Compared with other fermentation processes in food industry, cocoa bean fermentation is uncontrolled and not standardized. A detailed mechanistic understanding can therefore be relevant for cocoa bean quality control. Starting from an existing mathematical model of cocoa bean fermentation we analyse five additional biochemical mechanisms derived from the literature. These mechanisms, when added to the baseline model either in isolation or in combination, were evaluated in terms of their capacity to describe experimental data. In total, we evaluated 32 model variants on 23 fermentation datasets. We interpret the results from two perspectives: (1) success of the potential mechanism, (2) discrimination of fermentation protocols based on estimated parameters. The former provides insight in the fermentation process itself. The latter opens an avenue towards reverse-engineering empirical conditions from model parameters. We find support for two mechanisms debated in the literature: consumption of fructose by lactic acid bacteria and production of acetic acid by yeast. Furthermore, we provide evidence that model parameters are sensitive to differences in the cultivar, temperature control and usage of steel tanks compared with wooden boxes. Our results show that mathematical modelling can provide an alternative to standard chemical fingerprinting in the interpretation of fermentation data. Their fermentation plays a fundamental role as being responsible for eliminating undesired properties from freshly harvested beans, e.g. astringency and bitterness, besides yielding chocolate-related flavour and aroma precursor compounds  contained in vector θ.First, let us consider any of our proposed deterministic MIs represented in a general formθ are selected such that a set of data θ given If we assume that parameters θ, θ, P(θ), asHere, since equation  can be wT time steps, with N state variables being observed, equation (If we take into account that each component of equation  takes thθ that lead to a best agreement between xi(j) in equation , with a standard deviation term, σ, (caused by noise of any kind) allowing us to reformulate the total posterior distribution asFinally, as our purpose is to identify values of equation  and xi independent prior distributions for each parameter can take the same form, (ii) by introducing scale information of the original units in which the parameters of the models are originally measured, we can formulate weakly informative priors capable of covering all possible values in the scaled space ,32, and θ and σ were computed using a normal distribution with mean 0.5 and standard deviation of 0.3 for each element of the parameter vector θ and a Cauchy distribution with location 0 and scale of 1 for σ. With the purpose of avoiding estimates with negative values, both priors were truncated to the positive set of real numbers,Hence, posterior distributions of . 2.4.3θ, σ and f, were obtained by Markov chain Monte Carlo (MCMC) no-U-turn sampler (NUTS) method [rk45 for non-stiff systems by means of fourth- and fifth-order Runge–Kutta method [−6 for both, and a maximum number of steps of 1 × 104. All MIs were fitted to data by running four parallel Markov chains of 3000 iterations each, with 1000 of them used for warm-up. Sampling convergence was assessed by examining et al. [draws) and checking whether these were above 12.5%, meaning that as minimum 1000 ESS were obtained [The fit of MIs to experimental data was performed with Stan  via RSta) method . Each moa method ,40 with i et al. . In caseobtained   success of each MI across all data and (ii) predictive accuracy comparison of all MIs for each dataset.Under the umbrella of the proposed Bayesian framework, it is important to define what will be called from now on a w), non-successful fits were assigned values of zero. Furthermore, an observed success rate (OSR) and expected success rate (ESR) were determined on the basis of times where the model was satisfactorily fit to a given dataset. OSR is then defined by the ratio of the number of successful fits and the total of datasets fitted by at least one MI. ESR, used to properly compare the success rates of models with only a single additional mechanism with those models containing a combination of mechanisms, is defined as the product of the OSRs of the elementary MIs. For model variant MI, for example, the expected success rate is then the product of the observed success rates of the elementary models MI(1), MI(2) and MI(5).On the one hand, Bayesian model averaging (BMA ) weightsOn the other hand, all MIs that were suitably fitted to each dataset were compared by means of Pareto-smoothed importance sampling leave-one-out cross validation  with th. 2.6In an approach of linking our mathematical exploration of mechanisms with a real-life application  is a common approach), we studied whether projecting the obtained vectors of kinetic parameters on the PCA space allows for a separation (and hence classification) of experiments according to fermentation features (e.g. cocoa beans’ country of origin and used cultivar), which are of interest in both academic research and the chocolate industry.Consequently, six main features of fermentation trials were taken into account as groups and analysed via PCA over parameter estimates for all successful MIs. Of these, three consist of multiple classes: (i) country of origin, (ii) cacao cultivar, and (iii) fermentation method. The other three features are binary: (iv) use of starter culture, (v) turning of fermenting mass, and (vi) controlled temperature during fermentation. Experiments with features reported as unknown or missing were not considered for any PCA. Only the posterior samples contained within their 95% credible interval (CI) from each chain in the MCMC-NUTS runs were taken into account to perform PCA. The assessment of groups within the PCA results was realized only for features with more than one successful fit.Moreover, PCAs were also performed over subgroups of parameter defined by the type of parameter and its association with a certain microbial group. Considered subgroups consisted of: (i) all MI parameters, (ii) maximum specific growth rates, (iii) mortality rates, (iv) yield coefficients, (v) Y-related parameters, (vi) LAB-related parameters, and (vii) AAB-related parameters. No PCA was run over substrate saturation constants due to their known correlation with maximum growth rates . All PCADM, [j grouping class within an i feature and used to determine DM asi,j and PC2i,j respectively; and S−1 is the inverse of the covariance matrix between groups classes [Finally, pairwise squared Mahalanobis distances  were co classes ,49.DM were implemented in R using functions prcomp [pairwise.mahalanobis [Both, PCA and s prcomp  and pairalanobis , respect. 3. 3.1First, we want to understand how well the different models—the baseline model from  and the draws over 12.5% , indicating an acceptable ESS above 1000. A number of nine datasets reported by Lefeber et al. [et al. [et al. [et al. [mywb3 from Papalexandratou et al. [In general terms, MIs summed up to 1024 runs over 32 available datasets; of which, 207 resulted in successful fits with values of r et al. ,52, More [et al. , Bastos  [et al.  and Raci [et al.  were notu et al. ).Figureet al. [brwb1 and brwb2. Both were well fitted by MI(5), MI and MI; while MI and MI fitted brwb1 and MI fitted brwb2 only. Thus, MIs , , , , , ,  and  could not describe any dataset at all . For MIs containing single mechanisms, M(1), M(2), M(3), M(4) and M(5), BMAw were 0.084, 0.034, 0.075, 0.079 and 0.011, while OSR values were 0.52, 0.61, 0.83, 0.65 and 0.09, respectively. Among more complex combinations of mechanisms, BMAw ranged between 0.00 to a maximum of 0.141 reached by the combination of M2 and M3 ). Similarly, among these, MI had a maximum OSR of 0.74 as listed in From their computations, the baseline model (MI(0)) showed the highest values , we have the ESR to partially correct for this.Pertaining to ESRs, leaving out non-successful MIs, 2 out of 18 MIs showed higher values than their corresponding OSRs. These two MIs with higher ESRs correspond to iterations including M5  and MI). Leaving aside MI and MI due to be the only exceptions of M5 ending up in successful fits, in overall combinations of mechanisms seems to lead to increases of their OSR over ESR on describing different datasets despite not overpassing the OSR of the baseline model .. 3.3mywb3  and electronic supplementary material, figures S18–S23 for MIs, where M was not suitable).Next, we resort to the distributions of posterior probabilities, in order to assess differences in the quality of the fit for the different MIs. Among the 23 datasets that were fitted by at least one MI, posterior predictions describe their dynamics remarkably well. In each data collection, despite the presence of highly influential observations and sampling rates ranging from 6 to 17 data points, time courses are simulated to an acceptable level. Again we refer to the example shown in brpb1, brwb4, brst2 and niwb2 . The latter is also evident by higher BMA weights by such MIs .In terms of predictive accuracy among MIs fitted on each dataset, there were no outstanding differences on the basis of obtained PSIS-LOO deviance values that had overlapping standard errors between each other. Thus, is not surprising that for these cases posterior predictions resulted to be extremely similar . Nevertheless, slight distinct PSIS-LOO were observed towards favouring MIs involving M1 in for datasets brpb1 . However, the same does not seem to be clear for niwb2 where predictions made by M(1) and M overlay each other with no evident improvements for either MI .These subtle differences provided visually better fits by MI(1) with respect to MI for datasets . 3.4We now turn to the second question raised, namely whether the model parameters obtained by describing the datasets with all MIs are informative of the fermentation features behind the datasets. Via principal component analysis performed on the full parameter vectors or biologically meaningful subsets of the parameters, we want to assess whether distinct clusters emerge in agreement with differences in fermentation set-ups.After dropping the use of a starter culture as a feature , a total of 490 PCAs were performed from the remaining five features and seven parameters subsets. Note that MI did not converge for datasets representing more than one used fermentation method, and MI(1), MI, MI, MI, MI, MI, MI, MI, MI and MI were not capable of describing datasets with more than one class of controlled temperature .DM for cases with more than one pairwise comparison, medians of all DM were computed (In terms of group separation measured by . 3.4.1b)).PCAs with cultivar as feature of interest showed a consistent pattern of high values for un2 with explained variances of 13.91% by the first PCA component (PC1) and 9.85% by the second component (PC2). From its loading plot (b), parameters with negative loadings in PC1, mainly kY and kAAB determine the classes separation.ing plot b, parame. 3.4.2e)). The PCA of the set of all parameters for MI resulted in a clear separation of groups determined by the use of controlled and non-controlled temperature with PC1 and PC2 scores explaining 11.87% and 8.76% of variance, respectively (Temperature control showed its highest ectively . Likewis. 3.4.3c)). In this case, a clear separation between fermentations carried out in stainless-steel tanks and the rest of fermentation methods can be seen with total explained variance of 16.61% by PC1 and 9.97% by PC2 . Furthermore, parameters with positive loadings in PC1, namely kAAB seem to influence the separation between stainless-steel tank and the rest of methods ).Fermentation method as classification feature resulted in less clear separation patterns. Highest values of . 