Identification of severely malnourished children at high risk for death:

A metabolomics study

By: Matilda Elisabeth Arvidsson Kvissberg, BSc

S2140543 Supervisor: Dr. R.H.J. Bandsma, MD, PhD. Department of Pediatrics, University Medical Centre Groningen Location for Research: Physiology and Experimental Medicine. The Hospital for Sick Children, Toronto, Canada. © Copyright by Matilda Arvidsson-Kvissberg 2016

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ABSTRACT Introduction: Severe acute malnutrition (SAM) accounts for over half a million child deaths per year, with an inpatient mortality up to as high as 30%. SAM can present as either severe wasting, known as marasmus, or as bilateral pitting edema, known as kwashiorkor. Mortality in SAM is associated to hypoglycemia, indicating metabolic dysfunction. By comparing the metabolic urine profiles of children who die from SAM to children who recover we aim to better understand the pathophysiology of SAM and potentially uncover a non-invasive predictive biomarker for morality. Methods: We used untargeted nuclear magnetic resonance (NMR) spectroscopy to analyze the metabolic urine profiles of 69 children with SAM. Using sex and age corrected generalized linear models we compared the metabolic profiles of children who died to those who survived, and children with kwashiorkor to children with marasmus. Results: Three metabolites differed significantly between the deceased and recovered group: lactic acid (0.29 mM vs 0. 12 mM, p < 0.001), succinic acid (0.09 mM vs 0.00 mM, p < 0.001) and ß-hydroxybutyric acid (0.02 mM vs 0.00 mM, p = 0.01). There was no significant difference between the metabolic profiles of children with kwashiorkor compared to children with marasmus. Discussion: The increased lactic acid and succinic acid levels indicate malnutrition-induced mitochondrial dysfunction leading to anaerobic glycolysis under aerobic conditions for energy production. The increase in ß-hydroxybutyric acid could be due to the utility of ketones as an energy source. Further studies are needed to determine the validity of these metabolites as biomarkers to predict poor clinical outcome. Introductie: Ernstige acute ondervoeding leidt tot meer dan een half miljoen sterfgevallen onder kinderen per jaar en een intramurale sterfte tot wel 30%. Ernstige acute ondervoeding kan zich op twee manieren presenteren: als marasmus en als kwashiorkor. Sterfte onder kinderen met ernstige acute ondervoeding wordt geassocieerd met hypoglykemie, wat duidt op metabole dysfunctie. Door het vergelijken van metabole urine profielen van kinderen die zijn overleden door ernstige acute ondervoeding met de profielen van kinderen die zijn hersteld, proberen we de pathofysiologie van ernstige acute ondervoeding beter te begrijpen en mogelijk een nieuwe non-invasieve biomarker voor sterfte te ontdekken. Methoden: De metabole urine profielen van 69 kinderen met ernstige acute ondervoeding zijn geanalyseerd met behulp van ongerichte NMR spectroscopie. Met behulp van gegeneraliseerde lineaire modellen, gecorrigeerd op geslacht en leeftijd, zijn de metabole profielen van kinderen die zijn overleden vergelijken met de metabole profielen van kinderen die het hebben overleefd. Ook zijn de metabole profielen van kinderen met kwashiorkor vergeleken met de metabole profielen van kinderen met marasmus. Resultaten: Drie metabolieten verschilden significant tussen de groep overleden kinderen en de groep kinderen die het hebben overleefd: melkzuur (0.29 mM vs 0. 12 mM, p < 0.001), barnsteenzuur (0.09 mM vs 0.00 mM, p < 0.001) en ß-hydroxyboterzuur (0.02 mM vs 0.00 mM, p = 0.01). Er was geen significant verschil tussen de metabole profielen van kinderen met kwashiorkor vergeleken met de metabole profielen van kinderen met marasmus. Discussie: Het verhoogde melk- en barnsteenzuur duidt op door ondervoeding geïnduceerde mitochondriële dysfunctie. Dit leidt tot energieproductie middels anaerobe glycolyse onder aerobe condities. Het verhoogde ß-hydroxyboterzuur kan komen doordat ketonen als energiebron worden gebruikt. Verder onderzoek is nodig om de bruikbaarheid van deze metabolieten als biomarkers voor slechte klinische uitkomst te bepalen.

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Acknowledgment

This project would not have been possible without my mentor Ms. Celine Bourdon, bioinformatician, and my supervisor Dr. Robert Bandsma, MD-PhD. I am incredibly grateful to Ms. Celine Bourdon for her incredible patience with teaching me the statistical techniques required to carry out this project, and her ability to make me appreciate and enjoy statistics. I am also grateful to my lab colleagues Nathan Swain and Emma Onverwagt for their general support and for keeping my spirits high.

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Table of Contents List of Abbreviations pg. 4 Introduction pg. 5 - 9 Methods pg. 10 - 11 Results pg. 12 – 16 Discussion pg. 17 - 22 Conclusion pg. 23 References pg. 24 - 26 Appendix A pg. 27 Appendix B pg. 28 - 30 Appendix C pg. 31 - 32 Appendix D pg. 33 - 34

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LIST OF ABBREVIATIONS AKBR - Acetotacetate and ß-hydroxybutyrate ratio

BSTFA - N2O-Bis(trimethylsilyl)trifluoroacetamide

GC-MS - Gas chromatography–mass spectrometry

HCAR2 - Hydroxycarboxylic acid receptor 2

HIF - Hypoxia-inducing factor

HPLC - High performance liquid chromatography

FAD - Flavin adenine dinucleotide

FDR - False discovery rate

KEGG - Kyoto Encyclopedia of Gene and Genome

LOB - Limit of Blank

LOD - Limit of Detection

MSTFA - N-Methyl-N-trifluoroacetamide

MUAC - Mid upper arm circumference

NAD - Nicotinamide adenine dinucleotide

NMR - Nuclear magnetic resonance

NRU - Nutritional rehabilitation unit

PEM - Protein energy malnutrition

PIM2 - Pediatric Index of Mortality 2

RNS - Reactive nitrogen species

ROS - Reactive oxygen species

SMPD - Small Molecule Pathway Database

TMCS - Trimethylchlorosilane

TMIC - The metabolomics innovation centre

WHO - World Health Organization

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INTRODUCTION RELEVANCE Malnutrition is a global burden which is directly or indirectly accountable for 3.1 million child deaths annually (1). Severe acute malnutrition (SAM) has particularly high case fatality rates (2) accounting for more than 500,000 child deaths per year, with some estimates of SAM related mortality as high as 1 million per year (3). SAM is commonly viewed as a consequence of household food insecurity whereas in reality the issue is more complex. Even when resources and highly trained staff are available to initiate treatment, inpatient mortality remains exceptionally high, up to 30% (2). This underscores that current treatment strategies are not effective in avoiding mortality in many of these children. Children with SAM present with either severe wasting or nutritional edema. Severe wasting, also known as marasmus, is defined as having a weight-for-height z score below 3 standard deviations or a mid-upper arm circumference (MUAC) of less than 115 mm. Whilst, nutritional edema or kwashiorkor is defined by the presence of bilateral pitting edema. It is currently unknown why malnourished children specifically develop kwashiorkor compared to marasmus. Different theories have been brought forth, the most common being that protein deficiency prevails in kwashiorkor whereas marasmus would be linked to a generalised lack of caloric intake (4). Infections have also been proposed to play a role in the form of SAM that a child develops (5) and another proposed mechanism is oxidative stress, which has shown to be associated with kwashiorkor (6,7). Oxidative stress is the lack of balance of reactive oxygen or nitrogen species (ROS/RNS) production and the ability to counter the oxidative attacks by antioxidants and other protective systems (8). ROS and RNS are free radicals produced during aerobic cellular processes and can oxidize any cellular structure (9) causing damage. Kwashiorkor, has been associated with an increase in oxidative stress markers (10) and decreased antioxidants (11). Historically, kwashiorkor showed higher mortality rates then children with marasmus, but this relationship has recently changed and it has been hypothesised to be due do the modulatory effects of comorbidities, such as HIV (12,13). It is unclear why certain children develop kwashiorkor, while other develop marasmus and further research is required to uncover the pathophysiological changes in these children. In 1999, the WHO first published treatment guidelines for SAM which helped standardize the clinical management and played a crucial role in improving outcomes of children with severe malnutrition. These guidelines have since been updated (2013), however, the WHO has declared that they have major limitations as treatment approaches are based on expert opinions and sparse scientific evidence often of “low" or "very low" quality (14). Another great challenge is that the children hospitalized for SAM rarely present with malnutrition alone and often have complications such as acute respiratory or enteric infections. Severe acute malnutrition is, on its own, a complex disorder and children with SAM show signs of disturbed hepatic and intestinal metabolism, with features of hypoalbuminemia, hepatic steatosis, diarrhea, and hypoglycemia (3,15). The intestinal metabolism disturbance can be seen through lipid (16,17) malabsorption and I recently published a systematic review indicating significant carbohydrate (18) malabsorption in severely malnourished children. Carbohydrate malabsorption can cause osmotic diarrhea, since the excessive carbohydrates pull water into the intestinal lumen leading to further dehydration. Hepatic disturbances in SAM are illustrated through hepatic steatosis, hypoglycemia and hypoalbuminemia. The metabolic disturbances associated with SAM can be assessed using different techniques that assess metabolism. One innovation is metabolomics, a technique that assesses the set of metabolites present in a sample, tissue, organism or cell.

