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Serum Uric Acid and Adiposity: Deciphering CausalityUsing a Bidirectional Mendelian RandomizationApproach Tanica Lyngdoh1, Philippe Vuistiner1, Pedro Marques-Vidal1, Valentin Rousson1, Ge´rard Waeber2, 1 Institute of Social and Preventive Medicine (IUMSP), Lausanne University Hospital, Lausanne, Switzerland, 2 Department of Medicine, Internal Medicine, CHUV, Lausanne, Background: Although the relationship between serum uric acid (SUA) and adiposity is well established, the direction of thecausality is still unclear in the presence of conflicting evidences. We used a bidirectional Mendelian randomization approachto explore the nature and direction of causality between SUA and adiposity in a population-based study of Caucasians aged35 to 75 years.
Methods and Findings: We used, as instrumental variables, rs6855911 within the SUA gene SLC2A9 in one direction, andcombinations of SNPs within the adiposity genes FTO, MC4R and TMEM18 in the other direction. Adiposity markers includedweight, body mass index, waist circumference and fat mass. We applied a two-stage least squares regression: a regression ofSUA/adiposity markers on our instruments in the first stage and a regression of the response of interest on the fitted valuesfrom the first stage regression in the second stage. SUA explained by the SLC2A9 instrument was not associated to fat mass(regression coefficient [95% confidence interval]: 0.05 [20.10, 0.19] for fat mass) contrasting with the ordinary least squareestimate (0.37 [0.34, 0.40]). By contrast, fat mass explained by genetic variants of the FTO, MC4R and TMEM18 genes waspositively and significantly associated to SUA (0.31 [0.01, 0.62]), similar to the ordinary least square estimate (0.27 [0.25,0.29]). Results were similar for the other adiposity markers.
Conclusions: Using a bidirectional Mendelian randomization approach in adult Caucasians, our findings suggest thatelevated SUA is a consequence rather than a cause of adiposity.
Citation: Lyngdoh T, Vuistiner P, Marques-Vidal P, Rousson V, Waeber G, et al. (2012) Serum Uric Acid and Adiposity: Deciphering Causality Using a BidirectionalMendelian Randomization Approach. PLoS ONE 7(6): e39321. doi:10.1371/journal.pone.0039321 Editor: Florian Kronenberg, Innsbruck Medical University, Austria Received December 29, 2011; Accepted May 19, 2012; Published June 19, 2012 Copyright: ß 2012 Lyngdoh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The CoLaus study was supported by research grants from the Swiss National Science Foundation (grant no: 33CSCO-122661), GlaxoSmithKline and theFaculty of Biology and Medicine of Lausanne, Switzerland. Tanica Lyngdoh is supported by a grant from the Swiss National Science Foundation (PRODOCPDFMP3_127393/1). Murielle Bochud is supported by the Swiss School of Public Health Plus (SSPH+). The funders had no role in study design, data collection andanalysis, decision to publish, or preparation of the manuscript.
Competing Interests: Peter Vollenweider and Ge´rard Waeber received an unrestricted grant from GlaxoSmithKline to build the CoLaus study. The other authorsreport no conflict of interest. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials.
muscle [8]. Several pieces of evidence are in line with this directionof causality. In longitudinal epidemiologic studies, baseline SUA High serum uric acid (SUA) is known to co-exist with the independently predicted weight gain [9], the development of components of metabolic syndrome including obesity [1–3].
impaired fasting glucose [10] or incident type 2 diabetes [10–13], Epidemiological studies found positive associations between SUA even in the absence of metabolic syndrome [13] or obesity [9,10] and different adiposity markers including waist circumference [4], at baseline. Analogously, baseline hyperuricemia independently body mass index (BMI) [4], waist-to-hip ratio [5] and body fat predicted 9-year incident hyperinsulinemia in the ARIC cohort [6,7]. Although the relationship between SUA and adiposity [14], which suggests that hyperuricemia is not merely the appears to be well-established in conventional observational consequence of hyperinsulinemia. Baseline hyperuricemia was analysis, it is difficult to ascertain if these associations are truly also an independent predictor of 5-year incident metabolic causal or are a consequence of bias or residual confounding.
syndrome in a population-based sample in Portugal [15].
