Uric acid is associated with adiposity factors, especially with fat mass reduction during weight loss in obese children and adolescents

Early-life gut mycobiome core species modulate metabolic health in mice

The gut microbiome causally contributes to obesity; however, the role of fungi remains understudied. We previously identified three core species of the infant gut mycobiome (Rhodotorula mucilaginosa, Malassezia restricta and Candida albicans) that correlated with body mass index, however their causal contributions to obesity development are unknown. Here we show the effects of early-life colonization by these fungal species on metabolic health in gnotobiotic mice fed standard (SD) or high-fat-high-sucrose (HFHS) diets. Each species resulted in bacterial microbiome compositional and functional differences. R. mucilaginosa and M. restricta increased adiposity in mice fed SD, while only R. mucilaginosa exacerbated metabolic disease. In contrast, C. albicans resulted in leanness and resistance to diet-induced obesity. Intestinal nutrient transporter expression was unaffected by the presence of fungi in jejunal enteroids, yet the immune landscape in white adipose tissue was distinctly impacted by each fungal species, suggesting that these phenotypes may be a result of fungal immune regulation. This work revealed that three common fungal colonizers have distinct causal influences on obesity and metabolic inflammation and justifies the consideration of fungi in microbiome research on host metabolism.

Interplay between BMI, neutrophil, triglyceride and uric acid: a case-control study and bidirectional multivariate mendelian randomization analysis

BACKGROUND

This study aims to explore the interplay between body mass index (BMI), neutrophils, triglyceride levels, and uric acid (UA). Understanding the causal correlation between UA and health indicators, specifically its association with the body's inflammatory conditions, is crucial for preventing and managing various diseases.

METHODS

A retrospective analysis was conducted on 4,286 cases utilizing the Spearman correlation method. BMI, neutrophil count, and triglyceride levels were determined as key exposure factors. To further investigate the causal correlation, a two-sample Mendelian randomization(MR) design was utilized, leveraging data from genome-wide association study (GWAS). Within this framework, and multivariable Mendelian randomization(MVMR) was applied to explore the linkage between multiple genetic variants and complex traits.

RESULTS

The study primarily focused on UA, employing genetic variation as a natural tool to assess the causal impact of various factors on UA. Spearman correlation analysis revealed significant association between UA and BMI (ρ = 0.230,p<0.01), neutrophils (ρ = 0.164,p<0.01), and triglyceride levels (ρ = 0.154,p<0.01). Additionally, two-sample MR analysis affirmed a reciprocal causal association between neutrophils and UA (OR = 1.035,95%CI:1.009-1.061,p = 0.008), as well as positive causal connection between UA and both BMI (OR = 1.083,95%CI:1.042-1.126,p<0.001) and triglyceride levels (OR = 1.090,95%CI:1.037-1.146,p<0.001). Neutrophils also demonstrated a positive causal linkage with BMI (OR = 1.034,95%CI:1.009-1.078,p = 0.012) and triglyceride levels (OR = 1.077,95%CI:1.033-1.122,p<0.001), and BMI exhibited a similar causal association with triglyceride levels (OR = 1.300,95%CI:1.212-1.385,p<0.001). These findings shed light on the causal networks connecting UA, BMI, neutrophils, and triglyceride levels. By integrating Spearman correlation analysis with various MR study designs, this study provided a robust framework for identifying key factors influencing hyperuricemia and related health issues, thereby enhancing our understanding of the interplay between inflammatory markers and these health indicators.

CONCLUSIONS

Our study presents strong evidence of the complex interconnection between BMI, neutrophils, triglyceride, and UA, revealing complex causal links and highlighting potential inflammatory states as key mediators. The findings may contribute to a better understanding of these factors and potentially lead to improved clinical outcomes and patients' health.

High-Intensity Interval Training Reduces Liver Enzyme Levels and Improves MASLD-Related Biomarkers in Overweight/Obese Girls

BACKGROUND/OBJECTIVES

Despite the abundant body of evidence linking high-intensity interval training (HIIT) to cardiometabolic markers, little is known about how HIIT affects liver enzymes, particularly in obese adolescents. This study aimed to investigate the effects of HIIT on metabolic dysfunction-associated steatotic liver disease (MASLD)-related biomarkers in overweight/obese adolescent girls.

