Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data.

INTRODUCTION: Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models to predict inpatient mortality in a paediatric population in a low/middle-income country. METHOD: We retrospectively analysed hospital record data from 0-59 months old children admitted to the ICU of Dhaka hospital of International Centre for Diarrhoeal Disease Research, Bangladesh. Five commonly used ML models- logistic regression, least absolute shrinkage and selection operator, elastic net, gradient boosting trees (GBT) and random forest (RF), were evaluated using the area under the receiver operating characteristic curve (AUROC). Top predictors were selected using RF mean decrease Gini scores as the feature importance values. RESULTS: Data from 5669 children was used and was reduced to 3505 patients (10% death, 90% survived) following missing data removal. The mean patient age was 10.8 months (SD=10.5). The top performing models based on the validation performance measured by mean 10-fold cross-validation AUROC on the training data set were RF and GBT. Hyperparameters were selected using cross-validation and then tested in an unseen test set. The models developed used demographic, anthropometric, clinical, biochemistry and haematological data for mortality prediction. We found RF consistently outperformed GBT and predicted the mortality with AUROC of ≥0.87 in the test set when three or more laboratory measurements were included. However, after the inclusion of a fourth laboratory measurement, very minor predictive gains (AUROC 0.87 vs 0.88) resulted. The best predictors were the biochemistry and haematological measurements, with the top predictors being total CO2, potassium, creatinine and total calcium. CONCLUSIONS: Mortality in children admitted to ICU can be predicted with high accuracy using RF ML models in a real-life data set using multiple laboratory measurements with the most important features primarily coming from patient biochemistry and haematology.

Data on microRNA expression, predicted gene targets and pathway analysis in response to different concentrations of a cranberry proanthocyanidin-rich extract and its metabolite 3-(4-hydroxyphenyl)-propionic acid in intestinal Caco-2BBe1 cells.

Cranberry-derived proanthocyanidin (PAC) is processed by the gut microbiota to produce 3-(4-hydroxyphenyl)-propionic acid (HPPA), among other metabolites. These data are in support of the article entitled, "Cranberry proanthocyanidin and its microbial metabolite 3,4-dihydroxyphenylacetic acid, but not 3-(4-hydroxyphenyl)-propionic acid, partially reverse pro-inflammatory microRNA responses in human intestinal epithelial cells," published in Molecular Nutrition and Food Research [1]. Here we describe data generated by nCounterⓇ Human v3 miRNA Expression Panel of RNA obtained from Caco-2BBe1 cells exposed to two different concentrations of cranberry extract rich in PAC (50 µg/ml or 100 µg/ml) or 3-(4-hydroxyphenyl)-propionic acid (5 µg/ml or 10 µg/ml) for 24 h, then stimulated with 1 ng/ml of IL-1ß or not (mock) for three hours. The raw data are publicly available at the NCBI GEO database GSE237078. This work also includes descriptive methodological procedures, treatment-responsive microRNA (miRNA) expression profiles in Caco-2BBe1 cells, and in silico mRNA gene target and pathway enrichment analyses of significantly differentially expressed miRNAs (q < 0.001). Cranberry and its components have recognized health benefits, particularly in relation to combatting inflammation and pathogenic bacterial adhesion. These data will be valuable as a reference to study the response of intestinal cells to other polyphenol-rich food sources, analyze gut microbial responses to cranberry and its metabolites in different cell lines and mammalian hosts to elucidate individualized effects, and to delineate the role of the gut microbiota in facilitating the benefits of cranberry. Moreover, these data will aid in expanding our knowledge on the mechanisms underlying the benefits of cranberry and its components.

New approaches and technical considerations in detecting outlier measurements and trajectories in longitudinal children growth data.

