Dataset Information

Higher plasma CXCL12 levels predict incident myocardial infarction and death in chronic kidney disease: findings from the Chronic Renal Insufficiency Cohort study.

ABSTRACT: Genome-wide association studies revealed an association between a locus at 10q11, downstream from CXCL12, and myocardial infarction (MI). However, the relationship among plasma CXCL12, cardiovascular disease (CVD) risk factors, incident MI, and death is unknown.We analysed study-entry plasma CXCL12 levels in 3687 participants of the Chronic Renal Insufficiency Cohort (CRIC) Study, a prospective study of cardiovascular and kidney outcomes in chronic kidney disease (CKD) patients. Mean follow-up was 6 years for incident MI or death. Plasma CXCL12 levels were positively associated with several cardiovascular risk factors (age, hypertension, diabetes, hypercholesterolaemia), lower estimated glomerular filtration rate (eGFR), and higher inflammatory cytokine levels (P < 0.05). In fully adjusted models, higher study-entry CXCL12 was associated with increased odds of prevalent CVD (OR 1.23; 95% confidence interval 1.14, 1.33, P < 0.001) for one standard deviation (SD) increase in CXCL12. Similarly, one SD higher CXCL12 increased the hazard of incident MI (1.26; 1.09,1.45, P < 0.001), death (1.20; 1.09,1.33, P < 0.001), and combined MI/death (1.23; 1.13-1.34, P < 0.001) adjusting for demographic factors, known CVD risk factors, and inflammatory markers and remained significant for MI (1.19; 1.03,1.39, P = 0.01) and the combined MI/death (1.13; 1.03,1.24, P = 0.01) after further controlling for eGFR and urinary albumin:creatinine ratio.In CKD, higher plasma CXCL12 was associated with CVD risk factors and prevalent CVD as well as the hazard of incident MI and death. Further studies are required to establish if plasma CXCL12 reflect causal actions at the vessel wall and is a tool for genomic and therapeutic trials.

SUBMITTER: Mehta NN

PROVIDER: S-EPMC4132636 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Higher plasma CXCL12 levels predict incident myocardial infarction and death in chronic kidney disease: findings from the Chronic Renal Insufficiency Cohort study.

Mehta Nehal N NN Matthews Gregory J GJ Krishnamoorthy Parasuram P Shah Rhia R McLaughlin Catherine C Patel Parth P Budoff Matthew M Chen Jing J Wolman Melanie M Go Alan A He Jiang J Kanetsky Peter A PA Master Stephen R SR Rader Daniel J DJ Raj Dominic D Gadegbeku Crystal A CA Shah Rachana R Schreiber Marty M Fischer Michael J MJ Townsend Raymond R RR Kusek John J Feldman Harold I HI Foulkes Andrea S AS Reilly Muredach P MP

European heart journal 20131204 31

<h4>Aims</h4>Genome-wide association studies revealed an association between a locus at 10q11, downstream from CXCL12, and myocardial infarction (MI). However, the relationship among plasma CXCL12, cardiovascular disease (CVD) risk factors, incident MI, and death is unknown.<h4>Methods and results</h4>We analysed study-entry plasma CXCL12 levels in 3687 participants of the Chronic Renal Insufficiency Cohort (CRIC) Study, a prospective study of cardiovascular and kidney outcomes in chronic kidney ...[more]

PMID: 24306482

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Project description:ImportanceAccurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.ObjectiveTo evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.Design, setting, and participantsThis cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.Main outcomes and measuresThree machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample.ResultsA total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates.Conclusions and relevanceIn this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.

Project description:Rationale & objectiveFew studies have examined incident type 2 diabetes mellitus (T2DM) in chronic kidney disease (CKD). Our objective was to examine rates of and risk factors for T2DM in CKD, using several alternative measures of glycemic control.Study designProspective cohort study.Setting & participants1,713 participants with reduced glomerular filtration rates and without diabetes at baseline, enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study.PredictorsMeasures of kidney function and damage, fasting blood glucose, hemoglobin A1c (HbA1c), HOMA-IR (homeostatic model assessment of insulin resistance), demographics, family history of diabetes mellitus (DM), smoking status, medication use, systolic blood pressure, triglyceride level, high-density lipoprotein cholesterol level, body mass index, and physical activity.OutcomeIncident T2DM (defined as fasting blood glucose ≥ 126mg/dL or prescription of insulin or oral hypoglycemic agents).Analytical approachConcordance between fasting blood glucose and HbA1c levels was assessed using κ. Cause-specific hazards modeling, treating death and end-stage kidney disease as competing events, was used to predict incident T2DM.ResultsOverall T2DM incidence rate was 17.81 cases/1,000 person-years. Concordance between fasting blood glucose and HbA1c levels was low (κ for categorical versions of fasting blood glucose and HbA1c = 13%). Unadjusted associations of measures of kidney function and damage with incident T2DM were nonsignificant (P ≥ 0.4). In multivariable models, T2DM was significantly associated with fasting blood glucose level (P = 0.002) and family history of DM (P = 0.03). The adjusted association of HOMA-IR with T2DM was comparable to that of fasting blood glucose level; the association of HbA1c level was nonsignificant (P ≥ 0.1). Harrell's C for the models ranged from 0.62 to 0.68.LimitationsLimited number of outcome events; predictors limited to measures taken at baseline.ConclusionsThe T2DM incidence rate among individuals with CKD is markedly higher than in the general population, supporting the need for greater vigilance in this population. Measures of glycemic control and family history of DM were independently associated with incident T2DM.

Dataset Information

Higher plasma CXCL12 levels predict incident myocardial infarction and death in chronic kidney disease: findings from the Chronic Renal Insufficiency Cohort study.

Publications

Higher plasma CXCL12 levels predict incident myocardial infarction and death in chronic kidney disease: findings from the Chronic Renal Insufficiency Cohort study.

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