerritsen', Augustinus A.M. Hart2, Mijna Hadders-Algra3,
Jan M. Ruijter4, Pieter Tamminga', Majid Mirmiran3 and Henk Spekreijse4
'Department of Neonatology and
department of Clinical Epidemiology and Biostatistics
Academical Medical Center, University of Amsterdam, Emma Childrens' Hospital department of Medical Physiology, University of Groningen
4The Netherlands Ophthalmic Research Institute and Laboratory of Medical Physics
Netherlands Institute for Brain Research
3.1 Abstract
Background The factors producing the differences in composition of human milk and the mechanisms determining the patterns of its changing composition are still unclear. In this study the effects of gestational age at delivery (GA), postnatal age (PNA) and postmenstrual age (PMA = PNA + GA, an indicator of autonomous developmental processes not affected by the moment of birth) on macronutrient composition of very preterm milk were studied. Methods Total nitrogen, fat, lactose and carbohydrate concentration, energy density and 24-hour volume were determined in 282 24-24-hour milk samples collected at weekly intervals (day 7-55 of lactation) from 79 women delivering their babies between 25 and 29 weeks of gestation.
Results Gestational age-related differences were found for carbohydrate concentration only: carbohydrate concentration was lower with increasing GA. Postnatal age was related to a decrease in total nitrogen and increase in lactose concentration. Postmenstrual age was not related to milk composition.
Conclusions Our data indicate that postnatal age strongly influences the development of very preterm human milk composition, while gestational age affects carbohydrate content with a negligible effect on the nutritional value of the milk.
We conclude that in accordance with current opinion in paediatrics human milk is the best source of nutrient even for very preterm ( < 30 weeks' gestational age) infants.
46 Chapter 3
3.2 Introduction
Human milk has long been recommended as the ideal nutrient source for full term neonates, but there is still controversy concerning its suitability for the preterm infant (1). Most studies comparing the macronutrient composition of preterm human milk with full term human milk found a difference in composition, preterm milk having a higher nitrogen content and a higher nutritional value than full term milk (2-14). These findings gave rise to the common consent that when a mother gives birth prematurely her milk is more suitable for her child than full term milk. It is also known that the composition of milk shows considerable differences with the stages of lactation (1-4,6,11,15-19).
The underlying factors producing the differences in composition and the mechanisms leading to the patterns of change in composition are still unclear. In this study we have examined the influence of gestational age at delivery (GA) and duration of lactation (postnatal age, PNA) and, when indicated, of postmenstrual age (PM A = GA + PN A) on the changing macronutrient composition of preterm milk in the group of mothers giving birth before 30 weeks of gestation. Mathematically, PMA is the simple addition of GA and PNA, but developmentally PMA is an independent time measure, reflecting endogenously generated maturational processes from conception onwards (20). As the moment of conception is usually uncertain, it is common practice to use PMA for documenting developmental age.
3.3 Materials and methods
3.3.1 Milk Donors
Milk samples (n = 311) were obtained at weekly intervals from 79 mothers who had given birth before 30 weeks of gestation and who had the intention to breast-feed their infants. Gestational age was determined by the first day of the last menstrual period of the mother. This was confirmed either by an ultrasound examination during early pregnancy or a maturational assessment of the preterm infant with the help of the Dubowitz score (21). 13 mothers gave birth in the 25th or 26th week of gestation, 20 mothers in the 27th week, 21 mothers in the 28th week and 25 mothers in the 29th week.
Macronutrient content of preterm human milk 47
Medical Center, Amsterdam.
3.3.2 Collection of milk samples
The collection of the 24-h samples started as soon as there was sufficient milk to both feed the child and take an aliquot of 25 ml for analysis. Samples were taken for as long as the infant stayed in our hospital and milk production was adequate. Mothers pumped their breasts manually or mechanically, collecting the milk in sterile (deionized) bottles. The number and time of expressions varied per mother, according to their own habits. All expressions were pooled over 24 hours, mixed thoroughly and volume was measured. All samples were stored at -20 °C until analysis.
