Next Article in Journal
Association between Dietary Niacin Intake and Migraine among American Adults: National Health and Nutrition Examination Survey
Previous Article in Journal
Food Additives Associated with Gut Microbiota Alterations in Inflammatory Bowel Disease: Friends or Enemies?
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Body Composition and “Catch-Up” Fat Growth in Healthy Small for Gestational Age Preterm Infants and Neurodevelopmental Outcomes

by
Laura E. Lach
1,*,
Katherine E. Chetta
1,
Amy L. Ruddy-Humphries
1,
Myla D. Ebeling
1,
Mathew J. Gregoski
2 and
Lakshmi D. Katikaneni
1
1
Division of Neonatology, Department of Pediatrics, Medical University of South Carolina, Charleston, SC 29425, USA
2
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(15), 3051; https://doi.org/10.3390/nu14153051
Submission received: 30 June 2022 / Revised: 8 July 2022 / Accepted: 22 July 2022 / Published: 25 July 2022
(This article belongs to the Section Clinical Nutrition)

Abstract

:
To examine the growth and body composition of small for gestational age (SGA) and appropriate for gestational age (AGA) very low birth weight infants (VLBW) and their outpatient neurodevelopmental outcomes. From 2006–2012, VLBW infants (n = 57 of 92) admitted to the Neonatal Intensive Care Unit (NICU) had serial air displacement plethysmography (ADP) scans and were followed as outpatients. Serial developmental testing (CAT/CLAMS, Peabody Gross Motor Scales) and anthropometrics were obtained from n = 37 infants (29 AGA and 8 SGA) and analyzed via repeated measures analyses of variances. The percentage of body fat, percentage of lean mass, and weight gain were statistically significant between SGA and AGA groups at the first ADP assessment. There was no difference between the two groups in outpatient neurodevelopmental testing. Weight gain as “catch-up” body fat accrual occurs by 67 weeks of PMA. This catch-up growth is associated with normal SGA preterm neurodevelopment as compared to AGA preterm infants.

1. Introduction

Optimizing nutrition for preterm infants continues to be a challenge to curb postnatal growth faltering in the neonatal intensive care unit (NICU), especially in the small for gestational age (SGA) and very low birth weight (VLBW) population. Studies show growth of VLBW infants is associated with better neurodevelopmental outcomes [1,2,3,4]. Previous work on the body composition of the preterm infant associated with hospital weight gain [2] and fat-free mass gain while in the NICU with better neurodevelopmental outcomes across multiple studies [2,3,4,5,6]. This is of crucial significance as SGA infants are at risk for poor neurodevelopmental outcomes [7].
Prematurity has been linked to later metabolic disease given increased early adiposity [8,9]. Few studies have suggested that rapid growth may lead to worse metabolic outcomes in adulthood [10,11,12]. There may be short-term benefits to increased rapid weight gain in the domains of survival and neurodevelopment, but this is controversial [10].
The link between premature infant growth and neurodevelopment is observed in multiple studies [2,3,4,5]. Ehrenkranz et al. demonstrated that faster weight gain and head growth in the NICU was associated with higher cognitive and motor scores and reduced cerebral palsy at 18–22 months [2]. In-hospital gain of fat-free mass is associated with better neurologic and motor outcomes at one-year corrected age in VLBW infants as demonstrated by Ramel et al. [6]. Small for gestational age (<10th percentile) infants are at especially increased risk for delayed neurodevelopment [7]. Studies have linked improved neurodevelopmental outcomes of preterm infants with respect to post-discharge nutrition, however, do not correlate with the post-discharge body composition [13].
The Capute Scales Cognitive Adaptive Test/Clinical Linguistic and Auditory Milestone Scale (CAT/CLAMS) is a standardized tool to assess pediatric development. This test has been established with acceptable sensitivity and specificity to detect a neurodevelopmental delay in the premature population and correlates well with the Bayley Scales of Infant Development-II [14,15]. The CAT/CLAMS Developmental Quotients (DQ) and Peabody Developmental Motor scales are both utilized at the Medical University of South Carolina’s (MUSC) outpatient NICU follow-up clinic. As more premature infants are surviving into adulthood with advances in medical and nutritional therapy, long-term observational studies are needed to establish outcomes of neurodevelopment and optimal growth. Our study aimed to examine the growth and body composition of VLBW SGA and VLBW appropriate for gestational age (AGA) infants over time and their neurodevelopmental outcomes at a level IV NICU.

