International Journal of Cardiology 171 (2014) 390–397

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Neutrophil lymphocyte ratio significantly improves the Framingham risk score in prediction of coronary heart disease mortality: Insights from the National Health and Nutrition Examination Survey-III Neeraj Shah a,⁎, Valay Parikh b, Nileshkumar Patel a, Nilay Patel c, Apurva Badheka d, Abhishek Deshmukh e, Ankit Rathod f, James Lafferty b a

Department of Medicine, Staten Island University Hospital, Staten Island, NY, United States Department of Cardiology, Staten Island University Hospital, Staten Island, NY, United States c Heart & Vascular Institute, Detroit Medical Center, Detroit, MI, United States d Department of Cardiology, Wayne State University School of Medicine, Detroit, MI, United States e Department of Cardiology, University of Arkansas for Medical Science, Little Rock, AR, United States f Department of Cardiology, Cedars Sinai Medical Center, Los Angeles, CA, United States b

a r t i c l e

i n f o

Article history: Received 13 September 2013 Received in revised form 25 November 2013 Accepted 14 December 2013 Available online 23 December 2013 Keywords: Neutrophil lymphocyte ratio (NLR) Framingham risk score (FRS) Coronary heart disease (CHD) Reclassification

a b s t r a c t Background: Neutrophil lymphocyte ratio (NLR) has been shown to predict cardiovascular events in several studies. We sought to study if NLR predicts coronary heart disease (CHD) in a healthy US cohort and if it reclassifies the traditional Framingham risk score (FRS) model. Methods: We performed post hoc analysis of National Health and Nutrition Examination Survey-III (1998–94) including subjects aged 30–79 years free from CHD or CHD equivalent at baseline. Primary endpoint was death from ischemic heart disease. NLR was divided into four categories: b1.5, ≥1.5 to b3.0, 3.0–4.5 and N 4.5. Statistical analyses involved multivariate Cox proportional hazards models as well as discrimination, calibration and reclassification. Results: We included 7363 subjects with a mean follow up of 14.1 years. There were 231 (3.1%) CHD deaths, more in those with NLR N 4.5 (11%) compared to NLR b 1.5 (2.4%), p b 0.001. Adjusted hazard ratio of NLR N 4.5 was 2.68 (95% CI 1.07–6.72, p = 0.035). There was no significant improvement in C-index (0.8709 to 0.8713) or area under curve (0.8520 to 0.8531) with addition of NLR to FRS model. Model with NLR was well calibrated with Hosmer–Lemeshow chi-square of 8.57 (p = 0.38). Overall net reclassification index (NRI) was 6.6% (p = 0.003) with intermediate NRI of 10.1% (p b 0.001) and net upward reclassification of 5.6%. Absolute integrated discrimination index (IDI) was 0.003 (p = 0.039) with relative IDI of 4.3%. Conclusions: NLR can independently predict CHD mortality in an asymptomatic general population cohort. It reclassifies intermediate risk category of FRS, with significant upward reclassification. NLR should be considered as an inflammatory biomarker of CHD. © 2013 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Cardiovascular disease is the leading cause of death in United States. Statistics from 2009 show nearly 600,000 annual deaths due to cardiovascular disease, out of which 385,000 deaths were due to coronary heart disease (CHD) [1]. The annual cost attributed to CHD in US is around $109 billion [1]. CHD has a long latent period during which the subjects remain asymptomatic. In order to reduce the burden of CHD, an important step is to institute appropriate primary prevention

⁎ Corresponding author at: Department of Internal Medicine, Staten Island University Hospital, 475 Seaview Avenue, Staten Island, NY 10305, United States. Tel.: +1 914 826 1033. E-mail address: [email protected] (N. Shah). 0167-5273/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2013.12.019

measures in asymptomatic, apparently healthy population at risk. Traditional risk assessment strategies for CHD like the Framingham risk score (FRS) rely on markers like age, sex, hypertension, diabetes, hyperlipidemia and smoking. Unfortunately, conventional risk prediction models like FRS often fail to identify a considerable proportion of individuals at risk of developing CHD. Almost half of individuals who develop CHD have only 1 or none of these traditional risk factors [2,3]. Hence there is a need to improve the currently existing risk assessment models for more accurate prediction of CHD risk, so that targeted preventive measures can be instituted. The 2010 American College of Cardiology Foundation (ACCF)/ American Heart Association (AHA) guidelines [4] recommend that initially global risk scores like FRS should be applied to all asymptomatic individuals for risk assessment. Persons at low risk do not need any further testing. Persons at high risk should be subjected to intensive

