Although the adverse health effects of consuming ultra-processed foods (UPFs) are well-documented, limited evidence exists on their impact during lactation. This study examined the association between maternal UPF consumption, feeding practices, and malnutrition in breastfed infants. A cross-sectional analysis was conducted with 111 mother-child pairs up to 150 days postpartum. Infant feeding practices were assessed using WHO indicators, and malnutrition was evaluated by length-for-age and BMI-for-age. Maternal dietary intake was estimated using two 24 h recalls, and UPF consumption was classified by the Nova classification. Dyads were grouped based on the highest UPF consumption quartile, and adjusted binary logistic regression was applied. UPFs accounted for 26% of the maternal diet on average. While 71.2% of infants were exclusively breastfed, one-third were overweight, and 11.7% were stunted. High maternal UPF consumption (>32% of energy) was associated with increased odds of malnutrition by BMI-for-age (wasting or overweight) (OR 3.38; 95% CI: 1.29-8.83) and stunting (OR 3.89; 95% CI: 1.04-14.58). Feeding practices showed no association. These findings highlight that maternal UPF consumption is associated with malnutrition odds in breastfed infants in the population assessed, emphasizing the need for dietary guidance during lactation to improve infant health outcomes.
Juliana Morais de Sousa, Danielle Soares Bezerra, Lara Virginia Pessoa de Lima et al.
BACKGROUND: Child undernutrition remains a pressing global concern, with Pakistan among the most affected countries in South Asia. Despite recognition of its multifaceted causes, the relative importance of key risk factors remains insufficiently understood. This study investigates the relative significance of associated risk factors among preschool-age children in Punjab, Pakistan's most populous province. METHODS: Using data from the 2018 Punjab Multiple Indicator Cluster Survey (MICS), this study analyses 38,495 children aged 0-59 months. Binary logistic regression models were employed to estimate the effects of 25 risk factors on stunting, underweight, and wasting, selected through a review of literature. The analysis was conducted for the full sample (children aged 0-59 months) and two age sub-groups: infants aged 0-5 months and children aged 6-35 months, selected to reflect key developmental stages. Environmentally modifiable risk factors were ranked by effect size to identify high-priority intervention areas. RESULTS: Five key predictors were consistently associated with stunting and underweight: household poverty, low maternal education, short birth intervals, underage motherhood, and low birth weight (LBW). For wasting, significant determinants included LBW, low maternal education, incomplete vaccination, and recent fever. Among children aged 6-35 months, inadequate dietary diversity was a major contributor, while in infants (0-5 months), lack of exclusive breastfeeding and unassisted delivery were most influential. Box plot analysis revealed that older children face higher and more variable undernutrition risks, whereas infants benefit from protective factors such as breastfeeding and neonatal care-highlighting the importance of age-specific interventions. CONCLUSIONS: The findings underscore the urgent need for integrated, age-adapted strategies that address both structural and behavioural determinants of child undernutrition. Effective interventions must combine poverty reduction, maternal education, and healthcare access with targeted nutrition programs for infants and toddlers. These insights offer a critical evidence base to inform policies aimed at improving child nutrition and health in Punjab and similar contexts.
