An APOE ε4–Uric Acid Axis Underpins Mesoamerican Nephropathy

An APOE ε4–Uric Acid Axis Underpins Mesoamerican Nephropathy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Brief Communication An APOE ε4–Uric Acid Axis Underpins Mesoamerican Nephropathy Iván Landires, Karen Courville, Raúl Cumbrera, Norman Bustamante, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7880187/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Mesoamerican Nephropathy (MeN) is a form of chronic kidney disease of non-traditional origin that has become a major public health concern among agricultural workers in Central America, where extreme heat and dehydration are common occupational hazards. In this study, we combined long-term clinical follow-up with exome sequencing to explore the contribution of genetic susceptibility to MeN. We identified a significant interaction between the APOE ε4 allele and elevated serum uric acid (SUA) levels, which together markedly increased the risk of disease. Patients carrying APOE ε4 showed higher SUA concentrations, while SUA values followed a clear gradient—highest in MeN cases, intermediate in heat-exposed but unaffected workers, and lowest in unexposed controls. These results indicate that uric acid regulation is shaped by both genetic and environmental factors. The findings suggest that urate-lowering therapies already used in clinical practice could be repurposed as preventive interventions for heat-exposed populations at risk of MeN. Health sciences/Risk factors Biological sciences/Genetics/Genetic association study Figures Figure 1 Figure 2 Figure 3 Introduction Mesoamerican Nephropathy (MeN) is a chronic kidney disease of unknown aetiology (CKDu) that carries significant morbidity and mortality in Central America 1 , 2 . The highest prevalence of this condition has been observed in young male agricultural workers, although it also occurs in other occupations involving prolonged metabolic strain and recurrent heat stress 3 . The genetic risk factors for MeN have only recently been the subject of investigation 4 . Genetic variation in the apolipoprotein E ( APOE ) gene differentially modulates lipoprotein metabolism across organs, including the brain, heart, and kidneys 5 . Recent studies have demonstrated that the APOE ε4 allele functions as a pleiotropic metabolic and immune modulator, rather than a gene specific to Alzheimer's disease 6 . This has been shown to contribute to a spectrum of chronic inflammatory and degenerative disorders through its pro-inflammatory immune signature 6 . A substantial body of evidence suggests that this APOE -driven cellular effect extends beyond the nervous system. Indeed, APOE alleles, genotypes, and haplotypes have been demonstrated to confer differential risk to diverse phenotypes, including hypercholesterolemia, cardiovascular and metabolic disorders. 5 , 7 . In the context of chronic kidney disease (CKD), the apolipoprotein E ( APOE ) gene is expressed in the kidney and may influence susceptibility to metabolic and vascular injury 8 . However, over the past four decades, a new epidemic of CKD has emerged among agricultural and manual workers in tropical and subtropical regions 1 , 2 , 9 . This form of disease cannot be explained by conventional risk factors such as diabetes, hypertension, or primary glomerular pathology 2 , 3 . Instead, there is a strong association with occupational heat exposure, dehydration and repeated physical exertion 2 , 3 . In Central America, this non-traditional condition is known as Mesoamerican Nephropathy (MeN)—a distinctive form of CKD characterised by progressive renal dysfunction, early onset, and high mortality rates among otherwise healthy adults 1 , 2 . The Pan American Health Organization (PAHO) defines MeN as a decline in renal function with a glomerular filtration rate (GFR) of less than 60 ml/min/1.73 m², in the absence of traditional CKD risk factors such as diabetes, hypertension or glomerular disease 1 . Although it is most prevalent among agricultural workers, MeN also affects individuals in other occupations who are chronically exposed to thermal and metabolic stress. This makes it a sentinel model of environmentally driven kidney injury and climate-sensitive disease. While the underlying pathophysiology of MeN is not fully understood, it is believed that chronic exposure to heavy metals, pesticides, high temperatures and dehydration may play a role 2 , 9 . A previous study revealed that elevated serum uric acid (SUA) differentiates MeN from traditional forms of CKD, aligning with substantial evidence associating hyperuricemia with kidney dysfunction, oxidative stress, and vascular injury 10 , 11 . Emerging mechanistic data indicates that elevated SUA may contribute to endothelial dysfunction, inflammasome activation, and tubular oxidative damage, thereby establishing a biologically plausible association between environmental stressors and renal pathogenesis 12 – 14 . Several studies have also reported an association between APOE haplotypes and SUA levels in humans 15 . This prompted us to examine the relationship between MeN, SUA and APOE variation. Given that next-generation sequencing (NGS) has become a leading research and diagnostic tool in kidney disease, the present study applied whole-exome sequencing (WES) to identify functional APOE variants, including single nucleotide polymorphisms (SNPs) and insertions/deletions (Ins/Del), and to test the hypothesis that APOE variation interacts with SUA to modulate susceptibility to MeN under heat stress 16 . Together, these findings redefine MeN as a disease caused by a gene-environment interaction, thus positioning elevated uric acid as a modifiable metabolic driver of renal injury. This conceptual framework suggests the potential for clinically available urate-lowering therapies to be repurposed for precision prevention in heat-exposed and genetically susceptible populations 17 – 19 . Methods Individuals A broad group of specialties, i.e. , nephrologists, geneticists, epidemiologists, and bioinformaticians, exhaustively followed a cohort of 61 MeN patients (cases), and four control groups, all from the provinces of Coclé, Herrera and Los Santos in Central Panama: ( i ) 30 exposed healthy individuals (EHI), under heat stress and agricultural work, matching the environment conditions that MeN patients are exposed to, ( ii ) 30 non-exposed healthy individuals (UHI), ( iii ) 32 individuals with CKD caused by Diabetes Mellitus (CKDDM), and ( iv ) 36 individuals with CKD associated with High Blood Pressure (CKDHBP). MeN was diagnosed according to the definition proposed by the PAHO and had been followed for more than 7 years. A more detailed description of the clinical criteria and follow-up has been presented elsewhere 1 , 11 . DNA extraction, preparation of libraries, and next-generation sequencing Comprehensive WES and bioinformatic analyses were applied to both cases and controls 16 as follows: Ten millilitres (10mL) of venous blood was drawn from the forearm vein of consenting study participants (EDTA tetrasodium anticoagulant). Samples were briefly preserved at 4°C before extraction of genomic DNA using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). DNA concentration was measured using a Qubit 4TM Fluorometer (Thermo Fisher Scientific, USA). DNA samples with a concentration above 1.0 µg were used to prepare sequencing libraries. Agilent liquid phase hybridization was applied to enrich whole exons. The SureSelect Human All ExonV6 r2 (Agilent Technologies, CA, USA) was used for sequencing and capture library preparation. Genomic DNA was randomly fragmented to 180–280 bp using the Nextera DNA Flex kit from Illumina. The DNA fragments were then end-polished, A-tailed, and ligated with the full-length adapter for Illumina sequencing. Fragments with specific indexes were hybridized with more than 543,872 biotin-labelled probes after pooling. Then, magnetic beads coated with streptavidin were used to capture 334,401 exons from 20,967 genes. After PCR amplification and quality control, libraries were sequenced. Bioinformatics Analysis All high-quality data were mapped to the human genome assembly (GRCh38) using the BWA-MEM algorithm 20 .The aligned files were processed with the Genome Analysis Tool Kit (GATK) to recalibrate base quality, realign indels, and remove duplicates 21 . This was followed by SNP and INDEL discovery and genotyping according to GATK Best Practices recommendations. All variant calls underwent recalibration of quality scores and filtering to remove low-quality variants. Genetic data were imported into Golden Helix®’s SVS 8.8.3 (Bozeman, MT, USA). Quality control was performed, as recommended in several previous papers 22 : ( i ) fitting to Hardy-Weinberg equilibrium ( P -value > 0.05/ m , where m is the number of markers included for analysis); ( ii ) a minimum genotype call rate of 90%; ( iii ) and presence of two alleles and control for heterozygosis’ excess (weighting by expected f , inbreeding-coefficient). Genotypic and allele frequencies were estimated by maximum likelihood. Variants with a minor allele frequency (MAF) ≥ 0.01 were classified as common and otherwise as rare. Exonic variants with potential functional effects were identified using annotations in the database for functional predictions of nonsynonymous SNPs (dbNSFP, GRCh38/hg38 genome assembly). This filter uses PolyPhen-2, Provean, SIFT, Mutation Taster, Gerp++, PhyloP, and Mutation Assessor to predict a variant’s deleterious effect and is fully implemented in the SVS 8.3.3 Variant Classification module 23 . We also investigated the presence of variants associated with clinical disorders and annotated them according to ClinVar 24 . Targeted Analysis of the APOE Gene Variation We employed a multiple-locus logistic regression (LR) mixed model, which utilizes a LR with logit link to model the binary response (0: not affected; 1: affected by MeN), using a design matrix consisting of the numerical covariates and indicator coefficients (i.e., one-hot encoding) for categorical predictors, to estimate the regression coefficients. In this model, we introduced numerical covariates ( i.e. , age, glycaemia, hemoglobin, SUA, serum sodium and potassium, creatinine, and ureic