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The long-term effects of blood urea nitrogen levels on cardiovascular disease and all-cause mortality in diabetes: a prospective cohort study

BMC Cardiovascular Disorders volume 24, Article number: 256 (2024) Cite this article

Abstract

Background

The long-term effects of blood urea nitrogen(BUN) in patients with diabetes remain unknown. Current studies reporting the target BUN level in patients with diabetes are also limited. Hence, this prospective study aimed to explore the relationship of BUN with all-cause and cardiovascular mortalities in patients with diabetes.

Methods

In total, 10,507 participants with diabetes from the National Health and Nutrition Examination Survey (1999–2018) were enrolled. The causes and numbers of deaths were determined based on the National Death Index mortality data from the date of NHANES interview until follow-up (December 31, 2019). Multivariate Cox proportional hazard regression models were used to calculate the hazard ratios (HRs) and 95% confidence interval (CIs) of mortality.

Results

Of the adult participants with diabetes, 4963 (47.2%) were female. The median (interquartile range) BUN level of participants was 5 (3.93–6.43) mmol/L. After 86,601 person-years of follow-up, 2,441 deaths were documented. After adjusting for variables, the HRs of cardiovascular disease (CVD) and all-cause mortality in the highest BUN level group were 1.52 and 1.35, respectively, compared with those in the lowest BUN level group. With a one-unit increment in BUN levels, the HRs of all-cause and CVD mortality rates were 1.07 and 1.08, respectively. The results remained robust when several sensitivity and stratified analyses were performed. Moreover, BUN showed a nonlinear association with all-cause and CVD mortality. Their curves all showed that the inflection points were close to the BUN level of 5 mmol/L.

Conclusion

BUN had a nonlinear association with all-cause and CVD mortality in patients with diabetes. The inflection point was at 5 mmol/L.

Peer Review reports

Background

Diabetes affects 1 in 11 individuals worldwide and is one of the major risk factors of death [1]. In 2021, 537 million adults lived with diabetes worldwide, and this number is expected to increase up to 700 million by 2045 [2]. Diabetes has several complications, and cardiovascular disease (CVD) is one of its main complications [3]. Therefore, the early identification of the modifiable risk factors to prevent mortality among patients with diabetes is of utmost importance.

Blood urea nitrogen (BUN) is a biochemical indicator derived from protein metabolism and excretion by the liver and kidneys [4] and is commonly used to measure protein intake and evaluate the status of renal function [5]. BUN levels are affected by several factors, including dehydration, protein intake, gastrointestinal bleeding, and liver disease; previous studies reported that BUN is not only used for evaluating the status of renal function [6]. Evidence from previous experiments suggested that the circulating urea levels have a direct impact on pancreatic beta cell function and lead to insulin secretory deficiency due to the production of oxidative stress and O-linked beta-N-acetylglucosamine in murine models [7, 8]. Based on the epidemiological evidence, a national prospective cohort study including 1,337,452 United States (US) veterans without diabetes conducted by Xie et al. concluded that BUN levels were positively associated with the occurrence of diabetes [9]. Similarly, the association was confirmed in a large Chinese cohort, and elevated BUN levels increased the prevalence of diabetes [10]. Moreover, Luo et al. found BUN levels on admission among patients with intracranial hemorrhage were associated with an increased risk of in-hospital death and 1-year mortality [11]. The results of a retrospective cohort of 2,008 Chinese participants shown higher BUN levels to serum albumin ratios were associated with an increased risk of short-term mortality [12]. However, existing prospective studies have not explored the role of BUN on long-term outcomes in patients with diabetes. This study aimed to explore the correlation of BUN levels with all-cause and CVD mortality in patients with diabetes using a nationally representative prospective cohort.

