Bariatric surgery in severely obese adolescents improves major comorbidities including hyperuricemia

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Metabolism www.metabolismjournal.com

Bariatric surgery in severely obese adolescents improves major comorbidities including hyperuricemia Andreas Oberbach a, b , Jochen Neuhaus c , Thomas Inge d , Katharina Kirsch e , Nadine Schlichting f , Susann Blüher f, g , Yvonne Kullnick f , Joachim Kugler b , Sven Baumann h , Holger Till i,⁎ a

Department of Cardiac Surgery, University of Leipzig, Heart Center Leipzig, Leipzig, Germany University of Dresden, Department of Health Sciences/Public Health, Dresden, Germany c Department of Urology, University of Leipzig, Leipzig, Germany d Division of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA e Department of Cardiology, Heart Center, University of Leipzig, Leipzig, Germany f Integrated Research and Treatment Center (IFB) Adiposity Diseases, University Leipzig, Leipzig, Germany g Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany h Helmholtz Centre for Environmental Research, Department of Metabolomics, Leipzig, Germany i Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria b

A R T I C LE I N FO Article history:

AB S T R A C T Objective. Serum uric acid (sUA) is believed to contribute to the pathogenesis of metabolic

Received 12 September 2013

comorbidities like hypertension, insulin-resistance (IR) and endothelial dysfunction (EDF) in

Accepted 15 November 2013

obese children. The present pilot study investigated the association between sUA concentrations and loss of body weight following laparoscopic sleeve gastrectomy (LSG)

Keywords: Uric acid Laparoscopic sleeve gastrectomy (LSG) Roux-Y gastric bypass (RYGB) Morbid obesity Adolescents

or laparoscopic Roux-en-Y-gastric bypass (RYGB) in severely obese adolescents. Materials/Methods. 10 severely obese adolescents underwent either LSG (n = 5) or RYGB (n = 5). 17 normal weight, healthy, age- and gender-matched adolescents served as a normal weight peer group (NWPG). Pre- and 12 months postoperatively, sUA and relevant metabolic parameters (glucose homeostasis, transaminases, lipids) were compared. Results. Preoperatively, sUA was significantly elevated in patients with severe obesity compared to NWPG. Twelve months after LSG and RYGB, a significant decrease in sUA, BMI, CVD risk factors, hepatic transaminases, and HOMA-IR was observed. Reduction in SDS-BMI significantly correlated with changes in sUA. Conclusions. sUA levels and metabolic comorbidities improved following bariatric surgery in severely obese adolescents. The impact of changes in sUA on long-term clinical complications of childhood obesity deserves further study. © 2014 Elsevier Inc. All rights reserved.

Abbreviations: LSG, laparoscopic sleeve gastrectomy; RYGB, Roux-en-Y gastric bypass; BMI, body mass index; sBP, systolic blood pressure; dBP, diastolic blood pressure; ALAT, alanine aminotransferase; ASAT, aspartate aminotransferase; γ-GT, γ-glutamyl transferase, HOMA-IR, homeostasis model assessment-insulin-resistance; sUA, serum uric acid; CVD, cardiovascular disease; FPG, fasting plasma glucose; FPI, fasting plasma insulin; TG, triglycerides; MRM, multiple reaction monitoring. ⁎ Corresponding author. Department of Paediatric and Adolescent Surgery, Medical University of Graz, Auenbruggerplatz 34, A-8036 Graz. Tel.: +43 316 385 13762; fax: +43 316 385 13775. E-mail address: [email protected] (H. Till). 0026-0495/$ – see front matter © 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.metabol.2013.11.012

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1.