BACKGROUND: Growth studies rely on longitudinal measurements, typically represented as trajectories. However, anthropometry is prone to errors that can generate outliers. While various methods are available for detecting outlier measurements, a gold standard has yet to be identified, and there is no established method for outlying trajectories. Thus, outlier types and their effects on growth pattern detection still need to be investigated. This work aimed to assess the performance of six methods at detecting different types of outliers, propose two novel methods for outlier trajectory detection and evaluate how outliers affect growth pattern detection. METHODS: We included 393 healthy infants from The Applied Research Group for Kids (TARGet Kids!) cohort and 1651 children with severe malnutrition from the co-trimoxazole prophylaxis clinical trial. We injected outliers of three types and six intensities and applied four outlier detection methods for measurements (model-based and World Health Organization cut-offs-based) and two for trajectories. We also assessed growth pattern detection before and after outlier injection using time series clustering and latent class mixed models. Error type, intensity, and population affected method performance. RESULTS: Model-based outlier detection methods performed best for measurements with precision between 5.72-99.89%, especially for low and moderate error intensities. The clustering-based outlier trajectory method had high precision of 14.93-99.12%. Combining methods improved the detection rate to 21.82% in outlier measurements. Finally, when comparing growth groups with and without outliers, the outliers were shown to alter group membership by 57.9 -79.04%. CONCLUSIONS: World Health Organization cut-off-based techniques were shown to perform well in few very particular cases (extreme errors of high intensity), while model-based techniques performed well, especially for moderate errors of low intensity. Clustering-based outlier trajectory detection performed exceptionally well across all types and intensities of errors, indicating a potential strategic change in how outliers in growth data are viewed. Finally, the importance of detecting outliers was shown, given its impact on children growth studies, as demonstrated by comparing results of growth group detection.

A novel systematic pipeline for increased predictability and explainability of growth patterns in children using trajectory features.

OBJECTIVE: Longitudinal patterns of growth in early childhood are associated with health conditions throughout life. Knowledge of such patterns and the ability to predict them can lead to better prevention and improved health promotion in adulthood. However, growth analyses are characterized by significant variability, and pattern detection is affected by the method applied. Moreover, pattern labelling is typically performed based on ad hoc methods, such as visualizations or clinical experience. Here, we propose a novel pipeline using features extracted from growth trajectories using mathematical, statistical and machine-learning approaches to predict growth patterns and label them in a systematic and unequivocal manner. METHODS: We extracted mathematical and clinical features from 9577 children growth trajectories embedded with machine-learning predictions of the growth patterns. We experimented with two sets of features (CAnonical Time-series Characteristics and trajectory features specific to growth), developmental periods and six machine-learning classifiers. Clinical experts provided labels for the detected patterns and decision rules were created to associate the features with the labelled patterns. The predictive capacity of the extracted features was validated on two heterogenous populations (The Applied Research Group for Kids and the 2004 Pelotas Birth Cohort, based in Canada and Brazil, respectively). RESULTS: Features predictive ability measured by accuracy and F1 score was ≥ 80% and ≥ 0.76 respectively in both cohorts. A small number of features (n = 74) was sufficient to distinguish between growth patterns in both cohorts. Slope, intercept of the trajectory, age at peak value, start value and change of the growth measure were among the top identified features. CONCLUSION: Growth features can be reliably used as predictors of growth patterns and provide an unbiased understanding of growth patterns. They can be used as tool to reduce the effort to repeat analysis and variability concerning anthropometric measures, time points and analytical methods, in the context of the same or similar populations.

A novel shape-based approach to identify gestational age-adjusted growth patterns from birth to 11 years of age.

Child growth patterns assessment is critical to design public health interventions. However, current analytical approaches may overlook population heterogeneity. To overcome this limitation, we developed a growth trajectories clustering pipeline that incorporates a shape-respecting distance, baseline centering (i.e., birth-size normalized trajectories) and Gestational Age (GA)-correction to characterize shape-based child growth patterns. We used data from 3945 children (461 preterm) in the 2004 Pelotas Birth Cohort with at least 3 measurements between birth (included) and 11 years of age. Sex-adjusted weight-, length/height- and body mass index-for-age z-scores were derived at birth, 3 months, and at 1, 2, 4, 6 and 11 years of age (INTERGROWTH-21st and WHO growth standards). Growth trajectories clustering was conducted for each anthropometric index using k-means and a shape-respecting distance, accounting or not for birth size and/or GA-correction. We identified 3 trajectory patterns for each anthropometric index: increasing (High), stable (Middle) and decreasing (Low). Baseline centering resulted in pattern classification that considered early life growth traits. GA-correction increased the intercepts of preterm-born children trajectories, impacting their pattern classification. Incorporating shape-based clustering, baseline centering and GA-correction in growth patterns analysis improves the identification of subgroups meaningful for public health interventions.