nitrogen), categorical predictors (i.e., APOE genotype) and interaction terms (of up to three dimensions, i.e. , either the APOE genotype or haplotype predictors interacting with age and glucose, for example). This approach allows us to compare a reduced LR model , which only includes the dependent variable, and a full LR model ( i.e. , saturated model) including all relevant variables. A likelihood ratio (LRT) comparing both models was conducted to assess the contribution of genetic variants and interaction terms to model fitting 25 . Furthermore, we evaluated the effect of different-sized haplotypes , ranging from two to four markers, using a moving window that covers the variation in the APOE gene. After the estimation process, the resulting P- values were corrected for multiple testing using the false discovery rate (FDR) 26 . Finally, we test the effect of the APOE variation on the SUA 0 (initial uric acid blood levels before treatment) levels by introducing this variable as the dependent variable and applying a multiple regression analysis, following the same methodology and theory presented above under the hypothesis that APOE variation affects SUA levels. Given the complexity of transmission patterns and as this research is part of an exploratory enterprise, we model different forms of Mendelian transmission and maximize models following the additive, dominant, and recessive inheritance. All these methods are implemented in SVS 8.3.3. To evaluate differences in SUA levels among the four groups (MeN, EHI, UHI and CKDDMI) we use a one-way ANOVA. Post-hoc comparisons were performed using Tukey’s test, reporting P -values adjusted for multiple comparisons. The overall ANOVA results and Tukey-adjusted P -values were graphically represented using violin plots with overlaid means and significance annotations. All analyses and visualizations were conducted in R version 4.5.0 with the RStudio IDE (2024.09.1 + 394, Cranberry Hibiscus). P -values below 0.05 were considered statistically significant. A summary of the methods is presented in Fig. 1. Results Baseline serum uric acid levels distinguish MeN cases from controls Table 1 presents the variation and contrast of demographic and paraclinical variables for the MeN cases and controls. After correction for multiple comparisons, only the SUA 0 (baseline serum uric acid levels before treatment) and SUA 1 (serum uric acid levels after treatment initiation) levels were significantly higher in MeN cases than in controls. Other clinical and genotypic characteristics are reported in Table 1S of the Supplementary Material. Table 1. Demographic and paraclinical characteristics of individuals included in this study. Variable Cases ( n =61) Controls ( n =90) P Mean (SD) Range Mean (SD) Range [min-max] [min-max] Age 56.08 (13.67) 22-78 61.87 (13.28) 23-92 0.049 SBP 148.31 (25.3) 110-200 146.21 (21.03) 100-200 0.215 DBP 83.43 (13.86) 50-120 77.51 (9.65) 40-95 0.007 GLU 105.59 (17.88) 71-155 141.35 (79.18) 60-491 0.048 CR 5.4 (5.32) 0.8-22 5.88 (5.78) 0.7-20.6 0.803 BUN 37.74 (23.51) 9.6-98 40.63 (29.17) 9-135 0.743 SUA 0 8.04 (1.77) 4.8-13 6.08 (1.89) 1.9-11.6 <0.00001 SUA 1 6.87 (1.31) 4-10.1 6.08 (1.89) 1.9-11.6 0.026 Na 137.14 (4.32) 122-145.3 137.22 (4.15) 126-144.8 0.231 K 4.56 (0.75) 2.4-7.3 4.39 (0.72) 2-6.6 0.216 Hb 13 (2.1) 8-18.1 12.31 (2.43) 6.9-17.1 0.695 Abbreviations: BUN, blood urea nitrogen; CR, creatinine; GLU, glucose; K, potassium; Hb, hemoglobin; Na, sodium; SBP, diastolic blood pressure; SBP, systolic blood pressure; SD, standard deviation; SUA 0 , baseline serum uric acid levels before treatment; SUA 1 , serum uric acid levels after treatment initiation; P : P -value. Statistically significant results at 5% are shown in bold . Whole-exome sequencing confirms population homogeneity between cases and controls The whole-exome analysis resulted in the genotyping of 3,741,135 variants, including SNPs and Ins/Del. From this total set, 136,831 variants were randomly pruned and used to estimate the IBS matrix, which allowed both the control of the random genomic noise and the assessment of potential stratification effects between cases and controls. The index of fixation, also known as the F st statistic, enabled us to exclude the possibility of microdifferentiation among the cases and controls. As suggested by many authors, F st values below 0.05 indicate the absence of genetic microdifferentiation and homogeneity between populations. In our case, the F st estimated between the case and control groups was 0.04 (standard deviation = 0.005), defining population homogeneity ( i.e. , absence of stratification). The process of variant filtering is presented in more detail in Figure 1. Genetic association analysis identifies APOE variants linked to MeN susceptibility Twelve variants harbored in the APOE genomic region were selected for association analyses. Table 2 shows the results of the genetic association analysis using a full LR model with MeN affection status as the dependent variable. This model included genetic variants and a fixed window size of six markers as covariates, and SUA 0 , age, and glucose as interactions terms. Eight variants showed significant differences when comparing the full model to a reduced model without these variants using a LRT. Table 2. APOE variants conferring susceptibility to MeN in this study. Variant Chr Position Ref/Alt P LRT -log 10 ( P LRT ) P FDR 19:44905910-SNV 19 44,905,910 C/G 2.8x10 -9 8.55 2.8x10 -9 19:44905978-SNV 19 44,905,978 G/A 2.8x10 -9 8.55 3.2x10 -9 19:44906026-SNV 19 44,906,026 C/A 2.8x10 -9 8.55 3.2x10 -9 19:44906731-SNV 19 44,906,731 C/T 2.8x10 -9 8.55 3.2x10 -9 19:44906745-SNV 19 44,906,745 G/A 2.8x10 -9 8.55 3.2x10 -9 19:44908329-SNV 19 44,908,329 C/T 2.1x10 -9 8.68 5.5x10 -9 19:44908420-SNV 19 44,908,420 C/G 1.8x10 -9 8.74 7.1x10 -9 19:44908442-Ins 19 44,908,442 -/TC 1.8x10 -9 8.74 7.1x10 -9 Abbreviations: Chr, chromosome; FDR, false discovery rate; LRT, likelihood ratio test; Ref/Alt, reference and alternate alleles. APOE ε4 carriers exhibit elevated baseline uric acid levels At the first clinical assessment, SUA 0 in MeN patients without any medical treatment ( n =16 ) carrying the APOE ε4 allele were significantly higher compared to MeN patients who do not carry the APOE ε4 allele ( n =45) ( Table 3 ). This finding is supported by a linear regression model of SUA 0 levels against variants within the APOE gene and relevant covariates ( Table 4 ). These results highlight the role of the APOE ε4 allele not only as a modifier of MeN risk but also as a potential regulator of SUA levels. Table 3. Clinical characteristics of MeN patients with APOE ε4 haplotype compared to non-carriers. Characteristic Non- APOE ε 4 carriers ( n = 45) APOE ε4 carriers ( n = 16) P Median (IQR), Age, years 59 (46,67) 57 (45,66) 0.83 * Gender n (%) n (%) Men 43 (95.56) 15 (93.75) 1** Women 2 (4.44) 1 (6.25) Employment Agricultural workers 38 (84.4) 12 (75.0) 0.68 Others 7 (15.6) 4 (25.0) Median blood biochemistry values Median (IQR) Median (IQR) Glucose, mg/dL 105 (95,117) 100 (95,113) 0.51 Creatinine, mg/dL 2.47 (1.43,10.66) 3.2 (1.32, 5.89) 0.6 Blood urea nitrogen, mg/dL 34 (19,51) 33.5 (15.2,68.0) 0.95 Uric acid, mg/dL (SUA 0 ) 7.2 (6.35, 9) 8.7 (7.7, 10.3) 0.0326 Sodium, mEq/L 137 (136,140) 138 (136,140) 0.91 Potassium, mEq/L 4.46 (4.2,4.9) 4.3 (4.1, 5.08) 0.83 Hemoglobin, mg/dL 13.3 (11.8, 14.45) 12.6 (11.3,13.9) 0.297 * Mann-Whitney test; ** Fisher’s exact test. IQR, interquartile range; P , P -value. Statistically significant results at 5% are shown in bold . Non- APOE ε 4 carriers ( n = 45) include APOE*E3 / APOE*E3 ( n = 38) + APOE*E2 / APOE*E3 ( n = 7) haplotypes. APOE ε 4 carriers ( n = 16) include APOE*E4 / APOE*E3 ( n = 15) + APOE*E4 / APOE*E4 ( n = 1) haplotypes. Table 4. APOE variants modifying SUA 0 levels in individuals with MeN. Abbreviations as in Table 2. Predictor Chr Position Ref/Alt P LRT -log 10 ( P LRT ) 19:44908684-SNV 19 44,908,684 T/C 0,04 1.40 19:44905910-SNV 19 44,905,910 C/G 0,09 1.05 19:44908822-SNV 19 44,908,822 C/T 0,17 0.77 19:44906745-SNV 19 44,906,745 G/A 0,47 0.33 Serum uric acid levels exhibit a heat-exposure gradient across study groups To follow up on these findings, we compared SUA levels between a group of 60 MeN patients and those in the EHI and UHI control groups to dissect the environmental effects of high-temperature and agricultural work on SUA. We found statistically significant differences in the SUA levels of these three groups. Specifically, MeN patients showed the highest SUA level values, followed by healthy individuals exposed to heat stress (EHI group), and unexposed healthy donors (UHI group) (Figure 3). Discussion MeN is one of the most enigmatic and devastating forms of chronic kidney disease of unknown cause, affecting agricultural workers across Central America disproportionately 27 . Despite four decades of research, the precise cause of the condition remains unclear. In this study, we present a longitudinal genomic and clinical investigation of MeN, encompassing more than seven years of systematic follow-up. Our design, integrating whole-exome sequencing with detailed phenotyping, enabled direct comparison between MeN patients, heat-exposed and unexposed healthy controls, and individuals with diabetes- or hypertension-associated CKD. This approach demonstrates that functional variation within the APOE locus interacts with elevated SUA to markedly increase susceptibility to MeN, thus uncovering a mechanistic bridge between genetic predisposition and environmental stress. The data reveal a clear gradient in SUA concentration across exposure groups: highest in MeN cases, intermediate in heat-exposed but unaffected agricultural workers, and lowest in unexposed controls. This monotonic distribution reflects the convergence of genetic and environmental influences on renal metabolism, highlighting that APOE ε4 carriers are metabolically primed to accumulate uric acid under thermal stress. The APOE ε4 allele, a well-established risk factor for Alzheimer's and cardiovascular disease, is identified in this study as a modifier of metabolic and endothelial vulnerability in the kidney. These findings extend the pleiotropic role of APOE beyond the nervous and vascular systems 28-31 , demonstrating its