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Elevated uric acid levels, mortality and cognitive impairment in children with severe malaria

Nature Medicine volume 31, pages 777–787 (2025) Cite this article

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Abstract

We investigated the role of uric acid in the pathogenesis of severe malaria (SM) in two independent cohorts of children with SM. Hyperuricemia (blood uric acid ≥ 7 mg dl−1) was present in 25% of children with SM and was associated with increased in-hospital mortality and postdischarge mortality in both cohorts. Increased blood uric acid levels were also associated with worse scores in overall cognition in children with SM < 5 years old in both cohorts. Hemolysis of infected red blood cells and impaired renal excretion of uric acid were the primary drivers of hyperuricemia in SM. Hyperuricemia was associated with multiple complications of SM, including acute kidney injury, acidosis, impaired perfusion, coma and intestinal injury with increases in the abundance of Gram-negative uricase-producing pathobionts (Escherichia and Shigella) in the stool. Clinical trials evaluating uric acid-lowering medications as adjunctive therapy for children with SM should be considered to improve survival and protect neurodevelopment.

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Fig. 1: Study design and participant flowchart.
The alternative text for this image may have been generated using AI.
Fig. 2: Uric acid levels and hyperuricemia in children with SM compared to CC and potential contributors to hyperuricemia.
The alternative text for this image may have been generated using AI.
Fig. 3: In-hospital mortality in children with SM according to hyperuricemia and complications of SM.
The alternative text for this image may have been generated using AI.
Fig. 4: Postdischarge mortality in children with SM according to hyperuricemia and SMA.
The alternative text for this image may have been generated using AI.
Fig. 5: Association between uric and cognitive outcomes and markers of brain injury.
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Fig. 6: Mechanisms and potential therapeutic targets of malaria-induced hyperuricemia.
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Data availability

De-identified data are available on request to the corresponding author. All data requests that comply with limitations contained in the informed consent form signed by the participants of the trial will be granted access to the data within 1 month of the request.

Code availability

Code used in the primary analysis is available on the following GitHub repository: https://github.com/caitbond8/Uric_acid_sev_malaria_statistical_code.

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Acknowledgements

We are grateful to the participants and caregivers in this trial and to the study team. This work was funded by the National Institutes of Health (NIH)/National Institutes of Health National Institute of Neurological Disorders and Stroke (R01NS055349 to C.C.J.), the Fogarty International Center (D43 TW010928 to C.C.J.), a Ralph W. and Grace M. Showalter Young Investigator Award to A.L.C., NIH/National Institute of Allergy and Infectious Diseases (R01AI165946 to A.L.C.), NIH/National Institute of Neurological Disorders and Stroke (1R01NS105910 to A.R.), NIH/National Heart Lung and Blood Institute (1R01HL150145 to A.R. and 3R01HL150145-02S1 to M.V.), NIH (R21AI151349 to A.R. and A.L.C.), NIH/National Institute of Allergy and Infectious Disease (R01AI148525 to N.W.S. and C.C.J.), NIH National Center for Advancing Translational Sciences (TL1TR002531), Clinical and Translational Sciences Award to O.J.B. and a fellowship award from the NIH/National Institute of Diabetes and Digestive and Kidney Diseases (grant 5T32DK120524-05 to C.L.). The funders had no role in study design, data collection, analysis, interpretation or the decision to publish.

Author information

Author notes

  1. These authors contributed equally: Chandy C. John, Andrea L. Conroy.

Authors and Affiliations

  1. Ryan White Center for Pediatric Infectious Diseases and Global Health, Indiana University School of Medicine, Indianapolis, IN, USA

    Caitlin Bond, Olivia J. Bednarski, Dibyadyuti Datta, Giselle Lima-Cooper, Keerthi Udumula, Deepali Balasubramani, Claire Liepmann, Nathan W. Schmidt, Chandy C. John & Andrea L. Conroy

  2. Department of Paediatrics and Child Health, Makerere University College of Health Sciences, Kampala, Uganda

    Ruth Namazzi

  3. Aga Khan University East Africa Medical College, Nairobi, Kenya

    Robert O. Opoka

  4. Child Health and Development Centre, Makerere University College of Health Sciences, Kampala, Uganda

    Anthony Batte

  5. Department of Microbiology, New York University School of Medicine, New York City, NY, USA

    Marilyn Vasquez & Ana Rodriguez

  6. Department of Psychiatry, Makerere University College of Health Sciences, Kampala, Uganda

    Paul Bangirana

  7. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

    Marco Abreu & Tae-Hwi Schwantes-An

  8. Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA

    Yi Zhao

  9. Division of Nephrology, Indiana University and the Roudebush Indianapolis VA Medical Center, Indianapolis, IN, USA

    Tarek M. El-Achkar

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  1. Caitlin Bond
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  2. Olivia J. Bednarski
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Contributions

C.B. wrote the primary draft of the manuscript and all revisions. C.B., A.L.C. and Y.Z. conducted primary study statistical analysis. O.B. and N.W.S. conducted and analyzed microbiome data. D.D. performed and analyzed brain biomarker testing. R.N. was the primary study pediatrician and supervisor. R.O.O. oversaw the conduct of the primary study. G.L.-C., K.U. and D.B. performed study analysis and quality control of uric acid testing data. A.B. and T.M.E.-A. provided analysis and interpretation of renal complications of hyperuricemia. M.A. and A.R. performed testing and analysis of xanthine oxidase levels. C.L., M.A. and T.-H.S.-A. conducted and analyzed studies on genetic testing. C.C.J. obtained primary funding for the study and was responsible for study conduct, analysis and interpretation. A.L.C. supervised, analyzed and interpreted laboratory testing and was responsible for the interpretation of how lab findings related to clinical outcomes in the study. All authors contributed to the writing and revision of the manuscript.