3.3.3 Chemical analysis
Total nitrogen concentration (mg/100 g) was determined using Kjeldhal analysis (22). Crude protein was calculated by multiplying Kjeldhal nitrogen by 6.38. Fat (g/100 g) was determined according to the method of Roese-Gottlieb (22). Lactose (g/100 g) was deter-mined using an enzymatic procedure (23). Carbohydrate (g/100 g) was calculated as:
Carbohydrate = dry matter - protein - fat - ashes,
dry matter being determined as the mass left after evaporation (vacuum) at
102 CC and ashes being determined as the mass left after glowing at 550 °C (22). Gross
total energy content (kJoules/100 g) was calculated as:
Energy = (Protein * 5.65 + Fat * 9.25 + Carbohydrate * 3.95) * 4.18,
with protein, fat and carbohydrate in g/100 g milk, the multiplying factors in kcal/g and 4.18 being the conversion factor in kJoules/kcal to calculate kJoules (6).
3.3.4 Statistical analysis
To evaluate GA, PNA or PMA effects on the macronutrient composition of very premature human milk unbalanced repeated measurements analysis of covariance with structured covariance matrices was performed (24). This technique allows for missing data which are estimated implicitly from the available data. Only 24-h samples taken before 56 days of lactation (n = 282) were used in the statistical analysis as the older lactational ages lacked sufficient samples for reliable statistics. PMA being the same as G A + PNA makes it
48 Chapter 3 impossible to analyze all three effects (PNA, PMA and GA) in one model. Therefore analysis of GA and PNA effects was first performed for each nutrient and energy density using the following model:
nutrient/energy density = GA + PNA + 24-h volume effect, and for 24-h volume using the model:
24-h volume = G A + PNA.
When a statistically significant (p<0.05) GA effect in this model was found, PNA was replaced by PMA in order to decide whether the combined effect of GA and PNA could be explained by a single PMA effect. This would be the case if GA lost its significance in the PMA model. If G A remained significant in this model as well, this was seen as an indication of an independent G A effect.
Time scales of PNA and PMA were both divided in 11 time intervals, for PNA between day 7 to 55 (interval: 4-5 days) and for PMA between day 183 to 258 (interval: 7 days). GA was used as a between-mofher grouping variable with four categories: 25-26 weeks, 27 weeks, 28 weeks and 29 weeks. The 24-h milk volume was used as a time-varying covaria-te in the nutrient and energy density analysis.
To test the assumptions of the model and to check on outliers, analysis of residuals was per-formed from the unbalanced repeated measurements analysis. When indicated, a Box-Cox transformation (25) was applied and the effect of outliers was analysed. When a significant overall GA or time effect was found it was tested which groups or periods differed by calculating contrasts.
3.4 Results
3.4.1 24-h Milk volume
Based on the residual analysis this variable was log transformed. The amount of milk produced by the mothers during a 24-hour period was not found to be related to GA or PNA (table 3.1-3.3).
M G o E Sa a > a T) b' T) -i LH js •n a G M >> O O CU i-1 -^ i> •a ,—,
i 2
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0 0 1) ca t-i •o >-. J3 O , 0 G 1) 00 O o o. o O. 00 O o 00 o 00 o o oo o o J3 £ -si-rs p o > S od o. 00 •* OS ^r Tf •* t-; cs 00 Os en o en en sÓ en CS od CS od
CS es' en u-i CS r-' es' en •<a- un es un r- t- 00 un es 00 -tf M-00 en en O m o en en O en en os CS OS CS en oo cs SO 00 cs es 00 es oo es es r- Os es TT o en •st-un Os u-i -st-un o UI r-•st >o Os en t'-es ON •ST O O O O o o o o O o O [-; t- OS CS SO CS so en -st-cs SO en en es 00 en •st o en r~ t~ r-' r^ t-- r~ r~ r~ r~ r-' r- r~ 00 en so r- 00 un o un o un Un 00 en CN un o o o o o o o o O O o o co un r-os r- -st-00 oo r- en O es OS en O CS en es oo un un un in un m *r> so un SO so un un r-o i— en as 00 SO r-SO «"I SO en t- O SO en •st- en r-o o o o O o O o O O o r- •* •* oo -st-en NO •* en r-en O •st t— en en en O en ^t-co m en en en en en en en en en en r-o O t-o so O SO O •sr o o Un O o en O so O o o o o o o o o o o O oo en en CS ON cs as cs oo cs m CS en cs CS cs es es o es CS un es o o o o o o O o o o o o en co CS NO o CS 1r-es en en cs oo SD CS Os SO CS SO es CS 00 en es un en es o un •<*• r-CS cs m o en o o en un en SO SO en so en Os en O Os en 00 un en OS es en - Os r- SO en O u-i un •st- SO Tt- CS en es es - •st-es oo es r-CS OO CS Os cs o en en CS en en en -st-en un en so en r-en t-en só CS CS oó cs o\ cs Ó en en CS en en en •4 en un en SO en so es — CS m ^r i/n sO r^ 00 Os O - 13 00 CN Os •st c oo II c