Research design and methods

Participants

Patients who participated in the National Health and Nutrition Examination Survey (NHANES) and those included in the 10 consecutive cycle datasets (1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018) were enrolled in the present study. The NHANES includes adults and children from 0 to 80 years of age. Briefly, the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention used complex, multistage, stratified methods to collect a representative sample of the US population. NHANES covers several variables, which are mainly related to the demographic, laboratory, dietary, examination, and important questionnaire data. These questionnaire variables were used by trained professionals during in-home and mobile examination center (MEC) interviews. More detailed information about NHANES is available online (https://www.cdc.gov/nchs/nhanes). The NHANES study was approved by the NCHS research ethics review board (protocols 98 − 12, 2005-06, 2011-17, and 2018-01). Patients aged ≥ 18 years who provided written informed consent upon enrollment were included in the study. Those who self-reported as pregnant were excluded (n = 1,305). Diabetes was ascertained according to the following criteria: [1] self-reported physician-diagnosed diabetes or current use of oral antihyperglycemic agents or insulin [2], a glycated hemoglobin A1c (HbA1c) level of ≥ 6.5% [3], a fasting blood glucose level of ≥ 7.0 mmol/L, and/or [4] a postprandial 2-h plasma glucose level of ≥ 11.1 mmol/L [13]. Patients with diabetes [1] with unavailable follow-up information (n = 19) [2], diagnosed with cancer at baseline (n = 1,739), and [3] with missing information on BUN levels (n = 717) were also excluded. Ultimately, 10,507 patients with diabetes were included in the analysis (Supplemental Fig. S1).

Exposure and outcomes

BUN levels were measured using the enzymatic conductivity rate method, which is described in detail in the NHANES. The reference range for BUN levels was 3.1–9.5 mmol/L for adults. The outcomes were based on the mortality data of the National Death Index, which is an NCHS-centralized database, from the date of NHANES interview until follow-up (December 31, 2019) [14]. All underlying causes of death in the US population were precisely recorded following the International Statistical Classification of Diseases, 10th Revision (ICD-10). The ICD-10 codes I60–I69, I20–I51, I13, I11, and I00–I09 were used to determine the cause of CVD death, and malignant neoplasm was ascertained using the C00–C97 codes [15]. The follow-up interval was calculated using the difference between the last known date recorded in the mortality file for each individual and the date of the initial examination.

Covariates

Sociodemographic information on age, marital status (widowed, divorced, married, separated, living with partner, and never married), race/ethnicity (other races, non-Hispanic White, other Hispanics, non-Hispanic Black, and Mexican–American), physical activity (inactive, active, and insufficiently active), smoking history (never, current, and former smokers), educational degree (< high school level, high school level, and > high school level), and alcohol intake (none, heavy, and low-to-moderate drinker) were obtained using standardized interview questionnaires. Household income was measured using the family income-to-poverty ratio (PIR). Patients who smoked ≥ 100 cigarettes, ≥ 100 cigarettes and then stopped smoking, and < 100 cigarettes were considered current, former, and never smokers, respectively [2]. During leisure time, patients who did not perform physical exercise were categorized as “inactive.” Those who engaged in moderate activity with metabolic equivalents of 3–6 at least five times each week or in strenuous exercise with a metabolic equivalent of > 6 at least thrice per week, which met the recommended levels of leisure–time activity, were categorized as “active”; those who were neither inactive nor did not meet the criteria for the active group were categorized as “insufficiently active” [16]. Alcohol status was determined based on the 24-h dietary intake measured during the MEC interview. Men consuming 0.1–27.9 g of alcohol per day and women consuming 0.1–13.9 g of alcohol per day were classified as moderate drinkers, men consuming > 28 g of alcohol per day and women consuming > 14 g of alcohol per day were classified as heavy drinkers, and patients who consumed 0 g of alcohol per day were classified as nondrinkers. Healthy Eating Index 2015 (HEI-2015) was introduced to measure the diet quality [17]. Body mass index (BMI) was subdivided into three categories (≥ 30.0, 25.0–29.9, and < 25.0 kg/m2) [2]. The criteria for diagnosing baseline hypertension were as follows: [1] taking antihypertensive medications or [2] having a systolic blood pressure of ≥ 130 mmHg and/or a diastolic blood pressure of ≥ 80 mmHg [18]. The diagnosis of dyslipidemia was based on the following conditions: [1] high-density lipoprotein levels of < 40 mg/dL in men and < 50 mg/dL in women [2], a total cholesterol (TC) level of ≥ 200 mg/dL [3], a low-density lipoprotein level of ≥ 130 mg/dL [4], a triglyceride level of ≥ 150 mg/dL, and/or [5] taking lipid-lowering medications [19]. Data on diabetes duration and use of diabetes medications, including oral hypoglycemic drugs and insulin, were collected when the patients were interviewed during medical visits. The homeostatic model assessment of insulin resistance (HOMA-IR) was calculated using the formula adopted in previous studies [20]. A 24-h recall interview was performed to collect information on the patient’s daily diet [21]. We calculated the total energy intake (TEI) using the US Department of Agriculture Automated Multiple-Pass Method [22]. The baseline CVD was diagnosed by a professional physician. The baseline levels of biochemical markers, including serum cotinine, serum calcium, serum TC, urine albumin, and serum triglycerides, were strictly analyzed according to the laboratory standards. Further experimental details were also documented in the NHANES. The duration of diabetes and the medications used were obtained through interviews during medical visits.