Further, the relationship between SUA and adiposity is compli- Experimental studies have shown that allopurinol, a xanthine cated by evidence suggesting the possibility of causality in both oxidase inhibitor that inhibits SUA synthesis, was able to prevent weight gain in fructose-fed rats [16]. Similarly, rats administered Some hypothesized that SUA mediates obesity and other uricase inhibitors to induce hyperuricemia, developed features of features of metabolic syndrome by reducing endothelial nitric oxide and decreasing insulin-mediated glucose uptake in skeletal Serum Uric Acid and Adiposity: MR Approach Conversely, others suggest that hyperinsulinemia (along with and weight was measured to the nearest 0.1 kg using a SecaH scale accompanying obesity) reduces urinary uric acid clearance with (Hamburg, Germany). These instruments were calibrated regu- subsequent elevation of SUA levels [18,19]. Also, the fact that a larly. Body mass index was defined as weight divided by height in genetic risk score robustly associated with SUA was not associated meter squared. Waist circumference was measured with a non- with fasting glucose or insulin levels in the CHARGE consortium stretchable tape and the mean of two measurements expressed in speaks against a causal role of uric acid on hyperinsulinemia [20].
centimeters was used for the analyses. Fat mass (in percent of the Longitudinal epidemiologic studies found baseline BMI [21] or total body weight) was assessed by electrical bioimpedance using weight gain [22] to predict the development of hyperuricemia the BodystatH 1500 analyzer (Isle of Man, British Isles). Fat mass during follow-up. Furthermore, weight loss is known to lower SUA (in kilograms) was calculated from the percentage of fat mass levels [23–25], which suggests that adiposity leads to hyperurice- mia. Hence, further investigations to clarify the nature and Venous blood samples were collected after an overnight fasting.
direction of the causal link between SUA and adiposity are Most clinical assays were performed by the CHUV Clinical Laboratory on fresh blood samples. Serum creatinine was As far as we are aware, the relationship between SUA and measured by Jaffe kinetic compensated method (2.9%21.5% adiposity has not been previously explored using the principles of maximum inter and intra-batch coefficients of variation) and uric Mendelian randomization, a method that allows disentangling acid by uricase-PAP (1.0%20.5%). Glomerular filtration rate causation from association in the presence of confounding [26]. In (GFR) was estimated using the abbreviated Modification of the a large population-based CoLaus study of Caucasians, we used Diet in Renal Disease (MDRD) formula: 1866(serum creatinine SUA and adiposity-related genetic variants as instruments in a [mmol/L]/88.4)(21.154)6age(20.203)6F, where F = 1 for men and bidirectional Mendelian randomization approach to explore the links between SUA and adiposity. We performed a Mendelianrandomization analysis to determine 1) if adiposity markers such as increased weight, BMI, waist circumference or fat mass are a Nuclear DNA was extracted from whole blood for whole consequence of elevated SUA or 2) if adiposity leads to genome scan analysis. Genotyping was performed using Affyme- trix 500 K SNP chip, as recommended by the manufacturer SUA is known to have a high (25 to 70%) heritability [27] and (Affymetrix, Inc., Santa Clara, California, USA). Persons with less recent genome-wide association studies have identified SLC2A9 to than 95% genotyping efficiency overall (or ,90% efficiency on have a strong association with SUA levels [28,29], explaining either array; n = 399) and persons with possible gender inconsis- about 1.2–6.0% of the variance in SUA concentration [30].
tencies (n = 5) were removed. Monomorphic SNPs, SNPs with less Amongst the adiposity-related genetic variants, we chose single than 70% genotyping efficiency, SNPs with minor allele frequency nucleotide polymorphisms (SNPs) within the most common major less than 1%, and/or not in the Hardy-Weinberg proportions were adiposity genes FTO, MC4R and TMEM18, all of which have been excluded. A hundred and twenty-nine, 20, 56 and 124 SNPs, recognized to be associated with obesity and explaining a variance 100 kb upstream and downstream of the FTO, MC4R, TMEM18 and SLC2A9 genes respectively, were considered for the presentanalyses.