3.4.4a) and (d)). For this case PCA from MI explains a total variance of 45.8% for PC1 and 25.89% for PC2. Furthermore, a clear separation between Brazil with respect to Ecuador and Ghana, is defined over PC1. From its loadings, parameters with positive loadings in PC1 (YLA|LAB and kLAB) are clearly denoting the separation between classes . Lastly, turning of fermenting mass did not show any separation either for MI nor any subset of parameters.In contrast to previous features, country of origin and turning of fermenting mass have lower . 4. 4.1We have assessed a series of mathematical model variants (or MIs) for cocoa bean fermentation in terms of two levels of plausibility: success of each run of a MI over a given dataset, and success of each MI to adequately describe the whole range of fermentation datasets. In this sense, failure in fitting a certain MI might be a consequence of practical non-identifiability of the parameters caused by weakly informative observations or priors, misspecification of the model , and eveIn general, the mechanisms discussed here have shown in most cases that their stand-alone and concomitant inclusion in the baseline model lead to convincing OSRs values, in particular for M2, M3 and M4. We can consider a lack of success in some runs involving these mechanisms is the product of numerically conflicting combinations of mechanisms rather than possible misspecification of the whole MI itself.On the other hand, wide fitting failure patterns in runs involving M5  and to aIn the following, we will elaborate on the implications of fitting failure and plausibility of each mechanism considered in descending order relative to their OSR of the stand-alone inclusion in the baseline model.et al. [Starting with M3, production of Ac by Y’s metabolism has not been hypothesized from direct experimental measurements as is the case for other mechanisms. Instead, indirect kinetic studies of isolated strains have shown such an ability of some species of Y . This pret al. . Hence, With respect to M2, in light of the recent characterization of fructophilic lactic acid bacteria (FLAB) in cocoa bean fermentation processes , the levSimilarly to M3, the reasoning behind M4 relies on indirect characterization of Y strains capable of metabolizing LA into EtOH  via theiMIs employing M1 and M5 resulted in the lowest OSRs among all, both stand-alone and combined with other mechanisms. However, important distinctions need to be made between these two. First, M1 is formulated on the basis of several experimental studies that have brought evidence of metabolites diffusing into the bean ,14,16,17brwb1 and brwb2 reported by Papalexandratou et al. [Second, regardless of the widely accepted mechanism of AAB consuming Ac once EtOH concentration has reached minimum levels in the fermenting mass ,4,6,31, u et al. , where ddowb1 and niwb2.Finally, after reviewing all above-mentioned causes of fitting failure and how they could have affected success ratios of each MI, it is plausible to assume that harsher combinations of such causes are responsible for failing to fit any model to nine datasets (see §3.1) as well for some others that were scarcely described, e.g. . 4.2Here, we would like to discuss general aspects of the posterior distributions and how they allow us to further assess the different MIs. We will focus on the agreement of the obtained posteriors with values in the literature as well as practical non-identifiability of parameters.For parameter agreement, let us consider the set of posterior distributions for MI fitted over several datasets . As stated in , among aYLA|AAB, YEtOH|AAB and ghhp1, ecwb1, ecwb2 and brpb1 .This assumption seems to be supported by parameters' posteriors under the scope of different MIs. By the inclusion of extra terms acting upon the dynamics, in which non-identifiable parameters are suspected to exist, their wide ranges should be visibly reduced. In fact, focusing on examples already mentioned in   and MI. From our perspective, this is a clear illustration, how ODE-based modelling, rather than the usual methods of chemical fingerprinting –59, can An example is the clear differences in fermentation features identified through kinetic parameter estimates of MI for cultivar, controlled temperature and fermentation methodology. Regarding cultivar, similar findings have been reported for biochemical characterization studies where the same cultivars used in different countries have shown to be part of similar classes within PCA . This woIn a similar fashion, grouping of fermentation trials dependent on whether they were performed under controlled temperature settings, reflects how kinetic parameters of MI might be influenced by this feature. In this regard, explicit inclusion of temperature in these models would be a natural option. From our point of view, we firmly consider that incorporation of temperature in this modelling scheme would be beneficial in case remarkable improvements on the assessing statistics presented here were seen. However, explicitly incorporating temperature as a dynamical variable did not dramatically change either PSIS-LOO, posterior predictions or parameter ranges towards more biological plausibility .Furthermore, classification of trials with respect to methodology also tends to lead to a clear separation with a special emphasis on trials performed in stainless-steel tanks fitted with MI(2). This finding suggests that the use of stainless-steel tanks affects kinetic parameters, making them distinct from other methods. Besides, it becomes an indication that inclusion of M2 seems to be driving this difference, as well as other feature discriminations. The latter observation is based on the loading plots that for all these PCAs are determined by parameters related to M2, such as Lastly, it is worth remarking that including all parameters’ posterior draws contained in their 95% CI for conducting PCAs plays an important role in finding more meaningful differences between the features. This conclusion comes from a former approach where we performed PCAs over the medians of each chain of successful MIs instead. In such an exercise, besides obvious quantitative differences between its PCs and their counterparts reported here , the same classification of the features was observed, with the exception of a noticeable separation between all countries  rather than the overlap between Ghana and Ecuador shown in electronic supplementary material, figure S29, which could have been mistakenly ignored if it were not by considering the posterior distributions’ uncertainties.. 5The series of MIs presented here constitute a first kinetic exploration of the plausibility of regulatory dynamics of cocoa bean fermentation not considered in our previous modelling , but lonOur scheme also allows us to conclude that loss of metabolites by physical phenomena (M1) is quite minimal, relative to their consumption and formation rates emphasizing the importance of microbial biochemical processes. Furthermore, it also offers quantitative evidence that a widely hypothesized mechanism, M5 (over-oxidation of acetic acid by acetic acid bacteria), does not agree with experimental data.With reference to fermentation features, the rich set of parameter estimation results grants for interpretation on three levels: (i) We find that the parametrized time courses separate different fermentation features with different quality. Across all models, origin countries seem to only have a small influence on systematic time course differences. By contrast, temperature and cultivar seem to have a strong effect on fermentation dynamics (and hence on systematic differences in the resulting parameter vectors). (ii) Orthogonal to this view, we can assess which model versions lead to better discrimination fermentation features compared with the basic fermentation model from . This coLastly, this work commends that in a pure sense of describing fermentation dynamics in the pulp, inclusion of temperature as a dynamical variable does not add improvements to fits obtained under the proposed scheme. However, future advancements in cocoa bean fermentation modelling might find it necessary to take it into account for a more refined description of more detailed experimental data and to capture its reported importance in mediating important dynamics, for instance, its role in diffusion processes of acids into the bean .Click here for additional data file.


The use of the direct oral anticoagulants dabigatran, rivaroxaban, apixaban and edoxaban (DOACs) offers some major advantages over warfarin and other vitamin K antagonists (VKAs). One advantage is the possibility to use a fixed dose in normal-weight patients, overweight patients and patients with obesity. However, the “one size fits all” strategy raised a concern regarding the possibility to undertreat patients with a high body mass index. No randomized controlled trials (RCTs) have ever compared VKAs and DOACs in this population. We analyzed data from the literature on DOAC pharmacokinetics and pharmacodynamics, results from the four pivotal phase III trials on non-valvular atrial fibrillation, retrospective observational studies and metanalyses. While we are aware of the limitation imposed by the absence of specific RCTs, we propose the position of the Italian Association of Hospital Cardiologists (ANMCO) on the use of DOACs in patients with obesity based on the existing evidence. Morbid, extreme or severe obesity is defined as a BMI of ≥40 kg/m2 [Overweight and obesity are two conditions characterized by abnormal and excessive fat accumulation that may impair health . ObesityThe weight status classification associated with BMI categories is presented in BMI is the most used obesity measure in the scientific literature but has some limitations.It cannot take into account body compositions, particularly excess fat mass, which is the main determinant of the clinical and functional consequences of obesity. Furthermore, BMI does not provide any information about fat location, which has a relevant impact on cardiovascular risk . DespiteIn Italy, 35% of the population is overweight, and more than 10% is obese [In the United States of America, nearly 40% of adults met the criteria for obesity in 2017–2018, with approximately 9% meeting the criteria for grade 3 obesity .In 2015, 107.7 million children and 603.7 million adults worldwide were estimated to have obesity [Obesity and atrial fibrillation (AF) are closely related through epidemiological and pathophysiological relationships. Atrial fibrillation (AF) is the most common sustained arrhythmia in adult individuals , accountThe increasing age of the population and the increasing prevalence of other AF risk factors such as hypertension, coronary artery disease, diabetes mellitus, heart failure, chronic kidney disease, obesity, chronic obstructive pulmonary disease and obstructive sleep apnea explain the higher burden of AF that is expected in the coming decades ,12,13,14AF and obesity patient populations partially overlap, and both are experiencing epidemic growth ,11,12,14Since 2009, the introduction of four new direct oral anticoagulants (DOACs) into clinical practice has changed anticoagulant therapy for non-valvular AF.Dabigatran, rivaroxaban, apixaban and edoxaban offer some improvements in both efficacy and safety ,22,23,24For these reasons, DOACs are generally considered a better option in non-valvular AF, and current guidelines recommend them over warfarin or other vitamin K antagonists (VKAs) ,19,20, dHowever, some controversy still exists. No RCTs have been specifically conducted in patients with obesity. Available data are derived from post hoc analyses, metanalyses, retrospective analyses, observational studies and small non-randomized studies.None of these four drugs need dose adjustment in patients with overweight and obesity, while a reduced dose must be considered in low-body weight patients on apixaban or edoxaban.Pharmacokinetic (PK) data in extreme obesity support the suspicion that patients with a high BMI could be underexposed to DOACs and thus undertreated .Obesity could affect drug pharmacokinetics by increasing the volume of distribution and altering drug