Statistical analyses

Free Statistics software version 1.4 was used to perform statistical analysis, which was based on the R statistical software package (http://www.R-project.org, the R Foundation) [23]. Categorical and normally distributed continuous variables were presented as numbers (n) with percentages (%) and as means with standard deviations, and nonnormally distributed continuous variables were presented as medians with interquartile ranges. The patients were divided into quarters (Q1–Q4) according to BUN values. The difference between the four groups was compared by one-way ANOVA tests (continuous variables with normal distribution and homogeneity of variance), Kruskal-Wallis test (continuous variables with nonnormal distribution), and χ2 test (categorical variables). A P-value of < 0.05 was considered significant. Person-time was calculated based on either [1] the interval between the date of NHANES interview and death and [2] the interval between the date of NHANES interview and follow-up (December 31, 2019), whichever came first. Kaplan–Meier analysis and log-rank test were used in the univariate survival analysis. The hazard ratios (HRs) and 95% confidence intervals (CIs) of CVD, cancer, and all-cause mortality were calculated using the multivariate Cox proportional hazards regression models. In Model 1, none of the variables were adjusted. Variables such as gender, race/ethnicity, and age were included in Model 2. Model 3 was fully adjusted for gender, race/ethnicity, age, PIR; educational status; marital status; BMI; HbA1c and serum TC levels; diabetes medication use; diabetes duration; insulin therapy; serum cotinine, calcium, serum triglyceride, urine albumin, magnesium, and vitamin D levels; alcohol intake; multivitamin supplement use; kidney disease; TEI; HEI; HOMA-IR; hypertension; hyperlipidemia; CVD; smoking status; and physical activity.

Subgroup analysis was performed according to age (≤ 60 or > 60 years), BMI level (< 30 or ≥ 30 kg/m2), gender, race/ethnicity (others or non-Hispanic White), alcohol intake status, smoking status, diabetes duration (≤ 3 or > 3 years), physical activity, and HbA1c level (< 7% or ≥ 7%). To explore the potential interaction effects between subgroups, likelihood ratio tests were conducted to assess the corresponding multiplicative interaction terms, and P values were also calculated. Moreover, a series of sensitivity analyses were performed. First, patients who died within 2 years after the baseline examination were excluded to reduce the possibility of reverse causality. Second, considering that dietary protein intake plays a critical role in the BUN levels, the sensitivity analysis was additionally adjusted for protein intake. Third, severe liver disease may decrease the BUN levels; therefore, we excluded patients with liver disease. Fourth, as part of the sensitivity analysis, we also determined the association of BUN with mortality among patients with normal BUN levels (3.1–9.5 mmol/L). Fifth, considering that other potential confounding factors may still exist, the inverse probability of treatment weighting (IPTW) method was used to compare the Q1 and Q4 groups. Covariates used in the model were gender, age, BMI, race/ethnicity, PIR, alcohol intake, HOMA-IR, smoking status, HbA1c level, physical activity, diabetes duration, and kidney disease. We used restricted cubic splines(RCS) with three knots to visualize the dose-response association between BUN and mortality. In addition, generalized additive models were used to explore the potential nonlinear association of BUN levels with mortality. When a nonlinear association was found in the models, a recursive algorithm was used to calculate the inflection points in order to determine the link between BUN and mortality; a two-piecewise linear regression was also performed.

