A nomogram for predicting aldosterone-renin ratio in patients with hypertension

A nomogram for predicting aldosterone-renin ratio in patients with hypertension | 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 Article A nomogram for predicting aldosterone-renin ratio in patients with hypertension Xuehan Li, Yulu Yang, Changhu Liu, Jiacheng Wu, Jianwu Huang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4942905/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : It’s necessary to assess activity of renin-angiotensin-aldosterone system(RAAS) in patients with hypertension by means of orthostatic aldosterone-renin ratio(ARR) which is demanding and not available to those primary hospitals. A novel and portable prediction tool is highly desirable to distinguish abnormal ARR in those patients and guide hypertension therapy to some degree. Methods : Clinical characteristics and laboratory data of 1,212 patients with hypertension were collected for modeling and randomly divided into a training cohort (865 of 1,212, 70%) and an internal validation cohort (347 of 1,212, 30%). Then, predictors for ARR were extracted to construct a nomogram model based on regression analysis of the training set. Receiver operating characteristics (ROC), calibration plots and decision curve analyses (DCA) were applied to evaluate the model. Conclusions : Five predictors were adopted to the nomogram including Na-K ratio, gender, serum chloridion(Cl - ), estimated glomerular filtration rate(eGFR) and urinary pH. Based on this nomogram, the area under the curve(AUC) was 0.756 (95% CI: 0.71-0.80, p < 0.05) in the training set and 0.725 (95% CI: 0.64-0.81, p < 0.05) in the validation set. The calibration curves exhibited great agreement between the predictive risk of the model and the actual risk and the DCA also showed good clinical benefit. Conclusion : We have firstly developed a novel nomogram to predict abnormal ARR in hypertensive individuals based on routine biochemical variables. Health sciences/Cardiology Health sciences/Endocrinology Health sciences/Risk factors aldosterone-renin-ratio Na-K ratio hypertension nomogram primary hospitals Figures Figure 1 Figure 2 Introduction Hypertension, including essential hypertension and secondary hypertension, is the leading cause of cardiovascular and cerebrovascular diseases, with >1.3 billion individuals with high blood pressure worldwide[1]. Secondary hypertension is mainly involved in PA, renal artery stenosis, pheochromocytoma, Cushing’s syndrome and obstructive sleep apnea syndrome. Among these, it’s well estimated that PA featured by aldosterone overproduction and hypokalemia accounts for 5-10% of secondary hypertension[2]. Aldosterone functioning as a regulator of body fluid and electrolyte, and renin that influences vascular activity are recognized as the significant factors in progression of hypertension. Orthostatic ARR calculated by plasma aldosterone concentration(PAC) and plasma renin activity(PRA) is the standard screening method for PA since Hiramatsu discoverded this dramatically screening strategy[3]. Hundemer GL et al. demonstrated that high ARR indicative of renin-independent aldosteronism is responsible for increased arterial stiffness, exacerbated adverse cardiac remodeling and increased left ventricular hypertrophy[4]. Hence, unless the utilization of an antihypertensive drug matched to the underlying pathophysiology and mechanism behind the hypertension can it lead to a larger BP reduction. In fact, ARR is more than associated with diagnosis of PA. Vecchiola A et al. demonstrated that ARR could be a more sensitive biomarker of obesity, metabolic syndrome, and endothelial damage in patients without PA than aldosterone or renin alone[5]. Since components of the RAAS are present on bone cells, Holloway-Kew KL et al. suggested that men with high possibility of PA are more likely to a high level of ARR resulted in negative impact on bone health[6]. Furthermore, during COVID-19 pandemic, researchers found that novel coronavirus infects cells via the angiotensin converting enzyme 2 (ACE2) receptor[7]. Moreover, high ARR may be a risk factor for COVID-19 hospitalization[8] while low ARR as well as high soluble ACE2 are supposed to affect COVID-19 severity[9]. Notably, the measurement of PRA which is highly sensitive and widely used in international guidelines compared with direct renin concentration(DRC), requires the initial generation of angiotensin I from angiotensinogen followed by a radioimmunoassay step[10], which makes it time-consuming and costly. Although many studies indicate that DRC is more cost-effective and displays similar screening accuracy like PRA[11,12], we still need large scale clinical researches to validate. Chemiluminescence immunoassay is another method to assess PRA but the wide-recognized drawback of it is cross-reactivity which is is difficult to be completely avoided, especially for the low-activity blood samples[10]. Generally, PAC and PRA measurements are mainly carried out in tertiary hospitals or research centers and it may take some time before they are widely introduced to primary care settings. Therefore, it’s imperative to establish a clinical model for ARR, as the first step to recognize the activity of RAAS in hypertension patients to guide drug therapy as well as help screening PA. Previously, known predictive models primarily lay emphasis on diagnosis of PA[13,14] and neglect the ARR measurement. Aldosterone is capable to increase sodium ion reabsorption and promotes potassium ion excretion, thus, this study aims to distinguish abnormal ARR(＞37) in hypertension patients by developing a nomogram model based on Na-K ratio accompanied with other biochemical parameters. It’s the first model for predicting ARR and can help clinicians especially working in those primary hospitals to estimate RAAS activity better in hypertension patients and guide antihypertensive therapy. Methods Ethic statements: Institutional Ethics Committee of Wuhan Union Hospital approved study design and data analysis(2023-0673), and waived need for informed consent due to retrospective nature of the study and due to use of anonymized data. We declare that all research was performed in accordance with relevant regulations. Patients We reviewed 2,126 records from patients who visited and completed orthostatic ARR test, at Wuhan Union Hospital between January 1, 2019 and April 30, 2024 in the above mentioned hospital, a tertiary hospital. We excluded patients: whose data were incomplete or missing (n=540); whose age under 18 or beyond 65(n=318); who were diagnosed with diabetes mellitus(DM) and treated with insulin(n=8); whose renal function was damaged with eGFR <60ml/（min/1.73 m 2 ）(n=48); who were pregnant or under menstrual period(n=0); patients with hypohepatia(glutamic-pyruvic transaminase＞35IU/dL)(n=0) or severe heart failure(NYHA II-IV)(n=0). Finally, complete data from 1212 patients were analyzed (Fig. 1). Orthostatic ARR test Blood samples were collected at midmorning after the patient had been up (sitting, standing, or walking) for at least 2 hours and orthostatic for 5-15 minutes[15]. To minimize hemolysis, blood samples were maintained at room temperature (and not on ice) during delivery to the laboratory, with the plasma component quickly frozen for storage after centrifugation. ARR was calculated from the PAC and PRA with units of pg/mL and pg/mL respectively. Data collection and potential predictors Following clinical variables were reviewed from electronic medical records: age, gender, body mass index(BMI), systolic blood pressure(SBP), diastolic blood pressure(DBP), hypertension grades[15], current oral antihypertensive drugs, ARR, serum calcium(Ca 2+ )[16], serum Cl - , Na-K ratio[14], serum magnesium, serum phosphorus[16], fasting blood-glucose(FBG)[17], total cholesterol[18], triglyceride[18], LDL-C[18], HDL-C[18], urea nitrogen, creatinine, eGFR, uric acid[19] and urinary pH[20]. Statistical analysis A total of 1212 patients were divided randomly into two groups: 70%(n=865) as training set , 30%(n=347) as validation set. Continuous variables are presented as mean ± standard deviation or medians and inter-quartile spacing, and categorical variables are expressed as percentages. In training set, comparison of clinical characteristics was performed between normal ARR(ARR≤37) and abnormal ARR(ARR >37) groups, using students’t test for normally distributed continuous variables, Wilcoxon rank-sum test for non-normally distributed variables and chi-square test for categorical variables. Next, univariate logistic regression analysis was conducted in the training set to identify the variables associated with ARR. The magnitude of the association was expressed by odds ratio (OR value) with a 95% confidence interval (95% CI). P < 0.05 was considered statistically significant. The variables with statistical significance ( p value < 0.05) in the previous univariable logistic analysis were selected for a step-backward multivariate logistic regression analysis to identify the independent risk factors ( p < 0.05) for the prediction of ARR. In training or validation set, discriminatory performance of the nomogram model was assessed by AUC, and an AUC ≥ 0.70 was