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• W2551896507 abstract "The aim of the present study was to examine the association between peripheral differential leukocyte counts and dyslipidemia in a Chinese hypertensive population. A total of 10,866 patients with hypertension were enrolled for a comprehensive assessment of cardiovascular risk factors using data from the China Stroke Primary Prevention Trial. Plasma lipid levels and total leukocyte, neutrophil, and lymphocyte counts were determined according to standard methods. Peripheral differential leukocyte counts were consistently and positively associated with serum total cholesterol (TC), LDL cholesterol (LDL-C), and TG levels (all P < 0.001 for trend), while inversely associated with HDL cholesterol levels (P < 0.05 for trend). In subsequent analyses where serum lipids were dichotomized (dyslipidemia/normolipidemia), we found that patients in the highest quartile of total leukocyte count (≥7.6 × 109 cells/l) had 1.64 times the risk of high TG [95% confidence interval (CI): 1.46, 1.85], 1.34 times the risk of high TC (95% CI: 1.20, 1.50), and 1.24 times the risk of high LDL-C (95% CI: 1.12, 1.39) compared with their counterparts in the lowest quartile of total leukocyte count. Similar patterns were also observed with neutrophils and lymphocytes. In summary, these findings indicate that elevated differential leukocyte counts are directly associated with serum lipid levels and increased odds of dyslipidemia. The aim of the present study was to examine the association between peripheral differential leukocyte counts and dyslipidemia in a Chinese hypertensive population. A total of 10,866 patients with hypertension were enrolled for a comprehensive assessment of cardiovascular risk factors using data from the China Stroke Primary Prevention Trial. Plasma lipid levels and total leukocyte, neutrophil, and lymphocyte counts were determined according to standard methods. Peripheral differential leukocyte counts were consistently and positively associated with serum total cholesterol (TC), LDL cholesterol (LDL-C), and TG levels (all P < 0.001 for trend), while inversely associated with HDL cholesterol levels (P < 0.05 for trend). In subsequent analyses where serum lipids were dichotomized (dyslipidemia/normolipidemia), we found that patients in the highest quartile of total leukocyte count (≥7.6 × 109 cells/l) had 1.64 times the risk of high TG [95% confidence interval (CI): 1.46, 1.85], 1.34 times the risk of high TC (95% CI: 1.20, 1.50), and 1.24 times the risk of high LDL-C (95% CI: 1.12, 1.39) compared with their counterparts in the lowest quartile of total leukocyte count. Similar patterns were also observed with neutrophils and lymphocytes. In summary, these findings indicate that elevated differential leukocyte counts are directly associated with serum lipid levels and increased odds of dyslipidemia. Atherosclerosis is a multi-step chronic inflammatory disease. Largely characterized by the formation of lipid and immune-cell-containing plaques in the intima of large and mid-sized arteries (1Libby P. Lichtman A.H. Hansson G.K. Immune effector mechanisms implicated in atherosclerosis: from mice to humans.Immunity. 2013; 38: 1092-1104Abstract Full Text Full Text PDF PubMed Scopus (481) Google Scholar), it is often the underlying cause of CVDs and stroke (1Libby P. Lichtman A.H. Hansson G.K. Immune effector mechanisms implicated in atherosclerosis: from mice to humans.Immunity. 2013; 38: 1092-1104Abstract Full Text Full Text PDF PubMed Scopus (481) Google Scholar, 2Hansson G.K. Hermansson A. The immune system in atherosclerosis.Nat. Immunol. 