clearance as well as other pharmacodynamic effects. Traditional anticoagulants are dosed either based on laboratory testing (VKAs) or by weight (as is the case for heparin). Thus, when fixed-dose DOACs became the mainstay of anticoagulation, many clinicians were concerned about their efficacy and safety in patients who have obesity, since dosing is not weight-based, and laboratory monitoring is not commonly performed.The aim of this position paper is to review the available literature and propose the position of the Italian Association of Hospital Cardiologists (ANMCO) regarding the use of DOACs in patients with obesity and AF. In this paper, non-valvular atrial fibrillation refers to AF in the absence of a mechanical prosthetic heart valve or moderate to severe mitral stenosis.The pharmacokinetics (PK) and pharmacodynamics (PD) of DOACs have already been reported in detail in the scientific literature ,27,28,29PK differences in patients with obesity, as compared to those with normal weight, can potentially induce a different drug exposure and therefore a different magnitude of the anticoagulation effect .Drug absorption is a complex event that depends on drug features and specific properties of the host system. Gut permeability , liver mDrug distribution is the most controversial PK topic. Lipophilic drugs are expected to easily diffuse in fat tissue, and thus a higher volume of distribution (Vd) should be found in patients with extreme obesity. This is true for diazepam  but not Drug metabolism and excretion are mainly controlled by hepatic and renal functions. These two variables together with Vd determine the drug elimination half time.Vd ,38, renaIn severe obesity, the glomerular filtration rate (GFR) and renal plasma flow (RPF) were 51% and 31% greater than these values in control subjects .In patients with obesity, an increase in cytochrome P450 2E1 (CYP2E1) activity and phase II conjugation activity has been observed .For these reasons, several drug regimens are weight-adjusted, though this is not the case with DOACs, which are approved in fixed doses in patients with a high BMI ,31,32,33Kubitza et al. , in a siBarsam et al.  evaluateUsing data from seven clinical trials including 4918 patients with different approved indications of rivaroxaban, Willmann et al.  demonstrLiesenfeld et al.  analyzedCmax and AUC extrapolated to infinity, respectively, in low-weight patients when compared with the reference body weight patients. Conversely, the same parameters were reduced by 31% and 23% in higher-BMI subjects with a significant inverse relationship between apixaban exposure and both body weight and BMI. Anti-factor Xa activity showed a direct linear relationship with the apixaban plasma concentration. In light of the small slope, the authors considered the relationship between BMI and drug exposure “unlikely to be clinically meaningfully”.In a phase I study, Upreti et al.  investigIn a retrospective study on 38 patients with obesity (median body weight 132.5 kg), Piran et al.  analyzedBoriani et al.  analyzedFrom a theoretical point of view, DOACs with a low Vd, high protein binding and limited renal elimination should be less affected by body weight ; howeverDosing the plasma drug concentration at steady state could be a simple solution to solve the problem, but most guidelines ,19,20,50p < 0.0001) but the same coagulations biomarkers, such as D-dimer and prothrombin fragment 1 + 2 levels, and clinical outcome [For example, in the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial, patients receiving a lower apixaban dose compared with those receiving a full dose had a lower median AUC at steady state  guidelines  , mid-range BMI  and high BMI . No differences in efficacy or safety were found in the high-BMI group between dabigatran and warfarin.Ezekowitz et al.  presenteIn conclusion, about 24,000 patients with obesity were included in the four phase III registration trials . The useMetanalyses have investigated the effects of different weight classes compared to normal-weight patients on thromboembolic and bleeding events ,59,60. EThese observations are the basis of the so-called “obesity paradox” hypothesis: counterintuitive observations suggesting that patients with overweight or obesity may have a better prognosis than healthy-weight patients in terms of cardiovascular events ,62,63,64In metanalyses including data from RCTs only, DOAC treatment resulted in efficacy and safety at least comparable to warfarin in overweight and obese patients ,59,65,66Both Wang et al.  and Zhou2) and class 3 (BMI ≥ 40 kg/m2) obese patients.Additional metanalyses evaluated data from both RCTs and real-life studies ,67,68. G2 found no significant reduction in stroke or systemic embolism, but a significant reduction in major bleeding. Additionally, in this analysis, the results were driven by real-life studies.The analysis highlighted a better efficacy and safety of DOACs over VKAs in terms of stroke, systemic embolism and major bleeding in both groups and an even greater benefit in terms of the intracranial hemorrhage (ICH) incidence rate . NotablyFollowing an initial metanalysis including two post hoc analyses of the ARISTOTLE trial and three retrospective studies  with sim2, BMI 18.5 to <30 kg/m2, BMI 30 to <40 kg/m2 and BMI ≥ 40 kg/m2. Furthermore, patients were divided according to the type of anticoagulant prescribed: dabigatran, rivaroxaban, apixaban, edoxaban or warfarin.Recently, Barakat et al.  presenteConsidering all patients receiving DOACs as a group, the outcome of 17,640 patients in the treatment with warfarin was compared with that of 18,454 patients receiving DOACs: 1754 patients on warfarin were morbidly obese vs. 2170 on DOACs.BMI was not an independent predictor of ischemic stroke, significant bleeding or hemorrhagic stroke. Similar results were obtained considering BMI as a categorical or a continuous variable. Increasing BMI was associated with a small but statistically significant reduction in the risk of all-cause mortality.As compared with warfarin, DOACs showed improved safety and effectiveness across all BMI groups, including underweight and morbidly obese patients, at 3.8 years of follow-up. However, this observational study may have some limitations related to uncontrolled confounders.In the Anticoagulants for Reduction In Stroke: Observational Pooled analysis on Health outcomes And Experience of Patients (ARISTOPHANES) study, Deitelzweig et al.  analyzedBerger et al.  assessedWe identified seven scientific society guidelines or position papers on AF management ,73,74,75The most exhaustive guidelines on DOAC use in overweight, obese and extremely obese patients are from the Scientific and Standardization Committee (SSC) of ISTH  and the The guidelines of the SSC of the ISTH published in 2016 were the first society statement to raise concern about the use of DOACs in patients with obesity . GuidancThe recommendation to use standard dosing in people with a weight equal to or less than 120 kg or a BMI equal to or less than 40 kg/m2;The suggestion to not use DOACs in patients with a weight of more than 120 kg or a BMI of more than 40 kg/m2;The suggestion to evaluate, in patients with the characteristics of point 2, a drug-specific peak and trough level using a specific assay depending on the drugs used.In 2018, the EHRA Practical Guide on the use of non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation  reportedIn 2018, the ESC working group on thrombosis assembled a task group that published a consensus statement on antithrombotic management related to body mass . The con2 or a weight of >120 kg.In the 2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation , specifiThe conclusions of the most recent EHRA practical guide on non-VKA oral anticoagulants are summarized in 2 and a weight of 50–120 kg, DOACs can be safely used as usual. Between a BMI of 40 and 50 kg/m2 and a weight of 120 and 140 kg, the use of DOACs can be considered, but data are less robust. Over these upper limits, the need for careful individual clinical evaluation and DOAC plasma level measurements increases. According to international society recommendations, the use of VKAs may be considered a better option in this setting, and treatment individualization is mandatory.Briefly, at a BMI of 17.5–40 kg/m2 and a weight of 140 kg, scarce data are available. DOAC plasma level measurements or the use of VKAs may be reasonable [In conclusion, guidelines direct special attention to DOAC use in people with extreme obesity. DOACs can be used, but individualized management should guide clinicians, possibly with the help of DOAC plasma level measurement. Over a BMI of 50 kg/masonable .Plasma level measurements may aid clinicians in decision making but should only be used under the guidance of an expert team.The use of DOACs offers some major advantages with respect to warfarin and other VKAs ,19,20.One advantage is the possibility to use a fixed dose in patients with a high BMI, irrespective of patient size.However, the “one size fits all” strategy has raised a reasonable concern since different PKs in extreme BMI could induce a different drug exposure and thus a different anticoagulation effect .To test this hypothesis, an RCT in patients with extreme obesity should be conducted, comparing each DOAC against VKAs. Assuming a power of 80% and an alpha error of 0.05, a non-inferiority study would need at least 3000 patients in the DOAC arm and 3000 patients in the warfarin arm. It is unlikely that such a study can be carried out soon.However, PK data from populations with obesity, when available, are reassuring about drug exposure ,46,47,48A different number of studies have been performed for each DOAC. Rivaroxaban and apixaban are the most studied .Additionally, the use of warfarin in populations with obesity presents some problems and limitations. VKAs suffer from important food–drug interactions .There is an up to 40% prevalence of binge eating disorder or food addiction in extreme obesity ,78, whicMany patients with extreme obesity are affected by some deficits in executive function, such as inhibitory control or attentional bias . The simThe lack of prospective, randomized and controlled studies comparing each DOAC with VKAs in populations with extreme obesity means there is limited knowledge on this subject. However, knowledge is also limited in other important settings. For example, the use of DOACs in the specific setting of cardioversion has been evaluated in three deliberately designed undersized RCTs ,81,82.These three studies were undersized because from 20,000 to 48,000 patients would have been necessary to test non-inferiority, making it impossible. For this reason, other criteria different from powered RCTs have been considered acceptable.Furthermore, in patients affected by acute coronary syndrome, clopidogrel, ticagrelor, prasugrel and aspirin are commonly used in extremely obese patients, though no RCTs are available comparing each drug to the previous standard of care .It is reasonable to consider the use of DOACs as safe and effective in all weight classes.Considering our knowledge limitations, DOACs should be preferred unless specific reasons to the contrary are identified. Rivaroxaban and apixaban have more data and stronger evidence. Edoxaban should be avoided if a patient has a creatinine clearance superior to 95 mL/min, a frequent possibility with extreme BMI, as specified in the US package insert .2 must be individualized and determined together with a multidisciplinary team since data are lacking and special expertise is needed.Treatment in patients with a BMI equal or superior to 50 kg/mChecking drug-specific peak and trough levels is currently not a completely satisfactory solution for both theoretical and practical reasons, such as the lack of a clear correlation between drug levels, effective anticoagulation and outcome. Measurement of DOAC serum levels can be considered in specific situations if expertise is available, but current knowledge does not suggest routine daily use.