Neurodevelopment and Recovery From Wasting.

BACKGROUND AND OBJECTIVES: Acute illness with malnutrition is a common indication for hospitalization among children in low- and middle-income countries. We investigated the association between wasting recovery trajectories and neurodevelopmental outcomes in young children 6 months after hospitalization for an acute illness. METHODS: Children aged 2 to 23 months were enrolled in a prospective observational cohort of the Childhood Acute Illness & Nutrition Network, in Uganda, Malawi, and Pakistan between January 2017 and January 2019. We grouped children on the basis of their wasting recovery trajectories using change in mid-upper arm circumference for age z-score. Neurodevelopment was assessed with the Malawi Developmental Assessment Tool (MDAT development-for-age z-score [DAZ]) at hospital discharge and after 6 months. RESULTS: We included 645 children at hospital discharge (mean age 12.3 months ± 5.5; 55% male); 262 (41%) with severe wasting, 134 (21%) with moderate wasting, and 249 (39%) without wasting. Four recovery trajectories were identified: high-stable, n = 112; wasted-improved, n = 404; severely wasted-greatly improved, n = 48; and severely wasted-not improved, n = 28. The children in the severely wasted-greatly improved group demonstrated a steep positive MDAT-DAZ recovery slope. This effect was most evident in children with both wasting and stunting (interaction wasted-improved × time × stunting: P < .001). After 6 months, the MDAT DAZ in children with wasting recovery did not differ from community children. In children who never recovered from wasting, there remained a significant delay in MDAT DAZ scores. CONCLUSIONS: Neurodevelopment recovery occurred in parallel with wasting recovery in children convalescing from acute illness and was influenced by stunting.

Nordic dietary patterns and cardiometabolic outcomes: a systematic review and meta-analysis of prospective cohort studies and randomised controlled trials.

AIMS/HYPOTHESIS: Nordic dietary patterns that are high in healthy traditional Nordic foods may have a role in the prevention and management of diabetes. To inform the update of the EASD clinical practice guidelines for nutrition therapy, we conducted a systematic review and meta-analysis of Nordic dietary patterns and cardiometabolic outcomes. METHODS: We searched MEDLINE, EMBASE and The Cochrane Library from inception to 9 March 2021. We included prospective cohort studies and RCTs with a follow-up of ≥1 year and ≥3 weeks, respectively. Two independent reviewers extracted relevant data and assessed the risk of bias (Newcastle-Ottawa Scale and Cochrane risk of bias tool). The primary outcome was total CVD incidence in the prospective cohort studies and LDL-cholesterol in the RCTs. Secondary outcomes in the prospective cohort studies were CVD mortality, CHD incidence and mortality, stroke incidence and mortality, and type 2 diabetes incidence; in the RCTs, secondary outcomes were other established lipid targets (non-HDL-cholesterol, apolipoprotein B, HDL-cholesterol, triglycerides), markers of glycaemic control (HbA1c, fasting glucose, fasting insulin), adiposity (body weight, BMI, waist circumference) and inflammation (C-reactive protein), and blood pressure (systolic and diastolic blood pressure). The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was used to assess the certainty of the evidence. RESULTS: We included 15 unique prospective cohort studies (n=1,057,176, with 41,708 cardiovascular events and 13,121 diabetes cases) of people with diabetes for the assessment of cardiovascular outcomes or people without diabetes for the assessment of diabetes incidence, and six RCTs (n=717) in people with one or more risk factor for diabetes. In the prospective cohort studies, higher adherence to Nordic dietary patterns was associated with 'small important' reductions in the primary outcome, total CVD incidence (RR for highest vs lowest adherence: 0.93 [95% CI 0.88, 0.99], p=0.01; substantial heterogeneity: I2=88%, pQ<0.001), and similar or greater reductions in the secondary outcomes of CVD mortality and incidence of CHD, stroke and type 2 diabetes (p<0.05). Inverse dose-response gradients were seen for total CVD incidence, CVD mortality and incidence of CHD, stroke and type 2 diabetes (p<0.05). No studies assessed CHD or stroke mortality. In the RCTs, there were small important reductions in LDL-cholesterol (mean difference [MD] -0.26 mmol/l [95% CI -0.52, -0.00], pMD=0.05; substantial heterogeneity: I2=89%, pQ<0.01), and 'small important' or greater reductions in the secondary outcomes of non-HDL-cholesterol, apolipoprotein B, insulin, body weight, BMI and systolic blood pressure (p<0.05). For the other outcomes there were 'trivial' reductions or no effect. The certainty of the evidence was low for total CVD incidence and LDL-cholesterol; moderate to high for CVD mortality, established lipid targets, adiposity markers, glycaemic control, blood pressure and inflammation; and low for all other outcomes, with evidence being downgraded mainly because of imprecision and inconsistency. CONCLUSIONS/INTERPRETATION: Adherence to Nordic dietary patterns is associated with generally small important reductions in the risk of major CVD outcomes and diabetes, which are supported by similar reductions in LDL-cholesterol and other intermediate cardiometabolic risk factors. The available evidence provides a generally good indication of the likely benefits of Nordic dietary patterns in people with or at risk for diabetes. REGISTRATION: ClinicalTrials.gov NCT04094194. FUNDING: Diabetes and Nutrition Study Group of the EASD Clinical Practice.