2011; 12: 204-212Crossref PubMed Scopus (1610) Google Scholar, 3Legein B. Temmerman L. Biessen E.A. Lutgens E. Inflammation and immune system interactions in atherosclerosis.Cell. Mol. Life Sci. 2013; 70: 3847-3869Crossref PubMed Scopus (213) Google Scholar). Traditionally, atherosclerosis was thought to result from passive lipid accumulation in the arterial vessel wall. However, a growing body of evidence suggests that the pathogenesis of atherosclerosis is much more complex than formerly believed. Immune cells and inflammation play a key role in its pathogenesis in conjunction with hyperlipidemia (4Schaftenaar F. Frodermann V. Kuiper J. Lutgens E. Atherosclerosis: the interplay between lipids and immune cells.Curr. Opin. Lipidol. 2016; 27: 209-215Crossref PubMed Scopus (158) Google Scholar). However, how lipids interact with the immune system is largely unknown. There is some limited evidence (5Tall A.R. Yvan-Charvet L. Cholesterol, inflammation and innate immunity.Nat. Rev. Immunol. 2015; 15: 104-116Crossref PubMed Scopus (804) Google Scholar, 6Simon A. Cholesterol metabolism and immunity.N. Engl. J. Med. 2014; 371: 1933-1935Crossref PubMed Scopus (39) Google Scholar) showing that increased levels of LDL promote cholesterol accumulation in the matrix beneath the endothelial cell layer of blood vessels (7Moore K.J. Tabas I. Macrophages in the pathogenesis of atherosclerosis.Cell. 2011; 145: 341-355Abstract Full Text Full Text PDF PubMed Scopus (1814) Google Scholar) and promote an inflammatory response in the artery wall, driving the process of atherosclerosis. This is opposed by HDL, which promotes cellular efflux of cholesterol and reduces inflammation (5Tall A.R. Yvan-Charvet L. Cholesterol, inflammation and innate immunity.Nat. Rev. Immunol. 2015; 15: 104-116Crossref PubMed Scopus (804) Google Scholar). The early inflammatory response to retained cholesterol is enhanced by oxidative modification of the LDL cholesterol (LDL-C) trapped in the vessel wall (7Moore K.J. Tabas I. Macrophages in the pathogenesis of atherosclerosis.Cell. 2011; 145: 341-355Abstract Full Text Full Text PDF PubMed Scopus (1814) Google Scholar). Accumulated oxidized LDL leads to decreased cholesterol efflux from macrophage foam cells (8Ouimet M. Wang M.D. Cadotte N. Ho K. Marcel Y.L. Epoxycholesterol impairs cholesteryl ester hydrolysis in macrophage foam cells, resulting in decreased cholesterol efflux.Arterioscler. Thromb. Vasc. Biol. 2008; 28: 1144-1150Crossref PubMed Scopus (22) Google Scholar), amplifies the toll-like receptor signaling in macrophages (9Stewart C.R. Stuart L.M. Wilkinson K. van Gils J.M. Deng J. Halle A. Rayner K.J. Boyer L. Zhong R. Frazier W.A. et al.CD36 ligands promote sterile inflammation through assembly of a Toll-like receptor 4 and 6 heterodimer.Nat. Immunol. 2010; 11: 155-161Crossref PubMed Scopus (1091) Google Scholar, 10Zhu X. Owen J.S. Wilson M.D. Li H. Griffiths G.L. Thomas M.J. Hiltbold E.M. Fessler M.B. Parks J.S. Macrophage ABCA1 reduces MyD88-dependent Toll-like receptor trafficking to lipid rafts by reduction of lipid raft cholesterol.J. Lipid Res. 2010; 51: 3196-3206Abstract Full Text Full Text PDF PubMed Scopus (246) Google Scholar), and activates endothelial cells. Activation of arterial endothelial cells leads to recruitment of blood-borne monocytes into atherosclerotic lesion sites within the vessel wall (11Glass C.K. Witztum J.L. Atherosclerosis. the road ahead.Cell. 2001; 104: 503-516Abstract Full Text Full Text PDF PubMed Scopus (2636) Google Scholar, 12Mestas J. Ley K. Monocyte-endothelial cell interactions in the development of atherosclerosis.Trends Cardiovasc. Med. 2008; 18: 228-232Crossref PubMed Scopus (391) Google Scholar) and augments the production of cytokines and chemokines (7Moore K.J. Tabas I. Macrophages in the pathogenesis of atherosclerosis.Cell. 2011; 145: 341-355Abstract Full Text Full Text PDF PubMed Scopus (1814) Google Scholar) that interact with cognate chemokine receptors on monocytes. Accumulation of monocytes and monocyte-derived ma­crophages in the wall of large arteries leads to chronic inflammation and the development and progression of atherosclerosis. A large number of epidemiological studies has intensively evaluated multiple markers of inflammation as potential risk factors for the development of atherosclerosis and CVDs, such as high-sensitivity C-reactive protein (hsCRP), interleukin-6, and total leukocyte and its subset counts [neutrophil counts (13Guasti L. Dentali F. Castiglioni L. Maroni L. Marino F. Squizzato A. Ageno W. Gianni M. Gaudio G. Grandi A.M. et al.Neutrophils and clinical outcomes in patients with acute coronary syndromes and/or cardiac revascularisation. A systematic review on more than 34,000 subjects.Thromb. Haemost. 2011; 106: 591-599Crossref PubMed Scopus (159) Google Scholar, 14Gillum R.F. Mussolino M.E. Madans J.H. Counts of neutrophils, lymphocytes, and monocytes, cause-specific mortality and coronary heart disease: the NHANES-I epidemiologic follow-up study.Ann. Epidemiol. 2005; 15: 266-271Crossref PubMed Scopus (102) Google Scholar), monocyte counts (15Kato A. Takita T. Furuhashi M. Maruyama Y. Kumagai H. Hishida A. Blood monocyte count is a predictor of total and cardiovascular mortality in hemodialysis patients.Nephron Clin. Pract. 2008; 110: c235-c243Crossref PubMed Scopus (20) Google Scholar), and lymphocyte percentages (15Kato A. Takita T. Furuhashi M. Maruyama Y. Kumagai H. Hishida A. Blood monocyte count is a predictor of total and cardiovascular mortality in hemodialysis patients.Nephron Clin. Pract. 2008; 110: c235-c243Crossref PubMed Scopus (20) Google Scholar)]. Previous studies have found strong evidence of association between the frequency of leukocytes and/or its subsets, or the neutrophil/lymphocyte ratio (NLR) and vascular disease morbidity and mortality. Misialek et al. (16Misialek J.R. Bekwelem W. Chen L.Y. Loehr L.R. Agarwal S.K. Soliman E.Z. Norby F.L. Alonso A. Association of white blood cell count and differential with the incidence of atrial fibrillation: the Atherosclerosis Risk in Communities (ARIC) study.PLoS One. 2015; 10: e0136219Crossref PubMed Scopus (15) Google Scholar) prospectively examined the relationship between total white blood cell (WBC) count with incident atrial fibrillation (AF) in the Atherosclerosis Risk in Communities study and found that high total WBC, neutrophil, and monocyte counts were each associated with higher AF risk, while lymphocyte count was inversely associated with AF risk. A study by Sharma et al. (17Sharma K.H. Shah K.H. Patel I. Patel A.K. Chaudhari S. Do circulating blood cell types correlate with modifiable risk factors and outcomes in patients with acute coronary syndrome (ACS)?.Indian Heart J. 2015; 67: 444-451Abstract Full Text Full Text PDF PubMed Scopus (7) Google Scholar) indicated that among patients with evidence of acute coronary syndrome, those who were hypertensive, diabetic, or habitual smokers had significantly higher levels of total WBC, neutrophil, NLR, and platelet/lymphocyte ratio. The authors also demonstrated that neutrophil, lymphocyte, and total WBC counts, along with their ratios, predicted mortality. However, there is a dearth of reports on the association of total leukocyte and its subset counts with cardiovascular risk factors leading to atherosclerotic vascular diseases in Asian populations. Recently, a cross-sectional study from a population of 2,953 healthy Japanese men and women (18Oda E. Kawai R. Aizawa Y. Lymphocyte count was significantly associated with hyper-LDL cholesterolemia independently of high-sensitivity C-reactive protein in apparently healthy Japanese.Heart Vessels. 