Understanding the role of the gut in undernutrition: what can technology tell us?

Abstract

Gut function remains largely underinvestigated in undernutrition, despite its critical role in essential nutrient digestion, absorption and assimilation. In areas of high enteropathogen burden, alterations in gut barrier function and subsequent inflammatory effects are observable but remain poorly characterised. Environmental enteropathy (EE)-a condition that affects both gut morphology and function and is characterised by blunted villi, inflammation and increased permeability-is thought to play a role in impaired linear growth (stunting) and severe acute malnutrition. However, the lack of tools to quantitatively characterise gut functional capacity has hampered both our understanding of gut pathogenesis in undernutrition and evaluation of gut-targeted therapies to accelerate nutritional recovery. Here we survey the technology landscape for potential solutions to improve assessment of gut function, focussing on devices that could be deployed at point-of-care in low-income and middle-income countries (LMICs). We assess the potential for technological innovation to assess gut morphology, function, barrier integrity and immune response in undernutrition, and highlight the approaches that are currently most suitable for deployment and development. This article focuses on EE and undernutrition in LMICs, but many of these technologies may also become useful in monitoring of other gut pathologies.

Keywords: gastrointestinal function; malnutrition.

Figure 1

Pathology and domains of gut dysfunction. (A) Diagram illustrating the authors’ interpretation of the pathology of EE (blue boxes) showing links to broader features of gut dysfunction and undernutrition (black boxes). (B) Domains of gut function that appear to be aberrant in EE. Functional tests that report across each domain may shed new light on both the domains of dysfunction and the degree of dysfunction in various forms of undernutrition. EE, environmental enteropathy.

Figure 2

Capsule technologies for imaging, sensing and sampling. (A, B) Wireless capsule endoscopy images of the small intestine in (A) a healthy volunteer and (B) a patient with coeliac disease. Reprinted from Ciaccio et al , Copyright (2010), with permission from Elsevier. (C) Photograph of a TC-OCT system. Reprinted from Gora et al , Copyright (2018), with permission from Elsevier. (D) Example TC-OCT data collected in the small intestine demonstrating the capability to provide direct imaging of villi over large areas. Reprinted from Gora et al , Copyright (2018), with permission from Elsevier. (E) Photograph of a magnetically actuated wireless sampling capsule (shown on graph paper for scale). Republished with permission of The American Society of Mechanical Engineers, from Simi et al ; permission conveyed through Copyright Clearance Center, Inc. (F, G) Model (F) and photograph (G) of the HemoPill sensing capsule for detection of intestinal bleeding. 1, recess in which blood is detected; 2, light detector; 3, light sources; 4, antenna for data transmission. Reprinted by permission from Springer Nature, from Schostek et al , Copyright (2016). TC-OCT, tethered capsule optical coherence tomography.

Figure 3

Portable sequencing using the MinION (Oxford Nanopore Technologies). (A) Photograph of the MinION handheld sequencing tool developed by Oxford Nanopore Technologies (source: https://nanoporetech.com/products/minion). Red arrow indicates where liquid samples are deposited for analysis. (B) Photograph of multiple MinION systems deployed for sequencing of Ebola. Reprinted by permission from Springer Nature, from Quick et al , Copyright (2016). (C) Example MinION data collected from a mixture of bacterial, viral and mammalian tissue (Mus musculus) material onboard the International Space Station demonstrating the suitability for remote deployment. Reproduced from Castro-Wallace et al under the terms of the Creative Commons CC BY license.

Figure 4

Point-of-care breath tests. (A) A portable breath sample collection tool (ReCIVA breath sampler, Owlstone Medical) for use in conjunction with the breath biopsy central analysis facility (source: https://www.owlstonemedical.com/products/reciva/). (B) Helicobacter pylori infection data collected using a non-invasive ¹³C urea breath test in Pakistani infants demonstrating the suitability of breath tests for use in large cohorts. Reprinted from Nizami et al with permission from Wolters Kluwer Health, Inc. The Creative Commons license does not apply to this content (figure 4B). Use of the material in any format is prohibited without written permission from the publisher, Wolters Kluwer Health, Inc. Please contact permissions@lww.com for further information. (C) Diagram of a compact and alignment-free gas sensing system based on FERS. Reprinted with permission from Hanf et al. Copyright (2014) American Chemical Society. (D) Example FERS data demonstrating the capability to detect and identify ¹³C at low concentrations. Due to the linear relationship between the FERS signal intensity and gas pressure (and because different isotopes exhibit spectral peaks at distinct frequencies), FERS spectra can be interpreted to extract concentrations of desired gaseous species (eg, ¹³C) simply through quantification of the intensity of specific peaks in the spectra (assuming an appropriate calibration procedure has been performed). Reprinted with permission from Hanf et al. Copyright (2014) American Chemical Society. FERS, fibre-enhanced Raman spectroscopy.

Figure 5

Smartphone-based detectors suitable for assessment of immune function (and other biomarkers) at point of care. (A) Image of smartphone-based reader for LFIA strip-based rapid tests for ALP activity in milk. No ALP is indicated by a visible band, ALP+ samples are indicated by the absence of a band (left). Br of bands is quantified on smartphone images using MATLAB (right). Adapted from Yu et al , Copyright (2015), with permission from Elsevier. (B) Image of a smartphone adapted for use as a spectrometer using a photonic crystal biosensor (left). Schematic of the optical components of the detector (centre). Example of label-free detection of changes in peak wavelength caused by the binding of a range of porcine IgG concentrations to an immobilised layer of protein (right). Low IgG concentration was detected by one photonic crystal sensor (green, low sensor) and high IgG concentration by a separate sensor (orange, high sensor). Adapted with permission from Gallegos et al , Copyright (2013), Royal Society of Chemistry. (C) Image (left) and schematic (centre) of a smartphone-based light microscope, which could be used for differential cell counts or label-free CD4+ T-cell detection. Comparative examples of immune cells imaged using a standard light microscope versus the smartphone device (right). Adapted with permission from Tseng et al , Copyright (2010), Royal Society of Chemistry. (D) Image (left) and schematic (centre) of a smartphone adapted for use as a fluorescence microscope, which has been trialled for detection of Mycobacterium tuberculosis-infected sputum samples (example of labelled beads shown on the right). Adapted from Breslauer et al underthe terms of the Creative Commons CC BY license. (E) Example of how a lab-on-a-chip functional assay can be combined with a smartphone-based detector for point-of-care immune function assays. A schematic of the combined device (left: in this example, the lab-on-a-chip is lined with a renal adenocarcinoma cell line for assays of kidney function) and readouts from two functional assays using the smartphone-based detector (right: quantification of agglutination in response to a nephrotoxin and fluorescent imaging of cellular responses to the same toxin). Adapted from Cho et al , Copyright (2016), with permission from Elsevier. ALP, alkaline phosphatase; Br, brightness; CCD, charge-coupled device; LED, light-emitting diode; LFIA, lateral flow immunoassay.