Secondary Logo

Journal Logo

Original Articles: Nutrition

Environmental Enteropathy, Micronutrient Adequacy, and Length Velocity in Nepalese Children: the MAL-ED Birth Cohort Study

Morseth, Marianne S.; Henjum, Sigrun; Schwinger, Catherine; Strand, Tor A.†,‡; Shrestha, Sanjaya K.†,§; Shrestha, Binob§; Chandyo, Ram K.†,||; Ulak, Manjeswori; Torheim, Liv Elin

Author Information
Journal of Pediatric Gastroenterology and Nutrition: August 2018 - Volume 67 - Issue 2 - p 242-249
doi: 10.1097/MPG.0000000000001990

Abstract

What Is Known

  • Environmental enteropathy combined with poor micronutrient intake likely contributes to the global burden of growth faltering in children.
  • The fecal markers alpha-1-antitrypsin, myeloperoxidase, and neopterin have been linked to decreases in height-for-age, but associations with length velocity z score have not been assessed.

What Is New

  • Associations between fecal markers and length velocity z score were weak.
  • Environmental enteropathy score (based on all 3 fecal markers) and myeloperoxidase were significantly associated with length velocity z score from 9 to 24 months.
  • These associations were slightly modified by nutrient density adequacy.

Child undernutrition is a widespread global health problem, with the majority of affected children living in Sub-Saharan Africa and South Asia (1). Although the global prevalence of stunting decreased from 33% to 23% between 2000 and 2016, the prevalence of wasting was 8% in 2016 (2). The causes of growth faltering are complex and include intrauterine growth retardation (3), diarrhea (4), and inadequate infant and young child feeding (IYCF) practices (5), while a recent systematic review showed heterogeneous results for associations between environmental enteropathy (EE) and impaired growth (6). The consequences of growth faltering for affected children are cognitive deficits and reduced human capital (7) and increased risk of infections (8) and mortality (9).

Dietary interventions among children in LMICs generally fail to achieve normal linear growth (10). This may be partly caused by weaning environments with high exposure to environmental pathogens (11), resulting in chronic inflammation of the intestine and malabsorption of nutrients, described as EE, or more recently, environmental enteral dysfunction (EED) (12). Histologically, EE presents as villous blunting, crypt hyperplasia, inflammation in the epithelium and lamina propria as well as damaged tight junction integrity and microerosions, increasing the risk of systemic inflammation (12). Biopsies of healthy children are considered invasive and efforts are made to validating simple, non-invasive biomarkers associated with EE and subsequent growth faltering (11,13). Among the biomarkers investigated for this purpose are fecal alpha-1-antitrypsin (AAT) (marker for protein losing enteropathy), myeloperoxidase (MPO) (marker of neutrophil activity) and neopterin (NEO) (marker of Th1 immune activation) (14).

While Bhaktapur has higher socioeconomic status than national averages (15), micronutrient adequacy in our sample was extremely low (16) and multiple micronutrient deficiency has been shown to histologically mimic EE in animal models (17). Previous studies have demonstrated associations between MPO and short-term (3 months) (18) and AAT, MPO, and NEO and long-term (6 months) (14) changes in height (ΔHAZ). While ΔHAZ uses the same scale (z scores) independent of age, length velocity assesses expected growth incorporating different growth velocities across infancy and childhood, and may thus be a more suitable method to assess determinants of growth faltering (19). The aim of this study was to assess the association between intestinal inflammation and length velocity z scores (LVZ) and whether these associations were influenced by micronutrient adequacy among 9 to 24 months old children in Bhaktapur, Nepal.