Introduction

Metabolic dysfunction and related cardiovascular diseases in obese adults have prompted interest in studying similar conditions in obese children, to estimate the potential impact on the long term health [1]. Specifically, recent evidence suggests that serum uric acid (sUA) concentrations were strongly associated with body weight in adults [2] and obese children [3]. Hyperuricemia is believed to independently play a major role in the pathogenesis of IR and CVD [4,5] in obese individuals, because sUA enters adipocytes through a uric acid-specific transporter and plays a role in regulating the production of macrophage chemoattractant protein 1 (MCP-1). In addition, sUA may participate in the production of adiponectin [6]. Interestingly, MCP-1 is a well-known protein that modulates IR and fatty acid catabolism [7]. Moreover sUA affects various vasoactive mediators and reduces NO production in cultured pulmonary endothelial cells [8]. Increased NO metabolism is essential in the regulation of arterial hypertension, a condition that leads to CVD in the long-term [9,10]. Several long-term longitudinal studies have demonstrated that childhood obesity was associated with a cluster of risk factors for the later manifestation of CVD in adulthood, such as atherosclerosis and hypertension [11,12]. It is reasonable to assume that effective treatment of childhood obesity may reduce those risk factors including hyperuricemia [13]. Behavioral and lifestyle interventions for children and adolescents with severe obesity often fail to achieve sustainable weight loss and thus fail to reverse associated comorbidities [14]. Bariatric surgery has been increasingly suggested as an effective treatment option to achieve sustainable weight loss in severely obese adolescents [15]. The most commonly used procedures for adolescents today include LSG and RYGB. Both techniques improve metabolic and cardiovascular comorbidities such as insulin resistance (IR), an impaired glucose metabolism and CVD in this age group [16,17]. Current evidence indicates that both procedures have similar effects on body weight, food intake, and parameters of lipid and glucose homeostasis despite their surgical differences [18]. Studies focusing on the regulation of sUA in children/ adolescents following weight loss therapies remain sparse. In this pilot study we have for the first time investigated the consequences of LSG and RYGB on sUA, glucose and lipid metabolism in severely obese adolescents. We hypothesized that 1) sUA levels would be more elevated in severely obese adolescents compared to lean subjects, 2) sUA levels would decrease following surgical weight loss, and 3) decreases in sUA levels would correlate with change in weight and other important clinical parameters postoperatively.

2.

Methods

The study was performed in accordance with the principles of Good Clinical Practice [19] and the Declaration of Helsinki [20]. Informed consent was obtained from all subjects and/or from a legally authorized representative before initiation of any

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study related activities. Three cohorts were included in this multicenter study: LSG (n = 5) from the University of Leipzig, RYGB (n = 5) from Cincinnati Children’s Hospital (NIH R03DK068228; protocol # 05-05-14 approved by CCHMC Institutional Review Board [IRB]). According to present guidelines for weight loss surgery [21], severely obese adolescents in both centers were identified for bariatric surgery only after failing conservative therapy. A normal weight, healthy, ageand gender-matched peer group (NWPG) from our previously described cohort (URL: http://www.clinicaltrials.gov. Identifier: NCT00176371 [22]) served as a lean comparison group. Measurements were performed at baseline and at 12-month follow-up. All subjects were characterized anthropometrically and clinically. Laboratory measurements were carried out in a blinded manner at a certified laboratory in the University of Leipzig, including the frozen specimens from Cincinnati Children’s Hospital.

2.1.

Cohort definitions

Children over the 90th, 97th and 99.8th BMI percentiles were considered to be overweight, obese and severely obese, respectively, according to the current national reference values [23].

2.2.

Sample collection and laboratory data analysis

All blood samples were collected between 8:00 and 10:00 a.m. after an overnight fast. Fasting plasma glucose (FPG) and fasting plasma insulin (FPI) were measured by commercial kits (Glucose Assay Kit II, BioVision, CA, USA; Human Insulin ELISA Kit, Alpco Diagnostics, Salem, NH, USA). IR was evaluated applying the homeostasis model assessment (HOMA) and defined as HOMA > 2.7 (HOMA-IR) [24]. Serum hepatic enzyme activities (ALAT, ASAT, γGT) were measured by colorimetric enzymatic assays (Alanine Aminotransferase Activity Assay Kit, BioVision; Aspartate Aminotransferase Activity Assay Kit, BioVision; IDTox γ-GlutamylTransferase Enzyme Assay Kit, IDLabs, ON, Canada). Analysis of total cholesterol (Chol), high-density lipoprotein cholesterol (HDLc), low-density lipoprotein cholesterol (LDLc) and triglycerides (TG) was determined by the Integra 700 Analyzer (Roche Diagnostic, Hoffmann–La Roche, Ltd, Basel, Switzerland) using the standard enzymatic method from the Biochemical Laboratory instructions.

2.3.