considered to have a relatively good discrimination. Hosmer-Lemeshow (H-L) test was used to evaluate diagnostic consistency of the model and to draw smooth-fitting curves based on actual and predicted probabilities. A p value > 0.05 in the H-L test suggested a good consistency between the new model and standard diagnostic criteria. Calibration curves were plotted to describe the consistency between predicted and actual possibilities of ARR. We compared predictive accuracy among this nomogram model using DCA. Abnormal ARR probability threshold are ranges between the mininum points at DAC just separated from the ALL line and the maximal point at DAC firstly contacting with the NONE line. The statistical analyses were performed using SPSS Statistics v.26.0 (IBM, Armonk, NY) and R Software v.4.3.1(The R Project for Statistical Computing, http://www.r-project.org). Results Clinical characteristics of patients with hypertension Of the 1212 participants included in the training and validation analysis, no significant differences were observed between two groups(Supplementary Table1). In training set, patients with abnormal ARR showed higher age[52.1±7.91, p < 0.05]and were more likely to be female[68 vs 46, p < 0.05](Table1). Moreover, they are feathered with higher Na-K ratio[38.58±3.73, p < 0.05], higher urinary pH[6.28±0.5, p =0.03], lower serum calcium[2.24±0.12, p =0.005], lower eGFR[96.07±13.92, p =0.001] and uric acid[342.31±88.44, p =0.002]. In both training set and validation set, FBG, serum Cl - , triglyceride, LDL-C and HDL-C showed no correlation with the levels of ARR. Predictors and construction of the nomogram model Ten predictors were highly associated with ARR in the training set by univariate logistic regression, including age, gender, Na-K ratio, Cl - , Na + , K + , Ca 2+ , urinary pH, eGFR and uric acid(Table2). To avoid the influence of multicollinearity, Na + and K + were not included in the multivariate logistic regression since Na-K ratio was considered as the dominant factor. Finally, five protential predictors without multicollinearity were screened, formula for calculate score(Table2): ARR=0.166*Na-K ratio+0.114*Cl - +0.438*Urinary pH-0.026*eGFR-0.1.137*Gender(Female=0, Male=1). Based on these results, we construct a nomogram to predict whether ARR is abnormal in hypertension patients(Fig 2A). The AUC and calibrated with 1000 bootstrap samples were further used to evaluate this nomogram. The AUCs were 0.756 [95% CI, 0.71-0.80, p < 0.05] with sensitivity of 64.8%, specificity of 70.8% in training set(Fig 2B), and 0.725 [95% CI, 0.64-0.81, p < 0.05] with sensitivity of 69.7%, specificity of 67.2% in validation set(Supplemetary Fig1A ), respectively. Finally, calibration curve of the nomogram indicated great prediction consistency between new model in training set(Fig 2C) and validation set(Supplementary Fig 1B). And the DCA curves showed that the nomogram could predict the abnormal ARR in patients with hypertension well(Fig 2D and Supplemetary Fig 1C). Discussion Given the importance role of RAAS in progression and treatment of hypertension as well as the screening test of PA, there is a need and challenge to develop better predictive tools to identify patients suspected of having high ARR, based on some clinical characteristics and simple biochemical indexes. Our results suggests that women and those with higher serum Na-K ratio or serum chloridion or in patients with higher urinary pH but lower eGFR are more likely to manifest abnormal ARR. The meaning of ARR is more than the criteria of PA screening test. Not only it is a miniature of RAAS which plays a pivotal role in cardiovascular diseases, but also associated with the severity of COVID-19. Hence, our study firstly construct a model to distinguish abnormal ARR in hypertension patients and provide a novel approach to estimate activity of RAAS especially in those small scale hospitals lack of radioimmunoassay. It’s obvious that the excessive production of aldosterone directly leads to high serum sodium and low serum potassium, indicating high Na-K ratio. We also attempt to excavate various numeric combination of serum sodium and potassium by utilizing ROC to compare prediction efficiency and Na-K ratio demonstrates the highest predictive value. Thus, we choose Na-K ratio as the predictor to enlarge effects of aldosterone and it really exhibits great correlation in our model. Furthermore, it’s not surprising to find that serum Cl - and urinary pH are also involved in the model. According to previous studies, aldosterone could mediate Cl − absorption and HCO 3 − secretion in the cortical collecting duct by regulating pendrin, an aldosterone-sensitive Cl - /HCO 3 − exchanger[21,22], which directly leads to an increase in serum Cl - and promote Urine alkalization. In our model, female and eGFR are considered to have negative correlation with ARR. Hermidorff MM et al. suggests that G protein-coupled estrogen receptor-1 (GPER-1) was identified as the third estrogen receptor and could mediate some aldosterone-induced rapid effects in several tissues[23], which means estrogen in women will compete for GPER-1 with aldosterone and undermine aldosterone’s effects. As a matter of factor, there are loads of evidence that estrogen may have great benefits in preventing apoptosis and necrosis of cardiac and endothelial cells by mediating signaling through PI3K, Akt, and ERK 1/2 [24]. As for the assessment of eGFR, it’s a fundamental tool which is mainly applied to public health and researches, especially the diagnose, stage, as well as manage chronic kidney disease (CKD). Sim JJ et al. demonstrated that each 10-unit increase in PRA was associated with OR for CKD prevalence of 1.3 (1.2-1.4), suggesting mean PRA increased with decline of eGFR[25]. The modulation of RAAS especially preservation of vascular tone can lead to detrimental outcomes in vascular system and in kidney eGFR is the best choice to evaluate function of glomerular capillary. Another study carried out by Hannemann A et al. suggests that ARR or PAC are inversely associated with the eGFR in the general population in their linear regression models adjusted for sex, age, serum triglyceride concentrations, etc[26]. In particular, they adjusted the model for systolic and diastolic blood pressures, indicating that aldosterone may have affected eGFR through blood pressure-independent mechanisms. Thus, aldosterone and renin all contributes to change of eGFR, and in our model the negative correlation between eGFR and ARR may be the composite effect of all two hormones. Before our study, there are several researches about diagnose of PA through constructing different models[13-15]. In addition, there are also plenty of studies to explore biochemical as well as clinical characteristics in PA, which built a steady foundation of our study[27-29]. Meanwhile, Kuo CC et al. harbored the view that the ARR is a satisfactory tool for screening, but it is still far from an efficient mass screening method from an availability perspective and they developed a new simple formula which they think is comparable to ARR by combining body mass index (BMI) and serum potassium to urine potassium clearance (PUKC) ratio[30]. However, well-conducted clinical trials with sufficient sample sizes are still needed to validate it. It's worth noting that hypertension is frequently associated with those factors of metabolic syndrome, including obesity, dyslipidemia and glucose homeostasis, all of which accelerate the process of atherosclerosis[31]. For example, Fallo F et al. reviewed that an imbalanced release of glucose owing to insulin resistance tends to be associated with aldosterone overproduction 31 .Therefore, we adopted FBP, total cholesterol, triglyceride, LDL-C and HDL-C to investigate whether the link between these interrelated risk factors of metabolic origin and ARR. Although no difference was observed in our study between two groups, large studies are needed to further explore the relationship. According to the guideline, certain drugs, such as diuretics, angiotensin-converting enzyme Inhibitors(ACEIs), angiotensin receptor blockers (ARBs), β-adrenergic blockers, and calcium channel blockers (CCBs), should be discontinued before the measurement of ARR[32]. Limited by the retrospective analysis, we are unable to intervene this process. However, we investigated exact oral antihypertensive drugs which further classified as false-positive and false-negative drugs clearly. In training set, different drugs really have an impact on ARR, but in the following univariable logistic analysis they showed no correlation. Meanwhile, it was also ajusted in the following analysis. This study contains several limitations. (1) Although this single-center study collecting data from a tertiary hospital, it may be more representative and compelling as a multi-center study. (2) Selection bias is inevitable in retrospective studies. (3) Whether this model can be extended to primary hospitals and used as the common step to screen activity of RAAS in hypertension patients as well as possibility of PA still needs more external validation data support. In summary, we have firstly developed a novel nomogram to predict whether ARR is abnormal in hypertensive individuals using simple routine examinations, which may help clinicians plan whether the hypertension patient needs more endocrine screening to identify PA and needs relevant drugs to inhibit activity of RAAS. In addition, it provides a new tool for some primary hospitals to check ARR. Abbreviations ARR aldosterone-renin ratio RAAS renin-angiotensin-aldosterone system ROC receiver operating characteristics DCA calibration plots and decision curve analyses Cl- chloridion eGFR estimated glomerular filtration rate PA primary aldosteronism PAC plasma aldosterone concentration PRA plasma renin activity ACE2 angiotensin converting enzyme 2 DRC direct renin concentration NYHA New York Heart Association BMI body mass index SBP systolic blood pressure DBP diastolic blood pressure Ca2+ calcium FBG fasting blood-glucose OR odds ratio AUC area under the ROC curve GPER-1 G protein-coupled estrogen receptor-1 CKD chronic kidney disease PUKC serum potassium to urine potassium clearance ARBs angiotensin receptor blockers CCBs calcium channel blockers ACEIs angiotensin-converting enzyme Inhibitors Declarations Institutional Ethics Committee of Wuhan Union Hospital approved study design and and waived need for informed consent due to retrospective nature of the study and due to use of anonymized data. All authors are agreeable for the publication and the data are provided in the supplementary materials. We declare that no funding was received for conducting this study that could be construed as a potential conflict of interest. The authors had the following roles in the writing of this report: Xuehan Li and Yulu Yang are responsible for the study design, data collection, analysis, and manuscript drafting; Zhihua Qiu and Zihua Zhou are responsible for the study design, critical discussion, and review; Changhu Liu, Jiacheng Wu, Jianwu Huang, Hao chen and Yalei Wang are responsible for the data collection and analysis. Data availability Relevant research data is provided within the supplementary information files. References Guzik TJ, Nosalski R, Maffia P, Drummond GR. Immune and inflammatory mechanisms in hypertension. Nat Rev Cardiol. 2024 Jun;21(6):396-416. Epub 2024 Jan 3. Funder JW, Carey RM. Primary Aldosteronism: Where Are We Now? Where to From Here? 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Baseline of characteristics in the training and validation sets Variables Training set(n=865) Validation set(n=347) Abnormal ARR(n=114) Normal ARR(n=751) P value Abnormal ARR(n=33) Normal ARR(n=314) P value Age[years] 52.1±7.91 47.61±11.76 0.000 54.21±8.09 48.71±11.34 0.001 Gender[Female(%)] 68(59.6%) 256(34.0%) 0.000 20 123 0.017 BMI[kg/m 2 ] 25.41±3.92 26.00±3.85 0.131 25.57±4.30 25.97±4.61 0.624 SBP [mmHg] 143.60±20.92 144.87±21.40 0.547 150.18±26.09 145.11±20.88 0.287 DBP [mmHg] 92.40±16.66 92.17±16.34 0.888 91.18±14.20 90.14±15.94 0.693 Hypertension Grades 0.133 0.405 Normal blood pressure 40 243 8 107 Grade 1 41 217 15 99 Grade 2 16 173 6 58 Grade 3 17 118 4 50 Oral antihypertensive Drugs 0.002 0.318 No oral drugs 34 309 8 115 False positive drugs 6 8 2 7 False negative drugs 50 302 13 117 Both 24 132 10 75 Serum calcium[mmol/L] 2.24±0.12 2.27±0.13 0.005 2.25±0.12 2.28±0.13 0.161 Serum chloridion[mmol/L] 102.96±2.77 102.33±2.82 0.027 103.53±2.60 102.74±2.82 0.110 Serum magnesium[mmol/L] 0.87±0.08 0.89±0.12 0.078 0.87±0.08 0.89±0.08 0.259 Serum phosphorus[mmol/L] 1.10±0.21 1.09±0.19 0.659 1.10±0.26 1.12±0.19 0.713 FBG[mmol/L] 5.55±1.38 5.33±1.32 0.116 5.23±0.75 5.31±1.34 0.628 Serum sodium[mmol/L] 142.11±1.94 141.08±2.12 0.000 142.65±2.09 141.20±2.30 0.001 Serum potassium[mmol/L] 3.71±0.33 3.88±0.33 0.000 3.70±0.36 3.85±0.34 0.028 Na-K ratio 38.58±3.73 36.61±3.25 0.000 38.88±3.77 36.95±3.48 0.008 Serum cholesterol[mmol/L] 4.38±1.00 4.49±1.01 0.284 4.44±0.91 4.42±0.97 0.908 Serum triglyceride[mmol/L] 1.80±1.21 1.91±1.71 0.393 2.01±1.71 1.73±1.31 0.373 LDL-C[mmol/L] 2.54±0.80 2.67±0.87 0.099 2.60±0.76 2.63±0.81 0.802 HDL-C[mmol/L] 1.11±0.29 1.10±0.30 0.757 1.09±0.25 1.12±0.28 0.575 Urea nitrogen[mmol/L] 5.32±1.38 5.22±1.41 0.461 5.06±1.43 5.35±1.45 0.270 Creatinine[umol/L] 68.24±15.99 71.09±16.27 0.079 66.52±16.49 70.36±15.77 0.209 eGFR[ml/min/1.73 m 2 ] 96.07±13.92 100.96±14.56 0.001 96.92±13.64 100.13±14.36 0.208 Uric acid[umol/L] 342.31±88.44 370.30±100.54 0.002 334.36±91.92 364.06±99.97 0.088 Urinary pH 6.28±0.50 6.13±0.50 0.003 6.24±0.45 6.06±0.48 0.035 Continuous variables are presented as mean ± standard deviation or medians and inter-quartile spacing and categorical variables are expressed as percentages. Significance is marked in bold. Student’ t test (continuous variables) and Pearson’ chi-square test (categorical variables) was performed to compare between normal ARR and abnormal ARR groups . ARR aldosterone-renin-ratio, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, eGFR estimated glomerular filtration rate, FBG fast blood-sugar. Table 2. Results of univariable and multivariate logistic analysis Univariable logistic analysis Multivariate logistic analysis OR(95% CI) P value β OR(95% CI) P value Age[years] 1.039(1.019, 1.060) 0.000 - Na-K ratio 1.168(1.106, 1.233 ) 0.000 0.166 1.181(1.112, 1.254) 0.000 Cl[mmol/L] 1.083(1.009, 1.162) 0.028 0.114 1.120(1.038, 1.210) 0.004 Na[mmol/L] 1.288(1.162, 1.427) 0.000 - Urinary pH 1.826(1.240, 2.690) 0.002 0.438 1.550(1.024, 2.345) 0.038 Ca[mmol/L] 0.074(0.012, 0.437) 0.004 - - K[mmol/L] 0.221(0.122, 0.403) 0.000 - - eGFR[ml/min/1.73 m 2 ] 0.978(0.964, 0.991) 0.001 -0.026 0.974(0.960, 0.989) 0.001 Uric acid 0.997(0.995, 0.999) 0.005 - - Gender[female = 1 male = 0] Female Ref. - Ref. - Male 0.350(0.234, 0.524) 0.000 -1.137 0.321(0.209, 0.492) 0.000 eGFR estimated glomerular filtration rate, CI confidence interval, OR odds ratio, The bold numbers are the variables with p < 0.05 Additional Declarations No competing interests reported. Supplementary Files Data.xlsx SupplementaryFig1C.pdf SupplementaryTable.docx SupplemetaryFig1A.pdf SupplemetaryFig1B.pdf Cite Share Download PDF Status: Posted 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-4942905","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":354963259,"identity":"7e491486-59f0-4cd5-982f-eae5a7324fed","order_by":0,"name":"Xuehan Li","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuehan","middleName":"","lastName":"Li","suffix":""},{"id":354963260,"identity":"9ed10a2c-36f9-4427-a158-f17e712ddba9","order_by":1,"name":"Yulu Yang","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yulu","middleName":"","lastName":"Yang","suffix":""},{"id":354963261,"identity":"13aaa9cb-5992-438e-b2c8-9eda304176ab","order_by":2,"name":"Changhu Liu","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Changhu","middleName":"","lastName":"Liu","suffix":""},{"id":354963262,"identity":"deda2857-91cd-4c7b-a63d-cbbbf54db415","order_by":3,"name":"Jiacheng Wu","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Wu","suffix":""},{"id":354963263,"identity":"1ddb04a3-4b70-43b5-ba50-9dd052ba142b","order_by":4,"name":"Jianwu Huang","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianwu","middleName":"","lastName":"Huang","suffix":""},{"id":354963264,"identity":"fa4aa5f8-d5ed-462b-9723-f86e226015cb","order_by":5,"name":"Hao chen","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"chen","suffix":""},{"id":354963265,"identity":"0a65dfb4-1753-439a-b090-aef22d01804d","order_by":6,"name":"Yalei Wang","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yalei","middleName":"","lastName":"Wang","suffix":""},{"id":354963266,"identity":"691f581b-28e8-49a8-963b-dd22250e409e","order_by":7,"name":"Zhihua Qiu","email":"","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Qiu","suffix":""},{"id":354963267,"identity":"9e037919-1021-491c-b18b-e093a537853a","order_by":8,"name":"Zihua Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIie3QsQrCMBCA4YRCXA5xvFJJn0CIBLpY+iwGoa6OHStCuhT6Ns6Vgi7V3c0+gNDRQcTqKEjj5pAfbssHdyHEZvvLoJsEgQ3W66Y1J3XIh1BtJBoTqmPJcalHYAL84rjHG6uUdhtNkER8kvYQmp5iN4eOeEpfVmQhg7KHODQPEPBNMoGkVNs+whwI3Lt4LbbTCCYEGAQezGPJkBoSBJCzcRlyBqr7ZGFwi1/U0/P1geBnh6Zpk4j3ko/Eb89tNpvN9qUnzSY6lXn6ZxkAAAAASUVORK5CYII=","orcid":"","institution":"Union Hospital, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zihua","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-08-20 07:30:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4942905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4942905/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66904831,"identity":"47c3defb-7013-4112-abda-4b77423f5f8f","added_by":"auto","created_at":"2024-10-17 17:43:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151245,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/bf0f87cbe1c51cba15a52f45.png"},{"id":66903852,"identity":"cf2ba570-6171-4b3d-a3ca-a502e998f1c2","added_by":"auto","created_at":"2024-10-17 17:35:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197997,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment and validation of nomogram model to predict ARR in hypertension patients. A Nomogram for predicting ARR in the training set. B The ROC curves of the model in the training set. C Calibration plots of the nomogram model in the training set. D DCA of the nomogram model in the training set.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/8a5d5321cfdcfa5fec3fd182.png"},{"id":66919652,"identity":"1a0d688c-eb45-4656-b554-4774e7cc4c50","added_by":"auto","created_at":"2024-10-18 03:53:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":803596,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/36082586-71ee-4b59-afe4-6922930c721e.pdf"},{"id":66903858,"identity":"dbb21f32-0af9-4ea8-884b-19c998d79959","added_by":"auto","created_at":"2024-10-17 17:35:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":214929,"visible":true,"origin":"","legend":"","description":"","filename":"Data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/d5dbe933cc9ae3210a8b6ded.xlsx"},{"id":66904832,"identity":"e4599678-1ee2-4583-845f-50e636d422ae","added_by":"auto","created_at":"2024-10-17 17:43:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5471,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig1C.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/5a763f2fb4a37f946de31bb1.pdf"},{"id":66903859,"identity":"d1125c46-ffa9-4be1-bee7-2373bca85907","added_by":"auto","created_at":"2024-10-17 17:35:32","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14084,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/932f18176f3eca9ca904ad66.docx"},{"id":66904833,"identity":"ea634993-97ca-4599-b26c-d77dff8b64bb","added_by":"auto","created_at":"2024-10-17 17:43:32","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5685,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemetaryFig1A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/6f6d33a0d72ca9967d2c1d9d.pdf"},{"id":66903857,"identity":"d9c5fad4-cd2d-4a92-9d90-1a2431c3ed3b","added_by":"auto","created_at":"2024-10-17 17:35:32","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":5583,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemetaryFig1B.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4942905/v1/3f4c78065564f3800f0c4741.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A nomogram for predicting aldosterone-renin ratio in patients with hypertension","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension, including essential hypertension and secondary hypertension, is the leading cause of cardiovascular and cerebrovascular diseases, with \u0026gt;1.3 billion individuals with high blood pressure worldwide[1]. Secondary hypertension is mainly involved in PA, renal artery stenosis, pheochromocytoma, Cushing\u0026rsquo;s syndrome and obstructive sleep apnea syndrome. Among these, it\u0026rsquo;s well estimated that PA featured by aldosterone overproduction and hypokalemia accounts for 5-10% of secondary hypertension[2]. Aldosterone functioning as a regulator of body fluid and electrolyte, and renin that influences vascular activity are recognized as the significant factors in progression of hypertension. Orthostatic ARR calculated by plasma aldosterone concentration(PAC) and plasma renin activity(PRA) is the standard screening method for PA since Hiramatsu discoverded this dramatically screening strategy[3]. Hundemer GL et al. demonstrated that high ARR indicative of renin-independent aldosteronism is responsible for increased arterial stiffness, exacerbated adverse cardiac remodeling and increased left ventricular hypertrophy[4]. Hence, unless the utilization of an antihypertensive drug matched to the underlying pathophysiology and mechanism behind the hypertension can it lead to a larger BP reduction.\u003c/p\u003e\n\u003cp\u003eIn fact, ARR is more than associated with diagnosis of PA. Vecchiola A et al. demonstrated that ARR could be a more sensitive biomarker of obesity, metabolic syndrome, and endothelial damage in patients without PA than aldosterone or renin alone[5]. Since components of the RAAS are present on bone cells, Holloway-Kew KL et al. suggested that men with high possibility of PA are more likely to a high level of ARR resulted in negative impact on bone health[6]. Furthermore, during COVID-19 pandemic, researchers found that novel coronavirus infects cells via the angiotensin converting enzyme 2 (ACE2) receptor[7]. Moreover, high ARR may be a risk factor for COVID-19 hospitalization[8] while low ARR as well as high soluble ACE2 are supposed to affect COVID-19 severity[9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, the measurement of PRA which is highly sensitive and widely used in international guidelines compared with direct renin concentration(DRC), requires the initial generation of angiotensin I from angiotensinogen followed by a radioimmunoassay step[10], which makes it time-consuming and costly. Although many studies indicate that DRC is more cost-effective and displays similar screening accuracy like PRA[11,12], we still need large scale clinical researches to validate. Chemiluminescence immunoassay is another method to assess PRA but the wide-recognized drawback of it is cross-reactivity which is is difficult to be completely avoided, \u0026nbsp; especially for the low-activity blood samples[10]. Generally, PAC and PRA measurements are mainly carried out in tertiary hospitals or research centers and it may take some time before they are widely introduced to primary care settings. Therefore, it\u0026rsquo;s imperative to establish a clinical model for ARR, as the first step to recognize the activity of RAAS in hypertension patients to guide drug therapy as well as help screening PA.\u003c/p\u003e\n\u003cp\u003ePreviously, known predictive models primarily lay emphasis on diagnosis of PA[13,14] and neglect the ARR measurement. Aldosterone is capable to increase sodium ion reabsorption and promotes potassium ion excretion, thus, this study aims to distinguish abnormal ARR(＞37) in hypertension patients by developing a nomogram model based on Na-K ratio accompanied with other biochemical parameters. It\u0026rsquo;s the first model for predicting ARR and can help clinicians especially working in those primary hospitals to estimate RAAS activity better in hypertension patients and guide antihypertensive therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eEthic statements: Institutional Ethics Committee of Wuhan Union Hospital approved study design and data analysis(2023-0673), and waived need for informed consent due to retrospective nature of the study and due to use of anonymized data. We declare that all research was performed in accordance with relevant regulations.\u003c/p\u003e\n\u003cp\u003ePatients\u003c/p\u003e\n\u003cp\u003eWe reviewed 2,126 records from patients who visited and completed orthostatic ARR test, at Wuhan Union Hospital between January 1, 2019 and April 30, 2024 in the above mentioned hospital, a tertiary hospital. We excluded patients: whose data were incomplete or missing (n=540); whose age under 18 or beyond 65(n=318); who were diagnosed with diabetes mellitus(DM) and treated with insulin(n=8); whose renal function was damaged with eGFR \u0026lt;60ml/（min/1.73 m\u003csup\u003e2\u003c/sup\u003e）(n=48); who were pregnant or under menstrual period(n=0); patients with hypohepatia(glutamic-pyruvic transaminase＞35IU/dL)(n=0) or severe heart failure(NYHA II-IV)(n=0). Finally, complete data from 1212 patients were analyzed (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOrthostatic ARR test\u003c/p\u003e\n\u003cp\u003eBlood samples were collected at midmorning after the patient had been up (sitting, standing, or walking) for at least 2 hours and orthostatic for 5-15 minutes[15]. To minimize hemolysis, blood samples were maintained at room temperature (and not on ice) during delivery to the laboratory, with the plasma component quickly frozen for storage after centrifugation. ARR was calculated from the PAC and PRA with units of pg/mL and pg/mL respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData collection and potential predictors\u003c/p\u003e\n\u003cp\u003eFollowing clinical variables were reviewed from electronic medical records: age, gender, body mass index(BMI), systolic blood pressure(SBP), diastolic blood pressure(DBP), hypertension grades[15], current oral antihypertensive drugs, ARR, serum calcium(Ca\u003csup\u003e2+\u003c/sup\u003e)[16], serum Cl\u003csup\u003e-\u003c/sup\u003e, Na-K ratio[14], serum magnesium, serum phosphorus[16], fasting blood-glucose(FBG)[17], total cholesterol[18], triglyceride[18], LDL-C[18], HDL-C[18], urea nitrogen, creatinine, eGFR, uric acid[19] and urinary pH[20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 1212 patients were divided randomly into two groups: 70%(n=865) as training set , 30%(n=347) as validation set. Continuous variables are presented as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;standard deviation or medians and inter-quartile spacing, and categorical variables are expressed as percentages. In training set, comparison of clinical characteristics was performed between normal ARR(ARR\u0026le;37) and abnormal ARR(ARR \u0026gt;37) groups, using students\u0026rsquo;t test for normally distributed continuous variables, Wilcoxon rank-sum test for non-normally distributed variables and chi-square test for categorical variables. Next, univariate logistic regression analysis was conducted in the training set to identify the variables associated with ARR. The magnitude of the association was expressed by odds ratio (OR value) with a 95% confidence interval (95% CI). \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant. The variables with statistical significance (\u003cem\u003ep\u0026nbsp;\u003c/em\u003evalue \u0026lt; 0.05) in the previous univariable logistic analysis were selected for a step-backward multivariate logistic regression analysis to identify the independent risk factors (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) for the prediction of ARR. In training or validation set, discriminatory performance of the nomogram model was assessed by AUC, and an AUC\u0026nbsp;\u0026ge;\u0026nbsp;0.70 was considered to have a relatively good discrimination. Hosmer-Lemeshow (H-L) test was used to evaluate diagnostic consistency of the model and to draw smooth-fitting curves based on actual and predicted probabilities. A \u003cem\u003ep\u003c/em\u003e value \u0026gt; 0.05 in the H-L test suggested a good consistency between the new model and standard diagnostic criteria. Calibration curves were plotted to describe the consistency between predicted and actual possibilities of ARR. We compared predictive accuracy among this nomogram model using DCA. Abnormal ARR probability threshold are ranges between the mininum points at DAC just separated from the ALL line and the maximal point at DAC firstly contacting with the NONE line. The statistical analyses were performed using SPSS Statistics v.26.0 (IBM, Armonk, NY) and R Software v.4.3.1(The R Project for Statistical Computing, http://www.r-project.org).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinical characteristics of patients with hypertension\u003c/p\u003e\n\u003cp\u003eOf the 1212 participants included in the training and validation analysis, no significant differences were observed between two groups(Supplementary Table1). In training set, patients with abnormal ARR showed higher age[52.1\u0026plusmn;7.91, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05]and were more likely to be female[68 vs 46, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05](Table1). Moreover, they are feathered with higher Na-K ratio[38.58\u0026plusmn;3.73, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05], higher urinary pH[6.28\u0026plusmn;0.5, \u003cem\u003ep\u003c/em\u003e=0.03], lower serum calcium[2.24\u0026plusmn;0.12, \u003cem\u003ep\u003c/em\u003e=0.005], lower eGFR[96.07\u0026plusmn;13.92, \u003cem\u003ep\u003c/em\u003e=0.001] and uric acid[342.31\u0026plusmn;88.44, \u003cem\u003ep\u003c/em\u003e=0.002]. In both training set and validation set, FBG, serum Cl\u003csup\u003e-\u003c/sup\u003e, triglyceride, LDL-C and HDL-C showed no correlation with the levels of ARR.\u003c/p\u003e\n\u003cp\u003ePredictors and construction of the nomogram model\u003c/p\u003e\n\u003cp\u003eTen predictors were highly associated with ARR in the training set by univariate logistic regression, including age, gender, Na-K ratio, Cl\u003csup\u003e-\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, urinary pH, eGFR and uric acid(Table2). To avoid the influence of multicollinearity, Na\u003csup\u003e+\u003c/sup\u003eand K\u003csup\u003e+\u003c/sup\u003e were not included in the multivariate logistic regression since Na-K ratio was considered as the dominant factor. Finally, five protential predictors without multicollinearity were screened, formula for calculate score(Table2): ARR=0.166*Na-K ratio+0.114*Cl\u003csup\u003e-\u003c/sup\u003e+0.438*Urinary pH-0.026*eGFR-0.1.137*Gender(Female=0, Male=1).\u003c/p\u003e\n\u003cp\u003eBased on these results, we construct a nomogram to predict whether ARR is abnormal in hypertension patients(Fig 2A). The AUC and calibrated with 1000 bootstrap samples were further used to evaluate this nomogram. The AUCs were 0.756 [95% CI, 0.71-0.80, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05] with sensitivity of 64.8%, specificity of 70.8% in training set(Fig 2B), and 0.725 [95% CI, 0.64-0.81, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05] with sensitivity of 69.7%, specificity of 67.2% in validation set(Supplemetary Fig1A ), respectively. Finally, calibration curve of the nomogram indicated great prediction consistency between new model in training set(Fig 2C) and validation set(Supplementary Fig 1B). And the DCA curves showed that the nomogram could predict the abnormal ARR in patients with hypertension well(Fig 2D and Supplemetary Fig 1C).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven the importance role of RAAS in progression and treatment of hypertension as well as the screening test of PA, there is a need and challenge to develop better predictive tools to identify patients suspected of having high ARR, based on some clinical characteristics and simple biochemical indexes. Our results suggests that women and those with higher serum Na-K ratio or serum chloridion or in patients with higher urinary pH but lower eGFR are more likely to manifest abnormal ARR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe meaning of ARR is more than the criteria of PA screening test. Not only it is a miniature of RAAS which plays a pivotal role in cardiovascular diseases, but also associated with the severity of COVID-19. Hence, our study firstly construct a model to distinguish abnormal ARR in hypertension patients and provide a novel approach to estimate activity of RAAS especially in those small scale hospitals lack of radioimmunoassay.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt\u0026rsquo;s obvious that the excessive production of aldosterone directly leads to high serum sodium and low serum potassium, indicating high Na-K ratio. We also attempt to excavate various numeric combination of serum sodium and potassium by utilizing ROC to compare prediction efficiency and Na-K ratio demonstrates the highest predictive value. Thus, we choose Na-K ratio as the predictor to enlarge effects of aldosterone and it really exhibits great correlation in our model. \u0026nbsp;Furthermore, it\u0026rsquo;s not surprising to find that serum Cl\u003csup\u003e-\u003c/sup\u003e and urinary pH are also involved in the model. According to previous studies, aldosterone could mediate Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e absorption and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e secretion in the cortical collecting duct by regulating pendrin, an aldosterone-sensitive Cl\u003csup\u003e-\u003c/sup\u003e/HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e exchanger[21,22], which directly leads to an increase in serum Cl\u003csup\u003e-\u003c/sup\u003e and promote Urine alkalization. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our model, female and eGFR are considered to have negative correlation with ARR. Hermidorff MM et al. suggests that G protein-coupled estrogen receptor-1 (GPER-1) was identified as the third estrogen receptor and could mediate some aldosterone-induced rapid effects in several tissues[23], which means estrogen in women will compete for GPER-1 with aldosterone and undermine aldosterone\u0026rsquo;s effects. As a matter of factor, there are loads of evidence that estrogen may have great benefits in preventing apoptosis and necrosis of cardiac and endothelial cells by mediating signaling through PI3K, Akt, and ERK 1/2 [24]. As for the assessment of eGFR, it\u0026rsquo;s a fundamental tool which is mainly applied to public health and researches, especially the diagnose, stage, as well as manage chronic kidney disease (CKD). Sim JJ et al. demonstrated that each 10-unit increase in PRA was associated with OR for CKD prevalence of 1.3 (1.2-1.4), suggesting mean PRA increased with decline of eGFR[25]. The modulation of RAAS especially preservation of vascular tone can lead to detrimental outcomes in vascular system and in kidney eGFR is the best choice to evaluate function of glomerular capillary. Another study carried out by Hannemann A et al. suggests that ARR or PAC are inversely associated with the eGFR in the general population in their linear regression models adjusted for sex, age, serum triglyceride concentrations, etc[26]. In particular, they adjusted the model for systolic and diastolic blood pressures, indicating that aldosterone may have affected eGFR through blood pressure-independent mechanisms. Thus, aldosterone and renin all contributes to change of eGFR, and in our model the negative correlation between eGFR and ARR may be the composite effect of all two hormones.\u003c/p\u003e\n\u003cp\u003eBefore our study, there are several researches about diagnose of PA through constructing different models[13-15]. In addition, there are also plenty of studies to explore biochemical as well as clinical characteristics in PA, which built a steady foundation of our study[27-29]. Meanwhile, Kuo CC et al. harbored the view that the ARR is a satisfactory tool for screening, but it is still far from an efficient mass screening method from an availability perspective and they developed a new simple formula which they think is comparable \u003ca href=\"https://www.sciencedirect.com/topics/medicine-and-dentistry/aldosterone-to-renin-ratio\" title=\"Learn more about to ARR from ScienceDirect's AI-generated Topic Pages\"\u003eto ARR\u003c/a\u003e by combining \u003ca href=\"https://www.sciencedirect.com/topics/medicine-and-dentistry/body-mass-index\" title=\"Learn more about body mass index from ScienceDirect's AI-generated Topic Pages\"\u003ebody mass index\u003c/a\u003e (BMI) and \u003ca href=\"https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/potassium-blood-level\" title=\"Learn more about serum potassium from ScienceDirect's AI-generated Topic Pages\"\u003eserum potassium\u003c/a\u003e to \u003ca href=\"https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/potassium-urine-level\" title=\"Learn more about urine potassium from ScienceDirect's AI-generated Topic Pages\"\u003eurine potassium\u003c/a\u003e clearance (PUKC) ratio[30]. However, well-conducted clinical trials with sufficient sample sizes are still needed to validate it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt\u0026apos;s worth noting that hypertension is frequently associated with those factors of metabolic syndrome, including obesity, dyslipidemia and glucose homeostasis, all of which accelerate the process of atherosclerosis[31]. For example, Fallo F et al. reviewed that an imbalanced release of glucose owing to insulin resistance tends to be associated with aldosterone overproduction\u003csup\u003e31\u003c/sup\u003e.Therefore, we adopted FBP, total cholesterol, triglyceride, LDL-C and HDL-C to investigate whether the link between these interrelated risk factors of metabolic origin and ARR. Although no difference was observed in our study between two groups, large studies are needed to further explore the relationship.\u003c/p\u003e\n\u003cp\u003eAccording to the guideline, certain drugs, such as diuretics, angiotensin-converting enzyme Inhibitors(ACEIs), angiotensin receptor blockers (ARBs),\u0026nbsp;\u0026beta;-adrenergic blockers, and calcium channel blockers (CCBs), should be discontinued before the measurement of ARR[32]. Limited by the retrospective analysis, we are unable to intervene this process. However, we investigated exact oral antihypertensive drugs which further classified as false-positive and false-negative drugs clearly. In training set, different drugs really have an impact on ARR, but in the following univariable logistic analysis they showed no correlation. Meanwhile, it was also ajusted in the following analysis.\u003c/p\u003e\n\u003cp\u003eThis study contains several limitations. (1) Although this single-center study collecting data from a tertiary hospital, it may be more representative and compelling as a multi-center study. (2) Selection bias is inevitable in retrospective studies. (3) Whether this model can be extended to primary hospitals and used as the common step to screen activity of RAAS in hypertension patients as well as possibility of PA still needs more external validation data support.\u003c/p\u003e\n\u003cp\u003eIn summary, we have firstly developed a novel nomogram to predict whether ARR is abnormal in hypertensive individuals using simple routine examinations, which may help clinicians plan whether the hypertension patient needs more endocrine screening to identify PA and needs relevant drugs to inhibit activity of RAAS. In addition, it provides a new tool for some primary hospitals to check ARR.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARR aldosterone-renin ratio\u003c/p\u003e\n\u003cp\u003eRAAS renin-angiotensin-aldosterone system\u003c/p\u003e\n\u003cp\u003eROC receiver operating characteristics\u003c/p\u003e\n\u003cp\u003eDCA calibration plots and decision curve analyses\u003c/p\u003e\n\u003cp\u003eCl- chloridion\u003c/p\u003e\n\u003cp\u003eeGFR estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003ePA primary aldosteronism\u003c/p\u003e\n\u003cp\u003ePAC plasma aldosterone concentration\u003c/p\u003e\n\u003cp\u003ePRA plasma renin activity\u003c/p\u003e\n\u003cp\u003eACE2 angiotensin converting enzyme 2\u003c/p\u003e\n\u003cp\u003eDRC direct renin concentration\u003c/p\u003e\n\u003cp\u003eNYHA New York Heart Association\u003c/p\u003e\n\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003eSBP systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eCa2+ calcium\u003c/p\u003e\n\u003cp\u003eFBG fasting blood-glucose\u003c/p\u003e\n\u003cp\u003eOR odds ratio\u003c/p\u003e\n\u003cp\u003eAUC area under the ROC curve\u003c/p\u003e\n\u003cp\u003eGPER-1 G protein-coupled estrogen receptor-1\u003c/p\u003e\n\u003cp\u003eCKD chronic kidney disease \u003c/p\u003e\n\u003cp\u003ePUKC serum potassium to urine potassium clearance\u003c/p\u003e\n\u003cp\u003eARBs angiotensin receptor blockers \u003c/p\u003e\n\u003cp\u003eCCBs calcium channel blockers\u003c/p\u003e\n\u003cp\u003eACEIs angiotensin-converting enzyme Inhibitors\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eInstitutional Ethics Committee of Wuhan Union Hospital approved study design and and waived need for informed consent due to retrospective nature of the study and due to use of anonymized data. All authors are agreeable for the publication and the data are provided in the supplementary materials. We declare that no funding was received for conducting this study that could be construed as a potential conflict of interest. The authors had the following roles in the writing of this report: Xuehan Li and Yulu Yang are responsible for the study design, data collection, analysis, and manuscript drafting; Zhihua Qiu and Zihua Zhou are responsible for the study design, critical discussion, and review; Changhu Liu, Jiacheng Wu, Jianwu Huang, Hao chen and Yalei Wang are responsible for the data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelevant research data is provided within the supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGuzik TJ, Nosalski R, Maffia P, Drummond GR. Immune and inflammatory mechanisms in hypertension. Nat Rev Cardiol. 2024 Jun;21(6):396-416. Epub 2024 Jan 3.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFunder JW, Carey RM. Primary Aldosteronism: Where Are We Now? Where to From Here? Hypertension. 2022 Apr;79(4):726-735. Epub 2022 Jan 24.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRossi GP, Bernini G, Caliumi C, Desideri G, Fabris B, Ferri C, et al. PAPY Study Investigators. A prospective study of the prevalence of primary aldosteronism in 1,125 hypertensive patients. J Am Coll Cardiol. 2006 Dec 5;48(11):2293-300. Epub 2006 Nov 13.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHundemer GL, Agharazii M, Madore F, Vaidya A, Brown JM, Leung AA, et al. Subclinical Primary Aldosteronism and Cardiovascular Health: A Population-Based Cohort Study. Circulation. 2024 Jan 9;149(2):124-134. Epub 2023 Nov 30.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVecchiola A, Fuentes CA, Barros ER, Mart\u0026iacute;nez-Aguayo A, Garc\u0026iacute;a H, Allende F, et al. The Aldosterone/Renin Ratio Predicts Cardiometabolic Disorders in Subjects Without Classic Primary Aldosteronism. Am J Hypertens. 2019 Apr 22;32(5):468-475.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHolloway-Kew KL, Anderson KB, Rufus-Membere P, Tembo MC, Sui SX, Hyde NK, et al. Associations Between Aldosterone-Renin-Ratio and Bone Parameters Derived from Peripheral Quantitative Computed Tomography and Impact Microindentation in Men. Calcif Tissue Int. 2023 Nov;113(5):496-510.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThomas G. Renin-angiotensin system inhibitors in COVID-19. Cleve Clin J Med. 2020 May 14.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eParikh NI, Arowolo F, Durstenfeld MS, Nah G, Njoroge J, Vittinghoff E, et al. Hospitalized Patients With COVID-19 Have Higher Plasma Aldosterone-Renin Ratio and Lower ACE Activity Than Controls. J Endocr Soc. 2022 Sep 22;6(12):bvac144.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAkin S, Schriek P, van Nieuwkoop C, Neuman RI, Meynaar I, van Helden EJ, et al. A low aldosterone/renin ratio and high soluble ACE2 associate with COVID-19 severity. J Hypertens. 2022 Mar 1;40(3):606-614.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu Z, Jin L, Zhou W, Zhang C. The spectrum of plasma renin activity and hypertension diseases: Utility, outlook, and suggestions. J Clin Lab Anal. 2022 Nov;36(11):e24738.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi T, Ma Y, Zhang Y, Liu Y, Fu T, Zhang R, et al. Feasibility of Screening Primary Aldosteronism by Aldosterone-to-Direct Renin Concentration Ratio Derived from Chemiluminescent Immunoassay Measurement: Diagnostic Accuracy and Cutoff Value. Int J Hypertens. 2019 Jul 2;2019:2195796.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGlinicki P, Jeske W, Bednarek-Papierska L, Kruszyńska A, Gietka-Czernel M, Rosłonowska E, et al. The ratios of aldosterone / plasma renin activity (ARR) versus aldosterone / direct renin concentration (ADRR). J Renin Angiotensin Aldosterone Syst. 2015 Dec;16(4):1298-305.\u003c/li\u003e\n \u003cli\u003eWang MH, Li NF, Luo Q, Wang GL, Heizhati M, Wang L, et al. Development and validation of a novel diagnostic nomogram model to predict primary aldosteronism in patients with hypertension. Endocrine. 2021 Sep;73(3):682-692.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLin W, Gan W, Feng P, Zhong L, Yao Z, Chen P, et al. Online prediction model for primary aldosteronism in patients with hypertension in Chinese population: A two-center retrospective study. Front Endocrinol (Lausanne). 2022 Aug 2;13:882148.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang Q, Dong H, Li HW, Zheng ZH, Liu YZ, Hua YH, et al. Development of a diagnostic model for pre-washout screening of primary aldosteronism. J Endocrinol Invest. 2024 Mar 27.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLoh HH, Yee A, Loh HS. Bone health among patients with primary aldosteronism: a systematic review and meta-analysis. Minerva Endocrinol. 2019 Dec;44(4):387-396.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKwak MK, Lee JY, Kim BJ, Lee SH, Koh JM. Effects of Primary Aldosteronism and Different Therapeutic Modalities on Glucose Metabolism. J Clin Med. 2019 Dec 12;8(12):2194.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBerends AMA, Buitenwerf E, Gruppen EG, Sluiter WJ, Bakker SJL, Connelly MA, et al. Primary aldosteronism is associated with decreased low-density and high-density lipoprotein particle concentrations and increased GlycA, a pro-inflammatory glycoprotein biomarker. Clin Endocrinol (Oxf). 2019 Jan;90(1):79-87.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNamba M, Kikuchi K, Komura H, Suzuki S, Satoh N, Ohtomo T, et al. [Study on uric acid metabolism in patients with primary aldosteronism]. Nihon Naibunpi Gakkai Zasshi. 1992 Jan 20;68(1):51-61. Japanese.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYamashita T, Shimizu S, Koyama M, Ohno K, Mita T, Tobisawa T, et al. Screening of primary aldosteronism by clinical features and daily laboratory tests: combination of urine pH, sex, and serum K. J Hypertens. 2018 Feb;36(2):326-334.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRoyaux IE, Wall SM, Karniski LP, Everett LA, Suzuki K, Knepper MA, et al. Pendrin, encoded by the Pendred syndrome gene, resides in the apical region of renal intercalated cells and mediates bicarbonate secretion. Proc Natl Acad Sci U S A. 2001 Mar 27;98(7):4221-6.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWall SM, Kim YH, Stanley L, Glapion DM, Everett LA, Green ED, et al. NaCl restriction upregulates renal Slc26a4 through subcellular redistribution: role in Cl- conservation. Hypertension. 2004 Dec;44(6):982-7.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHermidorff MM, de Assis LV, Isoldi MC. Genomic and rapid effects of aldosterone: what we know and do not know thus far. Heart Fail Rev. 2017 Jan;22(1):65-89.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKnowlton AA, Lee AR. Estrogen and the cardiovascular system. Pharmacol Ther. 2012 Jul;135(1):54-70.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSim JJ, Shi J, Calara F, Rasgon S, Jacobsen S, Kalantar-Zadeh K. Association of plasma renin activity and aldosterone-renin ratio with prevalence of chronic kidney disease: the Kaiser Permanente Southern California cohort. J Hypertens. 2011 Nov;29(11):2226-35.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHannemann A, Rettig R, Dittmann K, V\u0026ouml;lzke H, Endlich K, Nauck M, et al. Aldosterone and glomerular filtration--observations in the general population. BMC Nephrol. 2014 Mar 10;15:44.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang W, Li Y, Li Q, Zhang T, Wang W, Mo D, et al. Developing a research database of primary aldosteronism: rationale and baseline characteristics. BMC Endocr Disord. 2021 Jun 29;21(1):137.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYoon M, Hong N, Ha J, Lee CJ, Ku CR, Rhee Y, et al. Prevalence and clinical characteristics of primary aldosteronism in a tertiary-care center in Korea. Hypertens Res. 2022 Sep;45(9):1418-1429.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLana A, Alexander K, Castagna A, D\u0026apos;Alessandro A, Morandini F, Pizzolo F, et al. Urinary Metabolic Signature of Primary Aldosteronism: Gender and Subtype-Specific Alterations. Proteomics Clin Appl. 2019 Jul;13(4):e1800049.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKuo CC, Wu VC, Tsai CW, Huang KH, Wang SM, Li BC, et al. Combining body mass index and serum potassium to urine potassium clearance ratio is an alternative method to predict primary aldosteronism. Clin Chim Acta. 2011 Aug 17;412(17-18):1637-42.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFallo F, Pilon C, Urbanet R. Primary aldosteronism and metabolic syndrome. Horm Metab Res. 2012 Mar;44(3):208-14.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFunder JW, Carey RM, Mantero F, Murad MH, Reincke M, Shibata H, et al. The Management of Primary Aldosteronism: Case Detection, Diagnosis, and Treatment: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2016 May;101(5):1889-916. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline of characteristics in the training and validation sets\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"94%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36%;\"\u003e\n \u003cp\u003eTraining set(n=865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 37%;\"\u003e\n \u003cp\u003eValidation set(n=347)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003eAbnormal ARR(n=114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003eNormal ARR(n=751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003eAbnormal ARR(n=33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003eNormal ARR(n=314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAge[years]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e52.1\u0026plusmn;7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e47.61\u0026plusmn;11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e54.21\u0026plusmn;8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e48.71\u0026plusmn;11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGender[Female(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e68(59.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e256(34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBMI[kg/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e25.41\u0026plusmn;3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e26.00\u0026plusmn;3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e25.57\u0026plusmn;4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e25.97\u0026plusmn;4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSBP [mmHg]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e143.60\u0026plusmn;20.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e144.87\u0026plusmn;21.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e150.18\u0026plusmn;26.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e145.11\u0026plusmn;20.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eDBP [mmHg]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e92.40\u0026plusmn;16.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e92.17\u0026plusmn;16.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e91.18\u0026plusmn;14.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e90.14\u0026plusmn;15.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;Grades\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eNormal blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGrade 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eOral antihypertensive Drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eNo oral drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eFalse positive drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eFalse negative drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14%;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum calcium[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e2.24\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e2.27\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.25\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.28\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum chloridion[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e102.96\u0026plusmn;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e102.33\u0026plusmn;2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e103.53\u0026plusmn;2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e102.74\u0026plusmn;2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum magnesium[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.87\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.87\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum phosphorus[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.10\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.09\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.10\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eFBG[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e5.55\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e5.33\u0026plusmn;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e5.23\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e5.31\u0026plusmn;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum sodium[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e142.11\u0026plusmn;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e141.08\u0026plusmn;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e142.65\u0026plusmn;2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e141.20\u0026plusmn;2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum potassium[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e3.71\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e3.88\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e3.70\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e3.85\u0026plusmn;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eNa-K ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e38.58\u0026plusmn;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e36.61\u0026plusmn;3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e38.88\u0026plusmn;3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e36.95\u0026plusmn;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum cholesterol[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e4.38\u0026plusmn;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e4.49\u0026plusmn;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4.44\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e4.42\u0026plusmn;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSerum triglyceride[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.80\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.91\u0026plusmn;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.01\u0026plusmn;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.73\u0026plusmn;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eLDL-C[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e2.54\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e2.67\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.60\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e2.63\u0026plusmn;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eHDL-C[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e1.10\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.09\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eUrea nitrogen[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e5.32\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e5.22\u0026plusmn;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e5.06\u0026plusmn;1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e5.35\u0026plusmn;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCreatinine[umol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e68.24\u0026plusmn;15.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e71.09\u0026plusmn;16.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e66.52\u0026plusmn;16.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e70.36\u0026plusmn;15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eeGFR[ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e96.07\u0026plusmn;13.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e100.96\u0026plusmn;14.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e96.92\u0026plusmn;13.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e100.13\u0026plusmn;14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eUric acid[umol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e342.31\u0026plusmn;88.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e370.30\u0026plusmn;100.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e334.36\u0026plusmn;91.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e364.06\u0026plusmn;99.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.088\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eUrinary pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e6.28\u0026plusmn;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003e6.13\u0026plusmn;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6.24\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13%;\"\u003e\n \u003cp\u003e6.06\u0026plusmn;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eContinuous variables are presented as mean \u0026plusmn; standard deviation or medians and inter-quartile spacing and categorical variables are expressed as percentages. Significance is marked in bold. Student\u0026rsquo; t test (continuous variables) and Pearson\u0026rsquo; chi-square test (categorical variables) was performed to compare between normal ARR and abnormal ARR groups .\u003c/p\u003e\n\u003cp\u003eARR aldosterone-renin-ratio, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, eGFR estimated glomerular filtration rate, FBG fast blood-sugar.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Results of univariable and multivariate logistic analysis\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33%;\"\u003e\n \u003cp\u003eUnivariable logistic analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30%;\"\u003e\n \u003cp\u003eMultivariate logistic analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eAge[years]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e1.039(1.019, 1.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eNa-K ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e1.168(1.106, 1.233 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e1.181(1.112, 1.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eCl[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e1.083(1.009, 1.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e1.120(1.038, 1.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eNa[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e1.288(1.162, 1.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eUrinary pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e1.826(1.240, 2.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e1.550(1.024, 2.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eCa[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e0.074(0.012, 0.437)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eK[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e0.221(0.122, 0.403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eeGFR[ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e0.978(0.964, 0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e0.974(0.960, 0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eUric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e0.997(0.995, 0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eGender[female = 1 male = 0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e0.350(0.234, 0.524)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003e-1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19%;\"\u003e\n \u003cp\u003e0.321(0.209, 0.492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eeGFR estimated glomerular filtration rate, CI confidence interval, OR odds ratio, The bold numbers are the variables with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"aldosterone-renin-ratio, Na-K ratio, hypertension, nomogram, primary hospitals","lastPublishedDoi":"10.21203/rs.3.rs-4942905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4942905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: It’s necessary to assess activity of renin-angiotensin-aldosterone system(RAAS) in patients with hypertension by means of orthostatic aldosterone-renin ratio(ARR) which is demanding and not available to those primary hospitals. A novel and portable prediction tool is highly desirable to distinguish abnormal ARR in those patients and guide hypertension therapy to some degree.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Clinical characteristics and laboratory data of 1,212 patients with hypertension were collected for modeling and randomly divided into a training cohort (865 of 1,212, 70%) and an internal validation cohort (347 of 1,212, 30%). Then, predictors for ARR were extracted to construct a nomogram model based on regression analysis of the training set. Receiver operating characteristics (ROC), calibration plots and decision curve analyses (DCA) were applied to evaluate the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Five predictors were adopted to the nomogram including Na-K ratio, gender, serum chloridion(Cl\u003csup\u003e-\u003c/sup\u003e), estimated glomerular filtration rate(eGFR) and urinary pH. Based on this nomogram, the area under the curve(AUC) was 0.756 (95% CI: 0.71-0.80, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) in the training set and 0.725 (95% CI: 0.64-0.81, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) in the validation set. The calibration curves exhibited great agreement between the predictive risk of the model and the actual risk and the DCA also showed good clinical benefit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: We have firstly developed a novel nomogram to predict abnormal ARR in hypertensive individuals based on routine biochemical variables.\u003c/p\u003e","manuscriptTitle":"A nomogram for predicting aldosterone-renin ratio in patients with hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 17:35:27","doi":"10.21203/rs.3.rs-4942905/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4819f5a0-35d4-4af0-a6ea-2162fb1f11c8","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37730267,"name":"Health sciences/Cardiology"},{"id":37730268,"name":"Health sciences/Endocrinology"},{"id":37730269,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-10-18T03:53:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 17:35:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4942905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4942905","identity":"rs-4942905","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}