Inflammation is considered as an integral part of the pathophysiology of atherosclerosis and the associated ischemic cardiovascular complications.1 Accordingly, elevations of plasma levels of several markers of systemic inflammation have been found in patients with documented atherosclerotic deposition at different arterial territories,2-5 including at the extracranial carotid district.6 Further support for the association between systemic inflammation and the risk of atherosclerotic cardiovascular disease (ASCVD) is provided by the results of large longitudinal studies7-9 and recent clinical trials with anti-inflammatory drugs10, 11 in patients with ASCVD.
Neutrophils are the predominant circulating leucocytes; they are key participants in innate immune response and also influence effector cells of adaptive immunity.12, 13 In addition to being observed in atherosclerotic plaques, the participation of neutrophils during various stages of atherosclerosis has been documented.14 As the effector cells of the adaptive immune system, lymphocytes are closely associated with atherosclerosis, with some subsets possessing pro-inflammatory and pro-atherogenic properties and some others exerting anti-inflammatory and anti-atherogenic effects.15
Both elevated neutrophil and reduced lymphocyte counts have been associated with impaired ASCVD prognosis.16, 17 Thus, the neutrophil to lymphocyte ratio (NLR) has been explored either as a possible inflammation metric for the detection of the presence of carotid atherosclerosis in case–control and cross-sectional studies18-20 or as a predictor of ASCVD events in prospective studies.21
A direct cross-sectional association between NLR and carotid intima-media thickness (cIMT) has been found in different studies.22-26 However, this relationship has not been always confirmed.23, 27-29 In addition, the prospective association between NLR and cIMT progression has not been investigated so far. Hence, further cross-sectional, and prospective data from large cohorts exploring the link between NLR and carotid atherosclerosis are warranted.
Similarly, the longitudinal investigation of the association between NLR and ASCVD events has produced variable results. In particular, most studies reported a positive association,30-32 whereas others failed to replicate the same result in patients without overt ASCVD.33-36 In addition, NLR cut-offs, multivariable adjustment and time to ASCVD events appeared to strongly influence the prospective relationship between NLR and ASCVD risk.30, 31, 33-36 Hence, the independent impact of NLR, both as a continuous and categorical variable, on the dynamics of carotid atherosclerosis and ASCVD risk requires further evaluation especially in patients in the primary prevention of ASCVD.
Current guidelines suggest the use of risk-enhancing factors, including inflammation biomarkers, in order to improve ASCVD risk estimates particularly in subjects at moderate-intermediate risk.37, 38 The IMPROVE study is a prospective multicenter longitudinal study exploring determinants of the presence and progression of carotid atherosclerosis and predictors of ASCVD events after correction for a consistent number of potential confounders.39, 40 Importantly, this cohort study includes subjects at moderate-to-high baseline ASCVD risk, which makes it particularly suitable for searching potential ASCVD risk-enhancing factors. Given the clinical need of robust predictors of ASCVD risk,37, 38 the role of some inflammation biomarkers in improving ASCVD risk reclassification37, 38 and the controversial impact of NLR on both atherosclerosis and ASCVD events in the different clinical settings (e.g., primary vs. secondary cardiovascular prevention),30-32 the association of NLR with atherosclerosis presence and progression, as well as with ASCVD events occurrence was explored in the large cohort of the IMPROVE study.
2 METHODS 2.1 SubjectsDesign and methods of the IMPROVE study have been previously reported.39 In brief, 3703 subjects were recruited, aged 54–79 years, with at least three CVD risk factors, but without overt cardio- or cerebrovascular event at baseline. Seven centers in five European countries – Finland (Kuopio, two centers), France (Paris), Italy (Milan and Perugia), The Netherlands (Groningen) and Sweden (Stockholm), participated in the enrollment.
Methods for laboratory analyses have been previously reported.40 The NLR was calculated as the neutrophil count divided by the lymphocyte count. Subjects with white blood cells count of 10,000/mm3 or more, with overt inflammatory diseases or on corticosteroid treatment were excluded. Glomerular filtration rate (GFR) was estimated by Cockcroft–Gault formula.
The occurrence of combined VEs, including coronary VEs (myocardial infarction [MI], sudden cardiac death, angina pectoris, any revascularization or surgical intervention of the coronary arteries), cerebro-VEs (ischemic stroke, transient ischemic attack, any revascularization or surgical intervention of the carotid arteries) and lower extremity artery disease (LEAD) events (new diagnosis of intermittent claudication, any revascularization or surgical intervention of lower limb arteries) were recorded at months 15 and 30 by regular visits, and at the end of follow-up (average 36.2 months) by phone interview. The occurrence of VEs and death of all participants was validated by local specialists, and adjudicated by a designated specialist, who was unaware of the relevant clinical history and cIMT data. The sample size considered for this report is 3341, since white blood cells measurement was not available in 51 subjects, 82 subjects had white blood cells levels of 10,000/mm3 or above, 85 had current inflammatory disease, 144 were on corticosteroid treatment.
2.2 Ultrasonographic assessmentThe ultrasonographic assessment of carotid arteries of the IMPROVE study was performed as previously described.39, 40 The ultrasonographic variables were measured centrally by trained readers at the ultrasound reading center in Milan (Italy) at baseline and measurements were repeated 15 months later. In each carotid segment (1 cm length), both mean (mean) and maximal (max) IMT were evaluated. Composite variables cIMTmean, cIMTmax and cIMTmean-max refer to the whole carotid tree. cIMTmean is the average of 1st cm of common carotid artery (1st CC) proximal to the bifurcation IMTmean, common carotid artery (CC) IMTmean, carotid artery bifurcation (BIF) IMTmean and internal carotid artery (ICA) IMTmean. cIMTmax is the greatest value among 1st CC-IMTmax, CC-IMTmax, BIF-IMTmax and ICA-IMTmax. cIMTmean-max is the average of 1st CC-IMTmax, CC-IMTmax, BIF-IMTmax, and ICA-IMTmax.
To evaluate changes of cIMT over time, ultrasonographic measurements were repeated at 15 months using the same ultrasonographic protocol (positions and angles of ultrasound transducer with respect to the neck) used at baseline. Carotid IMT change for each ultrasonographic variable, expressed in mm/year, was calculated as the difference between the 15-month measurement and the corresponding baseline value divided by the length of the intervening time period. The Fastest-cIMTmax-progr, that is, the greatest value chosen among the progressions of cIMTmax, was also assessed, as a measure of the maximal focal progression of cIMT.
2.3 Ethical considerationsThe Ethics Committees of all participating institutions approved the IMPROVE study, which complied with the Declaration of Helsinki. Written informed consent was obtained from all participants.
2.4 Statistical analysisNLR was calculated and participants were grouped according to NLR quartiles (division points: 1.34, 1.73, 2.24). Descriptive and comparative statistical analyses have been performed. In two-tailed tests, probability values less than 0.05 have been considered statistically significant. Logarithmic transformation was applied to skewed variables.
Multiple linear regression analyses have been performed with each ultrasonographic measure as the dependent variable, in the entire population. In model 1 covariates were NLR and latitude. In Model 2 covariates were age, sex, and latitude. In model 3, also smoking status (current vs. former or never), body mass index (BMI), systolic blood pressure (SBP), glucose, low-density-lipoprotein cholesterol (LDL-C), and C-reactive protein (CRP) were added as covariates. Model 4 included as covariates also waist-hip ratio (WHR), diastolic blood pressure (DBP), high-density lipoprotein cholesterol (HDL-C), triglyceride, GFR, lipid-lowering treatment, and anti-hypertensive treatment. Also, adjusted association between neutrophil and lymphocyte count and cIMT measures were calculated by linear regression analyses. Furthermore, the associations between NLR and cIMT measures were analyzed in subgroups divided by median age, sex, BMI, smoking status, history of diabetes and hypertension, and median CRP levels. Since the different measures of cIMT are not independent of each other, no correction was made for multiple tests.
Cox regression analyses have been used to estimate adjusted hazard ratios (HRs). In model 1, adjusted for latitude, HRs for combined VEs, coronary VEs, peripheral VEs (i.e., lower extremities atherosclerotic disease [LEAD] events plus cerebrovascular events) and cerebro-VEs have been calculated for each NLR quartile and for NLR as continuous log-transformed variable. In model 2, age and sex were added as covariates. In model 3, also smoking status (current vs. former or never), BMI, SBP, DBP, glucose, LDL-C, HDL-C, triglyceride and CRP were added as covariates. Model 4 included WHR, CRP, GFR, lipid-lowering treatment, and anti-hypertensive treatment as covariates. A sample of about 190 VEs was 80% power to deem as significant, with alpha = 0.05, an adjusted HR of 1.23 for one SD of NLR, assuming a total R2 of 0.25 among the covariates included in the model. HRs of combined VEs for each NLR quartile were also calculated in subgroups divided by median age, sex, BMI, smoking status, history of diabetes and hypertension, and median CRP levels. In addition, adjusted HRs for combined VEs have been calculated for neutrophil and lymphocyte count quartiles and for neutrophil and lymphocyte count as continuous Log-transformed variables. Survival functions, over a 36.2 month follow-up, have been generated to compare event-free survival between NLR quartiles. All the analyses have been performed using the SPSS statistical package v. 22.0 (IBM statistics).
3 RESULTSThe baseline characteristics of 3341 subjects, categorized according to NLR quartiles, are described in Table 1.
TABLE 1. Characteristics of IMPROVE study participants according to NLR quartiles NLR quartiles 1st 2nd 3rd 4th NLR range ≤1.33 >1.33, ≤1.73 >1.73, ≤2.24 >2.24 Number of subjects 838 835 838 830 Age, years 63.1 (58.7, 66.9) 64.2 (59.5, 67.3) 64.8 (59.8, 67.3) 65.8 (60.5, 67.5) Sex, males % 42.2% 46.8% 48.3% 54.9% Current smoke, % 36.4 37.6 35.7 37.3 Diabetes, % 25.7 24.4 22.9 22.0 Hypertension, % 68.1 69.3 74.8 74.1 Lipid-lowering, % 45.0 53.3 52.3 52.1 Anti-hypertensive, % 49.2 56.7 60.5 61.2 Anti-inflammatory, % 18.1 18.3 17.1 21.1 Hypoglycemic, % 17.5 16.8 15.8 15.8 Body Mass Index, kg/m2 27.0 (24.4, 29.8) 26.9 (24.7, 29.4) 26.2 (23.8, 28.7) 26.2 (23.8, 28.7) Waist/Hip ratio 0.91 (0.85, 0.97) 0.92 (0.86, 0.97) 0.92 (0.86, 0.97) 0.93 (0.87, 0.98) Systolic Blood Pressure, mmHg 140 (130–154) 140 (130, 152) 140 (130, 153) 140 (130, 152) Diastolic Blood Pressure, mmHg 82 (75, 90) 81 (75, 88) 81 (75, 88) 80 (75, 88) White blood cells, n/mm3 5600 (4760, 6400) 5700 (4800, 6600 5900 (5100, 6900) 6300 (5400, 7290) Neutrophil count, n/mm3 2580 (2163, 3001) 3068 (2610, 3600) 3583 (3023, 4127) 4222 (3578, 4970) Lymphocyte count, n/mm3 2381 (2017, 2843) 2002 (1711, 2356) 1808 (1550, 2115) 1459 (1242, 1701) Blood glucose, mmol/L 5.56 (4.95, 6.40) 5.50 (4.89, 6.30) 5.50 (4.90, 6.17) 5.50 (5.00, 6.23) Total Cholesterol, mg/dl 216 (186, 245) 212 (183, 243) 208 (184, 239) 207 (178, 236) Triglyceride, mg/dl 118 (84, 177) 116 (81, 165) 115 (83, 165) 110 (79, 158) HDL-Cholesterol, mg/dl 47 (40, 58) 46 (39, 56) 46 (39, 56) 47 (39, 56) LDL-Cholesterol, mg/dl 139 (112, 165) 137 (111, 165) 135 (110, 162) 132 (106, 159) CRP, mg/dl 1.51 (0.57, 2.97) 1.74 (0.71, 3.35) 2.05 (0.91, 3.83) 1.98 (0.78, 4.03) GFR, ml/min 81 (67, 95) 81 (68, 97) 81 (68, 95) 79 (67, 94) Latitude, degrees 53 (45, 62) 53 (45, 62) 53 (45, 59) 53 (45, 62) cIMTmean, mm 0.834 (0.728, 0.975) 0.851 (0.733, 0.998) 0.851 (0.745, 0.998) 0.867 (0.760, 1.023) cIMTmax, mm 1.76 (1.35, 2.32) 1.84 (1.39, 2.50) 1.85 (1.45, 2.50) 2.03 (1.48, 2.61) cIMTmean-max, mm 1.30 (1.10, 1.59) 1.34 (1.11, 1.63) 1.35 (1.14, 1.64) 1.39 (1.15, 1.69) PF CC- IMTmean, mm 0.700 (0.639, 0.756) 0.698 (0.645, 0.763) 0.707 (0.646, 0.757) 0.704 (0.651, 0.770) Fastest-cIMTmax-progr, mm/year 0.201 (0.104, 0.332) 0.176 (0.094, 0.324) 0.215 (0.109, 0.357) 0.190 (0.100, 0.366) Note: Values are median (25th, 75th percentile) or percentage. Abbreviations: CC, common carotid; CRP, C-reactive protein; GFR, glomerular filtration rate; HDL, high density lipoproteins; cIMT, intima media thickness; LDL, low density lipoprotein; NLR, neutrophil-lymphocyte ratio; PF, plaque-free.Increasing age and a higher percentage of male subjects, current smokers and hypertensive subjects and lower prevalence of diabetes were observed with increasing NLR quartiles. Also, in higher NLR quartiles, increased CRP levels and reduced levels of total and LDL cholesterol and triglycerides were observed (Table 1). The use of lipid lowering drugs was higher among subjects in the first quartile of NLR compared to those in the higher quartiles.
3.1 NLR and carotid atherosclerosisWith increasing NLR quartiles, higher values of cross-sectional measures of carotid atherosclerosis (cIMTmean, cIMTmax and cIMTmean-max) were observed (Table 1). No differences were found in fastest-cIMTmax-progr across NLR quartiles.
When used as a continuous variable, NLR was positively associated with all measures of baseline cIMT but not with Fastest-cIMTmax-progr (Table 2, Model 1), after correction for latitude. NLR was not associated with baseline cIMT variables or changes in cIMT in multiple linear regression analyses after further adjustment for age and sex (Table 2, Model 2) and after further adjustment for other covariates (Table 2, Models 3 and 4). The analysis was repeated for neutrophil and lymphocyte counts separately. Neutrophil count was positively associated with cross-sectional cIMT measures, but such an association was lost after full adjustment for covariates (Table S1). A positive association of lymphocyte count with cIMTmean, and cIMTmean-max emerged after adjustment for age, sex, and latitude (Table S2, Model 2). However, such an association was not confirmed after further adjustment (Table S2, Models 3 and 4).
TABLE 2. Associations between NLR and measures of cIMT Multivariable linear regression Model 1 Model 2 Model 3 Model 4 β p Value β p Value β p Value β p Value cIMTmean 0.044 0.007 0.003 0.849 0.003 0.856 −0.007 0.673 cIMTmax 0.054 0.001 0.023 0.154 0.019 0.242 0.007 0.670 cIMTmean-max 0.045 0.006 0.008 0.607 0.007 0.680 −0.005 0.749 Fastest-cIMTmax-progr 0.003 0.865 −0.011 0.529 −0.020 0.276 −0.019 0.313 Notes: Model 1: adjusted for latitude. Model 2: adjusted for age, sex, and latitude. Model 3: adjusted for covariates in Model 2 plus body mass index, systolic blood pressure, glucose, smoking status, LDL-cholesterol and C-reactive protein. Model 4: adjusted for covariates in Model 3 plus waist-hip ratio, diastolic blood pressure, HDL-cholesterol, triglyceride, glomerular filtration rate, lipid-lowering treatment and antihypertensive treatment. Abbreviations: cIMT, carotid intima media thickness; NLR, neutrophil-to- lymphocyte ratio.Associations between NLR and cIMT measures were also analyzed in subgroups defined by sex, median age, BMI, smoking status, history of diabetes and hypertension, and median CRP levels (Figures S1–S4). No significant association between NLR and cross-sectional cIMT measures, after adjustment for confounders, was observed in any of the subgroups.
3.2 NLR and ASCVD eventsDuring the median 36.2-month follow-up period of the 3341 subjects considered in this study, a total of 190 combined VEs were recorded, of these 110 were coronary VEs, 66 were cerebro-VEs and 14 LEAD events.
Table 3 shows HRs and 95% confidence interval for VEs for one SD increase of log-transformed NLR. There was no significant association between NLR and the risk of combined, coronary, peripheral, and cerebro-VEs. When using NLR as a categorical variable, no significant differences in terms HRs for VEs were found across NLR quartiles (Table 4). Combined event-free survival curves among NLR quartiles are presented in Figure 1.
TABLE 3. Hazard ratios (95% confidence interval) for VEs for one SD increase of log-transformed NLR Model 1 Model 2 Model 3 Model 4 NLR Combined VEs 1.01 (0.87, 1.16) 0.99 (0.85, 1.14) 0.95 (0.82, 1.10) 0.98 (0.84, 1.14) Coronary VEs 0.93 (0.77, 1.13) 0.91 (0.76, 1.10) 0.91 (0.74, 1.11) 0.94 (0.76, 1.14) Peripheral VEs 1.12 (0.90, 1.39) 1.10 (0.88, 1.37) 1.00 (0.80, 1.26) 1.03 (0. 82, 1.31) Cerebro-VEs 1.13 (0.89, 1.44) 1.10 (0.86, 1.41) 1.02 (0.79, 1.31) 1.04 (0.81, 1.34) Notes: Model 1: adjusted for latitude. Model 2: adjusted for age, sex, and latitude. Model 3: adjusted for covariates in Model 2 plus body mass index, systolic blood pressure, glucose, smoking status, LDL-cholesterol and C-reactive protein. Model 4: adjusted for covariates in Model 3 plus waist-hip ratio, diastolic blood pressure, HDL-cholesterol, triglyceride, glomerular filtration rate, lipid-lowering treatment and antihypertensive treatment. Abbreviations: NLR, neutrophil to lymphocyte ratio; VEs, vascular events. TABLE 4. Hazard ratios of VEs according to NLR quartiles NLR quartiles Number of subjects with/without events Model 1 Model 2 Model 3 Model 4 Combined VEs 1st 45/793 1.00 1.00 1.00 1.00 2nd 51/784 1.16 (0.77, 1.72) 1.09 (0.73, 1.63) 1.06 (0.70, 1.59) 1.22 (0.80, 1.85) 3rd 42/796 0.94 (0.62, 1.43) 0.86 (0.57, 1.31) 0.81 (0.52, 1.24) 1.00 (0.64, 1.56) 4th 52/778 1.16 (0.78, 1.72) 1.01 (0.67, 1.51) 1.94 (0.62, 1.42) 1.30 (0.84, 2.04) Coronary VEs 1st 30/808 1.00 1.00 1.00 1.00 2nd 30/805 1.02 (0.61, 1.70) 0.96 (0.57, 1.60) 0.93 (0.55, 1.56) 1.07 (0.63, 1.82) 3rd 21/817 0.71 (0.40, 1.23) 0.63 (0.37, 1.12) 0.67 (0.38, 1.17) 0.82 (0.46, 1.47) 4th 29/801 0.97 (0.58, 1.61) 0.83 (0.49, 1.39) 0.87 (0.51, 1.49) 1.19 (0.67, 2.11) Peripheral VEs 1st 15/823 1.00 1.00 1.00 1.00 2nd 21/814 1.42 (0.73, 2.76) 1.36 (0.70, 2.63) 1.32 (0.68, 2.56) 1.50 (0.76, 2.98) 3rd 21/817 1.41 (0.73, 2.73) 1.31 (0.68, 2.55) 1.05 (0.53, 2.08) 1.33 (0.66, 2.69) 4th 23/807 1.53 (0.80, 2.93) 1.37 (0.71, 2.64) 1.06 (0.54, 2.08) 1.47 (0.72, 3.01) Cerebro-VEs 1st 12/826 1.00 1.00 1.00 1.00 2nd 19/816 1.60 (0.77, 3.29) 1.52 (0.74, 3.13) 1.48 (0.72, 3.05) 1.69 (0.81, 3.52) 3rd 15/823 1.25 (0.59, 2.67) 1.16 (0.54, 2.49) 0.91 (0.42, 2.00) 1.08 (0.48, 2.41) 4th 20/810 1.66 (0.81, 3.40) 1.48 (0.72, 3.04) 1.21 (0.58, 2.52) 1.50 (0.69, 3.26) Note: Models and abbreviations as in Table 3.Combined vascular event-free survival across NLR quartiles (log-rank p = 0.632). NLR, neutrophil-to-lymphocyte ratio
Even when analyzing the neutrophil and lymphocyte counts separately, either as continuous log-transformed variables or divided into quartiles, no significant associations with the risk of VEs emerged (Tables S3 and S4), except for an increase in HR in the fourth quartile of neutrophils, in the minimally adjusted model (Table S4 – Model 1), which was lost after further adjustment for other covariates. Furthermore, the analyses in subgroups defined by sex, age, BMI, smoking status, history of diabetes and hypertension and CRP levels confirmed the lack of association between NLR and combined VEs (Figure 2).
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