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METABOLOMICS Metabolomics is the study of metabolites that is often used to provide a biochemical fingerprint of the metabolic processes taking place within the body. These vary depending on the genetic background but also due to environmental influences on a living system which can be dynamic in time. Metabolomics can be divided into separation or detection techniques, or a combination. Separation techniques split the samples into different molecular subgroups. There are three types: gas chromatography (GC), high performance liquid chromatography (HPLC) and capillary electrophoresis (CE). GC is used to separate compounds that can withstand vaporization such as alanine, citrate, methol and lactic acid. The sample passes through a gas-stream (i.e. the mobile phase), which is usually helium. As the molecules funnel through, they interact with the non-volatile stationary phase to different extents depending on their chemical and physical properties. Thus, the molecules pass through the column at different rates and can be separated by their retention time. HPLC is very similar to GC but uses pressure instead of relying on gravity to push a solvent through the stationary phase. CE separates charged metabolites based on their electrophoretic mobility when voltage is applied. Detection techniques are either nuclear magnetic resonance spectroscopy (NMR) or mass spectrometry (MS). NMR measures the absorption of radiofrequency radiation by different nuclei within a chemical compound when placed under a powerful magnetic field. From the radiofrequency patterns, structural and chemical properties can be inferred and molecules identified. MS ionizes the sample and arranges it based on their mass to charge ratio. The mass to charge ratio is the charge of a particle divided by its mass (Q/m); however, in mass spectrometry the notation m/z is used instead of Q/m, were m refers to molecular or atom mass and z to the charge of the ion. These techniques are used to identify compounds in a bio-fluid and can provide vast amount of information. The different molecules can be measured with either an untargeted or targeted approach. Untargeted techniques aim to detect and measure all found metabolites simultaneously. Thus, massive amounts of data are collected and the role of many detected compounds may not yet be known. On the other hand, targeted techniques measure a large set of predefined metabolites, these metabolites are often selected based on a central hypothesis of interest. To quantify the predefined metabolites the analysis can be limited by a range or by using a chosen standard for the metabolites of interest. Metabolomics provides insight into the metabolic composition of different biofluids and through coupling to multi-variant statistical analysis massive quantities of data can be analyzed. As metabolomics allows for both insight into genetic influence and environmental influence on a sample, it is growing in its usage for detection of diseases. Additionally, by comparing the metabolic profiles of two groups, metabolomics can provide biomarkers associated to the different groups. The increase use of metabolomics has led to the formation of metabolomics databases that disclose normal concentrations of metabolites in different biofluids, their involvement in healthy and diseased pathways (http://www.hmdb.ca/). METABOLIC DISTURBANCE AND ITS ASSOCIATION TO MORTALITY IN SEVERE ACUTE MALNUTRITION Mortality in severe acute malnutrition is associated to a number of co-morbidities such as diarrhea (19,20), hypoglycemia (15), malaria (20), HIV, renal failure, and neurological impairment (3). This wide variety of comorbidities makes treatment of severely malnutrition complex and too often unsuccessful. Even though, these associations are well established it remains unclear through which specific pathways these disturbances lead to increased

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mortality, and further research is needed to better comprehend these associations. Apart from diagnosing high risk co-morbidities, identifying children with under nutrition who are at greatest risk of mortality is surprisingly difficult. Children are classified as high risk based on relatively non-specific indicators of nutritional status. Whilst anthropometry is low cost and easy to perform screening tool for risk assessment at a population level and in hospitals, the utility of such measurements for an individual child is less precise. There is a great need 1. to further understand the metabolic pathways associated to mortality and 2. to identify non-invasive biomarkers to identify children at high risk for mortality; this both to guide specialized care towards the children most in need. Analysing the metabolic profiles of children with SAM and their association with mortality, could enhance our understanding of the pathophysiological changes that take place during malnutrition and define a predictive biomarker for mortality. To date there are three clinical studies that look at metabolic profiles of children with SAM. Bartz et al. (21) illustrated that that fatty acid metabolism has a central role in the adaptation to severe acute malnutrition in children. This was illustrated by an association between low levels of leptin, an adipose tissue hormone, and mortality; furthermore, these patients had elevated plasma free fatty acids, ketones, and even-numbered acylcarnitines (21). Moreover, a study conducted in the lab of the Bandsma lab, where my project took place, by Di Giovanni et al. (22) focused on understanding the difference between metabolic profiles of marasmus and kwashiorkor. The study illustrated that kwashiorkor children had lower levels of amino acids compared to marasmus children, with the most notable reductions being seen in tryptophan and its derivative kynurenine (22). The most interesting finding of this study was the fact that the metabolic profiles of children at discharge was very different from the metabolic profiles of the community controls, indicating that these children are still suffering from metabolic dysfunction at discharge (22). A study by Semba et al. (23) used targeted metabolomics to uncover that stunted children had significantly lower serum certain essential amino acids, non-essential amino acids, and six different sphingolipids compared with non-stunted children. Another clinical study led by our lab illustrated that severely malnourished children had increased levels of bile acids in their serum but showed decreased bile acid synthesis rates (24). This illustrates that bile acid homeostasis is also dysregulated during malnutrition. Furthermore, Bartz et al. (21) illustrated a negative correlation between leptin plasma levels and mortality; however, this study was a single center study in a specific geographical region and had a small sample size. This warrants validation studies in different cohorts. Most of these studies have been carried up using blood samples, an invasive technique that requires a medically trained professional. A non-invasive technique would allow samples to be collected not just by a medical trained professional but also by community health workers. This would allow for more cost-effective sample collection in more locations. CLINICAL USE OF URINE Urine is a readily available and sterile bio-fluid that can provide insight into human health and disease. In the kidneys, fluid is filtered out from the blood flowing through the glomerular capillaries into the Bowman’s capsule. Most molecules, apart from proteins, are filtered out from the blood stream. A molecule’s filtration rate is inversely related to its size and depended on its charge. This filtrate travels serially through the proximal tubule, the loop of Henle, the distal tubule, and lastly through the collecting duct. During this process, different molecules are reabsorbed or excreted through active or passive transport ultimately forming urine to be excreted. Urine formation is glomerular filtration minus tubular reabsorption plus tubular