Results

The baseline characteristics of the study population are listed in Table 1 according to the BUN level quartiles. Out of the 10,570 patients with diabetes from the NHANES 1999–2018, only 4,963 (47.2%) women were included. The average age of patients was 58.1 ± 15.4 years. The median (interquartile range) BUN level was 5 (3.93–6.43) mmol/L. A higher BUN level was frequently reported among patients who were male, older age, Caucasian, had middle family income, were married and inactive, and had higher HOMA-IR level, shorter diabetes duration, and hypertension and hyperlipidemia. During an average follow-up period of 8.24 years, 2,441 deaths were documented, 858 of which were due to cardiovascular disease and 398 due to cancer. Kaplan–Meier analysis indicated an association between lower BUN levels and lower all-cause, CVD, and cancer mortality rates among patients with diabetes (Supplemental Fig. S2). In the fully adjusted model, BUN was associated with all-cause and CVD mortality rates, and the association of BUN with cancer mortality disappeared (Table 2). Compared with the lowest quartile of BUN levels, the HRs of all-cause and CVD mortality in the highest quartile group were 1.35 (95% CI, 1.18–1.54) (Ptrend < 0.001) and 1.52 (95% CI, 1.2–1.92) (Ptrend < 0.001) in the fully adjusted model, respectively. With a one-unit increment in BUN levels, the HRs of all-cause and CVD mortality were 1.07 (1.06–1.08) and 1.08 (1.06–1.1), respectively. Moreover, the stratified analyses indicated that these associations remained unchanged (Supplemental Figs. S3 and S4). Meanwhile, there was no association found between BUN and all-cause and CVD mortalities among the physically active or alcohol intake groups, largely due to the limited sample size. No significant interaction effects were observed between the subgroups.

Table 1 Baseline characteristics of patients with type 2 diabetes, according to quartiles of BUN
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Table 2 Associations of BUN level with all-cause, CVD, and cancer mortality in patients with diabetes from the NHANES 1999–2018 cohort
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In the sensitivity analyses, these associations were largely unchanged when patients who died early were excluded, dietary protein intake was considered, patients with liver disease were excluded, and the association among patients with normal BUN was analyzed. After IPTW, the HRs of all-cause and CVD mortality remained largely unchanged when compared with that after the primary analyses (Supplemental Tables S1 and S2).

A nonlinear association was found between log2-transformed BUN and all-cause and CVD mortalities in the results of the generalized additive models and smooth curve fittings. Both curves of all-cause and CVD mortalities showed a downward slope in HRs when the BUN level was < 2.322 mmol/L and an ascending slope in HRs when the BUN level was > 2.322 mmol/L (Fig. 1). The results of the threshold effect analysis indicated that BUN was negatively correlated with all-cause mortality when the BUN levels were less than the turning point; meanwhile, a positive correlation existed when the BUN levels were more than the turning point. However, BUN levels were not associated with CVD mortality when the levels were less than the turning point, while a positive correlation still existed when the BUN levels were more than the turning point. The inflection points were close to the BUN level of 5 mmol/L (Table 3).

Fig. 1
figure 1

Smooth curve fitting demonstrates the relationship between BUN levels after log2-transformation and risk of all-cause and CVD mortality. A: all-cause mortality; B: CVD mortality. The predicted risk for all-cause and CVD mortality in the y-axis and the BUN levels after log2 transformation in the x-axis. The black line and gray area represent the estimated values and their corresponding 95% confidence intervals, respectively. The adjused covariates included gender, age, race/ethnicity, PIR, educational status, marital status, BMI, HbA1c, serum total cholesterol, diabetes medication use, diabetes duration, Insulin therapy, Serum cotinine, calcium, serum triglycerides, urine albumin, magnesium, vitamin D, alcohol intake, multivitamin supplements use, kidney disease, TEI, HEI, HOMA-IR, hypertension, hyperlipidemia, CVD, smoke, physical activity

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Table 3 Threshold effect analysis of BUN levels on all-cause and CVD mortality based on segmented linear regression model
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Discussion

To the best of our knowledge, this study is the first to explore the correlation of BUN levels with CVD and all-cause mortality in patients with diabetes. In this large and nationally representative sample of US individuals with diabetes, a nonlinear association was observed between BUN and all-cause and CVD mortality. That is, the BUN levels should be controlled at appropriate levels, and significantly high and low BUN levels should be avoided to reduce the all-cause mortality. Moreover, higher BUN levels were positively associated with a higher prevalence of CVD mortality; hence, the BUN levels should be appropriately controlled.