METHODS

Thirty-three overweight/obese adolescent girls (age, 17.0 ± 1.15 yr.; body mass index, 33.3 ± 4.77 kg/m2) were randomly assigned to HIIT (n = 17) or control (n = 16) groups. The HIIT group participated in a nine-week HIIT program (three times weekly) without caloric restriction. Maximal aerobic speed, body composition indexes, blood pressure, MASLD-related biomarkers [liver enzymes (alanine aminotransferase (ALT) and aspartate aminotransferase (AST)), plasma lipids, uric acid, platelet count, and homeostasis model assessment index for insulin-resistance (HOMA-IR)] were examined at baseline and after the intervention.

RESULTS

Significant "time × group" interactions were found for body composition indexes, systolic blood pressure, maximal aerobic speed, liver enzymes ALT and AST, plasma lipids, glucose, and HOMA-IR. The HIIT program resulted in an increase in maximal aerobic speed (p = 0.035) and a decrease in body composition and plasma lipids (p < 0.01), systolic blood pressure (p = 0.011), ALT (p = 0.013), AST (p = 0.012), and HOMA-IR (p = 0.01), but no significant changes in uric acid and platelet count. None of these markers changed in the control group.

CONCLUSIONS

HIIT resulted in an improvement in MASLD-related biomarkers. HIIT could be an effective exercise therapy to prevent and reverse MASLD in adolescents with obesity.

Phosphorus modifies the association between body mass index and uric acid: Results from NHANES 2007-2018

INTRODUCTION

Studies in recent years have shown that high uric acid causes harm to the human body, which has become a serious public health problem. Elevated serum uric acid has been shown to be associated with obesity, but the relationship between BMI and uric acid (UA) remains controversial. Although the association between BMI and UA has been well studied, the effect of phosphorus levels in vivo on this association remains unclear. This study aimed to determine the relationship between BMI and serum uric acid and the effect of phosphorus on the relationship between the two.

RESEARCH DESIGN AND METHODS

The present study analyzed data from the National Health and Nutrition Examination Survey (NHANES) continuous 2007-2018 cycle. We included 10786 participants aged 20 years and over. Multivariable linear regression was performed to assess the association between BMI and serum uric acid. phosphorus was stratified into low phosphorus (<3.3 mg/dl), middle phosphorus (3.3-3.9 mg/dl) and high phosphorus (>3.9 mg/dl). Correction of the effect of phosphorus was assessed by testing the interaction between BMI and UA in multivariate linear regression.

RESULTS

In this cross-sectional study, we found that BMI was positively associated with UA in the female population but not significantly in the male population or in the total population. In multiple regression analysis, UA was 0.51 higher in the highest female BMI group than in the lowest group (p = 0.0001). The relationship between BMI and UA differed significantly by gender under the influence of phosphorus, with men and women in Model II having a greater elevation of UA in men than in women within most groups. (BMI >30, phosphorus >3.9 mg/dl, β:0.83 95% CI: 0.43, 1.23 vs β: 0.79 95% CI: 0.30, 1.29). In addition, phosphorus significantly altered the positive association between BMI and UA in most models.

CONCLUSION

Our results indicate significant associations between BMI and uric acid in women, with higher BMI values likely to be associated with a higher risk of hyperuricemia, suggesting that uric acid levels in obese people should be closely monitored in clinical practice. Phosphorus and BMI have an interactive effect in elevating UA and should be noted as indicators of phosphorus in clinical practice.

Uricase biofunctionalized plasmonic sensor for uric acid detection with APTES-modified gold nanotopping