METHODS

Design and Subjects

This study was part of the Etiology, Risk Factors, and Interactions of Enteric Infections and Consequences for Child Health and Development study (MAL-ED). Data were collected in Bhaktapur, a peri-urban community situated 15 km east of Kathmandu, the capital of Nepal. The number of children to be included each week was based on a pre-study census providing information on the expected birth rate and the target sample size (>200 children). Participants were enrolled within 17 days from birth and followed up until 24 months. Children who were singletons, born to mothers >16 years of age, weighed >1500 g at birth and were without indications of serious disease were eligible. Out of 275 children screened, 240 were included. Data collection took place between February 2011 and February 2014. In this article, we used data of children aged 9 to 24 months only. This was due to a change in methodology for dietary data collection after 9 months (20) enabling calculation of micronutrient adequacy after this age only. Data were divided into 5 time slots (9–12, 12–15, 15–18, 18–21, and 21–24 months). The study received ethical approval from Nepal Health Research Council (NHRC) and the Walter Reed Institute of Research (Silver Springs, MD) and all parents signed informed consent forms. Further information on design and methodology is reported elsewhere (21).

Anthropometry

Anthropometric measurements were performed monthly. A standard length board (ShorrBoard; Weigh and Measure, LLC, Olney, MD) was used to measure length, and an infant scale (Seca, Chino, CA) was used to measure weight. If concern was raised about measurements, raw values were plotted on growth curves on site. If deviations from previous values were substantial, measurements were redone immediately. To monitor the quality of the data, 10% of measurements each month were duplicated within 24 hours by a supervisor. Anthropometric data were double entered into a database. The site data supervisor checked for discrepancies and missing data. If needed, new measurements were performed, generally within 48 hours. Following an external quality control implausible increments in subsequent measurements (>1.5 kg for weight and >3.5 cm for length) were returned for review by the study site.

Intestinal Inflammation Markers

Routine stool samples were collected monthly for children <12 months, then quarterly up to 36 months age (21). Stool samples collected at 9, 12, 15, 18, and 21 months were used in the analyses. The samples were stored for processing at −70°C without fixative (22). The concentrations AAT, MPO, and NEO were measured by ELISA tests at Walter Reed/AFRIMS Research Unit, Katmandu, Nepal, using initial dilutions of 1:500 ng/mL for MPO (ALPCO, Salem, NH) and AAT (BioVendor, Candler, NC) and 1:1000 nmol/L for NEO (GenWay Biotech, San Diego, CA). Tests showing out of range values were run again at a 2-fold higher or lower (as appropriate) concentration (22). In order to avoid overly diluting the biomarker concentrations, we excluded stool samples collected <7 days after a diarrheal episode or at the same time as the urine sample for the lactulose:mannitol test of intestinal permeability inherent in MAL-ED protocol (13). Due to highly skewed distributions (with few very high values), the variables were log2 transformed to obtain normality and to ease the interpretation of results.

The EE score was calculated from weighting factors from a principal component analysis of the natural log of the 3 biomarkers and percentile scores for AAT, MPO, and NEO, based on methodology by Kosek et al (14). The EE score (range 0–10) was calculated as follows:  

where categories were defined as 0 (≤25th percentile), 1, (25–75th percentile), or 2 (≥75th percentile) (14).

Diet and Socioeconomic Status

Food intake was assessed by 24 hour recall, with separate forms for recipes. To calculate nutrient intake, the FAO International Network of Food Data Systems (INFOODS) database for Asia (23) was mainly used. Parity and socioeconomic status were assessed by questionnaire at 12 months. Socioeconomic status was assessed by a WAMI (Water, Assets, Maternal education and Income) index, with scores ranging from 0 to 1 (24).

Nutrient Density Adequacy

Context specific desired nutrient density and nutrient density adequacy (NDA) of complementary foods was calculated for 10 micronutrients: thiamin, riboflavin, niacin, vitamin B6, folate, vitamin C, vitamin A, calcium, iron, and zinc, based on methodology by Dewey et al (25). Mean nutrient density adequacy (MNDA) was calculated as the mean of individual NDAs for all 10 micronutrients each capped at 100%. Further details on assessment of dietary intake and calculations of MNDA are reported elsewhere (16).

Statistical Analysis

Statistical Package for Social Science (SPSS) version 24.0 (IBM Corp., Armonk, NY) was used to analyze data. Continuous data were presented as mean and standard deviation (SD) if normally distributed, and as median and interquartile range (IQR) if not normally distributed. Growth velocity was calculated based on the WHO 2009 standards (26).