All tests were performed using Stata 11 (StataCorp, College The CoLaus study is a cross-sectional population-based study Station, TX, USA). Continuous variables were summarized as conducted in Lausanne, Switzerland. Details of the study have mean (standard deviation [SD]) while categorical variables as been previously described [32]. Briefly, a simple, non-stratified number of subjects and percentages. We used t test and x2 test to random sample of 19,830 participants, corresponding to 35% of compare the distribution of covariates according to sex.
the source population, was drawn, of which 6184 participants were Pearson’s partial correlation coefficient test was used to estimate included. Inclusion criteria included a written informed consent, the correlation of SUA with adiposity markers and Fischer’s Z age between 35–75 years and being of Caucasian origin. The transformation to compare the correlation coefficients between study was approved by the Ethics Committee of the University of men and women. We performed a bidirectional Mendelian Lausanne. Recruitment began in June 2003 and ended in May randomization to 1) assess the causality in the direction of SUA causing elevated adiposity and 2) reverse causality i.e. elevatedadiposity levels leading to hyperuricemia. In the former, we chose as instrumental variable the SNP with the best F-statistics (Table 1) Participants attended the outpatient clinic at Centre Hospitalier from the linear regressions between the SNPs within and around Universitaire Vaudois (CHUV) in the morning after an overnight the SLC2A9 gene and SUA level, in the overall sample and fast. They were asked to continue taking their medication as usual.
separately by sex. We identified rs6855911, rs7442295 and This examination included detailed questionnaire, physical rs7669607 as the best SNP in the overall sample, men and women examination with anthropometric measures by trained and respectively. These variants have been identified to be related to certified field interviewers and laboratory testing. In the present SUA in earlier studies [34,35]. In the latter case, using the SNPs analysis, smoking was defined as present if the participant reported within and around the FTO, MC4R and TMEM18 genes to be current smoker at the time of examination and alcohol separately did not result in strong instruments. To identify consumption was defined as present for participants who report sufficiently strong instruments (i.e. an F-statistics .10) [36], we drinking alcohol at least once a day. Diuretic use was assessed by carried out a systematic combination of three SNPs from the three recording all the prescribed drugs taken by the participants and genes separately for each adiposity traits in the overall sample and was considered as present if participants were using drugs also by sex. Combinations of four SNPs from the adiposity genes belonging to any class of diuretics. Height was measured to the did not lead to significantly better instruments. Based on the nearest 5 mm using a SecaH height gauge (Hamburg, Germany), genotypes of FTO, MC4R and TMEM18, a score was created for Serum Uric Acid and Adiposity: MR Approach every individual SNP, coded as 0-homozygote for the non-risk transformed dependent and independent variables were standard- allele, 1-heterozygote and 2-homozygote for the risk allele. When ized and results from regression models expressed as 1 SD change combining the SNPs, we summed up their scores using an additive in the outcome corresponding to a 1 SD increase in exposure (note coding for the number of alleles associated with higher adiposity that the significance of the results would remain the same without levels. This resulted in an ordinal variable with seven categories standardization). We tested for interaction by sex using the sex- coded from 0 to 6. Further, we present the distribution of SUA specific results from the second stage and the following test across genotypes of the SLC2A9 rs6855911 and adiposity markers men-bwomen )/! (S.Emen +S.Ewomen ) where b and S.E is across adiposity-related SNPs individually or as genetic scores in the standardized beta coefficient and standard error respectively.