Macronutrient content of preterm human milk 51
Table 3.3 P-values for the effect of gestational age at delivery, postnatal or postmenstrual age (if analysed) and 24h milk volume on total nitrogen , fat , lactose , carbohydrate -and energy content of 24-h milk (model a) -and P-values for the effect of gestational age at delivery and postnatal age on 24-h milk volume (model b), from mothers delivering preterm. Statistical method was an unbalanced repeated measurements analysis of covariance with structured covariance matrices using the models:
a. variable = G A + PN A (or PMA*) + 24-h volume effect b. 24-h volume effect = G A + PN A
variable gestational age postnatal or 24-h volume at delivery postmenstrual" age effect
effect effect < 0.0001 total nitrogen 0.85 < 0.0001 < 0.0001 fat 0.25 0.69 0.31 lactose 0.25 < 0.0001 < 0.0001 carbohydrate 0.0065 0.057 < 0.0001 0.013 0.77* < 0.0001 energy 0.45 0.011 0.15 24-h volume 0.95 0.89 -3.4.2 Total nitrogen
Changes in total nitrogen were related to PN A: an increase in postnatal age was associated with a significant decrease in total nitrogen content (table 3.1-3.3). Moreover, total nitrogen content was related to 24-h milk volume: an increase in 24-h milk volume of 100 ml was associated with a decrease in total nitrogen content of 8 mg (SE 2).
3.4.3 Fat
Fat content was not found to be related to GA or PNA (table 3.1-3.3), nor was it found to be affected by 24-h milk volume.
3.4.4 Lactose
52 Chapter 3 content was not found to be related to GA, but lactose content increased highly significantly with increasing PNA (table 3.1-3.3). Milk volume affected lactose content too: an increase in 24-h milk volume of 100 ml was associated with an increase in lactose content of 0.14 g (SE 0.02).
3.4.5 Carbohydrate
Based on the residual analysis this variable was transformed with exponent 4.5. An effect of GA on total carbohydrate was found, mainly due to differences between carbohydrate content at 28 weeks of GA and the carbohydrate content found at 25-26 and 27 weeks of G A (table 3.4). A trend towards PNA effect on total carbohydrate content was found (p = 0.057) (table 3.1-3.3). Analysis of a PMA effect was therefore indicated, confirming an independent G A effect on carbohydrate while no PMA effect was found. An increase in 24-h milk volume of 100 ml was associated with an increase in total carbohydrate content of0.10g(SE0.02).
3.4.6 Energy content
Energy content was found neihter to be related to GA or PNA (table 3.1-3.3), nor to be affected by 24-h milk volume.
3.5 Discussion
3.5.1 Introduction
The present study, like others, showed that very preterm milk composition changes during lactation (3-6,26,27). To account for the observed time effect we evaluated the effect of three time parameters: postnatal age, gestational age at birth and - a novelty - postmenstrual age. The major findings of our study are that developmental changes in milk composition are largely determined by postnatal age, minimally by gestational age at birth and not at all by postmenstrual age. This means that preterm human milk composition is not determined by autonomous developmental processes related to the moment of conception, but that the maternal body adapts to the moment of precocious delivery. 24-Hour milk volume itself did not show a dependence on the gestational age at delivery, postnatal or postmenstrual age. But using volume as a time-varying covariate in our analysis we found that total nitrogen
Macronutrient content of preterm human milk 53
content decreases while lactose and carbohydrate content increase when 24-h milk volume increases, which is in accordance with the literature (2,7,9,17). One should be aware of the limitation of our finding namely no postnatal age effect on 24-hour milk volume. Factors such as stress of delivering extremely preterm infants, high tech neonatal intensive care unit and the absence of breast feeding etc. all contributed to this finding.