Uremic toxins are a heterogeneous group of molecules that accumulate in the body due to the progression of chronic kidney disease (CKD). These toxins are associated with kidney dysfunction and the development of comorbidities in patients with CKD, being only partially eliminated by dialysis therapies. Importantly, drugs used in clinical treatments may affect the levels of uremic toxins, their tissue disposition, and even their elimination through the interaction of both with proteins such as albumin and cell membrane transporters. In this context, protein-bound uremic toxins (PBUTs) are highlighted for their high affinity for albumin, the most abundant serum protein with multiple binding sites and an ability to interact with drugs. Membrane transporters mediate the cellular influx and efflux of various uremic toxins, which may also compete with drugs as substrates, and both may alter transporter activity or expression. Therefore, this review explores the interaction mechanisms between uremic toxins and albumin, as well as membrane transporters, considering their potential relationship with drugs used in clinical practice. Kidney diseases were the 10th leading cause of death worldwide in 2019, according to the World Health Organization (WHO). Their mortality rate has increased approximately 37.4% in 19 years. One of the main consequences of the loss of renal function is an accumulation of uremic toxins in the body, affecting the various tissues and organs, including the cardiovascular system -labeled metformin uptake by OCT2, suggesting some type of interaction between these compounds and the transporter [OCT2 is encoded by the ompounds ,119,120.ompounds ,121. Likompounds . Cheung nsporter .In vitro studies have also shown that creatinine, methylguanidine, guanidine, putrescine, and TMAO uremic toxins are substrates of OCT2 ,124,125.The interaction between drugs and OCT2 may influence the transport of uremic toxins. Studies have shown that serum creatinine levels may be increased with the use of drugs that compete for OCT2, impairing the tubular secretion of this uremic toxin ,127. CiaOCT2 expression in the kidneys is reduced in CKD. Studies with nephrectomized rats have shown a decrease in Oct2 levels in the kidneys ,130. HowSLC22A1 gene, is mainly found in the basolateral membrane of hepatocytes and may be involved in the uptake of guanidinovaleric acid [SLC22A3 gene and widely distributed by tissues, including skeletal muscle, placenta, kidney, and epithelial cells of choroid plexus [The transport capacity of uremic toxins related to other OCT isoforms is still unclear. OCT1 is encoded by the ric acid ,118. On d plexus . Althougd plexus ,121. Howd plexus .2 and COOH terminations in the cytoplasmic space [Organic anion-transporting polypeptides (OATPs) belong to the organic solute carrier (SLCO) gene family, which is within the SLC superfamily. This group is characterized by mainly transporting amphipathic organic anions, including compounds larger than 300 Da, but they may also have neutral and positively charged molecules as substrates . The stric space . Eleven ic space .SLCO4C1 gene and is highlighted by interacting with some uremic toxins and having its expression altered in CKD. This transporter is mainly found in the kidneys, in which it participates in excreting several molecules [OATP4C1 is encoded by the olecules . OATP4C1olecules ,140,141.olecules . Interesolecules . In contolecules . A reducolecules . Similarolecules . These dSLCO1B1 and SLCO1B3 genes, respectively. Both transporters had their expression reduced in hepatocytes exposed to uremic plasma from patients with CKD [Uremic toxins can also affect the expression and activity of OATPs present in the liver and, therefore, influence the metabolism of other compounds, including drugs. This is the case of OATP1B1 and OATP1B3, encoded by the with CKD . The trawith CKD . The dowwith CKD ,146.SLC20A1 and SLC20A2 genes, respectively, part of the SLC20 gene family. PiTs structurally contain 12 transmembrane domains, with both NH2 and COOH terminations in the extracellular space, with transport based on the Na+ concentration gradient to translocate Pi [Type-III sodium-dependent phosphate transporters 1 and 2 (PiT-1 and PiT-2) are encoded by the ocate Pi ,148. Botocate Pi . Studiesocate Pi ,151.Patients with CKD develop hyperphosphatemia, especially in the more advanced stages, which is related to several comorbidities in these patients . HyperphSLC34A2 gene, part of the SLC34 family. This transporter is responsible for the uptake of Pi in the intestine [Another important Pi transporter is NaPi2B which is encoded by the ntestine . Nicotinntestine . Howeverntestine .A synthesis of in vitro, animal, and clinical studies that link uremic toxins with cellular influx transporters, especially in CKD, is presented in SLC47A1 and SLC47A2 genes, respectively. Both mediate the cellular efflux of various compounds, particularly organic cations, presenting a large overlap with OCT substrates [Multidrug and toxin extrusion (MATEs) are membrane proteins which are part of the SLC47 family, which is included in the SLC superfamily. The main representatives are MATE1 and MATE2-K, encoded by the bstrates ,159. Basbstrates ,160. MATbstrates .MATEs are related to the mediated transport of both uremic toxins and various drugs used in clinical treatments. Studies have shown that both MATE1 and MATE2-K have creatinine and guanidines as substrates ,161. GesABCG2 gene and forms homodimers in the cell membrane to efflux a wide variety of substrates, including uremic toxins [2 termination in the cytoplasm [Breast cancer resistance protein (BCRP) is encoded by the c toxins . BCRP beytoplasm ,167. Likytoplasm ,167. BCRytoplasm ,170,171.ytoplasm ,173.Abcg2-knockout mice with chronic kidney dysfunction had high plasma levels as well as low renal clearance of IS compared to the control group, suggesting that BCRP is essential for renal elimination of this uremic toxin [ABCG2 gene [BCRP is widely known for its drug transport, being primarily related to the pharmacokinetics of drugs used in cancer therapies. In recent years, BCRP has been shown to interact with several uremic toxins, especially in the transport of these compounds from the intracellular compartment to the renal lumen in the tubular secretion process. Mutsaers et al. (2015) demonstrated that BCRP inhibition with KO143 leads to increased intracellular PCS levels, indicating that this uremic toxin is a substrate for the transporter . In anotic toxin . It was ic toxin . Other sic toxin ,178,179.CG2 gene ,181,182.CG2 gene .ABCC2 and ABCC4 genes, respectively. Both transporters are found in the apical membrane of proximal tubule cells, participating in the tubular secretion of endogenous and exogenous compounds. Moreover, MRP2 and MRP4 are also found in the liver, intestine, and brain capillary endothelium [Multidrug resistance-associated proteins (MRPs) are cell efflux transporters that interact with a wide variety of substrates. MRPs are part of the ABCC gene family which belongs to the ABC superfamily, characterized by ATP hydrolysis-dependent activity. The main members of this group are MRP2 and MRP4 encoded by the othelium ,186,187.othelium ,172,188.othelium ,172,189.p-cresyl glucuronide; however, the uptake of these toxins in vesicles expressing MRP4 was not observed [Although MRPs are considered potential transporters for uremic toxins, this interaction has been little explored. Mutsaers et al. (2011) observed that MRP4 activity is inhibited in the presence of IS, hippuric acid, kynurenic acid, IAA, and phenylacetic acid, which may suggest an interaction between these uremic toxins and the transporter . In anotobserved . In addiobserved . Like otobserved . Furtherobserved ,191,192.The main findings from in vitro and animal studies that investigated uremic toxins and their relationship to cellular efflux transporters are listed in Uremic toxins are important factors contributing to the pathogenesis of CKD, and their elimination by dialysis therapies is limited. PBUTs specifically have a high affinity for serum albumin, and their removal by dialysis is still a challenge, although some studies have explored strategies that break this interaction, including using drugs that also have an affinity for albumin. Uremic toxins reach various tissues and organs through the bloodstream. Several uremic toxins are capable of interacting with membrane transporters, mediating their entry or exit from the cell and may cause biological effects under uremic conditions, such as cell dysfunction. Membrane transporters are particularly important in renal clearance and elimination by other routes of uremic toxins and drugs. Therefore, there may be competition between toxins and drugs as substrates of the transporter or even inhibition of its activity. Another relevant outcome is the change in the expression of these transporters in CKD. Consequently, tissue disposition and the elimination of uremic toxins as well as drugs may be affected. Despite this, clinical studies addressing this issue are still scarce. Altogether, it is important to evaluate the interaction of drugs and uremic toxins via albumin or transporters and their impact on the clinical course of patients with CKD. In addition, studies in this area could contribute to the development of new therapeutic strategies to improve the removal of uremic toxins by dialysis.


