Confounded by obesity and modulated by urinary uric acid excretion, sleep‐disordered breathing indirectly relates to hyperuricaemia in males: A structural equation model

Lai, Chao-Hung; Huang, Ren-Jing; Wong, James K. S.; Chang, Shen-Wen; Chung, Ai-Hui; Chi, Yung-Chun; Yu, Yi-Chen; Lee, Shin-Da; Ting, Hua

doi:10.1111/jsr.13108

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…The conversion of some metabolites, such as saturated fatty acids, involves producing inflammatory factors that may cause an inflammatory response in HUA patients ( Berg, Seyedsadjadi & Grant, 2020 ). Obesity and hyperlipidemia have been shown to be risk factors for hyperuricemia ( Lai et al, 2021 ). According to research, BMI, waist circumference, and body fat percentage are associated with hyperuricemia ( Rivera-Paredez et al, 2019 ).…”

Section: Survey Methodologymentioning

confidence: 99%

The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics

You

2022

PeerJ

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Background Hyperuricemia and gout are a group of disorders of purine metabolism. In recent years, the incidence of hyperuricemia and gout has been increasing, which is a severe threat to people’s health. Several studies on hyperuricemia and gout in proteomics and metabolomics have been conducted recently. Some literature has identified biomarkers that distinguish asymptomatic hyperuricemia from acute gout or remission of gout. We summarize the physiological processes in which these biomarkers may be involved and their role in disease progression. Methodology We used professional databases including PubMed, Web of Science to conduct the literature review. This review addresses the current landscape of hyperuricemia and gout biomarkers with a focus on proteomics and metabolomics. Results Proteomic methods are used to identify differentially expressed proteins to find specific biomarkers. These findings may be suggestive for the diagnosis and treatment of hyperuricemia and gout to explore the disease pathogenesis. The identified biomarkers may be mediators of the link between hyperuricemia, gout and kidney disease, metabolic syndrome, diabetes and hypertriglyceridemia. Metabolomics reveals the main influential pathways through small molecule metabolites, such as amino acid metabolism, lipid metabolism, or other characteristic metabolic pathways. These studies have contributed to the discovery of Chinese medicine. Some traditional Chinese medicine compounds can improve the metabolic disorders of the disease. Conclusions We suggest some possible relationships of potential biomarkers with inflammatory episodes, complement activation, and metabolic pathways. These biomarkers are able to distinguish between different stages of disease development. However, there are relatively few proteomic as well as metabolomic studies on hyperuricemia and gout, and some experiments are only primary screening tests, which need further in-depth study.

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Section: Survey Methodologymentioning

confidence: 99%

The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics

You

2022

PeerJ

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“…In the literature there are studies that investigated serum levels of uric acid in patients with OSA. These studies re-ported an association between OSA and high uric acid levels (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). However, there are only a few studies evaluating the association between levels of uric acid and OSA severity or NH (14,15,19).…”

Section: Discussionmentioning

confidence: 99%

“…It is known that SRBD are associated with obesity, systemic inflammation, hypertension and increased risk of cardiovascular diseases (1,(8)(9)(10)(11)(12). There are several previous studies that investigate the relationship between serum uric acid and OSA (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). However, studies evaluating the relationship between severity of OSA, NH and hyperuricemia are limited (14,15,19).…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Serum Uric Acid Levels and Sleep-Related Breathing Disorders in Obese Subjects

Pıhtılı¹,

Bingöl²,

Kıyan³

2021

J Istanb Fac Med

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Objective: We examined the serum uric acid (UA) levels among patients with sleep-related breathing disorders (SRBD) and the relationship between UA levels and obstructive sleep apnea (OSA) severity, nocturnal hypoxemia (NH) and obesity hypoventilation syndrome (OHS).Material and Method: Subjects with body mass index (BMI) ≥30 kg/m 2 who, according to polysomnography results, have SRBD were included. Demographics, anthropometric measurements and serum UA were recorded. The apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) were recorded. The OSA severity was graded as mild (AHI 5-14 events/hour), moderate (AHI 15-29 events/hour) or severe (AHI ≥30 events/hour). NH was considered when sleep time spent with SpO 2 <90% was ≥30%. PaCO 2 >45 mmHg was considered OHS. Results:The study included 159 patients with SRBD (79 female, 80 males, age: 50.0±10.5 years, BMI: 40.1±5.7 kg/m 2 ). Seventy-six had OHS and 83 had pure OSA. NH was detected in 25.8% of patients. UA levels were higher in subjects with NH (6.22±1.37 mg/dl, 5.67±1.51 mg/dl, p=0.008). UA levels were similar in patients with OHS and pure OSA (5.91±1.25 mg/dl, 5.72±1.68 mg/ dl, p=0.1). UA levels increased as OSA severity increased (mild: 5.03±1.41 mg/dl, moderate: 5.46±1.55 mg/dl, severe: 6.19±1.8 mg/dl, p=0.039). UA levels were higher in males with OSA (6.86±1.64 mg/dl, 4.84±1.10 mg/dl, p<0.001). UA levels were correlated with neck circumference (r=0.366, p<0.001), waist/ hip ratio (r=0.358, p<0.001), OSA severity (r=0.224, p=0.005), AHI (r=0.250, p=0.001), ODI (r=291, p<0.001) and sleep time with SpO 2 <90% (r=0.228, p=0.004). Male gender, waist/hip ratio and NH were factors independently related with UA levels (retrospectively; p<0.001, p=0.03, p=0.03). Conclusion:In obese subjects with SRBD, serum UA levels were associated with male gender, NH and OSA severity.

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Confounded by obesity and modulated by urinary uric acid excretion, sleep‐disordered breathing indirectly relates to hyperuricaemia in males: A structural equation model

Abstract: Ching-Hsiang Lai and Ren-Jing Huang share equal contribution. Shin-Da Lee and Hua Ting share equal contribution.

Cited by 2 publications

References 38 publications

The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics

The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics

Relationship Between Serum Uric Acid Levels and Sleep-Related Breathing Disorders in Obese Subjects

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