Association between gut MIcrobiota, GROWth and Diet in peripubertal children from the TARGet Kids! cohort (The MiGrowD) study: protocol for studying gut microbiota at a community-based primary healthcare setting.

INTRODUCTION: The gut microbiota interacts with diet to affect body health throughout the life cycle. Critical periods of growth, such as infancy and puberty, are characterised by microbiota remodelling and changes in dietary habits. While the relationship between gut microbiota and growth in early life has been studied, our understanding of this relationship during puberty remains limited. Here, we describe the MIcrobiota, GROWth and Diet in peripubertal children (The MiGrowD) study, which aims to assess the tripartite growth-gut microbiota-diet relationship at puberty. METHODS AND ANALYSIS: The MiGrowD study will be a cross-sectional, community-based study involving children 8-12 years participating in the TARGet Kids! COHORT: TARGet Kids! is a primary healthcare practice-based research network in Canada. Children will be asked to provide a stool sample, complete two non-consecutive 24-hour dietary recalls and a pubertal self-assessment based on Tanner Stages. Anthropometry will also be conducted. The primary outcome is the association between gut microbiota composition and longitudinal growth from birth until entry into the study. Anthropometrics data from birth will be from the data collected prospectively through TARGet Kids!. Body mass index z-scores will be calculated according to WHO. The secondary outcome is the association between gut microbiota, diet and pubertal stage. ETHICS AND DISSEMINATION: Ethics approval has been obtained by the Hospital for Sick Children and St. Michael's Hospital-Unity Health, and the University of Toronto. Results will be disseminated in the public and academic sector, including participants, TARGet Kids! primary healthcare physicians teams, scientists via participation in the TARGet Kids! science and physician meetings, conferences and publications in peer-reviewed journals. The MiGrowD study results will help researchers understand the relationships underlying growth, gut microbiota and pubertal maturation in children.

Cranberry Proanthocyanidin and Its Microbial Metabolite 3,4-Dihydroxyphenylacetic Acid, but Not 3-(4-Hydroxyphenyl)-Propionic Acid, Partially Reverse Pro-Inflammatory microRNA Responses in Human Intestinal Epithelial Cells.

