Improving gut health and growth in early life: a protocol for an individually randomised, two-arm, open-label, controlled trial of a synbiotic in infants in Kaffrine District, Senegal

INTRODUCTION

Infants exposed to enteropathogens through poor sanitation and hygiene can develop a subclinical disorder of the gut called environmental enteric dysfunction (EED), characterised by abnormal intestinal histology and permeability. EED can contribute to stunting through reduced digestion and absorption of nutrients, increased susceptibility to infections, increased systemic inflammation and inhibition of growth hormones. EED can be apparent by age 12 weeks, highlighting the need for early intervention. Modulating the early life gut microbiota using synbiotics may improve resistance against colonisation of the gut by enteropathogens, reduce EED and improve linear growth.

METHODS AND ANALYSIS

An individually randomised, two-arm, open-label, controlled trial will be conducted in Kaffrine District, Senegal. Infants will be recruited at birth and randomised to either receive a synbiotic containing two Bifidobacterium strains and one Lactobacillus strain, or no intervention, during the first 6 months of life. The impact of the intervention will be evaluated primarily by comparing length-for-age z-score at 12 months of age in infants in the intervention and control arms of the trial. Secondary outcome variables include biomarkers of intestinal inflammation, intestinal integrity and permeability, gut microbiota profiles, presence of enteropathogens, systemic inflammation, growth hormones, epigenetic status and episodes of illness during follow-up to age 24 months.

DISCUSSION

This trial will contribute to the evidence base on the use of a synbiotic to improve linear growth by preventing or ameliorating EED in a low-resource setting.

TRIAL REGISTRATION NUMBER

PACTR202102689928613.

Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings.

Evaluation of Acceptability, Functionality, and Validity of a Passive Image-Based Dietary Intake Assessment Method in Adults and Children of Ghanaian and Kenyan Origin Living in London, UK

BACKGROUND

Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs.

METHODS

Wearable camera devices were used to capture food intake on eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, nutrient intake, and the relative validity of the method compared to the weighed food records.

RESULTS

The Pearson and Intraclass correlation coefficients of estimates of intakes of food, energy, and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and the weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%.

CONCLUSIONS

Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records.

Biofortified and fortified maize consumption reduces prevalence of low milk retinol, but does not increase vitamin A stores of breastfeeding Zambian infants with adequate reserves: a randomized controlled trial

BACKGROUND

Replacement of conventional staples with biofortified or industrially fortified staples in household diets may increase maternal breast milk retinol content and vitamin A intakes from complementary foods, improving infant total body stores (TBS) of vitamin A.

OBJECTIVES

To determine whether biofortified or industrially fortified maize consumption by Zambian women and their breastfeeding infants could improve milk retinol concentration and infant TBS.

METHODS

We randomly assigned 255 lactating women and their 9-mo-old infants to a 90-d intervention providing 0 µg retinol equivalents (RE)/d as conventional maize or ∼315 µg RE/d to mothers and ∼55 µg RE/d to infants as provitamin A carotenoid-biofortified maize or retinyl palmitate-fortified maize. Outcomes were TBS, measured by retinol isotope dilution in infants (primary), and breast milk retinol, measured by HPLC in women (secondary).

RESULTS

The intervention groups were comparable at baseline. Loss to follow-up was 10% (n = 230 mother-infant pairs). Women consumed 92% of the intended 287 g/d and infants consumed 82% of the intended 50 g/d maize. The baseline geometric mean (GM) milk retinol concentration was 1.57 μmol/L (95% CI: 1.45, 1.69 μmol/L), and 24% of women had milk retinol <1.05 μmol/L. While mean milk retinol did not change in the biofortified arm (β: 0.11; 95% CI: -0.02, 0.24), the intervention reduced low milk retinol (RR: 0.42; 95% CI: 0.21, 0.85). Fortified maize increased mean milk retinol (β: 0.17; 95% CI: 0.04, 0.30) and reduced the prevalence of low milk retinol (RR: 0.46; 95% CI: 0.25, 0.82). The baseline GM TBS was 178 μmol (95% CI: 166, 191 μmol). This increased by 24 µmol (± 136) over the 90-d intervention period, irrespective of treatment group.

CONCLUSIONS

Both biofortified and fortified maize consumption improved milk retinol concentration. This did not translate into greater infant TBS, most likely due to adequate TBS at baseline. This trial was registered at clinicaltrials.gov as NCT02804490.

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.

Development and Validation of an Objective, Passive Dietary Assessment Method for Estimating Food and Nutrient Intake in Households in Low- and Middle-Income Countries: A Study Protocol

Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460).