Multiple linear regression models were constructed to assess the associations between EE score, AAT, MPO, and NEO (independent variables) and LVZ (dependent variable), respectively. Models were constructed both for 3- and 6-month growth periods starting at the time of fecal marker sampling. Models were adjusted for baseline (at the beginning of each time slot) HAZ, child's gender, WAMI, and diarrhea (proportion of days in time slot). Only regression models including the growth period 21 to 24 months were additionally adjusted for breast-feeding status (yes or no). Then models were adjusted for MNDA in addition to the above mentioned variables. We tried adjusting for stool consistency, but due to very little variation among participants, it was excluded. Apart from child's gender and breast-feeding status all variables were continuous.

Generalized estimating equations (GEE) models with first order autoregressive (AR-1) covariance matrix were used to assess these associations for the entire follow-up period. Similar adjustments to those used in multiple linear regression models were included.

Associations between quartile groups (≤25, 25–75, and ≥75th percentile) of AAT, MPO, and NEO and 3-month LVZ were also assessed. Models were adjusted for baseline (at the beginning of the time slot) HAZ and diarrhea (proportion of days in time slot).

RESULTS

Baseline characteristics of the mother and child pairs are presented in Table 1. The median (IQR) number of years of education among mothers was 9 (6,10), and the median (IQR) monthly household income (in USD) was 157 (100, 248). Furthermore, the median (IQR) WAMI-score was 0.72 (0.63, 0.81), and all households had access to improved water and sanitation. Mean (SD) length was 50 (2.1) cm, and 12% of children were stunted at birth. The majority (53%) of children were male.

TABLE 1
TABLE 1:
Baseline characteristics, mother-child pairs, Bhaktapur, Nepal

EE score, biomarker concentrations, LVZ and child feeding practices are presented in Table 2. Median (IQR) EE score remained stable at 5 (3,7) throughout follow-up. Concentrations of fecal markers decreased gradually through time slots (with the largest decrease seen for MPO), apart from an increase in NEO at 12 to 15 months. About 70% of our participants in the first 3 and 57% in the last time slot had AAT concentrations above reference values for healthy populations (0.27 mg/g) (27). For MPO, 80% had concentrations above reference value (2000 ng/mL) (28) in the first 2, and 50% in the last time slot, while for NEO, all participants in all time slots had concentrations well above (from 14 to 30 times) the reference value (70 nmol/L) (29). Mean LVZ was lowest at 15 to 18 months (−0.72, SD 1.12) while median (IQR) MNDA was stable at about 40 (35, 50) % up to 21 months and was 49 (40, 56) % in the final time slot. Virtually all children were breastfed up to 21 months.

TABLE 2
TABLE 2:
Environmental enteropathy score, fecal biomarker concentrations, length velocity z scores and feeding practices, children 9 to 24 months, Bhaktapur, Nepal

Results from multiple linear regression models and GEE models for associations between EE score, fecal biomarkers and LVZ scores for 3- and 6-month growth periods are presented in Tables 3 and 4, respectively. For 3-month growth periods, associations for separate time slots were overall weak with few significant findings. EE score (−0.03, CI −0.05, 0) and MPO (−0.03, CI −0.06, 0) were significantly associated with LVZ for the whole follow-up period when MNDA was not adjusted for. Adjusting for MNDA made no changes to the estimates for individual time slots, but slightly weakened the negative association between EE score and MPO and LVZ for the whole follow-up period. For 6-month growth periods, only MPO was significantly associated with lower LVZ for the whole follow-up period. Very little variation was explained in our models, with the highest adjusted R2 found for AAT (5%) at 18 to 21 months.