2. Materials and Methods

From 2006–2012, 366 infants admitted to MUSC’s level IV NICU underwent an air displacement plethysmography (ADP) using the PeaPod (Cosmed, Concord, CA, USA) as part of clinical care. After discharge, all VLBW infants were followed, outpatient. To clarify the association between neurodevelopmental outcomes and body composition, we chose to exclude several severe co-morbidities and sample a population of healthy preterm infants. A total of 92 VLBW SGA and VLBW AGA infants were selected for analysis after excluding necrotizing enterocolitis, retinopathy of prematurity stage 3, and grade III-IV intraventricular hemorrhage. A total of 57 infants had serial ADP assessments, while admitted to the MUSC NICU and as an outpatient. The average time of the first ADP assessment was 44 ± 8 weeks postmenstrual age (PMA) and the second ADP at 67 ± 6 weeks PMA. Infants receiving an ADP evaluation were not on mechanical or non-invasive ventilation and did not have intravenous fluids.
After discharge, infants were then followed serially at outpatient at MUSC’s high risk NICU clinic where neurodevelopmental testing (CAT/CLAMS and Peabody Gross Motor) was performed along with anthropometric measurements including height, weight and head circumference at each outpatient visit. Chronological and adjusted age CAT, CLAMS, and Gross Motor DQs were obtained at each outpatient NICU clinic visit over four consecutive visits. Infants who had four consecutive outpatient visits were included to trend their neurodevelopment over time (n = 37, 29 AGA, and 8 SGA). ADP assessments were analyzed using independent samples t-test between VLBW SGA and VLBW AGA groups for anthropometric and body composition. Repeated measures analyses of variances were completed across four outpatient visits and for weight, length, head circumference, adjusted age CAT DQ, adjusted age CLAMS DQ, adjusted age gross motor DQ between VLBW SGA and VLBW AGA groups with Bonferroni adjusted pairwise comparisons, (i.e., α = 0.05 ÷ number of comparisons).
Very low birth weight infants were started on either mother’s own milk or donor breast milk per feeding protocol on admission with parental nutrition supplement until they reach full enteral feeds (160 mL/kg/day). Enteral feeds were initiated within six hours of admission if the infant remained clinically stable. Infants were fortified to 24 kcal/oz on day of life 5–12 and reached full enteral feeds of 160 mL/kg/day by day 7–16 of life, corresponding to birth weight feeding protocol (Supplemental Tables S1–S5). Infants were discharged home on enteral feeds of mother’s own milk fortified to 24 kcal/oz with human milk fortifier or formula (Similiac® Neosure, Abbott Nutrition, https://abbottnutrition.com/ (accessed on 6 June 2022)) with fortification of 24 kcal/oz or 27 kcal/oz at clinician discretion based on in hospital growth patterns. This project was designated as exempted research by the MUSC Institutional Review Board and consent was not required to be obtained.

3. Results

The demographics of all infants included in our study are shown in Table 1. Of the total cohort, the average birth weight was 1124 ± 183 g, and gestational age was 30 ± 2 weeks. A total of 57 infants had serial anthropometric data from birth, two ADP assessments at 44 ± 8 weeks postmenstrual age (PMA), and the second ADP assessment average was 67 ± 6 weeks PMA. Of these infants, 36 were consecutively followed outpatient for neurodevelopmental testing. Of the final 36 infants, the average gestational age was 30 ± 2 weeks and birth weight 1135 ± 185 g. At the time of discharge, 6 of 8 (75%) of SGA infants were discharged on maternal breast milk fortified to 24 kcal/oz with human milk fortifier, and 19 of 28 (68%) of the AGA cohort were discharged on the same fortified maternal breast milk regimen.
Anthropometric results from birth to ADP evaluations are shown in Table 2. There was a statistically significant difference in weight gain (grams/day) between SGA and AGA cohorts from birth to the first ADP assessment (p < 0.05); however, there was no statistical difference in weight gain between the two groups between the first and second ADP. Linear growth and head circumference growth were not statistically significant between the SGA and AGA groups over time as shown in Table 2.
Fifty-seven VLBW infants had serial body composition assessments which are displayed in Table 3. At the first ADP assessment, there was a statistically significant difference between percent fat (14.3% vs. 18.8%) and percent lean mass (85.7% vs. 81.2%) between the AGA and SGA groups (p < 0.05). However, at the second ADP assessment, there was no longer a statistical difference between the SGA and AGA body composition as the SGA cohort of VLBW infants increased fat from 14.3% to 19.2%.
Table 4 shows the neurodevelopmental and anthropometric data across four outpatient visits of the VLBW SGA and AGA groups (n = 37, n = 8 SGA, n = 29 AGA). The average adjusted age from visits 1, 2, 3, and 4 were 3.0 ± 1.5 months, 7.0 ± 2.7 months, 11.7 ± 3.4 months, and 17.3 ± 3.8 months, respectively. There was a statistically significant difference between the weight of SGA and AGA cohorts at the first outpatient visit as the SGA group was smaller; however, we did not see the difference between the two cohorts at visits 2–4. There was statistical significance between the head circumferences of the two groups across all outpatient visits; however, we did not see a difference between the cohort’s linear growth. We did not see a significant difference between the adjusted CAT, adjusted CLAMS, and adjusted gross motor DQs for each of the outpatient follow-up visits between the two groups.