capacity to shape renal susceptibility under environmental extremes. From a mechanistic perspective, this interaction is biologically plausible and aligns with extensive evidence linking hyperuricemia to kidney injury 12,13 . Uric acid levels have been observed to increase in response to sustained periods of physical exertion and dehydration. This phenomenon has been attributed, at least in part, to the acceleration of purine catabolism and muscle breakdown that occurs under conditions of heat stress 32 . Consequently, hyperuricemia instigates both crystalline and non-crystalline injury pathways, resulting in the induction of inflammasome activation, oxidative stress, and endothelial dysfunction through the suppression of nitric oxide bioavailability 12,13,31,33 . As demonstrated by experimental models, uric acid has been shown to amplify afferent arteriolar resistance and promote tubulointerstitial inflammation 13,31,33,34,35 . This establishes it as a central mediator in the pathogenesis of heat-induced renal injury. The interplay between APOE variation and uric acid dysregulation carries direct therapeutic implications. Elevated SUA is not merely a bystander, but rather a modifiable metabolic driver at the core of the pathophysiology of MeN. This suggests that urate-lowering interventions could attenuate the renal damage cascade in genetically predisposed individuals. Randomised controlled trials of allopurinol and febuxostat in chronic kidney disease (CKD) and asymptomatic hyperuricemia have demonstrated slowed eGFR decline, improved endothelial function, and reduced oxidative stress 17-19 . Although these therapies have yet to be evaluated in heat-exposed agricultural populations, the present data identify a high-risk subgroup — APOE ε4 carriers with hyperuricemia — who may benefit most from preventive or early hypouricemic treatment. This conceptual framework redefines MeN as a targetable gene-environment interaction disorder, thereby establishing a novel paradigm for precision prevention in climate-vulnerable communities 36 . Beyond APOE , emerging genomic evidence supports a broader polygenic model for MeN and CKDu. A GWAS of Nicaraguans identified Native American ancestry as a risk factor and allelic variation in OPCML , a gene influencing vasopressin signalling, as protective 37 . Studies in South Asia have further implicated SLC13A3 , which codes for a renal dicarboxylate transporter, and KCNA10 , which codes for a voltage-gated potassium channel in the glomerular endothelium, as loci conferring susceptibility to CKDu 38,39 . More recently, NOS3 variants in Mexican populations were shown to predispose to CKDu by reducing nitric oxide synthesis and impairing endothelial homeostasis 40 . Despite regional heterogeneity, these genetic signals converge on shared pathways of vascular, metabolic, and tubular vulnerability under heat stress, and our findings suggest that uric acid may serve as a common downstream mediator linking these pathways to renal injury 12,40 . It should be noted that this study has several limitations. Despite the fact that the cohort was observed for a period of over seven years, the sample size remains relatively small in comparison to larger genetic studies, which may limit the study's ability to generalize its findings. Our analysis focused on the APOE locus; therefore, it is not possible to exclude the contribution of polygenic and epigenetic factors. SUA measurements were standardized under both field and clinical conditions, but unmeasured confounders – such as hydration status or diet – may have introduced variability. Finally, while our data provide strong evidence for a causal APOE –SUA interaction, prospective interventional trials are essential to determine whether urate-lowering therapies confer renal protection in heat-exposed individuals. Beyond the limitations mentioned, the study integrates long-term clinical follow-up, genomic profiling and environmental exposure into a unified framework. This framework redefines MeN as a disease of gene-environment interaction. By establishing a link between a common genetic variant, a modifiable metabolic pathway and an occupational exposure, our findings provide a valuable insight into a preventable mechanism of kidney injury in the context of global climate change 9 . Taken together, the results of the study provide compelling evidence that APOE ε4 acts as a genetic amplifier of metabolic risk in MeN through modulation of uric acid metabolism 12,28 . This suggests that environmental and metabolic stressors may be integrated into a coherent pathophysiological model. It has been demonstrated that agricultural work in conditions of extreme heat elevates uric acid levels even in healthy individuals. However, APOE ε4 carriers exhibit an exaggerated biochemical and endothelial response that predisposes them to irreversible renal injury. This study provides a novel perspective on MeN, redefining it not as an enigmatic regional nephropathy, but as a prototype of climate-sensitive, gene–environment-driven kidney disease. The study proposes that the prevention of this condition may be possible through the timely utilisation of safe, existing urate-lowering therapies 17-19,36 . Declarations Acknowledgments: Iván Landires, Karen Courville, and Virginia Núñez-Samudio are members of the Sistema Nacional de Investigación (S.N.I.), which is supported by Panama's Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). Authors' contributions: Conceptualization, I.L., K.C., M.A.B., and V. N-S.; methodology, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; software, I.L., M.A.B., and V. N-S.; validation, I.L., K.C., M.A.B., and V. N-S.; formal analysis, I.L., K.C., G.P.P., R.C., M.A.B., and V. N-S.; investigation, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; resources, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; data curation, I.L., G.P.P., R.C., M.A.B., and V. N-S.; writing—original draft preparation, I.L., and V. N-S.; writing—review and editing, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; visualization, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; supervision, I.L., K.C., M.A.B., and V. N-S.;; project administration, I.L., and V. N-S.;; funding acquisition, I.L., K.C., and V. N-S.;; All authors have read and agreed to the published version of the manuscript. Funding: Panama's "Secretaría Nacional de Ciencia, Tecnología e Innovación, SENACYT," projects IOMS19-013 and FID23-009, funded this study. Availability of data and materials : This published article includes all data generated or analyzed during this study. Ethics approval and consent to participate: The study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Interinstitutional Ethics Committee of the Social Security Fund and the National Directorate for Teaching and Research (Human Ethics approval No. CIEI-CSS-M-181-12019). No further administrative permissions were required to access the raw data used in this study, as it had been anonymized before use. Competing interests: The authors declare that they have no competing interests. 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Association of apolipoprotein E (ApoE) polymorphisms with risk of primary hyperuricemia in Uygur men, Xinjiang, China. Lipids Health Dis . 2015;14:25. Published 2015 Apr 12. doi:10.1186/s12944-015-0025-2 Groopman EE, Marasa M, Cameron-Christie S, et al. Diagnostic Utility of Exome Sequencing for Kidney Disease. N Engl J Med . 2019;380(2):142-151. doi:10.1056/NEJMoa1806891 Goicoechea M, de Vinuesa SG, Verdalles U, et al. Effect of allopurinol in chronic kidney disease progression and cardiovascular risk. Clin J Am Soc Nephrol . 2010;5(8):1388-1393. doi:10.2215/CJN.01580210 Sircar D, Chatterjee S, Waikhom R, et al. Efficacy of Febuxostat for Slowing the GFR Decline in Patients With CKD and Asymptomatic Hyperuricemia: A 6-Month, Double-Blind, Randomized, Placebo-Controlled Trial. Am J Kidney Dis . 2015;66(6):945-950. doi:10.1053/j.ajkd.2015.05.017 Kimura K, Hosoya T, Uchida S, et al. Febuxostat Therapy for Patients With Stage 3 CKD and Asymptomatic Hyperuricemia: A Randomized Trial. Am J Kidney Dis . 2018;72(6):798-810. doi:10.1053/j.ajkd.2018.06.028 Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics . 2009;25(14):1754-1760. doi:10.1093/bioinformatics/btp324 McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res . 2010;20(9):1297-1303. doi:10.1101/gr.107524.110 Bansal V, Libiger O, Torkamani A, Schork NJ. Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet . 2010;11(11):773-785. doi:10.1038/nrg2867 Liu X, Wu C, Li C, Boerwinkle E. dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs. Hum Mutat . 2016;37(3):235-241. doi:10.1002/humu.22932 Landrum MJ, Lee JM, Benson M, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res . 2018;46(D1):D1062-D1067. doi:10.1093/nar/gkx1153 Cordell HJ. Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet . 2002;11(20):2463-2468. doi:10.1093/hmg/11.20.2463 Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B. 1995;57(1):289–300. doi:10.1111/j.2517-6161.1995.tb02031.x Johnson RJ, Wesseling C, Newman LS. Chronic Kidney Disease of Unknown Cause in Agricultural Communities. N Engl J Med . 2019;380(19):1843-1852. doi:10.1056/NEJMra1813869 Hsu CC, Kao WH, Coresh J, et al. Apolipoprotein E and progression of chronic kidney disease. JAMA . 2005;293(23):2892-2899. doi:10.1001/jama.293.23.2892 Islam S, Noorani A, Sun Y, Michikawa M, Zou K. Multi-functional role of apolipoprotein E in neurodegenerative diseases. Front Aging Neurosci . 2025;17:1535280. Published 2025 Jan 29. doi:10.3389/fnagi.2025.1535280 Lumsden AL, Mulugeta A, Zhou A, Hyppönen E. Apolipoprotein E (APOE) genotype-associated disease risks: a phenome-wide, registry-based, case-control study utilising the UK Biobank. EBioMedicine . 2020;59:102954. doi:10.1016/j.ebiom.2020.102954 Johnson RJ, Sanchez Lozada LG, Lanaspa MA, Piani F, Borghi C. Uric Acid and Chronic Kidney Disease: Still More to Do. Kidney Int Rep . 2022;8(2):229-239. Published 2022 Dec 5. doi:10.1016/j.ekir.2022.11.016 Ebi KL, Capon A, Berry P, et al. Hot weather and heat extremes: health risks. Lancet . 2021;398(10301):698-708. doi:10.1016/S0140-6736(21)01208-3 Joosten LAB, Crişan TO, Bjornstad P, Johnson RJ. Asymptomatic hyperuricaemia: a silent activator of the innate immune system. Nat Rev Rheumatol . 