Our findings reveal the pathogenesis of peripheral nerve reinnervation after thermal injury, which will assist in selecting appropriate treatments in further research.In recent decades, the use of energy-based devices has substantially increased the incidence of iatrogenic thermal injury to nerves . While recovery of the nerve after thermal injury is important, the changes in neural structure, function, and peripheral inflammatory reactions postinjury remain unclear. This study is aimed at demonstrating the changes mentioned above during the acute, subacute, and chronic stages of nerve reinnervation after thermal injury. Spontaneous reinnervation was evaluated, including the neural structures, nerve conduction abilities, and muscle regeneration. These effects vary depending on the severity of thermal injury . Peripheral inflammatory reactions, as impediments to reinnervation, were found in significant numbers 3 days after thermal injury, exhibiting high expression of IL-1 Energy-based devices (EBDs) have been widely adopted for hemostasis in recent decades due to the easy application and effective reduction in blood loss , 2. HoweNerve thermal injury, as a less reversible type, was associated with obvious histologic damage –6. It haIt has been demonstrated that nerve reinnervation is evaluated by assessing not only the reconstruction of the neural and muscular complex but also the improvement of conductive function . EMG andThis study aimed at elucidating the nerve's morphological and functional changes, as well as the peripheral inflammatory reactions that occur during spontaneous reinnervation following thermal injury in a rat model. We hypothesize that the consequence of reinnervation differs from the severity of thermal injury, presents in a time-dependent manner, and may be regulated by peripheral inflammatory reactions.The study involved 48 Sprague-Dawley (SD) male rats weighing 200-220 g. As described in the previous study, all rats were anesthetized with intraperitoneal injection of pentobarbital  and maintained on spontaneous ventilation . As descα, IL-1β, and IL-10) were analyzed via the enzyme-linked immunosorbent assay (ELISA) to assess the peripheral inflammatory reactions of the nerve. Animals were sacrificed via intracardiac injection of potassium chloride solution at 3 days (representing subacute evidence) and 30 days (chronic evidence) after thermal injury , subacute (3 and 7 days), and chronic (15 and 30 days) stages after thermal injury. Functional changes of the nerve were evaluated via EMG, including signal amplitude, latency, and nerve conduction velocity (NCV). The transversely morphological changes, including the structures of the sciatic nerve and gastrocnemius muscle, were recorded by using a light scope of hematoxylin and eosin (H&E) staining, immunofluorescent (IF) staining, and transmission electron microscopy (TEM). The changes in serum cytokine levels , whereas the distances between the nerve and HS were 1 and 3 mm in the moderate and slight injury groups, respectively. Sham operation was designed as nerve exposure, but without thermal injury as the control group. The results of the EMG changes  were recorded to verify the severity of nerve thermal injury  . These pAs described previously in the protocol, the sciatic nerves were exposed in the anesthetized animal for electrophysiological testing . The stiTEM was conducted to record structural changes in the nerve, particularly the morphological characteristics of myelinated axons and myelinated sheaths. A portion of the nerve was removed, fixed in 2.5% glutaraldehyde overnight in the refrigerator, and then washed in phosphate-buffered saline (pH 7.2) for 3 times at 15 min each. Then, it was placed in 1% osmium tetroxide and washed 3 times for 1 h each. After, the samples were stained with 4% uranyl acetate for 30 min, dehydrated by a series concentration of ethanol, and then embedded in resin mixture . This was sectioned and observed under a TECNAI-10 transmission electron microscope (Phillips) .μm thickness, CM1850, LEICA, Germany), stained using IF markers, and observed under a fluorescence microscope . Nuclei were stained blue using 4,6-diamidino-2-phenylindole . Primary antibodies against the neurofilament  were used to identify axons and NMJ in the nerves and gastrocnemius muscles, respectively. Goat anti-mouse  was used as the secondary antibody.Tissue samples were sectioned  following the manufacturer's instructions. Briefly, a total of 5 mL of blood was collected from the exposed heart of the deeply anesthetized animals and incubated at room temperature for 30 min. The serum was obtained after centrifuging the whole blood at 2000 rpm at 4°C for 15 min and then analyzed using an ELISA kit. The concentrations of TNF-α, IL-1β, and IL-10 were measured through correlation with a standard curve. Blank disks were used as the control.Productions of TNF-t-test, Kruskal-Wallis test, Fisher's precise test, and one-way analysis of variance (ANOVA) using the SPSS software . Data were illustrated using the GraphPad Prism .Data were expressed as mean ± standard error, and statistical analysis was performed by Student's rd to 30th day after thermal injury. In the severe group, the NCV index was also significantly decreased but could not recover until 30 days after thermal injury. As depicted in Nerve electrophysiological changes, such as NCV, amplitude, and latency, were recorded via EMG to evaluate the nerve conductive abilities. The nerve conductive abilities were destroyed, which was exhibited as the decrease in NCV and amplitude, as well as latency prolongation after thermal injury. The NCV and amplitude indices vary from 0 to 1, with 0 and 1 corresponding to complete dysfunction and normal function, respectively. The NCV index was measured in each time point  after thermal injury to assess spontaneous nerve reinnervation recovery. In the slight group, the NCV index was impaired immediately but began to recover 3 hours after thermal injury. However, the NCV index decreased immediately after thermal injury in the moderate group and continued to decline over the following 3 days. It should be noted that the NCV index began to increase from the 3Nerve pathological changes were recorded via light and electron microscopy at 3 days after slight, moderate, and severe thermal injury . In subjThe macro- and micromorphological changes of the gastrocnemius muscle are recognized as a vital index to evaluate nerve functional recovery. At 30 days after thermal injury, the gross, microscopic, and IF staining changes of the gastrocnemius muscles were recorded in each group . Gross oThe number of intact NMJ is used as a vital index to evaluate functional nerve recovery. As an essential constituent of NMJ, neurofilaments in the gastrocnemius muscle were evaluated via IF staining to estimate the number of NMJ. The number of neurofilaments varied and was ranked in the following order: slight group > moderate group > severe group, with significant differences. Quantitatively, there was no significant difference in the number of NMJ between the slight thermal injury group and the control. However, in the moderate thermal injury group, 58 ± 2% of NMJ was fluorescently present compared to the control group. Additionally, there was only 9 ± 8% NMJ present in the severe group compared to the control group.To evaluate the functional recovery of the sciatic nerve, the number of neural fibers was recorded by IF staining (antineurofilament) at 30 days after thermal injury . IF evidμm2) was measured in the scope of TEM, and the results revealed that the number of macrophages was increased significantly, showing the tendency of Schwann cells to activate at 3 days after thermal injury in the moderate group. Moreover, as exhibited in α and IL-1β were increased, while the levels of IL-10 were reduced in the moderate and severe groups at 3 days after thermal injury. However, there was no significant difference in the levels of TNF-α, IL-1β, and IL-10 in the four groups at 30 days after thermal injury.The peripheral nerve injury environment consists of axons, Schwann cells, macrophages, and components of the extracellular matrix (ECM), such as neurotrophic growth factors (secreted by Schwann cells) and inflammatory cytokines (secreted by macrophages) . Neural β and TNF-α increasing and IL-10 decreasing in the peripheral nerve environment. Moreover, the severity of thermal injury influenced the degree of recovery and determined whether the nerve reinnervated spontaneously. This finding is consistent with the previous study and may be relevant to the phenomenon of nerve thermal tolerance [Spontaneous reinnervation of muscles after peripheral nerve injury has been reported in the previous studies , 14. Mosolerance .Acquiring the acute, subacute, and chronic evidence of functional and morphological changes after thermal injury is fundamental to elucidate the process of spontaneous reinnervation. During the acute stage of thermal injury, the conductive ability of the nerve is significantly impaired, but the degree of damage varies depending on the severity of thermal injury. During the subacute stage, the conductive ability is recovered in the slight group, but not in the moderate or severe group. Pathologic results also exhibit different consequences. In the slight group, the myelin sheaths are thick, and the neural axons are maintained as the concentric structure, whereas in the moderate group, the nerves maintained the complete structural integrity of the axons and myelin sheaths, but the damaged features were exhibited as loose myelin sheaths and reduced anchoring particles. In the severe group, the nerve exhibited features of coagulation and homogenization. Moreover, multiple bubbles were observed in the neural fibers and myelin sheaths, destroying the intact structures completely. In the chronic stage, nerve conduction experienced a gradual recovery period, and NCV returned to 45% compared to the normal at 30 days after thermal injury in the moderate group. Conversely, the conductive ability of the nerve could not be recovered in the severe group. These differences are also exhibited in morphological results. The neural fluorescent intensity in the moderate group was restored to 61% compared with the control, but only 11% had been detected in the severe group, which speculated that the number of neural fibers cannot restore if nerves are exposed to severe thermal injury.The consequence of neural transient or permanent damage varies. Therefore, it is important to find critical indices for predicting the consequence of neural reinnervation. EMG results, including NCV and signal amplitude, reflect the conductive ability of the nerve . In the The morphological change of the gastrocnemius muscle is also recognized as a vital index in evaluating functional recovery of nerves. When nerves are exposed to thermal injury, the nerve conductive ability is destroyed. Additionally, the number of intact NMJ in skeleton muscles is also significantly decreased , 20. Witβ and TNF-α but low expression of IL-10. All of these observations indicate the existence of peripheral inflammatory reactions after thermal injury, which may interfere in the early stage of neural recovery.Although it has been demonstrated that the nerve itself contributes to spontaneous reinnervation in the moderate group, neural function cannot recover at an early stage. Conversely, it is observed that nerve conduction ability was continuously damaged in 3 days after thermal injury. This abnormal finding is supported by a previous study, which can be explained by the theory of local inflammation after peripheral nerve injury , 22, 23.This study has some limitations. Firstly, animal models of nerve thermal injury were derived from HS, which mimic the surgical environment. Though estimated using an infrared thermodetector, the actual temperature cannot be measured to evaluate the severity of thermal injury. Secondly, the mechanisms of inflammatory reactions, especially the processes of macrophagic activation and proinflammatory cytokine release after thermal injury, warrant further investigation.However, despite these limitations, our study first investigates the process of neural spontaneous reinnervation after thermal injury by trying to elucidate underlying peripheral inflammatory reactions. Understanding the effects and mechanisms of spontaneous peripheral nerve reinnervation after thermal injury is helpful in bridging the subjects of medicine, biology, and bioengineering to improve reinnervation. In recent decades, various promising efforts in biology and bioengineering have been explored to improve reinnervation, including nerve conduits using an extracellular matrix, gene therapy with growth factors, and even 3D printed scaffolds –30. HoweOur results showed that the consequence of spontaneous peripheral nerve reinnervation varies, depending on the severity of thermal injury. When exposed to slight injury, neural function would recover in the acute stage. When exposed to moderate injury, nerve conductive ability is damaged significantly, requiring a gradual recovery period in the subacute and chronic stages. Lastly, when exposed to severe injury, both neural structures and functions were completely destroyed and were unable to reinnervate at all. Moreover, thermal injury generated inflammatory reactions in the peripheral nerve, which may interfere with neural recovery at the acute stage.