4. Discussion

These data support previously reported findings, specifically, that a healthy cohort of SGA VLBW infants start with less body fat than AGA infants but after 50–60 weeks of PMA, the differences in fat are minimized between the two groups [16,17,18].
Premature infants accumulate body fat after birth, and at term equivalent age, they have higher fat mass and less fat-free mass than their term counterparts [18,19,20,21]. Air displacement plethysmography (ADP) is a non-invasive validated method to obtain infant body composition in the premature infant population [22,23]. Studies show that around 50–60 weeks postmenstrual age (PMA) body fat seems to stabilize in premature infants [17,18]. Our data supports this, as we did not see a significant difference between the SGA and AGA cohorts at the second ADP assessment which was around 67 weeks PMA.
SGA infants have lower body fat percent at birth and show recovery of adiposity over time compared to AGA counterparts [16,21,24]. Roggero et al. have shown that preterm SGA infants have less body fat than AGA counterparts at birth, however, after three months have similar body composition to AGA infants [16]. Our data support the findings along with these previous studies, adding information about the SGA phenotype over time.
Our study adds to the literature on VLBW neurodevelopment with respect to growth. The infants followed had repeated neurodevelopmental testing, anthropometric assessments, and serial body composition assessments. These infants were specifically selected to represent an optimized sample of infants who did not have necrotizing enterocolitis, severe retinopathy of prematurity or grade III-IV intraventricular hemorrhage and represent a sample of healthy VLBW infants without significant co-morbidities.
From birth to the first anthropometric assessment, there was a statically significant difference in weight gain (grams/day) between the SGA and AGA cohorts (p = 0.003). Our data also demonstrated a significant difference in weight at the first outpatient visit where the mean age was 3.0 ± 1.5 months. This represents that the SGA cohort’s catch-up growth occurs between birth to around 3 months of adjusted age. As after the second ADP assessment (mean 67 weeks PMA) and between subsequent outpatient visits 2–4, there were no significant differences in growth (grams/day) and weight between SGA and AGA groups. Although the SGA cohort had significantly smaller head circumference than the AGA group, this did not impact their neurodevelopmental testing as we saw no difference across the Capute Scales CLAMS DQ, CAT DQ, and Gross Motor DQ for adjusted age. Linear growth between the SGA and AGA cohorts was also not significant from birth to outpatient follow-up.
The Capute scales have been noted for ease and speed of administration, which make the CAT/CLAMS a reasonable choice for assessment of early development by pediatric health care providers [25,26] as well as its high test-retest reliability in preterm infants [15]. The CAT/CLAMS uses a developmental quotient (DQ) which is calculated on a ratio formula of developmental age divided by chronologic or adjusted age multiplied by 100 [26]. The test gives a mean score of 100 with standard deviation of 15. The threshold used for developmental delay is a score of <70 (2 standard deviations below the mean) [27]. The Peabody Developmental Motor Scales is another instrument for measuring motor abilities which has been shown to have high test and re-test reliability in the VLBW population [28]. Of note, none of the infants in either group were below cut off (<70) for developmental delay which may have added to our above average testing scores. In a meta-analysis performed by Sacchi et al., SGA preterm infants across multiple studies had worse neurodevelopmental outcomes compared to AGA infants [7]. We suggest that poor neurodevelopmental outcomes seen in SGA infants may be more associated with co-morbidities than SGA status itself, as we were unable to appreciate poor neurodevelopmental outcomes in this cohort. These studies demonstrate the importance of early intervention to promote neurodevelopment in this vulnerable population.
Limitations of this study include the observational retrospective nature of this cohort. With this data review, some infants did not have serial ADP assessments or were not seen in serial outpatient visits, limiting the number of infants seen consistently in follow-up (37 of 92, 40%). Inconsistent follow-up was due to missed outpatient appointments or if infants did not need further interventional services from the clinic. We were unable to correct for maternal IQ or educational level, which was unknown in this cohort of infants. Given the structure of our NICU follow-up clinic, most infants at the time of outpatient follow-up were referred to South Carolina’s home therapy programs and most infants were involved in physical and occupational therapy services as a standard of care, which may have benefitted early neurologic testing scores. Lastly, our outpatient clinic utilizes the Capute Scales instead of Bayley for neurodevelopmental testing due to ease of administration with a shorter amount of training compared to the Bayley examinations. Additionally, our center promotes intensive post-discharge fortification of breastmilk and formula to 24 kcal/oz which is not a standard practice universally. More studies are needed on the association between discharge nutrition and neurodevelopmental outcomes. Moving forward with this research, observing a modern cohort of SGA VLBW and AGA VLBW infants utilizing the Bayley Infants Scales is warranted. Future studies should also be powered to detect a statistical difference between body fat and neurodevelopmental outcomes. Although modern nutritional changes for preterm infants have not substantially changed since this data collection, it would be prudent to study a modern cohort of infants prospectively.
It is possible there was no difference between neurodevelopment across the two groups given the SGA cohort grew as well as the AGA cohort with early catch-up growth demonstrated by significant body fat accrual and weight gain of the SGA cohort before 60 weeks PMA. Furthermore, the increase in body fat and early weight gain in this SGA population may play a role in neuroprotection as alluded to in Okada et al. [10] as we saw no differences in neurodevelopmental scores. Overall growth trajectories and early body fat accrual may positively impact VLBW neurodevelopment.