2012; 27: 377-383Crossref PubMed Scopus (16) Google Scholar) showed that lymphocyte count was significantly associated with high LDL-C, high TG, and low HDL cholesterol (HDL-C) in men and high LDL-C in women. Moreover, a prospective investigation from a Chinese population involving 1,287 patients with a mean age of 58 years provided convincing evidence that both neutrophil count and the Global Registry of Acute Coronary Events risk score are independent and joint predictors for major adverse cardiovascular events in patients with ST-elevation myocardial infarction (19Zhang S. Wan Z. Zhang Y. Fan Y. Gu W. Li F. Meng L. Zeng X. Han D. Li X. Neutrophil count improves the GRACE risk score prediction of clinical outcomes in patients with ST-elevation myocardial infarction.Atherosclerosis. 2015; 241: 723-728Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar). Our present study design is the first large-scale cross-sectional study in a Chinese hypertensive population to investigate the association between peripheral differential leukocyte counts (total leukocytes, neutrophils, and lymphocytes) and lipid profiles. We hypothesized that participants with elevated leukocyte counts would have increased dyslipidemia. All subjects came from the China Stroke Primary Prevention Trial (CSPPT, clinicaltrials.gov identifier: NCT00794885). The CSPPT was a large community-based, randomized, double-blind, and parallel-controlled trial with a total of 20,702 participants. It was designed to evaluate whether combination therapy with enalapril and folic acid is more effective in reducing first stroke than enalapril alone among Chinese adults with hypertension. Participants in the CSPPT study were deemed “relatively healthy” hypertensives without histories of myocardial infarction, stroke, heart failure, cancer, or serious mental disorder. Details regarding the inclusion/exclusion criteria, treatment assignment, and outcome measures of the trial have been previously described (http://clinicaltrials.gov/ct2/show/NCT00794885). In the present study, 15,486 patients were recruited from the Lianyungang region of Jiangsu Province. Among them, 11,345 participants had complete data on lipid measurements and total leukocyte, neutrophil, and lymphocyte counts, after excluding 446 participants who were taking anti-platelet and anti-hyperlipidemic medication. Moreover, after 13 outliers of leukocyte, neutrophil, and lymphocyte counts were excluded, the final analytic sample of 10,866 participants was obtained (as shown in Fig. 1). The present study was approved by the Ethics Committee of the Institute of Biomedicine, Anhui Medical University, Hefei, China. Written informed consent was obtained from each participant before data collection. All participants were administered a standardized questionnaire that requested information on occupation, medical history, past and current medications, and personal habits such as cigarette smoking and alcohol consumption. After an overnight fast, a venous blood sample was obtained from each subject. Serum or plasma samples were separated within 30 min of collection and stored at −70°C. The serum levels of TG, total cholesterol (TC), LDL-C, HDL-C, and fasting glucose were determined enzymatically with a commercially available assay kit (Hitachi P800, Holliston, MA). A complete blood count analysis including leukocytes was performed within 2 h of collection using a Beckman Coulter Gen-S automated analyzer (High Wycombe, UK), following the hospital laboratory policy. Resting seated blood pressure (BP) measurements were obtained using a mercury sphygmomanometer with an appropriate cuff size. BP was measured three times, with a 5 min rest period in between each and the average of the three measurements was used for statistical analyses. All analyses were