Hyperkalemia in Diabetes Mellitus Setting

Goia-Nishide, Kleber; Coregliano-Ring, Lucas; Rangel, Érika B.

doi:10.3390/diseases10020020

Cited by 15 publications

(10 citation statements)

References 42 publications

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“…The disposition of potassium in the body also affects sodium transport through Na+-K+-ATPase, [ 45 ] which in turn affects glucose reabsorption in the renal tubule through the sodium–glucose co-transporter [ 46 ]. In summary, these biochemical interactions between potassium and other biomarkers indicate that potassium measurement could be pivotal in predicting glycemic status [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…In patients with HF, both comorbidities and medications can increase the risk of hyperkalemia. Hyperglycemia can lead to the passage of water and K+ from the intracellular to the extracellular compartment, while a low insulin concentration reduces the activity of the Na-K pump [112]. Non-cardioselective beta-blockers could lead to an extracellular K+ shift through direct renin inhibition [113].…”

Section: Hyperkalemia In Hf and Ckdmentioning

confidence: 99%

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

Bonacchi,

Rossi,

Garofalo

et al. 2024

Biomedicines

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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.

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“…[4][5][6] Diabetic patients, especially those with CKD, are more susceptible to present with hyperkalemia due to renal disease progression. 7 Hyperkalemia is one of the prevalent reasons for hospitalization among CKD patients. Some observational studies have described the relationship between hyperkalemia and excessive mortality, 8 however, there is lack of research on the relationship between hyperkalemia and the length of hospitalization of advanced CKD patients with type two diabetes mellitus (T2DM).…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Li²,

Li³

et al. 2023

Pak J Med Sci

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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. doi: https://doi.org/10.12669/pjms.39.3.6874 How to cite this: Li X, Li X, Li B, Guo Y. Impact of hyperkalemia on hospitalization days in advanced chronic kidney disease patients with Type-2 diabetes mellitus: A prospective study. Pak J Med Sci. 2023;39(3):885-890. doi: https://doi.org/10.12669/pjms.39.3.6874 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hyperkalemia in Diabetes Mellitus Setting

Cited by 15 publications

References 42 publications

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

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

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

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

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