SCOPE: The molecular basis underlying the anti-inflammatory and anticarcinogenic properties of cranberries is incompletely understood. The effects of a cranberry proanthocyanidin-rich extract (PAC) and two of its gut microbial metabolites, 3,4-dihydroxyphenylacetic acid (DHPAA) and 3-(4-hydroxyphenyl)-propionic acid (HPPA), on intestinal epithelial cells microRNA (miRNA) expression and their downstream pathways at homeostasis and in inflammatory conditions, are investigated. METHODS AND RESULTS: The expression of 799 miRNAs is quantitatively assessed in differentiated Caco-2BBe1 cells pre-treated with PAC, DHPAA, or HPPA and stimulated with interleukin (IL)-1ß or not. PAC, DHPAA, and HPPA generate subsets of shared and distinct miRNA responses. At homeostasis, miRNAs affected by the metabolites, but not PAC, targeted genes enriched in kinase, Wnt, and growth factor signaling, cell growth and proliferation, apoptosis, and specific cancer pathways. In an inflammatory environment, PAC and DHPAA, but not HPPA, reverses the expression of 16 and two IL-1ß-induced miRNAs, respectively, regulating inflammatory and cancer pathways. CONCLUSION: miRNA modulation is a novel mechanism for PAC bioactivity in the gut. The gut microbiota may be necessary to unlock these effects at homeostasis and partially in inflammation.

Long-Term Trends (1994-2011) and Predictors of Total Alcohol and Alcoholic Beverages Consumption: The EPIC Greece Cohort.

The aim of this study was to evaluate the longitudinal changes in alcohol consumption (total alcohol and types of alcoholic beverages) of the Greek EPIC cohort participants (28,572) during a 17-year period (1994-2011), with alcohol information being recorded repeatedly over time. Descriptive statistics were used to show crude trends in drinking behavior. Mixed-effects models were used to study the consumption of total alcohol, wine, beer and spirits/other alcoholic beverages in relation to birth cohort, socio-demographic, lifestyle and health factors. We observed a decreasing trend of alcohol intake as age increased, consistent for total alcohol consumption and the three types of beverages. Older birth cohorts had lower initial total alcohol consumption (8 vs. 10 g/day) and steeper decline in wine, spirits/other alcoholic beverages and total alcohol consumption compared to younger cohorts. Higher education and smoking at baseline had a positive association with longitudinal total alcohol consumption, up to +30% (vs. low education) and more than +25% (vs. non-smoking) respectively, whereas female gender, obesity, history of heart attack, diabetes, peptic ulcer and high blood pressure at baseline had a negative association of -85%, -25%, -16%, -37%, -22% and -24% respectively. Alcohol consumption changed over age with different trends among the studied subgroups and types of alcohol, suggesting targeted monitoring of alcohol consumption.

Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques.

BACKGROUND: Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection. METHODS: We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features. RESULTS: Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%-89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures. CONCLUSION: Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns.

Does a parenteral nutrition decision support system for total nutrients improve prescription procedure and neonatal growth?

Background and objectives: Parenteral nutrition (PN) is an integral part of the nutritional support of critically ill neonates in the intensive care units (ICU). The evaluation of a decision support system for total nutrients (DSSFTN) is of great importance for clinical practice. This study's aim was to evaluate the impact caused by implementation of a DSSFTN on PN support and neonatal growth. This pilot work was supported by the hospital PN team (PNT) in order to assess possible benefits stemming from the use of DSSFTN.Materials and methods: DSSFTN development is based on the incorporation of pharmaceutical and therapeutic protocols. Thirty-eight neonates were recruited. Inclusion criteria included: patients should (a) be hospitalized in ICU, (b) receive PN support at least for 15 days, (c) have birth weight 550-1600 g. One exclusion criterion was applied: patients should have no inborn error of metabolism. 15 doctors prescribed PN for two groups of neonates. PN was calculated by doctors for Group 1 (19 neonates) and respectively was calculated by the DSSFTN (and checked by doctors) for Group 2 (19 neonates). A questionnaire was completed later by doctors to evaluate DSSFTN.Results: The implementation of DSSFTN led to appropriate composition and administration of PN. Growth was not significantly different between the study groups. Compliance with guidelines was observed. DSSFTN ameliorated intercommunication among doctors.Conclusions: The implementation of DSSFTN enables health professionals to facilitate the complex task of prescribing. It ensures the consistency of PN prescriptions, as it leads to appropriate dosing in all nutrients. DSSFTN provides real-time PN interventions (clinical conditions and enteral amounts are included additionally) and minimizes exposure to human errors.

Childhood severe acute malnutrition is associated with metabolic changes in adulthood.