2020;16(2):75-86. doi:10.1038/s41584-019-0334-3 Kano Y, Tanabe K, Kitagawa M, et al. Serum uric acid level is associated with renal arteriolar hyalinosis and predicts post-donation renal function in living kidney donors. PLoS One . 2025;20(3):e0320482. Published 2025 Mar 25. doi:10.1371/journal.pone.0320482 Romi MM, Arfian N, Tranggono U, Setyaningsih WAW, Sari DCR. Uric acid causes kidney injury through inducing fibroblast expansion, Endothelin-1 expression, and inflammation. BMC Nephrol . 2017;18(1):326. Published 2017 Oct 31. doi:10.1186/s12882-017-0736-x Shvetcov A, Johnson ECB, Winchester LM, et al. APOE ε4 carriers share immune-related proteomic changes across neurodegenerative diseases. Nat Med . 2025;31(8):2590-2601. doi:10.1038/s41591-025-03835-z Friedman DJ, Leone DA, Amador JJ, et al. Genetic risk factors for Mesoamerican nephropathy. Proc Natl Acad Sci U S A . 2024;121(49):e2404848121. doi:10.1073/pnas.2404848121 Nanayakkara S, Senevirathna ST, Parahitiyawa NB, et al. Whole-exome sequencing reveals genetic variants associated with chronic kidney disease characterized by tubulointerstitial damages in North Central Region, Sri Lanka. Environ Health Prev Med . 2015;20(5):354-359. doi:10.1007/s12199-015-0475-1 Kumari R, Tiwari S, Atlani M, Anirudhan A, Goel SK, Kumar A. Association of Single Nucleotide Polymorphisms in KCNA10 and SLC13A3 Genes with the Susceptibility to Chronic Kidney Disease of Unknown Etiology in Central Indian Patients. Biochem Genet . 2023;61(4):1548-1566. doi:10.1007/s10528-023-10335-7 Marín-Medina A, Gómez-Ramos JJ, Mendoza-Morales N, Figuera-Villanueva LE. Association between the Polymorphisms rs2070744, 4b/a and rs1799983 of the NOS3 Gene with Chronic Kidney Disease of Uncertain or Non-Traditional Etiology in Mexican Patients. Medicina (Kaunas) . 2023;59(5):829. Published 2023 Apr 24. doi:10.3390/medicina59050829 Additional Declarations There is NO Competing Interest. Supplementary Files image1.png Graphical Abstract Supplementarymaterial.AnAPOEXX4XXXUricAcidAxisUnderpinsMesoamericanNephropathyFV.docx An APOE ε4–Uric Acid Axis Underpins Mesoamerican Nephropathy Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7880187","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":532975936,"identity":"5b755a2d-54da-476c-8a05-0b2b1049e7cc","order_by":0,"name":"Iván Landires","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACNhDxgIGBsQ3E+MDAkECclgSoFsYZxGhhgGlpANLMPMRo4ZNIfvYgoeaObJ/04cOfbdvs8vjZGxgf/sDnMIk0c4OEY8+M2/jS0qRz25KLJXsOMBvz4NPCc8BMIoHtcGIbD48Zc24bc+KGGwls0vgcxsZz/JtEwj+QFv7Pny3b6kFa2H/idRh7j5lEYhvYFgZpRiADZAsDXoex95RJJPYdNm7jYTOT7Dl3PHFmz8FmaXxa5JvZt0l8+HZYdn4P8+MPP8qqE/vZmw9+xOcwVMAIjllwHBEN/pCieBSMglEwCkYKAAAaMEuiDWBz/AAAAABJRU5ErkJggg==","orcid":"","institution":"Instituto de Ciencias Médicas","correspondingAuthor":true,"prefix":"","firstName":"Iván","middleName":"","lastName":"Landires","suffix":""},{"id":532975937,"identity":"ef49a8b5-7ad2-42e3-b64f-5dc1406de210","order_by":1,"name":"Karen Courville","email":"","orcid":"","institution":"Instituto de Ciencias Médicas","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Courville","suffix":""},{"id":532975938,"identity":"69d22095-6a4e-439b-bbbb-ce1229a69f01","order_by":2,"name":"Raúl Cumbrera","email":"","orcid":"","institution":"Instituto de Ciencias Médicas","correspondingAuthor":false,"prefix":"","firstName":"Raúl","middleName":"","lastName":"Cumbrera","suffix":""},{"id":532975939,"identity":"b18c4e82-c701-4d36-8af1-6f88243d0ccb","order_by":3,"name":"Norman Bustamante","email":"","orcid":"","institution":"Hospital Dr. Gustavo N. Collado","correspondingAuthor":false,"prefix":"","firstName":"Norman","middleName":"","lastName":"Bustamante","suffix":""},{"id":532975940,"identity":"43fbaccb-0c97-4e67-98d5-91d2739b76a8","order_by":4,"name":"Gumercindo Pimentel-Peralta","email":"","orcid":"","institution":"Instituto de Ciencias Médicas","correspondingAuthor":false,"prefix":"","firstName":"Gumercindo","middleName":"","lastName":"Pimentel-Peralta","suffix":""},{"id":532975941,"identity":"8ecb9bdf-09c5-4074-bbb5-06706151bb9a","order_by":5,"name":"Jorge Vélez","email":"","orcid":"","institution":"Universidad del Norte","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Vélez","suffix":""},{"id":532975942,"identity":"6427262d-3254-4a6e-af87-f68023e677c0","order_by":6,"name":"Richard Johnson","email":"","orcid":"","institution":"University of Colorado","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Johnson","suffix":""},{"id":532975943,"identity":"73d01e42-6284-44a2-b02c-ca6f8cc8081d","order_by":7,"name":"Mauricio Arcos-Burgos","email":"","orcid":"","institution":"Department of Psychiatry, University of Antioquia","correspondingAuthor":false,"prefix":"","firstName":"Mauricio","middleName":"","lastName":"Arcos-Burgos","suffix":""},{"id":532975944,"identity":"840c037d-b72c-4dee-9218-23a37fa9ec74","order_by":8,"name":"Virginia Núñez-Samudio","email":"","orcid":"","institution":"Instituto de Ciencias Médicas","correspondingAuthor":false,"prefix":"","firstName":"Virginia","middleName":"","lastName":"Núñez-Samudio","suffix":""}],"badges":[],"createdAt":"2025-10-16 17:55:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7880187/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7880187/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94764709,"identity":"070d2c84-d897-4e3a-87dc-a4096623d96d","added_by":"auto","created_at":"2025-10-30 12:33:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":299533,"visible":true,"origin":"","legend":"\u003cp\u003eA summary of the methods\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7880187/v1/f73f33553fc8eabc5d1336f2.png"},{"id":94764711,"identity":"db613035-bf5e-4df0-8c35-d2c7fb212746","added_by":"auto","created_at":"2025-10-30 12:33:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137169,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic location of variants within the \u003cem\u003eAPOE \u003c/em\u003egene conferring susceptibility to MeN.\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7880187/v1/054505baab628a8921d92fd2.jpg"},{"id":94764710,"identity":"696e9064-6585-4157-a7c3-1da49e88d08b","added_by":"auto","created_at":"2025-10-30 12:33:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12523,"visible":true,"origin":"","legend":"\u003cp\u003eSerum uric acid levels (mg/dL) in MeN patients compared to control groups. MeN, Mesoamerican nephropathy (\u003cem\u003en\u003c/em\u003e=60); EHI, exposed healthy individuals (\u003cem\u003en\u003c/em\u003e=30), UHI: non-exposed healthy individuals (\u003cem\u003en\u003c/em\u003e=30).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7880187/v1/35ded12da7cfa5050fada72f.png"},{"id":94827290,"identity":"131b924a-aa27-4f45-be44-8da600678000","added_by":"auto","created_at":"2025-10-31 06:56:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1538431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7880187/v1/0ac57b13-3c9f-431c-b891-ac508b4831a3.pdf"},{"id":94824817,"identity":"49bd7c69-4bc7-48c3-9318-df3185609281","added_by":"auto","created_at":"2025-10-31 06:49:23","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1199777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7880187/v1/20bfab4ac89fab29c16b0552.png"},{"id":94764708,"identity":"1e011c2c-d342-4bfd-9954-d2e88d6f5061","added_by":"auto","created_at":"2025-10-30 12:33:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21356,"visible":true,"origin":"","legend":"\u003cp\u003eAn APOE ε4–Uric Acid Axis Underpins Mesoamerican Nephropathy\u003c/p\u003e","description":"","filename":"Supplementarymaterial.AnAPOEXX4XXXUricAcidAxisUnderpinsMesoamericanNephropathyFV.docx","url":"https://assets-eu.researchsquare.com/files/rs-7880187/v1/1fb82be38527059ce2081185.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"An APOE ε4–Uric Acid Axis Underpins Mesoamerican Nephropathy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMesoamerican Nephropathy (MeN) is a chronic kidney disease of unknown aetiology (CKDu) that carries significant morbidity and mortality in Central America\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The highest prevalence of this condition has been observed in young male agricultural workers, although it also occurs in other occupations involving prolonged metabolic strain and recurrent heat stress\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe genetic risk factors for MeN have only recently been the subject of investigation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Genetic variation in the apolipoprotein E (\u003cem\u003eAPOE\u003c/em\u003e) gene differentially modulates lipoprotein metabolism across organs, including the brain, heart, and kidneys\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Recent studies have demonstrated that the \u003cem\u003eAPOE ε4\u003c/em\u003e allele functions as a pleiotropic metabolic and immune modulator, rather than a gene specific to Alzheimer's disease\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This has been shown to contribute to a spectrum of chronic inflammatory and degenerative disorders through its pro-inflammatory immune signature\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A substantial body of evidence suggests that this \u003cem\u003eAPOE\u003c/em\u003e-driven cellular effect extends beyond the nervous system. Indeed, \u003cem\u003eAPOE\u003c/em\u003e alleles, genotypes, and haplotypes have been demonstrated to confer differential risk to diverse phenotypes, including hypercholesterolemia, cardiovascular and metabolic disorders.