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secretion. Urine composition is routinely measured to assess health status (e.g. dipstick tests are used to measure leukocyte count, glucoses, nitrates, hemoglobin, urobilinogen, bilirubin and protein). Urine contains inorganic salts and organic compounds, including proteins, hormones, and metabolites. Changes in metabolite profiles in urine samples indicates changes in biochemical pathways, such as changes in oxidative stress, inflammation or cellular metabolism. In the context of malnutrition urinary dipstick tests and urine culture are used to determine urinary tract infections, which is an indication for antibiotic treatment. Urine collection is a non-invasive technique that is easy to teach and carry out; therefore, making it useful as a biofluid for metabolic testing. URINE METABOLOMICS Urine metabolomics has been used since the 1970’s. A literature survey done by Bouatra et al. (25) in 2013 illustrated that with a combination of metabolomics techniques 2651 different metabolites can be identified and/or quantified. This extensive list of urine metabolites has been made available in a public database (http://www.urinemetabolome.ca/). These results were further validated, and 873 unique structures were identified and 806 were quantified. To our knowledge, metabolomics has been used in only three studies to analyze the metabolic profiles of urine from children with malnutrition. A recent study by Mayneris-Perxachs et al. (26) looked at the association between urinary metabolites and impaired growth and cognitive development in malnourished children. The study provided insight into the metabolic adaptations seen in severe malnutrition, such as increased N-methylnicotinamide and reduced β-aminoisobutyric acid excretion, which suggests a reduction in energy expenditure (26). This would be an adaptive response to undernutrition, with dire consequences since it reduces growth, cognitive function and metabolic function. Unfortunately, this study did not look at metabolic profile and mortality. Another urinary metabolomics study by Terán-García1 et al. (27) suggested that nearly all children with severe malnutrition demonstrate blocks in the pathways of propionate and/or fatty acid β-oxidation. Furthermore, this study illustrated abnormal levels of 3-hydroxybutyrate, lactate, 4-hydroxyphenyllactate, fumarate, succinate, and 4-hydroxyphenylacetate (27). This study had multiple limitations such as a small patient group, and the admission samples were taken once overt infection, dehydration, and metabolic acidosis had been controlled. It furthermore did not analyze metabolites association with mortality. The last study by Manary et al. (6) used urine metabolomics to illustrated increased levels of oxidative in children with kwashiorkor, through increased ortho-tyrosine which is a marker of damage by tyrosyl radicals. Our study looks at a larger patient group and looks at the association between mortality and metabolic profiles of children with SAM. AIM We aimed to compare the urinary metabolic profile of severely malnourished children that died during in-patient treatment to those that survived. This will help identify the metabolic dysregulations associated with mortality and may provide a non-invasive predictive biomarker for poor prognosis. Our secondary aims are to compare children with kwashiorkor to children with marasmus, to potentially find metabolic dysregulation that is associated with the different phenotypes. Our research questions are:

• What are the differences in urine metabolic profiles of children, with kwashiorkor malnutrition compared to those children with marasmic malnutrition?

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• What are the differences in urine metabolic profiles of children, under the age of five, who died from severe acute malnutrition compared to those that recovered?

• Can we identify a possible biomarker that can identify children at high risk of mortality? We hypothesis that there will be a difference in urine metabolic profiles of children who died from severe acute malnutrition compared to those who survived, and that these metabolic profile differences will indicative oxidative stress and mitochondrial dysfunction. Furthermore, we hypothesis that children with kwashiorkor malnutrition will illustrate a metabolic profile of greater oxidative stress than the marasmic patients.

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METHODS

SUBJECTS The samples were collected from children between the age of 6 to 60 months admitted to the tertiary hospital Queen Elisabeth Hospital in Blantyre, Malawi for the inpatient treatment of severe malnutrition. On admission to the nutritional rehabilitation unit (NRU) the children were screened for eligibility. Severe malnutrition was defined following the World Health Organization (WHO) standard of either a mid-upper arm circumference (MUAC) of less than or equal to 115 cm, a weight-for-height of less than or equal to -3 SD (based on the WHO growth standard) [(www.who.int/childgrowth/standards)], or bilateral pitting edema. Furthermore, children were tested for HIV and both children that were HIV negative and positive were included in the study. The exclusion criteria included children with a previous admission to the NRU that year, children that were hemodynamically unstable, children with a hematocrit level equal to or less than 15%, and children with severe neurological symptoms, such as convulsions and loss of consciousness. CLINICAL DATA AND SAMPLE COLLECTION Urine samples were collected upon admission and at three days following clinical stabilization. The samples were flash frozen and shipped to the metabolomics innovation centre (TMIC) in Edmonton, Canada for analysis. During the inpatient-treatment daily clinical and anthropometric data was collected, which included: weight, edema, stool frequency, presence of vomiting, hydration status, capillary refill, heart rate, saturation, temperature, and appetite through the recall of the mother. Additional, to the already mentioned data collected, further data was collected upon admission and discharge, which included: weight, length, MUAC, edema, breastfeeding status, other food intake, co-morbidities, HIV status, presence of diarrhea, duration of diarrhea, presence of anorexia, and duration of anorexia. During the time of this study at Queen Elisabeth’s Hospital I was carrying out my bachelor thesis

SAM Patients admitted to MOYO nutritional rehabilitation unit

Jan 2013-July 2013, n=509

Patients assessed for eligibility

n=97

Urine sample at admission,

n=69

Excluded patients with: Insufficient urine, n=10

Insufficient clincial data, n=2

Dischage,n=39

discharge, n=39

dead, n= 10discharge, n=16abscond, n= 4

Admission

Discharge

Figure 1. Flow chart of inclusion and exclusion of patients

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project at the nutritional rehabilitation clinic, MOYO, and partook in some data collection for this data collection. Furthermore, I, with the help of other students, transferred the clinical data into the online database and worked on ensuring that the database was complete. URINE METABOLOMICS ANALYSIS The urine analysis took place at TMIC in Edmonton, Canada using untargeted nuclear magnetic resonance (NMR) spectroscopy for the analysis of organic acids. For the untargeted NMR spectroscopy analysis, the urine samples were prepared by transferring a 300 µL aliquot of urine to a 1.5 mL Eppendorf tube followed by the addition of 35 µL D2O and 15 µL of a standard solution (3.73 mM DSS (disodium-2,2-dimethyl-2-silapentane-5-sulphonate), 233 mM imidazole, and 0.47% NaN3 in H2O, Sigma–Aldrich, Mississauga, ON). The urine sample (350 µL) was then transferred to a standard SHIGEMI microcell NMR tube. All 1H NMR spectra were collected on a 500 MHz Inova (Varian Inc., Palo Alto, CA) spectrometer equipped with either a 5 mm HCN Z-gradient pulsed-field gradient (PFG) room-temperature probe or a Z-gradient PFG Varian cold-probe. 1H NMR spectra were acquired at 25 °C using the first transient of the tnnoesy-presaturation pulse sequence. Spectra were collected with 64 transients using a 4s acquisition time and a 1s recycle delay. For certain confirmatory experiments, higher field (800 MHz Varian Inova) instruments and larger numbers of transients were used. STATISTICAL ANALYSIS Patient characteristics analysis was carried out using the stargazer package for R. To calculate the Z-scores for length for age, weight for age, weight for length and MUAC the WHO igrowup database was used (available at: http://www.who.int/childgrowth/software/en/). The igrowup database is made up of 8500 children from Brazil, Ghana, India, Norway, Oman and USA, providing an international standard that represents growth for children from birth to the age of five. The Fisher Exact Test was used to compare the nominal variables in the patient characteristics table, as the data was not normally distributed. For continuous variables, a two-way ANOVA was used. To assess differences in metabolite profiles between the children that died and children that survived, we conducted univariate analysis using generalized linear models (glm). P-values were FDR-corrected for all comparison tests performed, to account for the multiple analysis. R software was used for all analysis and figures were generated with Inkscape. Kyoto Encyclopedia of Genes and Genome (KEGG) and Small Molecule Pathway Database (SMPD) pathway search was conducted to determine the metabolomics pathways that the significant metabolites that differed between the two groups were involved in. The KEGG and SMPD search was limited to only human pathways and the following search terms were used: “lactic acid”, “lactate”, “succinic acid”, “succinate”, “3-hydroxybutyric acid”, “3-hydroxybutrate”, “beta hydroxybutyric acid”, and “beta hydroxybutrate”. Ms. Bourdon, bio-informatician from Dr. Robert Bandsma’s lab, mentored me through all statistical analysis for the metabolites; this especially regarding the choice of generalized linear models, the choice of gamma error structure instead of normal error structure, and providing analysis scripts previously developed that needed to be adapted for my project. Furthermore, I took a course on scripting in R.

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RESULTS CHARACTERISTICS OF PATIENTS WITH SAM Patient characteristics of all children with SAM are summarized in Table 1, except for those that absconded. Compared to those that recovered, children that died had a lower prevalence of kwashiorkor (71% vs 30%, p=0.03) and a lower MUAC at admission (10.1 ± 2.0 vs 11.9 ± 1.8, p=0.03). There was not a significant difference in age between the groups (24.8 months ± 11.4 vs 18 months ± 12.1, p=0.09) and the prevalence of HIV was comparable (31% vs 40%, p=0.72). There were more females in the group of children that died versus those that recovered, however, this was not statistically significant (60% vs 40%, p=0.33) As expected, the mean of most measures of anthropometry were lower in children that died compared to those that recovered; yet, only weight at admission and weight for length in the marasmus group were significantly different, 4.3 vs 5.5 (p= 0.02) and -5.9 vs -4.7 (p=0.003) respectively. The mean anthropometric indicators based on body weight were calculated separately in children with and without edema, as children with kwashiorkor present with misleading high weight measurements due to the significant accumulation of fluid (i.e. in marasmus: weight at admission (4.3 vs 5.5 , p= 0.02), weight for length (-5.9 vs -4.7 , p=0.003) , and weight for age Z-scores (-5.7 vs -5.3 , p=0.45); in kwashiorkor: weight at admission (-8.6 vs -8.8 , p=0.92), weight for length (-2.2 vs -2.1 , p=0.90) , and weight for age Z-scores (-3.6 vs -2.9 , p=0.41). Length for age and the duration of stay (9.7 ± 7.6 vs 10.7 ± 4.0, p= 0.71) were comparable between the children that died versus those that recovered. Patient characteristics are also presented in Appendix A as summary figures.

