Association of Dietary Niacin Intake with All-cause and Cardiovascular Mortality in Patients with Chronic Kidney Disease

Association of Dietary Niacin Intake with All-cause and Cardiovascular Mortality in Patients with Chronic Kidney Disease | 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 Association of Dietary Niacin Intake with All-cause and Cardiovascular Mortality in Patients with Chronic Kidney Disease yongqian Chi, Zhen Lu, Cuicui Liang, Chao Xuan, Fengqiang Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4772496/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 Niacin, also known as vitamin B3 or nicotinic acid (NA), exhibits beneficial effects on factors influencing the decline of kidney function. In chronic kidney disease (CKD) patients, the relationship between dietary niacin and mortality prognosis remains unclear. Methods The study involved 2,962 CKD patients from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 and followed for survival through December 31, 2019. Cox proportional hazards models were utilized to explore the association between dietary niacin intake and both all-cause mortality and cardiovascular disease (CVD) mortality. Additionally, restricted cubic splines and subgroup analyses were performed. Results During a median follow-up of 5.7 years, 631 deaths including 229 CVD deaths were recorded. In multivariable-adjusted Cox models, highest quartile of niacin intake compared with lowest quartile was associated with lower mortality risk. Hazard ratios were 0.71 (95% confidence interval [CI], 0.53–0.97) for all-cause mortality (P = 0.044 for trend) and 0.61 (95% CI, 0.41–0.91) for CVD mortality (P = 0.020 for trend). Conclusions The findings of this cohort study indicate a potential association between increased dietary niacin intake and reduced all-cause and cardiovascular mortality among patients with CKD. Health sciences/Cardiology Health sciences/Medical research Health sciences/Nephrology NHANES findings chronic kidney disease mortality niacin intake Figures Figure 1 Figure 2 INTRODUCTION In the United States, 14.0% of adults have moderate-risk or higher kidney disease, highlighting the global health issue of chronic kidney disease (CKD).[ 1 ] In 2017, 1.2 million people worldwide died from CKD, making it a significant contributor to increased burden of disease.[ 2 , 3 ] Approximately 40% of patients with end-stage kidney disease also have ischemic heart disease or heart failure and the mortality rate due to cardiovascular disease in patients undergoing dialysis is 10 to 30 times higher compared to the general population.[ 4 ] This underscores the significance of implementing secondary prevention measures for cardiovascular disease (CVD) in individuals with CKD. Niacin, also known as vitamin B3 or nicotinic acid (NA), is crucial for synthesizing nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP), coenzymes pivotal in cellular functions such as facilitating redox reactions and energy production.[ 5 ] Niacin could be acquired through diet, and rich dietary sources of niacin include meat, liver, fish, whole grains, and nuts.[ 6 ] Niacin exhibits beneficial effects on factors influencing the decline of kidney function, such as lipids, endothelial function, inflammation, and oxidative stress.[ 7 ] While animal studies have shown some promise in niacin supplementation for CKD and randomized controlled trials have indicated beneficial effect on CKD-related complications with niacin use such as hyperphosphatemia.[ 8 , 9 ] However, the impact of dietary niacin supplementation on mortality risk in CKD patients remains largely unknown. Our study aims to investigate the relationship between dietary niacin intake and the risk of all-cause and cardiovascular death in CKD patients. METHODS Study Design and Population The data for this cohort study were derived from seven consecutive waves of the National Health and Nutrition Examination Survey (NHANES) conducted by the U.S. Centers for Disease Control and Prevention (CDC) between 2005 and 2018. [ 10 ] This survey aims to evaluate the health and nutritional status of the U.S. population through physical examinations, laboratory tests, and questionnaire surveys. NHANES utilizes a complex multi-stage probability sampling method to collect a comprehensive dataset on health and nutrition. NHANES has received approval from the Ethical Review Board of the National Center for Health Statistics Research, with all participants providing informed consent. In the study, participants aged 20 years and above were analyzed using data from NHANES between 2005 and 2018. (Fig. 1 ) Exclusions from the study criteria included individuals under 20 years of age, those with missing death status data, individuals with any type of cancer, pregnant individuals, or those with unreasonable energy intake ( 3500 kcal/d for women and 4200 kcal/d for men) (n = 2636).[ 11 ] Additionally, participants with missing covariates (n = 1055) were further excluded, resulting in a final study sample of 2926 participants with CKD. Measurement of Dietary Niacin Intake The dietary interview component is a collaboration between the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (DHHS) known as What We Eat in America (WWEIA). All eligible NHANES participants underwent two 24-hour dietary recall interviews to report the types and quantities of food they had consumed in the 24 hours prior to the interview (from midnight to midnight). One dietary recall was conducted in person at the mobile examination center, while the second recall took place via telephone interview approximately 3 to 10 days after the initial one. Nutrient content and food ingredients for all foods were calculated using the USDA Dietary Studies Food and Nutrient Database (FNDDS). [ 12 ]For the purpose of this study, the daily niacin intake was determined by averaging the values from the participants' two dietary recalls, or using a single value if only one recall was available. Definition of CKD CKD is defined according to the KDIGO 2021 guidelines. Our study extracted urinary urine albumin-to-creatinine ratio (UACR) and eGFR data from NHANES, with eGFR calculated using the CKD-EPI equation.[ 13 ] For the purposes of this study, CKD patients were identified as those with eGFR < 60 mg/dl or UACR ≥ 30 mg/g. [ 14 ] Mortality Data Death data were collected by linking the NHANES database with the National Death Index records, and follow-up was conducted until December 31, 2019. The cause of death was determined by referencing codes from the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). In this study, all-cause death encompasses deaths from any cause, and CVD mortality was defined as deaths due to heart disease (ICD-10 codes I00–I09, I11, I13, and I20–I51) and cerebrovascular disease (ICD-10 codes I60–I69). Our study documented a total of 631 deaths, with 229 of them attributed to CVD. Assessment of Covariates Age was categorized into three groups: 39 years or younger, 40 to 59 years, and 60 years or older. Race and ethnicity were classified based on patient-reported selection in NHANES, including Mexican American, non-Hispanic black, non-Hispanic white, and other (included other Hispanic, other non-Hispanic, and non-Hispanic multiple races). Educational level was defined as below high school or high school and above. Marital status was divided into three categories: married, married/separated/widowed, and never married. Household income-to-poverty ratios were categorized into three groups: below 1.0, 1.0 to 3.0, and above 3.0. Smoking status was classified as never smokers (defined as lifetime smoking of less than 100 cigarettes), current smokers (defined as lifetime smoking of 100 cigarettes or more), and former smokers (defined as lifetime smoking of 100 cigarettes or more and quitting). Drinking status was dichotomized into non-drinkers (defined as consuming less than 12 drinks per year) and drinkers (defined as consuming at least 12 drinks per year). Body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) was categorized as less than 25.0, 25.0 to 29.9, and 30.0 or higher. Dyslipidemia was defined as meeting at least one of the following criteria: total cholesterol concentration ≥ 200 mg/dL, low-density lipoprotein cholesterol concentration ≥ 130 mg/dL, triglyceride concentration ≥ 150 mg/dL, or high-density lipoprotein cholesterol concentration ≤ 40 mg/dL. Diabetes mellitus (DM) was defined as meeting at least one of the following criteria: fasting blood glucose levels of ≥ 126mg/dL, glycated hemoglobin levels ≥ 6.5%, self-reported diabetes mellitus, use of insulin, or use of antidiabetic drugs. The Healthy Eating Index 2015 Edition (HEI-2015) is a dietary pattern assessment tool that is frequently utilized to evaluate diet quality. It is based on the healthy eating patterns outlined in the 2015–2020 Dietary Guidelines for Americans, as well as the 2020–2025 Dietary Guidelines for Americans. [ 15 , 16 ] HEI-2015 scores range from 0 to 100, with higher scores reflecting a healthier diet. Sel-reported data include doctor-diagnosed hypertension and hypercholesterolemia. Statistical Analysis Considering the intricate sampling design of NHANES, all analyses were conducted considering sample weights, strata, and primary sampling units. The weight was derived by dividing the dietary day 1 sample weight by the number of analysis periods. Categorical variables were presented as unweighted frequencies (weighted percentages) and compared using the χ 2 test with the Rao and Scott second-order correction. Continuous variables were displayed as medians (IQRS) and analyzed using the Kruskal-Wallis test for non-normally distributed data. A two-sided p-value of less than 0.05 was considered statistically significant. The statistical software R ( http://www.r-project.org ; version 4.3.3, The R Foundation) was used for all analyses. We utilized weighted Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% confidence intervals to investigate the relationship between dietary niacin intake and the risk of all-cause and cardiovascular deaths. The assumption of proportional hazards was evaluated using Schoenfeld residuals, and no violations were detected. Individual time was measured from the NHANES interview date to the date of death or the end of follow-up (December 31, 2019). Three models were developed for analysis. Model 1 did not include any covariate adjustments. Model 2 included adjustments for age and gender. Model 3 further adjusted for race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, diabetes, dyslipidemia, hypertension, high cholesterol and HEI-2015. In order to assess linear trends, a continuous variable was generated by assigning a median value to each category. Restricted cubic spline analyses were conducted to investigate potential nonlinear relationships between dietary niacin intake and both all-cause and cardiovascular disease mortality. Likelihood ratio tests were utilized to evaluate these nonlinear associations. Subgroup analysis involved stratification by gender, race and ethnicity, education level, BMI, household income to poverty ratio, smoking status, and diabetes. RESULTS Among 2926 CKD patients, 1796 individuals were aged 60 years or older (52.5%, weighted); 1585 were female (59.7%, weighted). Furthermore, 356 individuals (7.5%, weighted) were Mexican American, 868 (16.0%, weighted) were non-Hispanic black, 1199 (64.6%, weighted) were non-Hispanic white, and 503 (11.9%, weighted) were classified as other. The baseline characteristics of the 2926 CKD patients were summarized based on weighted quartiles of dietary niacin intake. This included 811 participants in quartile 1 (< 15.8 mg), 711 in quartile 2 (15.8–21.7 mg), 693 in quartile 3 (21.7–30.7 mg), and 711 in quartile 4 (≥ 30.7 mg), as presented in Table 1 . Compared to participants with the lowest niacin intake, those with higher dietary niacin intake were generally younger, more often male, more likely to drink, more likely to be married, had higher levels of education and family income. Similarities were observed across all 4 groups in terms of race and ethnicity, smoking status, BMI, dyslipidemia, hypertension, high cholesterol, and DM. Table 1. Baseline Characteristics of Participants with CKD in NHANES 2005 to 2018 a Dietary Niacin Quartiles Characteristic Overall Q1(<15.8mg/d b ) Q2(15.8-21.7 mg/d b ) Q3(21.7-30.7mg/d b ) Q4(≥30.7 mg/d b ) P value Patients, No 2926 811 711 693 711 NA Female 1585（59.7） 583(77.3) 418(68.5) 355(57.7) 229(35.3) <0.001 Age ≤39 376(16.7) 70(11.1) 76(13.1) 103(18.2) 127(21.1) <0.001 40-59 754(30.8) 166(25.1) 178(28.0) 188(34.5) 222(30.7) ≥60 1796(52.5) 575(63.8) 457(58.9) 402(47.2) 362(48.2) Race Mexican American 356(7.5) 93(6.2) 85(7.5) 92(7.9) 86(8.3) 0.265 Non-Hispanic Black 868(16.0) 249(17.4) 222(15.4) 191(15.2) 206(15.8) Non-Hispanic White 1199(64.6) 329(65.4) 298(67.7) 288(63.9) 284(61.6) Other c 503(11.9) 140(11.0) 106(9.4) 122(12.9) 135(14.3) Educational level <High school 816(19.9) 276(24.3) 204(21.7) 180(18.2) 156(15.6) 0.004 High School or above 2110(80.1) 535(75.7) 507(78.3) 513(81.8) 555(84.4) BMI <25.0 640(22.2) 176(23.0) 167(23.2) 153(22.2) 144(20.5) 0.921 25.0–29.9 870(28.0) 255(27.8) 210(28.0) 195(26.4) 210(30.0) ≥30.0 1606(49.7) 380(49.2) 334(48.9) 345(51.5) 357(49.5) Marital Status Married 1437(51.6) 359（43.8） 342（55.2） 328（47.3） 408（60.0） <0.001 Married Divorced/separated/widowed 1003(30.8) 329（39.9） 269（31.8） 236（30.6） 169（21.0） Never married 486(17.6) 123（16.3） 100（13.0） 129（22.1） 134（18.9） Drink 1606(57.8) 367(47.3) 408(56.3) 390(61.6) 441(65.9) <0.001 Poor 596(15.9) 200(20.8) 132(13.0) 138(15.9) 126(13.8) 0.003 Current smoker 482(15.7) 117(15.4) 119(14.0) 120(18.8) 126(14.7) 0.465 Dyslipidemia 1605(56.8) 452(58.6) 396(55.1) 357(55.5) 400(58.1) 0.632 Hypertension 1913(59.1) 560(60.4) 464(59.6) 459(61.8) 430(54.7) 0.247 High cholesterol 1489(48.6) 415(53.1) 343(42.7) 356(48.7) 375(50.0) 0.057 Diabetes 1220(35.5) 354(38.8) 295(34.8) 291(36.5) 280(31.8) 0.257 HEL-2015, median (IQR) 51(19-92) 50(19-82) 51(24-87) 51(24-85) 52(19-92) 0.012 a All estimates accounted for complex survey designs, and all percentages are weighted. b Daily dietary niacin intake. c Other race and ethnicity includes other Hispanic, other non-Hispanic, and multiracial individuals. Dietary Niacin Intake and All-Cause and CVD Mortality During the follow-up period, there were 1026 recorded deaths, with 386 CVD deaths. In model 1 (crude model), the hazard ratio (HR) for all-cause death in quartile 4 was 0.55 (95% CI, 0.44–0.70) compared to the reference group (P < 0.001 for trend) (Table 2 ). In model 2 adjusted for age and sex, the HR for all-cause death in quartile 4 was 0.58 (95% CI, 0.43–0.79) compared to the reference group (P = 0.001 for trend). In model 3 additionally adjusted for race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, DM, dyslipidemia, hypertension, high cholesterol and HEI-2015, the HR for all-cause death in quartile 4 was 0.71 (95% CI, 0.53–0.97) compared to the reference group (P = 0.044 for trend). The HR for CVD death in quartile 4 was 0.51 (95% CI, 0.35–0.77) in model 1 (P = 0.001 for trend), 0.56 (95% CI, 0.39–0.81) in model 2 (P = 0.003 for trend), and 0.61 (95% CI, 0.41–0.91) in model 3 (P = 0.020 for trend). The dose-response relationship between dietary niacin intake and all-cause and CVD mortality was shown in Fig. 2 . In restricted cubic spline analyses, we observed similar associations between dietary niacin intake and all-cause mortality (P = 0.012 for trend) or CVD mortality (P = 0.031 for trend) as observed in categorical analysis. Table 2 Hazard Ratios for All-Cause and CVD Mortality Among Participants with CKD in NHANES 2005 to 2018 Hazard ratio (95% CI) Model Q 1 Q 2 Q 3 Q 4 P trend All-cause mortality Model 1 b 1.00 [Reference] 0.82 [0.63, 1.06] 0.85 [0.64, 1.14] 0.55 [0.44, 0.70] < 0.001 Model 2 c 1.00 [Reference] 0.83 [0.65, 1.08] 0.91 [0.70, 1.19] 0.58 [0.43, 0.79] 0.001 Model 3 d 1.00 [Reference] 0.86 [0.66, 1.11] 0.90 [0.66, 1.21] 0.71 [0.53, 0.97] 0.044 CVD mortality Model 1 b 1.00 [Reference] 1.00 [0.63, 1.60] 1.06 [0.77, 1.46] 0.51 [0.35, 0.77] 0.001 Model 2 c 1.00 [Reference] 1.05 [0.68, 1.62] 1.15 [0.81, 1.64] 0.56 [0.39, 0.81] 0.003 Model 3 d 1.00 [Reference] 1.03 [0.66, 1.63] 1.09 [0.76, 1.58] 0.61 [0.41, 0.91] 0.020 a Daily dietary niacin intake. b Crude model. c Adjusted for age and sex. d Further adjusted for race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, diabetes, dyslipidemia, hypertension, high cholesterol and HEI-2015. Stratified Analyses Subgroup analyses were conducted to assess the relationship between dietary niacin intake and all-cause mortality. The analysis was stratified by sex, race and ethnicity, education level, family income to poverty ratio, BMI, smoking status, and DM. There were no significant interactions between the included stratification variables and niacin intake related all-cause mortality (Table 3 ). Table 3 Associations of Dietary Niacin Intake with All-Cause Mortality in Various Subgroups Among Participants with CKD in NHANES 2005-2018 a Hazard ratio (95% CI) Q1 Q2 Q3 Q4 P value for interaction Sex Male 1.00 [Reference] 0.93 [0.64, 1.35] 0.85 [0.52, 1.37] 0.67 [0.46, 0.98] 0.75 Female 1.00 [Reference] 0.82 [0.59, 1.14] 0.96 [0.67, 1.36] 0.74 [0.47, 1.16] Race Mexican American 1.00 [Reference] 1.27 [0.48, 3.40] 1.29 [0.48, 3.46] 0.78 [0.26, 2.29] 0.48 Non-Hispanic Black 1.00 [Reference] 0.72 [0.47, 1.11] 0.81 [0.49, 1.35] 1.08 [0.67, 1.76] Non-Hispanic White 1.00 [Reference] 0.85 [0.61, 1.19] 0.94 [0.64, 1.38] 0.63 [0.42, 0.95] Other 1.00 [Reference] 0.80 [0.41, 1.58] 0.47 [0.12, 1.81] 0.88 [0.36, 2.18] Educational level <High school 1.00 [Reference] 1.01 [0.61, 1.68] 0.90 [0.55, 1.48] 1.22 [0.68, 2.17] 0.35 High School or above 1.00 [Reference] 0.84 [0.63, 1.10] 0.91 [0.67, 1.25] 0.63 [0.45, 0.88] Family income to poverty ratio Poor 1.00 [Reference] 0.98 [0.49, 1.94] 0.85 [0.39, 1.87] 1.39 [0.67, 2.88] 0.21 Not poor 1.00 [Reference] 0.84 [0.64, 1.11] 0.91 [0.67, 1.25] 0.64 [0.46, 0.88] BMI <25.0 1.00 [Reference] 0.78 [0.48, 1.27] 0.53 [0.28, 1.01] 0.66 [0.39, 1.13] 0.07 25.0–29.9 1.00 [Reference] 0.73 [0.43, 1.23] 0.66 [0.44, 1.00] 0.77 [0.46, 1.31] ≥30.0 1.00 [Reference] 1.08 [0.75, 1.57] 1.26 [0.80, 1.98] 0.72 [0.45, 1.17] Smoking status never smoker 1.00 [Reference] 1.08 [0.75, 1.56] 1.06 [0.70, 1.60] 0.72 [0.45, 1.15] 0.36 current smoker 1.00 [Reference] 1.11 [0.52, 2.37] 0.76 [0.32, 1.83] 0.99 [0.43, 2.24] former smoker 1.00 [Reference] 0.61 [0.42, 0.90] 0.75 [0.46, 1.22] 0.62 [0.40, 0.95] Diabetes Yes 1.00 [Reference] 0.73 [0.49, 1.11] 0.94 [0.58, 1.54] 0.74 [0.46, 1.18] 0.61 No 1.00 [Reference] 1.02 [0.75, 1.37] 0.88 [0.60, 1.28] 0.71 [0.46, 1.10] a Adjusted for age, sex, race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, diabetes, dyslipidemia, hypertension, high cholesterol and HEI-2015; the strata variable was not included when stratifying by itself. DISCUSSION The study investigated the relationship between dietary niacin intake and the risk of all-cause mortality and CVD mortality in patients with CKD. We found that increased dietary niacin intake was associated with lower risk of all-cause and CVD mortality, even after accounting for various confounding factors. We observed similar findings in subgroup analyses, which demonstrated the robustness of our findings. Previous randomized controlled trial (RCT) showed the efficacy of niacin supplement in managing hyperphosphatemia in hemodialysis patients and can be considered a safe and cost-effective treatment.