TABLE 3
TABLE 3:
Linear models for associations between environmental enteropathy score, fecal biomarkers and 3-month length velocity z score, children 9 to 24 months, Bhaktapur, Nepal
TABLE 4
TABLE 4:
Linear models for associations between environmental enteropathy score, fecal biomarkers and 6-month length velocity z score, children 9 to 24 months, Bhaktapur, Nepal

For MPO, children in the high quartile had significantly lower LVZ (−0.47, CI −0.86, −0.07) than children in the low quartile in the 12–15-month time slot. Otherwise, no consistent relationship between quartile groups for fecal marker concentrations and LVZ was found either for separate time slots or throughout follow-up with 3-month growth periods (See Supplemental Table, Supplemental Digital Content 1, http://links.lww.com/MPG/B356, which shows associations between quartile groups for fecal markers and LVZ).

DISCUSSION

We found that associations between EE score or fecal markers for EE and LVZ were generally weak and few reached statistical significance. EE score and MPO for 3-month growth periods and MPO for 6-month growth periods were significantly associated with LVZ for the whole follow-up period. Adjusting for micronutrient adequacy made minor changes to the estimates for the entire follow-up period.

Our findings are in accordance with a study from the MAL-ED Bangladesh site where the EE score and MPO were significantly associated with 3 months ΔHAZ in children 12 to 21 months (18), and findings from the MAL-ED Brazil case-cohort study where MPO and AAT were associated with 2 to 6 months ΔHAZ in children 6–26 months old (11). It, however, contrasts previous findings by Kosek et al (14) based on all MAL-ED sites where EE score and all 3 fecal markers were significantly associated with 6 months ΔHAZ in children 3 to 15 months old. In our study, only MPO for the entire period of follow-up was significantly associated with linear growth over a 6-month period, while in the MAL-ED Bangladesh site, no significant associations were found. The study by Kosek et al reported fecal marker concentrations similar to our higher values (first 2 time slots), with MPO especially elevated, and had more participants and higher statistical power than our study (14), while fecal marker concentrations in the study from the MAL-ED Bangladesh site resembled the lower values in our sample (measured after 18 months age). Importantly, the associations are generally weak, similar to our findings. Finally, a lack of association between NEO and growth was also found in a recent study based on all MAL-ED sites (30). LVZ as a longitudinal growth indicator differs conceptually from ΔHAZ since it accounts for different variability of growth rates at different ages. Differences in estimates with the 2 methods are, however, expected to be small and substantial deviations from previous findings in our study population are unlikely.

Although especially EE score and MPO seem to be associated with longitudinal growth in the second year of life, independent of growth indicator used, the proportion of variance in growth explained by the fecal markers is low. This is likely in part caused by high variability in the fecal markers assessed (30). A study by Campbell et al. found that biomarker scores differed in their associations with sociodemographic characteristics, recent morbidities and prior anthropometry suggesting that they reflect multiple underlying biological processes underlining the numerous etiologies of EED (31). In line with this, McCormick et al (13), investigating environmental exposures and feeding practices influencing AAT, MPO, and NEO in MAL-ED, found that the variability in biomarker concentrations mainly seemed to be attributable to other child or environmental factors than those examined in the study (ie, SES, breast-feeding, season, birthweight). Large inter-individual variation combined with small effect sizes for growth and strong influences by age on biomarker concentrations limit the prognostic value of these fecal markers (32). Recent research also implies that the relationship between NEO and growth may be modified by MPO so that while NEO alone may reflect normal intestinal immune function, NEO in the presence of MPO indicate EED and possibly constrained growth (11,31). Furthermore, numerous biomarkers linked to intestinal- and systemic inflammation or microbial translocation are used to assess EE (6). Including more biomarkers in our analysis would likely have provided a more comprehensive assessment of the association between EE and growth. Finally, the utility of the fecal markers in describing EED has also been questioned since they correlate with the prevalence, activity and severity of other GI diseases and consequently are not specific to EED (12).

