Higher Blood Urea Nitrogen and Urinary Calcium: New Risk Factors for Diabetes Mellitus in Primary Aldosteronism Patients

Association of blood urea nitrogen to creatinine ratio with incident type 2 diabetes mellitus: A retrospective cohort study in the Chinese population

Renal dysfunction can lead to insulin resistance and increase the incidence of type 2 diabetes mellitus (T2DM). The blood urea nitrogen to creatinine ratio (NCR) is a frequently used indicator to assess renal dysfunction and differentiate between prerenal and intrinsic renal injury. However, the association between NCR and T2DM in the Chinese population remains unclear. Hence, this study aimed to investigate the association between NCR and the incidence of T2DM in the Chinese population. The relationship between NCR and T2DM was examined using the Cox proportional hazards model and curve fitting techniques. In addition, a comprehensive set of sensitivity and subgroup analyses were performed. All results were presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Between 2010 and 2016, 189,416 Chinese people were recruited from the Rich Healthcare Group for this retrospective cohort study. Of the participants, 3755 (19.8%) were diagnosed with T2DM during the follow-up period. After full adjustment, the Cox proportional hazards model revealed a positive connection between NCR and the incidence of T2DM (HR = 1.03, 95% CI: 1.02-1.04, P < .001). Compared with individuals with lower NCR Q1 (≤13.536), the multivariate HR for NCR and T2DM in Q2 (13.536-16.256), Q3 (16.256-19.638), Q4 (>19.638) were 1.08 (0.98-1.19), 1.16 (1.05-1.28), 1.39 (1.26-1.53). The higher NCR groups (≥20) had a higher ratio of T2DM (HR = 1.28, 95% CI: 1.18-1.38, P < .001) than the lowest NCR group (<20). These findings were validated using sensitivity and subgroup analyses. In conclusion, this study found a positive and independent association between NCR and the incidence of T2DM after adjusting for confounding variables.

A novel predictive model for optimizing diabetes screening in older adults

INTRODUCTION

The fasting blood glucose test is widely used for diabetes screening. However, it may fail to detect early-stage diabetes characterized by elevated postprandial glucose levels. Hence, we developed and internally validated a nomogram to predict the diabetes risk in older adults with normal fasting glucose levels.

MATERIALS AND METHODS

This study enrolled 2,235 older adults, dividing them into a Training Set (n = 1,564) and a Validation Set (n = 671) based on a 7:3 ratio. We employed the least absolute shrinkage and selection operator regression to identify predictors for constructing the nomogram. Calibration and discrimination were employed to assess the nomogram's performance, while its clinical utility was evaluated through decision curve analysis.

RESULTS

Nine key variables were identified as significant factors: age, gender, body mass index, fasting blood glucose, triglycerides, alanine aminotransferase, the ratio of alanine aminotransferase to aspartate aminotransferase, blood urea nitrogen, and hemoglobin. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.824 in the Training Set and 0.809 in the Validation Set. Calibration curves for both sets confirmed the model's accuracy in estimating the actual diabetes risk. Decision curve analysis highlighted the model's clinical utility.

CONCLUSIONS

We provided a dynamic nomogram for identifying older adults at risk of diabetes, potentially enhancing the efficiency of diabetes screening in primary healthcare units.

Effectiveness of Spironolactone in Reducing Osteoporosis and Future Fracture Risk in Middle-Aged and Elderly Hypertensive Patients

Objective

While the role of aldosterone in bone metabolism is well established, the specific effects of the widely used aldosterone antagonist, spironolactone, on bone health are not fully understood. This study aimed to investigate the effects of spironolactone on osteoporosis and future fracture risk in middle-aged and elderly hypertensive patients, revealing its potential benefits for bone health.

Methods

Propensity score matching was employed in this study to create matched groups of spironolactone users and non-users at a 1:4 ratio. We investigated the association between spironolactone use and the risk of osteoporosis using multivariate logistic regression analysis. Furthermore, we conducted multivariate linear regression analysis to explore the relationship between cumulative dosage and the FRAX score. Subgroup analysis was also performed to assess the effects under different stratification conditions.

Results

In both pre-match and post-match analyses, multivariable logistic regression revealed a significant reduction in the risk of osteoporosis in the spironolactone usage group (pre-match: odds ratios [OR] 0.406, 95% confidence interval [CI], 0.280-0.588; post-match: OR 0.385, 95% CI, 0.259-0.571). Furthermore, post-match multivariable linear regression demonstrated a clear negative correlation between cumulative spironolactone dosage and the FRAX score. Subgroup analyses consistently supported these findings.

Conclusion

This study offers evidence supporting the significant positive impact of the antihypertensive drug spironolactone on bone health, resulting in a substantial reduction in the risk of osteoporosis and future fractures in hypertensive patients. Future research should consider conducting large-scale, multicenter, randomized controlled trials to further investigate the long-term effects of spironolactone on bone health in hypertensive patients.

Association between blood urea nitrogen levels and the risk of diabetes mellitus in Chinese adults: secondary analysis based on a multicenter, retrospective cohort study

Background

As one of the recognized indicators of kidney function, blood urea nitrogen (BUN) is a key marker of metabolic diseases and other diseases. Currently, data on the relationship of BUN levels with the risk of diabetes mellitus (DM) in Chinese adults are sparse. This study aimed to investigate the correlation between BUN levels and DM risk in Chinese adults.

Data and methods

This study is a secondary analysis of a multicenter, retrospective cohort study with data from the Chinese health screening program in the DATADRYAD database. From 2010 to 2016, health screening was conducted on 211833 Chinese adults over the age of 20 in 32 locations and 11 cities in China, and there was no DM at baseline. Cox proportional hazards regression analysis assessed an independent correlation between baseline BUN levels and the risk of developing DM. The Generalized Sum Model (GAM) and smoothed curve fitting methods were used to explore the nonlinear relationship. In addition, subgroup analyses were performed to assess the consistency of correlations between different subgroups and further validate the reliability of the results.

Results

After adjusting for potential confounding factors (age, sex, etc.), BUN levels were positively correlated with the occurrence of DM (HR=1.11, 95% CI (1.00~1.23)). BUN level had a nonlinear relationship with DM risk, and its inflection point was 4.2mmol/L. When BUN was greater than 4.2mmol/L, BUN was positively correlated with DM, and the risk of DM increased by 7% for every 1 mmol/L increase in BUN (P<0.05). Subgroup analysis showed that a more significant correlation between BUN levels and DM was observed in terms of sex, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), alaninetransaminase (ALT), aspartate transaminase (AST), creatinine (Cr) and smoking status (interaction P<0.05).

Conclusion

High levels of BUN are associated with an increased risk of DM in Chinese adults, suggesting that active control of BUN levels may play an important role in reducing the risk of DM in Chinese adults.

A novel clinical-imaging nomogram for predicting primary aldosteronism in patients with hypertension

This study aimed to develop and validate the accuracy of a clinical-imaging index nomogram in predicting primary aldosteronism (PA) in patients with hypertension. This case-control study enrolled 404 hypertension patients in the First Affiliated Hospital of Nanjing Medical University, China, from April 2017 to September 2021. The patients were randomly divided into the training set (n = 283, 70%) and the validation set (n = 121, 30%). Univariate and multivariate logistic regression analyses were performed to identify independent predictors of PA, which were then used construct a nomogram. The receiver operating characteristic (ROC) curve and calibration plot were drawn to assess the predictive value. The accuracies of our nomogram and other known prediction models were compared using decision curve analyses (DCA). Four significant variables (history of hypokalemia [OR = 2.684, 95% CI: 1.281-5.623, P < 0.001], typical imaging feature [OR = 2.316, 95% CI: 1.166-4.601, P = 0.003], 24 h urine potassium [OR = 0.956, 95% CI: 0.932-0.980, P < 0.001], plasma renin activity [PRA] [OR = 1.423, 95% CI: 1.161-1.744, P < 0.001]) in the multivariate logistic regression analysis were sifted out, and used to build the nomogram. The predictive nomogram yielded an AUC of 0.890 (95% CI, 0.853-0.927) in the training set and 0.860 (95% CI, 0.793-0.927) in the validation set. Predicted and actual probability of PA matched well in the nomogram. Moreover, the DCA showed that the nomogram gained a net benefit in clinical practice in predicting PA when the threshold value was set between 0.1 and 1.0. Our four-variable nomogram was accurate in predicting PA patients and might be introduced into clinical management.

Development and validation of a simple-to-use nomogram for self-screening the risk of dyslipidemia

This study aimed to help healthy adults achieve self-screening by analyzing the quantitative relationship between body composition index measurements (BMI, waist-to-hip ratio, etc.) and dyslipidemia and establishing a logical risk prediction model for dyslipidemia. We performed a cross-sectional study and collected relevant data from 1115 adults between November 2019 and August 2020. The least absolute shrinkage selection operator (LASSO) regression analysis was performed to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 10 predictor variables (a "nomogram," see the precise definition in the text) was constructed to predict the risk of dyslipidemia in healthy adults. A calibration diagram, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to verify the model's utility. Our proposed dyslipidemia nomogram showed good discriminative ability with a C-index of 0.737 (95% confidence interval, 0.70-0.773). In the internal validation, a high C-index value of 0.718 was achieved. DCA showed a dyslipidemia threshold probability of 2-45%, proving the value of the nomogram for clinical application for dyslipidemia. This nomogram may be useful for self-screening the risk of dyslipidemia in healthy adults.

Recovery from diabetes mellitus in primary aldosteronism patients after adrenalectomy

BACKGROUND

The prevalence of diabetes mellitus (DM) was higher in primary aldosteronism (PA) patients. We aimed to evaluate the outcome of DM after adrenalectomy and determine the factors associated with that in PA patients.

METHODS

PA patients with DM (PA + DM patients) who received adrenalectomy were recruited into the study. The patients were classified into 3 groups based on their DM conditions after treatment, including "remission", "improved" and "unchanged" groups. Univariate and multivariate logistic regression analysis was conducted to uncover the preoperative factors affecting the outcome of DM after adrenalectomy.

RESULTS

A total of 54 PA + DM patients received adrenalectomy. After adrenalectomy, 16.7%, 33.3% and 50.0% of patients were classified into the "remission", "improved" and "unchanged" groups, respectively. The factors negatively associated with remission or improvement from DM after adrenalectomy were longer duration of hypertension (P = 0.029). Higher concentration of urinary magnesium (P = 0.031) and higher 24 h urinary potassium (P = 0.049) were factors negatively associated with the "remission" from DM after adrenalectomy.

CONCLUSIONS

Adrenalectomy was beneficial for the remission and improvement from DM in the half of PA patients with DM. Longer duration of hypertension, higher concentration of urinary magnesium and higher 24 h urinary potassium may prevent the remission and improvement from DM after adrenalectomy in PA patients. Examination of urinary electrolyte could be considered in PA patients with DM for predicting the outcome of DM after adrenalectomy.

Analysis of the Value of the Blood Urea Nitrogen to Albumin Ratio as a Predictor of Mortality in Patients with Sepsis

Purpose

At present, simple, accurate, and efficient prognostic tools for the evaluation of cases with early-stage sepsis in the emergency department (ED) are lacking. An increased blood urea nitrogen to albumin ratio (BAR) has previously been shown to be a valuable biomarker with predictive utility in several diseases. The relationship between BAR and sepsis patient outcomes, however, is not well-understood. This exploration was thus developed for the exploration of the link between BAR values and the short-term prognosis of cases with sepsis.

Methods

This was a retrospective cohort research of sepsis cases admitted to the West China Hospital of Sichuan University ED from July 2015 to June 2016. Laboratory data were collected upon ED admission, and 7-day all-cause mortality was the primary study endpoint. Relationships between BAR values and APACE II and SOFA scores were generated assessed with correlation coefficient heatmaps. Independent risk factors were identified through multivariate analyses, with the curves of receiver operating characteristic (ROC) being employed to gauge the value of BAR as a predictor of the risk of mortality in sepsis cases.

Results

In sum, 801 patients participated in the present investigation. BAR values were strongly correlated with APACHE II and SOFA scores. In a multivariate logistic regression assessment, BAR was identified as an independent predictor of mortality among patients with sepsis (HR=1.032, 95% CI: 1.010–1.055, P=0.004). BAR exhibited an AUC of 0.741 (95% CI: 0.688–0.793, P<0.001) when used to predict patient mortality risk, with 5.27 being the optimal BAR cut-off.

Conclusion

We found that BAR can be used as a reliable biomarker to predict mortality in patients with sepsis.

The Association Between the Triglyceride-to-High-Density Lipoprotein Cholesterol Ratio and the Risk of Progression to Diabetes From Prediabetes: A 5-year Cohort Study in Chinese Adults

OBJECTIVE

Evidence regarding the relationship between the triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-c) ratio and the risk of progression from prediabetes to diabetes remains limited. The purpose of our study was to investigate the relationship between the TG/HDL-C ratio and incident diabetes in prediabetic patients.

METHODS

This retrospective cohort study covered 32 regions and 11 cities in China and consecutively and non-selectively collected data from 15,017 patients with prediabetes who had received a health check from 2010 to 2016. Data were obtained from the DATADRYAD database (www.datadryad.org). The Cox proportional-hazards regression model with cubic spline functions and smooth curve fitting (cubic spline smoothing) was used to explore the non-linear relationship between the baseline TG/HDL-c ratio and the risk of diabetes in patients with prediabetes. In addition, we performed a series of sensitivity and subgroup analyses.

RESULTS

The mean age of the included individuals was 50.95 ± 13.48 years, and 9,745 (64.51%) were men. The median (interquartile range) TG/HDL-c ratio was 1.09 (0.69-1.72). During a median follow-up time of 3.05 years, 1,731 (11.46%) patients had a final diagnosis of diabetes. The analysis after adjusting for covariates showed that the TG/HDL-c ratio was positively related to incident diabetes in patients with prediabetes (HR = 1.111, 95% CI 1.061-1.164). Participants with the highest TG/HDL-c ratio (Q4) had higher diabetes incidence rates than those with the lowest TG/HDL-c ratio (Q1) (P < 0.001 for the trend). There was a non-linear relationship between the TG/HDL-c ratio and the risk of diabetes, and the inflection point of the TG/HDL-c ratio was 1.415. The effect sizes (HR) on the left and right sides of the inflection point were 1.336 (95% CI: 1.134-1.573) and 1.055 (95% CI: 0.988-1.126), respectively. The sensitivity analysis demonstrated the robustness of these results.

CONCLUSION

This study demonstrates a positive, non-linear relationship between the TG/HDL-c ratio and the risk of diabetes in Chinese patients with prediabetes. Aggressive intervention from a treatment perspective is required to lower the TG/HDL-c ratio below the inflection point (1.415) by lowering TG or increasing HDL-c levels.

Online prediction model for primary aldosteronism in patients with hypertension in Chinese population: A two-center retrospective study

BACKGROUND

The prevalence of primary aldosteronism (PA) varies from 5% to 20% in patients with hypertension but is largely underdiagnosed. Expanding screening for PA to all patients with hypertension to improve diagnostic efficiency is needed. A novel and portable prediction tool that can expand screening for PA is highly desirable.

METHODS

Clinical characteristics and laboratory data of 1,314 patients with hypertension were collected for modeling and randomly divided into a training cohort (919 of 1,314, 70%) and an internal validation cohort (395 of 1,314, 30%). Additionally, an external dataset (n = 285) was used for model validation. Machine learning algorithms were applied to develop a discriminant model. Sensitivity, specificity, and accuracy were used to evaluate the performance of the model.

RESULTS

Seven independent risk factors for predicting PA were identified, including age, sex, hypokalemia, serum sodium, serum sodium-to-potassium ratio, anion gap, and alkaline urine. The prediction model showed sufficient predictive accuracy, with area under the curve (AUC) values of 0.839 (95% CI: 0.81-0.87), 0.814 (95% CI: 0.77-0.86), and 0.839 (95% CI: 0.79-0.89) in the training set, internal validation, and external validation set, respectively. The calibration curves exhibited good agreement between the predictive risk of the model and the actual risk. An online prediction model was developed to make the model more portable to use.

CONCLUSION

The online prediction model we constructed using conventional clinical characteristics and laboratory tests is portable and reliable. This allowed it to be widely used not only in the hospital but also in community health service centers and may help to improve the diagnostic efficiency of PA.

Associations between urea nitrogen and risk of depression among subjects with and without type 2 diabetes: A nationwide population-based study

BACKGROUND

Depression and type 2 diabetes (T2D) are serious public health problems with irreversible health consequences and a significant economic burden on the healthcare system. Previous studies have suggested that blood urea nitrogen (BUN) was inversely longitudinally associated with incidence of diabetes and depression in adults, but few well-designed studies have examined the effects of status of T2D on the full range of relationship between BUN and depression.

METHODS

The analysis sample consisted of adults aged≥20 years from the 2007-2014 National Health and Nutrition Examination Survey (NHANES) who completed the Patient Health Questionnaire-9 (PHQ-9), involving 19,005 participants. By stratifying participants according to T2D status, we further assessed the difference between BUN and risk of depression in participants with and without T2D using multivariate logistic regression (interaction test).

RESULTS

In this cross-sectional study, the association between BUN and depression prevalence appeared to differ between the T2D and non-T2D groups (OR: 1.00, 95% Cl: 0.95-1.05 vs. OR: 0.89, 95% Cl: 0.85-0.93). In addition, there was evidence of an interaction between BUN levels and T2D status in reducing the risk of depression (P value for interaction = 0.032.) The relationship between BUN and depressive symptoms was significant in non-T2D subjects (P < 0.001), but not in T2D (P = 0.940).

CONCLUSIONS

Our findings suggest that there is a significant relationship between BUN and depression, and T2D status may influence the association between BUN and the risk of depression. Such findings require further prospective studies to provide more evidence.

Mechanism exploration of Gouqi-wentang formula against type 2 diabetes mellitus by phytochemistry and network pharmacology-based analysis and biological validation

BACKGROUND

The Gouqi-wentang formula (GQWTF) is a herbal formula used by Academician Xiao-lin Tong for the clinical treatment of T2DM. GQWTF is beneficial to qi, nourishes Yin, clears heat, and promotes fluid production, but the effective components and their mechanism of action remain unclear.

METHODS

The main components of GQWTF were detected by LC-MS, and the multi-target mechanisms of GQWTF in T2DM were elucidated using network pharmacology analysis, including target prediction, protein-protein interaction network construction and analysis, Gene Ontology (GO) terms, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway annotation, and other network construction. Finally, the efficacy of the GQWTF was verified using biological experiments.

RESULTS

First, the "herb-channel tropism" network suggested that GQWTF focuses more on treating diseases by recuperating the liver, which is considered as an important insulin-sensitive organ. Subsequently, a total of 16 active ingredients in GQWTF were detected and screened, and their biological targets were predicted. Then, "compound-target" network was constructed, where enrichment analysis of GQWTF targets reflected its potential pharmacological activities. After T2DM-related target identification, 39 cross targets of GQWTF and T2DM were obtained, and 30 key targets highly responsible for the beneficial effect of GQWTF on T2DM were identified by PPI analysis. GO analysis of these key targets showed that many biological processes of GQWTF in treating T2DM are key in the occurrence and development of T2DM, including components related to inflammatory/immune response, insulin, and metabolism. KEGG analysis revealed the regulation of multiple signalling pathways, such as insulin resistance, PPAR signalling pathway, FoxO signalling pathway, Fc epsilon RI signalling pathway, and pathways that influence diabetes primarily by regulating metabolism as well as other T2DM directly related pathways. Furthermore, a "formula-compound-pathway-symptom" network was constructed to represent a global view of GQWTF in the treatment of T2DM.

CONCLUSIONS

This study explored the mechanism of action of GQWTF in T2DM by multi-component and multi-target multi pathways, which could provide a theoretical basis for the development and clinical application of GQWTF.

Alteration of Gut Microbiota Relates to Metabolic Disorders in Primary Aldosteronism Patients

PURPOSE

This study aimed to determine the relationships among gut microbiota, primary aldosteronism (PA), and related metabolic disorders.

METHODS

The study enrolled 13 PA patients, 26 sex-matched primary hypertension patients, and 26 sex-matched healthy controls. Demographic and clinical characteristics such as age, body mass index (BMI), blood aldosterone-renin ratio, blood potassium, blood glucose, blood lipid parameters, and history of diabetes mellitus (DM) were compared between the three groups. The gut microbiota of each participant was examined by 16S rRNA gene sequencing. Spearman correlation analysis was performed to demonstrate the relationship between gut microbiota and clinical characteristics.

RESULTS

BMI and the percentage of DM in PA patients were higher than those in healthy controls (p < 0.05), but not higher than those in primary hypertension patients (p > 0.05). The gut microbiota of healthy controls and primary hypertension patients had a higher alpha diversity level than that of PA patients. PA patients had fewer short-chain fatty acid (SCFA)-producing genera (Prevotella, Blautia, Coprococcus, Anaerostipes, and Ruminococcus) and more inflammation-associated genera (Megamonas, Sutterella, and Streptococcus) than healthy controls (p < 0.05). The gut microbiota of PA patients was more inclined to encode microbial pathways involved in sugar metabolism, such as starch and sucrose metabolism and fructose and mannose metabolism. Blood potassium was negatively correlated with the relative abundance of Romboutsia (R = -0.364, q = 0.023). Diastolic blood pressure (DBP) was positively correlated with Romboutsia (R = 0.386, q = 0.015). Systolic blood pressure (SBP) was negatively correlated with Blautia (R = -0.349, q = 0.030).

CONCLUSIONS

The alteration of gut microbiota in PA patients, especially bacteria and pathways involved in inflammation, SCFAs, and sugar metabolism, may be associated with chronic metabolic disorders.

Association of Serum Uric Acid, Urea Nitrogen, and Urine Specific Gravity Levels at 16-18 Weeks of Gestation with the Risk of Gestational Diabetes Mellitus

OBJECTIVE

To evaluate the associations of serum uric acid (UA), urea nitrogen (UN), and urine specific gravity (USG) levels in the first trimester of pregnancy with the risk of gestational diabetes mellitus (GDM).

PATIENTS AND METHODS

A retrospective cohort study was conducted in 1,769 pregnant women aged 31.55 ± 3.91 years. UA, UN, and USG levels were measured during the 16-18th week of gestation. GDM was diagnosed by an oral 75 g glucose tolerance test during the 24-28th week of gestation.

RESULTS

A multivariate adjusted logistic regression analysis showed that UA levels in the highest quartile increased the risk of GDM by 55.7% (odds ratio [OR]: 1.557, 95% confidence interval [CI]: 1.055-2.298; p = 0.026) compared to those in the lowest quartile. USG levels in the second, third, and fourth quartiles increased the risk of GDM by 67.6% (95% CI: 1.090-2.421), 112.4% (95% CI: 1.446-3.119), and 94.5% (95% CI: 1.314-2.880), respectively, compared to those in the first quartile (p trend = 0.001). No significant association between UN levels and the GDM risk was observed. When the extreme composite biomarker score quartiles were compared, the adjusted OR (95% CI) for GDM was 1.909 (95% CI: 1.332-2.736). Age-stratified analyses revealed similar results in women aged ≤35 years only, but not in those aged >35 years.

CONCLUSION

Higher levels of UA and USG and a higher composite kidney function biomarker score during the 16-18th week of gestation were positively and independently associated with an increased risk of GDM.