Table1. Characteristics of children hospitalized for severe acute malnutrition that either recovered or died

All1 Recovery Death n=69 n=55 n=10 Female 31 (45%) 22 (40%) 6 (60%) HIV reactive 22 (32%)2 17 (31%) 4 (40%) Kwashiorkor 44 (64%) 39 (71%)* 3 (30%)* Age (months) 23.6 ± 11.3 24.8 ± 11.4 18.0 ± 12.1 Length (cm) 75.6 ± 8.2 76.9 ± 7.7 70.3 ± 9.0 MUAC (cm) 11.6 ± 1.9 11.9 ± 1.8* 10.1 ± 2.0*

edema 12.6 ± 1.4 12.6 ± 1.4 12.5 ± 1.9 non-edema 9.7 ± 1.1 10.0 ± 1.1 9.1 ± 0.9

Weight at admission (kg) 7.4 ± 2.4 7.8 ± 2.2 5.6 ± 2.4 edema 8.7 ± 1.8 8.8 ± 1.9 8.6 ± 2.0

non-edema 5.2 ± 1.1 5.5 ± 1.1* 4.3 ± 1.0* Weigth for Age (Z-score) -3.8 ± 1.8 -3.6 ± 1.7 -5.1 ± 1.5

edema -2.9 ± 1.4 -2.9 ± 1.4 -3.6 ± 1.3 non-edema -5.5 ± 1.0 -5.3 ± 1.0 -5.7 ± 1.1

Weight for Length (Z-score) -3.1 ± 2.0 -2.8 ± 1.8 -4.8 ± 2.1 edema -2.1 ± 1.5 -2.1 ± 1.5 -2.2 ± 1.8

non-edema -5.1 ± 1.0 -4.7 ± 0.9* -5.9 ± 0.6* Length for Age (Z-score) -3.2 ± 2.1 -3.0 ± 2.2 -3.4 ± 1.6 Duration of stay (days) 10.5 ± 4.8 10.7 ± 4.0 9.7 ± 7.6

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1 including data collected at admission of the 4 children that absconded; 2 one child had an unknown HIVstatus

Values are n (%), means ±SDs. Group differences were tested with either Fisher Exact test (used for proportions) or with two-way ANOVA (used for continuous variables). MUAC is midupper arm circumference. p-values <0.05 were considered to be significant and are indicated with a star*. DIFFERENCES IN URINE METABOLITES BETWEEN CHILDREN WITH KWASHIORKOR OR MARASMUS We probed a total of 56 metabolites (listed in Appendix B). Out of these, thirty were above the limit of detection (LOD) in at least 30% of samples in either the group that died or that recovered. The 30% limit was set based on previous literature, and it ensures that only metabolites that are detected in at least 30% of the total patient samples were included in the analysis. The LOD is the lowest concentration of a substance that can be detected and distinguished from a blank. It is calculated by adding 1.645 SD (90% confidence interval) to the limit of blank (LOB), which is the highest concentration of a substance in found in analysing a blank sample. The concentration levels of all 30 metabolites that passed the detection threshold are detailed in Appendix B. The results from the generalized linear model of marasmus vs kwashiorkor can be seen in table 2. There were no significant differences between the metabolic profiles of the children with marasmus versus the children with kwashiorkor. We accounted for the effect of age and sex on all metabolites levels as we wanted to gain insight into metabolic alterations based on the SAM phenotypes independently from age and sex. A gamma error structure was chosen after it became apparent that the residuals from the generalized linear models were not normally distributed. Their distribution had an exponential curve, therefore, better fitting a gamma variance. By assuming a gamma variance function for the generalized linear models, the observations on the left have a stronger influence than the observations on the right, which was reflected in our data. Due to multiple testing the p values were corrected by false discovery rate (FDR) method. FDR correction is necessary in our data since we carry out multiple analysis leading to an accumulation of false positives, which is corrected for by FDR. Table 2. Differences in metabolite levels in urine collected at admission between children with kwashiorkor and those with marasmus.

Kwashsiorkor vs Marasmus

estimate SEM p FDR p Azelaic Acid -0.002 0.003 0.41 0.74 Citric Acid 0.002 0.003 0.50 0.76 Ethylmalonic Acid -0.003 0.003 0.32 0.69 Glutaric Acid -0.005 0.003 0.06 0.66 Hippuric Acid 0.005 0.003 0.11 0.66 Homovanillic Acid -0.004 0.003 0.16 0.66 Lactic Acid -0.001 0.003 0.74 0.89 m-Hydroxyphenylacetic -0.004 0.003 0.17 0.66 Methylmalonic Acid 0.003 0.003 0.22 0.69

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O-Hydroxyphenylacetic Acid -0.004 0.003 0.13 0.66 Oxoglutaric Acid -0.002 0.003 0.56 0.80 p-Hydroxyphenylacetic Acid -0.006 0.003 0.04 0.66 Phenaceturic Acid -0.003 0.003 0.30 0.69 Pimelic Acid 0.003 0.003 0.34 0.69 Pyroglutamic Acid -0.003 0.003 0.27 0.69 Quinolinic Acid 0.001 0.003 0.85 0.89 Suberic Acid 0.001 0.003 0.86 0.89 Succinic Acid -0.003 0.003 0.34 0.69 Sumiki-s Acid -0.001 0.003 0.83 0.89 trans-Aconitic Acid 0.001 0.003 0.66 0.84 Vanillylmandelic Acid 0.002 0.003 0.47 0.75 2-Ethyl-3-hydroxypropionic -0.005 0.003 0.07 0.66 2-Hydroxybutyric -0.002 0.003 0.59 0.80 2-Methylsuccinic Acid 0.002 0.003 0.45 0.75 3-Hydroxy-2-methylbutyric 0.004 0.003 0.12 0.66 3-Hydroxybutyric Acid 0.001 0.003 0.86 0.89 3-Hydroxyisovaleric Acid 0.001 0.003 0.60 0.80 3-Methyladipic Acid 0.004 0.003 0.19 0.69 3-Methylglutaconic Acid 0.001 0.003 0.77 0.89 3-Methylglutaric 0.003 0.003 0.37 0.69 3-Phenyllactic -0.00002 0.003 0.99 0.99 4-Hydroxyhippuric Acid 0.003 0.003 0.26 0.69

Differences were tested using generalized linear models with a gamma error structure. P-values were corrected for multiple testing using false discovery rate method (FDR). FDR-corrected p-values <0.05 were considered to be significant and are indicated with a star*. DIFFERENCES IN URINE METABOLITES BETWEEN CHILDREN WITH SAM THAT DIED COMPARED TO THOSE THAT RECOVERED We probed a total of 56 metabolites (listed in Appendix B). Differences in metabolites for children that died compared to children that recovered was detected using generalized linear models with a gamma error structure. Out of the analyzed metabolites, 3 organic acid metabolites were significantly higher in children that died versus those that recovered while correcting for age and sex. These were lactic acid (0.29 mM vs 0. 12 mM, p < 0.001), succinic acid (0.09 mM vs 0.00 mM, p < 0.001) and ß-hydroxybutyric acid (0.02 mM vs 0.00 mM, p = 0.01). Their concentration levels split per group are also presented in boxplots (Figure 3). Boxplots for all the 30 metabolites can be found in Appendix C. We accounted for the effect of age and sex on all metabolites levels as we wanted to establish a metabolite marker that would be independently indicative of death beyond these clinical characteristics. Age is a confounding factor with mortality; therefore, if age had not been considered, the differences in metabolite levels would have been a reflection of age and not indicative of mortality per say.

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Table 3. Differences in metabolite levels in urine collected at admission between children with SAM who died and who recovered.