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the context of chronic kidney disease (CKD), the apolipoprotein E (\u003cem\u003eAPOE\u003c/em\u003e) gene is expressed in the kidney and may influence susceptibility to metabolic and vascular injury\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, over the past four decades, a new epidemic of CKD has emerged among agricultural and manual workers in tropical and subtropical regions\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This form of disease cannot be explained by conventional risk factors such as diabetes, hypertension, or primary glomerular pathology\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Instead, there is a strong association with occupational heat exposure, dehydration and repeated physical exertion\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In Central America, this non-traditional condition is known as Mesoamerican Nephropathy (MeN)\u0026mdash;a distinctive form of CKD characterised by progressive renal dysfunction, early onset, and high mortality rates among otherwise healthy adults\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The Pan American Health Organization (PAHO) defines MeN as a decline in renal function with a glomerular filtration rate (GFR) of less than 60 ml/min/1.73 m\u0026sup2;, in the absence of traditional CKD risk factors such as diabetes, hypertension or glomerular disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Although it is most prevalent among agricultural workers, MeN also affects individuals in other occupations who are chronically exposed to thermal and metabolic stress. This makes it a sentinel model of environmentally driven kidney injury and climate-sensitive disease.\u003c/p\u003e\u003cp\u003eWhile the underlying pathophysiology of MeN is not fully understood, it is believed that chronic exposure to heavy metals, pesticides, high temperatures and dehydration may play a role\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. A previous study revealed that elevated serum uric acid (SUA) differentiates MeN from traditional forms of CKD, aligning with substantial evidence associating hyperuricemia with kidney dysfunction, oxidative stress, and vascular injury\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Emerging mechanistic data indicates that elevated SUA may contribute to endothelial dysfunction, inflammasome activation, and tubular oxidative damage, thereby establishing a biologically plausible association between environmental stressors and renal pathogenesis\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Several studies have also reported an association between \u003cem\u003eAPOE\u003c/em\u003e haplotypes and SUA levels in humans\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This prompted us to examine the relationship between MeN, SUA and \u003cem\u003eAPOE\u003c/em\u003e variation.\u003c/p\u003e\u003cp\u003eGiven that next-generation sequencing (NGS) has become a leading research and diagnostic tool in kidney disease, the present study applied whole-exome sequencing (WES) to identify functional \u003cem\u003eAPOE\u003c/em\u003e variants, including single nucleotide polymorphisms (SNPs) and insertions/deletions (Ins/Del), and to test the hypothesis that \u003cem\u003eAPOE\u003c/em\u003e variation interacts with SUA to modulate susceptibility to MeN under heat stress\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTogether, these findings redefine MeN as a disease caused by a gene-environment interaction, thus positioning elevated uric acid as a modifiable metabolic driver of renal injury. This conceptual framework suggests the potential for clinically available urate-lowering therapies to be repurposed for precision prevention in heat-exposed and genetically susceptible populations\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eIndividuals\u003c/h2\u003e\u003cp\u003eA broad group of specialties, \u003cem\u003ei.e.\u003c/em\u003e, nephrologists, geneticists, epidemiologists, and bioinformaticians, exhaustively followed a cohort of 61 MeN patients (cases), and four control groups, all from the provinces of Cocl\u0026eacute;, Herrera and Los Santos in Central Panama: (\u003cem\u003ei\u003c/em\u003e) 30 exposed healthy individuals (EHI), under heat stress and agricultural work, matching the environment conditions that MeN patients are exposed to, (\u003cem\u003eii\u003c/em\u003e) 30 non-exposed healthy individuals (UHI), (\u003cem\u003eiii\u003c/em\u003e) 32 individuals with CKD caused by Diabetes Mellitus (CKDDM), and (\u003cem\u003eiv\u003c/em\u003e) 36 individuals with CKD associated with High Blood Pressure (CKDHBP). MeN was diagnosed according to the definition proposed by the PAHO and had been followed for more than 7 years. A more detailed description of the clinical criteria and follow-up has been presented elsewhere\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA extraction, preparation of libraries, and next-generation sequencing\u003c/h3\u003e\n\u003cp\u003eComprehensive WES and bioinformatic analyses were applied to both cases and controls\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e as follows: Ten millilitres (10mL) of venous blood was drawn from the forearm vein of consenting study participants (EDTA tetrasodium anticoagulant). Samples were briefly preserved at 4\u0026deg;C before extraction of genomic DNA using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). DNA concentration was measured using a Qubit 4TM Fluorometer (Thermo Fisher Scientific, USA). DNA samples with a concentration above 1.0 \u0026micro;g were used to prepare sequencing libraries. Agilent liquid phase hybridization was applied to enrich whole exons. The SureSelect Human All ExonV6 r2 (Agilent Technologies, CA, USA) was used for sequencing and capture library preparation. Genomic DNA was randomly fragmented to 180\u0026ndash;280 bp using the Nextera DNA Flex kit from Illumina. The DNA fragments were then end-polished, A-tailed, and ligated with the full-length adapter for Illumina sequencing. Fragments with specific indexes were hybridized with more than 543,872 biotin-labelled probes after pooling. Then, magnetic beads coated with streptavidin were used to capture 334,401 exons from 20,967 genes. After PCR amplification and quality control, libraries were sequenced.\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eAll high-quality data were mapped to the human genome assembly (GRCh38) using the \u003cem\u003eBWA-MEM\u003c/em\u003e algorithm\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.The aligned files were processed with the Genome Analysis Tool Kit (GATK) to recalibrate base quality, realign indels, and remove duplicates\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This was followed by SNP and INDEL discovery and genotyping according to GATK Best Practices recommendations. All variant calls underwent recalibration of quality scores and filtering to remove low-quality variants. Genetic data were imported into Golden Helix\u0026reg;\u0026rsquo;s SVS 8.8.3 (Bozeman, MT, USA). Quality control was performed, as recommended in several previous papers\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e: (\u003cem\u003ei\u003c/em\u003e) fitting to Hardy-Weinberg equilibrium (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05/\u003cem\u003em\u003c/em\u003e, where \u003cem\u003em\u003c/em\u003e is the number of markers included for analysis); (\u003cem\u003eii\u003c/em\u003e) a minimum genotype call rate of 90%; (\u003cem\u003eiii\u003c/em\u003e) and presence of two alleles and control for heterozygosis\u0026rsquo; excess (weighting by expected \u003cem\u003ef\u003c/em\u003e, inbreeding-coefficient). Genotypic and allele frequencies were estimated by maximum likelihood. Variants with a minor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.01 were classified as common and otherwise as rare. Exonic variants with potential functional effects were identified using annotations in the database for functional predictions of nonsynonymous SNPs (dbNSFP, GRCh38/hg38 genome assembly). This filter uses PolyPhen-2, Provean, SIFT, Mutation Taster, Gerp++, PhyloP, and Mutation Assessor to predict a variant\u0026rsquo;s deleterious effect and is fully implemented in the SVS 8.3.3 Variant Classification module\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We also investigated the presence of variants associated with clinical disorders and annotated them according to ClinVar\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eTargeted Analysis of the APOE Gene Variation\u003c/h3\u003e\n\u003cp\u003eWe employed a multiple-locus logistic regression (LR) mixed model, which utilizes a LR with logit link to model the binary response (0: not affected; 1: affected by MeN), using a design matrix consisting of the numerical covariates and indicator coefficients (i.e., one-hot encoding) for categorical predictors, to estimate the regression coefficients. In this model, we introduced numerical covariates (\u003cem\u003ei.e.\u003c/em\u003e, age, glycaemia, hemoglobin, SUA, serum sodium and potassium, creatinine, and ureic nitrogen), categorical predictors (i.e., \u003cem\u003eAPOE\u003c/em\u003e genotype) and interaction terms (of up to three dimensions, \u003cem\u003ei.e.\u003c/em\u003e, either the \u003cem\u003eAPOE\u003c/em\u003e genotype or haplotype predictors interacting with age and glucose, for example). This approach allows us to compare a \u003cem\u003ereduced LR model\u003c/em\u003e, which only includes the dependent variable, and \u003cem\u003ea full LR model\u003c/em\u003e (\u003cem\u003ei.e.\u003c/em\u003e, saturated model) including all relevant variables. A likelihood ratio (LRT) comparing both models was conducted to assess the contribution of genetic variants and interaction terms to model fitting\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, we evaluated the effect of \u003cem\u003edifferent-sized haplotypes\u003c/em\u003e, ranging from two to four markers, using a moving window that covers the variation in the \u003cem\u003eAPOE\u003c/em\u003e gene. After the estimation process, the resulting \u003cem\u003eP-\u003c/em\u003evalues were corrected for multiple testing using the false discovery rate (FDR)\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFinally, we test the effect of the \u003cem\u003eAPOE\u003c/em\u003e variation on the SUA\u003csub\u003e0\u003c/sub\u003e (initial uric acid blood levels before treatment) levels by introducing this variable as the dependent variable and applying a multiple regression analysis, following the same methodology and theory presented above under the hypothesis that \u003cem\u003eAPOE\u003c/em\u003e variation affects SUA levels.\u003c/p\u003e\u003cp\u003eGiven the complexity of transmission patterns and as this research is part of an exploratory enterprise, we model different forms of Mendelian transmission and maximize models following the additive, dominant, and recessive inheritance. All these methods are implemented in SVS 8.3.3.