5. Conclusions

The trajectory for catch-up growth of VLBW infants that reflects optimal neurodevelopment is still unknown. Our data reflects trends within a healthy preterm SGA cohort, whose increasing fat “catch-up” growth was not associated with the previously reported sub-optimal outcomes seen in this preterm SGA group and was comparable to the AGA group. Further studies should be completed to investigate the associations between body composition of preterm SGA infants, specifically adiposity within the NICU, neurodevelopment, and late-onset metabolic consequences.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14153051/s1, Table S1. Feeding Guidelines for Neonates 751–1000 gm Birth Weight; Table S2. Feeding Guidelines for Neonates ≤ 750 gm Birth Weight; Table S3. Feeding Guidelines for Neonates 1001–1250 gm Birth Weight; Table S4. Feeding Guidelines for Neonates 1251–1500 gm Birth Weight; Table S5. Feeding Guidelines for Neonates 1501–2000 gm Birth Weight; The NICU Enteral Feeding Guidelines [29,30,31,32,33,34,35,36,37,38,39,40,41].

Author Contributions

Conceptualization L.E.L. and L.D.K.; Data curation, L.D.K. and M.D.E., writing—original draft preparation, L.E.L.; writing—review and editing, L.E.L., K.E.C., L.D.K. and A.L.R.-H.; formal analysis, M.D.E. and M.J.G. This publication was supported, in part, by the National Center for Advancing Translational Sciences of the National Institutes of Health under Grant Number UL1 TR001450. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This is work is supported in part by NATS NIH KL2TR001452 (KEC). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was exempted as research by the Medical University of South Carolina’s Institutional Review Board.

Informed Consent Statement

Patient consent was not required by the Medical University of South Carolina’s review board as this was not human subject research.

Data Availability Statement

Data are available by request to the primary corresponding author at [email protected].