Mortality estimate SEM p FDR p Azelaic Acid 0.001 0.004 0.74 0.85 Citric Acid -0.002 0.004 0.65 0.83 Ethylmalonic Acid 0.003 0.004 0.48 0.76 Glutaric Acid -0.002 0.004 0.50 0.76 Hippuric Acid -0.006 0.003 0.08 0.39 Homovanillic Acid -0.004 0.004 0.32 0.60 Lactic Acid -0.012 0.003 0.00 > 0.001 * m-Hydroxyphenylacetic -0.004 0.003 0.21 0.56 Methylmalonic Acid 0.001 0.004 0.77 0.85 O-Hydroxyphenylacetic Acid -0.002 0.004 0.68 0.83 Oxoglutaric Acid 0.000 0.004 0.98 0.98 p-Hydroxyphenylacetic Acid -0.001 0.004 0.81 0.86 Phenaceturic Acid -0.008 0.003 0.01 0.12 Pimelic Acid 0.000 0.004 0.97 0.98 Pyroglutamic Acid 0.002 0.004 0.67 0.83 Quinolinic Acid -0.005 0.003 0.17 0.54 Suberic Acid 0.004 0.004 0.25 0.56 Succinic Acid -0.012 0.003 0.00 > 0.001 * Sumiki-s Acid -0.003 0.003 0.40 0.72 trans-Aconitic Acid 0.002 0.004 0.58 0.83 Vanillylmandelic Acid -0.008 0.003 0.02 0.12 2-Ethyl-3-hydroxypropionic 0.004 0.004 0.23 0.56 2-Hydroxybutyric -0.005 0.003 0.15 0.52 2-Methylsuccinic Acid -0.006 0.003 0.10 0.41 3-Hydroxy-2-methylbutyric 0.001 0.004 0.73 0.85 3-Hydroxybutyric Acid -0.010 0.003 0.00 0.01 * 3-Hydroxyisovaleric Acid 0.002 0.004 0.64 0.83 3-Methyladipic Acid -0.004 0.004 0.26 0.56 3-Methylglutaconic Acid -0.004 0.003 0.27 0.56 3-Methylglutaric -0.003 0.004 0.47 0.76 3-Phenyllactic -0.007 0.003 0.04 0.20 4-Hydroxyhippuric Acid -0.004 0.004 0.28 0.56

Differences were tested using generalized linear models with a gamma error structure. P-values were corrected for multiple testing using false discovery rate method (FDR). FDR-corrected p-values <0.05 were considered to be significant and are indicated with a star*.

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Figure 2. Boxplots of urine metabolites that differed between children with SAM who died compare to children who survived.

Boxplots of metabolites that differed significantly between children with SAM that died compared to those that recovered. A) lactic acid, B) succinic acid, C) ß-hydroxybutyric acid. The midline of the boxplots indicates the median and the upper and lower limits of the box are the inter quartile range (IQR). Numbers in each group are as follows: death, n = 10; discharge, n=55. PATHWAY ANALYSIS OF URINE METABOLITES THAT COULD BE PREDICTIVE OF MORTALITY IN CHILDREN WITH SAM A KEGG pathway and SMPD pathway search was carried out to determine in which metabolic pathways lactic acid, succinic acid and ß-hydroxybutyric acid were involved. The used search terms were “lactic acid”, “lactate”, “succinic acid”, “succinate”, “3-hydroxybutyric acid”, “3-hydroxybutrate”, “beta hydroxybutyric acid”, and “beta hydroxybutrate”. Results are detailed in table 3. Lactate and succinate were found to be both involved in pathways of pyruvate metabolism, central carbon metabolism in cancer, propanate metabolism, and cAMP signaling pathway. Specific KEGG annotated pathways were not found for beta-hydroxybutyric acid. In the SMPD search lactate and succinate were found to be involved in the Warburg Effect, and ß-hydroxybutrate was found to be involved in Fatty acid biosynthesis. Table 3. KEGG and SMPD pathway analysis search results for lactate, succinate, and ß-hydroxybutyrate. In bold are the pathways that are found in both the lactate and succinate search.

Metabolite KEGG Pathway SMPD Pathway Lactate Pyruvate metabolism Warburg Effect

Central carbon metabolism in cancer

Pyruvate Dehydrogenase Complex Deficiency

Propanoate metabolism Gluconeogenesis cAMP signaling pathway Methylmalonate Semialdehyde

Dehydrogenase Deficiency Fructose and mannose metabolism Glutaminolysis and Cancer Metabolic pathways Glycogen Storage Disease Type 1A

(GSD1A) or Von Gierke Disease Glycolysis / Gluconeogenesis Glycolysis HIF-1 signaling pathway Pyruvate Metabolism Glucagon signaling pathway Glycine and Serine Metabolism

Lactic Acid

20

15

10

5

0

Death Discharge

Con

cent

ratio

n (m

M)

Death Discharge

Succinic Acid

1.5

1.0

0.5

0.0

Death Discharge

C. Beta Hydroxybutyric Acid

0.00

0.10

0.20

A. B.

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Succinate Pyruvate metabolism Warburg Effect Central carbon metabolism in

cancer The oncogenic action of Succinate

Propanoate metabolism 4-Hydroxybutyric Aciduria/Succinic Semialdehyde Dehydrogenase Deficiency

cAMP signaling pathway The oncogenic action of Fumarate Oxidative phosphorylation Pyruvate Carboxylase Deficiency Phenylalanine metabolism Mitochondrial complex II deficiency Alanine, aspartate and glutamate

metabolism GABA-Transaminase Deficiency

Butanoate metabolism Aspartate Metabolism Sulfur metabolism Tyrosine metabolism Mitochondrial Electron Transport

Chain Carbon metabolism

ß-hydroxybutyrate Fatty Acid biosynthesis

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DISCUSSION METABOLITES KWASHIORKOR VS MARASMUS There was no significant difference in our study between the metabolite concentrations of children who had kwashiorkor compared to the children who had marasmus. A previous study in our lab illustrated a distinct difference in plasma metabolomics profile between children who had the kwashiorkor form of malnutrition versus children who had the marasmic form (22). This study looked at amino acids, biogenic amines, sphingomyelins, acylcarnitines, and phosphatidylcholines through metabolomics in plasma (22), these could not have been detected in our urine samples since it requires another technique in urine analysis that we did not apply, LC-MS (25). Furthermore, we are still waiting to receive the metabolomics urine results for thiols from the TMIC. Thiols are crucial in the response to oxidative stress and these metabolites might differ between the two groups, while organic acids do not. Another reason why we did not see a difference between the two groups, could be that we had a lower n number for our marasmic group. Furthermore, there is a third phenotype of SAM, marasmic-kwashiorkor, these children are wasted with some bilateral pitting edema. We chose to include patients with any pitting edema into the kwashiorkor group, while they classified clinical as marasmic-kwashiorkor. This could have skewed the metabolic profiles making the kwashiorkor group less distinguishable from the marasmic group. METABOLITE DIFFERENCES IN CHILDREN WHO RECOVER VERSUS POOR CLINICAL OUTCOME By comparing the urine metabolic organic acid profile of 10 children who died to 59 children who recovered from severe acute malnutrition, we have gained insight into metabolic pathway alterations preceding death. Additionally, we have uncovered an association between mortality and three urine organic acids, in this cohort, indicating three possible predictive biomarkers for mortality in SAM. The age and sex corrected glm analysis illustrates an association between mortality in children with severe malnutrition and the three organic acids: lactic acid (p<0.001), succinic acid (p<0.001), and ß-hydroxybutyric acid (p=0.01). For additional background information on these organic acids consult Appendix D. The mean for the lactic acid was 0.12 mM for the recovered patients and 0.29 mM in the patients that died (p < 0.001); furthermore, lactic acid was detected in 100% of the samples. The boxplot for lactic acid in figure 2 illustrates that the differences between the two groups is outlier driven. Three patients had extremely high lactic acid values in their urine (8.44 mM, 13.25 mM, and 22.21 mM). Without these outliers, there might not have a been an association between lactic acid and mortality; however, due to our small n number for the children who died from severe malnutrition we cannot exclude these outliers to see if the association of mortality and lactic acid would still be apparent. Our data does not indicate that any increase in lactic acid is directly correlated to an increased risk of mortality, but instead that a high amount of lactic acid in urine is associated to mortality. This finding needs to be validated in a larger, independent cohort. If our findings are validated, a certain urine lactic acid concentration could be used as a cut off for increased risk of mortality. Numerous studies on adult and some on pediatric patients have illustrated an association between elevated plasma lactic acid levels and mortality. A study by Bai et al. (28) illustrated