\u003c/p\u003e\u003cp\u003eTo evaluate differences in SUA levels among the four groups (MeN, EHI, UHI and CKDDMI) we use a one-way ANOVA. Post-hoc comparisons were performed using Tukey\u0026rsquo;s test, reporting \u003cem\u003eP\u003c/em\u003e-values adjusted for multiple comparisons. The overall ANOVA results and Tukey-adjusted \u003cem\u003eP\u003c/em\u003e-values were graphically represented using violin plots with overlaid means and significance annotations. All analyses and visualizations were conducted in R version 4.5.0 with the RStudio IDE (2024.09.1\u0026thinsp;+\u0026thinsp;394, Cranberry Hibiscus). \u003cem\u003eP\u003c/em\u003e-values below 0.05 were considered statistically significant. A summary of the methods is presented in \u003cb\u003eFig.\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline serum uric acid levels distinguish MeN cases from controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e presents the variation and contrast of demographic and paraclinical variables for the MeN cases and controls.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAfter correction for multiple comparisons, only the SUA\u003csub\u003e0\u0026nbsp;\u003c/sub\u003e(baseline serum uric acid levels before treatment) and SUA\u003csub\u003e1\u0026nbsp;\u003c/sub\u003e(serum uric acid levels after treatment initiation) levels were significantly higher in MeN cases than in controls. Other clinical and genotypic characteristics are reported in Table 1S of the Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDemographic and paraclinical characteristics of individuals included in this study.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases (\u003cem\u003en\u003c/em\u003e=61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls (\u003cem\u003en\u003c/em\u003e=90)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[min-max]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[min-max]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e56.08 (13.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e22-78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e61.87 (13.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e23-92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e148.31 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e110-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e146.21 (21.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e83.43 (13.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e50-120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e77.51 (9.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e40-95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eGLU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e105.59 (17.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e71-155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e141.35 (79.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e60-491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e5.4 (5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.8-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5.88 (5.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.7-20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e37.74 (23.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e9.6-98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e40.63 (29.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9-135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSUA\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e8.04 (1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4.8-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6.08 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.9-11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.00001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSUA\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e6.87 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e4-10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6.08 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.9-11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eNa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e137.14 (4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e122-145.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e137.22 (4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e126-144.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e4.56 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2.4-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.39 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eHb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e13 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e8-18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12.31 (2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6.9-17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: BUN, blood urea nitrogen; CR, creatinine; GLU, glucose; K, potassium; Hb, hemoglobin; Na, sodium; SBP, diastolic blood pressure; SBP, systolic blood pressure; SD, standard deviation; SUA\u003csub\u003e0\u003c/sub\u003e, baseline serum uric acid levels before treatment; SUA\u003csub\u003e1\u003c/sub\u003e, serum uric acid levels after treatment initiation; \u003cem\u003eP\u003c/em\u003e: \u003cem\u003eP\u003c/em\u003e-value. Statistically significant results at 5% are shown in \u003cstrong\u003ebold\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhole-exome sequencing confirms population homogeneity between cases and controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-exome analysis resulted in the genotyping of 3,741,135 variants, including SNPs and Ins/Del. From this total set, 136,831 variants were randomly pruned and used to estimate the IBS matrix, which allowed both the control of the random genomic noise and the assessment of potential stratification effects between cases and controls. The index of fixation, also known as the \u003cem\u003eF\u003csub\u003est\u003c/sub\u003e\u003c/em\u003e statistic, enabled us to exclude the possibility of microdifferentiation among the cases and controls. As suggested by many authors, \u003cem\u003eF\u003csub\u003est\u003c/sub\u003e\u003c/em\u003e values below 0.05 indicate the absence of genetic microdifferentiation and homogeneity between populations. In our case, the \u003cem\u003eF\u003csub\u003est\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eestimated between the case and control groups was 0.04 (standard deviation = 0.005), defining population homogeneity (\u003cem\u003ei.e.\u003c/em\u003e, absence of stratification). The process of variant filtering is presented in more detail in \u003cstrong\u003eFigure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic association analysis identifies APOE variants linked to MeN susceptibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwelve variants harbored in the \u003cem\u003eAPOE\u003c/em\u003e genomic region were selected for association analyses. \u003cstrong\u003eTable 2\u003c/strong\u003e shows the results of the genetic association analysis using a full LR model with MeN affection status as the dependent variable. This model included genetic variants and a fixed window size of six markers as covariates, and SUA\u003csub\u003e0\u003c/sub\u003e, age, and glucose as interactions terms. Eight variants showed significant differences when comparing the full model to a reduced model without these variants using a LRT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003evariants conferring susceptibility to MeN in this study.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef/Alt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eLRT\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-log\u003csub\u003e10\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eLRT\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eFDR\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44905910-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,905,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44905978-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,905,978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.2x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44906026-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,906,026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eC/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.2x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44906731-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,906,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.2x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44906745-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,906,745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.2x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44908329-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,908,329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.1x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e5.5x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44908420-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,908,420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e7.1x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e19:44908442-Ins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44,908,442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e-/TC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.8x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e7.1x10\u003csup\u003e-9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: Chr, chromosome; FDR, false discovery rate; LRT, likelihood ratio test; Ref/Alt, reference and alternate alleles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPOE \u0026epsilon;4 carriers exhibit elevated baseline uric acid levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the first clinical assessment, SUA\u003csub\u003e0\u003c/sub\u003e in MeN patients without any medical treatment (\u003cem\u003en\u003c/em\u003e=16\u003cem\u003e)\u003c/em\u003e carrying the \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eallele were significantly higher compared to MeN patients who do not carry the \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eallele (\u003cem\u003en\u003c/em\u003e=45) (\u003cstrong\u003eTable 3\u003c/strong\u003e). This finding is supported by a linear regression model of SUA\u003csub\u003e0\u003c/sub\u003e levels against variants within the \u003cem\u003eAPOE\u003c/em\u003e gene and relevant covariates (\u003cstrong\u003eTable 4\u003c/strong\u003e). These results highlight the role of the \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eallele not only as a modifier of MeN risk but also as a potential regulator of SUA levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3.