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Lipoprotein(a) and cardiovascular disease in diabetic patients.

ABSTRACT: Lipoprotein(a) (Lp[a]) is a LDL-like particle consisting of an ApoA moiety linked to one molecule of ApoB(100). Recent data from large-scale prospective studies and genetic association studies provide highly suggestive evidence for a potentially causal role of Lp(a) in affecting risk of cardiovascular disease (CVD) in general populations. Patients with Type 2 diabetes display clustered metabolic abnormalities and elevated risk of CVD. Lower plasma Lp(a) levels were observed in diabetic patients in several recent studies. Epidemiology studies of Lp(a) and CVD risk in diabetic patients generated inconsistent results. We recently found that Lp(a)-related genetic markers did not predict CVD in two diabetic cohorts. The current data suggest that Lp(a) may differentially affect cardiovascular risk in diabetic patients and in the general population. More prospective studies, Mendelian randomization analysis and functional studies are needed to clarify the causal relationship of Lp(a) and CVD in diabetic patients.

SUBMITTER: Qi Q

PROVIDER: S-EPMC3488449 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Lipoprotein(a) and cardiovascular disease in diabetic patients.

Qi Qibin Q Qi Lu L

Clinical lipidology 20120801 4

Lipoprotein(a) (Lp[a]) is a LDL-like particle consisting of an ApoA moiety linked to one molecule of ApoB(100). Recent data from large-scale prospective studies and genetic association studies provide highly suggestive evidence for a potentially causal role of Lp(a) in affecting risk of cardiovascular disease (CVD) in general populations. Patients with Type 2 diabetes display clustered metabolic abnormalities and elevated risk of CVD. Lower plasma Lp(a) levels were observed in diabetic patients ...[more]

PMID: 23136583

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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:Diabetic kidney disease (DKD) is heterogeneous in terms of proteinuria. Patients with DKD who present with low-grade proteinuria are more likely to have nephrosclerosis rather than traditional diabetic nephropathy. The amount of proteinuria might reflect the underlying pathology of renal failure and influence the prognosis after dialysis initiation. Clinical implications of proteinuria at the start of dialysis have not been confirmed, while greater proteinuria is associated with higher risk of cardiovascular disease (CVD) in the predialysis stages of chronic kidney disease. We performed a retrospective multicenter cohort study enrolling incident hemodialysis patients with diabetes. Patients were stratified using proteinuria quartiles. We examined the association of proteinuria quartiles with types of subsequent CVD. Among the enrolled 361 patients, the estimated mean glomerular filtration rate and proteinuria was 5.4 mL/min/1.73 m2 and 6.3 g/gCr, respectively. Lower quartile of proteinuria (cut-offs: 3.0, 5.4, and 8.8 g/gCr) was significantly associated with male, older age, and history of atherosclerotic CVD including coronary artery disease, peripheral arterial disease, and cerebral infarction (Ptrend<0.05). Kidney size was smaller in patients with lower levels of proteinuria. Patients with higher levels of proteinuria were more likely to have proliferative diabetic retinopathy (Ptrend<0.05). Multivariate competing risk analysis revealed that the first quartile of proteinuria was associated with a greater risk of atherosclerotic CVD than the third quartile (subhazard ratio [95% confidence interval]: 2.04 [1.00-4.14]). This association was attenuated after additional adjustments for history of atherosclerotic CVD. Furthermore, patients with lower quartiles of proteinuria were more likely to die of atherosclerotic CVD than those with non-atherosclerotic CVD (Ptrend = 0.01). Diabetic patients with lower proteinuria at dialysis initiation were characterized by severer macroangiopathy, as shown by a more atrophic kidney and higher prevalence of past atherosclerotic CVD. Hence, they are at a high risk of developing atherosclerotic CVD.

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Lipoprotein(a) and cardiovascular disease in diabetic patients.

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Lipoprotein(a) and cardiovascular disease in diabetic patients.

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