The slight tendency for MNDA to weaken the negative associations between intestinal inflammation and LVZ supports studies showing improvements in EED in Zambian adults (33), and transient improvements in EED with micronutrient (34) supplementation in 12 to 35 months old Malawian children (35). Micronutrient supplementation in combination with growth monitoring and health education and/or supplementary food and psychosocial stimulation also improved intestinal permeability in a study among severely undernourished 6 to 24 months old Bangladeshi children (36). In children with EED, nutrient absorption may decrease due to reduced intestinal absorptive surface (37). To our knowledge, the extent to which a scarce pool of micronutrients is allocated into combatting systemic inflammation, gut maturation and repair or growth is largely unknown. Although the children included in the study from Malawi received the WHO micronutrient requirements daily with good compliance, a 24-week follow-up only showed only modest improvements in EED, which led the authors to conclude that MN supplements alone is insufficient to unequivocally ameliorate EED (35). Furthermore, in the study from Bangladesh, the authors attributed the main improvements in EED to weight gain (36), which may partly be linked to gut microbiota immaturity seen in children with low WHZ. This immaturity increases susceptibility to intestinal inflammation (38), reduces growth hormone (GH) sensitivity and growth rate in the host (39). It is thus possible that energy sufficiency plays a greater role than micronutrient sufficiency in the relationship between EE and growth.

The main advantage of this study was frequent measurements of EE, anthropometry and diet across a critical time point in children's lives, allowing longitudinal analyses of relationships between EE, micronutrient adequacy and growth. Also, detailed assessment of diarrhea incidence (several times per week) was a major strength, and allowed for improved quality of data for fecal marker concentrations. Growth velocity is suggested to be more sensitivity in capturing influencing factors such as previous growth experience (40) and season (41), and has a greater potential to assess short-term consequences (42) than measures of attained growth. Further, recruitment was from a relatively homogenous setting. Although fewer fecal samples were available for the 9–12 and 15–18-month time slots, likely due to an increased burden of diarrhea, attrition was relatively low (15%) in the MAL-ED Nepal cohort.

Growth velocities have drawbacks such as a higher instability in subsequent short-time periods (43). Three-month periods were, however, chosen because they represent a balance between the influence of measurement error and the ability to represent current growth (44). Six-month growth periods were added to enable comparisons with other main publications on the subject (14,18), where it has been argued that assessing longer growth periods produce more clinically relevant results than shorter growth periods (14). These, however, did not change the association to a great extent. Furthermore, although P value adjustments such as the Bonferroni correction may not be required when conducting exploratory analysis, multiple regression models, such as in our study, increases the likelihood of significant results occurring by chance (45). Finally, due to the multifactorial etiology of malnutrition and varying importance of different risk factors, the generalizability of our study is limited to populations with similar characteristics.

In summary, associations between EE score, fecal markers and LVZ were weak and varied with biomarkers and age. EE score and MPO were significantly associated with 3-month growth and MPO with 6-month growth in Nepali children 9 to 24 months of age. Further studies to establish the usefulness of these biomarkers in assessing EE and risk of growth retardation in different settings and for different age groups are warranted.

Acknowledgments

The authors thank the staff, children, and caregivers of the MAL-ED Bhaktapur site for their contributions.