N. Shah et al. / International Journal of Cardiology 171 (2014) 390–397

preventive measures. Persons at intermediate risk may be considered to undergo further stratification by using either serum or radiologic markers, as long as these tests are cost effective [4]. The serum markers proposed by ACCF/AHA [4] include C-reactive protein (CRP), brain natriuretic peptide (BNP), hemoglobin A1c and lipoprotein associated phospholipase A2. The role of neutrophil lymphocyte ratio (NLR) as a biomarker for CHD in asymptomatic individuals is not clear. NLR is a simple ratio of the absolute neutrophil and lymphocyte counts obtained on the differential section of leukocyte count of a complete blood count (CBC) and is a marker of inflammation. NLR has been shown to be associated with worse outcomes in patients with acute coronary syndromes and established CHD [5–12]. However, there is a lack of data regarding its role in healthy individuals free from CHD at baseline. We sought to study the value of NLR to predict CHD related mortality in the National Health and Nutrition Examination Survey-III (NHANES-III) cohort which is representative of a healthy US general population cohort. 2. Methods 2.1. Study sample and design NHANES-III, conducted by the National Center for Health Statistics, includes data from oral surveys and general health examinations. It was designed to assess the demographic, socioeconomic, dietary and overall health status of a representative sample from all 50 States. Of the individuals selected to participate, 30,818 subjects (with 19,215 adults ≥18 years age) completed the health examination [13]. Our study inclusion criteria were subjects aged 30–79 years who did not have a history of coronary heart disease (CHD), diabetes mellitus (DM) or peripheral vascular disease (PVD). Study exclusion criteria were: 1) History of CHD, 2) DM, 3) Symptomatic PVD, 4) Inflammatory & autoimmune conditions like rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) or gout, which may cause an elevation in the neutrophil–lymphocyte ratio, 5) Malignancies (except skin cancer), which may also modify the NLR, 6) Oral corticosteroid therapy (since corticosteroids elevate the NLR), 7) Missing data on mortality, FRS covariates or NLR. After applying the study exclusion criteria 7363 subjects were included as the final cohort in our analysis (Supplementary Fig. 1).

2.2. Definitions and measurements Hypertension was defined as a self-reported history of high blood pressure or participants currently taking medications for high blood pressure. Blood pressure was measured as the average of systolic and diastolic blood pressure taken over six or fewer measurements. Blood pressure measurements were taken using a mercury sphygmomanometer according to standardized blood pressure measurement protocols recommended by American Heart Association at that time. Hyperlipidemia was defined as participants with a self-reported history of high cholesterol or those taking medications for high cholesterol. Low density lipoprotein (LDL) was calculated using the formula: LDL (mg/dL) = Total cholesterol − HDL (High density lipoprotein) − (0.20 × Serum triglycerides). Smoking was defined as participants who currently smoked and have smoked at least 100 cigarettes in their lifetime. Diabetes mellitus was defined as self-reported history of diabetes mellitus or participants currently taking insulin or anti-diabetic medications or those with a hemoglobin A1c (HbA1c) level of 6.5% or greater. History of CHD was defined as self-reported history of myocardial infarction (“heart attack”) or self-reported history of chest pain suggestive of chronic stable angina. Family history of CHD was defined as history of “heart attack” (i.e. myocardial infarction) in first degree relatives (mother, father, brother or sister) before the age of 50 years. Symptomatic PVD was defined as selfreported history of pain in lower extremities which was not present at rest, occurred while walking and was relieved by resting or standing still. GFR was estimated using the Modification of Diet in Renal Disease (MDRD) equation incorporating age, sex, race and serum creatinine in the equation. C-reactive protein (CRP) was measured using latexenhanced nephelometry [14]. For quantification of CRP, particles consisting of a polystyrene core and a hydrophilic shell were used in order to link covalently to anti-CRP antibodies. This method did not measure high sensitivity CRP (hsCRP). For the measurement of neutrophil and lymphocyte counts, the Coulter Counter Model S-PLUS JR (with Coulter histogram differential), which is a quantitative, automated hematology analyzer, was used [14]. (http://www.cdc.gov/nchs/data/nhanes/nhanes3/cdrom/nchs/manuals/ labman.pdf).

2.3. Framingham Risk Score (FRS) The Framingham Risk Score (FRS) referred to in this paper was calculated as the risk of developing hard coronary heart disease (hard CHD) as per ATP-III guidelines [15] (http://www.framinghamheartstudy.org/risk/hrdcoronary.html). The covariates used in estimating the FRS were: age, sex, total cholesterol, HDL (high density lipoprotein), systolic blood pressure and smoking.

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2.4. Outcome The primary end point of our study was hard CHD event (as per ATP-III guidelines) defined in the NHANES-III population as death due to ischemic heart disease. The NHANES-III linked mortality public-use file was used to obtain the mortality status. It provided mortality data through December 31, 2006. The mortality information was obtained by National Center of Health Statistics (NCHS) from death certificates, matching records of the National Death Index (NDI), social security administration or centers for Medicare and Medicaid services (CMS) [13]. Underlying causes of death were provided by death certificate data contained in the same mortality files and were classified according to the tenth revision of International Classification of Diseases, Injuries and Causes of death (ICD-10). ICD-10 codes for death due to ischemic heart disease (I20–I25) were used to define the primary endpoint. Person-months of follow up after the interview date were also provided in the NHANES-III linked mortality file. 2.5. Statistical methods NHANES-III had a complex nonrandom multistage stratified sample design. All analyses were performed using the designated weighting that was specified in the NHANES-III dataset to minimize biases [16]. The total NHANES-III pseudostratum was used as our stratum variable. The total NHANES-III pseudoprimary sampling unit was used as our survey sampling unit, and the total mobile examination center final weight as our sampling unit weight. NLR was divided into four categories based on the following cut-offs: NLR b 1.5 (used as reference), NLR ≥ 1.5 to b3.0, NLR 3.0–4.5 and NLR N 4.5. Baseline characteristics of the study population were studied in the overall population and the four categories of NLR. For categorical variables, chi-square analysis was used to evaluate group differences. For continuous variables, one way-ANOVA and Kruskal Wallis tests were used to evaluate group differences depending upon distribution of the variable. Multivariate Cox proportional hazards models were built in order to assess the effect of NLR on mortality from CHD. Four multivariate models were created: 1) Model A (n = 7363) which included NLR, age, sex, race (patient demographics only), 2) Model B (n = 7353) which included model A covariates plus BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol & HDL, 3) Model C (n = 7295) which included model B covariates plus CRP, and 4) Model D (n = 7250) which included model C covariates plus GFR. Other markers: Similar analysis was performed for CRP and individual components of NLR viz. absolute neutrophil count (ANC) (ANC N 7700/mm3 was considered high) and absolute lymphocyte count (ALC) (ALC b 1500/mm3 was considered low) in order to evaluate if these markers independently predicted CHD related death in our population. Discrimination, calibration and reclassification: In order to understand the role of addition of NLR to the FRS risk score, we created two multivariate models: One with FRS covariates alone (age, sex, systolic BP, smoking, total cholesterol and HDL cholesterol) and another with FRS covariates plus NLR. Global fit of the model containing NLR was assessed by −2 log likelihood ratio test. Likelihood ratio tests the significance of adding of a new biomarker (NLR) separately to the model, with p b 0.05 considered statistically significant [17]. Discrimination is defined as whether or not the new marker has the ability to distinguish those who experience an event from those who do not. The discriminatory ability of models was tested using the Harrell's C-statistic (involving survival data) and area under the receiver operating curve (ROC) (involving logistic regression data), both of which test the predictive accuracy of the model. C-statistic means probability that the predicted risk is higher for an event compared to a non-event [18] whereas area under curve (AUC) reflects area under curve of a ROC plot of sensitivity vs. 1-specificity [18]. Their values range from 0.5 to 1 with a value of 0.5 implying no discrimination and values closer to 1 signifying higher discrimination. We also assessed calibration which means how closely the predicted probabilities of risk reflect the actual observed risk. A model in which the number of observed events align well with the number of actual events, demonstrates good calibration. Calibration was assessed by Hosmer–Lemeshow (HL) chi2 test which compares observed and predicted outcomes over deciles of risk [19] in two multivariate models, one incorporating FRS covariates alone and another incorporating FRS covariates plus NLR. A HL chi2 b 20 and p N 0.05 reflects good calibration. The lower the value of HL chi2, the better is the calibration. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were computed with lower values of AIC and BIC implying better fit. Discrimination indices only signify whether the predicted risk will be higher in case of events compared to non-events, but what is more relevant clinically is whether addition of a new biomarker (NLR) to the model will accurately stratify individuals into clinically significant higher and lower categories of risk [17]. Hence clinically, reclassification becomes more important. Two approaches, namely “net reclassification index (NRI)” and “integrated discrimination index (IDI)” provide quantitative estimates of correct reclassifications [20]. NRI calculates reclassification tables separately for those who experience an event and those who do not. In the categories of risk, NRI quantifies the overall correct movement, which is upwards in those who have events and downwards in those who do not [20]. We divided the cohort into four risk categories: 5%, 5–10%, 10–20%, and N20% risk of developing CHD death over the study period. We then calculated NRI to see whether addition of NLR to the FRS risk model led to any significant reclassification of CHD risk. NRI was calculated separately for the entire cohort and the intermediate risk (5–20%) sub-group. We also computed event NRI as net upward reclassification for events and non-event NRI as net downward reclassification for non-events.

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Integrated discrimination index (IDI) is defined as difference in the discrimination slopes of the models with and without NLR. It is the difference between improvement in average sensitivity and any potential increase in one minus specificity [20]. It can be calculated as (EY1 − EX1) + (EX0 − EY0), where EX1 and EX0 represent the mean expected probabilities of events and nonevents, respectively, for the model without the new marker and EY1 and EY0 represent the mean expected probabilities of events and nonevents, respectively, for the model with the new marker [21]. Relative IDI is an index used when the incidence of events is relatively small [21], and it can be calculated as ([EY1 − EY0] / [EX1 − EX0]) − 1. Comparison of NLR and CRP: Since both NLR and CRP are inflammatory markers, correlation between NLR and CRP was assessed using Spearman correlation. Similar to NLR, discrimination, calibration and reclassification was carried out using CRP, in order to compare its performance to NLR in our population. Statistical analysis was performed using STATA SE 11.1 (STATA Corp. LP, College Station, Texas). A 2-sided p value of 0.05 was considered statistically significant.

3. Results We included 7363 subjects, who took part in NHANES-III from 1988 to 1994. This was representative of 89,80,0451 (almost 9 million) US general population. The mean follow up period was 14.1 ± 3.2 years. The number of CHD related deaths over this period was 231 (3.14%), signifying overall low risk in the population. 3.1. Baseline characteristics Table 1 shows the baseline characteristics in the overall population and in the 4 categories of NLR. Mean age of the population was 48.3 ± 13.9 years. There were 3541 (48.1%) males and 3178 (43.2%) subjects were of Caucasian descent. Hypertension was prevalent in 1701 (23.1%), hypercholesterolemia in 1314 (17.9%) and smoking in 2022 (27.5%) subjects. Subjects with higher values of NLR (N4.5) were more likely to be older, of Caucasian race, have a higher prevalence of hypertension & smoking and have a lower BMI, HDL cholesterol, GFR and higher CRP level.

Fig. 1. Figure showing survival curves for CHD related mortality in the four categories of NLR.

NLR (p for logrank test b 0.001). Multivariate Cox regression models showed that NLR (N 4.5) was an independent predictor of CHD mortality in all four models (A, B, C, D) (Table 2). In the full model including CRP (model D), the adjusted hazard ratio (HR) of NLR N 4.5 was 2.68 (95% confidence interval (CI) 1.07–6.72, p = 0.035). Other independent predictors of CHD mortality were age, gender, systolic blood pressure, smoking and CRP (Table 4). Incorporating NLR as a continuous variable in the multivariate models continued to demonstrate NLR as an independent predictor of CHD death. Adjusted HR for NLR (continuous) in the full model (model D) was 1.17 (95% CI 1.03–1.34, p = 0.02) (Table 3). 3.3. Discrimination, calibration and reclassification Likelihood ratio (LR) test for global fit (after addition of NLR to the FRS model) was significant with LR chi-square 6.15, p = 0.013, thus signifying that the model with NLR fit the data well.

3.2. NLR and CHD risk Higher NLR was associated with greater incidence of CHD related death. In patients with NLR b 1.5, ≥1.5– b 3.0, 3.0–4.5 and N 4.5, there were respectively 51 (2.4%), 140 (3.2%), 25 (3.7%) and 15 (11%) CHD related deaths during the follow up period (chi-square p b 0.001). Fig. 1 shows survival curves for CHD mortality in the four categories of

3.3.1. Discrimination Harrell's C-index for model containing NLR plus FRS covariates was 0.8713 compared to 0.8709 for the model containing FRS covariates only (p = 0.74). Similarly, AUC for the model containing NLR plus FRS

Table 1 Baseline characteristics of NHANES-III study population in the four categories of neutrophil lymphocyte ratio (NLR). Variable

Overall population (n = 7363)

NLR (b1.5) (n = 2146)

NLR (≥1.5–b 3.0) (n = 4407)

NLR (3.0–4.5) (n = 673)

NLR (N4.5) (n = 137)

p-Value

Age Male gender White race Hypertension Hyperlipidemia Current smoking Family history of CHD BMI⁎ Average systolic BP Average diastolic BP Total cholesterol HDL CRP† GFR‡

48.34 ± 13.91 3541 (48.1%) 3178 (43.2%) 1701 (23.1%) 1314 (17.9%) 2022 (27.5%) 580 (7.9%) 27.16 ± 5.46 124.83 ± 18.20 76.05 ± 10.13 208.22 ± 42.48 51.71 ± 15.91 0.43 ± 0.74 74.56 ± 14.81

47.63 ± 13.29 1002 (46.7%) 605 (28.2%) 504 (23.5%) 401 (18.7%) 578 (26.9%) 151 (7.0%) 27.25 ± 5.45 124.49 ± 18.24 76.43 ± 10.27 209.09 ± 43.34 53.08 ± 16.45 0.36 ± 0.44 76.32 ± 14.65

48.49 ± 14.04 2150 (48.8%) 2110 (47.9%) 984 (22.3%) 774 (17.6%) 1182 (26.8%) 368 (8.4%) 27.20 ± 5.39 124.75 ± 17.99 75.89 ± 9.95 208.52 ± 42.58 50.97 ± 15.52 0.41 ± 0.62 74.02 ± 14.53

49.04 ± 14.40 328 (48.7%) 385 (57.2%) 172 (25.6%) 114 (16.9%) 220 (32.7%) 55 (8.2%) 26.73 ± 5.99 125.77 ± 18.75 75.78 ± 10.61 203.82 ± 39.08 51.97 ± 16.64 0.63 ± 1.34 72.94 ± 15.99

51.04 ± 16.29 61 (44.5%) 78 (56.9%) 41 (29.9%) 25 (18.3%) 42 (30.7%) 6 (4.4%) 26.55 ± 5.16 128.08 ± 21.19 76.12 ± 11.55 206.51 ± 40.45 52.93 ± 15.03 1.26 ± 2.13 72.21 ± 17.75

0.058 0.341 b0.001 0.057 0.644 0.011 0.12 0.002 0.15 0.444 0.065 b0.001 b0.001 b0.001

NLR = Neutrophil lymphocyte ratio. CHD = Coronary heart disease. BMI = Body mass index. HDL = High density lipoprotein. CRP = C-reactive protein. GFR = Glomerular filtration rate estimated using the Modification of diet in renal disease (MDRD) formula. ⁎ There are 7353 subjects with available data for BMI. † There are 7304 subjects with available data for CRP. ‡ There are 7263 subjects with available data for GFR.

N. Shah et al. / International Journal of Cardiology 171 (2014) 390–397 Table 2 Table showing adjusted hazard ratio (HR) of NLR N 4.5 in predicting CHD mortality in the four multivariate models. Note: NLR b 1.5 is considered as referent. Model

n

Adj. HR

95% CI

p

Model A Model B Model C Model D

7363 7353 7295 7250

4.30 3.28 2.69 2.68

1.84–10.0 1.38–7.81 1.04–6.94 1.07–6.72

0.001 0.008 0.041 0.035

Adj. HR = Adjusted Hazard Ratio, 95% CI = 95% confidence interval, n = no. of observations in the model. Model A includes NLR, age, sex and race. Model B includes NLR, age, sex, race, BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol & HDL. Model C includes NLR, age, sex, race, BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol, HDL and CRP. Model D includes NLR, age, sex, race, BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol, HDL, CRP and GFR. NLR = Neutrophil lymphocyte ratio. CHD = Coronary heart disease. BMI = Body mass index. HDL = High density lipoprotein. CRP = C-reactive protein. GFR = Glomerular filtration rate estimated using the Modification of diet in renal disease (MDRD) formula.

covariates was 0.8531 compared to 0.8520 for the model containing FRS covariates only (p = 0.45). Thus, there was a trend of improvement in C-index and AUC after addition of NLR, but this trend was not statistically significant (Table 5). 3.3.2. Calibration HL chi square for the model containing FRS covariates alone was 9.15 (p = 0.33) and with the addition of NLR to the model, it further decreased to 8.57 (p = 0.38), thus signifying that the model with NLR was well calibrated. There were minor differences in the AIC and BIC of the two models with the model containing NLR having a slightly lower AIC and slightly higher BIC (Table 5). 3.3.3. Reclassification There was a net improvement in reclassification of FRS after addition of NLR by 6.6% (Table 5, Table 6), which was statistically significant (p = 0.003). Net upward reclassification for those who had an event (CHD death) was 6.1% (p = 0.006) and net downward reclassification

Table 3 Table showing adjusted hazard ratio (HR) of NLR (as a continuous variable) in predicting CHD mortality in the four multivariate models. Model

n

Adj. HRa

95% CI

p

Model A Model B Model C Model D

7363 7353 7295 7250

1.22 1.20 1.17 1.17

1.10–1.35 1.07–1.35 1.03–1.34 1.03–1.34

b0.001 0.002 0.021 0.02

Adj. HR = Adjusted Hazard Ratio, 95% CI = 95% confidence interval, n = no. of observations in the model. Model A includes NLR, age, sex and race. Model B includes NLR, age, sex, race, BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol & HDL. Model C includes NLR, age, sex, race, BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol, HDL and CRP. Model D includes NLR, age, sex, race, BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol, HDL, CRP and GFR. NLR = Neutrophil lymphocyte ratio. CHD = Coronary heart disease. BMI = Body mass index. HDL = High density lipoprotein. CRP = C-reactive protein. GFR = Glomerular filtration rate estimated using the Modification of diet in renal disease (MDRD) formula. a The adjusted hazard ratios are expressed per unit increase in NLR.

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Table 4 Independent multivariate predictors of CHD related death in NHANES-III population. Variable

Adj. HR

95% CI

P-value

NLR N 4.5a Age Female gender Systolic BP Current smoking CRP N 1 mg/Lb

2.68 1.11 0.54 1.01 2.76 1.82

1.07–6.72 1.09–1.13 0.37–0.80 1.00–1.03 1.92–3.98 1.07–3.12

0.035 b0.001 0.003 0.041 b0.001 0.029

Note: The multivariate Cox proportional hazards model is adjusted for NLR, age, sex, race, BMI, hypertension, hypercholesterolemia, smoking, family history of CHD, systolic blood pressure (SBP), total cholesterol, HDL, CRP and GFR (Model D). NLR = Neutrophil lymphocyte ratio. CHD = Coronary heart disease. BMI = Body mass index. HDL = High density lipoprotein. CRP = C-reactive protein. GFR = Glomerular filtration rate estimated using the Modification of diet in renal disease (MDRD) formula. a NLR b 1.5 is considered as reference. b CRP b =1 mg/L is considered as reference.

for those who did not have an event was 0.5% (p = 0.028) (Tables 5, 6). For subjects at intermediate risk of events (5–20%), NLR demonstrated an even higher NRI of 10.1% (p b 0.001). For the intermediate risk category, net upward reclassification for events was 5.6% and net downward reclassification for non-events was 4.5%. The absolute IDI for NLR was 0.003 (p = 0.039) and relative IDI was 4.3%.

3.3.4. Comparison with CRP The Spearman correlation coefficient between NLR and CRP was 0.11, p b 0.001, thus signifying some degree of association between NLR and CRP. Although CRP was an independent predictor of CHD death in multivariate analysis (Supplementary Table 1), there was no significant discrimination or reclassification after addition of CRP to FRS in our study population (Supplementary Table 1).

3.4. Individual cell counts Both absolute neutrophil count and absolute lymphocyte count, when considered alone, were not predictive of CHD mortality in multivariate Cox regression models (Supplementary Table 1). Moreover, there was no significant discrimination or reclassification after addition of individual neutrophil or lymphocyte counts to the FRS model (Supplementary Table 1).

4. Discussion Our study showed that neutrophil lymphocyte ratio (NLR) is an independent predictor of cardiovascular mortality in a nationally representative general population cohort. Moreover, NLR accurately reclassified those in the intermediate risk category of the Framingham Risk Score (FRS) as having lower or higher probability of cardiovascular mortality. NLR has been shown to be predictive of all cause and cardiovascular mortality in patients with acute coronary syndromes (ACS) [5–9], chronic ischemic heart disease [10], diabetes mellitus [11] or those undergoing coronary angiography [12]. On the other hand, some studies have reported borderline significance or no significance of NLR on mortality or adverse clinical events in patients with ACS [22,23]. Moreover, most of these studies focus on in-hospital or short term mortality outcomes; hence the effect of NLR on long term mortality in a cohort of asymptomatic healthy individuals is unclear. To our knowledge, this is one of the first studies showing predictive value of NLR on long term (14 year) mortality in a large general population cohort free from CHD at baseline.

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Table 5 Discrimination, calibration and reclassification after addition of NLR to the model containing FRS covariates. Model with FRS covariates alone

Model with FRS covariates plus NLR

p-value

Discrimination C-index AUC

0.8709 (0.8526–0.8892) 0.8520 (0.8310–0.8730)

0.8713 (0.8528–0.8897) 0.8531 (0.8322–0.8741)

0.74 0.45

Calibration Hosmer–Lemeshow chi2 Likelihood ratio test AIC BIC

9.15, p = 0.33 LR chi2 = 6.15 1666.03 1714.36

8.57, p = 0.38

– 0.013 – –

Reclassification NRI Intermediate NRI Event NRI Non-event NRI Absolute IDI Relative IDI

6.6% (2.2%–10.9%) 10.1% (5.5%–14.8%) 6.1% (1.7%–10.4%) 0.5% (0.05%–0.9%) 0.003 (0.0002–0.0063) 4.31%

1661.89 1717.12

0.022 b0.001 0.006 0.028 0.039 –

Note: Values shown in parentheses represent 95% confidence interval of that statistic. FRS covariates include age, sex, total cholesterol, HDL (high density lipoprotein), systolic blood pressure and smoking. FRS = Framingham risk score. NLR = Neutrophil lymphocyte ratio. AUC = Area under receiver operating characteristic (ROC) curve. AIC = Akaike information criterion. BIC = Bayesian information criterion. NRI = Net reclassification index. IDI = Integrated discrimination index.

4.1. Neutrophils, lymphocytes and NLR in CHD With the recognition of the role of inflammation in atherosclerosis [24,25], the interest in immune response in atherosclerosis has grown [26]. Inflammation may not only play a role in initiation of atherosclerosis and plaque formation but may also regulate integrity of the plaque cap [27], disruption of which can result in an acute event. Initial studies showed that elevated total WBC count was associated with increased mortality and worse outcomes after acute myocardial infarction (MI) [28–30]. Subsequently, focus turned to the differential leucocyte count with studies showing higher neutrophil counts associated with worse outcomes patients with MI [31] or at high risk of CHD [12]. Studies have shown continuous recruitment of neutrophils in all stages of atherosclerosis, from initiation to plaque rupture [32–34]. After emigration

into vessel walls, neutrophils produce proinflammatory and atherogenic effects by interacting with other cell types [35]. This highly inflammatory state can make the atherosclerotic plaque unstable and prone to rupture leading to acute coronary syndromes [33]. Low lymphocyte counts have also been associated with worse outcomes in patients with CHD and unstable angina [12,36,37], however, the exact mechanism is poorly understood. It is proposed that lower lymphocyte counts may represent higher physiologic stress due to cortisol release and reflect a poorly regulated immune response [11]. Regulatory T-cells, a subset of lymphocytes, have been shown to play an inhibitory role in atherosclerosis [38,39], possibly by controlling and regulating the inflammatory response. Studies have demonstrated that NLR is a marker of systemic inflammation [40] and that it correlates with C-reactive protein (CRP) [41].

Table 6 Risk of CHD mortality in the NHANES-III population in models with and without NLR. Model with FRS covariates without NLR

Model with FRS covariates plus NLR b5%

5–10%

Risk reclassification 10–20%

N = 20%

Total

Lower

Higher

Subjects with CHD events b5% 5–10% 10–20% N = 20% Total Event NRI (95% CI, p)

60 5 1 56 0 4 0 0 61 65 6.1% (1.7%–10.4%, p = 0.006)

0 6 66 1 73

0 0 9 23 32

65 63 79 24 231

NA 1 4 1 6

5 6 9 NA 20

Subjects without CHD events b5% 5–10% 10–20% N = 20% Total Non-event NRI (95% CI, p) Overall NRI (95% CI, p)

5789 43 71 614 0 62 0 0 5860 719 0.5% (0.05%–0.9%, p = 0.028) 6.6% (2.2%–10.9%, p = 0.003)

0 48 390 11 449

1 0 17 86 104

5833 733 469 97 7132

NA 71 62 11 144

44 48 17 NA 109

CHD = Coronary Heart Disease. NLR = Neutrophil lymphocyte ratio. FRS = Framingham risk score. NRI = Net reclassification index. 95% CI = 95% Confidence Interval.

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NLR is a ratio of absolute neutrophil and absolute lymphocyte counts thus representing two inversely related immune pathways, one representing uncontrolled inflammation and the other representing a quiescent immune pathway [11]. It is a more stable measure than the individual cell counts and less affected by acute conditions which change one of the individual cell counts. Prior studies have also shown NLR to be superior to the individual cell counts [6,7]. In our study, we show that neutrophil and lymphocyte counts when considered individually neither predict CHD mortality nor significantly reclassify the FRS. 4.2. NLR as a biomarker for CHD in asymptomatic individuals—reclassification of FRS Several biomarkers have been shown to be predictive of cardiovascular (CV) risk. Demonstration of a statistically significant association alone between the biomarker and CV outcome in a multivariate model is not sufficient for judging its utility to predict CV events. The biomarker should be able to accurately predict subclinical CV disease in order to target individuals for primary CV disease prevention measures. Therefore, the criteria of discrimination, calibration and reclassification have been proposed to assess the significance of a new biomarker in prediction of CV risk [42]. Reclassification, especially of individuals at “intermediate risk”, influences the clinical decision making and is the most clinically relevant statistical parameter. Upward reclassification is more important since it changes the management by reclassifying individuals to higher risk categories, thus identifying those who need more aggressive preventive measures [42] like diet modifications, lifestyle modifications and adherence to pharmacologic therapy. On the other hand, downward reclassification to lower risk categories does not significantly change management. In our study, we show that NLR is a significant predictor of CHD death in a multivariate model (HR 1.17 per unit increase in NLR, p = 0.02). With regards to discrimination indices, NLR showed only a marginal improvement in C-index and AUC which was not statistically significant, however, it must be noted that it is difficult for new predictors to raise the C-index when the existing model already discriminates well [42]. In our study, model with FRS covariates alone already had high C-index and AUC values (0.8709 and 0.8520 respectively); hence it was difficult for NLR to raise these values significantly. The salient finding of our study was significant reclassification of the FRS by NLR with an overall NRI of 6.6% (p = 0.003). A considerable proportion of individuals were reclassified upwards with a net upward reclassification for events being 6.1%. The NRI was even higher in individuals in intermediate risk category (10.1%, p b 0.001) and 5.6% of individuals were reclassified upwards from intermediate to high risk. Thus, addition of NLR to the FRS is likely to influence the CHD prevention measures in at least 5–6% of asymptomatic low to moderate risk individuals. Our study supports the notion that NLR provides useful information in addition to what is available with currently established traditional risk factors. Prior studies have shown other parameters to be associated with cardiovascular disease in asymptomatic individuals, for example, serum markers like C-reactive protein [43], N-terminal B-type natriuretic peptide (NT-proBNP) [44], hemoglobin A1c [45], high sensitivity troponin-T [46], lipoprotein associated phospholipase A2 (LpPLA2) [47], interleukin-6 [48], fasting C-peptide [49], cystatin C [50], homocysteine [21], mid-region proadrenomedullin [51], urinary markers like urinary albumin [52,53], electrocardiographic changes [54–56], radiologic markers like carotid intima-media thickness (CIMT) [57,58] and coronary calcium score [59,60] and vascular markers like anklebrachial pressure index (ABPI) [61,62] and aortic pulse wave velocity [63,64]. Some of these markers only demonstrate significant association with CV disease in a multivariate model and some of them also exhibit improved discrimination, however, only a few of them demonstrate significant reclassification. Recent reviews [42,65] have listed the following markers to have statistically significant net reclassification indices: hs-CRP (NRI 5%), hemoglobin A1c (NRI 5%), urinary albumin (NRI 2–

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13%), EKG abnormalities (NRI 7%), ABPI (NRI 4–10%), CIMT (NRI 8–10%) and coronary artery calcium (CAC) (NRI 14–25%). Of all these markers, CAC has the highest reclassification index and is currently used as a tool for further risk stratification in patients with intermediate risk of CHD. According to AHA 2007 guidelines [66], CAC is recommended as a screening test in asymptomatic patients with intermediate risk (10–20%) of CHD. Those with a CAC of greater than 400 have a ten year CHD risk similar to a person with diabetes mellitus or peripheral vascular disease [66]. Except for CAC, all other studied markers have a NRI in the range of 5–10%, which is comparable to the NRI of NLR demonstrated in our study. Some studies have undertaken measurements of multiple biomarkers with the aim improving CHD risk prediction. An example is the Framingham Offspring Study [67] which incorporated BNP, CRP, urinary albumin, homocysteine and renin and showed a small improvement in C-index for CV events (0.01). The Swedish Malmo Diet and Cancer cohort study [68] incorporated BNP and pro-adrenomedullin and showed a small improvement in C-index (0.01) with 15% overall NRI, but the upward reclassification was only 4%. The Uppsala Longitudinal Study of Adult Men [69] incorporated troponin I, NT pro BNP, CRP and cystatin C and showed an improvement in C-index by 0.06 with a NRI of 26%. The MORGAM study [70] incorporated NT proBNP, CRP and troponin I and showed improvement in C-index by 0.03 and NRI of 11%. A study from Women's Health Initiative [71] incorporated 5 biomarkers and showed an increase in C-index by 0.02 with a NRI of 6.5%. 4.3. NLR and CRP Both NLR and CRP are markers of inflammation. In our study, we observed a weak but statistically significant correlation between NLR and CRP (correlation coefficient = 0.11, p b 0.001). We also showed that CRP was an independent predictor of CHD mortality in our study. NLR continued to remain an independent predictor of CHD mortality even after adjusting for CRP, thus implying that the predictive value of NLR is independent of its relation to CRP. Some studies have in fact shown NLR to be a stronger predictor of cardiovascular mortality compared to CRP [72]. Although our study showed no significant reclassification of FRS with CRP, the CRP measurement in NHANES-III did not employ high sensitive assays (hs-CRP). The laboratory assays measuring hsCRP were not developed until mid-1990s [43]. High sensitive CRP is a more accurate representation of basal levels of CRP and has been demonstrated as a marker for future cardiovascular events in several large scale prospective studies [73–76]. Prior studies have shown that hsCRP significantly reclassifies FRS with a NRI of around 5–8% [77,78]. In Swedish Malmo Diet Cancer Cohort [68] and Women's Health Study [79], hs-CRP demonstrated an intermediate risk NRI of 16–20%, however, upward reclassification (which is more likely to change management) was only 3–4% [42]. In our study, we demonstrate significant reclassification of FRS, with an intermediate risk NRI of 10.1% and upward reclassification of 5.6%, when NLR is added to a model containing FRS. Thus, NLR can be considered at least equivalent to, if not better than, CRP or hs-CRP as an inflammatory biomarker of coronary atherosclerosis. 4.4. Other criteria for NLR as a biomarker In addition to adding new information to the traditional risk model, other criteria proposed to evaluate new biomarkers are: ease of measurement, cost-effectiveness, safety and replication in different prospective cohorts [80,81]. NLR is cheap, safe and easy to obtain compared to previously listed markers of CHD, since it entails a simple and inexpensive CBC test. CBC is performed routinely and periodically in the outpatient setting and hence measurement of NLR does not require any extra testing. Moreover, the predictive value of NLR for CV events has been described in prior prospective and retrospective studies [10–12].

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4.5. Limitations Our study was a retrospective observational study, thereby lacking interventions. Our study findings are not applicable to individuals with inflammatory and autoimmune disorders, malignancies and those on corticosteroid therapy, since they were excluded from our analysis. Our data was cross-sectional in nature thereby precluding us from accounting for diseases developing in the follow-up period. We did not have follow-up data on non-fatal myocardial infarction; hence “hard CHD events” were limited to death from CHD. We did not have follow-up data on neutrophil and lymphocyte counts to account for transient changes or effects of changes in NLR over time. NLR is not a specific marker of CHD, since it has also been shown to be elevated in systemic infections [82,83], inflammatory conditions [84,85] and cancer [86–88]. There are no established cut-off values for NLR, however, in our study we showed NLR greater than 4.5 to be significantly associated with risk of CHD mortality which is consistent with prior findings by Horne et al. [12] who showed that NLR greater than 4.71 predicts cardiovascular risk. 4.6. Future directions Current evidence only supports serial measurements of LDL [89] and CRP [90,91] as circulating biomarkers of CHD. The effects of serial measurements of NLR and changes in NLR over time are unknown. Moreover, the modifying effects of medications like statins on NLR have not been studied. Future studies may involve studying whether medications cause serial reductions in NLR and whether reductions in NLR over time translate into CHD risk reduction. In order to study these effects, large scale randomized controlled trials (RCTs) need to be done. NLR may be also combined with other biomarkers to increase the NRI and improve CHD risk prediction. 5. Conclusions NLR is a potential independent predictor of CHD mortality in a cross sectional US population. Adding NLR to the Framingham risk score model marginally improves model discrimination and significantly improves model calibration. There is significantly better reclassification of individuals in intermediate risk category of FRS with NRI of 10.1% and upward reclassification of 5.6%. NLR fulfills the criteria to be considered as a biomarker for predicting future CHD risk in asymptomatic, apparently healthy individuals. Future prospective studies and RCTs are warranted to validate these findings and to establish standardized cut-off values for NLR. 5.1. Recommendations Based on our study findings, we recommend that NLR be recognized as a biomarker for CHD. We recommend that more aggressive primary prevention measures for CHD risk reduction be instituted in asymptomatic individuals in intermediate risk category of FRS with high NLR values (especially those with NLR greater than 4.5). Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijcard.2013.12.019. References [1] Kochanek KD, Xu JQ, Murphy SL, Miniño AM, Kung HC. Deaths: Final Data for 2009. Natl Vital Stat Rep 2011;60(3):1–115. [2] Khot UN, Khot MB, Bajzer CT, et al. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA 2003;290(7):898–904. [3] Greenland P, Knoll MD, Stamler J, et al. Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA 2003;290(7):891–7. [4] Greenland P, Alpert JS, Beller GA, et al. ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation 2010;122(25):2748–64.

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