[ 17 ] With respect to cardiovascular outcomes, two RCTs with arteriography as the primary endpoint indicated a significant reduction in cardiovascular events in the group receiving combined drug therapy, which included niacin, compared to the placebo group.[ 18 , 19 ] Our study aligns with these findings, while additionally illustrating the association between increased dietary niacin intake and better prognosis in CKD patients. The relationship between dietary niacin intake and mortality risk may have multiple underlying mechanisms. These include reducing oxidative stress, decreasing intestinal phosphorus absorption, lowering triglyceride levels, and increasing HDL particle number and function.[ 7 ] Oxidative stress has been established as a factor in the advancement of kidney disease. [ 20 ] Oxidative stress in CKD patients is linked to various complications including atherosclerosis, anemia, and hypertension.[ 21 ] Niacin has been shown to have a role in inhibiting oxidative stress, reducing inflammation in blood vessels.[ 22 ] Previous studies have shown that low-dose niacin can effectively improve dyslipidemia and decrease serum phosphorus levels.[ 23 ] Based on a meta-analysis of cardiovascular events in patients with CKD, it was found that higher levels of serum phosphorus are linked to a higher likelihood of experiencing cardiovascular events.[ 24 ] Niacin is commonly utilized to lower triglyceride levels and/or raise HDL-C levels. The relationship between triglyceride and HDL-C levels and the progression of CKD has been proved, although the precise mechanisms involved remain uncertain.[ 25 ] Our results align with existing evidence and are biologically plausible, yet the precise molecular pathways connecting dietary niacin intake and mortality risk in CKD patients warrant additional exploration. Our study indicates that dietary supplementation with niacin could be beneficial for individuals with CKD. This can be achieved by incorporating niacin-rich foods like chicken, fish, whole grains, and nuts into the diet, or by utilizing niacin supplements.[ 6 ] Nevertheless, it is important to be aware that excessive niacin consumption may result in liver toxicity and hyperglycemia.[ 26 , 27 ] Therefore, niacin supplementation should be supervised by a healthcare provider. Despite utilizing the representative large cohort in US individuals and conducting subgroup analyzes to ensure robustness of results, this study has several limitations. Firstly, the research design was observational and could not establish causal relationships. Secondly, the National Death Index may have limitations in accurately classifying cardiovascular death outcomes.[ 28 ] Thirdly, although niacin intake data were collected through two 24-hour dietary recalls, recall bias may still be present. Fourthly, removing individuals with missing covariates may lead to the introduction of selection bias. Furthermore, there is a possibility that certain potential confounding factors were not fully taken into account, leading to the presence of unknown confounding factors that could not be entirely ruled out. CONCLUSIONS This cohort study in CKD patients suggests a potential association between higher niacin intake and lower risk of all-cause mortality and CVD. The findings highlight the need for further research to explore the dose-response relationship between niacin intake and outcomes in patients with CKD to establish optimal intake levels. Declarations Funding: This work was supported by the National Natural Science Foundation of China (Grant Nos. 82370272), Shandong Provincial Natural Science Foundation, China (ZR2023MH337) Author Contribution C.Y.., L.Z., L.C..and X.C. designed the model and the computational framework. C.Y. and L.Z. analysed the data. J.H.. and G.J.. were involved in planning and supervised the work. X.F.., L.C.., L.Z.. aided in interpreting the results. C.Y.. and L.Z. wrote the main manuscript text. Data Availability The data supporting the findings of this study can be obtained from NHANES (https://www.cdc.gov/nchs/nhanes/index.htm). References Johansen, K.L., et al., US Renal Data System 2023 Annual Data Report: Epidemiology of Kidney Disease in the United States . Am J Kidney Dis, 2024. 83(4S1): p. A8-A13. Collaboration, G.B.D.C.K.D., Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 . Lancet, 2020. 395(10225): p. 709–733. DALYs, G.B.D. and H. Collaborators, Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 . Lancet, 2018. 392(10159): p. 1859–1922. Sarnak, M.J., et al., Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention . Hypertension, 2003. 42(5): p. 1050–65. Meyer-Ficca, M. and J.B. Kirkland, Niacin. Adv Nutr, 2016. 7(3): p. 556–8. Mielgo-Ayuso, J., et al., Dietary Intake and Food Sources of Niacin, Riboflavin, Thiamin and Vitamin B(6) in a Representative Sample of the Spanish Population. The Anthropometry, Intake, and Energy Balance in Spain (ANIBES) Study dagger . Nutrients, 2018. 10(7). Streja, E., et al., Niacin and progression of CKD . Am J Kidney Dis, 2015. 65(5): p. 785–98. Cho, K.H., et al., Niacin ameliorates oxidative stress, inflammation, proteinuria, and hypertension in rats with chronic renal failure . Am J Physiol Renal Physiol, 2009. 297(1): p. F106-13. Malhotra, R., et al., The Effect of Extended Release Niacin on Markers of Mineral Metabolism in CKD . Clin J Am Soc Nephrol, 2018. 13(1): p. 36–44. Centers for Disease Control and Prevention. About the National Health and Nutrition Examination Survey. [cited 2024 April 1]; Available from: https://www.cdc.gov/nchs/nhanes/about_nhanes.htm . Satija, A., et al., Healthful and Unhealthful Plant-Based Diets and the Risk of Coronary Heart Disease in U.S. Adults . J Am Coll Cardiol, 2017. 70(4): p. 411–422. National Health and Nutrition Examination Survey. 2005–2006 Data documentation, codebook, and frequencies. [cited 2024 April 1]; Available from: https://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/DR1TOT_D.htm . Ji, H., et al., eGFRs from Asian-modified CKD-EPI and Chinese-modified CKD-EPI equations were associated better with hypertensive target organ damage in the community-dwelling elderly Chinese: the Northern Shanghai Study . Clin Interv Aging, 2017. 12: p. 1297–1308. Kidney Disease: Improving Global Outcomes Glomerular Diseases Work, G., KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int, 2021. 100(4S): p. S1-S276. U.S. Department of Health and Human Services and U.S. Department of Agriculture, Dietary Guidelines for Americans 2015–2020 . 8th ed. U.S. Department of Agriculture, 2015. U.S. Department of Agriculture and U.S. Department of Health and Human Services, Dietary Guidelines for Americans, 2020–2025. 9th ed. U.S. Department of Agriculture, 2021. Ahmed, H.M., et al., The efficacy and safety of niacin on hyperphosphatemia in ESRD patients undergoing hemodialysis: randomized controlled trial . The Egyptian Journal of Internal Medicine, 2022. 34(1): p. 33. Whitney, E.J., et al., A randomized trial of a strategy for increasing high-density lipoprotein cholesterol levels: effects on progression of coronary heart disease and clinical events . Ann Intern Med, 2005. 142(2): p. 95–104. Brown, B.G., et al., Simvastatin and niacin, antioxidant vitamins, or the combination for the prevention of coronary disease . N Engl J Med, 2001. 345(22): p. 1583–92. Honda, T., Y. Hirakawa, and M. Nangaku, The role of oxidative stress and hypoxia in renal disease . Kidney Res Clin Pract, 2019. 38(4): p. 414–426. Daenen, K., et al., Oxidative stress in chronic kidney disease . Pediatr Nephrol, 2019. 34(6): p. 975–991. Ganji, S.H., et al., Niacin inhibits vascular oxidative stress, redox-sensitive genes, and monocyte adhesion to human aortic endothelial cells . Atherosclerosis, 2009. 202(1): p. 68–75. Jin Kang, H., et al., Effects of low-dose niacin on dyslipidemia and serum phosphorus in patients with chronic kidney disease . Kidney Res Clin Pract, 2013. 32(1): p. 21–6. Major, R.W., et al., Cardiovascular disease risk factors in chronic kidney disease: A systematic review and meta-analysis . PLoS One, 2018. 13(3): p. e0192895. Li, Y., et al., Hypertriglyceridemic waist phenotype and chronic kidney disease in a Chinese population aged 40 years and older . PLoS One, 2014. 9(3): p. e92322. Parra, J.L. and K.R. Reddy, Hepatotoxicity of hypolipidemic drugs . Clin Liver Dis, 2003. 7(2): p. 415–33. Goldberg, R.B. and T.A. Jacobson, Effects of niacin on glucose control in patients with dyslipidemia . Mayo Clin Proc, 2008. 83(4): p. 470–8. Olubowale, O.T., et al., Comparison of Expert Adjudicated Coronary Heart Disease and Cardiovascular Disease Mortality With the National Death Index: Results From the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study . J Am Heart Assoc, 2017. 6(5). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4772496","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342417462,"identity":"fecb3896-e159-42a2-addd-82196b107306","order_by":0,"name":"yongqian Chi","email":"","orcid":"","institution":"Department of Cardiology, The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"yongqian","middleName":"","lastName":"Chi","suffix":""},{"id":342417463,"identity":"17fd4254-9703-4810-afcb-9098e59be79e","order_by":1,"name":"Zhen Lu","email":"","orcid":"","institution":"Department of Cardiology, The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Lu","suffix":""},{"id":342417464,"identity":"314066ab-7ae4-4fc2-95b5-efa684e10d0e","order_by":2,"name":"Cuicui Liang","email":"","orcid":"","institution":"Qingdao Municipal Health Commission Hospital development center","correspondingAuthor":false,"prefix":"","firstName":"Cuicui","middleName":"","lastName":"Liang","suffix":""},{"id":342417465,"identity":"87fee433-de24-4c97-9d42-a654931cdcb8","order_by":3,"name":"Chao Xuan","email":"","orcid":"","institution":"Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Xuan","suffix":""},{"id":342417466,"identity":"76629755-ca9e-4b08-8b08-fe00a0982770","order_by":4,"name":"Fengqiang Xu","email":"","orcid":"","institution":"Department of Cardiology, The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Fengqiang","middleName":"","lastName":"Xu","suffix":""},{"id":342417467,"identity":"0387fa3c-1ec0-4e4d-b011-c7a0928d4c1b","order_by":5,"name":"Congjuan Luo","email":"","orcid":"","institution":"Department of Nephrology, the Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Congjuan","middleName":"","lastName":"Luo","suffix":""},{"id":342417468,"identity":"eab4c558-5d6e-45b7-9013-ffbf7e07c5eb","order_by":6,"name":"Zhexun Lian","email":"","orcid":"","institution":"Department of Cardiology, The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Zhexun","middleName":"","lastName":"Lian","suffix":""},{"id":342417469,"identity":"9edd6f35-e845-43e5-9e44-fa719a72a2f1","order_by":7,"name":"Hongwei Ji","email":"","orcid":"","institution":"Tsinghua Medicine, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Ji","suffix":""},{"id":342417470,"identity":"bb33c1d8-6e86-430f-a4cc-c037f809b9c8","order_by":8,"name":"Junjie Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYJACAwYGNh4G9gYGCSCHsYF4LTwHSNACARIJRGqRj0g+UMy7g0+GX/Lxxts8DDayGw4wP3uAT4vhjbQEY94zbDySs9OKrXkY0ow3HGAzN8CrZUaOgTFvGxuPwe0cM2kehsOJGw7wsEkQpcX+5hmQlv+EtchLwGyR4AFpOUBYiwHPswTDuUAtEmfSii3nGCQbzzzMZobflvbkYwZv247Z87cf3njjTYWdbN/x5mf4bTnAwAYMn2NgNhgxMONTD7KlgYH5AQNDDVTLKBgFo2AUjAIsAAAb2kA8W38KTAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Cardiology, The Affiliated Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-07-20 09:51:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4772496/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4772496/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63407039,"identity":"c60b13aa-216a-441a-9c90-beb600cdbaf2","added_by":"auto","created_at":"2024-08-27 21:49:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271945,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart Illustrating Selection of the Study Population in NHANES From 2005 to 2018\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4772496/v1/01c20f5c482384dea5df7009.jpeg"},{"id":63407041,"identity":"f31c3886-32d0-4ece-b3f7-3e0ddc951b51","added_by":"auto","created_at":"2024-08-27 21:49:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of Dietary Niacin Intake With All-Cause and CVD Mortality Among Individuals With CKD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHazard ratios were adjusted for age, sex, race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, diabetes, dyslipidemia, hypertension, high cholesterol and HEI-2015. Shaded areas represent 95% CIs. CVD indicates cardiovascular disease and CKD, Chronic kidney disease\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4772496/v1/54859de134da2d9e090857bb.jpg"},{"id":64170568,"identity":"cb830082-7ea0-4fa0-8a6d-c76f495d703b","added_by":"auto","created_at":"2024-09-09 10:47:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1003950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4772496/v1/c8be2720-d67c-4fe0-a6da-0d8dc47945ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Dietary Niacin Intake with All-cause and Cardiovascular Mortality in Patients with Chronic Kidney Disease","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn the United States, 14.0% of adults have moderate-risk or higher kidney disease, highlighting the global health issue of chronic kidney disease (CKD).[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] In 2017, 1.2\u0026nbsp;million people worldwide died from CKD, making it a significant contributor to increased burden of disease.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Approximately 40% of patients with end-stage kidney disease also have ischemic heart disease or heart failure and the mortality rate due to cardiovascular disease in patients undergoing dialysis is 10 to 30 times higher compared to the general population.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] This underscores the significance of implementing secondary prevention measures for cardiovascular disease (CVD) in individuals with CKD.\u003c/p\u003e \u003cp\u003eNiacin, also known as vitamin B3 or nicotinic acid (NA), is crucial for synthesizing nicotinamide adenine dinucleotide (NAD) and nicotinamide adenine dinucleotide phosphate (NADP), coenzymes pivotal in cellular functions such as facilitating redox reactions and energy production.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Niacin could be acquired through diet, and rich dietary sources of niacin include meat, liver, fish, whole grains, and nuts.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Niacin exhibits beneficial effects on factors influencing the decline of kidney function, such as lipids, endothelial function, inflammation, and oxidative stress.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWhile animal studies have shown some promise in niacin supplementation for CKD and randomized controlled trials have indicated beneficial effect on CKD-related complications with niacin use such as hyperphosphatemia.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] However, the impact of dietary niacin supplementation on mortality risk in CKD patients remains largely unknown. Our study aims to investigate the relationship between dietary niacin intake and the risk of all-cause and cardiovascular death in CKD patients.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThe data for this cohort study were derived from seven consecutive waves of the National Health and Nutrition Examination Survey (NHANES) conducted by the U.S. Centers for Disease Control and Prevention (CDC) between 2005 and 2018. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] This survey aims to evaluate the health and nutritional status of the U.S. population through physical examinations, laboratory tests, and questionnaire surveys. NHANES utilizes a complex multi-stage probability sampling method to collect a comprehensive dataset on health and nutrition. NHANES has received approval from the Ethical Review Board of the National Center for Health Statistics Research, with all participants providing informed consent.\u003c/p\u003e \u003cp\u003eIn the study, participants aged 20 years and above were analyzed using data from NHANES between 2005 and 2018. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) Exclusions from the study criteria included individuals under 20 years of age, those with missing death status data, individuals with any type of cancer, pregnant individuals, or those with unreasonable energy intake (\u0026lt;\u0026thinsp;600 or \u0026gt;\u0026thinsp;3500 kcal/d for women and \u0026lt;\u0026thinsp;800 or \u0026gt;\u0026thinsp;4200 kcal/d for men) (n\u0026thinsp;=\u0026thinsp;2636).[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Additionally, participants with missing covariates (n\u0026thinsp;=\u0026thinsp;1055) were further excluded, resulting in a final study sample of 2926 participants with CKD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of Dietary Niacin Intake\u003c/h2\u003e \u003cp\u003eThe dietary interview component is a collaboration between the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (DHHS) known as What We Eat in America (WWEIA). All eligible NHANES participants underwent two 24-hour dietary recall interviews to report the types and quantities of food they had consumed in the 24 hours prior to the interview (from midnight to midnight). One dietary recall was conducted in person at the mobile examination center, while the second recall took place via telephone interview approximately 3 to 10 days after the initial one. Nutrient content and food ingredients for all foods were calculated using the USDA Dietary Studies Food and Nutrient Database (FNDDS). [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]For the purpose of this study, the daily niacin intake was determined by averaging the values from the participants' two dietary recalls, or using a single value if only one recall was available.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of CKD\u003c/h2\u003e \u003cp\u003e CKD is defined according to the KDIGO 2021 guidelines. Our study extracted urinary urine albumin-to-creatinine ratio (UACR) and eGFR data from NHANES, with eGFR calculated using the CKD-EPI equation.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] For the purposes of this study, CKD patients were identified as those with eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mg/dl or UACR\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMortality Data\u003c/h2\u003e \u003cp\u003eDeath data were collected by linking the NHANES database with the National Death Index records, and follow-up was conducted until December 31, 2019. The cause of death was determined by referencing codes from the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). In this study, all-cause death encompasses deaths from any cause, and CVD mortality was defined as deaths due to heart disease (ICD-10 codes I00\u0026ndash;I09, I11, I13, and I20\u0026ndash;I51) and cerebrovascular disease (ICD-10 codes I60\u0026ndash;I69). Our study documented a total of 631 deaths, with 229 of them attributed to CVD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Covariates\u003c/h2\u003e \u003cp\u003eAge was categorized into three groups: 39 years or younger, 40 to 59 years, and 60 years or older. Race and ethnicity were classified based on patient-reported selection in NHANES, including Mexican American, non-Hispanic black, non-Hispanic white, and other (included other Hispanic, other non-Hispanic, and non-Hispanic multiple races). Educational level was defined as below high school or high school and above. Marital status was divided into three categories: married, married/separated/widowed, and never married. Household income-to-poverty ratios were categorized into three groups: below 1.0, 1.0 to 3.0, and above 3.0. Smoking status was classified as never smokers (defined as lifetime smoking of less than 100 cigarettes), current smokers (defined as lifetime smoking of 100 cigarettes or more), and former smokers (defined as lifetime smoking of 100 cigarettes or more and quitting). Drinking status was dichotomized into non-drinkers (defined as consuming less than 12 drinks per year) and drinkers (defined as consuming at least 12 drinks per year). Body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) was categorized as less than 25.0, 25.0 to 29.9, and 30.0 or higher. Dyslipidemia was defined as meeting at least one of the following criteria: total cholesterol concentration\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, low-density lipoprotein cholesterol concentration\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dL, triglyceride concentration\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, or high-density lipoprotein cholesterol concentration\u0026thinsp;\u0026le;\u0026thinsp;40 mg/dL. Diabetes mellitus (DM) was defined as meeting at least one of the following criteria: fasting blood glucose levels of \u0026ge;\u0026thinsp;126mg/dL, glycated hemoglobin levels\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, self-reported diabetes mellitus, use of insulin, or use of antidiabetic drugs. The Healthy Eating Index 2015 Edition (HEI-2015) is a dietary pattern assessment tool that is frequently utilized to evaluate diet quality. It is based on the healthy eating patterns outlined in the 2015\u0026ndash;2020 Dietary Guidelines for Americans, as well as the 2020\u0026ndash;2025 Dietary Guidelines for Americans. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] HEI-2015 scores range from 0 to 100, with higher scores reflecting a healthier diet. Sel-reported data include doctor-diagnosed hypertension and hypercholesterolemia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eConsidering the intricate sampling design of NHANES, all analyses were conducted considering sample weights, strata, and primary sampling units. The weight was derived by dividing the dietary day 1 sample weight by the number of analysis periods. Categorical variables were presented as unweighted frequencies (weighted percentages) and compared using the χ\u003csup\u003e2\u003c/sup\u003e test with the Rao and Scott second-order correction. Continuous variables were displayed as medians (IQRS) and analyzed using the Kruskal-Wallis test for non-normally distributed data. A two-sided p-value of less than 0.05 was considered statistically significant. The statistical software R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; version 4.3.3, The R Foundation) was used for all analyses.\u003c/p\u003e \u003cp\u003eWe utilized weighted Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% confidence intervals to investigate the relationship between dietary niacin intake and the risk of all-cause and cardiovascular deaths. The assumption of proportional hazards was evaluated using Schoenfeld residuals, and no violations were detected. Individual time was measured from the NHANES interview date to the date of death or the end of follow-up (December 31, 2019). Three models were developed for analysis. Model 1 did not include any covariate adjustments. Model 2 included adjustments for age and gender. Model 3 further adjusted for race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, diabetes, dyslipidemia, hypertension, high cholesterol and HEI-2015. In order to assess linear trends, a continuous variable was generated by assigning a median value to each category.\u003c/p\u003e \u003cp\u003eRestricted cubic spline analyses were conducted to investigate potential nonlinear relationships between dietary niacin intake and both all-cause and cardiovascular disease mortality. Likelihood ratio tests were utilized to evaluate these nonlinear associations. Subgroup analysis involved stratification by gender, race and ethnicity, education level, BMI, household income to poverty ratio, smoking status, and diabetes.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAmong 2926 CKD patients, 1796 individuals were aged 60 years or older (52.5%, weighted); 1585 were female (59.7%, weighted). Furthermore, 356 individuals (7.5%, weighted) were Mexican American, 868 (16.0%, weighted) were non-Hispanic black, 1199 (64.6%, weighted) were non-Hispanic white, and 503 (11.9%, weighted) were classified as other. The baseline characteristics of the 2926 CKD patients were summarized based on weighted quartiles of dietary niacin intake. This included 811 participants in quartile 1 (\u0026lt;\u0026thinsp;15.8 mg), 711 in quartile 2 (15.8\u0026ndash;21.7 mg), 693 in quartile 3 (21.7\u0026ndash;30.7 mg), and 711 in quartile 4 (\u0026ge;\u0026thinsp;30.7 mg), as presented in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. Compared to participants with the lowest niacin intake, those with higher dietary niacin intake were generally younger, more often male, more likely to drink, more likely to be married, had higher levels of education and family income. Similarities were observed across all 4 groups in terms of race and ethnicity, smoking status, BMI, dyslipidemia, hypertension, high cholesterol, and DM.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1. Baseline Characteristics of Participants with CKD in NHANES 2005 to 2018\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.797927461139896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.680483592400691%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.8860103626943%\" colspan=\"4\"\u003e\n \u003cp\u003eDietary Niacin Quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.635578583765112%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003eQ1(\u0026lt;15.8mg/d\u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003eQ2(15.8-21.7 mg/d\u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003eQ3(21.7-30.7mg/d\u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003eQ4(\u0026ge;30.7 mg/d\u003csup\u003eb\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003ePatients, No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e2926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e1585（59.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e583(77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e418(68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e355(57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e229(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026le;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e376(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e70(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e76(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e103(18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e127(21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;40-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e754(30.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e166(25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e178(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e188(34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e222(30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e1796(52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e575(63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e457(58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e402(47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e362(48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e356(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e93(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e85(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e92(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e86(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" rowspan=\"4\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Non-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e868(16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e249(17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e222(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e191(15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e206(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Non-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e1199(64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e329(65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e298(67.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e288(63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e284(61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e503(11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e140(11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e106(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e122(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e135(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eEducational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003e\u0026lt;High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e816(19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e276(24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e204(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e180(18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e156(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003eHigh School or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e2110(80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e535(75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e507(78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e513(81.