Assessment of glycemia in chronic kidney disease

Hassanein, Mohamed; Shafi, Tariq

doi:10.1186/s12916-022-02316-1

Cited by 18 publications

(10 citation statements)

References 71 publications

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“…These two indicators have been proven to be associated with higher risk of CVDs (32,33). Lower eGFR indicates worse renal function, which may be the results of arteriosclerosis (34) or abnormal glucose metabolism (35). However, after adjusting for covariates, in the final CVDMCM model, age, smoking and SBP were included as positive predictor of CVDs, and total bilirubin and ABI were included as negative predictor.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Shan

Zhang

Zhao

et al. 2022

Front. Cardiovasc. Med.

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BackgroundDeath due to cardiovascular diseases (CVD) increased significantly in China. One possible way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those males at risk of CVD and provide targeted intervention.MethodsWe conducted a retrospective cohort study of 2,331 Chinese males without CVD at baseline to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008–2016) and at least one revisit record by September 2019. With the full cohort, we conducted three models: A model with Framingham CVD risk model predictors; a model with predictors selected by univariate cox proportional hazard model adjusted for age; and a model with predictors selected by LASSO algorithm. Among them, the optimal model, CVDMCM, was obtained based on the Akaike information criterion, the Brier's score, and Harrell's C statistic. Then, CVDMCM, the Framingham CVD risk model, and the Wu's simplified model were all validated and compared. All the validation was carried out by bootstrap resampling strategy (TRIPOD statement type 1b) with the full cohort with 1,000 repetitions.ResultsCVDMCM's Harrell's C statistic was 0.769 (95% CI: 0.738–0.799), and D statistic was 4.738 (95% CI: 3.270–6.864). The results of Harrell's C statistic, D statistic and calibration plot demonstrated that CVDMCM outperformed the Framingham CVD model and Wu's simplified model for 4-year CVD risk prediction.ConclusionsWe developed and internally validated CVDMCM, which predicted 4-year CVD risk for Chinese males with a better performance than Framingham CVD model and Wu's simplified model. In addition, we developed a web calculator–calCVDrisk for physicians to conveniently generate CVD risk scores and identify those males with a higher risk of CVD.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Shan

Zhang

Zhao

et al. 2022

Front. Cardiovasc. Med.

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“…The kidneys significantly impact glucose homeostasis via glucose glomerular filtration, reabsorption through sodium-glucose cotransporters (SGLT), and generation [38]. Hemoglobin A1C is the gold standard for glycemic assessment in CKD patients, but it is affected by hematologic factors.…”

Section: Blood Sugar Controlmentioning

confidence: 99%

“…Glycated albumin has been proposed as a marker of glycemic control in CKD patients. However, it is affected by proteinuria, and further studies are needed to evaluate its role in diabetic CKD [38]. Continuous glucose monitoring (CGM) device is a minimally invasive method that can monitor glycemic control in CKD patients.…”

Section: Blood Sugar Controlmentioning

confidence: 99%

Lifestyle Modifications and Nutritional and Therapeutic Interventions in Delaying the Progression of Chronic Kidney Disease: A Review

Alkhatib¹,

Diaz²,

Varma³

et al. 2023

Cureus

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Chronic kidney disease (CKD) is a debilitating progressive illness that affects more than 10% of the world's population. In this literature review, we discussed the roles of nutritional interventions, lifestyle modifications, hypertension (HTN) and diabetes mellitus (DM) control, and medications in delaying the progression of CKD. Walking, weight loss, low-protein diet (LPD), adherence to the alternate Mediterranean (aMed) diet, and Alternative Healthy Eating Index (AHEI)-2010 slow the progression of CKD. However, smoking and binge alcohol drinking increase the risk of CKD progression. In addition, hyperglycemia, altered lipid metabolism, low-grade inflammation, over-activation of the renin-angiotensin-aldosterone system (RAAS), and overhydration (OH) increase diabetic CKD progression. The Kidney Disease: Improving Global Outcomes (KDIGO) guidelines recommend blood pressure (BP) control of <140/90 mmHg in patients without albuminuria and <130/80 mmHg in patients with albuminuria to prevent CKD progression. Medical therapies aim to target epigenetic alterations, fibrosis, and inflammation. Currently, RAAS blockade, sodium-glucose cotransporter-2 (SGLT2) inhibitors, pentoxifylline, and finerenone are approved for managing CKD. In addition, according to the completed Study of Diabetic Nephropathy with Atrasentan (SONAR), atrasentan, an endothelin receptor antagonist (ERA), decreased the risk of renal events in diabetic CKD patients. However, ongoing trials are studying the role of other agents in slowing the progression of CKD.

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“…KDIGO guidelines recommend a target hemoglobin A1c (HbA1c) ranging from < 6.5 to < 8.0% unless the patient is at increased risk of hypoglycemia [12]. However, as Hassanein and Shafi pointed out in this collection, HbA1c is a less reliable marker for diabetes control due to the concomitant presence of anemia in people with DKD, particularly in those with advanced CKD [16]. Nevertheless, to date, it remains the gold standard method to monitor blood glucose control in this high-risk population.…”

Section: From Screening To Treatment Advancesmentioning

confidence: 99%

“…As one can realize, none of these markers are reliable in a population with DKD. Therefore, we suggest continuous glucose monitoring as an emerging tool to achieve glycemic control, but certainly, more studies are required to validate its efficacy in this population [16].…”

Section: From Screening To Treatment Advancesmentioning

confidence: 99%

From screening to treatment: the new landscape of diabetic kidney disease

Cervantes

Hanouneh

Jaar

2022

BMC Med

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Globally, diabetes mellitus is the leading cause of chronic kidney disease (CKD), and it is predicted to increase in the following years. Despite its high prevalence, CKD remains under diagnosed. In this BMC Medicine collection of articles on diabetic kidney disease (DKD), we place in context the importance of screening and early detection of DKD and the most accurate tools to monitor for optimal glycemic control in this his risk population. Further, we address this population's risk for severe complications such as stroke and all-cause mortality. We close this editorial by summarizing recent advances in management of this vulnerable population of patients with DKD, including guideline-directed medical therapy, novel treatments, and predictors of treatment failure.

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Assessment of glycemia in chronic kidney disease

Cited by 18 publications

References 71 publications

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Lifestyle Modifications and Nutritional and Therapeutic Interventions in Delaying the Progression of Chronic Kidney Disease: A Review

From screening to treatment: the new landscape of diabetic kidney disease

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