performed using EmpowerStats (http://www.empowerstats.com) and the statistical package R (3.2.3 version). Leukocyte counts were divided into quartiles to create a categorical variable. Data were presented as mean ± SD or proportions. Comparisons between groups were performed using chi-square tests for categorical variables and ANOVA for continuous variables. Because baseline leukocyte counts had a skewed distribution, multiple linear regression analyses were used to assess the associations between log-transformed baseline leukocyte counts and serum lipid levels (including log-transformed TC and HDL-C). Odds ratios (ORs) and 95% confidence intervals (CIs) of those having high lipid levels were estimated by multiple logistic regression analyses with the lowest quartile as the reference class. Adjusted smoothing spline plots of lipid levels by leukocyte counts were created. Multiple logistic regression analyses were also used to verify an interaction scale for diabetes × leukocyte counts categories on lipid profiles. Diabetes was defined as a fasting plasma glucose concentration greater than or equal to 7.0 mmol/l (20Chinese Diabetes Society China guideline for type-2 diabetes.Chinese Journal of Diabetes. 2012; 20: 81-117Google Scholar), or self-reported diabetes paired with the use of hypoglycemic medication. Serum lipids were dichotomized (dyslipidemia/normolipidemia). High TC was defined as TC ≥200 mg/dl; high TG was defined as TG ≥150 mg/dl; high LDL-C was defined as LDL-C ≥130 mg/dl; and low HDL-C was defined as HDL-C ≤40 mg/dl (21Joint Committee for Developing Chinese guidelines on Prevention and Treatment of Dyslipidemia in Adults. 2007. Chinese guidelines on prevention and treatment of dyslipidemia in adults [Article in Chinese]. Zhonghua Xin Xue Guan Bing Za Zhi. 35: 390–419.Google Scholar). Patients with diastolic BP (DBP) ≥90 mmHg or systolic BP (SBP) ≥140 mmHg or, who were currently taking antihypertensive medication, were defined as having hypertension (22Wang J.G. Chinese hypertension guidelines.Pulse (Basel). 2015; 3: 14-20Crossref PubMed Google Scholar). Trend tests were calculated by modeling the differential leukocyte count quartile categories as continuous variables. A two-sided P value <0.05 was considered to be significant. The cross-sectional population in the current study consisted of 10,866 hypertensive patients with an average age of 59.5 ± 7.6 years (Table 1). The mean total leukocyte count was 6.6 ± 1.8 × 109 cells/l (median = 6.3 × 109 cells/l; ranged from 0.6 × 109 cells/l to 17.1 × 109 cells/l); the mean neutrophil count was 3.9 ± 1.4 × 109 cells/l (median = 3.7 × 109 cells/l; ranged from 0.3 × 109 cells/l to 13.1 × 109 cells/l); the mean lymphocyte count was 2.1 ± 0.6 × 109 cells/l (median = 2.0 × 109 cells/l; ranged from 0.3 × 109 cells/l to 6.3 × 109 cells/l). The baseline demographic and clinical characteristics and laboratory measurements of the enrolled subjects by quartile of total leukocyte, neutrophil, and lymphocyte counts are summarized in supplemental Tables S1–S3. In summary, patients with higher total leukocyte, neutrophil, and lymphocyte counts consistently showed higher LDL-C, TC, and TG levels, but lower HDL-C levels.TABLE 1Baseline demographic and clinical parameters in total hypertensive patientsVariablesMean ± SDAnthropometricsN10,866Age (years)59.5 ± 7.6SBP (mmHg)168.1 ± 20.8DBP (mmHg)95.0 ± 11.8BMI (kg/m2)25.6 ± 3.6Platelet (109/l)256.6 ± 90.9RBC (1012/l)4.7 ± 0.7Lymphocyte (109/l)2.1 ± 0.6Neutrophil (109/l)3.9 ± 1.4Total leukocytes (109/l)6.6 ± 1.8CREA (μmol/l)65.1 ± 18.8GLU (mmol/l)6.1 ± 1.8Albumin (g/l)49.2 ± 5.6TC (mg/dl)218.6 ± 45.2TG (mg/dl)143.6 ± 66.1LDL-C (mg/dl)138.8 ± 40.9HDL-C (mg/dl)51.3 ± 14.1Diabetes [N (%)]1,474 (13.6)Sex [N (%)]Male4,157 (38.3)Female6,709 (61.7)Smoking