BACKGROUNDSevere acute malnutrition (SAM) is a major contributor to global mortality in children under 5 years. Mortality has decreased; however, the long-term cardiometabolic consequences of SAM and its subtypes, severe wasting (SW) and edematous malnutrition (EM), are not well understood. We evaluated the metabolic profiles of adult SAM survivors using targeted metabolomic analyses.METHODSThis cohort study of 122 adult SAM survivors (SW = 69, EM = 53) and 90 age-, sex-, and BMI-matched community participants (CPs) quantified serum metabolites using direct flow injection mass spectrometry combined with reverse-phase liquid chromatography. Univariate and sparse partial least square discriminant analyses (sPLS-DAs) assessed differences in metabolic profiles and identified the most discriminative metabolites.RESULTSSeventy-seven metabolite variables were significant in distinguishing between SAM survivors (28.4 ± 8.8 years, 24.0 ± 6.1 kg/m2) and CPs (28.4 ± 8.9 years, 23.3 ± 4.4 kg/m2) (mean ± SDs) in univariate and sPLS-DA models. Compared with CPs, SAM survivors had less liver fat; higher branched-chain amino acids (BCAAs), urea cycle metabolites, and kynurenine/tryptophan (KT) ratio (P < 0.001); and lower ß-hydroxybutyric acid and acylcarnitine/free carnitine ratio (P < 0.001), which were both associated with hepatic steatosis (P < 0.001). SW and EM survivors had similar metabolic profiles as did stunted and nonstunted SAM survivors.CONCLUSIONAdult SAM survivors have distinct metabolic profiles that suggest reduced ß-oxidation and greater risk of type 2 diabetes (BCAAs, KT ratio, urea cycle metabolites) compared with CPs. This indicates that early childhood SAM exposure has long-term metabolic consequences that may worsen with age and require targeted clinical management.FUNDINGHealth Research Council of New Zealand, Caribbean Public Health Agency, Centre for Global Child Health at the Hospital for Sick Children. DST is an Academic Fellow and a Restracomp Fellow at the Centre for Global Child Health. GBG is a postdoctoral fellow of the Research Foundation Flanders.

Maternal diet and breastfeeding duration of infants after NICU hospitalization in Greece: a cohort study.

OBJECTIVES: To investigate the role of maternal diet, personal characteristics and willingness to breastfeed on breastfeeding duration of hospitalized neonates as well as to evaluate the mothers' dietetic intake based on the national recommendations. METHODS: A sample of 161 pregnant women from Athens, Greece was followed up during pregnancy, labor and the first 40 weeks of lactation. The participants attended breastfeeding classes and were interviewed regarding their nutritional habits, personal characteristics and breastfeeding intention. A multivariable logistic regression, adjusted for maternal age, smoking, weeks of gestation, body mass index, mode of delivery was conducted in order to estimate the adjusted odds ratios of breastfeeding for at least 6 months for consuming additional serves of fruit or vegetables from the recommended by the national guidelines. RESULTS: The adjusted odds ratios for breastfeeding at 6 months was 2.15 (p = 0.05) for women consumed ≥3.5 servings of fruits/day. Moreover, the participants reported low conformity with the national dietetic guidelines. CONCLUSIONS: Mothers who consumed the recommended by the NDG fruit servings/day breast-fed their hospitalized newborns for a longer period. Despite the fact that our participants were highly motivated and willing to breast-feed, we argue that this relationship is highly unlikely to be biological.

Evaluation of Barley's Beta-glucan Food Fortification through Investigation of Intestinal Permeability in Healthy Adults.

