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

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

Funding Information: AZ is a part-time research associate at INQUIS Clinical Research (formerly Glycemic Index Laboratories), a contract research organisation, and a consultant for the Glycemic Index Foundation. AJG has received consulting fees from Solo GI Nutrition and an honorarium from the Soy Nutrition Institute. LC was a Mitacs Elevate postdoctoral fellow jointly funded by the Government of Canada and the Canadian Sugar Institute. She was previously employed as a casual clinical coordinator at INQUIS Clinical Research. TAK has received research support from the CIHR, the International Life Science Institute (ILSI) and the National Honey Board. He has been an invited speaker at the Calorie Control Council Annual Meeting for which he received an honorarium. EMC reports grants from the Natural Sciences and Engineering Research Council of Canada and the CIHR while this study was being conducted, has received research support from Lallemand Health Solutions and Ocean Spray, and has received consultant fees and speaker and travel support from Danone and Lallemand Health Solutions (all are outside this study). DR is director of Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases at Merkur University Hospital, Zagreb, Croatia. He is the president of the Croatian Society for Diabetes and Metabolic Disorders of the Croatian Medical Association. He serves as an Executive Committee member of the Croatian Endocrine Society, Croatian Society of Obesity and Croatian Society for Endocrine Oncology. He was a board member and secretary of IDF Europe and is currently the chair of the IDF Young Leaders in Diabetes (YLD) Programme. He has served as an Executive Committee member of the Diabetes and Nutrition Study Group of the EASD and currently serves as an Executive Committee member of the Diabetes and Cardiovascular Disease Study Group of the EASD. He has served as principal investigator or co-investigator in clinical trials for AstraZeneca, Eli Lilly, MSD, Novo Nordisk, Sanofi Aventis, Solvay and Trophos. He has received travel support, speaker fees and honoraria for advisory board engagements and/or consulting fees from Abbott, Amgen, AstraZeneca, Bayer, Belupo, Boehringer Ingelheim, Eli Lilly, LifeScan – Johnson & Johnson, the International Sweeteners Association, Krka, Medtronic, Mediligo, Mylan, Novartis, Novo Nordisk, MSD, Pfizer, Pliva, Roche, Salvus, Sandoz, Solvay, Sanofi Aventis and Takeda. HK is Director of Clinical Research at the Physicians Committee for Responsible Medicine, a non-profit organisation that provides nutrition education and research. JS-S reports serving on the board of and receiving grant support through his institution from the International Nut and Dried Fruit Council (INC) and the Eroski Foundation. He reports serving on the Executive Committee of the Instituto Danone Spain. He reports receiving research support from the Instituto de Salud Carlos III, Spain; Ministerio de Educación y Ciencia, Spain; the Departament de Salut Pública de la Generalitat de Catalunya, Catalonia, Spain; the European Commission; the California Walnut Commission, USA; Patrimonio Comunal Olivarero, Spain; La Morella Nuts, Spain; and Borges, Spain. He reports receiving consulting fees or travel expenses from Danone, the California Walnut Commission, the Eroski Foundation, the Instituto Danone Spain, Nuts for Life, the Australian Nut Industry Council, Nestlé, Abbot and Font Vella y Lanjarón. He is on the Clinical Practice Guidelines Expert Committee of the EASD and served on the Scientific Committee of the Spanish Agency for Food Safety and Nutrition and the Spanish Federation of the Scientific Societies of Food, Nutrition and Dietetics. He is a member of the International Carbohydrate Quality Consortium (ICQC) and an Executive Board Member of the Diabetes and Nutrition Study Group of the EASD. CWCK has received grants or research support from the Advanced Food and Materials Network, Agriculture and Agri-Food Canada (AAFC), the Almond Board of California, Barilla, the CIHR, the Canola Council of Canada, the International Nut and Dried Fruit Council, the International Tree Nut Council Nutrition Research and Education Foundation, Loblaw Brands, the Peanut Institute, Pulse Canada and Unilever. He has received in-kind research support from the Almond Board of California, Barilla, the California Walnut Commission, Kellogg Canada, Loblaw Brands, Nutrartis, Quaker (PepsiCo), the Peanut Institute, Primo, Unico, Unilever, WhiteWave Foods/Danone. He has received travel support and/or honoraria from Barilla, the California Walnut Commission, the Canola Council of Canada, General Mills, the International Nut and Dried Fruit Council, the International Pasta Organization, Lantmannen, Loblaw Brands, the Nutrition Foundation of Italy, the Oldways Preservation Trust, Paramount Farms, the Peanut Institute, Pulse Canada, Sun-Maid, Tate & Lyle, Unilever and White Wave Foods/Danone. He has served on the scientific advisory board for the International Tree Nut Council, International Pasta Organisation, McCormick Science Institute and Oldways Preservation Trust. He is a founding member of the ICQC and an Executive Board Member of the Diabetes and Nutrition Study Group of the EASD, is on the Clinical Practice Guidelines Expert