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that a high blood lactate level at admission is independently associated with in-hospital morality in critically ill children. Limited studies have analyzed the association between urinary lactic acid levels and mortality. However, there is a correlation between blood level and urinary levels of lactate (29), indicating that lactic urine content could possibly be used as a biomarker. In a low income setting urine is a more easily available biofluid and the study by Bartels et al. (29) indicated that concentration of urine lactic acid above 2 mM/L is pathological in children and can be detected by using 30 mg of CaC12 and about 30 mg of Ce(SO4)2 (29). This technique is not documented in other literature. The mean for urine succinic acid concentration was 0.09 mM in children who died from SAM and 0.00 mM in those that recovered (p < 0.001). Succinic acid was detected in 46% of the samples. The succinic acid boxplot in figure 2 illustrates the same outlier driven differences between the two groups. These outliers are the same three patients that had increased lactic acid concentration, and these patients had very high succinic acid levels in their urine (0.87 mM, 1.31 mM, 1.81 mM). The association between succinic acid and lactic acid should be analyzed, and the increased succinic acid levels also needs to be validated in an independent, larger cohort. The mean ß-hydroxybutyric acid levels were 0.00 mM in the recovered group and 0.09 in the group that died, and two of the patients with increased lactic and succinic acid levels had high levels of ß-hydroxybutyric acid (0.13 mM and 0.11 mM). Furthermore, three patients that recovered had high levels of ß-hydroxybutyric acid levels and this is illustrated in the boxplot of figure 2. As both groups had outliers the significant differences between these two groups was not outlier driven, and ß-hydroxybutyric acid was detected in 33% of the samples. The urine metabolite concentrations need to be corrected to creatinine, to control for the hydration status of the patients. This is currently being carried out. ß-hydroxybutyrate is used in determining the arterial ketone body ratio (AKBR), which is the arterial level of acetotacetate to ß-hydroxybutyrate. This ratio has been previously used as an indicator of the mitochondrial redox state and liver function (30). Yassen et al. (30) illustrated that AKBR was significantly lower in critically ill adult patients that died versus patients that survived. Urine ß-hydroxybutyrate is usually not tested but our findings indicate that this metabolite could potentially be a predictive biomarker for mortality in severely malnourished children. Elevated succinic acid levels has been associated with increased myocardial energy expenditure, which is positively associated with mortality in adult patients with heart failure (31). This study also illustrated an association between ß-hydroxybutyrate and myocardial energy expenditure and, furthermore, increased lactic acid levels (31). Heart failure is one of the contributing conditions to the high case-fatality rate in severely malnourished children (32). However, it is not stated in the patient files if these children suffered from heart failure or other myocardial dysfunctions, such as sepsis-associated myocardial dysfunction. The three metabolites that were associated to morality need to be validated as predictive biomarkers. Validating a biomarker for clinical outcome is a multistep process that involves: content validity, construct validity, and criteria validity (33). Content validity is how completely the biomarker covers the biological phenomenon or construct in question. Construct validity is the extent of how a biomarker measures what it is intended for. Criterion validity is the extent to which a biomarker is related to the outcome, and this is usually measured by sensitivity, specificity, and predictive power (33). The patient files of three children with especially high lactic and succinic acid were read to try to find a common reason that could explain these high findings. Compared to the other children whom died these three patients did not suffer from any common signs of malnutrition such as hypoglycemia or dehydration. Furthermore, one of the three did have metabolic acidosis and

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another had sepsis, but no specific illness was shared by the three. The other patient files from the children who died from severe malnutrition were analyzed looking for any pattern or striking similarities but none was found. Without the dehydration status of these children it cannot be excluded that the increases in metabolite concentration of certain patients is due to dehydration. Additionally, it is important to know the kidney function of these patients, as severe malnutrition can lead to organ failure and antiviral medication can cause glomeruli damage. Kidney function will be measured by looking at urine creatinine to albumin ratio, as well as, IL-18 urine levels. These analyses are also currently taking place. Additionally, as in all metabolomics studies these findings need to be validated in a new independent cohort, with a larger n in the mortality group. KEGG AND SMPDB ANALYSIS The KEGG and SMPDB analysis both indicated the lactic acid levels and succinic acid levels are associated with the ‘central carbon metabolism in cancer’ or Warburg Effect, which is a form of anaerobic glycolysis in aerobic conditions and it is predominantly seen in cancer cells. Glycolysis is the conversion of glucose into pyruvate, which takes place in the cytosol of cells. Under anaerobic conditions this pyruvate is then converted to lactate and energy, while in aerobic conditions the pyruvate enters the citric acid cycle and oxidative phosphorylation to produce energy. Aerobic glycolysis is the term used to not describe the breakdown of glucose during aerobic conditions, but breakdown of glucose in aerobic conditions using the anaerobic glycolysis pathway. This can be due to either a defect in mitochondria, which hinders the uptake of pyruvate or the malfunction of the citric acid cycle or electron transport chain, or through stimulation by growth factors (34). Aerobic glycolysis provides building block for nonessential amino acids and ribose for nucleotides, while promoting nicotinamide adenine dinucleotide phosphate (NADPH) formation along with lactic acid production (34). An increase in NADPH can be beneficial, in a state of malnutrition, as it increases production of glutathione, which defends against oxidative stress caused by reactive oxygen species (ROS) (34). Levels of oxidative stress has been illustrated to be associated with malnutrition, especially kwashiorkor (6,7), as well as, decreased glutathione levels (35). Furthermore, a study conducted by van Zutphen et al. (36) illustrated mitochondrial dysfunction in severely malnourished rats, specifically through reduced pyruvate uptake and a loss of complex I and IV proteins. The mitochondrial dysfunction, which can be due to increased oxidative stress will lead to further oxidative stress as the dysfunctional mitochondria will leak out free radicals producing more oxidative stress damage. The mitochondrial damage inhibits the cell from carrying out oxidative phosphorylation, which can lead to a switch to aerobic glycolysis. A study by Shatla et al. (37) illustrated an association between mitochondrial disorders and urinary lactic aciduria, succinic acid and the ketones: acetoacetate, beta-hydroxybutyrate. The increase in succinic acid could be caused by the mitochondrial dysfunction through a problem in the TCA cycle, as well as, the electron transport chain. Succinate + FAD (flavin adenine dinucleotide) is converted by the enzyme succinate dehydrogenase into fumarate + FADH2, as seen in figure 6. Succinate dehydrogenase is also referred to as complex II of the mitochondria electron transport chain and it couples the oxidation of succinate to the reduction of ubiquinone. Ubiquinone is the electron carrier from complex I to complex II to complex III in the electron transport chain, as depicted in figure 7. Levels of succinic acid can either be due to a decrease in availability or activity of succinate dehydrogenase or an increase in succinic acid precursors. In our urine metabolomics analysis, we did not see an increase in succinic acid precursors suggesting that the increase is due to succinate dehydrogenase inactivity or unavailability. The article by Teran-Garia et al. (27) illustrated significantly increased succinic acid level in children with SAM at

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admission and during receiving treatment. Other older studies have looked at the effect of starvation on succinic dehydrogenase with all illustrating different results with some illustrating a decrease in succinic dehydrogenase activity (38) especially in liver tissue (39), while others have showed increased succinic dehydrogenase activity (40). Figure 6. The citric acid cycle

The citric acid cycle or Krebs cycle takes place in the mitochondria under aerobic conditions and is the oxidation of acetyl CoA into ATP. Succinate dehydrogenase/complex II is the binding site for succinate which is oxidized into fumarate, the electron from the oxidation is used in the electron transport chain. Figure 7. The electron transport chain

The electron transport chain transfers electrons along its chain, while pumping out protons creating a membrane gradient. This gradient is used to produce ATP.