Current uric acid detection methodologies lack the requisite sensitivity and selectivity for point-of-care applications. Plasmonic sensors, while promising, demand refinement for improved performance. This work introduces a biofunctionalized sensor predicated on surface plasmon resonance to quantify uric acid within physiologically relevant concentration ranges. The sensor employs the covalent immobilization of uricase enzyme using 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) and N-Hydroxysuccinimide (NHS) crosslinking agents, ensuring the durable adherence of the enzyme onto the sensor probe. Characterization through atomic force microscopy and Fourier transform infrared spectroscopy validate surface alterations. The Langmuir adsorption isotherm model elucidates binding kinetics, revealing a sensor binding affinity of 298.83 (mg/dL)-1, and a maximum adsorption capacity of approximately 1.0751°. The biofunctionalized sensor exhibits a sensitivity of 0.0755°/(mg/dL), a linear correlation coefficient of 0.8313, and a limit of detection of 0.095 mg/dL. Selectivity tests against potentially competing interferents like glucose, ascorbic acid, urea, D-cystine, and creatinine showcase a significant resonance angle shift of 1.1135° for uric acid compared to 0.1853° for interferents at the same concentration. Significantly, at a low uric acid concentration of 0.5 mg/dL, a distinct shift of 0.3706° was observed, setting it apart from the lower values noticed at higher concentrations for all typical interferent samples. The uricase enzyme significantly enhances plasmonic sensors for uric acid detection, showcasing a seamless integration of optical principles and biological recognition elements. These sensors hold promise as vital tools in clinical and point-of-care settings, offering transformative potential in biosensing technologies and the potential to revolutionize healthcare outcomes in biomedicine.

A metabolomic investigation of serum perfluorooctane sulfonate and perfluorooctanoate

BACKGROUND

Exposures to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA), environmentally persistent chemicals detectable in the blood of most Americans, have been associated with several health outcomes. To offer insight into their possible biologic effects, we evaluated the metabolomic correlates of circulating PFOS and PFOA among 3,647 participants in eight nested case-control serum metabolomic profiling studies from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

METHODS

Metabolomic profiling was conducted by Metabolon Inc., using ultra high-performance liquid chromatography/tandem accurate mass spectrometry. We conducted study-specific multivariable linear regression analyses estimating the associations of metabolite levels with levels of PFOS or PFOA. For metabolites measured in at least 3 of 8 nested case-control studies, random effects meta-analysis was used to summarize study-specific results (1,038 metabolites in PFOS analyses and 1,100 in PFOA analyses).

RESULTS

The meta-analysis identified 51 and 38 metabolites associated with PFOS and PFOA, respectively, at a Bonferroni-corrected significance level (4.8x10-5 and 4.6x10-5, respectively). For both PFOS and PFOA, the most common types of associated metabolites were lipids (sphingolipids, fatty acid metabolites) and xenobiotics (xanthine metabolites, chemicals). Positive associations were commonly observed with lipid metabolites sphingomyelin (d18:1/18:0) (P = 2.0x10-10 and 2.0x10-8, respectively), 3-carboxy-4-methyl-5-pentyl-2-furanpropionate (P = 2.7x10-15, 1.1x10-17), and lignoceroylcarnitine (C24) (P = 2.6x10-8, 6.2x10-6). The strongest positive associations were observed for chemicals 3,5-dichloro-2,6-dihydroxybenzoic acid (P = 3.0x10-112 and 6.8x10-13, respectively) and 3-bromo-5-chloro-2,6-dihydroxybenzoic acid (P = 1.6x10-14, 2.3x10-6). Other metabolites positively associated with PFOS included D-glucose (carbohydrate), carotene diol (vitamin A metabolism), and L-alpha-aminobutyric acid (glutathione metabolism), while uric acid (purine metabolite) was positively associated with PFOA. PFOS associations were consistent even after adjusting for PFOA as a covariate, while PFOA associations were greatly attenuated with PFOS adjustment.

CONCLUSIONS

In this large metabolomic study, we observed robust positive associations with PFOS for several molecules. Further investigation of these metabolites may offer insight into PFOS-related biologic effects.

Associations between body mass index in different childhood age periods and hyperuricemia in young adulthood: the China Health and Nutrition Survey cohort study

BACKGROUND

Few studies have evaluated the specific age period in childhood when the association of body mass index with adult hyperuricemia begins to be operative. This study aimed to examine the associations between body mass index in different childhood age periods and the risk of adult hyperuricemia in China.

METHODS

The study cohort from the China Health and Nutrition Survey included 676 participants who were aged ≥ 18 years and had data on uric acid in 2009 with at least one measurement of body mass index in childhood surveys before 2009. There were 357, 365, 358, 427, and 432 observations in childhood age groups of ≤ 5 years, 6-9 years, 10-12 years, 13-15 years, and 16-18 years, respectively. Body mass index Z score was calculated based on 2000 Center for Disease Control and Prevention growth charts for the United States.

