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.

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 (HbA_1c, 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%, p_Q<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], p_MD=0.05; substantial heterogeneity: I²=89%, p_Q<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.

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 &amp; 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 &lt; .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.

Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset

Objectives

Children admitted to hospital with severe acute malnutrition (SAM) and acute illness can be challenging to nutritionally rehabilitate. There is limited understanding on predictors of weight gain during hospitalization in this vulnerable population. This work aimed to predict the weight gain in children using anthropometric, biochemical, clinical, and socio-demographic variables.

Methods

We included 5,044 children aged 0–59 months with SAM hospitalized in the Dhaka Hospital at icddr, b between 2011 and 2019. Surveillance data was collected during hospitalization and analyzed retrospectively. The 15% weight gain from hospital admission to discharge was considered as outcome because it is recommended as the transition criteria from facility to community-based management. We trained a Random Forest classifier to identify the best set of predictors of a 15% weight gain. A total of 78 features were considered. The developed diagnostic model was validated based on the area under the curve (AUC) between the true positive and the false positive rates.

Results

The classification of data based on the outcome (weight gain > 15%) created unbalanced classes, a larger group with < 15% changes in weight and a very small group with > 15% weight gain. To balance this data disparity, we finally included 263 children in this analysis. A model including 197 children (75% of the dataset) was identified in the training dataset, while the rest were used as a test dataset. Validation in the test dataset revealed an AUC of 69.05% when considering all 78 predictors. Among the top predictors were mid-upper arm circumference at admission, family income and breastfeeding duration.

Conclusions

This analysis revealed the role of socio-economic status as well as the importance of breastfeeding practices in attaining 15% weight gain from hospital admission to discharge in under five children treated for SAM. This finding has important implications for future work regarding childhood feeding practices and community-based detection of children with SAM.

Funding Sources

Joannah and Brian Lawson Center for Child Nutrition, Ontario Graduate Scholarship, Canadian Institutes of Health Research Healthy Cities Research Initiative, and icddr, b, Dhaka, Bangladesh.

The Association between Longitudinal BMI Patterns in Children and their Parents

Objectives

Parental (maternal and paternal) weight before and after pregnancy has been associated with an increased risk of obesity in the offspring. However, less is known on the longitudinal association between parental and child growth trajectories across early childhood. This work aims to describe parental and child body mass index (BMI) trajectory patterns from birth until adolescence and assess their association.

Methods

We analyzed 1537 single-child families participating in the Applied Research Group for Kids (TARGet Kids! ) cohort (ON, Canada). Children and their parents had repeated measurements of weight and length or height from child's birth to 13 years during scheduled visits with their primary care physician. These measurements were used to calculate BMI for parents and age and sex adjusted BMI z-scores (zBMI) for children based on the World Health Organization. Latent class mixed modeling was used to identify children and parental growth patterns. A loglinear analysis was conducted to study the tri-party interaction between child, maternal, paternal longitudinal patterns. Jaccard distance was used to assess cluster similarity.

Results

We identified two distinct patterns in children (normal, increasing obesity), mothers (normal, increasing obesity), and fathers (overweight, obesity). The three-way loglinear analysis indicated that the tri-party interaction between children, maternal and paternal growth patterns interaction was significant (χ2 (1) = 15.1, p < .001). 92.1% of children in the normal pattern had mothers in the normal pattern and fathers in the overweight pattern. Cluster similarity was 63% for mothers and children, but 12.5% between fathers and children, with most children from the increasing obesity group with a father in the overweight group.

Conclusions

There is a significant tri-party association between children, maternal and paternal BMI patterns from birth to adolescence. Future studies should aim in studying eating and other health behaviors associated with these relationships at the family level.

Funding Sources

Joannah and Brian Lawson Center for Child Nutrition, Ontario Graduate Scholarship, Canadian Institutes of Health Research.

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.

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.

Childhood severe acute malnutrition is associated with metabolic changes in adulthood

BACKGROUND

Severe 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.

METHODS

This 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.

RESULTS

Seventy-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 β-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.

CONCLUSION

Adult SAM survivors have distinct metabolic profiles that suggest reduced β-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.

FUNDING

Health 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.

Outlier Detection in Growth Data: Beyond Biologically Implausible Values

Objectives

Eliminating anthropometry measurement error and employing outlier and biological implausible values (BIV) detection methods adapted to longitudinal measurements is important for the study of growth. This work aimed to review and assess the accuracy of the available BIV and outlier detection methods and propose a growth trajectory outlier detection method.