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During prolonged fasting, an increase in ß-hydroxybutyrate is a physiological response and it ensures survival during times of limited food resources. However, as fasting continues ß-hydroxybutyrate and acetoacetate can progress into starvation ketosis. A study by Teran-Garia et al. (27) also looked at urine organic acids through metabolomics analysis to better understand the metabolic derangements of SAM. This study illustrated an increase in lactic acid, ß-hydroxybutyrate and succinate in some SAM patients (27). It would have been interesting to measure the level of acetoacetate in these samples; however, since we used a GC-MS technique just focused on organic acids, acetoacetate and acetone were not detected. Nevertheless, a study by Kanetakea et al. (41) found that ß-hydroxybutyrate mainly determines total ketone body concentration. One of the children with high ß-hydroxybutyrate levels did have metabolic acidosis; however, the other children with increased levels did not. Figure 8. A schematic overview of the metabolites of interest interactions

Glucose Glycogen Beta-hydroxybutyrate Propionate

Glucose-6-P

Dihydroxyacetone-P

Pyruvate

Acetyl-CoA

Succinate Succinyl-CoA

Propionyl-CoA

Citrate --

Isocitrate

2-Ketoglutanate

Succinyl-CoASuccinate

Fumarate *

Malate

Oxaloacetate

Glutamine

Lactate

TCA Cycle

MITOCHONDRIA

Malate

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A schematic overview of the organic acids measured in the urine of children with severe acute malnutrition who died and the children that recovered. Red indicates the organic acids that were significantly differ between the groups, blue indicates those that were detected but there was no difference between the groups, and black those who were not detected or not measured. * indicates those metabolites that were measured but did not pass the limit of detection. Figure 8 is a schematic overview of the organic acids measured in the urine of children with severe acute malnutrition who died and the children that recovered. The metabolomics urine analysis illustrated a significant increase in lactic acid, succinic acid and ß-hydroxybutyric acid. However, there was no change in citrate levels between the two malnourished patient groups and fumarate did not make the 30% detection cut off; furthermore, succinyl CoA, alpha-ketoglutarate and isocitrate were not detected in the urine samples. The increased succinic acid without changes to its precursor indicates that the increase is due to either an inactivity or unavailability of succinic dehydrogenase or complex II. This means that the results could be consistent with a dysfunction in the TCA cycle, which is further supported by previous research by our team (36). The increased lactic acid content can also be explained by mitochondrial dysfunction and reduced pyruvate uptake (36), leading to aerobic glycolysis. As previously stated severe malnutrition is associated with oxidative stress. Oxidative stress can both be caused by mitochondrial dysfunction and cause further mitochondrial dysfunction. This needs to be further validated by analyzing the role of oxidative stress in malnutrition induced mitochondrial dysfunction. We aimed to measure oxidative stress markers, thiols, in the urine of these children, however, these analyzes could not be completed on time. Based on this hypothesis, due to mitochondrial dysfunction the cell reverts to aerobic glycolysis, leading to increase in lactic acid, and the build-up of succinic acid through the TCA cycle malfunction. The increase in ß-hydroxybutyric acid could be due to an increase in acetyl-CoA leading to increased conversion into ß-hydroxybutyric acid. Furthermore, during hypoglycemic conditions the body uses ketogenesis for energy production and it begins with the conversion of β-oxidation-derived acetyl-coenzyme A (acetyl-CoA) into acetoacetate, which is then either converted into acetone or into ß-hydroxybutyrate. Acetoacetate and ß-hydroxybutyrate are exported into the blood stream for use by extrahepatic tissues. However, there was no hypoglycemia detected in the patients with increase ß-hydroxybutyric acid. LIMITATIONS The greatest limitation in our study is the missing thiol metabolomics data. The thiol analysis would have provided insight into the state of oxidative stress in these patients, which was what we hypothesized to see. These analyzes are not yet completed at the TMIC and I therefore had to complete the thesis without them. Another limitation is the small n number for the deceased group. With only 10 patients in the deceased group and 68 in the recovered group. Additionally, a healthy control group would have provided more insight into the metabolic alterations in children with SAM. Another limitation in our study is the lack of correction of metabolites to creatinine, which ensures that the concentration of metabolites is corrected to hydration status of the patient, and kidney function.

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CONCLUSION Childhood malnutrition contributes to 45% of all child death under the age of 5 (1). It is a complex condition often associated with multiple co-morbidities, and it has an remarkably high in-patient mortality of up to 30% (3). We carried out a urine metabolomics analysis of 69 children with severe malnutrition to gain insight into the metabolic alterations that take place during malnutrition, and furthermore, by looking at the association of these metabolites to mortality we aimed to uncover a predictive biomarker for mortality. We uncovered three organic acids that were associated to mortality; lactic acid (p < 0.001), succinic acid (p < 0.001), and ß-hydroxybutyric acid (p = 0.01). Through a KEGG and SMPD database search of these metabolites and which pathways they are involved in, we uncovered that increases in especially lactic and succinic acid was associated with the Warburg Effect. The Warburg Effect is seen in cancer cells and is the switch of from mitochondrial oxidative phosphorylation for a source of energy to aerobic glycolysis. We believe that oxidative stress during malnutrition causes mitochondrial damage. Through the mitochondrial damage, oxidative phosphorylation is inhibited and the cell adapts to the anaerobic glycolysis pathway under aerobic conditions for a source of energy. This leads to an increase in lactic acid and NADH, which forms glutathione an antioxidant that is important in the defense against oxidative stress. This hypothesis could explain the findings that this study uncovered. However, the association between mortality and the three organic acids needs to first be validated in a larger independent cohort. The metabolites need to be corrected to creatinine concentration and markers of kidney function.

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APPENDIX A

Duration of Stay Boxplot

25

20

15

10

5

Day

s

Death Recovered

Death Recovered

MUAC Admission Boxplot16

14

12

10

8

MU

AC

(cm

)

Age at Admission Boxplot

10

20

30

40

50

60

Death RecoveredA

ge (

mon

ths)

Death Recovered

Length Boxplot

90

80

70

60

Leng

th (

cm)

Death Recovered

Weight at Admission Boxplot

14

12

10

8

4

6

Wei

ght (

kg)

APPENDIX B

Supplemental Table 2. Metabolite concentrations in urine of children hospitalized for severe acute malnutrition that survived or died. Admission All Survived Died Detection

n=69 Detected n=59 Detected n=10 Detected Cut-off Median (IQR) (%) Median (IQR) (%) Median (IQR) (%) >30% Adipic Acid 0 (0-0) 4 0 (0-0) 3 0 (0-0) 10 - Azelaic Acid 0.03 (0.03-0.03) 77 0.03 (0.03-0.03) 80 0.03 (0-0.03) 60 Pass Benzoic Acid 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - Citric Acid 0 (0-0) 14 0 (0-0) 12 0 (0-0.23) 30 Pass Dodecanedioic 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - Ethylmalonic Acid 0 (0-0.07) 29 0 (0-0.07) 31 0 (0-0) 20 Pass Fumaric Acid 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - Glutaric Acid 0 (0-0.04) 42 0 (0-0.04) 41 0.02 (0-0.04) 50 Pass Hippuric Acid 0.12 (0-0.26) 67 0.12 (0-0.26) 68 0.21 (0-0.29) 60 Pass Homovanillic Acid 0 (0-0.03) 32 0 (0-0.02) 31 0.01 (0-0.06) 40 Pass HPHPA 0 (0-0) 6 0 (0-0) 5 0 (0-0.008) 10 - Lactic Acid 0.14 (0.11-0.28) 100 0.12 (0.1-0.26) 100 0.29 (0.16-6.5) 100 Pass Maleic 0 (0-0) 3 0 (0-0) 3 0 (0-0) 0 - Methylmalonic Acid 0 (0-0.1) 26 0 (0-0.05) 25 0 (0-0.075) 30 Pass N-acetyltyrosine 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - O-Hydroxyphenylacetic Acid 0 (0-0.04) 29 0 (0-0.04) 29 0 (0-0.03) 30 Pass Oxoglutaric Acid 0.1 (0.09-0.13) 86 0.1 (0.09-0.13) 83 0.11 (0.09-0.13) 100 Pass p-Hydroxybenzoic Acid 0 (0-0) 4 0 (0-0) 5 0 (0-0) 0 - p-Hydroxyphenylacetic Acid 0.08 (0.06-0.12) 100 0.08 (0.06-0.12) 100 0.08 (0.06-0.17) 100 Pass Phenaceturic Acid 0 (0-0) 17 0 (0-0) 15 0 (0-0.075) 30 Pass Pimelic Acid 0.02 (0.02-0.02) 77 0.02 (0.02-0.02) 78 0.02 (0.005-0.02) 70 Pass Propionylglycine 0 (0-0) 1 0 (0-0) 0 0 (0-0) 10 -