Previous studies have also shown that BUN levels were correlated with the prevalence of all-cause and CVD mortalities. A previous cohort study of 252 participants with pulmonary disease conducted by Tatlisu et al. [24] concluded that high BUN levels were correlated with augmented risks of all-cause mortality in hospitalized and cardiogenic shock patients. Results from another prospective cohort study of 603 patients with CVD showed an association between BUN and long-term mortality [25]. However, the nonlinear association of BUN with mortality has not been explored in the aforementioned studies. Previous prospective cohort studies have shown a U-shaped association between BUN concentration or BUN/creatinine (Cre) ratio and mortality. One cohort study of 42,038 participants found a U-shaped relationship between BUN/Cre ratio and all-cause mortality in the general population, but the association of BUN/Cre ratio with CVD mortality had not been found after adjusting for potential covariates [26]. Some studies have reported that BUN can be used to predict heart disease [27,28,29]. Moreover, BUN is a more valuable predictor of acute coronary syndrome than Cre [30, 31]. Another cohort study of 26,835 Chinese individuals also showed a nonlinear association of BUN with CVD mortality, and both high and low levels of BUN increased the prevalence of stroke mortality [32]. Although BUN is a relatively well-known indicator, little is known about its optimal level for patients with diabetes. Our findings suggest that maintaining the BUN levels close to the inflection point may be beneficial for preventing CVD or all-cause mortality in patients with diabetes. Moreover, we found that low levels of BUN were not correlated with CVD mortality among US patients with diabetes, which could be explained by the discrepancy in the study population [33,34,35].

The correlation of BUN with the risk of mortality is biologically plausible. In particular, the disturbance in glucose homeostasis is the potential basis of the association of BUN with mortality. Abnormal BUN levels indicate renal function impairment, and the kidneys play an important role in maintaining glucose homeostasis [36, 37]. Uremic metabolites (including urea and sulfates) can disrupt glucose homeostasis [9]. Experimental evidence also suggests that urea may be responsible for abnormal insulin levels [7, 8]. In addition to reflecting kidney function, elevated BUN levels may indicate a decrease in the amount of water in the body, thereby increasing the risk of stroke [38]. Furthermore, BUN levels are associated with neurohormone activation [39]. At the onset of acute coronary syndrome, the renal angiotensin system and sympathetic nervous system are activated, thus increasing the reabsorption of BUN [40].

This study has several major strengths. Most importantly, we used data from a large representative sample of the US population with a relatively long follow-up time; this study is the first to explore the relationship of BUN with mortality in patients with diabetes. Moreover, considering the profile of BUN levels, we additionally adjusted for dietary protein, excluded individuals with liver disease, and performed the analysis among patients with normal BUN values. In addition, using the well-documented variables in the NHANES database, we adjusted for several diabetes-related factors, including HOMA-IR, HbA1c levels, diabetes duration, medication use, and serum cotinine level.

Despite these strengths, the following limitations were considered. First, the causality of BUN on mortality is unclear based on the present study; hence, future studies should examine potential underlying mechanisms this causality. Second, as the NHANES only collected the baseline values of BUN from 1999 to 2019, changes in BUN levels during follow-up could not be obtained, and repeated measurements or the average values of BUN should be considered in later studies. Third, we excluded the influence of some factors on BUN levels, such as dietary protein and liver disease; however, residual factors exist. However, as part of the sensitivity, BUN within the normal range was still associated with mortality. Fourth, although we included several covariates, we could not adjust for all residual covariates. However, stratified analyses were performed to make the findings robust and to confirm their heterogeneity. The IPTW method was also introduced to address the potential covariates. Fifth, the types of diabetes were not well distinguished in NHANES. However, the findings from this study were likely more representative of individuals with type 2 diabetes, because it included participants aged 20 years or older. Sixth, our findings are based on US citizens with diabetes, and the limitation to generalizability should also be considered.

Conclusions

The nonlinear association of BUN with all-cause and CVD mortality exists in this large cohort of US patients with diabetes. Maintaining appropriate BUN levels may be beneficial to prevent all-cause or CVD mortality among patients with diabetes. Further clinical trials are also warranted to validate these results.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

Abbreviations

CVD:

Cardiovascular disease

Hb1Ac:

Glycohaemoglobin

NCHS:

National center for health statistics

NHANES:

National health and nutrition examination survey

BMI:

Body mass index

References

  1. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98.

    Article  PubMed  Google Scholar 

  2. Qiu Z, Chen X, Geng T, Wan Z, Lu Q, Li L, Zhu K, Zhang X, Liu Y, Lin X, Chen L, Shan Z, Liu L, Pan A, Liu G. Associations of serum carotenoids with risk of cardiovascular mortality among individuals with type 2 diabetes: results from NHANES. Diabetes Care. 2022;45(6):1453–61.