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e555(84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003e\u0026lt;25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e640(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e176(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e167(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e153(22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e144(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003e25.0\u0026ndash;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e870(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e255(27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e210(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e195(26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e210(30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003e\u0026ge;30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e1606(49.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e380(49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e334(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e345(51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e357(49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e1437(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e359（43.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e342（55.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e328（47.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e408（60.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003eMarried Divorced/separated/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e1003(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e329（39.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e269（31.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e236（30.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e169（21.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90566037735849%\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.037735849056602%\"\u003e\n \u003cp\u003e486(17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e123（16.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\"\u003e\n \u003cp\u003e100（13.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.283018867924529%\"\u003e\n \u003cp\u003e129（22.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.339622641509434%\"\u003e\n \u003cp\u003e134（18.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eDrink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e1606(57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e367(47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e408(56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e390(61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e441(65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e596(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e200(20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e132(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e138(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e126(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e482(15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e117(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e119(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e120(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e126(14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e1605(56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e452(58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e396(55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e357(55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e400(58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e1913(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e560(60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e464(59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e459(61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e430(54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eHigh cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e1489(48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e415(53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e343(42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e356(48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e375(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e1220(35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e354(38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e295(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e291(36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e280(31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.75862068965517%\"\u003e\n \u003cp\u003eHEL-2015, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.655172413793103%\"\u003e\n \u003cp\u003e51(19-92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e50(19-82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.793103448275861%\"\u003e\n \u003cp\u003e51(24-87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96551724137931%\"\u003e\n \u003cp\u003e51(24-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.10344827586207%\"\u003e\n \u003cp\u003e52(19-92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.620689655172415%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eAll estimates accounted for complex survey designs, and all percentages are weighted.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eDaily dietary niacin intake.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eOther race and ethnicity includes other Hispanic, other non-Hispanic, and multiracial individuals.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDietary Niacin Intake and All-Cause and CVD Mortality\u003c/h2\u003e \u003cp\u003eDuring the follow-up period, there were 1026 recorded deaths, with 386 CVD deaths. In model 1 (crude model), the hazard ratio (HR) for all-cause death in quartile 4 was 0.55 (95% CI, 0.44\u0026ndash;0.70) compared to the reference group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In model 2 adjusted for age and sex, the HR for all-cause death in quartile 4 was 0.58 (95% CI, 0.43\u0026ndash;0.79) compared to the reference group (P\u0026thinsp;=\u0026thinsp;0.001 for trend). In model 3 additionally adjusted for race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, DM, dyslipidemia, hypertension, high cholesterol and HEI-2015, the HR for all-cause death in quartile 4 was 0.71 (95% CI, 0.53\u0026ndash;0.97) compared to the reference group (P\u0026thinsp;=\u0026thinsp;0.044 for trend). The HR for CVD death in quartile 4 was 0.51 (95% CI, 0.35\u0026ndash;0.77) in model 1 (P\u0026thinsp;=\u0026thinsp;0.001 for trend), 0.56 (95% CI, 0.39\u0026ndash;0.81) in model 2 (P\u0026thinsp;=\u0026thinsp;0.003 for trend), and 0.61 (95% CI, 0.41\u0026ndash;0.91) in model 3 (P\u0026thinsp;=\u0026thinsp;0.020 for trend). The dose-response relationship between dietary niacin intake and all-cause and CVD mortality was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In restricted cubic spline analyses, we observed similar associations between dietary niacin intake and all-cause mortality (P\u0026thinsp;=\u0026thinsp;0.012 for trend) or CVD mortality (P\u0026thinsp;=\u0026thinsp;0.031 for trend) as observed in categorical analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard Ratios for All-Cause and CVD Mortality Among Participants with CKD in NHANES 2005 to 2018\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82 [0.63, 1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.64, 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55 [0.44, 0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 [0.65, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 [0.70, 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58 [0.43, 0.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 [0.66, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 [0.66, 1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 [0.53, 0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [0.63, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 [0.77, 1.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51 [0.35, 0.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 [0.68, 1.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 [0.81, 1.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 [0.39, 0.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 [0.66, 1.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 [0.76, 1.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61 [0.41, 0.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Daily dietary niacin intake.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Crude model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003e Adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ed\u003c/sup\u003e Further adjusted for race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, diabetes, dyslipidemia, hypertension, high cholesterol and HEI-2015.