Measuring of serum uric acid

sUA sample preparation was based on a modified method by Kim et al. [25]: serum samples (10 μl) were 10-fold diluted with water which included [1,3-15 N2]-UA (Eurisotop, Saarbrücken, Germany, final concentration 50 μmol/L) and treated with 20 μl 10% TCA (w/v). The samples were vortexed for 1 min and centrifuged at 15,000 × g for 2 min. Supernatants were loaded into autosampler vials and analyzed by LC-MS/MS. Analyses were carried out on an Agilent 1100 series binary HPLC system (Agilent Technologies, Waldbronn, Germany) coupled with an 4000 QTRAP™ mass spectrometer (AB Sciex, Concord, Canada) equipped with a TurboIon spray

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source. Liquid chromatography analyses were performed in gradient elution mode using Chromolith Flash RP-18e (25 × 4.6 mm, Merck, Darmstadt, Germany) at 40 °C with eluent A (0.1% formic acid in water) and eluent B (0.1% formic acid in acetonitrile) with a flow rate of 400 μl/min. The injection volume of each sample was 5 μl. The initial composition of the binary solvent was 100% A followed by a linear gradient to 0% A within 1.5 min and was held for 0.5 min. Afterwards the eluent composition was changed back to 100% A and held for another 1 min for reequilibration of the column. For sUA quantification, mass spectrometry (MS) was operated in negative ion and multiple reaction monitoring (MRM) mode. Data were acquired and analyzed using Analyst software version 1.4.2 (AB Sciex; AB SCIEX Headquarters, Framingham, MA, U.S.A.). Optimized MS-dependent parameters were as following: Ion spray voltage − 4000 V, nebulizer gas (GS1), auxiliary gas (GS2), curtain gas (CUR) and collision gas (CAD) were 40, 60, 20, 5 (arbitrary units), respectively. The source temperature was maintained at 400 °C. Declustering potential, collision energy, entrance potential and collision cell exit potential were optimized separately for each mass transition. The MRM-assay was performed by monitoring quantifier and qualifier mass transitions between m/z 167.0 to m/z 124.0 and m/z 167.0 to m/z 96.0 for UA and between m/z 169.0 to m/z 125.0 and m/z 129 to m/z 97.0 for [1,3-15 N2]-UA.UA and [1,315 N2]-UA stock solutions (10 μmol/L) were prepared in 0.3 mol/L KOH. Standard and spiking solutions were prepared by appropriate dilution of the stock solutions with water. The concentrations of UA in these standards ranged from 1 to 75 μmol/L. The calibration curves were obtained by using weighted (1/×) least-squares regression analysis and were made by the internal standard method, where the ratio of the areas was plotted against the concentration of analytes in the calibration solutions.

2.4.

Reactive hyperemia index (RHI)

Reactive hyperemia index (RHI) as an indicator of endothelial function (EF) was assessed only for NWPG using the EndoPAT® device as described elsewhere [26–28]. Measurements were performed in a thermo-neutral, quiet surrounding with children positioned lying, following an overnight fast.

2.5.

Statistical analysis

Statistical analyses were performed using the SPSS 18.0 package (SPSS Inc., Chicago, IL). Except for age, data in Table 1 are presented as means with 95% CI (mean (95% CI)). Fig. 1A data are presented as mean and SD. Normality of distribution of data was tested with the Kolmogorov– Smirnov test and equality of variances with Levene’s test. The data were logarithmically transformed, when appropriate (Fig. 1). Differences within groups (baseline vs. 12 months post) were tested for significance using paired t-test (two-tailed). ANOVA with repeated measurements were performed to test differences between the groups over time. The model was adjusted for weight. When data failed normal distribution for

group comparison, Kruskal–Wallis Test was applied following Dunn's Multiple Comparison Test to identify differences between the groups at baseline or after 12 months. Comparison of baseline vs. 12-month follow-up was done by Wilcoxon signed rank test. P-values < 0.05 were accepted as statistically significant. Partial correlation (Pearson’s) and linear regression coefficients were applied when examining the relationship between variables, both at baseline and separately changes of variables in non-obese and obese children. The statistical association between sUA concentrations and clinical parameters like age and gender was examined by multiple linear regression analysis.

3.

Results

3.1.

Characteristics of the study population at baseline

The clinical, anthropometric and metabolic characteristics of the 3 groups (NWPG, LSG, RYGB) at baseline and after 12 months are depicted in Table 1. Severely obese subjects undergoing LSG and RYGB had BMI values that were 169% and 280% higher respectively, than the NWPG. Additionally, other clinical characteristics of the NWPG and surgical groups were also significantly different at baseline (Table 1). Systolic blood pressure (sBP), FPI and HOMA-IR were significantly higher in LSG and RYGB cohorts than in the NWPG group. Significantly higher levels of TG were also found in both LSG and RYGB groups compared to NWPG (Table 1). Gamma glutamyl transferase (yGT), a marker associated with steatohepatitis, was also significantly higher in both severely obese groups compared to NWPG; ALAT was also higher in LSG compared to NWPG.

3.2.

Effect of LSG on metabolic characteristics

Twelve months postoperatively, LSG subjects showed significant reduction of body weight, SDS-BMI, and BMI (BMI decreased from 46.4 to 34.4 kg/m2) (Table 1). Furthermore, significant improvements were observed for HOMA-IR (− 51.9%) and ALAT (− 53.9%). Compared to NWPG, FPI was significantly lower (− 30%) in surgical cohorts. However sBP, FPG, and yGT were still significantly higher 12 months post LSG compared to NWPG. Interestingly, HOMA-IR, TG and ALAT had improved to normal range in LSG patients compared to NWPG (Table 1).

3.3.

Effect of RYGB on metabolic characteristics

RYGB subjects showed a significant decrease of BMI (decreased from 63 to 36.4 kg/m 2 ; − 41.8%) following 12 months, though their BMI was still outside of standard growth chart (> 99 BMI percentile, Table 1). Significant improvements compared to baseline were found for sBP (− 6.8%), FPI (−48.1%), HOMA-IR (−52%), HDLc (+ 21.4%) and ALAT (− 43.9%). Despite improvement between preoperative and postoperative values of sBP, both sBP (+ 19.6%) and dBP (+ 24.6%) were significantly higher in surgical subjects than in the NWPG. Also yGT levels were significantly higher than in the comparison group, possible due to referral bias.

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Table 1 – Phenotype characteristics of study population. NWPG

Severely obese children

Control baseline no. sex (female/male) age (years) BW BMI SDS-BMI Systolic BP (mmHg) Diastolic BP (mmHg)

glucose metabolism FPG (mmol/L) FPI (mU/L)

a

HOMA-IR

lipid metabolism Total c (mmol/L) HDLc (mmol/L) LDLc (mmol/L) TG (mmol/L)

liver enzymes ASAT (U/L) a ALAT (U/L) γGT

14.63 (12.5–16.5) 47.3 (43.2–51.4) 18.8 (18.37–19.28) 0.02 (−0.11–0.16) 103 (96–111) 66 (63–69)

12 months post 17 13/4 15.7 (13.5–16.6) 52.2 b (48.1–56.3) 19.9 (19.25–20.63) 0.14 b (0.01–0.27) 102 (97–109) 61 (56–66)

LSG baseline

14.7 (9.7–19.7) 127.3 c (112.1–142.5) 46.4 c (40.22–52.58) 4.1 c (3.6–4.6) 123 c (105–144) 72 (65–79)

12 months post 5 5/0 15.7 (10.6–20.8) 94.6 b d (83.6–105.7) 34.4 b d (29.09–39.71) 2.4 b d (1.4–3.4) 120 d (115–124) 65 (57–74)

RYGB baseline

12 months post

16.5 (14.6–18.5) 180.3 c (127.7–233) 63.0 c (49.48–76.52) 4.2 c (3.4–5.0) 131 c (124–138) 68 (62–74)

5 2/3 17.5 (15.6–19.4) 105.6 b d (70.2–141) 36.4 b d (27.08–45.72) 2.6 b d (1.7–3.5) 122 b d (117–126) 76 d (69–83)

4.3 (4.1–4.5) 16.1 (13.8–18.9) 2.4 c (2.3–2.8)

4.2 (4.0–4.5) 17.2 (14.7–20) 2.6 (2.4–2.8)

4.77 (4.76–4.78) 24.6 (20.4–29.6) 5.2 c (4.4–6.2)

4.7 d (4.6–4.8) 12.0 d (11.1–12.9) 2.5 b (2.3–2.7)

4.8 (4.4–5.2) 23.7 (19.1–29.3) 5.0 c (4.1–6.1)

4.4 (4.3–4.6) 12.3 b (10.8–14.1) 2.4 b (2.1–2.8)

4.3 (3.9–4.7) 1.4 (1.3–1.5) 2.5 (2.2–2.8) 0.99 (0.81–1.17)

4.1 b (3.8–4.4) 1.3 (1.2–1.4) 2.4 (2.1–2.7) 1.15 (0.88–1.42)

4.3 (4.1–4.5) 1.2 (0.8–1.5) 2.8 (2.6–2.9) 1.33 c (0.98–1.67)

4.5 (3.4–5.6) 1.5 (1.2–1.8) 2.5 (1.8–3.3) 0.8 (0.5–1.3)

4.2 (3.2–5.1) 1.1 (1.0–1.3) 2.5 (2.1–2.9) 1.34 c (1.08–1.59)

3.3 d (2.7–3.8) 1.4 b (1.2–1.5) 1.9 (1.5–2.3) 0.86 (0.66–1.05)

32.7 (30.2–35.4) 33.7 (32.1–35.3) 5.7 (3.8–8.7)

32.3 (29.5–35.4) 31.5 b (30–33) 6.7 (7.1–7.4)

36.7 (31.4–42.8) 54.9 c (32.0–77.8) 23.3 c (15.2–35.7)

32.4 (27.9–37.7) 25.3 b (15.9–34.7) 12.0 d (7.6–19.1)

34.4 (30.3–39.2) 34.6 (21.3–47.9) 22.3 c (16.8–29.7)

33.8 (25.8–44.3) 19.4 b (12.5–26.4) 16.7 (14.1–19.8)

P-values <0.05 were accepted as statistically significant. a Data are not normal distributed. b Dependent student T-Test for paired samples or Wilcoxon signed rank to identify time effects within patients after 12 months. c Compare groups at baseline to reference group. d Compare group after 12 months to reference group.

Interestingly, the total cholesterol (total c) was significantly lower in RYGB (− 19.5%) compared to NWPG at 12 months; HOMA-IR and TG normalized in surgical cohorts.

3.4.

Changes in sUA

sUA was 43.4% higher in LSG (379.3 ± 55.3 μmol/L; p < 0.015) and 54.1% higher in RYGB (407.4 ± 13.6 μmol/L; p < 0.003) obesity phenotypes at baseline compared to NPWG (264.4 ± 89.2 μmol/L; ANOVA comparison of all groups p < 0.001; Fig. 1A). Both LSG (261.4 ± 44 μmol/L; p < 0.028) and RYGB (291.4 ± 18.9 μmol/L; p < 0.001) led to a significant reduction of sUA levels (LSG: 31.1%; RYGB: 28.5%) after 12 months. ANOVA

comparison of all groups revealed no differences 12 months following intervention (p < 0.265; Fig. 1A).

3.5. Correlation of changes of sUA with other clinical changes To investigate whether changes in sUA concentrations (delta-sUA) were associated with clinical outcome, we correlated changes of all clinical parameters to delta-sUA levels. The changes were calculated as 12-month followup minus baseline. We found that delta-sUA was significantly correlated with delta-BW, delta-SDS-BMI and deltaLDLc (Table 2).

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Fig. 1 – Regulation of uric acid (UA). (A) Serum UA levels after 12 months in normal weight peer group (NWPG), laparoscopic sleeve gastrectomy cohort (LSG) and Roux-Y-gastric bypass cohort (RYGB). (B) Linear regression analysis of sUA (presented as log10 data) and reactive hyperemia index (RHI) as an indicator of endothelial function.

The pooled LSG and RYGB cohorts showed in addition significant correlation of delta-sUA and delta-dBP, while the correlation between delta-sUA and delta-LDLc was no longer observed.

3.6. Correlation of Reactive Hyperemia Index (RHI) with sUA In recent years, RHI has been evaluated as a marker for endothelial function. We found a significantly positive correlation between sUA and RHI (r2 = 0.79; p < 0.001, Fig. 1B) in NWPG.

4.

Discussion

In this study, we hypothesized that sUA levels would be markedly elevated at baseline in severely obese adolescents undergoing bariatric surgery, and that sUA levels would decrease postoperatively. Furthermore, we hypothesized that this decrease in sUA levels would correlate with change in weight and other important clinical parameters postoperatively. Indeed, we documented markedly elevated sUA levels in the severely obese adolescents which decreased significantly following operation. Decrease in sUA correlated with changes in BMI and dBP, suggesting a possible biological link

between improvement in sUA and improvement in risk of CVD. We found that the changes of sUA were accompanied by changes of body weight (Table 1). Interestingly although the body weight was still above normal range following bariatric surgery, HOMA-IR reflecting glucose homeostasis [18] was significantly lower as was ALAT reflecting liver metabolism (Table 1). Both findings support the view that these parameters may be improved by both surgery procedures [15–17]. However, there might be differences in the effectivity of LSG and RYGB on lipid metabolism, since only RYGB significantly improved total cholesterol (Table 1). The benefits of bariatric surgery for treatment of morbid obesity in adulthood are well documented [29]. As shown in a recent longitudinal study with 10-year follow-up cardiovascular risk was significantly diminished in severely obese patients who underwent biliopancreatic diversion [30]. Reports about the effects of bariatric surgery in adolescents are emerging, but the physiological effects and side-effects are still poorly understood [31]. The present study focused on the changes of sUA following two bariatric procedures in adolescents that almost certainly cause weight loss and metabolic change by distinct anatomic/physiologic mechanisms. Human studies have shown an important association between sUA and obesity comorbidities in childhood [3]. We found that extremely obese children had significantly higher

Table 2 – Multiple regression analysis of changes of serum uric acid (sUA) with changes in phenotype parameters. all cases (n = 27) β Δ Δ Δ Δ

body weight [kg] SDS-BMI LDLc dBP

a

0.54 0.43 0.33 -0.15

p-value 0.001 0.007 0.043 0.388

NWPG (n = 17) β

a

0.00 -0.15 0.15 0.18

p-value 0.999 0.573 0.562 0.542

LSG and RYGB (n = 10) βa 0.67 0.49 0.36 -0.43

p-value 0.002 0.019 0.101 0.047

Associations of changes (Δ) of body weight, SDS-BMI, LDLc and dBP with changes of sUA concentration (baseline − 12 months). Multiple regression models controlled for gender. No covariate was used. a Standarized regression coefficient-β.

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sUA levels compared to control subjects. These data agree with other recent findings showing that obesity is strongly associated with higher sUA, both in childhood and in adults [3,32]. Further, other studies have shown a link between sUA and obesity-related disorders such as hypertension, atherosclerosis, disturbed glucose and lipid metabolism in childhood and adolescence [3,33,34]. For instance, recent evidence supports the possibility that elevated sUA may influence the development of systolic hypertension. A high sUA level was observed in nearly 90% of adolescents with essential hypertension of recent onset [13]. In our study we correlated sUA with RHI in our NWPG and found a very close correlation, supporting the view that endothelial dysfunction might be directly related to elevated sUA [35,36]. Recently a molecular biological study on cultured pulmonary artery endothelial cells provided evidence that sUA induced arginase activity leading to inhibition of NO production [8]. NO is a major metabolite regulating smooth muscle cell contraction [37]. In summary, those results support the hypothesis that elevated sUA is a major risk factor for CVD in childhood and adolescence. It is of special importance that elevated cardiovascular risk factors in childhood will persist into adulthood, especially among those with increased body fat [11,38]. Therefore, lowering elevated sUA might be a promising therapeutic option to reduce hypertension [39]. Feig et al. showed that allopurinol medication reduced BP in adolescents with newly diagnosed hypertension [40]. Furthermore a recent meta-analysis supported the impact of allopurinol therapy, despite some conflicting results on its effect on BP in several studies [39]. In obese individuals, lowering of sUA by allopurinol might not be as effective due to the close correlation of sUA with visceral fat mass [41]. Therefore, treatment of severe obesity in childhood should optimally address both fat mass and sUA to have the greatest possible benefit in reducing cardiovascular risk. Indeed, others have also demonstrated that RYGB and LSG are effective interventions in severe obesity to improve obesity related comorbidities, such as hyperuricemia and hypertension, in a long-term manner [42,43]. While bariatric surgery certainly causes anatomic rearrangements of the gastrointestinal tract, the ultimate success of these operations relies on changes in lifestyle, food intake behavior, and almost certainly modulation of energy balance via changes in the gut–brain communication [42]. This is especially important, since it is known that as an example higher sugar sweetened beverage consumption is associated with higher serum uric acid levels in adolescents, which may lead to downstream adverse health outcomes [44]. Following LSG and RYGB, mean sUA levels decreased to levels seen in normal weight adolescents. Interestingly, we found marginal differences in the effects of LSG and RYGB on changes in this phenotype—both were associated with important reductions in sUA levels. The fact that similar reductions in sUA were seen after these very different surgical procedures, along with the strong association of delta-sUA with delta-body weight, supports our hypothesis that sUA concentration is influenced by factors that these procedures have in common, namely weight change, adiposity change, or dietary change. It is interesting to speculate, whether reducing white adipose tissue by bariatric surgery may be of particular

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importance in the finding of reduced sUA including purine nucleotide metabolism. On the other hand, the newly described myokine irisin was found to be strongly related to skeletal muscle ATP levels following an exercise intervention [45]. Additionally, circulating irisin levels were significantly downregulated 6 months after bariatric surgery, suggesting that irisin downregulation reflects changes in metabolically active muscle mass [45]. Interestingly, XO the key enzyme of UA metabolism is especially active in skeletal muscle. While reduction in lean mass is not a primary driver of weight change following bariatric procedures, it is possible that alterations in the metabolism or quantity of muscle mass following bariatric surgery could be one mechanism by which lower sUA levels are achieved postoperatively. Furthermore, reduced fat mass also leads to reduction of oxidative stress [46,47] and since UA is known to be a strong antioxidant, reduction of UA production in fat cells might reflect less ROS following weight loss. Moreover hyperinsulinemia is able to increase sUA via increased renal reabsorption of UA [48]. However, we observed lowering of FPI only in the RYGB cohort and we found no correlation of the delta-UA with delta-FPI. This small pilot study has certain limitations as well as strengths. The small sample size of surgical subjects may well be seen as a weakness of the study. However, the number was chosen to permit us to gain an initial experience working with rare biosamples from highly unique pediatric subjects in a new international collaborative. When using such specimens in this and other studies, we have strived to work in a stepwise fashion, piloting the work first to determine objectively how many samples are needed to answer specific questions, thus preserving scarce biospecimen resources to maximize their usefulness. Another potential weakness of the work is the fact that we did not have early timepoints (e.g. within the first weeks) to be able to assess possible weight independent effects of surgery. However, as with other pilot work, the findings of these analyses will permit a more robust definitive study to be completed which may well incorporate additional time-points which will address additional aims. On the other hand there are strengths of this pilot as well. This study explored a completely new area of adolescent bariatric science and yielded proof of concept data indicating that sUA metabolism may well be affected by two distinct types of weight loss surgery in adolescents. This pilot study also allowed this new international scientific group to establish sophisticated laboratory technology and standardize sUA measurements for all samples, simultaneously probing involvement of several metabolic pathways. Such data may justify and permit more precise and informed design of larger studies investigating weight loss dependent and weight loss independent metabolic changes in the future. Indeed, these preliminary findings should lead to further work to better understand the relationship between hyperuricemia and hyperinsulinemia in the context of weight loss surgery. sUA could be a potential target for therapies focused on reducing the risk of CVD [49] and related disorders [3,50,51] in childhood obesity. Future studies on larger cohorts should also address the basic mechanism of sUA changing following weight loss interventions. As indicated by the recent study of Hirsch et

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al. (2012), a larger study cohort could reveal if changes in sUA are related to comorbidities such as insulin resistance [52]. Furthermore, the sUA levels are higher in male adolescents than in female [53] and a larger study could elucidate gender effects on outcome of bariatric surgical procedures [54].

Authors contributions AO conceptualized and designed the study, participated in data collection, analyzed data, carried out the statistical analyses, wrote manuscript. JN collected data samples, analyzed data, wrote the manuscript. TI provided clinical care for patients involved in this study, supervised data and specimen collection, assisted with interpretation of clinical and metabolic data, revised the manuscript. KK collected data samples, analyzed data, interpreted data, revised manuscript. NS collected data samples, analyzed data, interpreted data, revised manuscript. SB collected data samples, analyzed data, interpreted data, revised manuscript. YK collected data samples, analyzed data, interpreted data, revised manuscript. JK designed study, provided financial support, carried out statistic analyses, revised manuscript. SB did sample measurements, analyzed data, revised manuscript. HT substantially contributed to the conception and design of the study, provided clinical care for patients involved in this study, supervised data and specimen collection, assisted with analysis and interpretation of clinical and metabolic data, critically reviewed and revised the manuscript. All authors approved the final manuscript as submitted.

Funding This work was supported by the Federal Ministry of Education and Research (BMBF), Germany (Integrated Research and Treatment Center IFB “Adiposity Diseases”, FKZ: 01E01001). National Institutes of Health sponsored “Adolescent Gastric Bypass and Diabetic Precursors” study (NIH R03DK068228; protocol # 05-05-14 approved by CCHMC Institutional Review Board [IRB]).

Acknowledgment We would like to express our gratitude to all children and their parents who participated in these studies.

Conflict of interest The authors declare no conflicts of interest.

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