Malnutrition remains one of the most pressing global health challenges, particularly in developing countries like Bangladesh, where it continues to significantly impact child health and contribute to chronic illness and high child mortality. Despite the potential of machine learning to improve malnutrition predictions, research in this area remains limited in the country. This study utilizes Bangladesh Demographic and Health Survey (BDHS) 2022 data to identify and quantify key determinants of under-five malnutrition (underweight, wasting, stunting) and evaluates various machine learning models for predicting malnutrition. By addressing a critical gap, this research provides deeper insights into the root causes of malnutrition in Bangladesh. Descriptive statistics were conducted to summarize the key characteristics of the dataset. Boruta algorithm was employed to identify important features related to malnutrition which were then used to evaluate several machine learning models, including K-Nearest Neighbors (KNN), Neural Networks (NN), Classification and Regression Trees (CART), XGBoost (XGBM), Support Vector Machines (SVM), and Random Forest (RF), in addition to the traditional logistic regression (LR) model. The best-performing model was selected to identify the most important factors contributing to malnutrition. The significance of these variables was further assessed using Feature Importance plot (Based on Gini Importance) and Shapley Additive Explanation (SHAP) values. Model performance was evaluated through various metrics, including accuracy, 95% Confidence Interval (CI), Cohen's kappa, sensitivity, specificity, F1 score and precision. The study examined a cohort of 7,910 children, reporting prevalence rates of 19% for stunting, 8% for wasting, and 17% for underweight. The Boruta algorithm identified 18 confirmed features for stunting, 22 for wasting, and 19 for underweight. For stunting, the Random Forest (RF) model outperformed other methods with an accuracy of 64.19%, 95% CI of (0.623, 0.666), Cohen's kappa of 0.158, sensitivity of 56.25%, specificity of 66.00%, F1 score of 0.750 and precision of 0.60. In wasting prediction, RF achieved the highest accuracy at 76.68%, 95% CI of (0.743, 0.787), Cohen's kappa of 0.049, sensitivity of 27.22%, specificity of 80.98%, F1 score of 0.865 and precision of 0.810. Similarly, for underweight, RF demonstrated superior performance with an accuracy of 68.18%, 95% CI of (0.662, 0.703), Cohen's kappa of 0.130, sensitivity of 43.02%, specificity of 73.48%, F1 score of 0.792 and precision of 0.735. Across all malnutrition types, the RF model consistently outperformed traditional logistic regression (LR) and other ML techniques in terms of accuracy, sensitivity, specificity, and other performance metrics. For stunting, key predictors identified in both the Shapley and Gini importance plots included mother's education, father's occupation, place of delivery, wealth index, birth order, and toilet facility; for wasting, significant predictors were antenatal care, unmet family planning, mother's BMI, birth interval, father's occupation, and television ownership; and for underweight, important factors included father's occupation, mother's education, child's age, birth order, wealth index, and place of delivery. This study highlights the effectiveness of Random Forest (RF) in predicting malnutrition outcomes-stunting, wasting, and underweight-using key features identified by the Boruta algorithm. While RF demonstrates moderate performance in predicting stunting and underweight, it shows strong predictive ability for wasting. This underscores RF's potential in guiding targeted interventions for wasting, though further improvements are needed for stunting and underweight predictions. Moreover, the study identifies key contributors for each malnutrition outcome. By pinpointing these determinants, the study provides actionable insights for designing targeted interventions to combat malnutrition more effectively. These findings align with the global development agenda, particularly Sustainable Development Goal (SDG) 2: Zero Hunger and SDG 3: Good Health and Well-being, reinforcing efforts to reduce malnutrition and improve child health outcomes in Bangladesh.
Tanzila Tamanna, Shohel Mahmud, Nahid Salma et al.
In low- and middle-income countries, undernutrition often coexists with rising obesity, creating a double burden of malnutrition (DBM). Our study employs a mathematical approach to examine how maternal socioeconomic status (SES) impacts child growth in Iranian children from infancy to age two, offering new insights into strategies for tackling both undernutrition and obesity. We used data from the PERSIAN Birth Cohort. SES was divided into quintiles using multiple correspondence analysis (MCA), and child growth was assessed with eight indicators. Missing data were handled via multiple imputation (MI). SES impacts on growth were analyzed using Generalized Estimating Equations (GEE), and BMI-Z was predicted from WAZ and HAZ through linear regression by SES and age. We explored obesity risk by comparing HAZ-to-WAZ ratios from GEE models with WAZ-to-HAZ beta ratios from regressions. Sensitivity analyses compared MI with complete-case analyses (CCA). 7169 neonates were assessed at 2, 4, 6, 12, and 24 months. SES increasingly improved WAZ with age, preventing underweight in higher SES groups. SES consistently improved HAZ, significantly enhancing stunting prevention in higher SES groups. The impact on BMI-Z increased with age, peaking in Very High SES, which shows a significant rise in obesity risk by 24 months. Comparing ratios from GEE and regression models, we found that GEE ratios were lower where BMI-Z increased. Sensitivity analysis confirmed MI and CCA consistency. Addressing DBM requires understanding how imbalanced increases in HAZ and WAZ heighten obesity risk. Future research should focus on targeted interventions to manage DBM effectively.
Golnaz Rezaeizadeh, Mohammad Ali Mansournia, Maryam Sharafkhah et al.