Chromatin and epigenetic alterations in cancer are responsible for a wide range of transcriptional changes that link DNA mutations to tumor phenotype. In this review, we explore studies describing recurrent epigenetic alterations in prostate cancer and highlight changes that occur during prostate carcinogenesis and progression to lethal treatment-resistant disease.The dysregulation of chromatin and epigenetics has been defined as the overarching cancer hallmark. By disrupting transcriptional regulation in normal cells and mediating tumor progression by promoting cancer cell plasticity, this process has the ability to mediate all defined hallmarks of cancer. In this review, we collect and assess evidence on the contribution of chromatin and epigenetic dysregulation in prostate cancer. We highlight important mechanisms leading to prostate carcinogenesis, the emergence of castration-resistance upon treatment with androgen deprivation therapy, and resistance to antiandrogens. We examine in particular the contribution of chromatin structure and epigenetics to cell lineage commitment, which is dysregulated during tumorigenesis, and cell plasticity, which is altered during tumor progression. Chromatin structure and epigenetics are intertwined but, nonetheless, distinct entities that have been implicated in prostate cancer (PC) disease initiation and progression. Here, we consider epigenetics to be the collection of DNA modifications such as DNA methylation. This definition of epigenetics can also include histone modifications and the binding of transcription factors (TFs) to DNA. Histone modifications are often referred to as epigenetic profiles as they determine chromatin states and nucleosome positioning, which, in turn, allows for DNA accessibility . TherefoPC continues to be a major cause of cancer-related death in men worldwide . AlthougAlong with others, we previously reported that the emergence of castration-resistance and AR overexpression are associated with chromatin reprogramming ,17,18,19Flavahan and colleagues first proposed the concept of epigenetic plasticity, by which alterations in the structure of the chromatin, or chromatin states, and epigenetic alterations would be able to confer the full range of cancer hallmarks by altering transcriptional regulation ,26. IndeSimilarly, it is well known that cellular lineage identity is defined by tightly regulated chromatin-related processes and the epigenetic landscape . The conDue to the dynamic sets of chromatin and epigenetic alterations and their reversibility, such alterations can play a significant role in driving both carcinogenesis and progression to treatment-resistant disease. These alterations can also be stochastically different in individual cells . TherefoIn this review, we highlight the contribution of chromatin- and epigenetics-related processes to prostate carcinogenesis and progression to treatment resistance. We explore the role of epigenetic regulation and TFs in lineage commitment and plasticity in the normal prostate and PC cells, the mutations and the altered expression of key genes coding for chromatin-associated proteins, alterations in DNA methylation patterns, and changes in the structure and 3D organization of the chromatin .Here, we collect evidence of the involvement of lineage commitment and cancer cell plasticity in PC initiation and resistance to treatment, respectively.NKX3-1, as it is an AR target gene and was not expressed after deletion of AR [The normal epithelium of the prostate secretory acini is composed of a layer of luminal cells oriented towards the acinar lumen and a basal cell layer that also includes rare neuroendocrine cells. The determinants of prostatic epithelial differentiation include master regulators such as AR, FOXA1, and NKX3-1. Talos and colleagues showed that these TFs were sufficient and required for differentiation of iPS cells of mouse fibroblast origin in prostatic cells engrafted in the renal capsule . Xie et on of AR . Dutta eon of AR . Of noteon of AR . Differeon of AR . The stron of AR ,34.HOXA13, HOXB13, and HOXD13) are still expressed in the luminal epithelium of the human adult prostate [NKX3-1 and HOXB13 together with AR [AR and FOXA1 have been found to also be important for the regulation of the Homeobox (HOX) A genes . The HOXprostate . In rece with AR .−, KRT8−, and SCGB1A1+) and hillock cells  with stem cell potential [Experiments in mice have shown that the prostate epithelium displays a regenerative capacity following repeated cycles of androgen deprivation ,39. Thisotential . Similarotential , althougotential ,41,42.Maitland and Collins have reviewed how the prostate epithelium constantly renews and the terminal differentiation of luminal cells is associated with a higher rate of apoptosis . The exiLineage plasticity refers to the reversal of the process of lineage commitment, either by dedifferentiation of the more differentiated cells or, in extreme cases, transdifferentiation directly (or via an intermediate) to another epithelial cell lineage. This is a process that is unlikely to occur in the normal prostate under physiological conditions. However, pathological processes or stressors such as inflammation may alter this scheme . AlteratLineage plasticity during prostate carcinogenesis is poorly understood. However, for example, the upregulation of c-MYC is a common early event  and has SPOP) showed that this mutation is sufficient to modify chromatin accessibility and binding of AR and FOXA1 at PC specific genomic sites [Cells with regenerative potential within the normal prostate tissue have been hypothesized to be the cells of origin for PC . Indeed,ic sites ,62. Furtic sites ,64.flox/flox mice [Tacstd2)-expressing luminal progenitor cells characterized by Guo et al. have similar transcriptional features to the club cells characterized by Henry et al., Karthaus et al., and Song et al., based on the presence of markers such as PIGR, PSCA, and KRT4 [The role of some luminal progenitor cells as possible cells of origin for PC is also supported by studies in Tmprss2-CreER;Ptenlox mice . The TACand KRT4 ,40,41,60and KRT4 . The autand KRT4 .Although the above-described studies suggest that multiple subpopulations of cells can give rise to PC ,47,48,50Treatment of metastatic HSPCs with ARSI in combination with ADT imposes a strong negative selection pressure on the cancer cell population. Multiple treatment resistance mechanisms arise from genetic and epigenetic alterations, leading to a rewiring of alternative bypass pathways driving tumor cell growth , but theNR3C1 (GR), POU3F2 (BRN2), TBX2, and NR2F1, leading to gene expression changes including a reduction in luminal and an increase in EMT markers [Chromatin reprogramming is a key feature of lineage plasticity during treatment resistance, for example by allowing reactivation of pathways that are normally active only during prostate tissue development ,64,72. Z markers .TP53 and RB1 that can lead to treatment resistance by allowing for diverse transcriptional programs [TP53 and RB1, respectively) cooperate to suppress expression of SOX2, a well-known factor of pluripotency, which, in part, explains the association of these factors with lineage plasticity [RB1 loss leads to a reconfiguration of E2F activity for increasing the production of antioxidants, protecting cells against doxorubicin in the CRPC cells that is not observed in the HSPC cells [Cellular plasticity has also been associated with alterations in tumor suppressor genes and chromatin reconfiguration, exemplified by the deletion of programs ,69,70. Pasticity ,73. UsinPC cells . These fPC cells .TP53 and RB1 suggests that t-NEPCs require these alterations for tumor selective reprogramming [LHX2 and ISL1 in human cells, which goes hand in hand with increased chromatin accessibility and the transcriptional output of neuroendocrine lineage-specific genes [ASCL1 and NKX2-1, has been implicated in this process [In the context of ARSI resistance, so-called treatment-induced neuroendocrine prostate cancers (t-NEPCs)  reprgramming ,15,73,76ic genes . NE-specic genes . The los process . Active  process .Ultimately, PC progression is a continuous process resulting from the selection pressures of treatment upon the tumor, the TME, and the host immune system .Many studies have illustrated how chromatin-associated factors affect both lineage commitment and lineage plasticity, which is likely to drive tumor progression ,68,77. PTP53, FOXA1, SPOP, and CDK12, were recurrently mutated in both the ICGC early stage and advanced CRPC tumors. The proportions of mutated genes from different functional categories did not differ significantly in the early versus treatment-resistant disease stages.Somatic mutation data from the International Cancer Genome Consortium (ICGC) prostate adenocarcinoma datasets  showed tn = 10), untreated PC (n = 16), and CRPC (n = 11) samples , luminal cells, and in glands from PC tissue, its use as an early carcinogenesis tissue marker has been suggested [GSTP1 and revealed several other promoters of genes, including RARβ, HIF3A and HAAO with functions related to tumor suppression, response to hypoxia, and microsatellite stability, respectively, which were hypermethylated in PC when compared to benign tissue [HOXD3, a gene involved in TGFβ signaling, and BMP7, which has been reported to suppress metastatic potential [One of the first findings linking alteration of DNA methylation patterns to PC was reported by Lee et al. . They shpromoter . The GSTal cells . As GSTPuggested ,104. Subuggested ,110,111.n tissue ,118. Simotential .The combination of DNA methylation and gene expression changes in respective genes can also be used to predict treatment outcomes. Panja et al. showed that a panel of as little as five methylation sites and mRNA expression changes in their site-harboring genes could be used to predict response to ADT in the TCGA PC cohort .Alterations in DNA methylation patterns in larger regions than promoters have also been linked to carcinogenesis. Fiano et al. showed that the hypomethylation of long interspersed nuclear element-1 (LINE-1), which represents about 15% of the human genome, is associated with PC mortality . It was Although methylation of 5′ carbon of cytosine (5mC) is the most commonly studied form of DNA methylation, there are also other types of DNA modifications associated with gene expression regulation that are less studied in PC: 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC), all formed from the 5mC oxidation process . StorebjERG, ETV1, ETV4, and FLI1) [SPOP, FOXA1, and IDH1 mutations also had distinct DNA methylation patterns. Interestingly, the IDH1-mutated PC subgroup had fewer DNA copy number alterations and was presented as an early onset PC subgroup. IDH1 mutations have been associated with a DNA hypermethylator phenotype also in gliomas and hematological malignancies [DNA methylation patterns have also been associated with specific genomic alterations in PC . The Cannd FLI1) . ERG fusnd FLI1) . In the gnancies ,124. Howgnancies . In distgnancies . The comgnancies .HOXD3 [DNA methylation in PC has also been studied in relation to changes in chromatin accessibility and histone modifications ,106,111.CRIP1, FLNC, RASGRF2, RUNX3, and HS3ST2 genes that are important for lineage commitment and differentiation have been associated with PC recurrence [SRD5A2 gene that encodes for one of three isozymes of 5α-reductase, is significantly associated with longer survival in CRPC patients [DNA methylation changes have been associated not only with PC carcinogenesis, but also with PC progression and treatment resistance. Promoter hypermethylation of currence ,127,128.patients .Peter et al. performed a genome-wide methylation analysis in cell lines representing PC progression and showed methylation changes associated with the development of CRPC and t-NEPC . They shTET2, DNMT3B, IDH1, and BRAF genes [AR, MYC, and ERG, was associated with DNA methylation in regulatory intergenic regions [In a large-scale study utilizing clinical specimens from 100 CRPC metastases, Zhao et al. showed that differential methylation preferably occurs at intergenic sites with regulatory functions . ApproxiAF genes . They al regions .We have profiled BPH, primary PC, and non-metastatic CRPCs, revealing that chromatin accessibility and DNA methylation are anticorrelated in these clinical specimens ,72. The These studies show that DNA methylation patterns are altered during progression and development of treatment resistance, but additional studies are needed to further elucidate passenger and driver events linked to DNA methylation in PC. The organization and structure