Acknowledgments

Thank you to Carrie Finch, and Allison Rohrer.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Korff, S.G.; Ross, J.; Morella, K.; Taylor, S.N. Investigation of a Common Clinical Approach to Poor Growth in Preterm Infants. Am. J. Perinatol. 2020, 37, 1462–1466. [Google Scholar] [CrossRef] [PubMed]
  2. Ehrenkranz, R.A.; Dusick, A.M.; Vohr, B.R.; Wright, L.L.; Wrage, L.A.; Poole, W.K. Growth in the Neonatal Intensive Care Unit Influences Neurodevelopmental and Growth Outcomes of Extremely Low Birth Weight Infants. Pediatrics 2006, 117, 1253–1261. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Belfort, M. Infant growth before and after term: Effects on Neurodevelopment in Preterm Infants. Pediatrics 2011, 128, e899–e906. [Google Scholar] [PubMed]
  4. Coviello, C.; Keunen, K.; Kersbergen, K.J.; Groenendaal, F.; Leemans, A.; Peels, B.; Isgum, I.; Viergever, M.A.; De Vries, L.S.; Buonocore, G.; et al. Effects of early nutrition and growth on brain volumes, white matter microstructure, and neurodevelopmental outcome in preterm newborns. Pediatr. Res. 2018, 83, 102–110. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Pfister, K.M.; Zhang, L.; Miller, N.C.; Ingolfsland, E.; Demerath, E.W.; Ramel, S.E. Early body composition changes are associated with neurodevelopmental and metabolic outcomes at 4 years of age in very preterm infants. Pediatr. Res. 2018, 84, 713–718. [Google Scholar] [CrossRef]
  6. Ramel, S.E.; Gray, H.L.; Christiansen, E.; Boys, C.; Georgieff, M.K.; Demerath, E.W. Greater Early Gains in Fat-Free Mass, but Not Fat Mass, Are Associated with Improved Neurodevelopment at 1 Year Corrected Age for Prematurity in Very Low Birth Weight Preterm Infants. J. Pediatr. 2016, 173, 108–115. [Google Scholar] [CrossRef]
  7. Sacchi, C.; Marino, C.; Nosarti, C.; Vieno, A.; Vesentin, S.; Simonelli, A. Association of Intrauterine Growth Restriction and Small for Gestational Age Status With Childhood Cognitive Outcomes: A Systematic Review and Meta-analysis. JAMA Pediatr. 2020, 174, 772–781. [Google Scholar] [PubMed]
  8. Morrison, K.M.; Ramsingh, L.; Gunn, E.; Streiner, D.; Van Lieshout, R.; Boyle, M.; Gerstein, H.; Schmidt, L.; Saigal, S. Cardiometabolic Health in Adults Born Premature with Extremely Low Birth Weight. Pediatrics 2016, 138, e20160515. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Markopoulou, P.; Papanikolaou, E.; Analytis, A.; Zoumakis, E.; Siahanidou, T. Preterm Birth as a Risk Factor for Metabolic Syndrome and Cardiovascular Disease in Adult Life: A Systematic Review and Meta-Analysis. J. Pediatr. 2019, 210, 69–80.e5. [Google Scholar] [CrossRef] [PubMed]
  10. Okada, T.; Takahashi, S.; Nagano, N.; Yoshikawa, K.; Usukura, Y.; Hosono, S. Early postnatal alteration of body composition in preterm and small-for-gestational-age infants: Implications of catch-up fat. Pediatr. Res. 2015, 77, 136–142. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Leunissen, R.W.J.; Kerkhof, G.F.; Stijnen, T.; Hokken-Koelega, A. Timing and Tempo of First-Year Rapid Growth in Relation to Cardiovascular and Metabolic Risk Profile in Early Adulthood. JAMA 2009, 301, 2234–2242. [Google Scholar] [CrossRef] [Green Version]
  12. Lapillonne, A.; Griffin, I.J. Feeding preterm infants today for later metabolic and cardiovascular outcomes. J. Pediatr. 2013, 162 (Suppl. S3), S7–S16. [Google Scholar] [CrossRef]
  13. Taylor, S.N.; Martin, C.R. Evidence-based Discharge Nutrition to Optimize Preterm Infant Outcomes. NeoReviews 2022, 23, e108–e116. [Google Scholar] [CrossRef]
  14. Kube, D.A.; Wilson, W.M.; Petersen, M.C.; Palmer, F.B. CAT/CLAMS: Its use in detecting early childhood cognitive impairment. Pediatr. Neurol. 2000, 23, 208–215. [Google Scholar] [CrossRef]
  15. McCurdy, M.; Bellows, A.; Deng, D.; Leppert, M.; Mahone, E.M.; Pritchard, A. Test-retest reliability of the Capute scales for neurodevelopmental screening of a high risk sample: Impact of test-retest interval and degree of neonatal risk. J. Neonatal-Perinatal Med. 2015, 8, 233–241. [Google Scholar] [CrossRef] [PubMed]