Committee for Nutrition Therapy of the EASD and is a Director of the Toronto 3D Knowledge Synthesis and Clinical Trials foundation. JLS has received research support from the Canadian Foundation for Innovation, the Ontario Research Fund, the Province of Ontario Ministry of Research, Innovation and Science, the CIHR, Diabetes Canada, the American Society for Nutrition (ASN), the International Nut and Dried Fruit Council Foundation, the National Honey Board (US Department of Agriculture [USDA] honey ‘Checkoff’ programme), the Institute for the Advancement of Food and Nutrition Sciences (IAFNS; formerly ILSI North America), Pulse Canada, the Quaker Oats Center of Excellence, the United Soybean Board (USDA soy ‘Checkoff’ programme), the Tate and Lyle Nutritional Research Fund at the University of Toronto, the Glycemic Control and Cardiovascular Disease in Type 2 Diabetes Fund at the University of Toronto (established by the Alberta Pulse Growers), the Plant Protein Fund at the University of Toronto (which has received contributions from IFF) and the Nutrition Trialists Fund at the University of Toronto (established by an inaugural donation from the Calorie Control Council). He has received food donations to support RCTs from the Almond Board of California, the California Walnut Commission, the Peanut Institute, Barilla, Unilever/Upfield, Unico/Primo, Loblaw Companies, Quaker, Kellogg Canada, WhiteWave Foods/Danone, Nutrartis and Dairy Farmers of Canada. He has received travel support, speaker fees and/or honoraria from the ASN, Danone, Dairy Farmers of Canada, FoodMinds, Nestlé, Abbott, General Mills, the Comité Européen des Fabricants de Sucre (CEFS), Nutrition Communications, the International Food Information Council (IFIC), the Calorie Control Council and the International Glutamate Technical Committee. He has or has had ad hoc consulting arrangements with Perkins Coie, Tate & Lyle, Phynova and INQUIS Clinical Research. He is a member of the European Fruit Juice Association Scientific Expert Panel and former member of the Soy Nutrition Institute Scientific Advisory Committee. He is on the Clinical Practice Guidelines Expert Committees of Diabetes Canada, the EASD, the Canadian Cardiovascular Society and Obesity Canada/Canadian Association of Bariatric Physicians and Surgeons. He serves or has served as an unpaid member of the Board of Trustees and an unpaid scientific advisor for the Food, Nutrition, and Safety Program (FNSP) and the Carbohydrates Committee of the IAFNS. He is a member of the ICQC, an Executive Board Member of the Diabetes and Nutrition Study Group of the EASD, and Director of the Toronto 3D Knowledge Synthesis and Clinical Trials foundation. His spouse is an employee of AB InBev. PM, EV, SBM, VC, US, UR, MU, A-MA, KH and IT declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Funding Information: Open access funding provided by University of Eastern Finland (UEF) including Kuopio University Hospital. The Diabetes and Nutrition Study Group of the EASD commissioned this systematic review and meta-analysis and provided funding and logistical support for meetings as part of the development of the EASD clinical practice guidelines for nutrition therapy. This work was also supported by the Canadian Institutes of Health Research (CIHR; reference no. 129920) through the Canada-wide Human Nutrition Trialists’ Network (NTN). The Diet, Digestive tract, and Disease (3D) Centre, funded through the Canada Foundation for Innovation and the Ministry of Research and Innovation’s Ontario Research Fund, provided the infrastructure for the conduct of this work. PM was funded by a Connaught Fellowship, an Onassis Foundation Fellowship and a Peterborough KM Hunter Charitable Foundation Scholarship. AZ was funded by a Toronto3D Postdoctoral Fellowship Award and a Banting and Best Diabetes Centre (BBDC) Fellowship in Diabetes Care. AJG was funded by a Nora Martin Fellowship in Nutritional Sciences, the Banting & Best Diabetes Centre Tamarack Graduate Award in Diabetes Research, the Peterborough K. M. Hunter Charitable Foundation Graduate Award and an Ontario Graduate Scholarship. LC was funded by a Mitacs Elevate Postdoctoral Fellowship Award. TAK was funded by a Toronto 3D Postdoctoral Fellowship Award. EMC held the Lawson Family Chair in Microbiome Nutrition Research at the Lawson Centre for Child Nutrition, Temerty Faculty of Medicine, University of Toronto. JS-S is partially supported by the Catalan Institution for Research and Advanced Studies (ICREA) under the ICREA Acadèmia programme. JLS was funded by a PSI Graham Farquharson Knowledge Translation Fellowship, Canadian Diabetes Association Clinician Scientist Award, CIHR Institute of Nutrition, Metabolism and Diabetes (INMD)/Canadian Nutrition Society (CNS) New Investigator Partnership Prize and BBDC Sun Life Financial New Investigator Award. Publisher Copyright: © 2022, The Author(s).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: I 2=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: I 2=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.Peer reviewe

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.</p

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

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

Abstract 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

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