Dataset Information

Unraveling the Heterogeneity of Hyperuricemia Using Untargeted Metabolomics and Statistical Modeling

ABSTRACT:

Background. Hyperuricemia is the second most prevalent metabolic disorder globally and exhibits pronounced heterogeneity in clinical practice. Only about 10% of individuals with asymptomatic hyperuricemia (HUA) progress to gout, yet the lack of specific biomarkers and precise therapeutic targets hinders accurate prediction of disease progression. The heterogeneity of hyperuricemia aims to elucidate potential mechanisms underlying its transition from asymptomatic to symptomatic stages.

Methods. A total of 107 serum samples were first analyzed, including 36 healthy controls (Con), and 30, 32 and 9 patients with HUA, gouty arthritis (gout), and gout complicated by uric acid nephrolithiasis (UAN), respectively. Untargeted metabolomics was conducted across the following four groups, namely, Con and HUA, HUA and gout, gout and UAN, and HUA and UAN, which helped to discover differential metabolites and key metabolic pathways involved in disease progression.

Results. Using advanced SCIEX ZenoTOF 7600-based untargeted metabolomics platform, 920 metabolites were identified in both positive and negative ionization modes. The assessment of quality control (QC) samples showed high precision in instrumental measurement with RSD values of the ion features less than 30% achieving to 91.76% and 89.73% respectively in the two ionization modes. Significant alterations of metabolites among the groups were observed, including 145, 82, 74 and 121 differential metabolites among the four groups. With high sensitivity and specificity, six of them were demonstrated to distinguish HUA, gout and UAN for the study of heterogeneity of hyperuricemia. Disruptions in phenylalanine, tyrosine, and tryptophan biosynthesis were prominent in symptomatic patients. Furthermore, four metabolic pathways remained consistently dysregulated throughout disease progression, which indicates their potentials as mechanistic drivers.

Conclusion.These findings provide promising biomarkers for early diagnosis and precision treatment of gout and its complications, while offering novel insights into the metabolic basis of heterogeneity of hyperuricemia.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

PROVIDER: MTBLS13094 | MetaboLights | 2025-10-05

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	a_MTBLS13094_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS13094_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS13094_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular
	m_MTBLS13094_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular

Items per page:

1 - 5 of 6

Similar Datasets

Project description:Increased plasma uric acid (hyperuricemia) has been associated with worse outcomes for chronic kidney disease (CKD). But some attempts to control uric acid (UA) in large-cohort clinical trials did not produce clinically meaningful benefits for CKD. Some studies suggest that only hyperuricemia with crystals, but not asymptomatic hyperuricemia promotes the progression of CKD. Salt-sensitivity (SS) in blood pressure is a prevalent trait that is sexually dimorphic and results in kidney damage. But the connection between hyperuricemia and SS hypertension (HTN) is still unclear. Here we tested the connection between the two using both male and female Dahl SS rats, a well-establish model of SS HTN. We hypothesized that mild asymptomatic hyperuricemia is beneficial in controlling the progression of SS HTN. A uricase inhibitor, oxonic acid (2%) (Oxo) was used to induce hyperuricemia and high-salt (HS) (4% NaCl) diet was used to induce SS HTN. After 3 weeks, in response to oxonic acid supplementation, both sexes showed a significant increase of UA in plasma compared to their respective HS-only controls (Males: 0.63 ±0.07 vs. 2.17 ±0.34; Females: 0.78 ±0.15 vs. 2.04 ±0.35 mg/dl, HS vs. HS/oxo). Interestingly, only male HS/oxo rats showed a significant increase in uricosuria (Males: 0.23 ±0.03 vs. 0.45 ±0.06; Femaels: 0.26 ±0.06 vs. 0.26 ±0.001 UA/Cre, HS vs. HS/oxo). Moreover, the mild hyperuricemia was associated with a significant attenuation of the progression and magnitude of the mean arterial pressure in male but not female rats (Males: 157 ±3 vs. 136 ±3; Females: 155 ±6 vs. 154 ±5 mmHg, HS vs. HS/oxo). Xanthine oxidase (XO) is one of the enzymes that produce UA, and its activity has been shown to affect HTN as well. Therefore, we examined the level of XO activity in the plasma after the treatment. While there was no difference in the activity based on the diet within each sex, females had significantly lower levels of XO activity compared to males in each of the diets. To further investigate the beneficial phenotype seen in male rats, we evaluate changes in the progression of renal pathology. The HS/oxo group compared to the HS group had a lower kidney weight/body weight ratio and lower protein cast accumulation, indicating lower kidney damage. Furthermore, the HS/Oxo treated males had less oxidative damage in their tubules than the HS-only males. Bulk-RNA seq done on the male kidneys revealed that attenuated HTN phenotype was associated with an increased expression in Mas1 (MAS receptor), Klk-1 (Kallikrein-1), and Pcsk6 (PCSK6 enzyme) which can all lead to the activation of different vasodilatory pathways. Our study showed that in male but not female Dahl SS rats, asymptomatic mild hyperuricemia accompanied by hyperuricosuria ameliorates the progression of SS HTN and protects kidneys from further damage. Thus, our findings challenge the notion of hyperuricemia being inherently detrimental to health and highlight that this is an oversimplified view of UA’s role in disease.

Project description:In this data set, we reported for the first time that huanglongbing disease (HLB) induces major changes in the expression of global genes in flavedo, vascular and juice vesicle tissues of citrus fruit. 68 Total samples were analyzed. cDNA generation, array analysis, and statistical tests were performed as a service at the Interdisciplinary Center for Biotechnology Research (ICBR) Microarray Core facility at the University of Florida (Gainesville, FL). The linear models were used for array analysis (Smyth GK et al. Bioinformatics, 2005, 2067-2075). The linear models were firstly used to assess differential expression, and then an empirical Bayes method was used to moderate the standard errors. 13 comparisons were performed for the study. The comparisons in Citrus sinensis cv. Hamlin included: SC vs. CC (genes that respond to infection in symptomatic vascular core); SJV vs. CJV (genes that respond to infection in symptomatic juice vesicle); SS vs. CS (genes that respond to infection in symptomatic seed); SP vs. CP (genes that respond to infection in symptomatic peel). The comparisons in Citrus sinensis cv. Valencia included: SP vs. HP (genes that respond to infection in symptomatic peel); ASP vs. HP (genes that respond to infection in asymptomatic peel); SP vs. ASP (genes that respond to infection in symptomatic peel compared to asymptomatic peel); SC vs. HC (genes that respond to infection in symptomatic vascular core); ASC vs. HC (genes that respond to infection in asymptomatic vascular core); SC vs. ASC (genes that respond to infection in symptomatic vascular core compared to asymptomatic vascular core); SJV vs. HJV (genes that respond to infection in symptomatic juice vesicle); ASJV vs. HJV (genes that respond to infection in asymptomatic juice vesicle); SJV vs. ASJV (genes that respond to infection in symptomatic juice vesicle compared to asymptomatic juice vesicle). ESTs with significant expression changes (P value <0.001; false discovery rate <0.01 with equal or higher than 2-fold changes in expression) were selected for further analysis.

Dataset Information

Unraveling the Heterogeneity of Hyperuricemia Using Untargeted Metabolomics and Statistical Modeling

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets