Temporal Dynamic of Hemoglobin Levels and Its Impact on Progression in Chronic Kidney Disease Stages 3- 4 Patients: A Retrospective Cohort Study

Temporal Dynamic of Hemoglobin Levels and Its Impact on Progression in Chronic Kidney Disease Stages 3- 4 Patients: A Retrospective Cohort Study | 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 Temporal Dynamic of Hemoglobin Levels and Its Impact on Progression in Chronic Kidney Disease Stages 3- 4 Patients: A Retrospective Cohort Study Shuqi Ye, Fan Zhu, Wenyuan Gan, Wenli Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3453502/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: Anemia is one of the most prevalent complications in patients with chronic kidney disease (CKD). Previous researches have indicated associations between anemia and renal outcomes. However, recurrent limitations in these studies are their exclusive reliance on baseline hemoglobin (Hb) levels, leaving the implications of longitudinal Hb fluctuations on the progression of CKD uncharted. Methods: In this cohort of 2,336 CKD stages 3-4 patients, we investigated the relationship between Hb levels, its temporal variations, and composite renal outcomes. Result: The average follow-up period was 552.5 days (289.3-971.0), with 834 individuals (35.7%) experiencing the composite renal endpoints. Using a fully adjusted cubic spline model with Hb treated as a continuous variable showed that the relationship between Hb and the renal composite outcomes was non-linear. Additionally, in an adjusted multivariate Cox model, participants in the Q4 (12-13 g/dL) and Q5 (>13 g/dL) groups with higher Hb levels had a significantly lower risk of composite renal outcomes compared to the Q1 group (<10 g/dL) (HR=0.57, 0.50, respectively, all p<0.001). A similar trend is observed in time-varying multivariate models that incorporate the longitudinal dynamics of Hb levels (HR=0.25, 0.25, respectively, all p<0.001). Conclusion: In the groups with higher Hb levels, the occurrence of renal endpoint events was notably lower than in the groups with lower Hb levels. Additionally, based on the longitudinal dynamics of Hb levels, the risk of renal endpoint events decreased progressively with rising Hb levels. Health sciences/Nephrology/Kidney diseases Health sciences/Risk factors Chronic kidney disease Anemia Hemoglobin longitudinal dynamics Chronic kidney disease progression. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic kidney disease (CKD) is a major global public health issue, with nearly one in ten people affected worldwide. According to the Global Burden of Disease (GBD) Study 2019, there are an estimated 697 million CKD cases globally 1 . Over an individual's lifetime, the overall risk for developing CKD stage 3a or higher is 59.1%. For stages 3b, 4, and 5, the risks stand at 33.6%, 11.5%, and 3.6%, respectively 2 .A severe potential outcome of CKD is end-stage renal disease (ESRD) which is associated with poorer clinical outcomes, including diminished quality of life, increased medical expenses, and a heightened economic strain 2-4 . Anemia as one of the most prevalent complications associated with CKD. A multi-center study reported that 41% of the 209311 participants exhibited low hemoglobin levels with criteria set at <13 g/dL for men and <12 g/dL for women 5 . The onset of anemia in CKD is attributed to a multifaceted mechanism, encompassing relative erythropoietin (EPO) deficiency, uremic-induced inhibitors of erythropoiesis, diminished erythrocyte lifespan, and disrupted iron homeostasis 6 . Studies have consistently indicated an association between anemia and heightened risks of mortality, hospitalization, major adverse cardiac events, and CKD progression. Notably, the severity of anemia proportionally raises these risks 7 , promoting the development of targeted treatment strategies for anemia. Recent findings suggest that anemia not only emerges as a byproduct of CKD but might also act as a precursor to CKD progression. The RENAAL study disclosed that even mild anemia in type 2 diabetic patients with nephropathy correlated with increased renal risks 8 . Prospective data from the CHARLS study underscored anemia as an autonomous risk factor for renal function decline among the middle-aged and elderly demographic 9 , parallel conclusions have been drawn from studies focusing on specific populations, such as adolescents 10 , those with IgA nephropathy 11 , and individuals with cardiovascular conditions 12 . However, a recurrent limitation in these studies is their exclusive reliance on baseline hemoglobin (Hb) levels, leaving the implications of longitudinal Hb fluctuations largely uncharted. This gap underscores the ambiguity surrounding the prognostic significance of Hb levels concerning CKD progression. In this research, we meticulously assessed both the baseline and time-varying Hb values in our endeavor to elucidate the intricate relationship between Hb levels and CKD progression risks within an extensive retrospective CKD patient cohort. Methods Study population Our current research was conducted in the central region of mainland China involving a multi-center study. The population consists of all patients who were diagnosed with CKD during their hospital stay or outpatient visits between January 1, 2017, and May 31, 2023. The total number of people included in the study is 39,698, of which 10,925 people have recorded kidney function data at least twice. According to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, patients were classified into CKD stages 3a, 3b, and 4 13 . Base on the estimated glomerular filtration rate (eGFR), there were 3430 patients with CKD stage 3-4, aged between 18-80 years. After excluding patients with missing values in the Hb levels, the final total number of participants in the study is 2,336 (figure 1). Patients were monitored from the date of diagnosis of CKD until a 40% reduction in eGFR from the baseline, the development of end-stage renal disease (ESRD), death, or loss to follow-up. The inclusion criteria were as follows: (1) patients diagnosed with CKD stages 3-4. (2) patients with a minimum of three outpatient visits or hospitalizations. (3) patients with at least three kidney function assessments. The exclusion criteria were as follows: (1) patients who have undergone hemodialysis (HD), peritoneal dialysis (PD), or kidney transplantation. (2) patients below 18 years or above 80 years. (3) patients with concurrent conditions such as pregnancy, malignant neoplasms, or acute active bleeding disorder. The study was registered in the Chinese Clinical Trial Registry (CHICTR) with the registration number, ChiCTR2200061199. The study was performed in accordance with the ethical principles of the Declaration of Helsinki, and all participants provided written informed consent. The study protocol was approved by the ethics committee of The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology (institutional review board approval number WHZXKYL2022-112). Variables and Covariates Collection We sourced our data from the hospital's electronic medical system. This includes demographics such as age, gender, comorbidities, medical history, blood pressure, and body mass index (BMI). For laboratory examinations, we collected data on blood counts, Hb, serum creatinine (Scr), blood urea nitrogen (BUN), serum uric acid (UA), albumin (Alb), D-dimmer, calcium, potassium, and phosphorus levels, N-Acetyl-β-D-Glucosaminidase (NAG), vitamin B12, folate, eGFR, C-reactive protein (CRP), iron profiles, lipid profiles, and urinary albumin to creatinine ratio (UACR), 24h proteinuria, parathyroid hormone (PTH). In terms of medications, we recorded if patients were on angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), β-Blockers, diuretics, erythropoiesis-stimulating agents (ESAs), and iron supplements. To guarantee the precision and consistency of our data, all entries were validated by two independent authors. Assessment Criteria Anemia in this study was characterized by hemoglobin levels falling below 13g/dL for men and 12g/dL for women, in line with the criteria set by the KDIGO 14 . To gauge the eGFR, we utilized the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation 15 . Hypertension was identified either by an average systolic blood pressure surpassing 140mmHg or an average diastolic reading exceeding 90mmHg. Additionally, any self-reported usage of antihypertensive medications or a personal history of hypertension also fell under this definition. The criteria for diabetes included a fasting plasma glucose level of 7.0mmol/L or higher, the use of hypoglycaemic agents, or any self-declared history of the condition. We derived the body mass index (BMI) by dividing an individual's weight (in kg) by the square of their height (in meters). Exposure and Outcomes of interests The primary exposures were the baseline and time-varying Hb levels. These levels were categorized and treated as categorical variables, split into five distinct groups: ＜10g/dL, 10-11g/dL, 11-12g/dL, 12-13g/dL, and ＞13g/dL. Furthermore, to further analyze time- varying Hb levels, Hb levels were also treated as continuous variables. A non-linear effect was modeled using a restricted cubic spline function to assess it. The composite renal outcomes manifested as an annual decline in eGFR (mL/min/1.73m^2/year). This decline was ascertained by plotting the eGFR values and determining the slope using a least-squares method. Another critical outcome was the emergence of ESRD throughout the follow-up duration. ESRD was demarcated by the commencement of renal replacement procedures, which includes either dialysis or undergoing a renal transplant. Statistical Analyses In the comparison of the baseline characteristics by Hb, continuous variates were analyzed by one-way analysis of variance, while categorical variates were analyzed by X 2 test. Cumulative incidences of outcome events were visualized using Kaplan-Meier curves. To evaluate independent associations between Hb and renal composite outcomes, two Cox proportional hazard regression models were separately adopted: (1) fixed Cox regression model with baseline values were examined to ascertain the association of long-term exposure with CKD progression. (2) time-varying models were assessed to account for time-dependent confounding and ascertain their short-term associations 16 . In time-varying analyses, all laboratory data were incorporated as time-dependent variables. The results of Cox proportional hazard regression models were presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Restricted cubic spline plots were used to assess the relation between Hb as a continuous variable with the study outcomes. To validate our findings, we performed sensitivity analyses. First, we excluded the subjects with eGFR ≥60 ml/min per 1.73 m2(CKD stage 1 and stage 2) due to the atypical clinical manifestations of CKD in these stages, which pose challenges for accurate diagnosis. And the subjects with eGFR ≥90 ml/min per 1.73 m 2 are considered close to normal kidney function, which may not represent the CKD population well. Second, we excluded the subjects with eGFR ≤15 ml/min per 1.73 m2 (stage 5), because the subjects with eGFR≤15 ml/min per 1.73 m 2 are relatively small in number, and may exaggerate the association between Hb level and study outcomes due to far-advanced CKD. Third, we repeated analyses using Hb categorized into quintiles, where the range of quintiles was ＜10(Q1), 10-11(Q2), 11-12(Q3), 12-13(Q4), ＞13(Q5) g/dL for baseline and time-varying Hb values. Two-sided p values <0.05 were considered statistically significant. Statistical analysis was performed using R version 3.6.1. Results Baseline characteristics of participants Table 1 provided the baseline demographic and clinical characteristics of the 2336 participants categorized by Hb quartiles: ＜10(Q1), 10-11(Q2), 11-12(Q3), 12-13(Q4), ＞13(Q5) g/dL. At baseline, 69.60% of the participants were male, with an average age of 64.91±10.61 years. The average Hb and eGFR were 11.76±2.42 g/dL and 39.84±12.86 ml/min/per 1.73 m2. Patients with lower Hb level were more likely to be thinner, had a lower diastolic blood pressure, and they were at a higher risk of thromboembolic events. In addition, these patients had a lower albumin, serum iron, serum uric acid levels and less inflamed than those with higher Hb level. Kidney function was more preserved and the amount of urinary protein excretion was lower in patients with higher Hb levels. No significant differences in age, comorbid conditions, systolic blood pressure, high-density lipoprotein cholesterol, parathyroid hormone, glycosylated hemoglobin, and medications like β-Blocker and CCB were observed across the hemoglobin quartile groups (P > 0.05, see Table 1 for details). Figure 2 contrasts Hb levels between participants who experienced renal composite endpoints and those who did not. Results suggested a lower distribution of Hb levels in the renal composite endpoint group, while the non-renal composite endpoint group exhibited relatively higher Hb levels. The incidence rate of the composite renal outcome The Kaplan Meier survival curves were employed to visualize the cumulative incidence of study outcomes in relation to Hb levels. These curves unveiled distinct differences in the risks of composite renal outcomes across Hb levels. Notably, the risks were markedly elevated in the Q1 and Q2 groups in contrast to the Q3, Q4, and Q5 cohorts (p<0.001 by Log-rank test, as depicted in Figure 3). For the entire study population, the cumulative incidence rate was set at 0.29 per 100 person-years. Breaking this down further, the Hb groups Q1 through Q5 had cumulative incidence rates of 0.09, 0.03, 0.06, 0.07, and 0.04 per 100 person-years, respectively. Intriguingly, as Hb levels ascended, there was a noticeable decline in the incidence of renal composite endpoints. This downward trend in incidence with rising Hb levels was statistically significant (P for trend<0.001), as illustrated in Figure 3. The results of univariate and multivariate analyses using Cox proportional-hazards regression model and Cox time-varying model. Univariate analysis revealed a few variables that did not have a significant association with the composite renal outcome. On the other hand, several factors exhibited a negative association with the composite renal outcome: hemoglobin, age, serum iron, serum albumin (HR=0.979, 0.986, 0.980, 0.994, 0.914, respectively, all p<0.05). Conversely, some variables demonstrated a positive correlation with the composite renal outcome: male gender, phosphorus, the use of iron and ESAs (HR=1.245, 1.737, 1.881, 2.179, respectively, all p<0.05). The multivariate analysis was conducted on the baseline data, validating that hemoglobin, age, serum Albumin (HR=0.507, 0.992, 0.959, respectively, all p<0.05) were negatively linked to the renal composite outcome, history of diabetes mellitus and hypertension (HR=1.237, 1.309, respectively, all p<0.05) has the positive correlation with the outcome (See details for Table 2). Further exploration with a Cox Time-Varying model for univariate and multivariate analysis supported these findings. The multi-variates time-varying hemoglobin (HR=0.969, 95% CI 0.966-0.972, p<0.05) continued to exhibit a negative association with the study outcomes, as detailed in Table 3. The effects of Hb level on the composite renal outcomes We constructed three different analytical models using the Cox proportional hazards regression model and Cox time-varying model to explore the relationship between hemoglobin levels and composite renal outcomes. In the unadjusted model (model 1), participants in the baseline Hb groups Q3(HR=0.59, 95% CI 0.48-0.71, p<0.05), Q4(HR=0.37, 95% CI 0.29-0.47, p<0.05), and Q5(HR=0.29, 95% CI 0.23-0.35, p<0.05) demonstrated a significant reduction in the risk of encountering the composite renal outcome when compared to the Q1 group. Specifically, an increase of 1g/dL of Hb was associated with a 41%, 63%, and 71% decrease in the risk of the study outcome for Q3, Q4, and Q5, respectively. This association was not changed in the minimally adjusted model, after adjustment for demographic factors, body mass index, and comorbidities (model 2). However, in a fully adjusted Cox model in which laboratory parameters and medications were additionally included (model 3), the risk of the composite renal outcome was significantly reduced in the Hb group only Q4 (HR=0.57, 95% CI 0.43-0.76, p<0.05) and Q5(HR=0.50, 95% CI 0.38-0.66, p<0.05), with Q3 losing its previous statistical significance (Table 4). The time-varying Cox model analysis consistently solidified the observation that throughout all three modeling approaches, it was evident that individuals with Hb levels above 10g/dL (Q3, Q4, and Q5) generally had a decreased risk of the composite renal outcome. Specifically, an increase of 1g/dL of Hb was associated with a 53%, 75%, and 86% decrease in the risk of the composite renal outcome for Q3 (HR=0.47, 95% CI 0.37-0.59, p<0.05), Q4 (HR=0.25, 95% CI 0.17-0.34, p<0.05), and Q5 (HR=0.14, 95% CI 0.10-0.20, p<0.05), compare with the Hb group Q1. This pattern of reduced risk with increasing Hb was depicted in the presented tables and figures (Table 4, Figure 4). Discussion In this comprehensive retrospective cohort study, we ventured into previously uncharted territory by examining the interplay between both baseline and time-varying Hb levels and the progression of CKD. Our findings showed that, compared with a reference category with Hb levels <10g/dL (Q1), both Hb levels of <13 and ≥12g/dL (Q4) and ≥13 g/dL (Q5) were associated with a decreased risk of disease progression in patients with moderate disease stages. The time-varying Cox analysis and restricted cubic spline plots further supported these findings, revealing a pivotal association: higher Hb levels, specifically those exceeding 12 g/dL, confer a protective effect against CKD progression in individuals with moderate disease stages. Conversely, Hb levels falling below the 12 g/dL threshold herald an escalated risk for adverse renal outcomes (Table 3, Figure 4). It's worth highlighting the strength of our analytical approach. By employing the Cox time-varying model, we adeptly navigated the challenges posed by time-dependent confounders. This not only enhanced the robustness of our results but also reaffirmed the associations we observed. Such insights underline the potential therapeutic implications of maintaining optimal Hb levels in CKD management, offering a promising avenue for future interventions and research. In essence, while numerous past studies have indicated a clear link between anemia and the progression of CKD, they predominantly focused on baseline Hb levels, potentially missing the dynamic evolution of anemia over time 9-12 . Seminal works like the RENNAL Study emphasized the exponential risk increase associated with even modest Hb reductions 8,17 . Similarly, studies targeting specific populations, such as those with type 2 diabetes mellitus or IgA nephropathy, have demonstrated the negative ramifications of low baseline Hb levels on renal outcomes 11,18 . Another interesting facet of our study was the exploration of factors beyond Hb. For instance, consistent with findings by Anand et al., we observed that elevated uric acid levels bore significant associations with kidney failure risk in earlier CKD stages. However, when it came to serum phosphorus levels, our conclusions deviated from established norms. While many studies, including the well-cited AASK, have flagged higher serum phosphorus levels as potential CKD aggravators, our data did not corroborate this. The discrepancy might emanate from inherent differences in study populations or methodological approaches. 19,20 Overall, our work underscores the significance of considering both static and dynamic Hb levels in CKD prognosis and highlights the intricate web of factors that influence renal outcomes. As science continues to progress, it's imperative to adopt holistic research methodologies that encompass both baseline measurements and their fluctuations over time, ensuring a more nuanced understanding of complex diseases like CKD. The relationship between anemia and CKD progression remains intricate and multifaceted. Several theories suggest that anemia exacerbates renal tissue hypoxia, activates harmful pathways like the renin-angiotensin aldosterone system, and amplifies cardiovascular risks – all of which could synergistically deteriorate kidney function 21 . Past studies have explored whether managing anemia could halt CKD progression. While traditional treatments like ESAs have produced mixed results, newer medications like HIF-PHIs offer promising avenues. we found that the use of ESAs has significant correlation with renal composite outcome in univariate Cox regression model (HR=2.179, 95%CI 1.808 to 2.625, P<0.001), but it was eliminated in multivariate Cox regression model (HR=0.913, 95%CI 0.714 to 1.168, P=0.471). Our research revealed a complex interplay between ESAs and CKD progression, suggesting that numerous factors from proteinuria to hypertension history could overshadow the direct impact of ESAs 22-28 . Despite its methodological rigor, our study, like all research endeavors, has limitations. Our observational approach, although powerful, can't ascertain causality. Some challenges, such as the potential for hidden bias or the static nature of our baseline measurements, are inherent in observational designs. Further, our focus on an Asian cohort limits the generalizability of our findings to other racial or ethnic groups. Conclusions Our research underscores the pivotal role of Hb levels in relation to the progression of CKD. A salient finding is the negative association between Hb levels, especially when exceeding 12g/dL, and the risk of CKD progression to ESRD. Furthermore, the introduction and utilization of time-varying variables in our analytical approach provide a more comprehensive understanding of the dynamic nature of CKD progression over time. This methodological advancement emphasizes the significance of accounting for fluctuations in key parameters, ensuring a more nuanced and accurate depiction of disease trajectories and outcomes. Declarations No conflicts of interest. All the authors declare that they have no conflict of interest. No financial contributions were made for this article. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1204-1222 (2020). https://doi.org:10.1016/s0140-6736(20)30925-9 Grams, M. E., Chow, E. K., Segev, D. L. & Coresh, J. 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Chronic hypoxia and tubulointerstitial injury: a final common pathway to end-stage renal failure. J Am Soc Nephrol 17 , 17-25 (2006). https://doi.org:10.1681/asn.2005070757 Rossert, J., Fouqueray, B. & Boffa, J. J. Anemia management and the delay of chronic renal failure progression. J Am Soc Nephrol 14 , S173-177 (2003). https://doi.org:10.1097/01.asn.0000070079.54912.b6 Alicic, R. Z., Rooney, M. T. & Tuttle, K. R. Diabetic Kidney Disease: Challenges, Progress, and Possibilities. Clin J Am Soc Nephrol 12 , 2032-2045 (2017). https://doi.org:10.2215/cjn.11491116 Anderson, A. H. et al. Time-updated systolic blood pressure and the progression of chronic kidney disease: a cohort study. Ann Intern Med 162 , 258-265 (2015). https://doi.org:10.7326/m14-0488 Iseki, K., Ikemiya, Y., Iseki, C. & Takishita, S. Proteinuria and the risk of developing end-stage renal disease. Kidney Int 63 , 1468-1474 (2003). https://doi.org:10.1046/j.1523-1755.2003.00868.x Lin, T. Y., Liu, J. S. & Hung, S. C. Obesity and risk of end-stage renal disease in patients with chronic kidney disease: a cohort study. Am J Clin Nutr 108 , 1145-1153 (2018). https://doi.org:10.1093/ajcn/nqy200 Basta, J. et al. Pharmacologic inhibition of RGD-binding integrins ameliorates fibrosis and improves function following kidney injury. Physiol Rep 8 , e14329 (2020). https://doi.org:10.14814/phy2.14329 Coresh, J. et al. Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies. Lancet Diabetes Endocrinol 7 , 115-127 (2019). https://doi.org:10.1016/s2213-8587(18)30313-9 Tables Table 1 . Baseline Characteristics of Patients According to Baseline Hemoglobin Levels Categorized Into 5 Groups Characteristics Quartiles by Hemoglobin, g/dL P value 13 (n=746,31.93%) Age, years 64.70±11.13 65.42±10.73 65.93±10.67 64.48±10.39 64.47±10.30 0.186 Gender (male, %) 0.44 0.53 0.66 0.79 0.92 0.951 Comorbidities, % DM 0.52 0.54 0.52 0.49 0.49 0.999 Hypertension 0.81 0.78 0.82 0.80 0.82 0.999 Gout Hyperlipidemia 0.10 0.21 0.14 0.30 0.19 0.28 0.21 0.32 0.22 0.34 0.999 0.999 BMI, kg/m² 23.43±6.57 23.85±4.55 24.03±4.43 24.23±3.88 24.42±3.97 0.006 SBP, mmHg 141.83±35.12 141.77±26.24 137.95±23.40 136.07±22.07 139.06±42.30 0.060 DBP, mmHg 75.98±13.32 78.49±14.66 78.4±13.55 78.64±14.11 82.38±27.60 <0.001 Laboratory parameter Hb, g/dL 8.44±1.27 10.47±0.28 11.46±0.28 12.43±0.28 14.45±1.15 <0.001 Alb, g/L 34.72±6.36 36.77±6.14 37.94±5.55 39.49±5.42 40.80±5.35 <0.001 BUN, mg/dL 13.27±5.37 11.74±4.28 10.87±3.93 10.28±3.63 9.17±3.18 <0.001 Scr, umol/L 207.64±75.36 185.85±65.93 173.06±60.38 162.72±52.18 147.45±41.11 <0.001 D-Dimer, ug/mL.FEU 1.61±2.49 1.41±4.24 1.32±2.77 1.14±3.26 0.84±1.69 <0.001 TC, mmol/L 4.18±1.31 4.51±1.57 4.41±1.42 4.34±1.29 4.50±1.40 0.001 LDL-C, mg/dL 2.40±0.97 2.61±1.17 2.62±1.06 2.53±0.92 2.69±1.10 0.298 HDL-C, mg/dL 1.08±0.36 1.08±0.32 1.04±0.30 1.04±0.28 1.04±0.29 0.063 Triglyceride, mg/dL 1.64±1.21 1.98±1.77 1.79±1.37 2.12±2.43 2.05±1.55 0.001 UA, umol/L 429.16±143.83 442.52±128.59 464.84±131.60 475.40±129.21 491.74±133.78 <0.001 eGFR, ml/min per 1.73 m2 32.99±13.17 36.73±12.94 39.19±12.46 41.65±11.66 45.53±10.34 <0.001 Serum iron, umol/L 10.85±6.28 10.64±5.14 11.76±5.14 13.92±7.45 14.28±6.34 <0.001 TIBC, mg/dL 45.52±13.20 47.46±11.94 47.99±11.26 51.13±14.21 49.90±11.62 <0.001 TS, % 24.80±14.29 23.22±11.29 25.21±11.56 27.48±11.21 29.33±12.78 <0.001 Ferritin, ng/mL 253.00±487.47 198.63±279.61 174.15±157.07 220.01±244.98 223.78±206.37 0.114 CRP, mg/L 2.62±4.74 1.85±3.43 2.72±5.00 2.79±5.27 1.96±4.42 0.011 24hproteinuria, mg/24h 2426.57±2630.47 2103.41±2190.83 1920.20±2201.27 1969.42±2320.19 1626.23±1978.06 0.008 UACR, mg/g 1418.48±2108.40 1394.49±2268.48 1001.18±1581.90 764.67±1425.14 750.67±1216.19 <0.001 WBC, ×10³/uL 6.68±3.62 6.95±2.40 7.15±2.59 7.23±2.94 7.54±2.73 <0.001 Potassium, mg/dL 4.54±0.78 4.40±0.70 4.42±0.58 4.43±0.53 4.26±0.51 <0.001 Calcium, mg/dL 2.23±0.23 2.32±0.21 2.33±0.21 2.37±0.19 2.40±0.20 <0.001 Phosphorus, mg/dL 1.23±0.31 1.18±0.24 1.14±0.22 1.14±0.27 1.09±0.24 <0.001 PTH, pg/mL 276.90±658.77 290.05±711.49 286.66±594.36 258.97±839.07 256.13±569.56 0.969 HbA1c, % 6.83±2.05 7.06±1.99 6.97±1.67 7.13±2.07 6.98±1.90 0.432 VitamineB12, pg/ml 423.93±445.00 343.75±251.84 312.72±236.67 347.42±298.06 335.87±276.17 0.007 Folate, ng/ml 21.13±13.86 20.28±12.73 16.98±10.26 17.98±10.63 16.18±8.69 <0.001 NAG, U/L 17.29±18.18 16.91±13.74 13.44±11.95 15.15±11.36 16.41±16.46 0.040 MCV, fL 90.62±8.99 91.34±6.34 92.08±6.20 92.20±5.52 93.26±5.15 <0.001 HCT, % 26.11±3.79 31.98±1.35 34.84±1.30 37.48±1.25 43.22±3.50 <0.001 MCH, pg 29.29±3.29 29.95±2.35 30.31±2.21 30.59±2.00 31.19±1.87 <0.001 Medication, % ACEI/ARB 0.338 0.373 0.488 0.452 0.546 0.998 βBlocker 0.372 0.458 0.380 0.430 0.412 0.083 Diurtics 0.513 0.489 0.462 0.382 0.378 0.999 CCB 0.628 0.589 0.615 0.572 0.567 0.179 ESAs 0.333 0.056 0.008 0.003 0.000 0.885 Iron 0.277 0.056 0.034 0.016 0.004 0.945 Lipid-modifying drugs 0.500 0.620 0.600 0.640 0.670 0.999 Values for categorical variables are given as number (percentage); values for continuous variables are given as mean ± SD or median (interquartile range). eGFR was calculated using the CKD-Epidemiology Collaboration equation. DM indicates diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; Hb, hemoglobin; Alb, albumin; BUN, blood urea nitrogen; Scr, serum creatinine; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; UA, serum uric acid; eGFR, estimated glomerular filtration rate; TIBC, total iron-binding capacity; TS, transferrin saturation; CRP, C-creative protein; UACR, urinary albumin to creatinine ratio; WBC, white blood cell; PTH, parathyroid hormone; HbA1c, glycosylated hemoglobin; NAG, N-Acetyl-β-D-Glucosaminidase S; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; HCT, hematocrit; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; CCB, calcium channel blocker; ESAs, erythropoiesis-stimulating agents. Table 2 : Univariate and Multivariate regression analysis to estimate baseline characteristics associated with the renal composite outcome. Variable Univariate Multivariate HR (95%CI) P value HR (95%CI) P value Age, years 64.91±10.61 0.986(0.980-0.992) <0.001 0.992(0.986-0.999) 0.034 Gender (male, %) 69.60% 1.254(1.088-1.445) 0.002 1.008(0.852-1.193) 0.921 Comorbidities, % DM 50.68% 1.457(1.270-1.672) <0.001 1.237(1.045-1.465) 0.013 Hypertension 81.03% 1.416(1.165-1.722) <0.001 1.309(1.060-1.616) 0.012 Gout 17.78% 0.554(0.443-0.693) <0.001 0.903(0.712-1.146) 0.404 Body mass index, kg/m² 24.03±4.79 0.979(0.963-0.996) 0.014 0.993(0.976-1.010) 0.419 SBP, mmHg 139.42±33.31 1.002(1.001-1.003) <0.001 … … DBP, mmHg 79.15±19.42 1.003(1.000-1.005) 0.019 … … Laboratory parameter Hb, g/dL 11.76±2.42 0.979(0.976-0.982) <0.001 0.507(0.388-0.663) <0.001 Alb, g/L 38.23±6.22 0.914(0.905-0.923) <0.001 0.959(0.944-0.975) <0.001 BUN, mg/dL 10.89±4.39 1.054(1.041-1.066) <0.001 … … Scr, umol/L 172.94±62.88 1.349(1.298-1.402) <0.001 … … Tc, mmol/L 4.40±1.40 1.094(1.044-1.146) <0.001 0.989(0.930-1.052) 0.732 Serum iron, umol/L 11.91±6.23 0.980(0.967-0.994) 0.004 1.028(1.000-1.056) 0.042 TIBC, mg/dL 47.63±12.64 0.993(0.986-0.999) 0.033 … … TSAT, % 25.71±12.95 0.993(0.987-1.000) 0.041 … … 24hproteinuria, mg/L 2030.74±2303.27 1.000(1.000-1.000) <0.001 1.584(1.455-1.725) <0.001 UPCR, mg/g 1057.78±1762.82 1.000(1.000-1.000) <0.001 … … WBC, ×10³/uL 7.16±2.94 0.986(0.964-1.010) 0.251 1.001(0.9751.027) 0.935 Potassium, mg/dL 4.38±0.63 1.059(0.952-1.178) 0.293 1.019(0.902-1.150) 0.760 Calcium, mg/dL 2.34±0.22 0.108(0.080-0.146) <0.001 0.439(0.293-0.656) <0.001 Phosphorus, mg/dL 1.15±0.27 1.737(1.404-2.148) <0.001 1.035(0.767-1.397) 0.817 PTH, pg/mL 272.81±663.55 1.000(1.000-1.000) 0.004 1.000(1.000-1.000) 0.001 NAG, U/L 16.04±15.25 1.008(1.002-1.014) 0.006 … … MCV, fL 92.06±6.63 0.989(0.979-0.999) 0.031 … … HCT, % 35.62±6.99 0.928(0.919-0.937) <0.001 … … MCH, pg 30.36±2.49 0.960(0.935-0.986) 0.002 … … Medication, % ACEI/ARB 46.98% 0.878(0.765-1.007) 0.064 0.872(0.747-1.018) 0.084 β-Blocker 40.66% 1.283(1.118-1.473) <0.001 … … Diurtics 43.80% 1.660(1.448-1.903) <0.001 1.117(0.958-1.303) 0.156 CCB 59.25% 1.863(1.604-2.165) <0.001 … … ESAs 8.53% 2.179(1.808-2.625) <0.001 0.913(0.714-1.168) 0.471 Iron 8.02% 1.881(1.537-2.303) <0.001 1.189(0.937-1.507) 0.152 Lipid-modifying drugs 60.60% 1.043(0.908-1.197) 0.548 0.912(0.778-1.070) 0.261 Values for categorical variables are given as number (percentage); values for continuous variables are given as mean ± SD or median (interquartile range). eGFR was calculated using the CKD-Epidemiology Collaboration equation. DM indicates diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; Hb, hemoglobin; Alb, albumin; BUN, blood urea nitrogen; Scr, serum creatinine; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; UA, serum uric acid; eGFR, estimated glomerular filtration rate; TIBC, total iron-binding capacity; TS, transferrin saturation; CRP, C-creative protein; UACR, urinary albumin to creatinine ratio; WBC, white blood cell; PTH, parathyroid hormone; HbA1c, glycosylated hemoglobin; NAG, N-Acetyl-β-D-Glucosaminidase S; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; HCT, hematocrit; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; CCB, calcium channel blocker; ESAs, erythropoiesis-stimulating agents. Table 3 : Univariate and Multivariate regression analysis to estimate Time-Varying characteristics associated with the renal composite outcome. Variable Univariate Multivariate HR (95%CI) P value HR (95%CI) P value Age, years 0.986(0.980-0.992) <0.001 0.984(0.978-0.991) <0.001 Gender (male, %) 1.238(1.069-1.445) <0.001 0.843(0.720-0.987) 0.034 DM 1.407(1.219-1.624) <0.001 1.295(1.115-1.504) 0.001 Hypertension 1.376(1.124-1.686) <0.001 1.472(1.190-1.821) <0.001 Body mass index, kg/m² 0.982(0.965-0.999) 0.042 0.999(0.982-1.016) 0.912 Time-varying Hb, g/dL 0.969(0.966-0.972) <0.001 0.973(0.970-0.976) <0.001 Alb, g/L 0.914(0.905-0.924) <0.001 0.956(0.942-0.970) <0.001 Potassium, mg/dL 1.075(0.964-1.199) 0.192 1.064(0.957-1.184) 0.249 Calcium, mg/dL 0.118(0.086-0.161) <0.001 0.449(0.299-0.674) <0.001 Phosphorus, mg/dL 1.723(1.385-2.143) <0.001 0.994(0.754-1.309) 0.965 ACEI/ARB 0.867(0.751-1.001) 0.052 0.866(0.742-1.010) 0.067 Diurtics 1.683(1.460-1.940) <0.001 1.134(0.973-1.323) 0.108 Lipid-modifying drugs 1.029(0.891-1.189) 0.697 1.072(0.915-1.256) 0.389 DM indicates diabetes mellitus; Alb, albumin; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker Table 4 . Association of Hb Levels between Composite Renal outcome in the Baseline and Time-Varying Cox Analysis Hb Level Events, n (%) Model 1 Model 2 Model 3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value Baseline Hb, g/dL <10 517(22.1) 1.00(Reference) 1.00(Reference) 1.00(Reference) 10-11 320(13.7) 0.79(0.65-0.97) 0.027 0.79(0.64-0.96) 0.022 0.94(0.75-1.18) 0.628 11-12 379(16.2) 0.59(0.48-0.71) <0.001 0.57(0.47-0.70) <0.001 0.81(0.64-1.03) 0.095 12-13 374(16.0) 0.37(0.29-0.47) <0.001 0.34(0.27-0.44) <0.001 0.57(0.43-0.76) <0.001 ＞13 746(32.0) 0.29(0.23-0.35) <0.001 0.25(0.20-0.32) <0.001 0.50(0.38-0.66) <0.001 Time-varying Hb, g/dL <10 … 1.00(Reference) 1.00(Reference) 1.00(Reference) 10-11 … 0.51(0.41-0.62) <0.001 0.49(0.40-0.61) <0.001 0.54(0.44-0.67) <0.001 11-12 … 0.38(0.30-0.47) <0.001 0.38(0.30-0.47) <0.001 0.47(0.37-0.59) <0.001 12-13 … 0.18(0.13-0.26) <0.001 0.18(0.13-0.25) <0.001 0.25(0.17-0.34) <0.001 ＞13 … 0.10(0.07-0.13) <0.001 0.09(0.06-0.12) <0.001 0.14(0.10-0.20) <0.001 Model 1: unadjusted; Model 2: adjusted for age, gender, comorbidities (diabetes mellitus, hypertension, hyperlipidemia, gout); Model 3: adjusted for model 2 plus body mass index, systolic blood pressure, diastolic blood pressure, laboratory findings (white blood cell count, albumin, blood urea nitrogen; serum creatinine, total cholesterol, serum uric acid, parathyroid hormone, glycosylated hemoglobin, lymphocyte, calcium, phosphorus, potassium, chlorine, Potassium, iron, transferrin saturation, proteinuria, blood urine), and meditation (angiotensin-converting enzyme inhibitor, angiotensin II receptor blocker, calcium channel blocker, erythropoiesis-stimulating agents, lipid-modifying drugs, diuretics, iron); Additional Declarations No competing interests reported. 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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-3453502","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":240876663,"identity":"0b90af4c-2ed6-469e-8278-23bb6495bff8","order_by":0,"name":"Shuqi Ye","email":"","orcid":"","institution":"Jianghan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuqi","middleName":"","lastName":"Ye","suffix":""},{"id":240876664,"identity":"a284350c-299d-4add-8cc8-62a2163c412b","order_by":1,"name":"Fan Zhu","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zhu","suffix":""},{"id":240876665,"identity":"fdd8f8d2-6cc7-46ea-9fdb-f9bc7b94a695","order_by":2,"name":"Wenyuan Gan","email":"","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenyuan","middleName":"","lastName":"Gan","suffix":""},{"id":240876666,"identity":"e1f9973a-e617-4b1f-9f85-85505201d974","order_by":3,"name":"Wenli Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACfvbmww8+GNTI8TMzH35AlBbJnmNphjMKjhlLtrOlGRClxeCGj4E0zwfmxA3neRQkiLNlBluCAY8BG+PmwzwMBgw1NtEEtfBLNx94IGEgw2x2mPfAA4ZjabkNBG2ZcyzBwMCAjc3sMF+CAWPDYcJaDG7kGEgkGDDzGDfzGEgQr+WAAbMEUBeRWsCB3GBwzEDiMDCQE4jxCygqH//5U1Pf338YGKc1NoS1oIIE0pSPglEwCkbBKMAFAFeGQAm+kGbhAAAAAElFTkSuQmCC","orcid":"","institution":"The Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2023-10-16 15:44:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3453502/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3453502/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44942375,"identity":"aea17d39-f7bc-4d17-aafa-8896803559cb","added_by":"auto","created_at":"2023-10-19 18:08:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrollment of patients into this study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients flow chart. CKD, chronic kidney disease.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3453502/v1/3087aba82121375134ff2257.jpg"},{"id":44943334,"identity":"96a16116-8ce3-4b73-afc7-2459936aac41","added_by":"auto","created_at":"2023-10-19 18:16:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197559,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData visualization of hemoglobin of all participants from the composite outcome and non-renal composite outcome groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data visualization indicated that the distribution level of Hb in the renal composite outcome group was lower. In contrast, the Hb level in the non-renal composite outcome group was relatively higher.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3453502/v1/031cd6e6ae1a8e84ea8bedb2.jpg"},{"id":44942377,"identity":"1007b21c-424f-458b-91da-2aae7a727354","added_by":"auto","created_at":"2023-10-19 18:08:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier analysis of incident renal composite endpoint-free survival based on Hb groups (log-rank, p\u0026lt;0.001).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan Meier survival curves were employed to visualize the cumulative incidence of study outcomes in relation to Hb levels. These curves unveiled distinct differences in the risks of composite renal outcomes across Hb levels. Notably, the risks were markedly elevated in the Q1 and Q2 groups in contrast to the Q3, Q4, and Q5 cohorts.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3453502/v1/778d11fe97cf541fb7512247.jpg"},{"id":44942376,"identity":"95d9b8d6-b973-445c-89e8-e70ece2e8bdd","added_by":"auto","created_at":"2023-10-19 18:08:21","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":385977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe non-linear relationship between hemoglobin and the risk of CKD progression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003ewe used a Cox proportional hazards regression model with cubic spline functions to evaluate the relationship between baseline Hb level and renal composite outcome. The result showed that the relationship between Hb and the renal composite outcome was non-linear, and revealed higher Hb levels, specifically those exceeding 12 g/dL, confer a protective effect against CKD progression. Hb levels failing below the 12g/dL threshold herald an escalated risk for adverse renal composite outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. \u003c/strong\u003ewe also used a Cox proportional hazards regression model with cubic spline functions and to evaluate the relationship between Time-varying Hb level and renal outcome. The results further validate and support the above conclusion.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3453502/v1/445917908b29c4c2bb22de0c.jpg"},{"id":52765261,"identity":"5074174e-1719-4813-bd73-9428afd29719","added_by":"auto","created_at":"2024-03-15 13:20:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":613115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3453502/v1/74029bc1-0556-4cd0-aaac-59561f8d7533.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal Dynamic of Hemoglobin Levels and Its Impact on Progression in Chronic Kidney Disease Stages 3- 4 Patients: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD)\u0026nbsp;is\u0026nbsp;a major global public health issue, with nearly one in ten people affected worldwide. According to the Global Burden of Disease (GBD) Study 2019, there are an estimated 697 million CKD cases globally\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;Over an individual\u0026apos;s lifetime,\u0026nbsp;the overall risk for developing CKD stage 3a or higher is 59.1%. For stages 3b, 4, and 5, the risks stand at 33.6%, 11.5%, and 3.6%, respectively\u003csup\u003e2\u003c/sup\u003e.A severe potential outcome of CKD is end-stage renal disease (ESRD) which is associated with poorer clinical outcomes, including diminished quality of life, increased medical expenses, and a heightened economic strain\u003csup\u003e2-4\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAnemia as one of the most prevalent complications associated with CKD. A multi-center study reported that 41% of the 209311 participants exhibited low hemoglobin levels with criteria set at \u0026lt;13 g/dL for men and \u0026lt;12 g/dL for women\u003csup\u003e5\u003c/sup\u003e.\u0026nbsp;The onset of anemia in CKD is attributed to a multifaceted mechanism, encompassing relative erythropoietin (EPO) deficiency, uremic-induced inhibitors of erythropoiesis, diminished erythrocyte lifespan, and disrupted iron homeostasis\u0026nbsp;\u003csup\u003e6\u003c/sup\u003e.\u0026nbsp;Studies have consistently indicated an association between anemia and heightened risks of mortality, hospitalization, major adverse cardiac events, and CKD progression. Notably, the severity of anemia proportionally raises these risks\u003csup\u003e7\u003c/sup\u003e, promoting the development of targeted treatment strategies for anemia.\u003c/p\u003e\n\u003cp\u003eRecent findings suggest that anemia not only emerges as a byproduct of CKD but might also act as a precursor to CKD progression.\u0026nbsp;The RENAAL study disclosed that even mild anemia in type 2 diabetic patients with nephropathy correlated with increased renal risks\u003csup\u003e8\u003c/sup\u003e. Prospective data from the CHARLS study underscored anemia as an autonomous risk factor for renal function decline among the middle-aged and elderly demographic\u003csup\u003e9\u003c/sup\u003e, parallel conclusions have been drawn from studies focusing on specific populations, such as adolescents\u003csup\u003e10\u003c/sup\u003e, those with IgA nephropathy\u003csup\u003e11\u003c/sup\u003e, and individuals with cardiovascular conditions\u003csup\u003e12\u003c/sup\u003e. However, a recurrent limitation in these studies is their exclusive reliance on baseline hemoglobin (Hb) levels, leaving the implications of longitudinal Hb fluctuations largely uncharted. This gap underscores the ambiguity surrounding the prognostic significance of Hb levels concerning CKD progression. In this research, we meticulously assessed both the baseline and time-varying Hb values in our endeavor to elucidate the intricate relationship between Hb levels and CKD progression risks within an extensive retrospective CKD patient cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur current research was conducted in the central region of mainland China involving\u0026nbsp;a multi-center study.\u0026nbsp;The population consists of all patients who were diagnosed with CKD during their hospital stay or outpatient visits between January 1, 2017, and May 31, 2023. The total number of people included in the study is 39,698, of which 10,925 people have recorded kidney function data at least twice. According to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, patients were classified into CKD stages 3a, 3b, and 4\u003csup\u003e13\u003c/sup\u003e. Base on the estimated glomerular filtration rate (eGFR), there were 3430 patients with CKD stage 3-4, aged between 18-80 years. After excluding patients with missing values in the Hb levels, the final total number of participants in the study is 2,336 (figure 1). Patients were monitored from the date of diagnosis of CKD until a 40% reduction in eGFR from the baseline, the development of end-stage renal disease (ESRD), death, or loss to follow-up. \u0026nbsp;The inclusion criteria were as follows: (1) patients diagnosed with CKD stages 3-4. (2) patients with a minimum of three outpatient visits or hospitalizations. (3) patients with at least three kidney function assessments. The exclusion criteria were as follows: (1) patients who have undergone hemodialysis (HD), peritoneal dialysis (PD), or kidney transplantation. (2) patients below 18 years or above 80 years. (3) patients with concurrent conditions such as pregnancy, malignant neoplasms, or acute active bleeding disorder.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was registered in the Chinese Clinical Trial Registry (CHICTR) with the registration number, ChiCTR2200061199. The study was performed in accordance with the ethical principles of the Declaration of Helsinki, and all participants provided written informed consent. The study protocol was approved by the ethics committee of The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology (institutional review board approval number WHZXKYL2022-112).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariables and Covariates Collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sourced our data from the hospital\u0026apos;s electronic medical system. This includes demographics such as age, gender, comorbidities, medical history, blood pressure, and body mass index (BMI). For laboratory examinations, we collected data on blood counts, Hb, serum creatinine (Scr), blood urea nitrogen (BUN), serum uric acid (UA), albumin (Alb), D-dimmer, calcium, potassium, and phosphorus levels, N-Acetyl-\u0026beta;-D-Glucosaminidase (NAG), vitamin B12, folate, eGFR, C-reactive protein (CRP), iron profiles, lipid profiles, and urinary albumin to creatinine ratio (UACR), 24h proteinuria, parathyroid hormone (PTH). In terms of medications, we recorded if patients were on angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), \u0026beta;-Blockers, diuretics, erythropoiesis-stimulating agents (ESAs), and iron supplements. To guarantee the precision and consistency of our data, all entries were validated by two independent authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssessment Criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnemia in this study was characterized by hemoglobin levels falling below 13g/dL for men and 12g/dL for women, in line with the criteria set by the KDIGO\u003csup\u003e14\u003c/sup\u003e. To gauge the eGFR, we utilized the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation\u003csup\u003e15\u003c/sup\u003e. Hypertension was identified either by an average systolic blood pressure surpassing 140mmHg or an average diastolic reading exceeding 90mmHg. Additionally, any self-reported usage of antihypertensive medications or a personal history of hypertension also fell under this definition. The criteria for diabetes included a fasting plasma glucose level of 7.0mmol/L or higher, the use of hypoglycaemic agents, or any self-declared history of the condition. We derived the body mass index (BMI) by dividing an individual\u0026apos;s weight (in kg) by the square of their height (in meters).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExposure and Outcomes of interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary exposures were the baseline and time-varying Hb levels. These levels were categorized and treated as categorical variables, split into five distinct groups:\u0026nbsp;＜10g/dL, 10-11g/dL, 11-12g/dL, 12-13g/dL, and\u0026nbsp;＞13g/dL. Furthermore, to further analyze time- varying Hb levels, Hb levels were also treated as continuous variables. A non-linear effect was modeled using a restricted cubic spline function to assess it.\u003c/p\u003e\n\u003cp\u003eThe composite renal outcomes manifested as an annual decline in eGFR (mL/min/1.73m^2/year). This decline was ascertained by plotting the eGFR values and determining the slope using a least-squares method. Another critical outcome was the emergence of ESRD throughout the follow-up duration. ESRD was demarcated by the commencement of renal replacement procedures, which includes either dialysis or undergoing a renal transplant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the comparison of the baseline characteristics by Hb, continuous variates were analyzed by one-way analysis of variance, while categorical variates were analyzed by X\u003csup\u003e2\u003c/sup\u003e test. Cumulative incidences of outcome events were visualized using Kaplan-Meier curves. To evaluate independent associations between Hb and renal composite outcomes, two Cox proportional hazard regression models were separately adopted: (1) fixed Cox regression model with baseline values were examined to ascertain the association of long-term exposure with CKD progression. (2) time-varying models were assessed to account for \u0026nbsp;time-dependent confounding and ascertain their short-term associations\u003csup\u003e16\u003c/sup\u003e. In time-varying analyses, all laboratory data were incorporated as time-dependent variables. The results of Cox proportional hazard regression models were presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Restricted cubic spline plots were used to assess the relation between Hb as a continuous variable with the study outcomes. To validate our findings, we performed sensitivity analyses. First, we excluded the subjects with eGFR \u0026ge;60 ml/min per 1.73 m2(CKD stage 1 and stage 2) due to the atypical clinical manifestations of CKD in these stages, which pose challenges for accurate diagnosis. And the subjects with eGFR \u0026ge;90\u0026nbsp;ml/min per 1.73 m\u003csup\u003e2\u003c/sup\u003e are considered close to normal kidney function, which may not represent the CKD population well. Second, we excluded the subjects with eGFR \u0026le;15 ml/min per 1.73 m2 (stage 5), because the subjects with eGFR\u0026le;15 ml/min per 1.73 m\u003csup\u003e2\u003c/sup\u003e are relatively small in number, and may exaggerate the association between Hb level and study outcomes due to far-advanced CKD. Third, we repeated analyses using Hb categorized into quintiles, where the range of quintiles was ＜10(Q1), 10-11(Q2), 11-12(Q3), 12-13(Q4), ＞13(Q5) g/dL for baseline and time-varying Hb values. Two-sided p values \u0026lt;0.05 were considered statistically significant. Statistical analysis was performed using R version 3.6.1.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 provided the baseline demographic and clinical characteristics of the 2336 participants categorized by Hb quartiles:\u0026nbsp;＜10(Q1), 10-11(Q2), 11-12(Q3), 12-13(Q4),\u0026nbsp;＞13(Q5) g/dL. At baseline, 69.60% of the participants were male, with an average age of 64.91\u0026plusmn;10.61 years. The average Hb and eGFR were 11.76\u0026plusmn;2.42 g/dL and 39.84\u0026plusmn;12.86 ml/min/per 1.73 m2. Patients with lower Hb level were more likely to be thinner, had a lower diastolic blood pressure, and they were at a higher risk of thromboembolic events. In addition, these patients had a lower albumin, serum iron, serum uric acid levels and less inflamed than those with higher Hb level. Kidney function was more preserved and the amount of urinary protein excretion was lower in patients with higher Hb levels. No significant differences in age, comorbid conditions, systolic blood pressure, high-density lipoprotein cholesterol, parathyroid hormone, glycosylated hemoglobin, and medications like \u0026beta;-Blocker and CCB were observed across the hemoglobin quartile groups (P \u0026gt; 0.05, see Table 1 for details). Figure 2 contrasts Hb levels between participants who experienced renal composite endpoints and those who did not. Results suggested a lower distribution of Hb levels in the renal composite endpoint group, while the non-renal composite endpoint group exhibited relatively higher Hb levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe incidence rate of the composite renal outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan Meier survival curves were employed to visualize the cumulative incidence of study outcomes in relation to Hb levels. These curves unveiled distinct differences in the risks of composite renal outcomes across Hb levels.\u0026nbsp;Notably, the risks were markedly elevated in the Q1 and Q2 groups in contrast to the Q3, Q4, and Q5 cohorts (p\u0026lt;0.001 by Log-rank test, as depicted in Figure 3).\u0026nbsp;For the entire study population, the cumulative incidence rate was set at 0.29 per 100 person-years. Breaking this down further, the Hb groups Q1 through Q5 had cumulative incidence rates of 0.09, 0.03, 0.06, 0.07, and 0.04 per 100 person-years, respectively. Intriguingly, as Hb levels ascended, there was a noticeable decline in the incidence of renal composite endpoints. This downward trend in incidence with rising Hb levels was statistically significant (P for trend\u0026lt;0.001), as illustrated in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe results of univariate and multivariate analyses using Cox proportional-hazards regression model and Cox time-varying model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis revealed a few variables that did not have a significant association with the composite renal outcome. On the other hand, several factors exhibited a negative association with the composite renal outcome: hemoglobin, age, serum iron, serum albumin (HR=0.979, 0.986, 0.980, 0.994, 0.914, respectively, all p\u0026lt;0.05). Conversely, some variables demonstrated a positive correlation with the composite renal outcome: male gender, phosphorus, the use of iron and ESAs (HR=1.245, 1.737, 1.881, 2.179, respectively, all p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe multivariate analysis was conducted on the baseline data, validating that hemoglobin, age,\u0026nbsp;serum Albumin (HR=0.507, 0.992, 0.959, respectively, all p\u0026lt;0.05) were negatively linked to the renal composite outcome, history of diabetes mellitus and hypertension\u0026nbsp;(HR=1.237, 1.309, respectively, all p\u0026lt;0.05)\u0026nbsp;has the positive correlation with the outcome (See details for Table 2).\u003c/p\u003e\n\u003cp\u003eFurther exploration with a Cox Time-Varying model for univariate and multivariate analysis supported these findings. The multi-variates time-varying hemoglobin (HR=0.969, 95% CI 0.966-0.972, p\u0026lt;0.05) continued to exhibit a negative association with the study outcomes, as detailed in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe effects of Hb level on the composite renal outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe constructed three different analytical models using the Cox proportional hazards regression model and Cox time-varying model to explore the relationship between hemoglobin levels and composite renal outcomes. In the unadjusted model (model 1), participants in the baseline Hb groups Q3(HR=0.59, 95% CI 0.48-0.71, p\u0026lt;0.05), Q4(HR=0.37, 95% CI 0.29-0.47, p\u0026lt;0.05), and Q5(HR=0.29, 95% CI 0.23-0.35, p\u0026lt;0.05) demonstrated a significant reduction in the risk of encountering the composite renal outcome when compared to the Q1 group. Specifically, an increase of 1g/dL of Hb was associated with a 41%, 63%, and 71% decrease in the risk of the study outcome for Q3, Q4, and Q5, respectively. This association was not changed in the minimally adjusted model, after adjustment for demographic factors, body mass index, and comorbidities (model 2). However, in a fully adjusted Cox model in which laboratory parameters and medications were additionally included (model 3), the risk of the composite renal outcome was significantly reduced in the Hb group only Q4 (HR=0.57, 95% CI 0.43-0.76, p\u0026lt;0.05) and Q5(HR=0.50, 95% CI 0.38-0.66, p\u0026lt;0.05), with Q3 losing its previous statistical significance (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe time-varying Cox model analysis consistently solidified the observation that throughout all three modeling approaches, it was evident that individuals with Hb levels above 10g/dL (Q3, Q4, and Q5) generally had a decreased risk of the composite renal outcome. Specifically, an increase of 1g/dL of Hb was associated with a 53%, 75%, and 86% decrease in the risk of the composite renal outcome for Q3 (HR=0.47, 95% CI 0.37-0.59, p\u0026lt;0.05), Q4 (HR=0.25, 95% CI 0.17-0.34, p\u0026lt;0.05), and Q5 (HR=0.14, 95% CI 0.10-0.20, p\u0026lt;0.05), compare with the Hb group Q1. This pattern of reduced risk with increasing Hb was depicted in the presented tables and figures (Table 4, Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this comprehensive retrospective cohort study, we ventured into previously uncharted territory by examining the interplay between both baseline and time-varying Hb levels and the progression of CKD. Our findings showed that, compared with a reference category with Hb levels \u0026lt;10g/dL (Q1), both Hb levels of \u0026lt;13 and \u0026ge;12g/dL (Q4) and \u0026ge;13 g/dL (Q5) were associated with a decreased risk of disease progression in patients with moderate disease stages. The time-varying Cox analysis and restricted cubic spline plots further supported these findings,\u0026nbsp;revealing a pivotal association: higher Hb levels, specifically those exceeding 12 g/dL, confer a protective effect against CKD progression in individuals with moderate disease stages. Conversely, Hb levels falling below the 12 g/dL threshold herald an escalated risk for adverse renal outcomes (Table 3, Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt\u0026apos;s worth highlighting the strength of our analytical approach. By employing the Cox time-varying model, we adeptly navigated the challenges posed by time-dependent confounders. This not only enhanced the robustness of our results but also reaffirmed the associations we observed. Such insights underline the potential therapeutic implications of maintaining optimal Hb levels in CKD management, offering a promising avenue for future interventions and research.\u003c/p\u003e\n\u003cp\u003eIn essence, while numerous past studies have indicated a clear link between anemia and the progression of CKD, they predominantly focused on baseline Hb levels, potentially missing the dynamic evolution of anemia over time\u003csup\u003e9-12\u003c/sup\u003e.\u0026nbsp;Seminal works like the RENNAL Study emphasized the exponential risk increase associated with even modest Hb reductions\u003csup\u003e8,17\u003c/sup\u003e. Similarly, studies targeting specific populations, such as those with type 2 diabetes mellitus or IgA nephropathy, have demonstrated the negative ramifications of low baseline Hb levels on renal outcomes\u003csup\u003e11,18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAnother interesting facet of our study was the exploration of factors beyond Hb. For instance, consistent with findings by Anand et al., we observed that elevated uric acid levels bore significant associations with kidney failure risk in earlier CKD stages. However, when it came to serum phosphorus levels, our conclusions deviated from established norms. While many studies, including the well-cited AASK, have flagged higher serum phosphorus levels as potential CKD aggravators, our data did not corroborate this. The discrepancy might emanate from inherent differences in study populations or methodological approaches.\u003csup\u003e19,20\u003c/sup\u003e Overall, our work underscores the significance of considering both static and dynamic Hb levels in CKD prognosis and highlights the intricate web of factors that influence renal outcomes. As science continues to progress, it\u0026apos;s imperative to adopt holistic research methodologies that encompass both baseline measurements and their fluctuations over time, ensuring a more nuanced understanding of complex diseases like CKD.\u003c/p\u003e\n\u003cp\u003eThe relationship between anemia and CKD progression remains intricate and multifaceted. Several theories suggest that anemia exacerbates renal tissue hypoxia, activates harmful pathways like the renin-angiotensin aldosterone system, and amplifies cardiovascular risks \u0026ndash; all of which could synergistically deteriorate kidney function\u003csup\u003e21\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePast studies have explored whether managing anemia could halt CKD progression. While traditional treatments like ESAs have produced mixed results, newer medications like HIF-PHIs offer promising avenues. we found that the use of ESAs has significant correlation with renal composite outcome in univariate Cox regression model (HR=2.179, 95%CI 1.808 to 2.625, P\u0026lt;0.001), but it was eliminated in multivariate Cox regression model (HR=0.913, 95%CI 0.714 to 1.168, P=0.471). Our research revealed a complex interplay between ESAs and CKD progression, suggesting that numerous factors from proteinuria to hypertension history could overshadow the direct impact of ESAs\u003csup\u003e22-28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite its methodological rigor, our study, like all research endeavors, has limitations. Our observational approach, although powerful, can\u0026apos;t ascertain causality. Some challenges, such as the potential for hidden bias or the static nature of our baseline measurements, are inherent in observational designs. Further, our focus on an Asian cohort limits the generalizability of our findings to other racial or ethnic groups.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur research underscores the pivotal role of Hb levels in relation to the progression of CKD. A salient finding is the negative association between Hb levels, especially when exceeding 12g/dL, and the risk of CKD progression to ESRD. Furthermore, the introduction and utilization of time-varying variables in our analytical approach provide a more comprehensive understanding of the dynamic nature of CKD progression over time. This methodological advancement emphasizes the significance of accounting for fluctuations in key parameters, ensuring a more nuanced and accurate depiction of disease trajectories and outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eNo conflicts of interest.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no conflict of interest. No financial contributions were made for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e396\u003c/strong\u003e, 1204-1222 (2020). https://doi.org:10.1016/s0140-6736(20)30925-9\u003c/li\u003e\n\u003cli\u003eGrams, M. E., Chow, E. K., Segev, D. L. \u0026amp; Coresh, J. 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A.\u003cem\u003e et al.\u003c/em\u003e Relationship of Estimated GFR and Albuminuria to Concurrent Laboratory Abnormalities: An Individual Participant Data Meta-analysis in a Global Consortium. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 206-217 (2019). https://doi.org:10.1053/j.ajkd.2018.08.013\u003c/li\u003e\n\u003cli\u003eBabitt, J. L. \u0026amp; Lin, H. Y. Mechanisms of anemia in CKD. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1631-1634 (2012). https://doi.org:10.1681/asn.2011111078\u003c/li\u003e\n\u003cli\u003ePalaka, E., Grandy, S., van Haalen, H., McEwan, P. \u0026amp; Darlington, O. The Impact of CKD Anaemia on Patients: Incidence, Risk Factors, and Clinical Outcomes-A Systematic Literature Review. \u003cem\u003eInt J Nephrol\u003c/em\u003e \u003cstrong\u003e2020\u003c/strong\u003e, 7692376 (2020). https://doi.org:10.1155/2020/7692376\u003c/li\u003e\n\u003cli\u003eKeane, W. F.\u003cem\u003e et al.\u003c/em\u003e The risk of developing end-stage renal disease in patients with type 2 diabetes and nephropathy: the RENAAL study. \u003cem\u003eKidney Int\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 1499-1507 (2003). https://doi.org:10.1046/j.1523-1755.2003.00885.x\u003c/li\u003e\n\u003cli\u003eYang, C.\u003cem\u003e et al.\u003c/em\u003e Anemia and Kidney Function Decline among the Middle-Aged and Elderly in China: A Population-Based National Longitudinal Study. \u003cem\u003eBiomed Res Int\u003c/em\u003e \u003cstrong\u003e2020\u003c/strong\u003e, 2303541 (2020). https://doi.org:10.1155/2020/2303541\u003c/li\u003e\n\u003cli\u003eFurth, S. L.\u003cem\u003e et al.\u003c/em\u003e The association of anemia and hypoalbuminemia with accelerated decline in GFR among adolescents with chronic kidney disease. \u003cem\u003ePediatr Nephrol\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 265-271 (2007). https://doi.org:10.1007/s00467-006-0313-1\u003c/li\u003e\n\u003cli\u003eOh, T. R.\u003cem\u003e et al.\u003c/em\u003e The Association between Serum Hemoglobin and Renal Prognosis of IgA Nephropathy. \u003cem\u003eJ Clin Med\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e (2021). https://doi.org:10.3390/jcm10020363\u003c/li\u003e\n\u003cli\u003eBansal, N.\u003cem\u003e et al.\u003c/em\u003e Anemia as a risk factor for kidney function decline in individuals with heart failure. \u003cem\u003eAm J Cardiol\u003c/em\u003e \u003cstrong\u003e99\u003c/strong\u003e, 1137-1142 (2007). https://doi.org:10.1016/j.amjcard.2006.11.055\u003c/li\u003e\n\u003cli\u003eStevens, P. E. \u0026amp; Levin, A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. \u003cem\u003eAnn Intern Med\u003c/em\u003e \u003cstrong\u003e158\u003c/strong\u003e, 825-830 (2013). https://doi.org:10.7326/0003-4819-158-11-201306040-00007\u003c/li\u003e\n\u003cli\u003eChapter 1: Diagnosis and evaluation of anemia in CKD. \u003cem\u003eKidney Int Suppl (2011)\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 288-291 (2012). https://doi.org:10.1038/kisup.2012.33\u003c/li\u003e\n\u003cli\u003eMatsushita, K.\u003cem\u003e et al.\u003c/em\u003e Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. \u003cem\u003eJama\u003c/em\u003e \u003cstrong\u003e307\u003c/strong\u003e, 1941-1951 (2012). https://doi.org:10.1001/jama.2012.3954\u003c/li\u003e\n\u003cli\u003eDekker, F. W., de Mutsert, R., van Dijk, P. C., Zoccali, C. \u0026amp; Jager, K. J. Survival analysis: time-dependent effects and time-varying risk factors. \u003cem\u003eKidney Int\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 994-997 (2008). https://doi.org:10.1038/ki.2008.328\u003c/li\u003e\n\u003cli\u003eMohanram, A.\u003cem\u003e et al.\u003c/em\u003e Anemia and end-stage renal disease in patients with type 2 diabetes and nephropathy. \u003cem\u003eKidney Int\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 1131-1138 (2004). https://doi.org:10.1111/j.1523-1755.2004.00863.x\u003c/li\u003e\n\u003cli\u003eKovesdy, C. P., Trivedi, B. K., Kalantar-Zadeh, K. \u0026amp; Anderson, J. E. Association of anemia with outcomes in men with moderate and severe chronic kidney disease. \u003cem\u003eKidney Int\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 560-564 (2006). https://doi.org:10.1038/sj.ki.5000105\u003c/li\u003e\n\u003cli\u003eSrivastava, A., Kaze, A. D., McMullan, C. J., Isakova, T. \u0026amp; Waikar, S. S. Uric Acid and the Risks of Kidney Failure and Death in Individuals With CKD. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 362-370 (2018). https://doi.org:10.1053/j.ajkd.2017.08.017\u003c/li\u003e\n\u003cli\u003eScialla, J. J.\u003cem\u003e et al.\u003c/em\u003e Mineral metabolites and CKD progression in African Americans. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 125-135 (2013). https://doi.org:10.1681/asn.2012070713\u003c/li\u003e\n\u003cli\u003eNangaku, M. Chronic hypoxia and tubulointerstitial injury: a final common pathway to end-stage renal failure. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 17-25 (2006). https://doi.org:10.1681/asn.2005070757\u003c/li\u003e\n\u003cli\u003eRossert, J., Fouqueray, B. \u0026amp; Boffa, J. J. Anemia management and the delay of chronic renal failure progression. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, S173-177 (2003). https://doi.org:10.1097/01.asn.0000070079.54912.b6\u003c/li\u003e\n\u003cli\u003eAlicic, R. Z., Rooney, M. T. \u0026amp; Tuttle, K. R. Diabetic Kidney Disease: Challenges, Progress, and Possibilities. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 2032-2045 (2017). https://doi.org:10.2215/cjn.11491116\u003c/li\u003e\n\u003cli\u003eAnderson, A. H.\u003cem\u003e et al.\u003c/em\u003e Time-updated systolic blood pressure and the progression of chronic kidney disease: a cohort study. \u003cem\u003eAnn Intern Med\u003c/em\u003e \u003cstrong\u003e162\u003c/strong\u003e, 258-265 (2015). https://doi.org:10.7326/m14-0488\u003c/li\u003e\n\u003cli\u003eIseki, K., Ikemiya, Y., Iseki, C. \u0026amp; Takishita, S. Proteinuria and the risk of developing end-stage renal disease. \u003cem\u003eKidney Int\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 1468-1474 (2003). https://doi.org:10.1046/j.1523-1755.2003.00868.x\u003c/li\u003e\n\u003cli\u003eLin, T. Y., Liu, J. S. \u0026amp; Hung, S. C. Obesity and risk of end-stage renal disease in patients with chronic kidney disease: a cohort study. \u003cem\u003eAm J Clin Nutr\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 1145-1153 (2018). https://doi.org:10.1093/ajcn/nqy200\u003c/li\u003e\n\u003cli\u003eBasta, J.\u003cem\u003e et al.\u003c/em\u003e Pharmacologic inhibition of RGD-binding integrins ameliorates fibrosis and improves function following kidney injury. \u003cem\u003ePhysiol Rep\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e14329 (2020). https://doi.org:10.14814/phy2.14329\u003c/li\u003e\n\u003cli\u003eCoresh, J.\u003cem\u003e et al.\u003c/em\u003e Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 115-127 (2019). https://doi.org:10.1016/s2213-8587(18)30313-9\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"775\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Baseline Characteristics of Patients According to Baseline Hemoglobin Levels Categorized Into 5 Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.16129032258064%\" colspan=\"5\" valign=\"top\" style=\"width: 79.3023%;\"\u003e\n \u003cp\u003eQuartiles by Hemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.354838709677419%\" rowspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\" style=\"width: 15.8026%;\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003cp\u003e(n=517,22.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e10-11\u003c/p\u003e\n \u003cp\u003e(n=320,13.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e11-12\u003c/p\u003e\n \u003cp\u003e(n=379,16.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e12-13\u003c/p\u003e\n \u003cp\u003e(n=374,16.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;13\u003c/p\u003e\n \u003cp\u003e(n=746,31.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e64.70\u0026plusmn;11.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e65.42\u0026plusmn;10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e65.93\u0026plusmn;10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e64.48\u0026plusmn;10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e64.47\u0026plusmn;10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eGender (male, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eComorbidities, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eGout\u003c/p\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e23.43\u0026plusmn;6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e23.85\u0026plusmn;4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e24.03\u0026plusmn;4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e24.23\u0026plusmn;3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e24.42\u0026plusmn;3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e141.83\u0026plusmn;35.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e141.77\u0026plusmn;26.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e137.95\u0026plusmn;23.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e136.07\u0026plusmn;22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e139.06\u0026plusmn;42.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e75.98\u0026plusmn;13.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e78.49\u0026plusmn;14.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e78.4\u0026plusmn;13.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e78.64\u0026plusmn;14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e82.38\u0026plusmn;27.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eLaboratory parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eHb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e8.44\u0026plusmn;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e10.47\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e11.46\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e12.43\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e14.45\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eAlb, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e34.72\u0026plusmn;6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e36.77\u0026plusmn;6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e37.94\u0026plusmn;5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e39.49\u0026plusmn;5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e40.80\u0026plusmn;5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eBUN, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e13.27\u0026plusmn;5.37 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e11.74\u0026plusmn;4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e10.87\u0026plusmn;3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e10.28\u0026plusmn;3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e9.17\u0026plusmn;3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eScr, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e207.64\u0026plusmn;75.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e185.85\u0026plusmn;65.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e173.06\u0026plusmn;60.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e162.72\u0026plusmn;52.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e147.45\u0026plusmn;41.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eD-Dimer, ug/mL.FEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e1.61\u0026plusmn;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u0026plusmn;4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.32\u0026plusmn;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u0026plusmn;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e4.18\u0026plusmn;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.51\u0026plusmn;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.41\u0026plusmn;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.34\u0026plusmn;1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.50\u0026plusmn;1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eLDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e2.40\u0026plusmn;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.61\u0026plusmn;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.62\u0026plusmn;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.53\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.69\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eHDL-C, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e1.64\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.98\u0026plusmn;1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.79\u0026plusmn;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.12\u0026plusmn;2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.05\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eUA, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e429.16\u0026plusmn;143.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e442.52\u0026plusmn;128.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e464.84\u0026plusmn;131.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e475.40\u0026plusmn;129.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e491.74\u0026plusmn;133.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eeGFR, ml/min per 1.73 m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e32.99\u0026plusmn;13.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e36.73\u0026plusmn;12.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e39.19\u0026plusmn;12.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e41.65\u0026plusmn;11.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e45.53\u0026plusmn;10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eSerum iron, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e10.85\u0026plusmn;6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e10.64\u0026plusmn;5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e11.76\u0026plusmn;5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e13.92\u0026plusmn;7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e14.28\u0026plusmn;6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eTIBC, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e45.52\u0026plusmn;13.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e47.46\u0026plusmn;11.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e47.99\u0026plusmn;11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e51.13\u0026plusmn;14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e49.90\u0026plusmn;11.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eTS, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e24.80\u0026plusmn;14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e23.22\u0026plusmn;11.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e25.21\u0026plusmn;11.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e27.48\u0026plusmn;11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e29.33\u0026plusmn;12.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eFerritin, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e253.00\u0026plusmn;487.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e198.63\u0026plusmn;279.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e174.15\u0026plusmn;157.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e220.01\u0026plusmn;244.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e223.78\u0026plusmn;206.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eCRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e2.62\u0026plusmn;4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.85\u0026plusmn;3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.72\u0026plusmn;5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.79\u0026plusmn;5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.96\u0026plusmn;4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003e24hproteinuria, mg/24h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e2426.57\u0026plusmn;2630.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2103.41\u0026plusmn;2190.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1920.20\u0026plusmn;2201.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1969.42\u0026plusmn;2320.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1626.23\u0026plusmn;1978.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eUACR, mg/g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e1418.48\u0026plusmn;2108.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1394.49\u0026plusmn;2268.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1001.18\u0026plusmn;1581.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e764.67\u0026plusmn;1425.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e750.67\u0026plusmn;1216.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eWBC, \u0026times;10\u0026sup3;/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e6.68\u0026plusmn;3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e6.95\u0026plusmn;2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e7.15\u0026plusmn;2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e7.23\u0026plusmn;2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e7.54\u0026plusmn;2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003ePotassium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e4.54\u0026plusmn;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.40\u0026plusmn;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.42\u0026plusmn;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.43\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e4.26\u0026plusmn;0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e2.23\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.32\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.33\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.37\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e2.40\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003ePhosphorus, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e1.23\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.18\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e1.09\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003ePTH, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e276.90\u0026plusmn;658.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e290.05\u0026plusmn;711.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e286.66\u0026plusmn;594.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e258.97\u0026plusmn;839.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e256.13\u0026plusmn;569.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eHbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e6.83\u0026plusmn;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e7.06\u0026plusmn;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e6.97\u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e7.13\u0026plusmn;2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e6.98\u0026plusmn;1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eVitamineB12, pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e423.93\u0026plusmn;445.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e343.75\u0026plusmn;251.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e312.72\u0026plusmn;236.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e347.42\u0026plusmn;298.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e335.87\u0026plusmn;276.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eFolate, ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e21.13\u0026plusmn;13.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e20.28\u0026plusmn;12.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e16.98\u0026plusmn;10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e17.98\u0026plusmn;10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e16.18\u0026plusmn;8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eNAG, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e17.29\u0026plusmn;18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e16.91\u0026plusmn;13.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e13.44\u0026plusmn;11.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e15.15\u0026plusmn;11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e16.41\u0026plusmn;16.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eMCV, fL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e90.62\u0026plusmn;8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e91.34\u0026plusmn;6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e92.08\u0026plusmn;6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e92.20\u0026plusmn;5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e93.26\u0026plusmn;5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eHCT, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e26.11\u0026plusmn;3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e31.98\u0026plusmn;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e34.84\u0026plusmn;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e37.48\u0026plusmn;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e43.22\u0026plusmn;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eMCH, pg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e29.29\u0026plusmn;3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e29.95\u0026plusmn;2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e30.31\u0026plusmn;2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e30.59\u0026plusmn;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e31.19\u0026plusmn;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eMedication, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eACEI/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003e\u0026beta;Blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.430\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eDiurtics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eCCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eESAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.534282018111256%\" valign=\"top\" style=\"width: 14.1243%;\"\u003e\n \u003cp\u003eLipid-modifying drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\" style=\"width: 15.8058%;\"\u003e\n \u003cp\u003e0.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.620\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.600\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.640\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.618369987063389%\" valign=\"top\"\u003e\n \u003cp\u003e0.670\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.373868046571798%\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eValues for categorical variables are given as number (percentage); values for continuous variables are given as mean \u0026plusmn; SD or median (interquartile range). eGFR was calculated using the CKD-Epidemiology Collaboration equation. DM indicates diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; Hb, hemoglobin; Alb, albumin; BUN, blood urea nitrogen; Scr, serum creatinine; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; UA, serum uric acid; eGFR, estimated glomerular filtration rate; TIBC, total iron-binding capacity; TS, transferrin saturation; CRP, C-creative protein; UACR, urinary albumin to creatinine ratio; WBC, white blood cell; PTH, parathyroid hormone; HbA1c, glycosylated hemoglobin; NAG, N-Acetyl-\u0026beta;-D-Glucosaminidase S; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; HCT, hematocrit; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; CCB, calcium channel blocker; ESAs, erythropoiesis-stimulating agents.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Univariate and Multivariate regression analysis to estimate baseline characteristics associated with the renal composite outcome.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.879746835443036%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.879746835443036%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.803797468354432%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.436708860759495%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.16953316953317%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.216216216216218%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.25061425061425%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e64.91\u0026plusmn;10.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.986(0.980-0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.992(0.986-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eGender (male, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e69.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.254(1.088-1.445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.008(0.852-1.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e50.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.457(1.270-1.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.237(1.045-1.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e81.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.416(1.165-1.722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.309(1.060-1.616)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eGout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e17.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.554(0.443-0.693)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.903(0.712-1.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e24.03\u0026plusmn;4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.979(0.963-0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.993(0.976-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e139.42\u0026plusmn;33.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.002(1.001-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e79.15\u0026plusmn;19.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.003(1.000-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eLaboratory parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eHb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e11.76\u0026plusmn;2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.979(0.976-0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.507(0.388-0.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eAlb, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e38.23\u0026plusmn;6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.914(0.905-0.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.959(0.944-0.975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eBUN, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e10.89\u0026plusmn;4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.054(1.041-1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eScr, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e172.94\u0026plusmn;62.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.349(1.298-1.402)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eTc, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e4.40\u0026plusmn;1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.094(1.044-1.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.989(0.930-1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eSerum iron, umol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e11.91\u0026plusmn;6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.980(0.967-0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.028(1.000-1.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eTIBC, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e47.63\u0026plusmn;12.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.993(0.986-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eTSAT, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e25.71\u0026plusmn;12.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.993(0.987-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e24hproteinuria, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e2030.74\u0026plusmn;2303.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.000(1.000-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.584(1.455-1.725)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eUPCR, mg/g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e1057.78\u0026plusmn;1762.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.000(1.000-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eWBC, \u0026times;10\u0026sup3;/uL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e7.16\u0026plusmn;2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.986(0.964-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.001(0.9751.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003ePotassium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e4.38\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.059(0.952-1.178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.019(0.902-1.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e2.34\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.108(0.080-0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.439(0.293-0.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003ePhosphorus, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.737(1.404-2.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.035(0.767-1.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003ePTH, pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e272.81\u0026plusmn;663.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.000(1.000-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.000(1.000-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eNAG, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e16.04\u0026plusmn;15.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.008(1.002-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eMCV, fL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e92.06\u0026plusmn;6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.989(0.979-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eHCT, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e35.62\u0026plusmn;6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.928(0.919-0.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eMCH, pg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e30.36\u0026plusmn;2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.960(0.935-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eMedication, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eACEI/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e46.98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e0.878(0.765-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.872(0.747-1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;-Blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e40.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.283(1.118-1.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eDiurtics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e43.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.660(1.448-1.903)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.117(0.958-1.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eCCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e59.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.863(1.604-2.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eESAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e8.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e2.179(1.808-2.625)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.913(0.714-1.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e8.02%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.881(1.537-2.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e1.189(0.937-1.507)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003eLipid-modifying drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.851500789889414%\" valign=\"top\"\u003e\n \u003cp\u003e60.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.327014218009477%\" valign=\"top\"\u003e\n \u003cp\u003e1.043(0.908-1.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\" valign=\"top\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.380726698262244%\" valign=\"top\"\u003e\n \u003cp\u003e0.912(0.778-1.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.162717219589258%\" valign=\"top\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eValues for categorical variables are given as number (percentage); values for continuous variables are given as mean \u0026plusmn; SD or median (interquartile range). eGFR was calculated using the CKD-Epidemiology Collaboration equation. DM indicates diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; Hb, hemoglobin; Alb, albumin; BUN, blood urea nitrogen; Scr, serum creatinine; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; UA, serum uric acid; eGFR, estimated glomerular filtration rate; TIBC, total iron-binding capacity; TS, transferrin saturation; CRP, C-creative protein; UACR, urinary albumin to creatinine ratio; WBC, white blood cell; PTH, parathyroid hormone; HbA1c, glycosylated hemoglobin; NAG, N-Acetyl-\u0026beta;-D-Glucosaminidase S; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; HCT, hematocrit; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; CCB, calcium channel blocker; ESAs, erythropoiesis-stimulating agents.\u0026nbsp;\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\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Univariate and Multivariate regression analysis to estimate Time-Varying characteristics associated with the renal composite outcome.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.628158844765345%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.628158844765345%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.794392523364486%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.794392523364486%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.986(0.980-0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.984(0.978-0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eGender (male, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.238(1.069-1.445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.843(0.720-0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.407(1.219-1.624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.295(1.115-1.504)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.376(1.124-1.686)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.472(1.190-1.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index, kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.982(0.965-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.999(0.982-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eTime-varying Hb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.969(0.966-0.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.973(0.970-0.976)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eAlb, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.914(0.905-0.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.956(0.942-0.970)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003ePotassium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.075(0.964-1.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.064(0.957-1.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.118(0.086-0.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.449(0.299-0.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003ePhosphorus, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.723(1.385-2.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.994(0.754-1.309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eACEI/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.867(0.751-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e0.866(0.742-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eDiurtics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.683(1.460-1.940)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.134(0.973-1.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.743682310469314%\" valign=\"top\"\u003e\n \u003cp\u003eLipid-modifying drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.029(0.891-1.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.56317689530686%\" valign=\"top\"\u003e\n \u003cp\u003e1.072(0.915-1.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.064981949458485%\" valign=\"top\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eDM indicates diabetes mellitus; Alb, albumin; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. Association of Hb Levels between Composite Renal outcome in the Baseline and Time-Varying Cox Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHb\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eEvents,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.961715160796324%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.961715160796324%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.961715160796324%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.881390593047033%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451942740286299%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.881390593047033%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451942740286299%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.881390593047033%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451942740286299%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eBaseline Hb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e517(22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e10-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e320(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.79(0.65-0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.79(0.64-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.94(0.75-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e11-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e379(16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.59(0.48-0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.57(0.47-0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.81(0.64-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e12-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e374(16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.37(0.29-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.34(0.27-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.57(0.43-0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e＞13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e746(32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.29(0.23-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.25(0.20-0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.50(0.38-0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eTime-varying Hb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e1.00(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e10-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.51(0.41-0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.49(0.40-0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.54(0.44-0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e11-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.38(0.30-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.38(0.30-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.47(0.37-0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e12-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.18(0.13-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.18(0.13-0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.25(0.17-0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.782542113323125%\" valign=\"top\"\u003e\n \u003cp\u003e＞13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.332312404287903%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.10(0.07-0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.09(0.06-0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.385911179173046%\" valign=\"top\"\u003e\n \u003cp\u003e0.14(0.10-0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575803981623277%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eModel 1: unadjusted; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eModel 2: adjusted for age, gender, comorbidities (diabetes mellitus, hypertension, hyperlipidemia, gout);\u003c/p\u003e\n \u003cp\u003eModel 3: adjusted for model 2 plus body mass index, systolic blood pressure, diastolic blood pressure, laboratory findings (white blood cell count, albumin, blood urea nitrogen; serum creatinine, total cholesterol, serum uric acid, parathyroid hormone, glycosylated hemoglobin, lymphocyte, calcium, phosphorus, potassium, chlorine, Potassium, iron, transferrin saturation, proteinuria, blood urine), and meditation (angiotensin-converting enzyme inhibitor, angiotensin II receptor blocker, calcium channel blocker, erythropoiesis-stimulating agents, lipid-modifying drugs, diuretics, iron);\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"Chronic kidney disease, Anemia, Hemoglobin longitudinal dynamics, Chronic kidney disease progression.","lastPublishedDoi":"10.21203/rs.3.rs-3453502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3453502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Anemia is one of the most prevalent complications in patients with chronic kidney disease (CKD). Previous researches have indicated associations between anemia and renal outcomes. However, recurrent limitations in these studies are their exclusive reliance on baseline hemoglobin (Hb) levels, leaving the implications of longitudinal Hb fluctuations on the progression of CKD uncharted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this cohort of 2,336 CKD stages 3-4 patients, we investigated the relationship between Hb levels, its temporal variations, and composite renal outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eThe average follow-up period was 552.5 days (289.3-971.0), with 834 individuals (35.7%) experiencing the composite renal endpoints. Using a fully adjusted cubic spline model with Hb treated as a continuous variable showed that the relationship between Hb and the renal composite outcomes was non-linear. Additionally, in an adjusted multivariate Cox model, participants in the Q4 (12-13 g/dL) and Q5 (\u0026gt;13 g/dL) groups with higher Hb levels had a significantly lower risk of composite renal outcomes compared to the Q1 group (\u0026lt;10 g/dL) (HR=0.57, 0.50, respectively, all p\u0026lt;0.001). A similar trend is observed in time-varying multivariate models that incorporate the longitudinal dynamics of Hb levels (HR=0.25, 0.25, respectively, all p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn the groups with higher Hb levels, the occurrence of renal endpoint events was notably lower than in the groups with lower Hb levels. Additionally, based on the longitudinal dynamics of Hb levels, the risk of renal endpoint events decreased progressively with rising Hb levels.\u003c/p\u003e","manuscriptTitle":"Temporal Dynamic of Hemoglobin Levels and Its Impact on Progression in Chronic Kidney Disease Stages 3- 4 Patients: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-19 18:08:16","doi":"10.21203/rs.3.rs-3453502/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":"158ce253-b872-4c59-aee0-764e945c83cc","owner":[],"postedDate":"October 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":25454827,"name":"Health sciences/Nephrology/Kidney diseases"},{"id":25454828,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-03-15T13:12:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-19 18:08:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3453502","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3453502","identity":"rs-3453502","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}