REFERENCES

1. Victora CG, de Onis M, Hallal PC, et al. Worldwide timing of growth faltering: revisiting implications for interventions. Pediatrics 2010; 125:e473–e480.
2. UNICEF; World Health Organization; World Bank Group. https://data.unicef.org/wp-content/uploads/2017/06/JME-2017_brochure_June-25.pdf. Accessed September 5, 2017.
3. Martorell R, Zongrone A. Intergenerational influences on child growth and undernutrition. Paediatr Perinat Epidemiol 2012; 26 (suppl 1):302–314.
4. Black RE, Victora CG, Walker SP, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 2013; 382:427–451.
5. Marriott BP, White A, Hadden L, et al. World Health Organization (WHO) infant and young child feeding indicators: associations with growth measures in 14 low-income countries. Matern Child Nutr 2012; 8:354–370.
6. Harper KM, Mutasa M, Prendergast AJ, et al. Environmental enteric dysfunction pathways and child stunting: a systematic review. PLoS Negl Trop Dis 2018; 12:e0006205.
7. Victora CG, Adair L, Fall C, et al. Maternal and child undernutrition: consequences for adult health and human capital. Lancet 2008; 371:340–357.
8. Black RE, Allen LH, Bhutta ZA, et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 2008; 371:243–260.
9. McDonald CM, Olofin I, Flaxman S, et al. The effect of multiple anthropometric deficits on child mortality: meta-analysis of individual data in 10 prospective studies from developing countries. Am J Clin Nutr 2013; 97:896–901.
10. Dewey KG, Adu-Afarwuah S. Systematic Review of the Efficacy and Effectiveness of Complementary Feeding Interventions in Developing Countries. Maternal and Child Nutrition. 2008; University of California, Davis, CA: Blackwell Publishing Ltd, 24–85.
11. Guerrant RL, Leite AM, Pinkerton R, et al. Biomarkers of environmental enteropathy, inflammation, stunting, and impaired growth in children in Northeast Brazil. PLoS One 2016; 11:e0158772.
12. Owino V, Ahmed T, Freemark M, et al. Environmental enteric dysfunction and growth failure/stunting in global child health. Pediatrics 2016; 138:e20160641.
13. McCormick BJ, Lee GO, Seidman JC, et al. Dynamics and trends in fecal biomarkers of gut function in children from 1–24 months in the MAL-ED study. Am J Trop Med Hyg 2017; 96:465–472.
14. Kosek M, Haque R, Lima A, et al. Fecal markers of intestinal inflammation and permeability associated with the subsequent acquisition of linear growth deficits in infants. Am J Trop Med Hyg 2013; 88:390–396.
15. Shrestha PS, Shrestha SK, Bodhidatta L, et al. Bhaktapur, Nepal: the MAL-ED birth cohort study in Nepal. Clin Infect Dis 2014; 59 (suppl 4):S300–S303.
16. Morseth MS, Torheim LE, Chandyo RK, et al. Severely inadequate micronutrient intake among children 9–24 months in Nepal-The MAL-ED birth cohort study. Matern Child Nutr 2017; 14:e12552.
17. Ueno PM, Oria RB, Maier EA, et al. Alanyl-glutamine promotes intestinal epithelial cell homeostasis in vitro and in a murine model of weanling undernutrition. Am J Physiol Gastrointest Liver Physiol 2011; 301:G612–G622.
18. Arndt MB, Richardson BA, Ahmed T, et al. Fecal markers of environmental enteropathy and subsequent growth in Bangladeshi Children. Am J Trop Med Hyg 2016; 95:694–701.
19. Argyle J. Approaches to detecting growth faltering in infancy and childhood. Ann Hum Biol 2003; 30:499–519.
20. Caulfield LE, Bose A, Chandyo RK, et al. Infant feeding practices, dietary adequacy, and micronutrient status measures in the MAL-ED study. Clin Infect Dis 2014; 59 (suppl 4):S248–S254.
21. The MAL-ED Network Investigators. The MAL-ED Study: a multinational and multidiciplinary approach to understand the relationship between enteric pathogens, malnutrition, gut physiology, physical growth, cognitive development, and immune responses in infants and children up to 2 years of age in resource-poor environments. Clinical Infectious Diseases 2014; 59 (suppl 4):S193–206.
22. Kosek M, Guerrant RL, Kang G, et al. Assessment of environmental enteropathy in the MAL-ED cohort study: theoretical and analytic framework. Clin Infect Dis 2014; 59 (suppl 4):S239–S247.