the overall sample to see how the specific SNPs relate to the Provided that the assumptions underlying Mendelian random- phenotype of interest in the CoLaus participants and used a non- ization are fulfilled, the regression coefficient obtained in the parametric test to assess for trend. In the latter case when using second stage can be interpreted as being the causal effect of the genetic scores to check for trends, we combined participants ‘‘explained’’ variable on the response of interest [37]. The first having scores of 0, 1 and 2 since the number of participants in assumption (i.e. the instrument is correlated with the explained these categories was small. We also reported the associations of SUA/adiposity), is usually considered to be met if the F-statistics SNP/SNP scores with markers in the hypothesized pathway (i.e.
calculated in the first stage regression is greater than 10 [36], SLC2A9 rs6855911 with adiposity markers and adiposity-related which is true in our context. We could partly check the second assumption (i.e. the instrument is unrelated to the confounders) by To explore the potential causal effect in both directions, we examining the association between the instruments and the applied a Mendelian randomization approach, also called two- potential confounders (as below) that were measured, as done by stage least squares (2 SLS) regression, using the instrumental others [38,39]. We found none of the measured confounders to be variables that we identified. In the first stage, we conducted an significantly associated with the instruments. The third assumption ordinary least square (OLS) regression, regressing SUA/adiposity (i.e. the instrument has an effect on the response of interest solely markers on our instruments (see Table 1 for the choices of via the explained variable), is difficult to verify from the data. We instruments in our context). In the second stage, we performed compared the estimates from the OLS and 2 SLS using the regression of the response of interest (e.g. SUA, BMI, weight etc.) Durbin-Hausman test. This process was repeated for each on the fitted values from the first stage regression, which will be association of interest in the overall sample and in the sex strata.
referred to as ‘‘explained’’ SUA/adiposity from here on. We We conducted both unadjusted and adjusted analyses controlling conducted the above analysis using the ivregress function in Stata for age, sex, smoking, alcohol use, GFR and diuretic use, 11. To meet the assumptions for linear regression, we used the covariates which can potentially influence the associations between most appropriate transformations for both the dependent and SUA and adiposity markers. To address the possibility of independent variables (weight and waist: log transformation; SUA confounding by population stratification, we included principal and fat mass: square root transformation; and BMI: inverse square components generated from genome-wide SNPs data as covariates root transformation). Further, to facilitate comparability between to the analysis. The significance level used for two-sided tests was the coefficients and ease interpretation of the results, both the Table 1. Association between SNPs chosen as instruments and intermediate phenotype.
Combined SNPs (instruments) within/around FTO, MC4R and TMEM18 for adiposity markers SNPs (instruments) within/around SLC2A9 for SUA BMI = body mass index; WC = waist circumference; SUA = serum uric acid; SNP = single-nucleotide polymorphism.
doi:10.1371/journal.pone.0039321.t001 Serum Uric Acid and Adiposity: MR Approach Table 3. Pearson’s partial correlation coefficient of adipositymarkers with serum uric acid according to sex.
Table 1 summarizes the combinations that produced the best instrument for the different adiposity traits in the overall sampleand by sex. Significant linear trends (either increasing or decreasing) were observed for the distribution of the phenotypesof interest across their respective genotypes or genetic scores (in the case of combined adiposity-related genetic variants) (Tables S1 and S2). Similar significant linear trends of SUA across genetic scores of adiposity-related genetic variants were noted (Table S3)but not for the distribution of adiposity markers across genotypes Of the 6184 participants, the range of missing genetic BMI = body mass index; WC = waist circumference.
information varied across the different SNPs (chosen as instru- aP value testing the difference in correlation coefficient between men and ments) of the SLC2A9 and adiposity-related genes: FTO (range of missing data: 557–695), MC4R (748), TMEM18 (650–1442) and Adjusted for age, smoking, alcohol use, estimated glomerular filtration rate SLC2A9 (590–963). No significant difference with regards to the (GFR) and diuretic use.
doi:10.1371/journal.pone.0039321.t003 phenotype of interest i.e. adiposity markers and SUA was notedbetween participants with and without missing genetic data. Data ,0.001), BMI (r = 0.35 vs. r = 0.28, P = 0.002), waist circumfer- was also missing for the adiposity markers: weight (n = 9), body ence (r = 0.36 vs. r = 0.29, P = 0.001), and fat mass (r = 0.35 vs.