3.5.2 Statistical analysis
The aim of our study was to get more insight into the patterns underlying the changes in nutrient concentration of preterm human milk, whereas other studies focused only on diffe-rences between milk obtained from mothers delivering their babies preterm vs term. In general the statistical analysis of previous studies has been done on mean nutrient values of small numbers of mothers at different postnatal days (5,8,10,12-14,17), thereby ignoring the considerable variability in milk volume and nutrient concentration which exists between and within individual mothers, particularly in preterm mothers (7,16,28). To allow correction for intra- and interindividual variations we collected longitudinal data of a total of 79 mothers and used unbalanced repeated measurement analysis. In this way we were able to correct for inter- as well as intraindividual variability. Moreover in our study possi-ble effects of 24-h volume on the observed differences in macronutrient concentrations between gestational, postnatal or postmenstrual age groups were controlled for by means of the statistical technique of covariance analysis.
3.5.3 Effect of gestational age
We found that total carbohydrate concentration was lower when the gestational age at delivery was higher (table 3.4). When differences in composition of very preterm human milk are found to be related to the gestational age at delivery this indicates that the event of birth interrupts the gestational developmental processes occurring in the mammary gland with a lasting effect on the composition of the milk produced. We have seen this only for the total carbohydrate content. An explanation for this might be that we only studied a small range of gestational ages, a broader range from week 25 till 36 might have led to a different conclusion.
54 Chapter 3
Table 3.4 Total carbohydrate and lactose content of preterm human milk for four gestational age at delivery groups
(values are means and standard deviations) gestational n carbohydrate lactose
age (weeks) (g/100g) (g/1.0Qi
0
Mean SD Mean SD 25-26 57 7.46 0.36 5.85 0.47 27 75 7.43 0.52 5.92 0.42T) 28 70 7.14 0.49*» 5.69 0.72 29 79 7.22 0.50 5.83 0.49*' 25-29 281 7.30 0.49 5.82 0.54
*> different from GA=25,26 (p<0.001) and GA=27 (p<0.02)
t )n = 76*)n=78
Taking into account that - 8 0 % of carbohydrate in human milk is considered to be lactose, we could have expected a gestational age at delivery effect for lactose as well. However, such an effect was absent (table 3.4), possibly due to the relatively large variation in the lactose-proportion of the carbohydrate content in our samples (64-93%).
3.5.4 Effect of postnatal age
Changes in composition related to postnatal age indicate that there is a relation with the lactational processes e.g. of the mammary gland, initiated at the moment of birth. Like others, we found such postnatal changes in protein (2-7,9-14,17) and lactose content (4,6,7,13,14). Similar postnatal changes are also seen in full term milk (1-4,6-11,14,16,17,19), indicating that both preterm and full term human milk composition change in a similar way (28). In our samples lactose content also changes relative to total carbohydrate content from 76% by day 7-10 to 80% by day 29-32 and 85% by day 51-55 (table 3.1), which is in accordance with what has been found in term human milk (29,30).
Macronutrient content of preterm human milk 55
3.5.5 Effect of postmenstrual age
Dependency of milk composition on PMA would imply that changes in milk composition are in accordance with developmental changes in the fetal-maternal unity. This could mean that the changes in milk composition are in accordance with the nutrient requirements of the infant at various developmental stages. We did not find such a 'ideological' relation for any of the studied nutrients.
In conclusion postnatal changes dominate the development of very preterm human milk composition. Gestational age affects only carbohydrate content with a minor net effect on the nutritional value of the milk.
Acknowledgements
First we want to thank all mothers for their milk samples and cooperation throughout the study. We also thank J.A. Boerma of the laboratories of Nutricia, The Netherlands, for the chemical analyses of the samples. We are grateful to J.G. Koppe and R. de Leeuw for their support of this project. We are also grateful to the referee of the journal we submitted this manuscript to before for critically reviewing it. Y.G.H. Maas and J. Gerritsen were financially supported by Nutricia, The Netherlands. This report is part of a study in fulfil-ment of the Degree in Philosophy in Science for Y.G.H. Maas.
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