    Article  CAS  PubMed  Google Scholar 

  3. Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16(7):377–90.

    Article  PubMed  PubMed Central  Google Scholar 

  4. van Veldhuisen DJ, Ruilope LM, Maisel AS, Damman K. Biomarkers of renal injury and function: diagnostic, prognostic and therapeutic implications in heart failure. Eur Heart J. 2016;37(33):2577–85.

    Article  PubMed  Google Scholar 

  5. You S, Zheng D, Zhong C, Wang X, Tang W, Sheng L, Zheng C, Cao Y, Liu CF. Prognostic significance of blood urea nitrogen in acute ischemic stroke. Circ J. 2018;82(2):572–8.

    Article  CAS  PubMed  Google Scholar 

  6. Dossetor JB. Creatininemia versus Uremia. The relative significance of blood urea nitrogen and serum creatinine concentrations in azotemia. Ann Intern Med. 1966;65(6):1287–99.

    Article  CAS  PubMed  Google Scholar 

  7. Koppe L, Nyam E, Vivot K, Manning Fox JEM, Dai XQ, Nguyen BN, Trudel D, Attané C, Moullé VS, MacDonald PE, Ghislain J, Poitout V. Urea impairs β cell glycolysis and insulin secretion in chronic kidney disease. J Clin Invest. 2016;126(9):3598–35612.

    Article  PubMed  PubMed Central  Google Scholar 

  8. D’Apolito M, Du X, Zong H, Catucci A, Maiuri L, Trivisano T, Pettoello-Mantovani M, Campanozzi A, Raia V, Pessin JE, Brownlee M, Giardino I. Urea-induced ROS generation causes insulin resistance in mice with chronic renal failure. J Clin Invest. 2010;120(1):203–13.

    Article  PubMed  Google Scholar 

  9. Xie Y, Bowe B, Li T, Xian H, Yan Y, Al-Aly Z. Higher blood urea nitrogen is associated with increased risk of incident diabetes mellitus. Kidney Int. 2018;93(3):741–52.

    Article  CAS  PubMed  Google Scholar 

  10. Li SN, Cui YF, Luo ZY, Lou YM, Liao MQ, Chen HE, Peng XL, Gao XP, Zhao D, Xu S, Wang L, Ma JP, Chen QS, Ping Z, Liu H, Zeng FF. Association between blood urea nitrogen and incidence of type 2 diabetes mellitus in a Chinese population: a cohort study. Endocr J. 2021;68(9):1057–65.

    Article  CAS  PubMed  Google Scholar 

  11. Luo H, Yang X, Chen K, Lan S, Liao G, Xu J. Blood creatinine and urea nitrogen at ICU admission and the risk of in-hospital death and 1-year mortality in patients with intracranial hemorrhage. Front Cardiovasc Med. 2022;9:967614.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang YY, Xia G, Yu D, Tu F, Liu J. The association of blood urea nitrogen to serum albumin ratio with short-term outcomes in Chinese patients with congestive heart failure: a retrospective cohort study. Nutr Metab Cardiovasc Dis. 2024;34(1):55–63.

    Article  CAS  PubMed  Google Scholar 

  13. Huang J, Hu L, Yang J. Dietary magnesium intake ameliorates the association between household pesticide exposure and type 2 diabetes: data from Nhanes, 2007–2018. Front Nutr. 2022;9:903493.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Skopp NA, Smolenski DJ, Schwesinger DA, Johnson CJ, Metzger-Abamukong MJ, Reger MA. Evaluation of a methodology to validate National Death Index retrieval results among a cohort of U.S. service members. Ann Epidemiol. 2017;27(6):397–400.

    Article  PubMed  Google Scholar 

  15. Rong S, Snetselaar LG, Xu G, Sun Y, Liu B, Wallace RB, Bao W. Association of skipping breakfast with cardiovascular and all-cause mortality. J Am Coll Cardiol. 2019;73(16):2025–32.

    Article  PubMed  Google Scholar 

  16. Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C, Buchner D, Ettinger W, Heath GW, King AC, et al. Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995;273(5):402–7.

    Article  CAS  PubMed  Google Scholar 

  17. Reedy J, Lerman JL, Krebs-Smith SM, Kirkpatrick SI, Pannucci TE, Wilson MM, Subar AF, Kahle LL, Tooze JA. Evaluation of the healthy eating Index-2015. J Acad Nutr Diet. 2018;118(9):1622–33.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Whelton PK, Carey RM, Aronow WS, Casey DE Jr., Collins KJ, Dennison Himmelfarb CD, DePalma SM, Gidding S, Jamerson KA, Jones DW, MacLaughlin EJ, Muntner P, Ovbiagele B, Smith SC Jr., Spencer CC, Stafford RS, Taler SJ, Thomas RJ, Williams KA, Sr., Williamson JD, Wright JT. APhA/ASH. 1979;71(6):e13–115. (Dallas T.:. Jr. ACC/AHA/AAPA/ABC/ACPM/AGS. Hypertension 2018.

    Google Scholar 

  19. National Cholesterol Education Program (NCEP). Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143–421.

    Article  Google Scholar 

  20. Nakamura K, Sakurai M, Miura K, Morikawa Y, Nagasawa SY, Ishizaki M, Kido T, Naruse Y, Nakashima M, Nogawa K, Suwazono Y, Nakagawa H. HOMA-IR and the risk of hyperuricemia: a prospective study in non-diabetic Japanese men. Diabetes Res Clin Pract. 2014;106(1):154–60.

    Article  CAS  PubMed  Google Scholar 

  21. Ahluwalia N, Dwyer J, Terry A, Moshfegh A, Johnson C. Update on NHANES dietary data: focus on collection, release, analytical considerations, and uses to inform public policy. Adv Nutr. 2016;7(1):121–34.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kligler B. Ask the experts: is there any real evidence that people who eat organic food are healthier? Explore (NY). 2007;3(6):640.

    Article  PubMed  Google Scholar 

  23. Huang J, Hu L, Yang J. Dietary zinc intake and body mass index as modifiers of the association between household pesticide exposure and infertility among US women: a population-level study. Environ Sci Pollut Res Int. 2023;30(8):20327–36.

    Article  CAS  PubMed  Google Scholar 

  24. Tatlisu MA, Kaya A, Keskin M, Avsar S, Bozbay M, Tatlisu K, Eren M. The association of blood urea nitrogen levels with mortality in acute pulmonary embolism. J Crit Care. 2017;39:248–53.

    Article  CAS  PubMed  Google Scholar 

  25. Özyıldız AG, Kalaycıoğlu E, Özyıldız A, Turan T, Çetin M. Blood urea nitrogen to left ventricular ejection fraction ratio is associated with long-term mortality in the stable angina pectoris patients. Eur Rev Med Pharmacol Sci. 2022;26(24):9250–7.

    PubMed  Google Scholar 

  26. Shen S, Yan X, Xu B. The blood urea nitrogen/creatinine (BUN/cre) ratio was U-shaped associated with all-cause mortality in general population. Ren Fail. 2022;44(1):184–1890.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Aronson D, Mittleman MA, Burger AJ. Elevated blood urea nitrogen level as a predictor of mortality in patients admitted for decompensated heart failure. Am J Med. 2004;116(7):466–73.

    Article  CAS  PubMed  Google Scholar 

  28. Matsue Y, van der Meer P, Damman K, Metra M, O’Connor CM, Ponikowski P, Teerlink JR, Cotter G, Davison B, Cleland JG, Givertz MM, Bloomfield DM, Dittrich HC, Gansevoort RT, Bakker SJ, van der Harst P, Hillege HL, van Veldhuisen DJ, Voors AA. Blood urea nitrogen-to-creatinine ratio in the general population and in patients with acute heart failure. Heart (Br Card Soc). 2017;103(6):407–13.

    CAS  Google Scholar 

  29. Zhen Z, Liang W, Tan W, Dong B, Wu Y, Liu C, Xue R. Prognostic significance of blood urea nitrogen/creatinine ratio in chronic HFpEF. Eur J Clin Investig. 2022;52(7):e13761.

    Article  CAS  Google Scholar 

  30. Kajimoto K, Minami Y, Sato N, Takano T. Investigators of the Acute Decompensated Heart failure syndromes (ATTEND) registry. Serum sodium concentration, blood urea nitrogen, and outcomes in patients hospitalized for acute decompensated heart failure. Int J Cardiol. 2016;222:195–201.