status [N (%)]Never7,688 (70.8)Former824 (7.6)Current2,352 (21.7)Alcohol consumption [N (%)]Never7,800 (71.8)Former715 (6.6)Current2,348 (21.6)Drug treatment [N, (%)]Antihypertensive drug use5,203 (47.9)Beta-blocker103 (0.9)Diuretics252 (2.3)Angiotensin receptor blocker12 (0.1)Calcium channel blockers693 (6.4)ACE-inhibitors898 (8.3)Glucose-lowering drugs177 (1.6)GLU, glucosamine; ACE, angiotensin converting enzyme; CREA, creatinine; RBC, red blood cell. Open table in a new tab GLU, glucosamine; ACE, angiotensin converting enzyme; CREA, creatinine; RBC, red blood cell. When analyzed as continuous variables, multiple linear regression models showed that baseline TC, TG, and LDL-C levels were positively associated with total leukocyte, neutrophil, and lymphocyte counts after adjusting for sex, age, baseline SBP, baseline DBP, BMI, alcohol consumption, smoking status, and diabetic status (all P < 0.05). However, baseline HDL-C level was inversely associated with total leukocyte, neutrophil, and lymphocyte counts after adjustment for the confounding factors listed above (supplemental Tables S4–S6). We found a dose-response association between baseline TC, TG, LDL-C, and HDL-C levels, and differential leukocyte counts. That is, the P for trend tests was statistically significant for baseline TC, TG, LDL-C, and HDL-C levels without a clear threshold. Multivariate adjusted smoothing spline plots suggest that serum TC, TG, and LDL-C levels increased with increasing total leukocyte, neutrophil, and lymphocyte counts, while HDL-C levels decreased as total leukocyte, neutrophil, and lymphocyte counts increased (as shown in Fig. 2A–C). Similar to differential leukocyte counts, baseline TC, TG, and LDL-C levels were positively associated with erythrocyte or platelet counts after adjusting for multiple covariables, while baseline HDL-C levels were inversely associated with erythrocyte counts (supplemental Tables S7, S8). We also detected an association between NLR and lipid profiles, but almost no significant trends were observed (supplemental Table S9). Each serum lipid variable was then analyzed as a binary variable (low/high) using multivariate logistic regression. As shown in TABLE 2, TABLE 3, TABLE 4, multivariate logistic regression analyses demonstrated that, after adjustment for age and sex and using the lowest quartile of total leukocytes as the reference, the ORs of having high TC increased in parallel with the quartiles of total leukocytes (ORs were 1.17, 1.39, and 1.41 from the second to the fourth quartiles, respectively, P < 0.001 for trend). The ORs of having high TG were 1.37, 1.67, and 1.85 from the second to the fourth quartiles, respectively (P < 0.001 for trend). The ORs of having high LDL-C were 1.18, 1.27, and 1.31 from the second to the fourth quartiles, respectively (P < 0.001 for trend). The ORs of having low HDL-C were 1.11, 1.18 and 1.23 from the second to the fourth quartiles, respectively (P < 0.001 for trend). Similar patterns between leukocyte subtypes (neutrophils and lymphocytes) and high TC, TG, and LDL-C levels remained significant after adjustment for confounding factors, including sex, age, baseline SBP, baseline DBP, BMI, alcohol consumption, smoking status, diabetic status, and previous medications (all adjusted P values <0.05), although the results did not hold for low HDL-C levels.TABLE 2Adjusted ORs (95% CI) for the association between quartiles of total leukocyte count and dyslipidemia by multivariate logistic regression modelsLeukocyte Count (×109 cells/l)Serum Lipids [Low/High (N)]Model IaModel 1: adjusted for sex and age.[OR (95% CI) P]P for TrendModel IIbModel 2: adjusted for sex, age, smoking status, alcohol consumption, SBP, DBP, BMI, and diabetes.[OR (95% CI) PP for TrendTC statuscHigh: TC ≥200 mg/dl, Low: TC <200 mg/dl.Q1 (0.6–5.3)1,046/1,5411<0.0011<0.001Q2 (5.4–6.3)970/1,6601.17 (1.05, 1.31) 0.0051.15 (1.03, 1.29) 0.016Q3 (6.4–7.5)926/1,8741.39 (1.24, 1.55) <0.0011.32(1.18,1.48) <0.001Q4 (7.6–17.1)930/1,9191.41 (1.26, 1.57) <0.0011.34(1.20,1.50) <0.001TG statusdHigh: TG ≥150 mg/dl, Low: TG <150 mg/dl.Q1 (0.6–5.3)1,819/7681<0.0011<0.001Q2 (5.4–6.3)1,682/9481.37 (1.22, 1.54) <0.0011.27 (1.13, 1.44) <0.001Q3 (6.4–7.5)1,667/1,1331.67 (1.49, 1.87) <0.0011.48(1.31, 1.66) <0.001Q4 (7.6–17.1)1,623/1,2261.85 (1.65, 2.07) <0.0011.64(1.46, 1.85) <0.001LDL-C statuseHigh: LDL-C ≥130 mg/dl, Low: LDL-C <130 mg/dl.Q1 (0.6–5.3)1,243/1,3441<0.0011<0.001Q2 (5.4–6.3)1,161/1,4691.18 (1.06, 1.31) 0.0041.15 (1.03, 1.28) 0.014Q3 (6.4–7.5)1,178/1,6221.27 (1.14, 1.42) <0.0011.21 (1.09, 1.35) <0.001Q4 (7.6–17.1)1,175/1,6741.31 (1.18, 1.46) <0.0011.24 (1.12, 1.39) <0.001HDL-C statusfHigh: HDL-C ≥40 mg/dl, Low: HDL-C <40 mg/dl.Q1 (0.6–5.3)477/2,11010.00210.261Q2 (5.4–6.3)529/2,1011.11 (0.97, 1.28) 0.1321.04 (0.90, 1.20) 0.588Q3 (6.4–7.5)587/2,2131.18 (1.03, 1.35) 0.0191.05 (0.92, 1.21) 0.457Q4 (7.6–17.1)616/2,2331.23 (1.07, 1.40) 0.0031.08 (0.94, 1.24) 0.260a Model 1: adjusted for sex and age.b Model 2: adjusted for sex, age, smoking status, alcohol consumption, SBP, DBP, BMI, and diabetes.c High: TC ≥200 mg/dl, Low: TC <200 mg/dl.d High: TG ≥150 mg/dl, Low: TG <150 mg/dl.e High: LDL-C ≥130 mg/dl, Low: LDL-C <130 mg/dl.f High: HDL-C ≥40 mg/dl, Low: HDL-C <40 mg/dl. Open table in a new tab TABLE 3Adjusted ORs (95% CI) for the association between quartiles of lymphocyte count and dyslipidemia by multivariate logistic regression modelsLymphocyte Count (×109 cells/l)Serum Lipids [Low/High (N)]Model IaModel 1: adjusted for sex and age.[OR (95% CI) P]P for TrendModel IIbModel 2: adjusted for sex, age, smoking status, alcohol consumption, SBP, DBP, BMI, and diabetes.[OR (95% CI) P]P for TrendTC statuscHigh: TC ≥200 mg/dl, Low: TC <200 mg/dl.Q1 (0.3–1.5)825/1,3231<0.0011<0.001Q2 (1.6–1.9)1,098/1,8571.05 (0.93, 1.18) 0.4241.03 (0.92, 1.16) 0.611Q3 (2.0–2.3)958/1,6851.07 (0.95, 1.21) 0.2391.04 (0.92, 1.17) 0.520Q4 (2.4–6.3)991/2,1291.29 (1.15, 1.45) <0.0011.24 (1.10, 1.40) <0.001TG statusdHigh: TG ≥150 mg/dl, Low: TG <150 mg/dl.Q1 (0.3–1.5)1,543/6051<0.0011<0.001Q2 (1.6–1.9)1,995/9601.21(1.07, 1.36) 0.0031.15 (1.01, 1.30) 0.034Q3 (2.0–2.3)1,583/1,0601.64 (1.45, 1.86) <0.0011.50 (1.32, 1.71) <0.001Q4 (2.4–6.3)1,670/1,4502.11 (1.87, 2.38) <0.0011.82 (1.61, 2.06) <0.001LDL-C statuseHigh: LDL-C ≥130 mg/dl, Low: LDL-C <130 mg/dl.Q1 (0.3–1.5)973/1,17510.00110.020Q2 (1.6–1.9)1,348/1,6090.99 (0.88, 1.10) 0.8190.97 (0.87, 1.09) 0.593Q3 (2.0–2.3)1,168/1,4751.03 (0.92, 1.16) 0.5731.00 (0.89, 1.13) 0.948Q4 (2.4–6.3)1,273/1,8581.18 (1.05, 1.32) 0.0041.12 (1.00, 1.26) 0.047HDL-C statusfHigh: HDL-C ≥40 mg/dl, Low: HDL-C <40 mg/dl.Q1 (0.3–1.5)356/1,7921<0.0011<0.001Q2 (1.6–1.9)560/2,3951.18 (1.02, 1.37) 0.0261.13 (0.97, 1.32) 0.107Q3 (2.0–2.3)579/2,0641.43 (1.23, 1.65) <0.0011.32 (1.13, 1.53) <0.001Q4 (2.4–6.3)714/2,4061.52 (1.32, 1.76) <0.0011.31 (1.13, 1.52) <0.001a Model 1: adjusted for sex and age.b Model 2: adjusted for sex, age, smoking status, alcohol consumption, SBP, DBP, BMI, and diabetes.c High: TC ≥200 mg/dl, Low: TC <200 mg/dl.d High: TG ≥150 mg/dl, Low: TG <150 mg/dl.e High: LDL-C ≥130 mg/dl, Low: LDL-C <130 mg/dl.f High: HDL-C ≥40 mg/dl, Low: HDL-C <40 mg/dl. Open table in a new tab TABLE 4Adjusted ORs (95% CI) for the association between quartiles of neutrophil count and dyslipidemia by multivariate logistic regression modelsNeutrophil Count (×109 cells/l)Serum Lipids [Low/High (N)]Model IaModel 1: adjusted for sex and age.[OR (95% CI) P]P for TrendModel IIbModel 2: adjusted for sex, age, smoking status, alcohol consumption, SBP, DBP, BMI, and diabetes.OR (95% CI) PP for TrendTC statuscHigh: TC ≥200 mg/dl, Low: TC <200 mg/dl.Q1 (0.3–2.8)966/1,5101<0.0011<0.001Q2 (2.9–3.6)1,035/1,8531.16 (1.04, 1.30) 0.0091.13 (1.01, 1.27) 0.029Q3 (3.7–4.5)921/1,7271.23 (1.10, 1.38) <0.0011.18 (1.05, 1.32) 0.006Q4 (4.6–13.1)950/1,9041.32 (1.18, 1.47) <0.0011.27 (1.13, 1.42) <0.001TG statusdHigh: TG ≥150 mg/dl, Low: TG <150 mg/dl.Q1 (0.3–2.8)1,680/7961<0.0011<0.001Q2 (2.9–3.6)1,780/11081.36 (1.21, 1.52) <0.0011.28 (1.14, 1.43) <0.001Q3 (3.7–4.5)1,611/1,0371.44 (1.28, 1.62) <0.0011.30 (1.15, 1.46) <0.001Q4 (4.6–13.1)1,720/1,1341.49 (1.33, 1.67) <0.0011.38 (1.23, 1.55) <0.001LDL-C statuseHigh: LDL-C ≥130 mg/dl, Low: LDL-C <130 mg/dl.Q1 (0.3–2.8)1,157/1,3191<0.00110.001Q2 (2.9–3.6)1271/1,6171.12 (1.01, 1.25) 0.0371.09 (0.98, 1.22) 0.104Q3 (3.7–4.5)1,145/1,5031.17 (1.04, 1.30) 0.0061.12 (1.00, 1.25) 0.054Q4 (4.6–13.1)1,184/1,6701.25 (1.12, 1.40) <0.0011.20 (1.08, 1.34) 0.001HDL-C statusfHigh: HDL-C ≥40 mg/dl, Low: HDL-C <40 mg/dl.Q1 (0.3–2.8)469/2,00710.10610.628Q2 (2.9–3.6)595/2,0931.11 (0.97, 1.27) 0.1361.04 (0.91, 1.20) 0.544Q3 (3.7–4.5)547/2,1011.11 (0.97, 1.27) 0.1441.01 (0.87, 1.16) 0.912Q4 (4.6–13.1)598/2,2561.13 (0.99, 1.29) 0.0771.05 (0.91, 1.21) 0.492a Model 1: adjusted for sex and age.b Model 2: adjusted for sex, age, smoking status, alcohol consumption, SBP, DBP, BMI, and diabetes.c High: TC ≥200 mg/dl, Low: TC <200 mg/dl.d High: TG ≥150 mg/dl, Low: TG <150 mg/dl.e High: LDL-C ≥130 mg/dl, Low: LDL-C <130 mg/dl.f High: HDL-C ≥40 mg/dl, Low: HDL-C <40 mg/dl. Open table in a new tab We further explored the relationship between total leukocyte, neutrophil, and lymphocyte counts and serum lipids among diabetics. As shown in TABLE 5, TABLE 6, TABLE 7, subgroup analyses stratified by diabetic status showed that the associations between differential leukocyte counts and high TC and LDL-C remained significant in patients without diabetes (all P values <0.001). However, there was no significant modification effect of diabetes on the associations of leukocyte counts with either high TG or low HDL-C risks. Additionally, interaction analyses found significant interaction terms between diabetes and neutrophil counts on TC and LDL-C risks (P = 0.033 and P = 0.024, respectively).TABLE 5Adjusted ORs (95% CI) for the association between dyslipidemia and the quartiles of leukocyte count by diabetic statusDiabetesNoYesLeukocyte Count (×109 cells/l)Serum Lipids [Low/High (N)]OR (95% CI) PP for TrendSerum Lipids [Low/High (N)]OR (95% CI) PP for TrendP for InteractionTC statusaHigh: TC ≥200 mg/dl, Low: TC <200 mg/dl.Q1 (0.6–5.3)975/1,3481<0.00171/19310.4780.552Q2 (5.4–6.3)884/1,4401.17 (1.04, 1.32) 0.00986/2190.94 (0.64, 1.37) 0.743Q3 (6.4–7.5)816/1,5631.35 (1.20, 1.53) <0.001110/3111.04 (0.73, 1.50) 0.810Q4 (7.6–17.1)810/1,5551.36 (1.21, 1.54) <0.001120/3641.09 (0.76, 1.55) 0.638TG statusbHigh: TG ≥150 mg/dl,Low: TG <150 mg/dl.Q1 (0.6–5.3)1,660/6631<0.001159/1051<0.0010.785Q2 (5.4–6.3)1,529/7951.25 (1.10, 1.42) 0.0007153/1521.51 (1.07, 2.13) 0.019Q3 (6.4–7.5)1,468/9111.45 (1.28, 1.65) <0.001199/2221.63 (1.18, 2.25) 0.003Q4 (7.6–17.1)1,402/9631.62 (1.43, 1.84) <0.001221/2631.79 (1.31, 2.46) <0.001LDL-C statuscHigh: LDL-C ≥130 mg/dl, Low: LDL-C <130 mg/dl.Q1 (0.6–5.3)1,150/1,1731<0.001" @default.
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• W2551896507 date "2017-01-01" @default.
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• W2551896507 title "Association of peripheral differential leukocyte counts with dyslipidemia risk in Chinese patients with hypertension: insight from the China Stroke Primary Prevention Trial" @default.
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• W2551896507 doi "https://doi.org/10.1194/jlr.p067686" @default.
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