23. FAO. International System of Food Data Systems (INFOODS). http://www.fao.org/infoods/infoods/tables-and-databases/asia/en/. Accessed February 28, 2018.
24. Psaki SR, Seidman JC, Miller M, et al. Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study. Popul Health Metr 2014; 12:8.
25. Working Group on Infant and Young Child Feeding Indicators. Developing and Validating Simple Indicators of Dietary Quality and Energy Intake of Infants and Young Children in Developing Countries: Summary of Findings From Analysis of 10 Data, Sets. Washington, DC: Food and Nutrition Technical Assistance Project (FANTA); 2006.
26. World Health Organization. WHO Child Growth Standards, WHO Department of Nutrition for Health and Development; 2009.
27. Beckmann GRA. Microbiology of the Intestine. Hannover: Schluttersche; 2000.
28. Saiki T. Myeloperoxidase concentrations in the stool as a new parameter of inflammatory bowel disease. Kurume Med J 1998; 45:69–73.
29. Ledjeff EA-DE, Witasek A, Fuchs D, et al. Neopterin concentrations in colon diasylate. Pteridines 2001; 12:155–160.
30. Kosek MN. MAL-ED Network Investigators. Causal pathways from enteropathogens to environmental enteropathy: findings from the MAL-ED Birth Cohort Study. EBioMedicine 2017; 18:109–117.
31. Campbell RK, Schulze KJ, Shaikh S, et al. Biomarkers of environmental enteric dysfunction among children in rural Bangladesh. J Pediatr Gastroenterol Nutr 2017; 65:40–46.
32. Colston JM, Penataro Yori P, Colantuoni E, et al. A methodologic framework for modeling and assessing biomarkers of environmental enteropathy as predictors of growth in infants: an example from a Peruvian birth cohort. Am J Clin Nutr 2017; 106:245–255.
33. Louis-Auguste J, Greenwald S, Simuyandi M, et al. High dose multiple micronutrient supplementation improves villous morphology in environmental enteropathy without HIV enteropathy: results from a double-blind randomised placebo controlled trial in Zambian adults. BMC Gastroenterol 2014; 14:5.
34. Herbison CE, Hickling S, Allen KL, et al. Low intake of B-vitamins is associated with poor adolescent mental health and behaviour. Prev Med 2012; 55:634–638.
35. Smith HE, Ryan KN, Stephenson KB, et al. Multiple micronutrient supplementation transiently ameliorates environmental enteropathy in Malawian children aged 12–35 months in a randomized controlled clinical trial. J Nutr 2014; 144:2059–2065.
36. Hossain MI, Nahar B, Hamadani JD, et al. Intestinal mucosal permeability of severely underweight and nonmalnourished Bangladeshi children and effects of nutritional rehabilitation. J Pediatr Gastroenterol Nutr 2010; 51:638–644.
37. Dewey KG, Mayers DR. Early child growth: how do nutrition and infection interact? Matern Child Nutr 2011; 7 (suppl 3):129–142.
38. Kane AV, Dinh DM, Ward HD. Childhood malnutrition and the intestinal microbiome. Pediatr Res 2015; 77:256–262.
39. Blanton LV, Barratt MJ, Charbonneau MR, et al. Childhood undernutrition, the gut microbiota, and microbiota-directed therapeutics. Science 2016; 352:1533.
40. Richard SA, McCormick BJ, Miller MA, et al. Modeling environmental influences on child growth in the MAL-ED cohort study: opportunities and challenges. Clin Infect Dis 2014; 59 (suppl 4):S255–S260.
41. Schwinger CL, Lunde TM, Andersen P, et al. Seasonal and spatial factors related to longitudinal patterns of child growth in Bwamanda, DR Congo. Earth Perspect 2014; 1:
42. Schwinger C, Fadnes LT, Van den Broeck J. Using growth velocity to predict child mortality. Am J Clin Nutr 2016; 103:801–807.
43. Zumrawi FY, Min Y, Marshall T. The use of short-term increments in weight to monitor growth in infancy. Ann Hum Biol 1992; 19:165–175.
44. Himes JH. Minimum time intervals for serial measurements of growth in recumbent length or stature of individual children. Acta Paediatr 1999; 88:120–125.
45. Altman DG. Practical statistics for medical research. London: Chapman & Hall/CRC; 1997.
Keywords:

anti-1-antitrypsin; Generalized Estimating Equation model; myeloperoxidase; neopterin; nutrient density adequacy

Supplemental Digital Content

Copyright © 2018 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition