Hyperlipidemia and mortality associated with diabetes mellitus co-existence in Chinese peritoneal dialysis patients

Association of urinary bisphenol A with hyperlipidemia and all-cause mortality: NHANES 2003-2016

BACKGROUND

The connection between urinary bisphenol A (BPA) and hyperlipidemia is still unclear, and few studies have evaluated whether urinary BPA affects mortality among individuals with hyperlipidemia. Therefore, we aimed to investigate the link between urinary BPA and hyperlipidemia and assess the impact of urinary BPA on mortality risk in subjects with hyperlipidemia.

METHODS

We analyzed data of the National Health and Nutrition Examination Survey from 2003 to 2016. Multivariable logistic analysis was performed to examine the relationship between urinary BPA and hyperlipidemia. Cox regression analysis was carried out to investigate the relationship between urinary BPA and all-cause mortality in subjects with hyperlipidemia.

RESULTS

This study included 8,983 participants, of whom 6,317 (70.3%) were diagnosed with hyperlipidemia. The results showed that urinary BPA was higher in participants with hyperlipidemia group than those without hyperlipidemia (3.87 ± 0.32 vs. 2.98 ± 0.14, P = 0.01). Urinary BPA levels were analyzed in tertiles. Compared with tertile 1 of BPA (reference), the odds ratio (95% confidence interval) of hyperlipidemia related to tertile 3 of BPA was 1.28 (1.11-1.48). The hazard ratio for all-cause death associated with the highest versus lowest tertile of urinary BPA was 1.20 (95% confidence interval: 1.01-1.44; P = 0.04) among participants with hyperlipidemia.

CONCLUSIONS

The study indicated a positive relationship between urinary BPA and the risk of hyperlipidemia. Urinary BPA was associated with a significantly higher risk of all-cause mortality in adults with hyperlipidemia.

Development and validation of prediction model for technique failure in peritoneal dialysis patients: An observational study

AIM

This study aimed to establish a prediction model in peritoneal dialysis patients to estimate the risk of technique failure and guide clinical practice.

METHODS

Clinical and laboratory data of 424 adult peritoneal dialysis patients were retrospectively collected. The risk prediction models were built using univariate Cox regression, best subsets approach and LASSO Cox regression. Final nomogram was constructed based on the best model selected by the area under the curve.

RESULTS

After comparing three models, the nomogram was built using the LASSO Cox regression model. This model included variables consisting of hypertension and peritonitis, serum creatinine, low-density lipoprotein, fibrinogen and thrombin time, and low red blood cell count, serum albumin, triglyceride and prothrombin activity. The predictive model constructed performed well using receiver operating characteristic curve and area under the curve value, C-index and calibration curve.

CONCLUSION

This study developed and verified a new prediction instrument for the risk of technique failure among peritoneal dialysis patients.

Association Between Peritoneal Glucose Absorption, Lipid Metabolism, and Cardiovascular Disease Risk in Nondiabetic Patients on Peritoneal Dialysis

BACKGROUND

Excessive sugar intake increases the energy metabolic burden and the risk of cardiovascular disease (CVD). Patients on peritoneal dialysis absorb much more glucose than the World Health Organization recommends, but the link to CVD is unclear.

OBJECTIVE

To identify the association between peritoneal glucose absorption, lipid metabolism, and CVD.

METHODS

We applied generalized additive mixed effects and mixed effects Cox proportional hazard models to evaluate the impact of peritoneal glucose absorption on lipid profiles and CVD risk. We performed subgroup analyses by using protein intake (normalized protein nitrogen appearance [nPNA] and normalized protein catabolic rate [nPCR] were used to assess protein intake) and high-sensitivity C-reactive protein (hs-CRP).

RESULTS

After multivariable adjustment, peritoneal glucose absorption per 10 g/d increase was associated with an increase in cholesterol of 0.145 (95% confidence interval [CI]: 0.086-0.204) mmol/L. No link with the total risk of CVD was observed; however, protein intake and hs-CRP levels affected the relationship between glucose absorption and CVD risk. Patients with values for nPNA and nPCR <1.0 g/(kg·d) were associated with a lower risk of CVD (hazard ratio [HR] 95% CI: 0.68 (0.46-0.98)) with glucose absorption per 10 g/d increase. While patients with hs-CRP levels ≥3 mg/d or values for nPNA or nPCR ≥1.0 g/(kg·d) were associated with a higher risk of CVD (HR 95% CI: 1.32 (1.07-1.63); 1.31 (1.02-1.68)) for glucose absorption per 10 g/d increase.

CONCLUSIONS

Our study found a positive correlation between peritoneal glucose absorption and lipid profiles. Increased glucose absorption was associated with a lower risk of CVD in lower protein intake patients and a higher risk of CVD in higher hs-CRP or protein intake levels in patients on peritoneal dialysis.

Complications of Peritoneal Dialysis Part II: Nonmechanical Complications

Peritoneal dialysis (PD) is a form of KRT that offers flexibility and autonomy to patients with ESKD. It is associated with lower costs compared with hemodialysis in many countries. Unlike mechanical complications that typical arise early in the course of treatment, noninfectious, nonmechanical complications often present late in patients who are established on PD. In this review, we first discuss abnormal-appearing drained dialysate, including hemoperitoneum, chyloperitoneum, and noninfectious cloudy dialysate. The underlying cause is frequently unrelated to PD. We then discuss encapsulating peritoneal sclerosis, a rare complication of PD. Finally, we review metabolic changes associated with PD and methods to mitigate its effects.

Design and clinical application of a risk prediction model for diabetic foot

OBJECTIVE

To construct and evaluate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes based on their clinical data, and to assist clinical healthcare professionals in identifying high-risk factors and developing targeted intervention measures.

METHODS

We retrospectively collected clinical data from 478 hospitalized patients with type 2 diabetes at the First Affiliated Hospital of Shantou University Medical College from January 2019 to December 2021. The patients were divided into a diabetic foot group (n=312) and a non-diabetic foot group (n=166) based on whether they had diabetic foot. The baseline data of both groups were collected. Univariate and multivariate analyses as well as logistic regression analysis were conducted to explore the risk factors for diabetic foot. A nomogram prediction model was established using the package "rms" version 4.3. The model was internally validated using the area under the receiver operating characteristic curve (AUC). Additionally, the decision curve analysis (DCA) was performed to evaluate the performance of the nomogram model.

RESULTS

The results from the logistic regression analysis revealed that being male, smoking, duration of diabetes, glycated hemoglobin, hyperlipidemia, and atherosclerosis were influencing factors for diabetic foot (all P<0.05). The AUC of the model in predicting diabetic foot was 0.804, with a sensitivity of 75.3% and specificity of 74.4%. Harrell's C-index of the nomogram prediction model for diabetic foot was 0.804 (95% CI: 0.762-0.844), with a threshold value of >0.675. The DCA findings demonstrated that the nomogram model provided a net clinical benefit.

CONCLUSION

The nomogram prediction model constructed in this study showed good predictive performance and can provide a basis for clinical workers to prevent and intervene in diabetic foot, thereby improving the overall diagnosis and treatment.

Low-Density Lipoprotein Cholesterol and Mortality in Peritoneal Dialysis

Background

In dialysis patients, lowering low-density lipoprotein cholesterol (LDL-C) did not provide benefits, which seemed implausible in clinical practice. We hypothesized a U-shaped association between LDL-C and mortality in dialysis patients.

Methods

In this multi-center retrospective real-world cohort study, 3,565 incident Chinese peritoneal dialysis (PD) patients between January 1, 2005, and May 31, 2020, were included. The associations between baseline LDL-C and mortality were examined using cause-specific hazard models.

Results

Of 3,565 patients, 820 died, including 415 cardiovascular deaths. As compared with the reference range (2.26-2.60 mmol/L), both higher levels of LDL-C (&gt; 2.60 mmol/L) and lower levels of LDL-C (&lt; 2.26 mmol/L) were associated with increased risks of all-cause mortality (hazard ratio [HR],1.35, 95% confidence index [CI], 1.09-1.66; HR 1.36, 95%CI, 1.13-1.64) and cardiovascular mortality (HR, 1.31, 95% CI, 1.10-1.72; HR, 1.64; 95% CI, 1.22-2.19). Malnutrition (albumin &lt; 36.0 g/L) modified the association between LDL-C and cardiovascular mortality (P for interaction = 0.01). A significantly increased risk of cardiovascular mortality was observed among patients with malnutrition and lower levels of LDL-C (HR 2.96, 95%CI 1.43-6.12) or higher levels of LDL-C (HR 2.81, 95%CI 1.38-5.72).

Conclusion

Low and high levels of LDL-C at the start of PD procedure were associated with increased all-cause and cardiovascular mortality risks. Malnutrition may modify the association of LDL-C with cardiovascular mortality.

Derivation and Validation of a Prediction Model of End-Stage Renal Disease in Patients With Type 2 Diabetes Based on a Systematic Review and Meta-analysis

OBJECTIVES

To develop and validate a model for predicting the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes.

METHODS

The derivation cohort was from a meta-analysis. Statistically significant risk factors were extracted and combined to the corresponding risk ratio (RR) to establish a risk assessment model for ESRD in type 2 diabetes. All risk factors were scored according to their weightings to establish the prediction model. Model performance is evaluated using external validation cohorts. The outcome was the occurrence of ESRD defined as eGFR<15 ml min^-1 1.73 m^-2 or received kidney replacement therapy (dialysis or transplantation).

RESULTS

A total of 1,167,317 patients with type 2 diabetes were included in our meta-analysis, with a cumulative incidence of approximately 1.1%. The final risk factors of the prediction model included age, sex, smoking, diabetes mellitus (DM) duration, systolic blood pressure (SBP), hemoglobin A1c (HbA1c), estimated glomerular filtration rate (eGFR), and triglyceride (TG). All risk factors were scored according to their weightings, with the highest score being 36.5. External verification showed that the model has good discrimination, AUC=0.807(95%CI 0.753-0.861). The best cutoff value is 16 points, with the sensitivity and specificity given by 85.33% and 60.45%, respectively.

CONCLUSION

The study established a simple risk assessment model including 8 routinely available clinical parameters for predicting the risk of ESRD in type 2 diabetes.

High Serum Uric Acid and High-Sensitivity C Reactive Protein Concentrations Predict Three-Year Cardiovascular Mortality in Patients Treated With Continuous Ambulatory Peritoneal Dialysis

AIMS

This study aims to access the predicting value of serum uric acid (UA) and high-sensitivity C reactive protein (hs-CRP) concentration on three-year cardiovascular-related mortality in patients performing continuous ambulatory peritoneal dialysis (CAPD).

METHODS

A total of267 CAPD patients [150 male (56.2%); mean age 48.93 ± 13.58 years] were included in our study. All patients had measured serum UA and hs-CRP concentration. A high-sensitivity particle-enhanced immunoturbidimetric assay determined serum hs-CRP; serum UA levels were determined using an enzymatic colorimetric assay. All patients were followed for three years to detect cardiovascular-related mortality by cardiologists and stroke specialists.

RESULTS

Mean serum UA level was 415.16 ± 84.28 µmol/L, 58.4% of patients had increased serum UA level. Median serum hs-CRP level was 2 (1-4) mg/L, 12.4% of patients had increased serum hs-CRP level. During 36 months of follow-up, 41 patients (15.4%) had cardiovascular-related mortality. The results of Cox proportional hazards regression showed that hypertension, diabetes, high serum UA and hs-CRP were risk factors that related to cardiovascular-related mortality (p<0.05). The receiver operating characteristic (ROC) curve and Kaplan-Meier analysis results showed that UA and hs-CRP level had predictive value for three-year cardiovascular-related mortality in CAPD patients [uric acid: area under the curve (AUC)=0.822; hs-CRP: AUC=0.834, p < 0.001].

CONCLUSION

High serum UA and hs-CRP levels were predictive factors of cardiovascular-related mortality in CAPD patients.