  16. Roggero, P.; Gianni, M.L.; Liotto, N.; Taroni, F.; Orsi, A.; Amato, O.; Morlacchi, L.; Piemontese, P.; Agosti, M.; Mosca, F. Rapid Recovery of Fat Mass in Small for Gestational Age Preterm Infants after Term. PLoS ONE 2011, 6, e14489. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Norris, T.; Ramel, S.E.; Catalano, P.; Ni Caoimh, C.; Roggero, P.; Murray, D.; Fields, D.A.; Demerath, E.W.; Johnson, W. New charts for the assessment of body composition, according to air-displacement plethysmography, at birth and across the first 6 mo of life. Am. J. Clin. Nutr. 2019, 109, 1353–1360. [Google Scholar] [CrossRef] [PubMed]
  18. Hamatschek, C.; Yousuf, E.I.; Möllers, L.S.; So, H.Y.; Morrison, K.M.; Fusch, C.; Rochow, N. Fat and Fat-Free Mass of Preterm and Term Infants from Birth to Six Months: A Review of Current Evidence. Nutrients 2020, 12, 288. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Al-Theyab, N.A.; Donovan, T.J.; Eiby, Y.A.; Colditz, P.B.; Lingwood, B.E. Fat trajectory after birth in very preterm infants mimics healthy term infants. Pediatr. Obes. 2019, 14, e12472. [Google Scholar] [CrossRef] [PubMed]
  20. Chmielewska, A.; Farooqi, A.; Domellöf, M.; Ohlund, I. Lean Tissue Deficit in Preterm Infants Persists up to 4 Months of Age: Results from a Swedish Longitudinal Study. Neonatology 2020, 117, 80–87. [Google Scholar] [CrossRef] [PubMed]
  21. Roggero, P.; Giani, M.L.; Amato, O.; Orsi, A.; Piemontese, P.; Cosma, B.; Morlacchi, B.; Mosca, F. Postnatal growth failure in preterm infants: Recovery of growth and body composition after term. Early Hum. Dev. 2008, 84, 555–559. [Google Scholar] [CrossRef] [PubMed]
  22. Forsum, E.; Olhager, E.; Törnqvist, C. An Evaluation of the Pea Pod System for Assessing Body Composition of Moderately Premature Infants. Nutrients 2016, 8, 238. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Nagel, E.; Hickey, M.; Teigen, L.; Kuchnia, A.; Curran, K.; Soumekh, L.; Earthman, C.; Demerath, E.; Ramel, S. Clinical Application of Body Composition Methods in Premature Infants. J. Parenter. Enter. Nutr. 2020, 44, 785–795. [Google Scholar] [CrossRef]
  24. Larsson, A.; Ottosson, P.; Törnqvist, C.; Olhager, E. Body composition and growth in full-term small for gestational age and large for gestational age Swedish infants assessed with air displacement plethysmography at birth and at 3-4 months of age. PLoS ONE 2019, 14, e0207978. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Vincer, M.J.; Cake, H.; Graven, M.; Dodds, L.; McHugh, S.; Fraboni, T. A Population-Based Study to Determine the Performance of the Cognitive Adaptive Test/Clinical Linguistic and Auditory Milestone Scale to Predict the Mental Developmental Index at 18 Months on the Bayley Scales of Infant Development-II in Very Preterm Infants. Pediatrics 2005, 116, e864–e867. [Google Scholar] [CrossRef] [Green Version]
  26. Macias, M.M.; Saylor, C.F.; Greer, M.G.; Charles, J.M.; Bell, N.; Katikaneni, L.D. Infant screening: The usefulness of the Bayley Infant Neurodevelopmental Screener and the Clinical Adaptive Test/Clinical Linguistic Auditory Milestone Scale. J. Dev. Behav. Pediatr. 1998, 19, 155–161. [Google Scholar] [CrossRef] [PubMed]
  27. Accardo, P.C.A. The Capute Scales: Cognitive Adaptive Test/Clinical Linguistic and Auditory Milestone Scale; Brooks Publishing: Pacific Groove, CA, USA, 2005; p. 136. [Google Scholar]
  28. Tavasoli, A.; Azimi, P.; Montazeri, A. Reliability and Validity of the Peabody Developmental Motor Scales-Second Edition for Assessing Motor Development of Low Birth Weight Preterm Infants. Pediatr. Neurol. 2014, 51, 522–526. [Google Scholar] [CrossRef]
  29. Caple, J.; Armentrout, D.; Huseby, V.; Halbardier, B.; Garcia, J.; Sparks, J.W.; Moya, F.R. Randomized, Controlled Trial of Slow Versus Rapid Feeding Volume Advancement in Preterm Infants. Pediatrics 2004, 114, 1597–1600. [Google Scholar] [CrossRef] [PubMed]
