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Addition of Metabolic Syndrome to Albuminuria Provides a New Risk Stratification Model for Diabetic Kidney Disease Progression in Elderly Patients.

ABSTRACT: Elderly patients with type 2 diabetes (T2DM) are more prone to developing diabetic kidney disease (DKD). Patients with DKD can develop albuminuria, and some studies have suggested an association between metabolic syndrome and albuminuria. The prevalence of both metabolic syndrome and albuminuria increases with age. We evaluated the association of these risk factors with worsening renal function and albuminuria progression in 460 T2DM patients with a mean age of 72 years. During the 5-year follow-up period, progression of albuminuria and worsening of renal function were observed in 97 (21.2%) and 23 (5.1%) patients, respectively. After adjusting for confounding factors, the group with metabolic syndrome had a higher multivariable-adjusted hazard ratio (HR) for worsening renal function (P?=?0.038) and albuminuria progression (P?=?0.039) than the group without metabolic syndrome. When patients were divided into four groups according to the presence of metabolic syndrome and/or albuminuria, the HR gradually increased. The group with both albuminuria and metabolic syndrome exhibited the highest cumulative incidence of worsening renal function (P?=?0.003). When we redefined metabolic syndrome to exclude the blood pressure (BP) component, similar results were obtained. We concluded that the presence of metabolic syndrome independently predicts the progression of renal disease in elderly patients with T2DM. The use of both metabolic syndrome and albuminuria provides a better risk stratification model for DKD progression than albuminuria alone.

SUBMITTER: Shih HM

PROVIDER: S-EPMC7176677 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Addition of Metabolic Syndrome to Albuminuria Provides a New Risk Stratification Model for Diabetic Kidney Disease Progression in Elderly Patients.

Shih Hong-Mou HM Chuang Shih-Ming SM Lee Chun-Chuan CC Liu Sung-Chen SC Tsai Ming-Chieh MC

Scientific reports 20200422 1

Elderly patients with type 2 diabetes (T2DM) are more prone to developing diabetic kidney disease (DKD). Patients with DKD can develop albuminuria, and some studies have suggested an association between metabolic syndrome and albuminuria. The prevalence of both metabolic syndrome and albuminuria increases with age. We evaluated the association of these risk factors with worsening renal function and albuminuria progression in 460 T2DM patients with a mean age of 72 years. During the 5-year follow ...[more]

PMID: 32321994

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Project description:Diabetic kidney disease (DKD) is a major burden in elderly patients with type 2 diabetes (T2DM). Low estimated glomerular filtration rate (eGFR+, <?60 mL/min/1.73 m2) and albuminuria (Alb+) are essential for the diagnosis of DKD, but their association with clinical variables and quality of care may be influenced by ageing.Here we investigated the association of clinical variables and quality of care measures with eGFR+ and Alb+ in 157,595 T2DM individuals participating to the Italian Association of Clinical Diabetologists (AMD) Annals Initiative, stratified by age.The prevalence of eGFR+ and Alb+ increased with ageing, although this increment was more pronounced for low eGFR. Irrespective of age, both the eGFR+ and Alb + groups had the worst risk factors profile when compared to subjects without renal disease, showing a higher prevalence of out-of target values of HbA1c, BMI, triglycerides, HDL-C, blood pressure and more complex cardiovascular (CVD) and anti-diabetic therapies, including a larger use of insulin In all age groups, these associations differed according to the specific renal outcome examined: male sex and smoking were positively associated with Alb+ and negatively with eGFR+; age and anti-hypertensive therapies were more strongly associated with eGFR+, glucose control with Alb+, whereas BMI, and lipid-related variables with both abnormalities. All these associations were attenuated in the older (>?75 years) as compared to the younger groups (<?65 years; 65-75 years), and they were confirmed by multivariate analysis. Notably, Q-score values <?15, indicating a low quality of care, were strongly associated with Alb+ (OR 8.54; P?<?0.001), but not with eGFR+.In T2DM patients, the prevalence of both eGFR and Albuminuria increase with age. DKD is associated with poor cardiovascular risk profile and a lower quality of care, although these associations are influenced by the type of renal abnormality and by ageing. These data indicate that clinical surveillance of DKD should not be unerestimated in old T2DM patients.

Project description:BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717-0.817), AUC of 0.780(95%CI: 0.721-0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

Project description:BackgroundAlthough the prevalence of obesity and metabolic syndrome (MetS) among dialysis patients has been exceeding than general population, little is known regarding obesity and MetS in non-dialysis chronic kidney disease (CKD). We aimed to find the magnitude of obesity and MetS and their associations with impaired renal function among type 2 diabetes mellitus (T2DM) patients.MethodsA national survey of T2DM patients was collected in the Thai National Health Security Office database during 2014-5. The sampling frame was designated as distinct geographic regions throughout the country. A stratified two-stage cluster sampling was used to select the study population. Anthropometry and 12-hour fasting blood samples were obtained by trained personnel. BMI of ?25 kg/m2 was classified as obesity. MetS was defined as having elevated waist circumference (>90 and >80 cm in men and women, respectively) plus any two of the followings: triglyceride ?150 mg/dL, HDL-C <40 in men or <50 mg/dL in women, blood pressure ?130/85 mmHg, and fasting blood sugar ?100 mg/dL. CKD was defined as an impaired renal function (eGFR <60 mL/min/1.73m2 according to the CKD-EPI equation). Logistic regression analysis was performed to examine the relationship between obesity and MetS with the presence of CKD.ResultsA total of 32,616 diabetic patients were finally recruited from 997 hospitals. The mean age was 61.5±10.9 years with 67.5% women. Of the participants, 35.4% were CKD patients. The prevalence of obesity was 46.5% in CKD and 54.1% in non-CKD patients with T2DM (p<0.001). In contrast, the prevalence of MetS in CKD patients was higher than their non-CKD counterparts (71.3 vs 68.8%, p<0.001). Moreover, there was an association between the prevalence of MetS with CKD stage from 3a to 5 (70.1, 72.3, 73.4, and 72.7%, respectively, p trend = 0.02). MetS, but not obesity, had a significant association with CKD in T2DM patients after adjusting for age, sex, and comorbidities [OR 1.14; 95% CI 1.06-1.22, p<0.001]. When stratified by each component of MetS, only high serum triglyceride and low HDL-C levels were increased in patients with CKD stage 4 and 5 compared with CKD stage 3 (p<0.001) and had a significant relationship with impaired renal function.ConclusionThere were relatively high prevalences of both obesity and MetS in T2DM patients. A higher prevalence of MetS, but lower prevalence of obesity, was observed among diabetic CKD group compared with their non-CKD counterparts. MetS, as a surrogate of insulin resistance, appeared to be more important than obesity in the development of impaired renal function in diabetic population.

Project description:Background and objectivesProtein carbamylation is a consequence of uremia and carbamylated lipoproteins contribute to atherogenesis in CKD. Proteins can also be carbamylated by a urea-independent mechanism, and whether carbamylated lipoproteins contribute to the progression of CKD has not been investigated.Design, setting, participants, & measurementsA case-control study was performed to determine whether there were changes in plasma levels of carbamylated lipoproteins in individuals with type 2 diabetes with eGFR >60 ml/min per 1.73 m2 compared with a group of age- and sex-matched healthy controls. A cohort of 1320 patients with type 2 diabetes with baseline eGFR ≥30 ml/min per 1.73 m2 was longitudinally followed up to evaluate the association between carbamylated lipoproteins and progression of CKD. The primary kidney outcome was defined as doubling of serum creatinine and/or initiation of KRT during follow-up. Plasma carbamylated LDLs and HDLs was measured by ELISA.ResultsIn individuals with diabetes with eGFR >60 ml/min per 1.73 m2, both plasma carbamylated LDL and HDL levels were higher compared with healthy controls (P<0.001). After a mean follow-up of 9 years of the diabetic cohort, individuals in the top quartile of carbamylated LDL (hazard ratio, 2.21; 95% confidence interval, 1.42 to 3.46; P<0.001) and carbamylated HDL (hazard ratio, 4.53; 95% confidence interval, 2.87 to 7.13; P<0.001) had higher risk of deterioration of kidney function compared with those in the lowest quartile. On multivariable Cox regression analysis, plasma carbamylated LDL was no longer associated with kidney outcome after adjusting for baseline eGFR and potential confounding factors. However, the association between plasma carbamylated HDL and kidney outcome remained significant and was independent of HDL cholesterol.ConclusionsPlasma carbamylated HDL but not carbamylated LDL was independently associated with progression of CKD in patients with type 2 diabetes.

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Addition of Metabolic Syndrome to Albuminuria Provides a New Risk Stratification Model for Diabetic Kidney Disease Progression in Elderly Patients.

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Addition of Metabolic Syndrome to Albuminuria Provides a New Risk Stratification Model for Diabetic Kidney Disease Progression in Elderly Patients.

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