    Article  PubMed  Google Scholar 

  31. Hong C, Zhu H, Zhou X, Zhai X, Li S, Ma W, Liu K, Shirai K, Sheerah HA, Cao J. Association of blood urea nitrogen with cardiovascular diseases and all-cause mortality in USA adults: results from NHANES 1999–2006. Nutrients. 2023;15(2):461.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Peng R, Liu K, Li W, Yuan Y, Niu R, Zhou L, Xiao Y, Gao H, Yang H, Zhang C, Zhang X, He M, Wu T. Blood urea nitrogen, blood urea nitrogen to creatinine ratio and incident stroke: the Dongfeng-Tongji cohort. Atherosclerosis. 2021;333:1–8.

    Article  CAS  PubMed  Google Scholar 

  33. Palmer ND, Hester JM, An SS, Adeyemo A, Rotimi C, Langefeld CD, Freedman BI, Ng MC, Bowden DW. Resequencing and analysis of variation in the TCF7L2 gene in African americans suggests that SNP rs7903146 is the causal diabetes susceptibility variant. Diabetes. 2011;60(2):662–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Juttada U, Kumpatla S, Parveen R, Viswanathan V. TCF7L2 polymorphism a prominent marker among subjects with type-2-Diabetes with a positive family history of diabetes. Int J Biol Macromol. 2020;159:402–5.

    Article  CAS  PubMed  Google Scholar 

  35. Campbell JM, Bellman SM, Stephenson MD, Lisy K. Metformin reduces all-cause mortality and diseases of ageing independent of its effect on diabetes control: a systematic review and meta-analysis. Ageing Res Rev. 2017;40:31–44.

    Article  CAS  PubMed  Google Scholar 

  36. Abe M, Kalantar-Zadeh K. Haemodialysis-induced hypoglycaemia and glycaemic disarrays. Nat Rev Nephrol. 2015;11(5):302–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Thomas SS, Zhang L, Mitch WE. Molecular mechanisms of insulin resistance in chronic kidney disease. Kidney Int. 2015;88(6):1233–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Coull BM, Beamer N, de Garmo P, genderton G, Nordt F, Knox R, Seaman GV. Chronic blood hyperviscosity in subjects with acute stroke, transient ischemic attack, and risk factors for stroke. Stroke. 1991;22(2):162–8.

    Article  CAS  PubMed  Google Scholar 

  39. Liu EQ, Zeng CL. Blood urea nitrogen and in-hospital mortality in critically ill patients with cardiogenic shock: analysis of the MIMIC-III database. BioMed Res Int. 2021;2021:5948636.

    PubMed  PubMed Central  Google Scholar 

  40. Conte G, Dal Canton A, Terribile M, Cianciaruso B, Di Minno G, Pannain M, Russo D, Andreucci VE. Renal handling of urea in subjects with persistent azotemia and normal renal function. Kidney Int. 1987;32(5):721–7.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank Dr. Zhang Jing for providing help with data collection for this study.

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Author notes

  1. Hongfang Liu, Xiaoqin Xin authors contributed equally to this work.

Authors and Affiliations

  1. Electrocardiography Department, Ganzhou Maternal and Child Health Hospital, Ganzhou, Jiangxi Province, 341000, China

    Hongfang Liu

  2. Department of Clinical Laboratory, Ganzhou People’s Hospital, Ganzhou, Jiangxi Province, 341000, China

    Xiaoqin Xin

  3. Department of Medical Genetic, Ganzhou Maternal and Child Health Hospital, Ganzhou, Jiangxi Province, 341000, China

    Jinghui Gan & Jungao Huang

Authors
  1. Hongfang Liu
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  2. Xiaoqin Xin
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  3. Jinghui Gan
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  4. Jungao Huang
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Contributions

HFL drafted the manuscript. XQX collected the clinical data and designed the study. JHG reviewed the data analyses. JGH proposed and designed the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jungao Huang.

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Ethics approval and consent to participate

The NHANES study was approved by the NCHS research ethics review board (protocols 98 − 12, 2005-06, 2011-17, and 2018-01). Patients aged ≥ 18 years who provided written informed consent upon enrollment were included in the study.

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Liu, H., Xin, X., Gan, J. et al. The long-term effects of blood urea nitrogen levels on cardiovascular disease and all-cause mortality in diabetes: a prospective cohort study. BMC Cardiovasc Disord 24, 256 (2024). https://doi.org/10.1186/s12872-024-03928-6

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  • DOI: https://doi.org/10.1186/s12872-024-03928-6

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

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