mass index (n = 9), waist circumference (n = 9) and fat mass We did not find any associations of the genetic variants with the The main demographic and clinical characteristics of CoLaus other measured confounders (Table S5), thereby verifying to some participants according to sex are summarized in Table 2. Men extent that the instruments were independent of the measured were slightly younger than women with a mean (SD) age of 52.6 confounders, which is an indication of the validity of the (10.8) years vs. 53.5 (10.7) years. SUA was significantly higher in men (361 (75.7) mmol/L) than in women (270.6 (67.2) mmol/L) aswell as the prevalence of reported alcohol consumption and The statistics from the first-stage regression between SLC2A9 smoking. With regards to adiposity, men had significantly higher SNPs used as instruments and SUA presented sufficient F-statistic weight, BMI and waist circumference (P,0.001 in all) while values (F = 170.47, 71.49 and 197.21 for rs6855911 in overall women had higher fat mass (P,0.001).
sample, rs7442295 in men and rs7669607 in women respectively, Table 3 displays the partial Pearson’s correlation coefficients of Table 1). Table 4 shows the associations between SUA explained SUA with the selected anthropometric phenotypes, separately for by rs6855911 and the selected markers of adiposity (as dependent men and women. SUA showed significant positive correlations variables) in the overall sample. Both crude and adjusted analyses with all traits (P,0.001). The correlations were stronger in women showed significant positive associations between SUA and all the than in men for weight (r = 0.33 vs. r = 0.24, P for sex difference selected adiposity markers (P,0.001) in the OLS regression.
However, in the 2 SLS regression using instrumental variables, weobserved no significant association with the adiposity traits. The Table 2. Demographic and clinical characteristics of CoLaus results obtained from 2 SLS do not provide evidence of a causal effect of SUA on adiposity markers. This is further substantiatedby the finding, in most cases, of a significant difference betweenthe OLS and 2 SLS standardized coefficients, as shown by the P- value obtained from the Durbin-Hausman test. Similarly, conducting the same analyses but using rs7442295 as instrument in men (Table S6) and rs7669607 as instrument in women (TableS7) resulted in similar conclusions, with the standardized coefficients derived from 2 SLS being close to zero for all the For the relationship between SUA and adiposity markers in the reverse direction, where SUA was used as the dependent variable, we obtained different combinations of SNPs that produced large enough F-statistics for the different adiposity traits separately in theoverall sample, in men and in women (Table 1). Table 5 describes the coefficients derived from the OLS and 2 SLS regressions in the overall sample using combinations of adiposity-related SNPs as instrumental variables. In both crude and adjusted OLS analyses, SUA was significantly positively associated with all the selected adiposity markers (P,0.001) in the overall sample. The associa- BMI = body mass index; GFR = estimated glomerular filtration rate (calculated tions obtained from the 2 SLS regression were similar to the OLS according to Modification in Diet in Renal Disease equation); WC = waist regression both in magnitude (in most cases) and direction, and remained significant in the unadjusted analyses. In fat mass, the aResults are presented as percentages.
association was significant even after adjustment (P = 0.048). Sex- Between-group comparisons by t-test, Chi-square test or Wilcoxon ranksumtest.
specific results are presented in Tables S8 and S9. We did not find any evidence for an interaction by sex (i.e. estimates did not Serum Uric Acid and Adiposity: MR Approach Table 4. Association of SUA (using rs6855911 from the SLC2A9 gene as instrument) with adiposity measures (dependent variableof interest) in the overall sample.
BMI = body mass index; SUA = serum uric acid; WC = waist circumference.
The b(95%CI) represents the association of SUA with adiposity markers as tested by the conventional epidemiological method (ordinary least square [OLS]) and by theinstrumental variable analysis in a two-stage least square (2 SLS) regression (so called Mendelian randomization approach whenever the instruments are geneticvariants). Similar magnitude and direction of coefficients derived from both the OLS and 2 SLS regressions suggest a causal effect of exposure (in this case SUA) on theoutcome of interest (in this case adiposity). Further, a P value2SLS ,0.05 against the null hypothesis favors a causal effect of SUA on adiposity.
aP value from the Durbin-Hausman test which compares the difference between estimates derived from the OLS and 2 SLS regressions.
Results are expressed as standardized regression coefficient (b) along with 95% confidence interval (CI).
Adjusted analysis controlled for age, sex, smoking, alcohol use, estimated glomerular filtration rate (GFR) and diuretic use.
doi:10.1371/journal.pone.0039321.t004 significantly differ in men and in women). The direction of models did not produce any relevant changes in the estimates (data association with BMI in men was reversed in the 2 SLS as opposed to the OLS results although this did not result in a significantdifference between the two coefficients (P value from Durbin- Hausman test = 0.671). Of interest is the observation that themagnitude of both the crude and adjusted coefficients was very Using a bidirectional Mendelian randomization approach in a similar in most cases, this being more apparent upon stratification population-based study of Caucasians aged 35 to 75 years, we tried by sex. The large confidence intervals in the 2 SLS associations to unravel the direction of causality between SUA and adiposity reflect the relative weakness of the instruments. Controlling for markers. SUA explained by SLC2A9 rs6855911 in the overall population stratification using principal components generated sample, by rs7442295 in men or by rs7669607 in women, was not from genome-wide SNPs data as covariates into the multivariable associated with any of the selected adiposity markers; the second-stage estimates from the instrumental variable approach were closeto zero. Thus, in the present study, we found no evidence to Table 5. Association of adiposity measures (using combined SNPs from the FTO, MC4R and TMEM18 gene as instrument) with SUA(dependent variable of interest) in the overall sample.
Ordinary least square (OLS) 2-stage least square (2 SLS) FTO rs1121980+ FTO rs1782322+ TMEM18 rs6755502 Crude FTO rs7193144+ FTO rs17823223+ TMEM18 rs10189761Crude FTO rs1121980+ FTO rs2665272+ TMEM18 rs6755502 Crude FTO rs1861868+ FTO rs8050136+ TMEM18 rs6755502 Crude BMI = body mass index; SNP = single-nucleotide polymorphism; SUA = serum uric acid; WC = waist circumference.
The b(95%CI) represents the association of SUA with adiposity markers as tested by the conventional epidemiological method (ordinary least square [OLS]) and by theinstrumental variable analysis in a two-stage least square (2 SLS) regression (so called Mendelian randomization approach whenever the instruments are geneticvariants). Similar magnitude and direction of coefficients derived from both the OLS and 2 SLS regressions suggest a causal effect of exposure (in this case adiposity) onthe outcome of interest (in this case SUA). Further, a P value2SLS ,0.05 against the null hypothesis favors a causal effect of adiposity on SUA.
aP value from the Durbin-Hausman test which compares the difference between estimates derived from the OLS and 2 SLS regressions.
Results are expressed as standardized regression coefficient (b) along with 95% confidence interval (CI).
Adjusted analysis controlled for age, sex, smoking, alcohol use, estimated glomerular filtration rate (GFR) and diuretic use.
doi:10.1371/journal.pone.0039321.t005 Serum Uric Acid and Adiposity: MR Approach suggest that SUA causally impacts on adiposity. By contrast, using insulin-mediated glucose uptake in skeletal muscle [8]. However, genetic variants of the FTO, MC4R and TMEM18 genes as estimates obtained in our analysis using an instrumental variable instruments to explain the effect of adiposity on SUA, we observed approach did not show an association in this direction. Consid- a causal positive association of weight and fat mass with SUA in ering that genetic variants are not influenced by confounding and the overall sample; the association of fat mass with SUA was that the instruments used for these analyses were sufficiently present in both men and women. This finding is not totally strong, our results are certainly of interest in that they provide unexpected and is compatible with the hypothesis that hyperin- some evidence against a causal association in this direction.
sulinemia, a consequence of overweight and obesity, enhances With respect to exploring causality in the other direction, i.e.
renal proximal tubular reabsorption of uric acid with subsequent SUA could be a consequence of excess fat accumulation, we took elevation of SUA levels [18]. Our findings are compatible with a advantage of the fact that obesity has a strong genetic component positive causal effect of adiposity on elevated SUA. This evidence with heritability estimates ranging from 65 to 80% [49].
is further supported by the observation that weight reduction leads Unfortunately, most genetic markers identified so far only explain to a fall in plasma uric acid levels [25]. Considering that a very small fraction of BMI or related continuous adiposity hyperuricemia is a strong risk factor for gout [40,41], a potential markers, so that we had to combine multiple instruments for this clinical implication of our results is that weight loss should analysis. The practice of combining variants from different genes decrease, and weight gain increase, gout incidence, as recently into an additive genetic score to improve instruments is not observed in a large prospective study [42]. However, we cannot uncommon [43,50,51] and has been shown to be an efficient rule out the possibility that these findings could reflect a failure to linear combination of individual instruments resulting in better fulfill the assumptions underlying Mendelian randomization.
precision of the instrumental variable estimator. This proved To the best of our knowledge, this is one of the few population- practical in order to ensure sufficiently strong instruments (as based studies to use a bidirectional Mendelian randomization evident by the F-statistic and R2) to fulfill the first assumption approach. Welsh et al. were among the first to have demonstrated underlying the approach. However, we acknowledge that this the usefulness of a bidirectional Mendelian randomization practice can also lead to an increase in bias of the estimates approach in unraveling the directional link between adiposity [52,53]. The current study focused on variants located within and and inflammation where the direction of relationship had not been around FTO, MC4R and TMEM18 that are amongst the genes otherwise proven [43]. The technique of Mendelian randomiza- most strongly associated with obesity traits [54] and also identified tion might help to surmount the problems that are often in earlier meta-analyses [55–58], despite the fact that the variance encountered in traditional observational epidemiology. The explained by these loci is small (1–2%) [31]. Although one can objective of most epidemiological research is to obtain conclusions argue that the instruments used for the associations in the direction that provide causal evidence. However, this is not always possible of adiposity causing elevated SUA are adequate but not sufficiently because of the unintended noise in the data resulting from the strong (as illustrated by the wide confidence intervals), we observed presence of known and unknown confounders, which are often consistent trends with weight, fat mass and waist circumference.
difficult to control for. In addition, there is the problem of reverse The 2 SLS estimates did not deviate much from the OLS causality as it is often difficult to determine which of the two estimates unlike what was found when we used the SLC2A9 variables of interest is the cause and which is the effect. Genetic variants can be thought of exposures that have been randomly The strengths of this study are its population-based design, the allocated at the time of gamete formation [44] and Mendelian large sample size and accessibility to detailed and relevant randomization approach as a natural randomized controlled trial information. However, our results have to be interpreted with [45]. A bidirectional Mendelian randomization approach using caution since the validity of a Mendelian randomization approach genetic variants, in our context where existing evidences on the in observational epidemiology relies partly on unverifiable direction of causality between SUA and adiposity is conflicting and assumptions. Some of the potential sources of residual confound- ing may arise due to pleiotropy and population stratification.
Recent genome-wide association studies have identified the Pleiotropy of genetic variants is difficult to address without solute carrier (SLC) family 2, member 9 (SLC2A9) gene, encoding examining all the biological pathways and this is often not possible a putative hexose transporter, to be strongly associated with SUA because of the lack of understanding on the exact underlying [29,34,35,46], including the SNP most significantly associated with mechanisms. However, we did not observe significant associations SUA in this study. The SLC2A9 gene explains a substantial between any of the instruments and potential confounders proportion (about 1–6%) of variance in SUA concentration [30] suggesting that the associations are unlikely to be mediated and the associations between these variants and SUA have been through biological pathway involving the measured confounders.
consistently replicated across studies [29,34,35,46]. Vitart et al Similarly, it is reasonable to speculate that residual confounding showed that the SLC2A9 gene has urate transport activity and from the association between the instruments and unmeasured found the most significant SLC2A9 SNPs for SUA to be associated confounders is minimal based on our findings of comparable crude with a low fractional excretion of uric acid [29].
and adjusted estimates (particularly in the direction of adiposity Conventional epidemiological studies show positive significant causing elevated SUA). We also did not find evidence of associations between SUA and adiposity markers (used as outcome confounding by population stratification in our data.
variable although not clearly stated) like BMI [9,47], waist-hip There are also other limitations in this study. First, the ratio [47] and body fat [6,48]. Except for Masuo et al. who adiposity-related genetic variants used as instruments were weak, reported that SUA predicted subsequent weight gain [9], these resulting in the estimates having wide confidence intervals and low studies did not clearly discuss causal associations and it is not precision. Second, the approach used here is not the classical possible to infer causality from them. The findings by Masuo et al.
Mendelian Randomization approach but a slight deviation from it and by others [10–15] are in line with previous hypothesis of a (which has been considered in Hernan et al [59]), since both the putative causal effect of uric acid on adiposity which states that SUA and adiposity-related genetic variants used as instruments are uric acid could mediate obesity and other features of the metabolic not the direct gene products. Thus, there is always a risk that the syndrome by reducing endothelial nitric oxide and decreasing proteins on the pathway work as confounders and drive the Serum Uric Acid and Adiposity: MR Approach association. Third, since we included only middle-aged Cauca- sians, the findings may not be generalizable to other populations.
Fourth, the approach of selecting the best genetic instrument in the CoLaus sample may be subject to over-fitting. Finally, an Association between the SNP/SNP scores with important issue is that the statistical power is, in general, not the same in both directions. In this regard, it is interesting to note that our confidence intervals of the instrumental variable analyses werein general wider when estimating a causal effect of adiposity on Association of SUA (using rs7442295 from the SUA than when estimating a causal effect of SUA on adiposity SLC2A9 gene as instrument) with adiposity measures (recall that since all variables are standardized, the effects are (dependent variable of interest) in men.
expressed on a similar scale, which allows such a comparison).
This means that we had more power in the direction where wecould not find a significant causal effect than in the direction Association of SUA (using rs7669607 from the where we found some significant causal effects (this being SLC2A9 gene as instrument) with adiposity measures consistent with the fact that we had a stronger instrument in the (dependent variable of interest) in women.
former direction). Thus, our non-significant causal effects of SUA on adiposity may not only be due to a lack of power.
In conclusion, using a bidirectional Mendelian randomization combined SNPs from the FTO, MC4R and TMEM18 gene approach, our findings suggest that elevated SUA is a consequence as instrument) with SUA (dependent variable of interest) rather than a cause of elevated adiposity. To our knowledge, this is the first study in which the relationship between SUA and adiposity has been explored using genetic tools. While futurestudies are essential to confirm these findings, our observations may shed some light on the uncertainty underlying this combined SNPs from the FTO, MC4R and TMEM18 gene pathophysiological link and highlight the usefulness of the as instrument) with SUA (dependent variable of interest) bidirectional Mendelian randomization approach to decipher the Genotype distribution of adiposity markers We are grateful to the participants of the CoLaus study and to the and SUA across the adiposity-related and SUA-related investigators who have contributed to the recruitment, in particular Yolande Barreau, Anne-Lise Bastian, Binasa Ramic, Martine Moranville,Martine Baumer, Marcy Sagette, Jeanne Ecoffey and Sylvie Mermoud for Conceived and designed the experiments: GW P. Vollenweider. Performed Distribution of SUA across scores of adiposity- the experiments: GW P. Vollenweider. Analyzed the data: TL MB P.
Vuistiner PMV VR. Wrote the paper: TL MB. Redrafting and finalizing of the final version: TL P. Vuistiner PMV VR GW P. Vollenweider MB.
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