RESULTS

Childhood body mass index Z scores measured at age ≤ 5 years, 6-9 years, 10-12 years, and 13-15 years had no statistical association with adult uric acid. In comparison, childhood body mass index Z scores measured at age 16-18 years were significantly associated with adult uric acid (β = 11.539, P = 0.007), and the strength of association was stronger in girls (β = 18.565, P = 0.002) than in boys (β = 9.209, P = 0.087). In addition, childhood body mass index Z scores measured at age 16-18 years were significantly associated with an increased risk of adult hyperuricemia (odds ratio = 1.323, 95% confidence interval = 1.003-1.746, P = 0.048), but not for other age groups.

CONCLUSION

The association between childhood body mass index and young adulthood hyperuricemia was influenced by childhood age.

The Effect of Body Adiposity and Alcohol Consumption on Serum Uric Acid: A Quantile Regression Analysis Based on the China National Health Survey

Adiposity and alcohol consumption are reported to be associated with a higher level of serum uric acid (SUA), but whether their effect differs on SUA percentile distribution is still unclear. In this study, we aimed to investigate how alcohol intake and body fat percentage (%BF) integrated with body mass index (BMI) influence the distribution of SUA in Chinese adults. Data from the China National Health Survey (CNHS) which included adults from 10 provinces of China were used (n = 31,746, aged 20–80 years, 40% male). %BF and BMI were integrated into eight expanded body composition groups to understand how excess body adiposity affects the distribution of SUA in the populational level. Self-report alcohol intake information was collected by face-to-face questionnaire interview. Quantile regression (QR) was used to analyze the data. We found that adiposity and alcohol consumption were associated with SUA, especially at the upper percentile in both sexes. In obese men, the QR coefficients at the 75th and 95th percentiles were 74.0 (63.1–84.9) and 80.9 (52.5–109.3) μmol/L, respectively. The highest quartile of %BF in men had a 92.6 (79.3–105.9) μmol/L higher SUA levels at its 95th percentile than the 5th quartile (p < 0.001). Compared with normal or underweight with the lowest %BF group (NWBF1), the obesity-highest %BF group (OBBF4) had the strongest positive effect on SUA, especially at the higher percentile of SUA. In BMI-defined normal or underweight participants, a higher quartile of %BF had greater effect size in all SUA percentiles. In men, current alcohol drinking had the strongest effect at the 95th percentile of SUA (QR coefficient: 31.8, with 95% CI: 22.6–41.0) comparing with 14.5, 95% CI of 8.4 to 20.6 in the 5th SUA percentile. High risk of alcohol consumption had a greater effect on SUA, especially in the higher SUA percentile. The observation of stronger association at the higher percentile of SUA suggests that decreasing body adiposity and alcohol intake at the populational level may shift the upper tails of the SUA distributions to lower values, thereby reducing the incidence of hyperuricemia.

Uric Acid Is Elevated in Children With Obesity and Decreases After Weight Loss

Introduction: Childhood obesity is an increasing condition associated with continuous obesity into adulthood and development of comorbidities. Adult studies show an association between serum uric acid (SUA) levels and body mass index (BMI). The aim of this retro perspective exploratory study was to investigate SUA in obese children and adolescents and the effects of a subsequent weight reduction. Materials and Methods: One hundred and seventy-one children (age 4-18), with obesity (i.e. BMI-SDS of +2 or higher) were included in a multifactorial lifestyle intervention. The children participating were annually measured for anthropometrics, blood samples and DEXA-scans for up to 3 years. Eighty-nine children were included for follow-up analysis. Results: After a follow-up of 20.7 ± 9.4 months a reduction in BMI-SDS of -0.34 ± 0.53 (p < 0.01) was observed. SUA was found to be positively associated with changes in BMI-SDS. SUA levels decreased in the 65 children who lost weight during the trial, conversely, SUA increased in the 23 children who gained weight during the trial (p < 0.01 between groups). Conclusion: SUA was found to correlate with measures of obesity and for the first time, this intervention demonstrates a positive relationship between SUA and weight reduction in children with obesity.