\u003c/strong\u003e Clinical characteristics of MeN patients with \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e haplotype compared to non-carriers.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026epsilon;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e4\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMedian (IQR), Age, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e59 (46,67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e57 (45,66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.83 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e43 (95.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e15 (93.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2 (4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e1 (6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eAgricultural workers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e38 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e12 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian blood\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ebiochemistry values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eGlucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e105 (95,117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e100 (95,113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2.47 (1.43,10.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e3.2 (1.32, 5.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eBlood urea nitrogen, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e34 (19,51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e33.5 (15.2,68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eUric acid, mg/dL (SUA\u003csub\u003e0\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7.2 (6.35, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e8.7 (7.7, 10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0326\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eSodium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e137 (136,140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e138 (136,140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003ePotassium, mEq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4.46 (4.2,4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e4.3 (4.1, 5.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eHemoglobin, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13.3 (11.8, 14.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003e12.6 (11.3,13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Mann-Whitney test; ** Fisher\u0026rsquo;s exact test. IQR, interquartile range; \u003cem\u003eP\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e-value. Statistically significant results at 5% are shown in \u003cstrong\u003ebold\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eNon- \u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026epsilon;\u003c/em\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45) include \u003cem\u003eAPOE*E3\u003c/em\u003e/\u003cem\u003eAPOE*E3\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38) + \u003cem\u003eAPOE*E2\u003c/em\u003e/\u003cem\u003eAPOE*E3\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7) haplotypes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026epsilon;\u003c/em\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16) include \u003cem\u003eAPOE*E4\u003c/em\u003e/\u003cem\u003e\u0026nbsp;APOE*E3\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15) + \u003cem\u003eAPOE*E4\u003c/em\u003e/\u003cem\u003eAPOE*E4\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1) haplotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003evariants modifying SUA\u003csub\u003e0\u003c/sub\u003e levels in individuals with MeN. Abbreviations as in Table 2.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"477\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef/Alt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eLRT\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-log\u003csub\u003e10\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eLRT\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e19:44908684-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e44,908,684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eT/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e19:44905910-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e44,905,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e19:44908822-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e44,908,822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e19:44906745-SNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e44,906,745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSerum uric acid levels exhibit a heat-exposure gradient across study groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo follow up on these findings, we compared SUA levels between a group of 60 MeN patients and those in the EHI and UHI control groups to dissect the environmental effects of high-temperature and agricultural work on SUA. We found statistically significant differences in the SUA levels of these three groups. Specifically, MeN patients showed the highest SUA level values, followed by healthy individuals exposed to heat stress (EHI group), and unexposed healthy donors (UHI group) \u003cstrong\u003e(Figure 3).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMeN is one of the most enigmatic and devastating forms of chronic kidney disease of unknown cause, affecting agricultural workers across Central America disproportionately\u003csup\u003e27\u003c/sup\u003e. Despite four decades of research, the precise cause of the condition remains unclear. In this study, we present a longitudinal genomic and clinical investigation of MeN, encompassing more than seven years of systematic follow-up. Our design, integrating whole-exome sequencing with detailed phenotyping, enabled direct comparison between MeN patients, heat-exposed and unexposed healthy controls, and individuals with diabetes- or hypertension-associated CKD. This approach demonstrates that functional variation within the APOE locus interacts with elevated SUA to markedly increase susceptibility to MeN, thus uncovering a mechanistic bridge between genetic predisposition and environmental stress.\u003c/p\u003e\n\u003cp\u003eThe data reveal a clear gradient in SUA concentration across exposure groups: highest in MeN cases, intermediate in heat-exposed but unaffected agricultural workers, and lowest in unexposed controls. This monotonic distribution reflects the convergence of genetic and environmental influences on renal metabolism, highlighting that\u0026nbsp;\u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carriers are metabolically primed to accumulate uric acid under thermal stress. The\u0026nbsp;\u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e allele, a well-established risk factor for Alzheimer\u0026apos;s and cardiovascular disease, is identified in this study as a modifier of metabolic and endothelial vulnerability in the kidney. These findings extend the pleiotropic role of \u003cem\u003eAPOE\u003c/em\u003e beyond the nervous and vascular systems\u003csup\u003e28-31\u003c/sup\u003e, demonstrating its capacity to shape renal susceptibility under environmental extremes.\u003c/p\u003e\n\u003cp\u003eFrom a mechanistic perspective, this interaction is biologically plausible and aligns with extensive evidence linking hyperuricemia to kidney injury\u003csup\u003e12,13\u003c/sup\u003e. Uric acid levels have been observed to increase in response to sustained periods of physical exertion and dehydration. This phenomenon has been attributed, at least in part, to the acceleration of purine catabolism and muscle breakdown that occurs under conditions of heat stress\u003csup\u003e32\u003c/sup\u003e. Consequently, hyperuricemia instigates both crystalline and non-crystalline injury pathways, resulting in the induction of inflammasome activation, oxidative stress, and endothelial dysfunction through the suppression of nitric oxide bioavailability\u003csup\u003e12,13,31,33\u003c/sup\u003e. As demonstrated by experimental models, uric acid has been shown to amplify afferent arteriolar resistance and promote tubulointerstitial inflammation\u003csup\u003e13,31,33,34,35\u003c/sup\u003e. This establishes it as a central mediator in the pathogenesis of heat-induced renal injury.