of the chromatin is the result of the activity of various proteins interacting with DNA. The negatively charged 2 nm DNA double helix wraps around positively charged histone octamers, creating the 10 nm fiber of adjacent nucleosomes. By continuous folding and looping, this primary structure is further compacted to higher order structures to form the 3D organization of the chromatin. Both DNA replication and RNA transcription require accessibility to DNA by reversible decondensation of these chromatin structures. Chromatin compaction and decondensation at certain loci are tightly regulated and cell type-specific. This is primarily achieved through the ability of chromatin-associated proteins to write , read , and erase  the histone code.The histone code is the set of histone modifications  defining the state of the chromatin. For instance, a chromatin region marked by methylation in lysine 4 of the histone 3 tail  and H3K36me1 could be defined as a promoter ,132,133,KLK3 (encoding PSA) promoter and enhancer regions upon androgen treatment in LNCaP cells [KLK3, thus changing the chromatin state to a more active one via their acetyl transferase activity [PC cells exhibit significant dysregulation of chromatin states . FirstlyaP cells ,137,138.activity . p300 haactivity . Moreoveactivity .KLK3, TMPRSS2, and NKX3-1, resulting in less methylated H3K9 and an active chromatin state [In addition to HATs, histone demethylases  have been reported to exhibit androgen stimulation-dependent recruitment to AR target genes such as in state ,143,144.in state , whereasin state . High lein state . Despitein state ,149. Ke in state . Genes min state .CDK1 and UBEC2, in CRPC tissues and cell models when compared to androgen-dependent cell lines and tumor tissue samples [Changes in chromatin states have also been associated with PC progression. The expression of the aforementioned HATs p300 and CBP was shown to be significantly higher in metastatic CRPC lesions than in local tumors . p300 fu samples . Consist samples . This de samples .These findings suggest that overall active chromatin state increases during PC progression and that the epigenome is reprogrammed to resemble stem-like cells. Supporting this, genes repressed and marked by H3K27me3 levels in normal prostate tissue and derepressed during PC progression are developmental regulators and homeobox proteins ,156. Yu The combination of aberrant functions of HATs, demethylases, and EZH2 all seem to contribute to increased active chromatin states in PC progression and treatment resistance. In line with this, Pomerantz et al. showed that the profiles of active enhancers defined by H3K27ac are similar in CRPC and prostate-specific fetal tissue, implicating reactivation of the developmental transcriptional programs in late-stage disease .Chromatin accessibility is facilitated by the chromatin remodeling complexes by either ejecting histones or full nucleosomes or by sliding nucleosomes to different positions in the DNA ,163. SevPTEN loss is associated with PC progression to CRPC  and has In addition to BRG1, increased expression of SWI/SNF complex subunits BAF57 and BAF155 has been described in PC ,171,172.Interestingly, the differential expression of certain SWI/SNF complex subunits has been linked to the progression of PC into NEPC . For exaAbnormal activation of chromatin-related proteins such as chromatin readers and the SWI/SNF complex and its subunits suggests an overall increase in accessible chromatin in gene regulatory regions in CRPC. Indeed, multiple studies have shown that relaxed chromatin correlates with PC progression ,173,174 KLK3 and TMPRSS2 transcription [FKBP5 and KLK2 [Similarly, the SWI/SNF complex has been shown to be an important transcriptional cofactor for AR activity ,189,190.cription ,188,190.The importance of bromodomain activity in modifying the structure of the chromatin in PC has been reviewed previously . Here, wThese findings demonstrate the abnormal function of chromatin accessibility regulators in PC and in the progression to treatment resistance. These changes precede the reprogramming of TF chromatin binding, especially the binding of the AR.There is an evident interplay between chromatin accessibility, chromatin remodelers, and TF activity in promoting prostate carcinogenesis and progression to CRPC by mediating transcriptional plasticity. Active chromatin is generally open and accessible, whereas repressed chromatin does not allow access to DNA . ExcludiIn addition to AR, its coregulators have been extensively studied during PC progression . AR cofaTMPRSS2-ERG fusions than late onset PCs, in addition to these tumors being generally more aggressive [The proportion of ETS fusion-positive tumors does not increase during progression of PC to CRPC , indicatgressive ,206.The AR cistrome expands during PC development and is further reprogrammed as the cancer progresses to CRPC, as shown by Sharma et al. and Pomerantz et al. ,63,64. TID1, ID3, and PFN2, which are androgen response element-independent AR target genes essential for CRPC growth in enzalutamide-resistant CRPC cells, patient-derived xenografts, and tissue samples [He et al. showed that AR binding converges toward the regulatory regions of genes such as  samples . The exp samples . Interes samples , and TET samples , mediate samples  and link samples , highlig samples . Example samples , and RUN samples . Express samples ,13,14,15 samples . Exposur samples . n-Myc–m samples , and the samples .These findings suggest that chromatin reprogramming can lead to the recruitment of additional TFs during PC progression to CRPC.In addition to nucleosome remodeling, chromatin accessibility, and chromatin state, the higher order chromatin organization influences gene expression . AlthougMultiple chromosome conformation capture sequencing studies from cell lines have demonstrated that the 3D organization of the PC genome is reprogrammed in comparison to normal cells ,221,222.Rhie et al. used normal and PC cell lines to study the association of TADs with different chromatin states . They shThe functional role of chromatin loops in the AR cistrome was further evaluated in a meta-analysis of protein–chromatin interactions  and active enhancer regions in PC cells . The resUBE2C locus in CRPC models [UBE2C has been shown by several groups to mediate castration-resistance [The pioneering factor FOXA1, together with the mediator complex component MED1, were first shown to facilitate chromatin looping at the C models . UBE2C hsistance ,228,229,Baca et al. showed that some regulatory regions gain active histone marks in NEPC in comparison to PC and that these regions form more physical contacts with genes exhibiting higher expression in NEPC than in PC . Thus, tGenomic rearrangements and breakpoints in PC have been shown to occur commonly at sites that are annotated as active enhancers in accessible chromatin regions or at sites that are transcriptionally active in PC datasets . A similPCAT43, PCAT61, and PCAT76) in the PMEPA1 gene locus are connected to the gene by a chromatin loop facilitated by AR and ERG. Depletion of the lncRNAs PCAT43 and PCAT61 significantly decreases the AR-mediated activation of PMEPA1, indicating that lncRNAs may function to converge some AR-mediated responses via established chromatin loops [Predisposing risk SNPs have also been shown to exhibit aberrant chromatin looping ,232,233.in loops .HOXA locus, repressing the expression of HOXA13 [HOXA13 suppression and its overexpression, which, in normal immortalized RWPE-1 cells, has been found to induce the activation of the HIPPO pathway [HOXA13 via the loss of a suppressive chromatin loop may be implicated in promoting prostate carcinogenesis and tumor progression.Analysis of the hotspot mutation locus 8q24, which is located in an enhancer and associated with increased PC risk, revealed that it is connected via chromatin loops to several oncogenes and is responsible for their regulation . Pioneerf HOXA13 . Deletio pathway . The act pathway ,236. ThuIn summary, although chromatin relaxation during PC progression to CRPC particularly occurs in enhancers as mentioned in earlier sections ,72, a stA thorough characterization of the processes leading to epigenetic and chromatin dysregulation will allow for a more refined classification of PC subtypes and provide rationale for novel therapeutic approaches for both HSPC and CRPC patients.NSD3 gene due to overexpression or missense mutation leads to the increased expression of other oncogenes via H3K36 dimethylation. This makes the NSD gene family an attractive therapeutic target. Similarly, Grbesa et al. showed that SPOP mutations increase chromatin accessibility in regions that coincide with AR binding sites in primary tumors and confer exquisite sensitivity to AR-targeted therapies due to the interplay of AR with the chromatin [Although genes with chromatin regulatory functions are known to be mutated and altered in terms of their expression in many cancer types, further studies are needed to understand the mechanisms by which the alteration of these genes leads to chromatin dysregulation and carcinogenesis. Recently, Yuan et al.  and Li ehromatin . Convershromatin .Another aspect that deserves further investigation is the presence of conserved shadow enhancers in the genome, which are alternative, redundant enhancers for the regulation of particular genes . The useTo date, epigenetic studies of PC have mostly been performed by bulk sequencing of individual tumors or in cell lines, and the full heterogeneity of PC phenotypes might still be severely underexplored. Single cell-based studies might be able to shed light on this complexity by increasing the analytical resolution and allowing for analysis of the transcriptome, chromatin structure, and epigenetics in the same cells.6-methyladenosine (m6A) is the most common post-transcriptional modification of mRNA, and its dysregulation via the METTL3 gene has been shown to play a role in prostate carcinogenesis and PC progression through modulation of the Wnt pathway [MYC mRNA methylation [In addition to DNA modifications, links are currently being explored between PC and epitranscriptomics such as mRNA methylation. N pathway  and MYC hylation . METTL3 hylation .Additionally, protein modifications can have further effects on PC biology. This is exemplified by the O-GlcNAc modification of proteins that are upregulated in early stage PC . Recent Limited focus on only cancer cells might also be too narrow a scope to understand the full complexity of the epigenetic landscape of prostate tumors. In PC, the TME consists of stromal cells such as smooth muscle, fibroblasts, endothelial cells, and leukocytes. The interplay of PC cells with the TME in resistance to ARSI has been revealed in recent single cell studies by Chen et al. and He et al. ,247. TheGSTP1 promoter were already suggested as a biomarker in the 1990s [Finally, given the importance of chromatin and epigenetic dysregulation in prostate carcinogenesis and PC progression to CRPC, many investigators have trialed and continue to trial so-called epidrugs and their efficacy in different clinical settings. A comprehensive review of the ongoing clinical trials testing small molecules in PC has been recently published by Kumaraswamy et al. . Here, whe 1990s , but deshe 1990s . As an ehe 1990s , includihe 1990s . The chahe 1990s .Chromatin- and epigenetics-related processes contribute to prostate carcinogenesis and prostate cancer progression to castration-resistance under selective treatment pressure. Prostate cancer shows evidence of progressive chromatin relaxation, reconfiguration of the 3D chromatin structure, alteration of specific histone modifications, and methylation patterns that contribute to so-called transcriptional plasticity and ultimately, provide the means of cancer cell adaptation to treatment. A total of 5% and 11% of genes coding for chromatin-associated proteins are recurrently mutated or aberrantly expressed in primary PC and in CRPC compared to the normal prostate tissue, respectively. In particular, recurrently mutated chromatin modifiers are highly expressed in CRPC, irrespective of their mutation frequency. Moreover, 6% of genes coding for chromatin-associated proteins are overexpressed during prostate carcinogenesis and treatment resistance. Targeting epigenetics- and chromatin-related processes in combination with existing therapeutic approaches seems to be an attractive strategy for providing greater benefits to prostate cancer patients. However, a deeper understanding of the underlying mechanisms involved and better biomarkers are needed to implement this strategy in the clinic.