Undernutrition affects thousands of children under five years old worldwide, and various factors are related to its onset, among which we highlight enteric infections and gastrointestinal barrier dysfunction. The cycle of intestinal infections and undernutrition has long-term consequences, such as cognitive deficits, poor growth, and metabolic diseases in adulthood. This review explores factors linked to childhood undernutrition, focusing on intestinal infections and markers of intestinal permeability that affect child development. This narrative review was conducted using Medline/PubMed, Web of Science, and Scopus, from July 2024 to March 2025. Studies involving children under five years old and addressing undernutrition, intestinal infections, or intestinal permeability markers were included. Exclusion criteria comprised studies without therapeutic focus, and books, case reports, or academic theses. No language restrictions were applied, and registration on global platforms was not required. Overall, the studies reported a close relationship between enteric pathogens, diarrheal and non-diarrheal stools, and undernutrition. Among the pathogens most frequently found in the feces of malnourished children were Shigella, enterotoxigenic Escherichia coli, enteroaggregative E. coli (EAEC), and Cryptosporidium. The studies also showed the relationship between gastrointestinal barrier function and undernutrition, with the deterioration of nutrient absorption and, consequently, repercussions on development, linear growth, and weight in children. Although the studies analyzed had different designs and heterogeneity in the age range of the studied children, it was possible to observe the relationship between the infection/undernutrition cycle. Future studies should optimize personalized nutrient-based therapies, assess long-term effects on gut health and growth, and explore the gut microbiome's role in enteric infection susceptibility and undernutrition.
Maria Clara da Cruz Carvalho, Samilly Albuquerque Ribeiro, Lélia Sales de Sousa et al.
Malnutrition remains a significant challenge in low- and middle-income countries, contributing to stunting, wasting, and micronutrient deficiencies. This study investigated the effect of locally formulated complementary foods on anthropometric parameters and micronutrient status in children aged 6 to 23 months. A randomized controlled trial involving 40 infants explored the effects of experimental diets, including Maize-Soybean, Maize-Soybean-Termite flour, Maize-Soybean-Fishbone powder, and Maize-Soybean-Termite-Fishbone-Pawpaw-Pumpkin (MaSoTFPP), and a control diet on children over a period of 90 days. Anthropometric measurements of weight, height, and mid-upper arm circumference (MUAC) as well as biochemical analyses of blood samples were carried out. Dietary intake was monitored over the 90-day feeding trial period. The data obtained were analyzed using Statistical Product for Service Solution, version 23. Significance was set at P<0.05. The findings revealed significant improvements in the anthropometric parameters, particularly in the MaSoTFPP group, which exhibited the largest percentage increase in height and MUAC. The children fed with the experimental diets demonstrated enhanced serum hemoglobin, iron, and zinc levels, which increased most significantly in the MaSoTFPP group (by 308%, 264%, and 58%, respectively). The study underscores the potential of incorporating locally available food crops in community-based management of acute malnutrition and supports the World Health Organization's recognition of fortified staple foods in improving child growth and development.
Ngozi Mercy Nnam, Peace Nwanneka Ani, Ngozi Elizabeth Chukwuemeka
BACKGROUND: Undernutrition is a global health concern, particularly in low-income countries, and can manifest as wasting, stunting and/or underweight. In Yemen, the recent armed conflict is likely to have significantly increased the incidence of undernutrition in young children, particularly those living in communities that were already experiencing socioeconomic and health disparities. AIM: To explore the risk factors associated with wasting, stunting and underweight in children aged between six months and five years living in one district of Yemen. METHOD: A cross-sectional study was conducted using a two-part questionnaire to collect anthropometric measurements and sociodemographic characteristics of children enrolled on an outpatient programme for undernourished children at three healthcare facilities in the Jiblah district, and of their families and households. RESULTS: Of the 120 children included in the study, 58 (48%) had wasting, 27 (23%) had stunting and 35 (29%) were underweight. Risk factors for undernutrition included being under the age of three years, being a girl, having had a low birthweight, having siblings under the age of five years, having a mother who is a housewife, living in a household relying on unimproved sources of drinking water, living in a rural area, and living in a low-income household. CONCLUSION: The nutritional status of children in Yemen is under continuing threat. The identification of risk factors for undernutrition may increase awareness of the issue and influence the policy decisions of the international community.
Noman Al-Hatemi, Ebraheem Al-Nawd, Laith Alosoufe et al.
BACKGROUND: India grapples with a dual burden of child wasting and stunting and stark socio-economic and regional disparities. OBJECTIVES: We aimed to better understand trends in the prevalence of childhood stunting and wasting, with an emphasis on its disproportionate effects on marginalized populations, by analyzing National Family Health Surveys (NFHS) data from 2005 to 2020. METHODS: We analyzed trends in the distribution of childhood height-for-age Z (HAZ) and weight-for-height Z (WHZ) scores using anthropometric data from 3 surveys, NFHS 3, 4, and 5. We examined disparities by wealth, caste, tribe, area of residence (rural compared with urban), and sex and mapped trends against India's evolving policy landscape. We also conducted a regression analysis of HAZ and WHZ risk factors. RESULTS: In the context of an evolving nutrition-centered policy landscape, disparities by level of wealth in both HAZ and WHZ decreased in the study period (HAZ: estimate = 0.27, confidence interval [CI]: 0.16, 0.38; and WHZ: estimate = 0.11, CI: 0.01, 0.22), though there were no improvements in disparities for marginalized castes (HAZ: estimate = 0.07, CI: 0.00, 0.13; and WHZ: estimate = 0.02, CI: -0.04, 0.08). Though they have narrowed, disparities by wealth, caste, tribe, and area of residence persist, with undernutrition at birth, as measured by HAZ and WHZ, a particularly acute problem. CONCLUSIONS: Although there have been significant reductions in disparities by wealth in mean HAZ and WHZ scores in India, persistent disparities by caste, tribe, and area of residence necessitate reinvestments in targeted interventions. Further, despite this narrowing of disparities and overall progress against stunting, anthropometric scores in early childhood continue to be low in India, especially with regard to wasting, indicating the importance of not only maternal nutrition and care for newborns but more comprehensive efforts to address poverty and other factors that contribute to undernutrition, with a focus on vulnerable populations.
Drishti Sharma, Pandara Purayil Vijin, Rajat Goyal et al.
OBJECTIVES: Immunization has been identified as a specific nutrition-related intervention to address the direct causes of stunting. This study examines the association between basic immunization status and stunting among toddlers in Indonesia. METHODS: We conducted a cross-sectional analysis using data from the 2021 Study of Indonesia's Nutritional Status. A total of 70 267 toddlers, aged 12 months to 59 months, were examined. Cox regression was employed to calculate adjusted prevalence ratios (aPRs) with their 95% confidence intervals (CIs). RESULTS: The prevalence of stunting among children aged 12 months to 59 months in Indonesia was 23.1%, while 74.9% of children under 5 years old had complete basic immunization status. Multivariable analysis revealed a statistically significant association between basic immunization status and the incidence of stunting. Relative to toddlers with complete basic immunization, those with incomplete (partial) immunization were at a 1.18 times higher risk of stunting (aPR, 1.18; 95% CI, 1.15 to 1.22). Furthermore, toddlers who had not been immunized at all faced an even greater stunting risk (aPR, 1.27; 95% CI, 1.17 to 1.37). This association persisted after adjusting for variables such as maternal education, economic status, and the child's birth weight. CONCLUSIONS: Basic immunization status influences the incidence of stunting in toddlers aged 12 months to 59 months. Thus, parents must ensure that their children's immunizations are completed according to the recommended schedule. Additionally, it is crucial to increase parental awareness regarding health service utilization, disease prevention, and the nutritional needs of toddlers.
OBJECTIVE: Disorders of gut-brain interaction (DGBI) and avoidant/restrictive food intake disorder (ARFID) share clinical characteristics. However, research on the prevalence of ARFID symptoms in youth with DGBI and the clinical presentation of DGBI youth with varying levels of ARFID symptoms is limited. METHOD: In this cross-sectional observational study, 38 adolescents (range 12-17 years; 71% female) with a diagnosed DGBI receiving treatment at a pediatric gastroenterology clinic and a primary caregiver completed the Nine Item ARFID Screen (NIAS). Gastrointestinal (GI), psychiatric, quality of life (QOL), and weight/growth outcomes were assessed via youth- and caregiver-reported questionnaires, anthropometrics, and a water load test. ARFID symptoms were characterized, and their associations with GI, psychiatric symptoms, QOL, and weight/growth outcomes were analyzed. RESULTS: Clinically significant ARFID symptoms were reported by 42% of youth and 55% of caregivers. ARFID symptoms were associated with more severe GI and psychiatric symptoms, lower QOL, and clinically significant weight loss or faltering growth. While associations became non-significant after false discovery rate correction, the effect sizes were medium to large, clearly demonstrating meaningful associations. Agreements between youth and caregiver NIAS reports were good to excellent. DISCUSSION: ARFID symptoms are common in adolescents with DGBI and associated with clinical severity. Further elucidating this common co-morbidity and developing guidelines for effective co-management are priorities.
Camden E Matherne, Hunna J Watson, Ricarda Schmidt et al.