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStratified Analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted to assess the relationship between dietary niacin intake and all-cause mortality. The analysis was stratified by sex, race and ethnicity, education level, family income to poverty ratio, BMI, smoking status, and DM. There were no significant interactions between the included stratification variables and niacin intake related all-cause mortality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of Dietary Niacin Intake with All-Cause Mortality in Various Subgroups Among Participants with CKD in NHANES 2005-2018\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value for\u003c/p\u003e \u003cp\u003einteraction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 [0.64, 1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.52, 1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67 [0.46, 0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82 [0.59, 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 [0.67, 1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74 [0.47, 1.16]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 [0.48, 3.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29 [0.48, 3.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78 [0.26, 2.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 [0.47, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81 [0.49, 1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 [0.67, 1.76]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 [0.61, 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 [0.64, 1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 [0.42, 0.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 [0.41, 1.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 [0.12, 1.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 [0.36, 2.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 [0.61, 1.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 [0.55, 1.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22 [0.68, 2.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 [0.63, 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 [0.67, 1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 [0.45, 0.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income to poverty ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 [0.49, 1.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.39, 1.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39 [0.67, 2.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 [0.64, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 [0.67, 1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64 [0.46, 0.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 [0.48, 1.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 [0.28, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66 [0.39, 1.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0\u0026ndash;29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 [0.43, 1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 [0.44, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77 [0.46, 1.31]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 [0.75, 1.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 [0.80, 1.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 [0.45, 1.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 [0.75, 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 [0.70, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 [0.45, 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 [0.52, 2.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76 [0.32, 1.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 [0.43, 2.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 [0.42, 0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 [0.46, 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62 [0.40, 0.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 [0.49, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 [0.58, 1.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74 [0.46, 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [Reference]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 [0.75, 1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 [0.60, 1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 [0.46, 1.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for age, sex, race and ethnicity, education level, household income to poverty ratio, marital status, smoking status, alcohol use status, BMI, diabetes, dyslipidemia, hypertension, high cholesterol and HEI-2015; the strata variable was not included when stratifying by itself.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study investigated the relationship between dietary niacin intake and the risk of all-cause mortality and CVD mortality in patients with CKD. We found that increased dietary niacin intake was associated with lower risk of all-cause and CVD mortality, even after accounting for various confounding factors. We observed similar findings in subgroup analyses, which demonstrated the robustness of our findings.\u003c/p\u003e \u003cp\u003ePrevious randomized controlled trial (RCT) showed the efficacy of niacin supplement in managing hyperphosphatemia in hemodialysis patients and can be considered a safe and cost-effective treatment.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] With respect to cardiovascular outcomes, two RCTs with arteriography as the primary endpoint indicated a significant reduction in cardiovascular events in the group receiving combined drug therapy, which included niacin, compared to the placebo group.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Our study aligns with these findings, while additionally illustrating the association between increased dietary niacin intake and better prognosis in CKD patients.\u003c/p\u003e \u003cp\u003eThe relationship between dietary niacin intake and mortality risk may have multiple underlying mechanisms. These include reducing oxidative stress, decreasing intestinal phosphorus absorption, lowering triglyceride levels, and increasing HDL particle number and function.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Oxidative stress has been established as a factor in the advancement of kidney disease. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Oxidative stress in CKD patients is linked to various complications including atherosclerosis, anemia, and hypertension.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Niacin has been shown to have a role in inhibiting oxidative stress, reducing inflammation in blood vessels.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Previous studies have shown that low-dose niacin can effectively improve dyslipidemia and decrease serum phosphorus levels.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Based on a meta-analysis of cardiovascular events in patients with CKD, it was found that higher levels of serum phosphorus are linked to a higher likelihood of experiencing cardiovascular events.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Niacin is commonly utilized to lower triglyceride levels and/or raise HDL-C levels. The relationship between triglyceride and HDL-C levels and the progression of CKD has been proved, although the precise mechanisms involved remain uncertain.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Our results align with existing evidence and are biologically plausible, yet the precise molecular pathways connecting dietary niacin intake and mortality risk in CKD patients warrant additional exploration.\u003c/p\u003e \u003cp\u003eOur study indicates that dietary supplementation with niacin could be beneficial for individuals with CKD. This can be achieved by incorporating niacin-rich foods like chicken, fish, whole grains, and nuts into the diet, or by utilizing niacin supplements.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Nevertheless, it is important to be aware that excessive niacin consumption may result in liver toxicity and hyperglycemia.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Therefore, niacin supplementation should be supervised by a healthcare provider.\u003c/p\u003e \u003cp\u003eDespite utilizing the representative large cohort in US individuals and conducting subgroup analyzes to ensure robustness of results, this study has several limitations. Firstly, the research design was observational and could not establish causal relationships. Secondly, the National Death Index may have limitations in accurately classifying cardiovascular death outcomes.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Thirdly, although niacin intake data were collected through two 24-hour dietary recalls, recall bias may still be present. Fourthly, removing individuals with missing covariates may lead to the introduction of selection bias. Furthermore, there is a possibility that certain potential confounding factors were not fully taken into account, leading to the presence of unknown confounding factors that could not be entirely ruled out.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis cohort study in CKD patients suggests a potential association between higher niacin intake and lower risk of all-cause mortality and CVD. The findings highlight the need for further research to explore the dose-response relationship between niacin intake and outcomes in patients with CKD to establish optimal intake levels.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant Nos. 82370272), Shandong Provincial Natural Science Foundation, China (ZR2023MH337)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.Y.., L.Z., L.C..and X.C. designed the model and the computational framework. C.Y. and L.Z. analysed the data. J.H.. and G.J.. were involved in planning and supervised the work. X.F.., L.C.., L.Z.. aided in interpreting the results. C.Y.. and L.Z. wrote the main manuscript text.