Objectives: Genetic variants and obesity are risk factors for hyperuricemia (HUA). Recent genome-wide association studies have identified ABCG2 rs2231142 as one of the most prominent genetic variants for HUA in an East Asian population. Nevertheless, no large-scale studies have demonstrated any interactive effects between this variant and obesity on serum urate level in Asians. This study aimed to determine the interaction of ABCG2 rs2231142 variant and body mass index (BMI) and its effect on risk of HUA in an East Asian population.Methods: The study was conducted using the Taiwan Biobank database, a population-based biomedical research database of patients with Taiwanese Han Chinese ancestry aged 30–70years between September 2014 and May 2017. Detailed physical information on participants were collected by questionnaires and genotyping using Affymetrix TWB 650K SNP chip. The primary outcome was HUA, defined as a serum uric acid level>7.0mg/dl. Odds ratio (OR) of HUA was analyzed using logistic regression models and the effects of interaction between ABCG2 rs2231142 variants and BMI on serum uric acid level were explored.Results: We identified 25,245 subjects, 4,228 (16.75%) of whom had HUA. The prevalence of HUA was 30% in men and 3.8% in women. The risk of HUA was significantly associated with ABCG2 rs2231142 risk T allele, with more HUA in TT genotype  and TG genotype  in men, and TT genotype  and TG genotype  in women, compared with their counterparts. Moreover, we found a strong genetic-environmental interaction associated with the risk of HUA. There was increased risk of HUA by the interaction of ABCG2 rs2231142 variant and BMI for TT genotype  and TG genotype  in men compared with the GG genotype in men, and for TT genotype  and TG genotype  in women compared with the GG genotype in women.Conclusion: The risk of HUA was markedly increased by the interaction of ABCG2 rs2231142 variant and BMI, both in men and in women. Body weight control and reduction in BMI are recommended in high-risk patients with the ABCG2 rs2231142 risk T allele. Hyperuricemia  is a major risk factor for gout, which is known as “the disease of kings and the king of diseases.” Acute gouty attacks cause severe pain, difficulty in walking, functional impairments, and major disability, as well as decreased productivity if left untreated. Moreover, HUA is associated with multiple comorbidities, including obesity, hypertension, type 2 diabetes, hyperlipidemia, cardiovascular diseases, chronic kidney diseases, stroke, osteoporosis, erectile dysfunction, obstructive sleep apnea, and is an independent predictor of premature mortality . The treABCG2), glucose transporter type 9 , and urate anion transporter 1 . ABCG2 encodes a high-capacity transporter for urate efflux and the ABCG2 rs2231142 single nucleotide polymorphism (SNP) is one of the most prominent genetic variants associated with HUA and gout . Other studies have also demonstrated that interactions between BMI and genetic variants affect serum urate concentrations and gout risk, but with conflicting results (n=459), the definition of obesity was obscure in a study of ABCG2 rs2231142 in East Asians  was positively correlated with serum uric acid levels in previous studies , a population-based biomedical research database of patients with Taiwanese Han Chinese ancestry aged 30–70years, without a history of cancer. The patients were enrolled from 29 recruitment centers throughout Taiwan and all of the participants provided informed consent. Our study cohort was composed of 25,245 individuals with genotyping information, serum uric acid reports and the demographics, medical history, lifestyle modality, body fat evaluation including BMI, waist circumference, and body fat percentage, as well as biochemical reports including serum creatinine, cholesterol and fasting glucose level, were all identified from the database. The study was conducted in accordance with the Declaration of Helsinki and was approved by the ethics committee of Taichung Veterans General Hospital’s Institutional Review Board (IRB no. CE16270B-2). All available data were achieved from TWB, which collected specimens and information in a complete and standardized procedure to fit researchers’ needs in different fields .https://taiwanview.twbiobank.org.tw/index; p<1×10−6, minor allele frequency<0.01, and a genotyping call rate less than 90% . After evaluating the quality of the isolated genomic DNA , it was than 90% .2) and overweight was defined as BMI≥24kg/m2 (for the East Asian population). Body fat percentage was the percentage of fat by weight  by the uricase method .t-test for continuous variables and Chi-square test for categorical variables were used to compare data between HUA and non-HUA controls. Odds ratios (OR) and 95% confidence interval (95% CI) of variables and ABCG2 rs2231142 on HUA were calculated by logistic regression. Multiple logistic regression models were also used for evaluating the effect of interaction of ABCG2 rs2231142 and BMI on HUA. All data were analyzed using R, version 4.0.0 software. Statistically significance was set at p values less than 0.05.The demographic information is shown as mean±standard deviation for continuous variables and number (percent) for categorical variables. Student’s p<0.001) but surprisingly, more men aged 30–45years had HUA (48%). Higher BMI , waist circumference , waist-hip ratio , and body fat percentage  were observed in both men and women with HUA, compared with their counterparts. Furthermore, those with HUA were more likely to be users of alcohol or betel nut, and exhibited higher systolic blood pressure, higher blood total cholesterol level, higher LDL level, higher triglyceride level, but lower HDL level, compared with those without HUA.Between September 2014 and May 2017, we identified 25,245 subjects in the final analysis, including 4,228 (16.75%) with hyperuricemia and 21,017 (83.25%) in the control group . The preABCG2 rs2231142 is shown in ABCG2 rs2231142 were found in the HUA group than in the control group, with a similar distribution in men  and women . The minor T allele of ABCG2 rs2231142 was 38.9% in the HUA population, compared with 28.9% in the control group in men, and 42.0 and 31.1% in females, respectively. Furthermore, the risk of HUA was significantly associated with ABCG2 rs2231142 risk T allele, and both TT and TG genotypes contributed to increased risk of HUA in men  and women  compared with their counterparts.The genotype frequency of p<0.001) and by 15% in women . In this study, we build a multiple logistic regression model for predicting serum uric acid levels based on ABCG2 rs2231142 genotypes. As ABCG2 rs2231142. In addition, we further characterized the relationship between BMI and serum uric acid levels when BMI exceeds 24 with different genotypes. Based on 3 BMI categories, the distributions of genotypes are almost the same for different gender  in men and by 205%  in women with the TG genotype, and elevated remarkably by 642%  in men and by 2,443%  in women with the TT genotype when the BMI increased by one unit.ABCG2 rs2231142 risk T allele or higher BMI in both men and women. Moreover, we demonstrated a strong genetic-environmental interaction associated with the risk of HUA. Individuals with elevated BMI in combination with the ABCG2 rs2231142 risk T allele had a much higher risk of HUA compared with the other subgroups. Of note, compared with those without risk factors, the ORs for the TT genotype of ABCG2 rs2231142 and higher BMI were 7.42  in men and 25.43  in women.This cohort study demonstrated a significantly increased chance of HUA in individuals with either the ABCG2 rs2231142 as BMI increased, especially for the TT genotype. The gene-environmental interaction between ABCG2 rs2231142 and BMI on the serum uric acid level could be explained by some reasons. The level of ABCG2 and URAT1 increased significantly in an obese mouse model, which might intensify reabsorption of urate  showed significant associations of the ABCG2 rs2231142 variant and BMI with serum uric acid level in males, but not in females (ABCG2 rs2231142 genotypes and obesity on serum uric acid level in Mexicans (ABCG2 rs2231142 TT vs. the GG genotype model (p=0.001), 4.56 (p<0.001), and 7.67 (p=0.002) for Caucasian, Mongoloid, and Polynesian populations carrying the ABCG2 rs2231142 TT genotype, respectively, compared with the GG genotype. Apart from genetic variants, dietary habits differed based on distinct ethnicities, and the effects of different diets on obesity status also varied. This has been confirmed in both animal and human studies. C57BL/6 mice fed with a cafeteria diet or a high-fat diet for 12weeks became obese (weight gain 179% on the cafeteria diet and 194% on the high-fat diet), but obesity was not found in those fed a standard chow diet or normal-fat diet . Further genetics research based on different ethnicities and subgroups are warranted. Second, the majority of subjects recruited from the Taiwan Biobank were healthy, and therefore the results may not be generalizable to hospital-based populations. Third, the BMI of participants was checked at a single time point and may not reflect the trends of BMI change related to the serum level of uric acid. This issue remains to be explored in future studies.ABCG2 rs2231142 risk allele and BMI on HUA. Based on the results of this study, the authors recommend the following. First, for high-risk patients with the ABCG2 rs2231142 risk T allele, body weight control and reduction in BMI are recommended to prevent hyperuricemia and possibly avoid development of gouty arthritis as well as associated comorbidities. Second, for high BMI subjects, the ABCG2 rs2231142 genotype should be checked and the serum uric acid level should also be assessed in those with the ABCG2 rs2231142 risk T allele. Third, for an individual with elevated BMI, the trend of serum uric acid should be monitored frequently with early implementation of a primary prevention strategy, such as dietary control, weight reduction, and lifestyle modification, in order to prevent development of hyperuricemia.This study sheds new light on the joint effect of ABCG2 rs2231142 variant and BMI, both in men and in women. Therefore, body weight control and reduction in BMI are strongly recommended in high-risk patients with the ABCG2 rs2231142 risk T allele. Prospective studies are needed to evaluate the effects of BMI change on serum uric acid level in individuals with the ABCG2 rs2231142 risk T allele, which is a high-risk gene.We conducted a case–control study using a population-based biomedical research database of subjects with Taiwanese Han Chinese ancestry. The risk of HUA was markedly increased by the interaction of The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of Taichung Veterans General Hospital’s Institutional Review Board (IRB no. CE16270B-1). The patients/participants provided their written informed consent to participate in this study.C-HL, Y-MC, and Y-JC conceived and designed the study. C-HL, Y-MC, P-CC, Y-JC, Y-CL, and I-CC performed the literature search and interpretation of data. Y-JC drafted the manuscript. P-CC conducted data extraction and methodological quality assessments, and performed the analysis. C-HL, P-CC, Y-MC, T-HH, Y-CL, J-CC, I-CC, and H-JL performed the critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.