  30. Cobb, B.A.; Carlo, W.A.; Ambalavanan, N. Gastric Residuals and Their Relationship to Necrotizing Enterocolitis in Very Low Birth Weight Infants. Pediatrics 2004, 113, 50–53. [Google Scholar] [CrossRef]
  31. Kennedy, K.; Tyson, J. Rapid versus slow rate of advancement of feedings for promoting growth and preventing necrotizing enterocolitis in parenterally fed low-birth-weight infants. Cochrane Database Syst. Rev. 1998, CD001241. [Google Scholar] [CrossRef]
  32. Preventing necrotizing enterocolitis in parenterally fed low birth weight infants (Review). Cochrane Database Syst. Rev. 2005, 4, 1–11.
  33. Mishra, S.; Agarwal, R.; Jeevasankar, M.; Deorari, A.K.; Paul, V.K. Minimal enteral nutrition. Indian J. Pediatr. 2008, 75, 267–269. [Google Scholar] [CrossRef] [PubMed]
  34. Mosqueda, E.; Sapiegiene, L.; Glynn, L.; Wilson-Costello, D.; Weiss, M. The use of minimal enteral nutrition in extremely low birthweight newborns. J. Perinatol. 2008, 28, 249–264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Premji, S.S.; Paes, B.; Jacobson, K.; Chessell, L. Evidence-based feeding guidelines for very low-birthweight infants. Adv. Neonatal Care 2002, 2, 5–18. [Google Scholar] [CrossRef]
  36. Rayyis, S.F.; Ambalavanan, N.; Wright, L.; Carlo, W.A. Randomized trial of “slow” versus “fast” feed advancements on the incidence of necrotizing enterocolitis in very low birth weight infants. J. Pediatr. 1999, 134, 293–297. [Google Scholar] [CrossRef]
  37. Reiter, P.D.; Thureen, P.J. Nutrition support in neonatology. In The Science and Practice of Nutrition Support; Gottschlich, M.M., Ed.; Kendall/Hunt Publishing Company: Dubuque, IA, USA, 2001; pp. 325–333. [Google Scholar]
  38. Rd, J.L.S.; Montgomery, D.; Alder, S.C.; Rn, D.K.L.; Gerstmann, D.R.; Christensen, R.D. Implementing Feeding Guidelines for NICU Patients <2000 g Results in Less Variability in Nutrition Outcomes. J. Parenter. Enter. Nutr. 2006, 30, 515–518. [Google Scholar] [CrossRef]
  39. Simmer, K. Aggressive nutrition for preterm infants—Benefits and risks. Early Hum. Dev. 2007, 83, 631–634. [Google Scholar] [CrossRef]
  40. Tyson, J.E.; Kennedy, K.A. Trophic feedings for parenterally fed infants (Review). Cochrane Database Syst. Rev. 2005, 3, 1–21. [Google Scholar]
  41. Yu, V.; Simmer, K. Enteral nutrition: Practical aspects, strategy and management. In Nutrition of the Preterm Infant: Scientific Basis and Practical Guidelines, 2nd ed.; Digital Educational Publishing, Inc.: Cincinnati, OH, USA, 2005; pp. 311–332. [Google Scholar]
Table 1. Demographics of VLBW AGA and SGA infants (n = 92).
Table 1. Demographics of VLBW AGA and SGA infants (n = 92).
DemographicSubgroupn of 92
SexMale43 (46.7)
Female49 (53.2)
RaceAfrican American59 (64.1)
Caucasian/Hispanic33 (35.9)
Growth classificationSmall for gestational age29 (31.5)
Appropriate for gestational age63 (68.4)
NutritionDays of TPN12.9 ± 7.5
Breast milk feeding during hospitalization85 (92)
Breast milk feeding at discharge47 (51)
Data displayed as count with percentage in parenthesis or mean ± standard deviation.
Table 2. SGA and AGA anthropometrics from birth to ADP evaluations (n = 57).
Table 2. SGA and AGA anthropometrics from birth to ADP evaluations (n = 57).
AnthropometricsTimeSGA (n = 14)AGA (n = 43)p-Value
Weight gain (grams/day)ADP119.8 ± 5.123.9 ± 4.10.003
ADP219.9 ± 2.820.9 ± 3.00.3
Length (cm/week)ADP11.0 ± 0.30.9 ± 0.20.5
ADP20.7 ± 0.10.8 ± 0.10.6
Head circumference (cm/week)Birth to discharge0.8 ± 0.30.8 ± 0.30.8
Small for gestational age infants (n = 14), appropriate for gestational age infants (n = 43) of 57 infants with serial ADP assessments. Data displayed as mean ± standard deviation with corresponding p values. ADP1 = the time from birth to first ADP evaluation, ADP2 = the time between first to second ADP assessment. The average time of the serial ADP assessments was 44 ± 8 weeks and 67 ± 6 weeks postmenstrual age, respectively.
Table 3. Body composition of SGA and AGA over time (n = 57).
Table 3. Body composition of SGA and AGA over time (n = 57).
ADPBody CompositionSGA (n = 14)AGA (n = 43)p-Value
ADP1% Fat14.3 ± 5.2 [9.4–23.5]18.8 ± 4.8 [11.7–34.1]0.004
% Lean85.7 ± 5.2 [76.5–90.6]81.2 ± 4.8 [65.9–88.3]0.004
ADP2% Fat19.2 ± 5.9 [9.7–30.8]17.8 ± 5.0 [4.0–29.0]0.37
% Lean80.8 ± 5.9 [69.2–90.3]82.2 ± 5.0 [71.0–96.0]0.40
Data displayed at mean ± standard deviation with respective range of data displayed in brackets and corresponding p-values for each ADP evaluation. ADP1 = first air displacement plethysmography assessment (mean 44.0 ± 8.1 weeks postmenstrual age). ADP2 = second air displacement plethysmography assessment (67.1 ± 6.1 weeks postmenstrual age). Body composition displayed as percentage fat or lean mass.
Table 4. Growth and neurodevelopmental scores of SGA and AGA over serial outpatient visits.
Table 4. Growth and neurodevelopmental scores of SGA and AGA over serial outpatient visits.
VariableVisitSGAAGA95% Confidence Interval
SGAAGA
Weight (kg)1 *4.6 ± 0.35.7 ± 0.23.9–5.25.3–6.0
26.4 ± 0.47.8 ± 0.25.5–7.37.3–8.2
37.7 ± 0.69.3 ± 0.36.6–8.98.7–9.9
49.9 ± 0.610.0 ± 0.38.7–11.210.2–11.5
Height (cm)156.6 ± 1.159.5 ± 0.654.4–58.958.3–60.7
263.9 ± 1.567.7 ± 0.860.9–66.966.1–69.3
370.4 ± 1.874.4 ± 1.066.6–74.172.5–76.4
478.0 ± 1.681.0 ± 0.874.8–81.279.4–82.7
Head circumference (cm)1 *37.2 ± 0.540.7 ± 0.236.2–38.240.2–41.2
2 *41.3 ± 0.544.1 ± 0.340.2–42.443.5–44.7
3 *43.6 ± 0.546.1 ± 0.342.5–44.645.5–46.7
4 *45.3 ± 0.547.6 ± 0.344.2–46.447.0–48.2
Adjusted CAT DQ1124.8 ± 9.4113.8 ± 5.0105.6–143.9103.5–124.0
296.8 ± 6.4109.2 ± 3.483.8–109.8102.3–116.2
3102.5 ± 6.1105.5 ± 3.390.1–114.998.9–112.1
497.4 ± 7.6104.8 ± 4.182.0–112.896.5–113.0
Adjusted CLAMS DQ1131.4 ± 12.8135.0 ± 6.8105.4–157.3121.0–148.8
2101.0 ± 7.8108.4 ± 4.285.2–116.899.9–116.8
3105.3 ± 6.999.1 ± 3.791.3–119.291.7–106.6
498.8 ± 8.098.5 ± 4.382.5–115.189.8–107.3
Adjusted gross motor DQ1112.6 ± 5.4124.3 ± 10.2101.5–123.6103.6–144.9
299.9 ± 3.795.0 ± 6.992.3–107.480.9–109.1
3103.1 ± 3.7103.9 ± 6.895.7–110.590.0–117.8
4105.8 ± 3.6106.8 ± 6.798.5–113.093.2–120.3
Adjusted developmental quotients correspond to adjusted age at time of developmental visit. Adjusted age is equivalent to chronologic age adjusted for prematurity. Data displayed as mean ± standard error with confidence intervals. Mean adjusted age in months with standard deviation of each outpatient visit 1–4 is 3.0 ± 1.5, 7.0 ± 2.7, 11.7 ± 3.4, 17.3 ± 3.8, respectively. * Statistically significant confidence intervals for each outpatient visit.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Lach, L.E.; Chetta, K.E.; Ruddy-Humphries, A.L.; Ebeling, M.D.; Gregoski, M.J.; Katikaneni, L.D. Body Composition and “Catch-Up” Fat Growth in Healthy Small for Gestational Age Preterm Infants and Neurodevelopmental Outcomes. Nutrients 2022, 14, 3051. https://doi.org/10.3390/nu14153051

AMA Style

Lach LE, Chetta KE, Ruddy-Humphries AL, Ebeling MD, Gregoski MJ, Katikaneni LD. Body Composition and “Catch-Up” Fat Growth in Healthy Small for Gestational Age Preterm Infants and Neurodevelopmental Outcomes. Nutrients. 2022; 14(15):3051. https://doi.org/10.3390/nu14153051

Chicago/Turabian Style

Lach, Laura E., Katherine E. Chetta, Amy L. Ruddy-Humphries, Myla D. Ebeling, Mathew J. Gregoski, and Lakshmi D. Katikaneni. 2022. "Body Composition and “Catch-Up” Fat Growth in Healthy Small for Gestational Age Preterm Infants and Neurodevelopmental Outcomes" Nutrients 14, no. 15: 3051. https://doi.org/10.3390/nu14153051

APA Style

Lach, L. E., Chetta, K. E., Ruddy-Humphries, A. L., Ebeling, M. D., Gregoski, M. J., & Katikaneni, L. D. (2022). Body Composition and “Catch-Up” Fat Growth in Healthy Small for Gestational Age Preterm Infants and Neurodevelopmental Outcomes. Nutrients, 14(15), 3051. https://doi.org/10.3390/nu14153051

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop