Lipid-related residual risk and renal function for occurrence and prognosis among patients with first-event acute coronary syndrome and normal LDL cholesterol
© Chien et al; licensee BioMed Central Ltd. 2011
Received: 12 October 2011
Accepted: 19 November 2011
Published: 19 November 2011
We investigated relationship of low levels of high density lipoprotein cholesterol (HDL-C), high levels of triglycerides, and renal function for the odds, prognosis and survival following acute coronary events among patients with a first event and normal low density lipoprotein cholesterol levels.
A case-control study based on 557 patients and 1086 matched control subjects was conducted. Case patients were followed up for survival with a median of 1.9 years. Participants in the higher quintiles of HDL-C had lower odds to develop acute coronary events (the adjusted odds ratios were 0.24 for the second, 0.24 for the third, 0.10 for the fourth and 0.05 for the fifth quintile). Patients with normal glomerular filtration rate were at a lower risk for all-cause death. However, a reverse association between triglycerides and death risk was found: patients with higher triglycerides were at a lower risk for all-cause death (adjusted relative risk, 0.38 for triglycerides ranging from 82 to 132.9 mg/dL, and 0.14 for triglycerides > = 133 mg/dL).
Low HDL-C was significantly associated with acute coronary events, and triglyceride levels as well as renal function were inversely related to all-cause deaths after the coronary event.
Lipid-related residual risk, including low levels of high density lipoprotein cholesterol (HDL-C) and high levels of triglycerides, has become a clinical target since the availability of aggressive low density lipoprotein cholesterol (LDL-C) lowering by statin treatment [1–4]. Evidence has shown about 70% increased relative risk among those with versus without low HDL-C and high triglycerides [5, 6]. In addition, a substantial incremental risk for further cardiovascular events was still apparent even in patients with LDL-C levels less than 70 mg/dL who had high triglycerides and low HDL-C [7, 8]. Residual risk profiles, including low HDL-C and high triglycerides among normal LDL-C, should be considered for prevention of coronary events [9, 10]. It is therefore necessary to investigate the role of lipid-related residual risk, including low HDL-C and high triglycerides, for the risk of the occurrence of coronary events.
In addition, chronic kidney disease status, estimated by lower glomerular filtration rates, is highly prevalent in Taiwan, with a prevalence rate of 12% in Taiwanese adults . Evidence has shown that an association between poor renal function and cardiovascular diseases is apparent [12, 13]. In addition, the role of lipid residual risk has not been investigated concurrently with renal function status in previous studies. Moreover, the data on the residual risk profiles for all-cause death after coronary events are scanty.
Therefore, it appears important to evaluate the prevalence of lipid-related residual risk, determine its association with acute coronary syndrome, and investigate the prognostic factors for survival after the events in Taiwanese patients. We investigated associations between low HDL-C, high triglycerides, and renal function status, for odds for coronary events among patients who had suffered from first events and were within normal LDL-C levels in this retrospective case control study, matching for age, sex, and LDL-C levels. We also conducted a cohort follow-up to record their survival status.
Basic distribution of case-control subjects
Basic characteristics of the study participants
Control subjects, n = 1086
Case patients, n = 557
Glomerular filtration rate, ml/min/1.73 m2
< = 60
Systolic BP, mmHg
Diastolic BP, mmHg
Total cholesterol, mg/dL
HDL cholesterol, mg/dL
LDL cholesterol, mg/dL
Serum creatinine, mg/dL
Estimated glomerular filtration rate, ml/min/1.73 m2
Associations with lipid-related residual risk as well as glomerular filtration rate and the occurrence of acute coronary syndrome
The number of case control subjects according to lipid quintiles and adjusted odds ratios in the study participants
HDL cholesterol, mg/dL
> = 53
> = 167
Glomerular filtration rate
> = 81.3
Subgroup analysis by obesity status* for the risk of acute coronary syndrome events in the study participants
Test for trend
BMI < 24.4
BMI > = 24.4
Predictive factors for mortality after acute coronary syndrome
The multiple Cox regression model for the risk of all-cause death among patients with first acute coronary syndrome events and normal LDL cholesterol levels
Gender, women vs. men
Age, 60-74.9 vs. 50-59.9 yrs
Age, > = 75 vs. 50-59 yr
History of hypertension
History of type 2 diabetes
Glomerular filtration rate, 60-89.9 vs. 60, ml/min/1.73 m2
Glomerular filtration rate, > = 90 vs. 60, ml/min/1.73 m2
HDL cholesterol, 38-47.9 vs. < 38 mg/dL
HDL cholesterol, > = 48 vs. < 38 mg/dL
Triglycerides, 82-132.9 vs. < 82 mg/dL
Triglycerides, > = 133 vs. < 82 mg/dL
In this matched case-control and cohort study among patients with first-event acute coronary syndrome and normal LDL-C levels, we demonstrated that increasing HDL-C was associated with lower odds for acute coronary syndrome events. HDL-C has been proven to be a protective factor for coronary artery disease. Epidemiological studies such as the Framingham Study  and the Prospective Cardiovascular Münster (PROCAM) study  have established that a low HDL-C is an independent risk factor for coronary events. Among the patients hospitalized due to coronary artery disease, low HDL-C was highly prevalent, with a prevalence rate of up to 55%. In addition, a low HDL-C level implies a high ratio of atherogenic apoB-containing lipoproteins to atheroprotective apoA-I [17, 18]. Low HDL-C is prevalent across all LDL-C levels, particularly so among patients with LDL-C levels less than 70 mg/dL, even with statin therapy (64%). Our study implies a significant inverse association of HDL-C for the odds of acute coronary syndrome among patients with normal LDL-C levels.
However, our data did not support an independent association between triglycerides and coronary events, after controlling for obesity and clinical disease status. A meta-analysis based on 10, 158 cases of coronary events from 262, 525 subjects in 29 studies showed a summarized odds ratio of 1.72 (95%CI 1.56-1.90) in a comparison of individuals in the top third with those in the bottom third oftriglycerides . However, the role of fasting triglycerides as the risk factor was still controversial. In addition, evidence has favored postprandial triglycerides, instead of fasting triglycerides, as the better predictor for coronary events [20, 21]. Although past evidence on combined low HDL-C and high triglycerides, "atherogenic dyslipidemia" predicting cardiovascular risk are available, our results did not support this hypothesis regarding the joint associations between HDL-C, triglycerides, and acute coronary events. The association was greatly attenuated after obesity and renal function were incorporated into the model, implying that the association between dyslipidemia and acute coronary events were modified according to the obesity and renal function status.
Evidence showing robust associations between increasing fasting trigylcerides and declining HDL-C/tirglyceride ratios with cardiovascular risk has been derived from prospective studies based on communities and hospitals [9, 19, 22, 23], and our hosptial-based case-control study results were consistent to previous findings. In addition, our further follow-up study showed that unexpected findings for the risk of all-cause deaths among patients who suffered from acute coronary events and had a normal LDL-C level. The patterns of statistical significance of the associations between triglycerides, odds for acute coronary syndrome, and mortality risk may be attributed from the following reasons. First, lipid-related residual risk is associated with acute coronary syndrome events, after which the patients develop wasting and then low triglycerides may affect survival . Recent evidence has shown that traditional atherosclerotic risk factors, such as obesity and lipid levels, are inversely associated with survival in patients with congestive heart failure and chronic diseases [24–26]. Second, body fat changes after the event, such as obesity distribution, inflammatory processes and endotoxin-lipoprotein interaction, may contribute to the role of triglycerides, which are highly associated with obesity status, as an inverse relationship to death among patients with chronic diseases . The interrelationshipe between obesity and triglycerides after coronary events may account for the attenuation of the association between triglyceride levels and coronary risk.
Our previous study based on healthy adults showed that renal function deterioration was related to cardiovascular and all-cause death mortality, and that the effect was additive with the metabolic syndrome components . In addition, a poor prognosis, including increased cardiovascular disease mortality, is associated with renal failure in chronic diseases [28–31]. The present study further demonstrated that poor renal function is also a poor prognostic factor for patients after acute coronary syndrome events. Therefore, identifying poor renal function and aggressively controlling the progress of renal function deterioration is crucial for improving the survival in patients experiencing a first coronary event.
This study has some limitations. First, our results are only applicable to middle-age and older patients with a normal LDL-C level due to the study design. Second, the problem of un-comparability of statin usages between cases and controls (20% in case patients and 8% in control subjects) may bias the association between lipid profiles and coronary events. Third, the follow-up period was relatively short, so the results are suitable for short-term prognosis and survival. In addition, we didn't specify the cause-specific death in the outcome assessment because of limited death cases due to cardiovascular events. Assessing all-cause mortality after acute cardiovascular events is a feasible way to evaluate the prognosis for secondary prevention .
In conclusion, low HDL-C was significantly associated with acute coronary events, and triglyceride levels as well as renal function were inversely related to all-cause deaths after the coronary events. Further secondary prevention studies on the relationship between lipids, renal function and coronary events may be warranted.
Study design and study participants
We divided this study into two parts. The first was a retrospective matched case-control study designed to examine the association between low HDL-C and high triglycerides, and the occurrence of acute coronary syndrome. The second was a longitudinal cohort study, following up the patients with acute coronary syndrome until December 31, 2009. The protocol was approved by the IRB, National Taiwan University Hospital, and written informed consent was not obtained due to chart review and un-labelling participants' identificiation.
Matched case-control study
The matched case-control study design was modified from the protocol of a multinational, multicenter case-control study . In brief, patients hospitalized due to acute coronary syndrome, including unstable angina, ST elevated myocardial infarction and non-ST elevated myocardial infarction, from 2006 to 2009 were included. From a retrospective chart review, patients with high LDL-C (> = 130 mg/dL) were excluded and we limited the age range to 50 to 85 years. Cases were defined as patients with a first coronary event (acute coronary syndrome, including myocardial infarction) admitted to intensive care units or explored in catheterization labs. A total of 557 case patients were recruited. Diagnosis was regarded as established if supported by electrocardiography criteria and appropriate cardiac biomarkers, otherwise diagnosis was considered as not definitively confirmed. The controls selected for the case-control study were participants free of coronary events being hospitalized for health checkups in the same hospital during the same period. The 1:2 matched controls (n = 1086) were randomly selected from the participants, with matching factors for age (50-60, 60-75, and > = 75 yrs), sex, and LDL-C level (< 70, 70-100, 100-130 mg/dL).
Prospective cohort study
The second part of this study was a prospective cohort study on the case patients. Information on mortality and causes of death were obtained by linking the identification numbers of the study subjects to a national databank provided by the National Health Administration, which was updated to the end of 2009. We defined all-cause death according to the death codes from the 9th or 10th versions of the International Classification of Diseases (ICD).
Clinical and laboratory measurements
Data were collected family history, lifestyle habits, and medical history, as well as anthropometric measurements, such as body weight, and body height, when available. Clinical variables were collected for each subject consisting of measures of body mass index, lipid profiles, blood pressures and glucose levels. Baseline hypertension and diabetes mellitus status were checked from the chart review.
We defined the baseline lipid profiles during the examination as those within the first 8 hours following onset of acute coronary syndrome. Lipid levels were measured in the control participants when they received the health checkup during the fasting status. Procedures for blood sampling and analytic methods were the same in case patients and control subjects and were performed as previously described . In brief, serum total cholesterol levels were measured using the CHOD-PAP method (Boehringer Mannheim, Germany) while HDL-C was measured following precipitation of apolipoprotein B-containing lipoproteins, with phosphotungstic acid and magnesium ions (Boehringer Mannheim, Germany). LDL-C levels were calculated if triglyceride levels were below 400 mg/dL. Triglycerides concentrations were measured by the GPO-DAOS method (Wako Co., Japan). All of the biochemical measurements, including the aforementioned lipid, uric acid, and creatinine concentrations, were measured using a Hitachi 7450 automated analyzer (Hitachi, Japan). All of the sample measurements were carried out in a single hospital. The coefficients of variation for the above measurements were around 5%.
Estimated glomerular filtration rates were calculated from serum creatinine measurements with the abbreviated MDRD equation :
All data were presented as mean and standard deviation for continuous variables and contingency tables for categorical data, and were listed by status of case patient and control subject. The t-test was used to test the differences between case patients and control subjects. Participants were categorized on the basis of quintiles of HDL-C, triglycerides, LDL-C and estimated glomerular filtration rate from the control subjects.
Multiple logistic regression models to adjust for potentially confounding variables, including age, sex, estimated glomerular filtration rate, body mass index (continuous variable), alcohol intake (nondrinker/current), smoking (yes/no), baseline hypertension (yes/no) and type 2 diabetes mellitus (yes/no) were applied to estimate the odds ratios and 95% confidence intervals (CI) by quintiles of HDL-C and triglycerides. In addition, to test for linear trends across lipid marker categories, we used the median lipid profile levels within quintiles as a continuous variable. Moreover, we examined whether the association between triglycerides and acute coronary syndrome odds differed according to weight status. Likelihood ratio tests were used to compare the model with the interaction terms and the model without the interaction terms. We also tested the goodness-of-fit for the model by using the Hosmer and Lemeshow test,  and the goodness-of-fit test was acceptable. Finally, we conducted joint analyses to evaluate potential additive associations of baseline HDL-C and triglycerides using the tertiles in the control subjects as the cutoff.
With regards to the cohort follow-up study, we constructed a multivariable Cox proportional hazards model to incorporate possible clinical variables in one model. To test the proportionality assumption in the Cox regression model, we generated the time dependent covariates by creating interactions of the covariates and a function of survival time and included them in the Cox model . The proportionality assumption for each covariate and overall model in the multiple Cox model were not rejected (all P values > 0.3).
In addition, we examined the non-linear relationship between lipids as well as filtration rates and the risk of coronary events and the survival non-parametrically with restricted cubic splines . Tests for non-linearity used the likelihood ratio test, comparing the model with only the linear term to the model with the linear and the cubic spline terms. The relationships between lipids and outcomes were plotted accordingly.
All statistical tests were two-tailed with a type I error of 0.05, and P values < 0.05 were considered statistically significant. Analyses were performed with SAS software version 9.1 (SAS Institute, Cary, NC).
Conflict of interest statement
The authors declare that they have no competing interests.
The authors thank the staff of National Taiwan University Hospital for their support. In addition, the authors express their thanks to Prof. Frank Sacks and Dr. Vincent Carey for their comments on the draft.
- Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002, 106 (25): 3143-3421.Google Scholar
- Graham I, Atar D, Borch-Johnsen K, Boysen G, Burell G, Cifkova R, Dallongeville J, De Backer G, Ebrahim S, Gjelsvik B: European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Eur Heart J. 2007, 28 (19): 2375-2414.View ArticlePubMedGoogle Scholar
- Buse JB, Ginsberg HN, Bakris GL, Clark NG, Costa F, Eckel R, Fonseca V, Gerstein HC, Grundy S, Nesto RW: Primary prevention of cardiovascular diseases in people with diabetes mellitus: a scientific statement from the American Heart Association and the American Diabetes Association. Circulation. 2007, 115 (1): 114-126.View ArticlePubMedGoogle Scholar
- Standards of medical care in diabetes--2008. Diabetes Care. 2008, 31 (Suppl 1): S12-54.Google Scholar
- Baigent C, Keech A, Kearney PM, Blackwell L, Buck G, Pollicino C, Kirby A, Sourjina T, Peto R, Collins R: Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90, 056 participants in 14 randomised trials of statins. Lancet. 2005, 366 (9493): 1267-1278.View ArticlePubMedGoogle Scholar
- Kearney PM, Blackwell L, Collins R, Keech A, Simes J, Peto R, Armitage J, Baigent C: Efficacy of cholesterol-lowering therapy in 18, 686 people with diabetes in 14 randomised trials of statins: a meta-analysis. Lancet. 2008, 371 (9607): 117-125. 10.1016/S0140-6736(08)60104-XView ArticlePubMedGoogle Scholar
- Cannon CP, Braunwald E, McCabe CH, Rader DJ, Rouleau JL, Belder R, Joyal SV, Hill KA, Pfeffer MA, Skene AM: Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med. 2004, 350 (15): 1495-1504. 10.1056/NEJMoa040583View ArticlePubMedGoogle Scholar
- LaRosa JC, Grundy SM, Waters DD, Shear C, Barter P, Fruchart JC, Gotto AM, Greten H, Kastelein JJ, Shepherd J: Intensive lipid lowering with atorvastatin in patients with stable coronary disease. N Engl J Med. 2005, 352 (14): 1425-1435. 10.1056/NEJMoa050461View ArticlePubMedGoogle Scholar
- Carey VJ, Bishop L, Laranjo N, Harshfield BJ, Kwiat C, Sacks FM: Contribution of High Plasma Triglycerides and Low High-Density Lipoprotein Cholesterol to Residual Risk of Coronary Heart Disease After Establishment of Low-Density Lipoprotein Cholesterol Control. The American Journal of Cardiology. 2010, 106 (6): 757-763. 10.1016/j.amjcard.2010.05.002PubMed CentralView ArticlePubMedGoogle Scholar
- Fruchart JC, Sacks FM, Hermans MP: Implications of the ACCORD lipid study: perspective from the Residual Risk Reduction Initiative (R(3)i). Curr Med Res Opin. 2010, 26 (8): 1793-1797. 10.1185/03007995.2010.489341View ArticlePubMedGoogle Scholar
- Wen CP, Cheng TY, Tsai MK, Chang YC, Chan HT, Tsai SP, Chiang PH, Hsu CC, Sung PK, Hsu YH: All-cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan. Lancet. 2008, 371 (9631): 2173-2182. 10.1016/S0140-6736(08)60952-6View ArticlePubMedGoogle Scholar
- Weiner DE, Tighiouart H, Amin MG, Stark PC, MacLeod B, Griffith JL, Salem DN, Levey AS, Sarnak MJ: Chronic kidney disease as a risk factor for cardiovascular disease and all-cause mortality: a pooled analysis of community-based studies. J Am Soc Nephrol. 2004, 15 (5): 1307-1315. 10.1097/01.ASN.0000123691.46138.E2View ArticlePubMedGoogle Scholar
- Chien KL, Hsu HC, Lee YT, Chen MF: Renal function and metabolic syndrome components on cardiovascular and all-cause mortality. Atherosclerosis. 2008, 197 (2): 860-867. 10.1016/j.atherosclerosis.2007.07.037View ArticlePubMedGoogle Scholar
- Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR: High density lipoprotein as a protective factor against coronary heart disease. The Framingham Study. Am J Med. 1977, 62 (5): 707-714. 10.1016/0002-9343(77)90874-9View ArticlePubMedGoogle Scholar
- Assmann G, Cullen P, Schulte H: Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation. 2002, 105 (3): 310-315. 10.1161/hc0302.102575View ArticlePubMedGoogle Scholar
- Sachdeva A, Cannon CP, Deedwania PC, Labresh KA, Smith SC, Dai D, Hernandez A, Fonarow GC: Lipid levels in patients hospitalized with coronary artery disease: an analysis of 136, 905 hospitalizations in Get With The Guidelines. Am Heart J. 2009, 157 (1): 111-117. e112, 10.1016/j.ahj.2008.08.010View ArticlePubMedGoogle Scholar
- Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J: Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004, 364 (9438): 937-952. 10.1016/S0140-6736(04)17018-9View ArticlePubMedGoogle Scholar
- Alsheikh-Ali AA, Lin JL, Abourjaily P, Ahearn D, Kuvin JT, Karas RH: Prevalence of low high-density lipoprotein cholesterol in patients with documented coronary heart disease or risk equivalent and controlled low-density lipoprotein cholesterol. Am J Cardiol. 2007, 100 (10): 1499-1501. 10.1016/j.amjcard.2007.06.058View ArticlePubMedGoogle Scholar
- Sarwar N, Danesh J, Eiriksdottir G, Sigurdsson G, Wareham N, Bingham S, Boekholdt SM, Khaw KT, Gudnason V: Triglycerides and the risk of coronary heart disease: 10, 158 incident cases among 262, 525 participants in 29 Western prospective studies. Circulation. 2007, 115 (4): 450-458. 10.1161/CIRCULATIONAHA.106.637793View ArticlePubMedGoogle Scholar
- Bansal S, Buring JE, Rifai N, Mora S, Sacks FM, Ridker PM: Fasting compared with nonfasting triglycerides and risk of cardiovascular events in women. JAMA. 2007, 298 (3): 309-316. 10.1001/jama.298.3.309View ArticlePubMedGoogle Scholar
- Nordestgaard BG, Benn M, Schnohr P, Tybjaerg-Hansen A: Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women. JAMA. 2007, 298 (3): 299-308. 10.1001/jama.298.3.299View ArticlePubMedGoogle Scholar
- Assmann G, Schulte H: Relation of high-density lipoprotein cholesterol and triglycerides to incidence of atherosclerotic coronary artery disease (the PROCAM experience). Prospective Cardiovascular Munster study. Am J Cardiol. 1992, 70 (7): 733-737. 10.1016/0002-9149(92)90550-IView ArticlePubMedGoogle Scholar
- Weverling-Rijnsburger AW, Jonkers IJ, van Exel E, Gussekloo J, Westendorp RG: High-density vs low-density lipoprotein cholesterol as the risk factor for coronary artery disease and stroke in old age. Arch Intern Med. 2003, 163 (13): 1549-1554. 10.1001/archinte.163.13.1549View ArticlePubMedGoogle Scholar
- Kalantar-Zadeh K, Horwich TB, Oreopoulos A, Kovesdy CP, Younessi H, Anker SD, Morley JE: Risk factor paradox in wasting diseases. Current Opinion in Clinical Nutrition & Metabolic Care. 2007, 10 (4): 433-442. 410.1097/MCO.1090b1013e3281a30594, 10.1097/MCO.0b013e3281a30594View ArticleGoogle Scholar
- Lavie CJ, Mehra MR, Milani RV: Obesity and heart failure prognosis: paradox or reverse epidemiology?. European Heart Journal. 2005, 26 (1): 5-7.View ArticlePubMedGoogle Scholar
- Speakman JR, Westerterp KR: Reverse Epidemiology, Obesity and Mortality in Chronic Kidney Disease: Modelling Mortality Expectations Using Energetics. Blood Purification. 2010, 29 (2): 150-157. 10.1159/000245642View ArticlePubMedGoogle Scholar
- Beddhu S: The Body Mass Index Paradox and an Obesity, Inflammation, and Atherosclerosis Syndrome in Chronic Kidney Disease. Seminars in Dialysis. 2004, 17 (3): 229-232. 10.1111/j.0894-0959.2004.17311.xView ArticlePubMedGoogle Scholar
- Culleton BF, Larson MG, Wilson PW, Evans JC, Parfrey PS, Levy D: Cardiovascular disease and mortality in a community-based cohort with mild renal insufficiency. Kidney Int. 1999, 56 (6): 2214-2219. 10.1046/j.1523-1755.1999.00773.xView ArticlePubMedGoogle Scholar
- Fried LF, Shlipak MG, Crump C, Bleyer AJ, Gottdiener JS, Kronmal RA, Kuller LH, Newman AB: Renal insufficiency as a predictor of cardiovascular outcomes and mortality in elderly individuals. J Am Coll Cardiol. 2003, 41 (8): 1364-1372. 10.1016/S0735-1097(03)00163-3View ArticlePubMedGoogle Scholar
- Garg AX, Clark WF, Haynes RB, House AA: Moderate renal insufficiency and the risk of cardiovascular mortality: results from the NHANES I. Kidney Int. 2002, 61 (4): 1486-1494. 10.1046/j.1523-1755.2002.00270.xView ArticlePubMedGoogle Scholar
- Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY: Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004, 351 (13): 1296-1305. 10.1056/NEJMoa041031View ArticlePubMedGoogle Scholar
- Carter AM, Catto AJ, Mansfield MW, Bamford JM, Grant PJ: Predictive Variables for Mortality After Acute Ischemic Stroke. Stroke. 2007, 38 (6): 1873-1880. 10.1161/STROKEAHA.106.474569View ArticlePubMedGoogle Scholar
- Fruchart JC, Sacks FM, Hermans MP, Assmann G, Brown WV, Ceska R, Chapman MJ, Dodson PM, Fioretto P, Ginsberg HN: The Residual Risk Reduction Initiative: a call to action to reduce residual vascular risk in dyslipidaemic patient. Diab Vasc Dis Res. 2008, 5 (4): 319-335. 10.3132/dvdr.2008.046View ArticlePubMedGoogle Scholar
- Chien KL, Hsu HC, Su TC, Lee YT: Consistency in genetic inheritance mode and heritability patterns of triglyceride vs. high density lipoprotein cholesterol ratio in two Taiwanese family samples. BMC Journal, Genetics. 2003, 4: 7-16.View ArticlePubMedGoogle Scholar
- Lopes-Virella M, Stone P, Ellis S, Colwell JA: Cholesterol determination in high-density lipoproteins separated by three different methods. ClinChem. 1977, 23 (5): 882-884.Google Scholar
- Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999, 130 (6): 461-470.View ArticlePubMedGoogle Scholar
- Hosmer DW, Lemeshow S: The multiple logistic regression model. Applied logistic regression. 1989, 25-37. New York: John Wiley & Sons, 1,Google Scholar
- Hosmer DW, Lemeshow S: Applied survival analysis: regression modeling of time to event data. 1999, New York: Wiley,Google Scholar
- Durrleman S, Simon R: Flexible regression models with cubic splines. Stat Med. 1989, 8 (5): 551-561. 10.1002/sim.4780080504View ArticlePubMedGoogle Scholar
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