Hyperkalemia in Diabetes Mellitus Setting

Dietary Guidelines Post Kidney Transplant: Is This the Missing Link in Recovery and Graft Survival?

The physiology of a transplanted kidney is affected from the moment it is separated from the donor. The risk of complications arising from surgery are highly associated with ischemic-reperfusion injury (IRI) due to the effects of hypoxia and oxidative stress during the procurement, preservation and reperfusion procedures. Hypoxia promotes the formation of reactive oxygen species (ROS) and it seems apparent that finding ways of optimising the metabolic milieu for the transplanted kidney would improve recovery and graft survival. Studies have demonstrated the benefits of nutrition and antioxidant compounds in mitigating the disturbance of energy supply to cells post-transplant and at improving long-term graft survival. Particularly in patients who may be nutritionally deficient following long-term dialysis. Despite the high incidence of allograft failure, a search of the literature and grey literature reveals no medical nutriti on therapy guidelines on beneficial nutrient intake to aid transplant recovery and survival. This narrative review aims to summarise current knowledge of specific macro and micronutrients and their effect on allograft recovery and survival in the perioperative period, up to 1-year post transplant, to optimise the metabolic environment and mitigate risk to graft injury.

Serum Potassium in Thyroid Cancer Patients With Hypothyroidism During Thyroid Hormone Withdrawal: A Retrospective Study

Background

Several case reports and a few studies have reported that hypothyroid patients have elevated serum potassium levels. However, hypothyroidism has not been widely accepted as a cause of hyperkalemia.

Objectives

This study aims to evaluate the incidence of hyperkalemia and factors influencing serum potassium levels in thyroid cancer patients with hypothyroidism during thyroid hormone withdrawal before radioactive iodine (RAI) treatment.

Methods

We conducted a retrospective review of electronic medical records from January 2017 to June 2021, involving 956 thyroid cancer patients post-thyroidectomy and undergoing RAI. Laboratory parameters, including serum potassium levels, were collected in both euthyroid (<1 year prior to RAI) and hypothyroid states.

Results

Among 508 patients (mean age 52 years, 79.3% female), hyperkalemia (potassium ⩾ 5.0 mEq/L) occurred in 2.8%, without severe hyperkalemia (potassium ⩾ 6.5 mEq/L). The hypothyroid state exhibited significantly higher serum potassium than the euthyroid state [4.16 (IQR, 3.94-4.41) vs 4.10 (IQR, 3.90-4.35) mEq/L, P < .01]. The mean change in potassium levels between the euthyroid and hypothyroid state was 0.05 ± 0.17 mEq/L. Pre-thyroid hormone withdrawal (euthyroid state) factors associated with serum potassium levels in the hypothyroid state included age, use of angiotensin-converting enzyme inhibitors, diabetes mellitus, serum BUN/creatinine, serum potassium levels, hemoglobin A1c (positive correlation); and thiazide use and eGFR (negative correlation). In the hypothyroid state, hyperkalemia was more likely in patients with serum potassium ⩾4.2 mEq/L (OR 9.36, P < .01) or free T4 ⩾1.38 ng/dL (OR 7.05, P < .01) during the euthyroid state.

Conclusions

The incidence of hyperkalemia was low in our hypothyroid cohorts. However, physicians should remain vigilant for cases with risk factors for developing hyperkalemia.

Defective natriuresis contributes to hyperkalemia in db/db mice during potassium supplementation

OBJECTIVES

Potassium supplementation reduces blood pressure and the occurrence of cardiovascular diseases, with K + -induced natriuresis playing a potential key role in this process. However, whether these beneficial effects occur in diabetes remains unknown.

METHODS

In this study, we examined the impact of high-K + intake on renal Na + /K + transport by determining the expression of major apical Na + transporters, diuretics responses (as a proxy for specific Na + transporter function), urinary Na + /K + excretion, and plasma Na + /K + concentrations in db/db mice, a model of type 2 diabetes mellitus.

RESULTS

Although db/m mice exhibited increased fractional excretion of sodium (FE Na ) and fractional excretion of potassium (FE K ) under high-K + intake, these responses were largely blunted in db/db mice, suggesting impaired K + -induced natriuresis and kaliuresis in diabetes. Consequently, high-K + intake increased plasma K + levels in db/db mice, which could be attributed to the abnormal activity of sodium-hydrogen exchanger 3 (NHE3), sodium-chloride cotransporter (NCC), and epithelial Na + channel (ENaC), as high-K + intake could not effectively decrease NHE3 and NCC and increase ENaC expression and activity in the diabetic group. Inhibition of NCC by hydrochlorothiazide could correct the hyperkalemia in db/db mice fed a high-K + diet, indicating a key role for NCC in K + -loaded diabetic mice. Treatment with metformin enhanced urinary Na + /K + excretion and normalized plasma K + levels in db/db mice with a high-K + diet, at least partially, by suppressing NCC activity.

CONCLUSION

Collectively, the impaired K + -induced natriuresis in diabetic mice under high-K + intake may be primarily attributed to impaired NCC-mediated renal K + excretion, despite the role of NHE3.

Sixteen prescribed Chinese herbal medicines provide time-dependent cardiorenal and survival benefits in patients with overall and advanced diabetic kidney disease: a real-world study in Taiwan

Background

A causal connection between oxidative stress and inflammation in diabetes, along with its associated renal and cardiovascular complications, has been established. Sixteen prescribed potentially renoprotective Chinese herbal medicines for diabetic kidney disease (PRCHMDKD), which are scientific Chinese medicine (botanical drug) and categorized into five classes (clearing heat, nourishing yin, dampness dispelling, tonifying qi, and harmonizing formulas), exhibit shared antioxidative properties and target multiple oxidative stress pathways. However, the time-response, cumulative effects, and safety (hyperkalemia risk) of these sixteen PRCHMDKD on cardiorenal and survival outcomes in patients with overall and advanced DKD remain unresolved.

Methods

This retrospective cohort study analyzed national health insurance claims data in 2000-2017. Four statistical methods, including Cox proportional hazards models, complementary restricted mean survival time (RMST), propensity score matching, and competing risk analysis for end-stage renal disease (ESRD), were employed to investigate this relationship. The study included 43,480 PRCHMDKD users and an equal number of matched nonusers within the overall DKD patient population. For advanced DKD patients, the cohort comprised 1,422 PRCHMDKD users and an equivalent number of matched nonusers.

Results

PRCHMDKD use in overall and advanced, respectively, DKD patients was associated with time-dependent reductions in adjusted hazard ratios for ESRD (0.66; 95% CI, 0.61-0.70 vs. 0.81; 0.65-0.99), all-cause mortality (0.48; 0.47-0.49 vs. 0.59; 0.50-0.70), and cardiovascular mortality (0.50; 0.48-0.53 vs. 0.61; 0.45-0.82). Significant differences in RMST were observed in overall and advanced, respectively, DKD patients, favoring PRCHMDKD use: 0.31 years (95% CI, 0.24-0.38) vs. 0.61 years (0.13-1.10) for ESRD, 2.71 years (2.60-2.82) vs. 1.50 years (1.03-1.98) for all-cause mortality, and 1.18 years (1.09-1.28) vs. 0.59 years (0.22-0.95) for cardiovascular mortality. Additionally, hyperkalemia risk did not increase. These findings remained consistent despite multiple sensitivity analyses. Notably, the cumulative effects of utilizing at least four or five classes and multiple botanical drugs from the sixteen PRCHMDKD provided enhanced renoprotection for patients with both overall and advanced DKD. This suggests that there is involvement of multiple targets within the oxidative stress pathways associated with DKD.

Conclusion

This real-world study suggests that using these sixteen PRCHMDKD provides time-dependent cardiorenal and survival benefits while ensuring safety for DKD patients.

The Association Between Hyperkalemia, Necrotic Bowel, and Cardiac Arrest Episodes: A Case Report

This case report presented the details of the case and outcomes of the relevant literature review to offer insights into how hyperkalemia triggers cardiac arrest and the resuscitation rate of patients with hyperkalemia. This report discusses how conditions such as hyperkalemia and necrotic bowel can lower patients' heart rate, with regard to patients' baseline cardiac condition. This report also justifies the possibility of patients experiencing cardiac arrest episodes before and after chest/abdomen surgeries.

Patient Characteristics and Outcomes of Hospitalized Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus: Observations from the German Claims Data-Based Cohort of the CaReMe-CKD Multinational Study

Introduction

Type 2 diabetes mellitus (T2DM) is a leading cause of chronic kidney disease (CKD) globally. Both conditions substantially worsen patients' prognosis. Current data on German in-hospital CKD cohorts are scarce. The multinational CaReMe study was initiated to evaluate the current epidemiology and healthcare burden of cardiovascular, renal and metabolic diseases. In this substudy, we share real-world data on CKD inpatients stratified for coexisting T2DM derived from a large German hospital network.

Methods

This study used administrative data of inpatient cases from 89 Helios hospitals from 01/01/2016 to 28/02/2022. Data were extracted from ICD-10-encoded discharge diagnoses and OPS-encoded procedures. The first case meeting a previously developed CKD definition (defined by ICD-10- and OPS-codes) was considered the index case for a particular patient. Subsequent hospitalizations were analysed for readmission statistics. Patient characteristics and pre-defined endpoints were stratified for T2DM at index case.

Results

In total, 48,011 patients with CKD were included in the present analysis (mean age ± standard deviation, 73.8 ± 13.1 years; female, 44%) of whom 47.9% had co-existing T2DM. Patients with T2DM were older (75 ± 10.6 vs 72.7 ± 14.9 years, p < 0.001), but gender distribution was similar to patients without T2DM. The burden of cardiovascular disease was increased in patients with T2DM, and index and follow-up in-hospital mortality rates were higher. Non-T2DM patients were characterised by more advanced CKD at baseline. Patients with T2DM had consistently higher readmission numbers for all events of interest, except for readmissions due to kidney failure/dialysis, which were more common in non-T2DM patients.

Conclusion

In this study, we present recent data on hospitalized patients with CKD in Germany. In this CKD cohort, nearly half had T2DM, which substantially affected cardiovascular disease burden, rehospitalization frequency and mortality. Interestingly, non-diabetic patients had more advanced underlying renal disease, which affected renal outcomes.

Initial eGFR Changes with SGLT2 Inhibitor in Patients With Type 2 Diabetes and Associations With the Risk of Abnormal Serum Potassium Level

BACKGROUND

Both high and low levels of serum potassium measurements are linked with a higher risk of adverse clinical events among patients with type 2 diabetes. The study was aimed at evaluating the implications of the various degrees of initial estimated glomerular filtration rate (eGFR) change on subsequent serum potassium homeostasis following sodium-glucose cotransporter-2 inhibitor (SGLT2i) initiation among patients with type 2 diabetes.

METHODS AND RESULTS

We used medical data from a multicenter health care provider in Taiwan and recruited 5529 patients with type 2 diabetes with baseline/follow-up eGFR data available after 4 to 12 weeks of SGLT2i treatment from June 1, 2016, to December 31, 2018. SGLT2i treatment was associated with an initial mean (SEM) eGFR decline of -3.5 (0.2) mL/min per 1.73 m2 in overall study participants. A total of 36.7% (n=2028) of patients experienced no eGFR decline, and 57.9% (n=3201) and 5.4% (n=300) of patients experienced an eGFR decline of 0% to 30% and >30%, respectively. Patients with an initial eGFR decline of >30% were associated with higher variability in consequent serum potassium measurement when compared with those without an initial eGFR decline. Participants with a pronounced eGFR decline of >30% were associated with a higher risk of hyperkalemia ≥5.5 (adjusted hazard ratio,4.59 [95% CI, 2.28-9.26]) or use of potassium binder (adjusted hazard ratio, 2.65 [95% CI, 1.78-3.95]) as well as hypokalemia events <3.0 mmol/L (adjusted hazard ratio, 3.21 [95% CI, 1.90-5.42]) or use of potassium supplement (adjusted hazard ratio, 1.87 [95% CI, 1.37-2.56]) following SGLT2i treatment after multivariate adjustment.

CONCLUSIONS

Physicians should be aware that the eGFR trough occurs shortly, and consequent serum potassium changes following SGLT2i initiation.

Pathophysiological Link and Treatment Implication of Heart Failure and Preserved Ejection Fraction in Patients with Chronic Kidney Disease

Heart failure with preserved ejection fraction (HFpEF) results from a complex interplay of age, genetic, cardiac remodeling, and concomitant comorbidities including hypertension, obesity, diabetes, and chronic kidney disease (CKD). Renal failure is an important comorbidity of HFpEF, as well as a major pathophysiological mechanism for those patients at risk of developing HFpEF. Heart failure (HF) and CKD are intertwined conditions sharing common disease pathways; the so-called "kidney tamponade", explained by an increase in intracapsular pressure caused by fluid retention, is only the latest model to explain renal injury in HF. Recognizing the different phenotypes of HFpEF remains a real challenge; the pathophysiological mechanisms of renal dysfunction may differ across the HF spectrum, as well as the prognostic role. A better understanding of the role of cardiorenal interactions in patients with HF in terms of symptom status, disease progression, and prognosis remains essential in HF management. Historically, patients with HF and CKD have been scarcely represented in clinical trial populations. Current concerns affect the practical approach to HF treatment, and, in this context, physicians are frequently hesitant to prescribe and titrate both new and old treatments. Therefore, the extensive application of HF drugs in diverse HF subtypes with numerous comorbidities and different renal dysfunction etiologies remains a controversial matter of discussion. Numerous recently introduced drugs, such as sodium-glucose-linked transporter 2 inhibitors (SGLT2i), constitute a new therapeutic option for patients with HF and CKD. Because of their protective vascular and hormonal actions, the use of these agents may be safely extended to patients with renal dysfunction in the long term. The present review delves into the phenotype of patients with HFpEF and CKD from a pathophysiological perspective, proposing a treatment approach that suggests a practical stepwise algorithm for the proper application of life-saving therapies in clinical practice.

Accurate correction model of blood potassium concentration in hemolytic specimens

BACKGROUND AND AIMS

The results of blood potassium can be seriously affected by specimen hemolysis which may interfere with clinicians' interpretation of test results. Redrawing blood and retesting may delay treatment time and it is not feasible for critically ill patients with difficulty in specimen collection. Therefore, it is significant to establish a mathematical model that can quickly correct the blood potassium concentration of hemolytic specimens.

MATERIALS AND METHODS

The residual blood samples from 107 patients at Peking University Third Hospital were collected to establish potassium correction model. Samples with different hemolysis indexes were obtained by ultrasonic crushing method. Blood potassium correction models of hemolysis specimens were established by linear regression and curve fitting using SPSS and MATLAB, respectively. In addition, blood samples from another 85 patients were used to verify the accuracy of the models and determine the optimal model.

RESULTS

Variation of potassium (ΔK) was 0.003HI-0.03 (R2 = 0.9749) in linear regression model which had high correlation in ΔK and HI, and the correction formula was Kcorrection = Khemolysis-0.003 × HI + 0.03. Average rate of potassium change (αaverage) was 0.003 ± 0.0002 mmol/L in curve fitting model, and correction formula was Kcorrection = Khemolysis-0.003 × HI, and both men and women can use the same correction model. The accuracy of linear regression model was 96.5 %, and there was statistical difference between the verification results and the measured values (p < 0.05), while the accuracy of curve fitting model was 100 %, and there was no statistical difference between the verification results and the measured values (p = 0.552). The model was validated in an independent set of samples and all were within the TEa of 6 % and the accuracy of 100 %.

CONCLUSIONS

Both linear regression and curve fitting models of potassium correction had high accuracy, and can effectively correct the potassium concentration of hemolytic specimens, while the curve fitting model have superior accuracy.

Hyperkalemia in CKD: an overview of available therapeutic strategies

Hyperkalemia (HK) is a life-threatening condition that often occurs in patients with chronic kidney disease (CKD). High serum potassium (sKsK) is responsible for a higher risk of end-stage renal disease, arrhythmias and mortality. This risk increases in patients that discontinue cardio-nephroprotective renin-angiotensin-aldosterone system inhibitor (RAASi) therapy after developing HK. Hence, the management of HK deserves the attention of the clinician in order to optimize the therapeutic strategies of chronic treatment of HK in the CKD patient. The adoption in clinical practice of the new hypokalaemic agents patiromer and sodium zirconium cyclosilicate (SZC) for the prevention and chronic treatment of HK could allow patients, suffering from heart failure and chronic renal failure, to continue to benefit from RAASi therapy. We have updated a narrative review of the clear variables, correct definition, epidemiology, pathogenesis, etiology and classifications for HK among non-dialysis CKD (ND CKD) patients. Furthermore, by describing the prognostic impact on mortality and on the progression of renal damage, we want to outline the strategies currently available for the control of potassium (K+) plasma levels.

Optimization of potassium management in patients with chronic kidney disease and type 2 diabetes on finerenone

INTRODUCTION

Patients with type 2 diabetes (T2DM) and chronic kidney disease (CKD) are at high risk of CKD progression and cardiovascular events. Despite treatment with renin-angiotensin system inhibitors and SGLT-2 inhibitors, the residual risk is substantial. There is preclinical and clinical evidence supporting a key role of mineralocorticoid receptor in cardiorenal injury in T2DM.

AREAS COVERED

Finerenone is a selective and nonsteroidal mineralocorticoid receptor antagonist that reduces -on preclinical studies- heart and kidney inflammation and fibrosis. Clinical trials have demonstrated that among patients with T2DM and CKD, finerenone reduces CKD progression and the risk of cardiovascular events. The incidence of adverse events is similar than for placebo. Permanent discontinuation of study drug due to hyperkalemia was low (1.7% of finerenone and 0.6% of placebo participants) as was the risk of hyperkalemia-related severe-adverse events (1.1%). We provide an overview of risk factors for hyperkalemia and management of serum potassium in people with CKD and T2DM on finerenone.

EXPERT OPINION

As finerenone increases potassium levels in a predictable way, patients at risk of hyperkalemia can be identified early in clinical practice and monitored for an easy management. This will allow people with T2DM and CKD to safely benefit from improved cardiorenal outcomes.

Impact of hyperkalemia on hospitalization days in advanced chronic kidney disease patients with Type-2 diabetes mellitus: A prospective study

Objective

This study aimed to explore the impact of hyperkalemia at admission on hospitalization days (HDs) among advanced chronic kidney disease patients (CKD) with type two diabetes mellitus (T2DM) in China.

Methods

A total of 270 CKD patients with T2DM were prospectively selected from January 1, 2020 to December 31, 2021. These patients were divided into Group-A (n = 150, serum potassium ≤ 5.5 mmol/L) and B (n = 120, serum potassium > 5.5 mmol/L). The comparison method between the two groups was taken. Linear correlation analysis was performed using the Spearman correlation method, and multivariate analysis was tested using linear regression.

Results

The study found statistically significant result between the two groups (Group-A vs Group-B): HDs (7.4 (5.3-11.2) vs 12.1 (8.2-16.5), p < 0.001), renin-angiotensin-aldosterone system inhibitors (RAASIs) (36.2% vs 55.8%, p = 0.014), systolic blood pressure (148.35 ± 19.51 vs 162.26 ± 21.31, p < 0.05), estimated glomerular filtration (eGFR) (20.35) (18.31-25.26) vs13.4 (12.50-18.50), p < 0.001, N-terminal pro-B type natriuretic peptide (NT-proBNP) (2245.42 ± 61.09 vs 3163.39 ± 85.15,p < 0.001), and Hb (88.45 ± 12.35 vs 72.26 ± 14.2, p = 0.023). Correlation analysis showed that HDs were positively correlated with age, serum potassium, systolic blood pressure, and NT-proBNP, while negatively with eGFR and Hb. After adjusting for relevant confounding variables, the multivariable linear regression analysis showed that hyperkalemia was an independent risk factor for HDs.

Conclusions

Hyperkalemia could be an independent risk factor increasing HDs of advanced CKD patients with T2DM.

Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data

There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.