Changes in Uric Acid Levels following Bariatric Surgery Are Not Associated with <em>SLC2A9</em> Variants in the Swedish Obese Subjects Study

<div><h3>Context and Objective</h3><p>Obesity and <em>SLC2A9</em> genotype are strong determinants of uric acid levels. However, data on <em>SLC2A9</em> variants and weight loss induced changes in uric acid levels are missing. We examined whether the changes in uric acid levels two- and ten-years after weight loss induced by bariatric surgery were associated with <em>SLC2A9</em> single nucleotide polymorphisms (SNPs) in the Swedish Obese Subjects study.</p> <h3>Methods</h3><p>SNPs (N = 14) identified by genome-wide association studies and exonic SNPs in the <em>SLC2A9</em> gene locus were genotyped. Cross-sectional associations were tested before (N = 1806), two (N = 1664) and ten years (N = 1201) after bariatric surgery. Changes in uric acid were compared between baseline and Year 2 (N = 1660) and years 2 and 10 (N = 1172). A multiple testing corrected threshold of P = 0.007 was used for statistical significance.</p> <h3>Results</h3><p>Overall, 11 of the 14 tested <em>SLC2A9</em> SNPs were significantly associated with cross-sectional uric acid levels at all three time points, with rs13113918 showing the strongest association at each time point (R² = 3.7−5.2%, 3.9×10⁻²²≤p≤7.7×10⁻¹¹). One SNP (rs737267) showed a significant association (R² = 0.60%, P = 0.002) with change in uric acid levels from baseline to Year 2, as common allele homozygotes (C/C, N = 957) showed a larger decrease in uric acid (−61.4 µmol/L) compared to minor allele carriers (A/X: −51.7 µmol/L, N = 702). No SNPs were associated with changes in uric acid from years 2 to 10.</p> <h3>Conclusions</h3><p>SNPs in the <em>SLC2A9</em> locus contribute significantly to uric acid levels in obese individuals, and the associations persist even after considerable weight loss due to bariatric surgery. However, we found little evidence for an interaction between genotype and weight change on the response of uric acid to bariatric surgery over ten years. Thus, the fluctuations in uric acid levels among the surgery group appear to be driven by the weight losses and gains, independent of <em>SLC2A9</em> genotypes.</p> </div

Cross-sectional associations between serum uric acid levels and <i>SLC2A9</i> SNPs in SOS bariatric surgery patients when number of subjects has been maximized locally.

<p>All models are adjusted for age, sex, and body weight. β values represent change in cross-sectional uric acid level (µmol/L) per copy of minor allele carried. To convert µmol/L to mg/dL divide values by 59.48.</p

Basic characteristics of SOS subjects with DNA and data for uric acid levels in the total sample and by surgery group.

<p>Main effect P value is for the main effect of surgery technique on variable of interest. For variables showing a significant main effect of surgery technique, post-hoc pair-wise comparisons were run to test the mean difference between the combined banding group (vertical banded gastroplasty and banding) and gastric bypass group. N represents the number of subjects with DNA and data for uric acid level at each time point. To convert µmol/L to mg/dL divide values by 59.48.</p

Inhibition of the mTOR-related expression network is correlated with gains in lean mass following RET.

<p>A) Quantitative SAM analysis was used to relate the change in RNA expression in response to 10 wk RET in 44 subjects. The change in gene expression was related to the change in lean mass (%) and a false discovery rate calculated based on permutation of the subject labels. Data were imported into IPA and 384 genes (FDR<5%) could be mapped to the data-base for up-stream analysis. An active rapamycin signature, equating to <i>inhibition</i> of mTOR signaling was discovered (Z-score = 2.8 for directional consistency; <i>P</i>-value for transcript overlap p = 1.4×10⁻³⁰). B) Given the strength of the negative statistical association between the rapamycin signature, we then plotted the data to establish the precise nature of the relationship. We presented the mean gains in lean mass by quartiles establishing that 25% of the subject demonstrated negligible changes in lean mass. C) We selected a representative subset of the genes from <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003389#pgen-1003389-g002" target="_blank">Figure 2A</a> and plotted the mean changes with respect to lean mass changes. This established that those with the greatest lean mass actually had a <i>reduction</i> in mTOR related genes with RET and not simple a lesser increase as one might have expected from first inspection of <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003389#pgen-1003389-g002" target="_blank">Figure 2A</a>.</p

Quantitative SAM analysis using a continuum of age versus gene expression produces network hubs that are activated with human muscle age.

<p>Using a total of 97 U133+2 Affymetrix gene-chips newly produced from two independent studies, the DRET study and the HERITAGE family study we produced a novel analysis that relied on the full age-range present in these data sets. A) We first found a set of genes that co-varied with age in the DRET study and then confirmed that 580 of these were also related to age in the HERITAGE study. Mitochondrial genes were not a feature of this linear age vs gene analysis. We then mapped the Affymetrix probe-sets to the IPA database and examined the up-stream analysis output. We found in IPA that the age-related dataset was consistent with the activation of the PGR (z-score = 2.6 and p-value = 0.001) and RXR (z-score = 2.0 and p-value = 0.0001) proteins and 5-fluorouracil agonism (Z-score = 2.2 and p-value = 0.0005). B) We noted that some members of these age-related networks were also associated with lean mass gains in humans. However about 50% of the common genes were positively associated with lean mass gain and age; and 50% were regulated in a discordant manner. Clearly some responses can be causal, some may be purely correlative and some may represent compensatory events.</p

Using principal component analysis to evaluate the relationship between physiological and acute protein signaling events to RET induced gains in lean mass.

<p>A) Change in lean mass following 20 wk RET and a number of physiological parameters which demonstrated the most variance were scaled to a common value and plotted using principal component analysis in R. Principal component (PC) 1 captured the major variance in lean mass gains across subjects however none of the commonly postulated physiological parameters varied with lean mass (linear regression analysis demonstrated no significant association also). PC2, the second largest proportion of independent variance also demonstrated no association between factors such as fiber type or age and gains in lean mass. B) Phospho-protein signaling 2 hr after a combined exercise and nutrition acute intervention (to promote anabolic signaling) were scaled and plotted with change in lean mass following 20 wk RET. Again these acute signaling events shared little common variance in either PC1 or PC2 with changes in lean mass with 20 wk RET.</p

Adverse and Excellent Responders to Regular Exercise in DREW^*.

*<p>A postmenopausal woman who follows the <i>2008 Physical Activity Guidelines for Americans</i> expends about 8 kcal/kg/week in her exercise program. The 4 kcal/kg/week is about 50% the current recommendation whereas the 12 kcal/kg/week is about 50% above the recommended dose.</p