OBJECTIVE: Intestinal permeability is an index of the adequate function of the intestinal barrier and its modification is associated with intestinal diseases. The aim of the study is to investigate the hypothesis that barley's beta-glucan can inhibit the alteration of intestinal permeability and maintain intestinal integrity after a period of consumption of a carbohydrate snack (cake) rich in sugars. METHODS AND DESIGN: Volunteers participated in a placebo-controlled intervention study for 1 month. In this double-blind methodology, they were randomly assigned to (1) the intervention group (daily consumption of one portion of cake fortified with barley's beta-glucan) or (2) the placebo group (daily consumption of the same cake without the enrichment). Intestinal permeability was assessed using the lactulose/mannitol test. SETTING: Athens, Greece. SUBJECTS: Twenty-three healthy volunteers (age > 40 years). RESULTS: Intestinal permeability did not differ between the 2 groups, both at the beginning and at the end of the intervention. In addition, the intestinal permeability was not significantly modified at the end of the intervention in each group. CONCLUSIONS: The results of the lactulose/mannitol test for the intervention and placebo groups were comparable. For healthy adults, the daily consumption of a simple cake (placebo) and the consumption of the cake fortified with barley's beta-glucan resulted in similar impact for intestinal permeability; thus, beta-glucans did not exert a protective role in intestinal permeability of healthy adults.

Investigation of multiple factors which may contribute to vitamin D levels of bedridden pregnant women and their preterm neonates.

OBJECTIVE: 25-Hydroxyvitamin D (25-OH-D) is the marker, which indicates vitamin D levels. The aim of this study was to investigate the possible factors, which contribute to serum 25-OH-D levels in bedridden mothers and their preterm neonates. METHODS: Twenty-six preterm neonates born during the period of 24-33 weeks of gestational age and 20 mothers (who experienced pregnancy complications) were recruited to the study. RESULTS: Five major results were obtained. (i) The 25-OH-D serum levels for preterm neonates and their mothers were found to possess strong correlation (ii) and both differed significantly in comparison with the optimal levels. (iii) An increase of mothers' 25-OH-D serum levels was associated with an increased possibility that the neonates would be measured to have normal 25-OH-D levels. (iv) Sex was not a key factor to neonates' 25-OH-D levels. (v) No correlation was found between mothers' 25-OH-D levels and their vitamin D3 supplement (400 IU/d during pregnancy). CONCLUSIONS: Due to insufficient exposure to sunlight and a diet not enriched with vitamin D, bedridden pregnant women suffer from vitamin D deficiency and pregnancy complications lead often to birth of preterm neonates with the same deficiency. Mothers should increase the total amount of vitamin D intake (food and supplement).

Parenteral MCT/ω-3 Polyunsaturated Fatty Acid-Enriched Intravenous Fat Emulsion Is Associated With Cytokine and Fatty Acid Profiles Consistent With Attenuated Inflammatory Response in Preterm Neonates: A Randomized, Double-Blind Clinical Trial.

BACKGROUND: Soybean oil-based intravenous fat emulsion (IVFE) administered to preterm neonates can induce oxidative stress and inflammatory response, which are associated with severe complications of prematurity. This study aimed to test the hypothesis that administration of medium-chain triglyceride (MCT)/ω-3 polyunsaturated fatty acid (PUFA)-enriched IVFE in preterm neonates is associated with a cytokine and fatty acid (FA) profile consistent with attenuated inflammatory response. PATIENTS/METHODS: In a double-blind randomized study, 60 preterm neonates (gestational age 26-32 weeks) were randomized to receive either MCT/ω-3 PUFA-enriched IVFE (intervention group) or soybean oil-based IVFE (control group). Serum biochemistry, tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-8, α-tocopherol, and FAs were assessed at baseline, on day of life 15, and day of life 30 or at the end of intervention. RESULTS: All cytokine levels changed significantly across the 3 time points, whereas the type of IVFE had a significant effect on final IL-6 and IL-8 levels, which were lower in the intervention group. The difference in final IL-6 and IL-8 levels remained significant after controlling for bronchopulmonary dysplasia and/or infection. α-Tocopherol and FA values changed significantly over time. MCT/ω-3 PUFA-enriched IVFE administration was associated with significantly higher α-tocopherol, eicosapentaenoic acid, docosahexaenoic acid, and ω-3 PUFAs and lower linolenic acid, total PUFA, and ω-6/ω-3 PUFA values compared with soybean oil-based IVFE. Both IVFEs were well tolerated. CONCLUSION: Compared with the soybean oil-based IVFE, the MCT/ω-3 PUFA-enriched IVFE is associated with a more favorable cytokine and FA profile consistent with attenuated inflammatory response in preterm neonates.