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study can be obtained from NHANES (https://www.cdc.gov/nchs/nhanes/index.htm).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohansen, K.L., et al., \u003cem\u003eUS Renal Data System 2023 Annual Data Report: Epidemiology of Kidney Disease in the United States\u003c/em\u003e. Am J Kidney Dis, 2024. 83(4S1): p. A8-A13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollaboration, G.B.D.C.K.D., \u003cem\u003eGlobal, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017\u003c/em\u003e. Lancet, 2020. 395(10225): p. 709\u0026ndash;733.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDALYs, G.B.D. and H. Collaborators, \u003cem\u003eGlobal, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017\u003c/em\u003e. Lancet, 2018. 392(10159): p. 1859\u0026ndash;1922.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarnak, M.J., et al., \u003cem\u003eKidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention\u003c/em\u003e. Hypertension, 2003. 42(5): p. 1050\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeyer-Ficca, M. and J.B. Kirkland, Niacin. Adv Nutr, 2016. 7(3): p. 556\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMielgo-Ayuso, J., et al., \u003cem\u003eDietary Intake and Food Sources of Niacin, Riboflavin, Thiamin and Vitamin B(6) in a Representative Sample of the Spanish Population. The Anthropometry, Intake, and Energy Balance in Spain (ANIBES) Study dagger\u003c/em\u003e. Nutrients, 2018. 10(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStreja, E., et al., \u003cem\u003eNiacin and progression of CKD\u003c/em\u003e. Am J Kidney Dis, 2015. 65(5): p. 785\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho, K.H., et al., \u003cem\u003eNiacin ameliorates oxidative stress, inflammation, proteinuria, and hypertension in rats with chronic renal failure\u003c/em\u003e. Am J Physiol Renal Physiol, 2009. 297(1): p. F106-13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalhotra, R., et al., \u003cem\u003eThe Effect of Extended Release Niacin on Markers of Mineral Metabolism in CKD\u003c/em\u003e. Clin J Am Soc Nephrol, 2018. 13(1): p. 36\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eCenters for Disease Control and Prevention. About the National Health and Nutrition Examination Survey.\u003c/em\u003e [cited 2024 April 1]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/about_nhanes.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/about_nhanes.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatija, A., et al., \u003cem\u003eHealthful and Unhealthful Plant-Based Diets and the Risk of Coronary Heart Disease in U.S. Adults\u003c/em\u003e. J Am Coll Cardiol, 2017. 70(4): p. 411\u0026ndash;422.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eNational Health and Nutrition Examination Survey. 2005\u0026ndash;2006 Data documentation, codebook, and frequencies.\u003c/em\u003e [cited 2024 April 1]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/DR1TOT_D.htm\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/DR1TOT_D.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, H., et al., \u003cem\u003eeGFRs from Asian-modified CKD-EPI and Chinese-modified CKD-EPI equations were associated better with hypertensive target organ damage in the community-dwelling elderly Chinese: the Northern Shanghai Study\u003c/em\u003e. Clin Interv Aging, 2017. 12: p. 1297\u0026ndash;1308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKidney Disease: Improving Global Outcomes Glomerular Diseases Work, G., \u003cem\u003eKDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases.\u003c/em\u003e Kidney Int, 2021. 100(4S): p. S1-S276.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Department of Health and Human Services and U.S. Department of Agriculture, \u003cem\u003eDietary Guidelines for Americans 2015\u0026ndash;2020\u003c/em\u003e. \u003cem\u003e8th ed.\u003c/em\u003e U.S. Department of Agriculture, 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Department of Agriculture and U.S. Department of Health and Human Services, \u003cem\u003eDietary Guidelines for Americans, 2020\u0026ndash;2025. 9th ed.\u003c/em\u003e U.S. Department of Agriculture, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed, H.M., et al., \u003cem\u003eThe efficacy and safety of niacin on hyperphosphatemia in ESRD patients undergoing hemodialysis: randomized controlled trial\u003c/em\u003e. The Egyptian Journal of Internal Medicine, 2022. 34(1): p. 33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitney, E.J., et al., \u003cem\u003eA randomized trial of a strategy for increasing high-density lipoprotein cholesterol levels: effects on progression of coronary heart disease and clinical events\u003c/em\u003e. Ann Intern Med, 2005. 142(2): p. 95\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, B.G., et al., \u003cem\u003eSimvastatin and niacin, antioxidant vitamins, or the combination for the prevention of coronary disease\u003c/em\u003e. N Engl J Med, 2001. 345(22): p. 1583\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonda, T., Y. Hirakawa, and M. Nangaku, \u003cem\u003eThe role of oxidative stress and hypoxia in renal disease\u003c/em\u003e. Kidney Res Clin Pract, 2019. 38(4): p. 414\u0026ndash;426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaenen, K., et al., \u003cem\u003eOxidative stress in chronic kidney disease\u003c/em\u003e. Pediatr Nephrol, 2019. 34(6): p. 975\u0026ndash;991.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanji, S.H., et al., \u003cem\u003eNiacin inhibits vascular oxidative stress, redox-sensitive genes, and monocyte adhesion to human aortic endothelial cells\u003c/em\u003e. Atherosclerosis, 2009. 202(1): p. 68\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Kang, H., et al., \u003cem\u003eEffects of low-dose niacin on dyslipidemia and serum phosphorus in patients with chronic kidney disease\u003c/em\u003e. Kidney Res Clin Pract, 2013. 32(1): p. 21\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajor, R.W., et al., \u003cem\u003eCardiovascular disease risk factors in chronic kidney disease: A systematic review and meta-analysis\u003c/em\u003e. PLoS One, 2018. 13(3): p. e0192895.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y., et al., \u003cem\u003eHypertriglyceridemic waist phenotype and chronic kidney disease in a Chinese population aged 40 years and older\u003c/em\u003e. PLoS One, 2014. 9(3): p. e92322.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra, J.L. and K.R. Reddy, \u003cem\u003eHepatotoxicity of hypolipidemic drugs\u003c/em\u003e. Clin Liver Dis, 2003. 7(2): p. 415\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldberg, R.B. and T.A. Jacobson, \u003cem\u003eEffects of niacin on glucose control in patients with dyslipidemia\u003c/em\u003e. Mayo Clin Proc, 2008. 83(4): p. 470\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlubowale, O.T., et al., \u003cem\u003eComparison of Expert Adjudicated Coronary Heart Disease and Cardiovascular Disease Mortality With the National Death Index: Results From the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study\u003c/em\u003e. J Am Heart Assoc, 2017. 6(5).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"NHANES findings, chronic kidney disease, mortality, niacin intake","lastPublishedDoi":"10.21203/rs.3.rs-4772496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4772496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNiacin, also known as vitamin B3 or nicotinic acid (NA), exhibits beneficial effects on factors influencing the decline of kidney function. In chronic kidney disease (CKD) patients, the relationship between dietary niacin and mortality prognosis remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e The study involved 2,962 CKD patients from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 and followed for survival through December 31, 2019. Cox proportional hazards models were utilized to explore the association between dietary niacin intake and both all-cause mortality and cardiovascular disease (CVD) mortality. Additionally, restricted cubic splines and subgroup analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring a median follow-up of 5.7 years, 631 deaths including 229 CVD deaths were recorded. In multivariable-adjusted Cox models, highest quartile of niacin intake compared with lowest quartile was associated with lower mortality risk. Hazard ratios were 0.71 (95% confidence interval [CI], 0.53\u0026ndash;0.97) for all-cause mortality (P\u0026thinsp;=\u0026thinsp;0.044 for trend) and 0.61 (95% CI, 0.41\u0026ndash;0.91) for CVD mortality (P\u0026thinsp;=\u0026thinsp;0.020 for trend).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings of this cohort study indicate a potential association between increased dietary niacin intake and reduced all-cause and cardiovascular mortality among patients with CKD.\u003c/p\u003e","manuscriptTitle":"Association of Dietary Niacin Intake with All-cause and Cardiovascular Mortality in Patients with Chronic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 21:49:05","doi":"10.21203/rs.3.rs-4772496/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":"326c6cb0-7d38-4975-8cb9-9f62ec29ae95","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36275481,"name":"Health sciences/Cardiology"},{"id":36275482,"name":"Health sciences/Medical research"},{"id":36275483,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2024-09-09T10:39:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-27 21:49:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4772496","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4772496","identity":"rs-4772496","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}