\u003c/p\u003e\n\u003cp\u003eThe interplay between \u003cem\u003eAPOE\u003c/em\u003e variation and uric acid dysregulation carries direct therapeutic implications. Elevated SUA is not merely a bystander, but rather a modifiable metabolic driver at the core of the pathophysiology of MeN. This suggests that urate-lowering interventions could attenuate the renal damage cascade in genetically predisposed individuals. Randomised controlled trials of allopurinol and febuxostat in chronic kidney disease (CKD) and asymptomatic hyperuricemia have demonstrated slowed eGFR decline, improved endothelial function, and reduced oxidative stress\u003csup\u003e17-19\u003c/sup\u003e. Although these therapies have yet to be evaluated in heat-exposed agricultural populations, the present data identify a high-risk subgroup \u0026mdash;\u0026nbsp;\u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carriers with hyperuricemia \u0026mdash; who may benefit most from preventive or early hypouricemic treatment. This conceptual framework redefines MeN as a targetable gene-environment interaction disorder, thereby establishing a novel paradigm for precision prevention in climate-vulnerable communities\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBeyond \u003cem\u003eAPOE\u003c/em\u003e, emerging genomic evidence supports a broader polygenic model for MeN and CKDu. A GWAS of Nicaraguans identified Native American ancestry as a risk factor and allelic variation in \u003cem\u003eOPCML\u003c/em\u003e, a gene influencing vasopressin signalling, as protective\u003csup\u003e37\u003c/sup\u003e. Studies in South Asia have further implicated \u003cem\u003eSLC13A3\u003c/em\u003e, which codes for a renal dicarboxylate transporter, and \u003cem\u003eKCNA10\u003c/em\u003e, which codes for a voltage-gated potassium channel in the glomerular endothelium, as loci conferring susceptibility to CKDu\u003csup\u003e38,39\u003c/sup\u003e. More recently, \u003cem\u003eNOS3\u003c/em\u003e variants in Mexican populations were shown to predispose to CKDu by reducing nitric oxide synthesis and impairing endothelial homeostasis\u003csup\u003e40\u003c/sup\u003e. Despite regional heterogeneity, these genetic signals converge on shared pathways of vascular, metabolic, and tubular vulnerability under heat stress, and our findings suggest that uric acid may serve as a common downstream mediator linking these pathways to renal injury\u003csup\u003e12,40\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIt should be noted that this study has several limitations. Despite the fact that the cohort was observed for a period of over seven years, the sample size remains relatively small in comparison to larger genetic studies, which may limit the study\u0026apos;s ability to generalize its findings. Our analysis focused on the \u003cem\u003eAPOE\u003c/em\u003e locus; therefore, it is not possible to exclude the contribution of polygenic and epigenetic factors. SUA measurements were standardized under both field and clinical conditions, but unmeasured confounders \u0026ndash; such as hydration status or diet \u0026ndash; may have introduced variability. Finally, while our data provide strong evidence for a causal \u003cem\u003eAPOE\u003c/em\u003e\u0026ndash;SUA interaction, prospective interventional trials are essential to determine whether urate-lowering therapies confer renal protection in heat-exposed individuals.\u003c/p\u003e\n\u003cp\u003eBeyond the limitations mentioned, the study integrates long-term clinical follow-up, genomic profiling and environmental exposure into a unified framework. This framework redefines MeN as a disease of gene-environment interaction. By establishing a link between a common genetic variant, a modifiable metabolic pathway and an occupational exposure, our findings provide a valuable insight into a preventable mechanism of kidney injury in the context of global climate change\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTaken together, the results of the study provide compelling evidence that\u0026nbsp;\u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e acts as a genetic amplifier of metabolic risk in MeN through modulation of uric acid metabolism\u003csup\u003e12,28\u003c/sup\u003e. This suggests that environmental and metabolic stressors may be integrated into a coherent pathophysiological model. It has been demonstrated that agricultural work in conditions of extreme heat elevates uric acid levels even in healthy individuals. However,\u0026nbsp;\u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carriers exhibit an exaggerated biochemical and endothelial response that predisposes them to irreversible renal injury. This study provides a novel perspective on MeN, redefining it not as an enigmatic regional nephropathy, but as a prototype of climate-sensitive, gene\u0026ndash;environment-driven kidney disease. The study proposes that the prevention of this condition may be possible through the timely utilisation of safe, existing urate-lowering therapies\u003csup\u003e17-19,36\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIv\u0026aacute;n Landires, Karen Courville, and Virginia N\u0026uacute;\u0026ntilde;ez-Samudio are members of the Sistema Nacional de Investigaci\u0026oacute;n (S.N.I.), which is supported by Panama\u0026apos;s Secretar\u0026iacute;a Nacional de Ciencia, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (SENACYT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, I.L., K.C., M.A.B., and V. N-S.; methodology, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; software, I.L., M.A.B., and V. N-S.; validation, I.L., K.C., M.A.B., and V. N-S.; formal analysis, I.L., K.C., G.P.P., R.C., M.A.B., and V. N-S.; investigation, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; resources, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; data curation, I.L., G.P.P., R.C., M.A.B., and V. N-S.; writing\u0026mdash;original draft preparation, I.L., and V. N-S.; writing\u0026mdash;review and editing, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; visualization, I.L., K.C., N.B., G.P.P., R.C., M.A.B., and V. N-S.; supervision, I.L., K.C., M.A.B., and V. N-S.;; project administration, I.L., and V. N-S.;; funding acquisition, I.L., K.C., and V. N-S.;; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003ePanama\u0026apos;s \u0026quot;Secretar\u0026iacute;a Nacional de Ciencia, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n, SENACYT,\u0026quot; projects IOMS19-013 and FID23-009, funded this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: This published article includes all data generated or analyzed during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Interinstitutional Ethics Committee of the Social Security Fund and the National Directorate for Teaching and Research (Human Ethics approval No. CIEI-CSS-M-181-12019). No further administrative permissions were required to access the raw data used in this study, as it had been anonymized before use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePan American Health Organization (PAHO). Epidemic of Chronic Kidney Disease in Agricultural Communities in Central America. Case definitions, methodological basis and approaches \u0026nbsp;for public health surveillance. Washington, DC: PAHO; 2017.\u003c/li\u003e\n \u003cli\u003eCorrea-Rotter R, Wesseling C, Johnson RJ. CKD of unknown origin in Central America: the case for a Mesoamerican nephropathy. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2014;63(3):506-520. doi:10.1053/j.ajkd.2013.10.062\u003c/li\u003e\n \u003cli\u003eHerath C, Jayasumana C, De Silva PMCS, De Silva PHC, Siribaddana S, De Broe ME. 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Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2013;9(2):106-118. doi:10.1038/nrneurol.2012.263\u003c/li\u003e\n \u003cli\u003eXue C, Nie W, Tang D, Yi L, Mei C. Apolipoprotein E gene variants on the risk of end stage renal disease. \u003cem\u003ePLoS One\u003c/em\u003e. 2013;8(12):e83367. Published 2013 Dec 13. doi:10.1371/journal.pone.0083367\u003c/li\u003e\n \u003cli\u003eElinder CG. Heat-induced kidney disease: Understanding the impact. \u003cem\u003eJ Intern Med\u003c/em\u003e. 2025;297(1):101-112. doi:10.1111/joim.20037\u003c/li\u003e\n \u003cli\u003eJohnson RJ, Nakagawa T, Jalal D, S\u0026aacute;nchez-Lozada LG, Kang DH, Ritz E. Uric acid and chronic kidney disease: which is chasing which?. \u003cem\u003eNephrol Dial Transplant\u003c/em\u003e. 2013;28(9):2221-2228. doi:10.1093/ndt/gft029\u003c/li\u003e\n \u003cli\u003eCourville K, Bustamante N, Hurtado B, et al. 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Elevated Serum Uric Acid Level Predicts Rapid Decline in Kidney Function. \u003cem\u003eAm J Nephrol\u003c/em\u003e. 2017;45(4):330-337. doi:10.1159/000464260\u003c/li\u003e\n \u003cli\u003eSun YP, Zhang B, Miao L, et al. Association of apolipoprotein E (ApoE) polymorphisms with risk of primary hyperuricemia in Uygur men, Xinjiang, China. \u003cem\u003eLipids Health Dis\u003c/em\u003e. 2015;14:25. Published 2015 Apr 12. doi:10.1186/s12944-015-0025-2\u003c/li\u003e\n \u003cli\u003eGroopman EE, Marasa M, Cameron-Christie S, et al. Diagnostic Utility of Exome Sequencing for Kidney Disease. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2019;380(2):142-151. doi:10.1056/NEJMoa1806891\u003c/li\u003e\n \u003cli\u003eGoicoechea M, de Vinuesa SG, Verdalles U, et al. 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Published 2023 Apr 24. doi:10.3390/medicina59050829\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7880187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7880187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMesoamerican Nephropathy (MeN) is a form of chronic kidney disease of non-traditional origin that has become a major public health concern among agricultural workers in Central America, where extreme heat and dehydration are common occupational hazards. In this study, we combined long-term clinical follow-up with exome sequencing to explore the contribution of genetic susceptibility to MeN. We identified a significant interaction between the \u003cem\u003eAPOE ε4\u003c/em\u003e allele and elevated serum uric acid (SUA) levels, which together markedly increased the risk of disease. Patients carrying \u003cem\u003eAPOE ε4\u003c/em\u003e showed higher SUA concentrations, while SUA values followed a clear gradient—highest in MeN cases, intermediate in heat-exposed but unaffected workers, and lowest in unexposed controls. These results indicate that uric acid regulation is shaped by both genetic and environmental factors. The findings suggest that urate-lowering therapies already used in clinical practice could be repurposed as preventive interventions for heat-exposed populations at risk of MeN.\u003c/p\u003e","manuscriptTitle":"An APOE ε4–Uric Acid Axis Underpins Mesoamerican Nephropathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 12:33:00","doi":"10.21203/rs.3.rs-7880187/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3096753c-2d8d-4d0f-b238-44a54552f711","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56660541,"name":"Health sciences/Risk factors"},{"id":56660542,"name":"Biological sciences/Genetics/Genetic association study"}],"tags":[],"updatedAt":"2025-10-30T12:33:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 12:33:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7880187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7880187","identity":"rs-7880187","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}