Corresponding author

Correspondence to Chandy C. John.

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The authors declare no competing interests.

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Nature Medicine thanks Hans Ackerman, Terrie Taylor and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Genes associated with uric acid levels at admission.

a, Uric acid measurement time points in children with severe malaria (SM) and community controls (CC). Transporters of uric acid reabsorption. b, Association between 14 variants and uric acid levels during severe malaria infection, adjusted for age, sex, site and disease severity (Lambaréné Organ Dysfunction Score). Red square and error bars represent a significant association with uric acid levels. c, Prevalence of SLC2A9 variant rs12498742 (location chr4: 9942428:A:G) G allele in cohort 2, Africa, and global populations (gnomAD genomes v3.1.2). d, Uric acid levels at admission and 1-month follow-up according to rs12498742 genotype in children who were admitted with severe malaria are shown in the box-whisker plots (box, median and IQR; whiskers, 1.5× IQR from the box edges; plus sign (+), mean). P-values shown in d are derived from the Kruskal–Wallis test. The figure is created with BioRender.com.

Extended Data Fig. 2 Hyperuricemia is associated with intestinal injury in severe malaria, and both hyperuricemia and intestinal injury are associated with increased abundance of specific pathobionts.

a, Diagram of high levels of uric acid damaging intestinal endothelium and leading to production of biomarkers of intestinal injury (TFF3 and I-FABP). Intestinal injury is defined as TFF3 >4.078 ng/mL and/or I-FABP >15.433 ng/mL (values >99th percentile of CC). b, Stacked bar chart comparing frequency of intestinal injury in children with hyperuricemia vs. normal uric acid. Two-sided p-value from chi-squared test. c, Differential abundance analysis of bacteria in stool in 417 children from cohort 2 shown in a radial phylogenetic tree. Significant increases in bacteria on admission are shown by white circle. Family is shown at the distal level. d, Stacked bar chart comparing frequency of families associated with higher bacterial abundance in children with hyperuricemia vs. normal uric acid. e, Relative abundance of bacteria (log-transformed) shown in the box plots box-whisker plots (box, median and IQR; whiskers, 1.5× IQR from the box edges; plus sign (+), mean). Two-sided p-values from the Wilcoxon rank sum test in children with intestinal injury (n = 67) vs. without intestinal injury (n = 349) are presented in box plot if significant after correcting for multiplicity using Benjamini–Hochberg false discovery rate (five comparisons). Detailed statistical test results and exact p-values are provided in Supplementary Table 19, corresponding to e. The figure is created with BioRender.com.

Extended Data Fig. 3 Association between uric acid levels and probability of in-hospital mortality in cohort 2.

a, Loess regression curve showing the probability of death as a function of uric acid levels in patients with severe malaria. Knots were selected at 3.8 mg/dL based on where the trend in the probability of death shifts from negative to positive and at 7 mg/dL to represent the threshold for hyperuricemia. b, Adjusted odds ratios (aOR) and 95% confidence intervals (CI) for the association between uric acid levels and in-hospital mortality using two-sided logistic regression with restricted cubic splines with knots at 3.8 and 7.0 mg/dL (based on loess smoothing), adjusted for age, sex and site. Odds ratios represent the risk associated with a 1-unit increase in uric acid within each spline segment.

Extended Data Fig. 4 Causal mediation pathway analysis between hyperuricemia and in-hospital mortality in cohort 2.

a, Causal pathway diagrams with adjusted odds ratios (OR) from binary logistic regression, adjusted for age, sex and site. Two-sided p-values shown in diagram and are not adjusted for multiple comparisons. b, The indirect and direct effects of hyperuricemia on in-hospital mortality with potential mediators, showing point estimates with 95% confidence intervals (error bars), obtained from causal mediation analysis. Sample sizes (independent biological samples from individual patients) were n = 595 for AKI and hyperuricemia; n = 594 for intestinal injury, metabolic acidosis and coma; and impaired n = 593 for perfusion. The percentage mediated (indirect effect divided by total effect) is presented to the right of the diagram in the diagram. All mediation analyses are adjusted for age, sex and site, with p-values from two-sided tests. All mediators were statistically significant (p < 0.05). For the relationship between AKI and mortality, mediation analysis was also performed for hyperuricemia as the potential mediator. AKI, acute kidney injury.

Extended Data Fig. 5 Flowchart of study participants with cognitive tests during follow-up among children included in the uric acid substudy.

Cohort 1 included four cognitive testing time points following enrollment and administered different cognitive tests for children <5 years of age and >5 years of age. As some children remained in the study but had missed cognitive testing at a given time point, the number of children remaining in a study is included on the upper righthand side of boxes, ‘following n’. Cohort 2 had one cognitive testing follow-up time point and only included children <5 years of age.

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Bond, C., Bednarski, O.J., Datta, D. et al. Elevated uric acid levels, mortality and cognitive impairment in children with severe malaria. Nat Med 31, 777–787 (2025). https://doi.org/10.1038/s41591-024-03430-8

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