Methods

We included 2354 infants from the Applied Research Group for Kids (TARGet Kids! ) cohort-based in Toronto (ON, Canada) that recruits healthy children from birth to 5 years of age. We considered infants with at least 8 length and weight measurements available between the 1st and the 24th month of age. Weight-for-length z-scores (wflz) were calculated using the WHO growth standards. Outlier measurements were randomly introduced in 5% of the wflz measurements using a normal distribution (μ = 0, σ = 1). We employed 4 outlier detection methods; an empirical detection method for BIV using the cut-offs derived from the WHO Child Growth Standards, a clustering method, a method based on cluster prototypes for individual outlier measurements and a method based on cluster prototypes for entire growth trajectories. Each method was applied individually and evaluated using the sensitivity and specificity indexes based on the manually introduced outliers. We also calculated the Kappa statistic to evaluate the agreement of each method against the manual outliers.

Results

After excluding premature (&lt;37 weeks), low birth weight (&lt;1500 g) neonates and children with missing length and weight measurements, we analyzed 393 children with a total of 3144 measurements. Sensitivity and specificity for the four methods ranged between 4.4%–55.0% and 83.7% −99.7%, respectively, with kappa being non-significant (P &gt; 0.05) only for the empirical. The clustering detection method reported a higher finding rate, while the empirical method found most of the BIV, but few of the rest of the outliers.

Conclusions

BIV account for a small portion of the possible outliers in growth datasets. We show that additional statistical or model-based methods are required for a more comprehensive outlier detection process, which has implications for growth analysis and nutritional assessment.

Funding Sources

Joannah and Brian Lawson Center for Child Nutrition, Connaught Fund, Onassis Foundation.

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.

Serum antioxidant capacity, biochemical profile and body composition of breast cancer survivors in a randomized Mediterranean dietary intervention study

PURPOSE

Increasing evidence suggests that Mediterranean Diet (MD) is correlated with reduced risk of breast cancer (BC) and cancer mortality, since it modifies patients' serum antioxidant capacity, body composition and biochemical parameters. The aim of the study was to investigate whether a dietary intervention based on MD has a beneficial effect on these factors.

METHODS

In this intervention study, seventy female BC survivors were randomly assigned to (1) the intervention group (personalized dietary intervention based on MD) and (2) the control group (received the updated American Cancer Society Guidelines on Nutrition and Physical Activity for Cancer Prevention and ad libitum diet). Both groups were assessed twice [beginning, end of study (after 6 months)] regarding their anthropometric and biochemical parameters, serum vitamin C, vitamin A, a-tocopherol and CoQ10 levels, dietary intake and adherence to MD. An additional intermediate analysis was conducted on participants' body composition and biochemical profile.

RESULTS

Concerning the intervention group, body weight, body fat mass, waist circumference, body mass index as well as HDL-cholesterol were significantly decreased (P < 0.2%). An increase was observed in the vitamin C levels in blood (P < 0.2%). In the control group, body weight, body fat mass and serum total cholesterol rose (P < 0.2%). At the end of the study the two groups were significantly different considering blood glucose, vitamin C, polyunsaturated fatty acids, vitamin A and a-tocopherol levels.

CONCLUSIONS

This randomized dietary intervention based on MD managed to ameliorate serum antioxidant capacity, body composition, adherence to MD and glycemic profile of postmenopausal BC survivors.

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).

Production of pyroglutamic acid by thermophilic lactic acid bacteria in hard-cooked mini-cheeses

Pyroglutamic acid is present in high amounts (0.5g/ 100g) in many cheese varieties-and particularly in extensively ripened Italian cheeses such as Grana Padano and Parmigiano Reggiano. An in vivo model system for cooked mini-cheese production and ripening acceleration was set up to demonstrate the ability of thermophilic lactic acid bacteria, used as a starter, to produce pyroglutamic acid (pGlu). In mini-cheeses stored at 38 and 30 degrees C for up to 45 d, all starters tested produced different amounts of pGlu. In descending order of pGlu production, the bacteria analyzed were: Lactobacillus helveticus, Lactobacillus delbrueckii subsp. bulgaricus, Streptococcus thermophilus, and Lactobacillus delbrueckii subsp. lactis. Evidence for the presence of glutamine to pGlu cyclase activity in lactic acid bacteria was provided. Cell lysates obtained from cultures of L. helveticus, L. delbrueckii subsp. bulgaricus, L. delbrueckii subsp. lactis, and S. thermophilus showed the ability to cyclize glutamine to pGlu, resulting in processing yields from 1.4 to 30.3%, depending on the subspecies. Formation of pGlu from free glutamine appeared to be similar to that observed using a glutamine-glutamine dipeptide substrate. Under the experimental conditions applied, pGlu aminopeptidase activity was only detected in L. helveticus. Thus, pGlu formation in long-ripened cooked cheese may depend on the activity of thermophilic lactic acid bacteria.