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Pyroglutamic Acid 0.11 (0-0.16) 74 0.1 (0.04-0.16) 75 0.13 (0.03-0.17) 70 Pass Quinolinic Acid 0 (0-0) 19 0 (0-0) 15 0 (0-0.16) 40 Pass Sebacic Acid 0 (0-0) 1 0 (0-0) 2 0 (0-0) 0 - Suberic Acid 0.02 (0.02-0.03) 86 0.02 (0.02-0.03) 88 0.02 (0.005-0.02) 70 Pass Succinic Acid 0 (0-0.09) 48 0 (0-0.09) 44 0.09 (0.02-0.69) 70 Pass Sumiki-s Acid 0.01 (0-0.01) 17 0.01 (0-0.01) 15 0.005 (0-0.018) 30 Pass Tiglylglycine 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - trans-Aconitic Acid 0.1 (0-0.11) 71 0.1 (0-0.11) 73 0.11 (0-0.11) 60 Pass trans-Ferulic Acid 0 (0-0) 3 0 (0-0) 3 0 (0-0) 0 - Uracil 0 (0-0) 3 0 (0-0) 2 0 (0-0) 10 - Vanillylmandelic Acid 0 (0-0.01) 29 0 (0-0.01) 27 0 (0-0.018) 40 Pass 1H-Indole-3-acetic Acid 0 (0-0) 10 0 (0-0) 10 0 (0-0) 10 - 2-5-Furandicaoxylic 0 (0-0) 1 0 (0-0) 0 0 (0-0) 10 - 2-Ethyl-3-hydroxypropionic 0 (0-0.01) 36 0 (0-0.01) 39 0 (0-0) 20 Pass 2-Hydroxy-3-methylvaleric 0 (0-0) 3 0 (0-0) 0 0 (0-0) 20 - 2-Hydroxyadipic 0 (0-0) 3 0 (0-0) 3 0 (0-0) 0 - 2-Hydroxybutyric 0 (0-0) 9 0 (0-0) 4 0 (0-0.01) 33 Pass 2-Hydroxyglutaric Acid 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - 2-Hydroxyisovaleric Acid 0 (0-0) 6 0 (0-0) 3 0 (0-0.01) 20 - 2-Methylsuccinic Acid 0 (0-0.01) 30 0 (0-0.01) 27 0.005 (0-0.01) 50 Pass 2-Oxoisovalericoime 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - 3-4-OH-3-MO-phenyl-lactic 0 (0-0) 0 0 (0-0) 0 0 (0-0) 0 - 3-Hydroxy-2-methylbutyric 0 (0-0) 6 0 (0-0) 7 0 (0-0) 0 Pass 3-Hydroxybutyric Acid 0 (0-0.02) 33 0 (0-0.02) 27 0.02 (0.01-0.1) 70 Pass 3-Methyladipic Acid 0 (0-0.04) 36 0 (0-0.04) 34 0.02 (0-0.04) 50 Pass 3-Methylglutaconic Acid 0 (0-0.03) 39 0 (0-0.03) 37 0.02 (0-0.04) 50 Pass 3-Methylglutaric 0 (0-0) 25 0 (0-0) 24 0 (0-0.02) 30 Pass 3-Phenyllactic 0 (0-0.03) 43 0 (0-0.03) 39 0.02 (0.005-0.03) 70 Pass 4-Hydroxyhippuric Acid 0.04 (0-0.06) 55 0.04 (0-0.06) 54 0.06 (0-0.09) 60 Pass 4-Hydroxymandelic 0 (0-0) 3 0 (0-0) 3 0 (0-0) 0 -

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4-Hydroxyphenyllactic Acid 0 (0-0) 6 0 (0-0) 5 0 (0-0) 10 - 5-Hydroxyhexanoic 0 (0-0) 1 0 (0-0) 2 0 (0-0) 0 -

1 Data are presented as median (IQR). 2 Metabolites ordered alphabetically. 3 Kwa, kwashiorkor; Mara, marasmus; nStt, non-stunted; SAM, severe acute malnutrition; Stt, Stunted.

APPENDIX C

Azelaic Acid

death discharge0.00

0.04

0.08

death discharge

3.0

0.0

1.5

Citric Acid

death discharge0.00

0.04

0.08Ethylmalonic Acid

death discharge

Glutaric Acid

0.00

0.06

0.03

death discharge

Hippuric Acid

0.0

0.8

0.4

death discharge

Homovanillic Acid

0.0

0.2

0.1

Methylmalnoic Acid

death discharge0.00

0.10

0.05

O Hydroxyphenlactic Acid

death discharge0.00

0.04

0.02

Oxoglutaric Acid

death discharge0.0

0.6

0.3

p Hydroxyphenylactic Acid

death discharge

0.10

0.60

0.35

death discharge0.0

0.6

0.3

Phenaceturic Acid Pimelic Acid

death discharge0.00

0.02

0.04

Pyroglutamic Acid

death discharge0

8

4

death discharge

Quinolinic Acid

0.00

0.30

0.15

Appendix C Boxplots of Metabolites co

ncen

tratio

n (m

M)

conc

entra

tion

(mM

)co

ncen

tratio

n (m

M)

conc

entra

tion

(mM

) Suberic Acid

death discharge0.00

0.10

0.05

death discharge

Sumiki Acid

0.00

0.14

0.07

- 33 -

APPENDIX C (part two)

Appendix C Boxplots of Metabolites

2-ethyl-3-hydroxyperopinoic

death discharge0.00

0.01

0.02

death discharge

2-hydroxybutyric

0.00

0.01

0.02

0.00

0.01

0.02

death discharge

2-methylsuccinic Acid 3-methyladipic Acid

death discharge0.00

0.05

0.03

3-methylglutaconic Acid

death discharge0.00

0.05

0.03

death discharge

3-methylglutaric

death discharge0.00

0.04

0.083-phenyllactic

0.00

0.01

0.024-hydroxyhippuric Acid

death discharge0.0

0.2

0.4

death discharge0.00

0.25

0.15

Trans Aconitic Acid Vanillylmandelic Acid

death discharge

conc

entra

tion

(mM

)co

ncen

tratio

n (m

M)

conc

entra

tion

(mM

)

3-hydroxy2-methylbutyric

death discharge0.00

0.08

0.04

0.00

0.02

0.04

- 34 -

APPENDIX D

ß-HYDROXYBUTRIC ACID ß-hydroxybutric acid or 3-hydroxybutric acid, with the chemical formula CH3CH(OH)CH2CO2H, is a type of ketone body that is produced during prolonged fasting. It is synthesized in the liver from acetoacetate by the enzyme beta-hydroxybutyrate dehydrogenase. Ketogenesis occurs in the mitochondrial matrix and it begins with the conversion of β-oxidation-derived acetyl-coenzyme A (acetyl-CoA) into acetoacetate, which is then either converted into acetone or into ß-hydroxybutyrate. Acetoacetate and ß-hydroxybutyrate are exported into the blood stream for use by extrahepatic tissues. The ß-hydroxybutyrate in extrahepatic mitochondria is converted into acetoacetate, which is then converted back into acetyl-CoA that can be used in the tricarboxylic acid cycle to produce ATP. This is illustrated in figure 6. For ketolytic activation of ß-hydroxybutyrate succinyl-CoA is required (Schutz), which is then coverted into succinate. Figure 6 Ketogenesis and ß-hydroxybutyrate (BHB) in ATP production

LACTIC ACID Lactic acid is an organic acid with the chemical formula CH3CH(OH)CO2H. It is formed through lactic acid fermentation, which is an anaerobic process that takes place in bacteria and certain animal cells that converts glucose or other simple carbohydrates into energy and lactate. In human cells it usually takes place during a limited time period when there is insufficient oxygen available for oxidative phosphorylation. Under oxygen deficient circumstances the NADH + H+ from glycolysis is oxidized to donate two hydrogen atoms to pyruvic acid forming NAD+ + lactic acid as seen in figure 3. During aerobic conditions lactic acid can be converted back into pyruvic acid that can be used as a better source for ATP. The ratio of NADH (reduced from of nicotinamide adenine dinucleotide) and NAD+ (oxidized form) in the cytoplasm of a cell defines the redox state of that cell. NADH is an electron donator and NAD+ is an electron acceptor. A reduced redox state with a low NAD/NADH ratio promotes lactic acid formation.

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Figure 3 Lactic acid fermentation pathway

SUCCINIC ACID Succinic acid has the chemical formula (CH2)2(CO2H)2 and is a component of the citric acid cycle or Krebs cycle, where it serves as an electron donor in the electron transport chain. Succinate + FAD (flavin adenine dinucleotide) is converted by the enzyme succinate dehydrogenase into fumarate + FADH2, as seen in figure 4. Succinate dehydrogenase is also referred to as complex II of the mitochondria electron transport chain and it couples the oxidation of succinate to the reduction of ubiquinone. Ubiquinone is the electron carrier from complex I to complex II to complex III in the electron transport chain, as depicted in figure 5. Succinyl-CoA is used to convert ß-hydroxybutyrate into acetyl-CoA and is reduced to succinate.

- 36 -