Sci Adv｜Dr. Wen Bijun et al. report the control of systemic inflammation and metabolic disorders in severely malnourished children

Editor-in-Chief | The United Nations adopted 17 UN Sustainable Development Goals in 2015 as a blueprint for a better global future
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One of the projects to achieve these goals is to end preventable deaths among children under 5 by 2030
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Yet in many parts of Africa, child mortality remains high
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1 in 13 children dies before the age of 5 [1]
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In these areas, the problem of child malnutrition is widespread
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Malnutrition, especially severe malnutrition (Severe Malnutrition), is an important factor that increases the risk of death [2]
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Severely malnourished children, despite being treated in hospitals according to World Health Organization (WHO) standards, have unexplained high mortality rates
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Existing treatments and research mostly focus on interventions for nutritional rehabilitation, and little is known about the underlying biomedical mechanisms responsible for the high mortality in these children
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Recently, Dr.
Bijun Wen from the Robert Bandsma Research Group of the Canadian Hospital for Sick Children and the University of Toronto, together with Dr.
James Njunge from the James Berkley Research Group of the University of Oxford, UK, published a paper entitled Systemic inflammation and metabolic disturbances underlie inpatient mortality in Science Advances.
Research paper among ill children with severe malnutrition
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For the first time, this study used multi-omics (proteomic), metabolomic (metabolomic), inflammatory factor (cytokine profile) molecular mapping technology, combined with machine learning (machine learning) analysis method, to 184 patients with severe malnutrition.
A detailed clinical case-control study
of blood samples collected from children on admission
found that children who died showed sepsis-like systemic immunity on admission compared with children who had recovered clinically .
Metabolic disturbance response, revealing that treatments that control inflammation and enhance mitochondrial energy metabolism may help reduce in-hospital mortality
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The study first selected deaths from the team's previous Randomized control trial in Malawi and Kenya, Africa Patients (Non-survivor, NS) and recovered patients (Survivor, S) were used as the case group and the control group, respectively
.

To control for confounding variables (confounders) that may affect the study relationship, the authors compared the age of the children in the case group and the control group, Anthropometric measurements and HIV infection status were matched, so that the severity of the disease at admission was relatively
similar
between the two groups.
A metabolomic profile that can measure a total of 206 metabolites was developed
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Differential metabolomic profiles identified 32 marker metabolites that were strongly associated with patient outcomes from treatment
.

Combined with supervised machine learning analyses, the authors found differential metabolomic profiles data strongly predict survival outcomes
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By using the bioinformatics platform (MetaboAnalyst [3]) to conduct in-depth research on metabolic metabolism, it was found that these metabolic differences are related to mitochondrial energy metabolism pathways (mitochondrial metabolic pathways), which are manifested as incompletely oxidized energy metabolites in the blood.
level increased significantly
.

revealed that mitochondrial energy metabolism disturbances were associated with fatal cases
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In order to reveal the biomolecular features and pathological mechanisms more comprehensively, the authors further performed high-throughput measurements of the proteome and inflammatory factors in blood
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The results showed that acute phase proteins (such as C-reactive protein) and multiple pro-inflammatory factors (such as IL-8, TNF-α) in the case group were significantly higher than those in the control group
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In addition, an integrated analysis with the metabolome found that acute proteins and proinflammatory factors in the case group were negatively correlated with the levels of the metabolite lysophospholipids
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This is similar to that observed in sepsis [4-6]
.

Overall, the case group exhibited elevated systemic acute inflammatory responses and features similar to molecular derangements in sepsis
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Finally, the authors conducted an integrated study of clinical profiles and biomolecular profiles using unsupervised machine learning
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The analysis further corroborated the association between immune metabolic disorders and survival outcomes, and identified patient sub-clusters associated with time to in-hospital death
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Among them, patients who died earlier after admission clustered into clusters with more pronounced biomolecular disturbances than other clusters
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Notably, biomolecular disorders are more important for cluster formation than clinical characterization
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In other words, although patients are clinically similar, there are survival-related differences in body function
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Overall, this study found that disturbances in systemic immunity and mitochondrial energy metabolism were associated with survival outcomes in severely malnourished hospitalized children through multi-omics analysis
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In low-resource settings, although hospitalized children with severe malnutrition are treated with antibiotics, treatment strategies tend to focus more on interventions for nutritional rehabilitation
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The study sheds new light on treatment priorities to reduce mortality in these children
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The research team is part of the CHAIN ​​Network (The Childhood Acute Illness & Nutrition Network) global research network, which is supported by the Bill & Melinda Gates Foundation
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The CHAIN ​​Network works to optimize the treatment and care of sick and malnourished children in low-resource settings to improve the survival, growth and development of vulnerable children
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Link to the original text: https://# Publisher: Eleven References 1.
https:// children-reducing-mortality2.
RE Black, CG Victora, SP Walker, ZA Bhutta, P.
Christian, M.
de Onis, M.
Ezzati, S.
Grantham-McGregor, J.
Katz, R.
Martorell, R.
Uauy, Maternal and child undernutrition and overweight in low-income and middle-income countries.
Lancet 382, ​​427–451 (2013).
3.
J.
Xia, DS Wishart, Using metaboanalyst 3.
0 for comprehensive metabolomics data analysis.
Curr.
Protoc.
Bioinformatics 55, 14.
10.
11–14.
10.
91 (2016).
4.
J.
Wang, Y.
Sun, S.
Teng, K.
Li, Prediction of sepsis mortality using metabolite biomarkers in the blood: A meta-analysis of death-related pathways and prospective validation.
BMC Med.
18, 83 (2020).
5.
DC Muller, A.
Kauppi, A.
Edin, A.
Gylfe, AB Sjostedt, A.
Johansson,Phospholipid levels in blood during community-acquired pneumonia.
PLOS ONE 14, e0216379 (2019).
6.
A.
Cambiaghi, BB Pinto, L.
Brunelli, F.
Falcetta, F.
Aletti, K.
Bendjelid, R.
Pastorelli, M.
Ferrario, Characterization of a metabolomic profile associated with responsiveness to therapy in the acute phase of septic shock.
Sci.
Rep.
7, 9748 (2017).
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