- Clinical characteristics of the subjects (Table 1):A total of 448 patients with PA, including 250 females (55.8%), were included in the study. The median NLR was 1.90, with 219 patients in the low NLR group (NLR<1.90) and 229 patients in the high NLR group (NLR ≥ 1.90). Compared with the low NLR group, the high NLR group had a greater white blood cell count, neutrophil count, hypertension grade, and baPWV but a lower lymphocyte count (P<0.05). There was no significant difference in other variables such as sex, age, BMI, or smoking status, between the two groups (P>0.05).
Table 1: Clinical characteristics of patients with primary aldosteronism
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)
Note: BMI: body mass index; WBC: white blood cell; NLR: ratio of neutrophils to lymphocytes; TC: total cholesterol; TG: triglyceride; HDL: high-density lipoprotein cholesterol; LDL: low-density lipoprotein cholesterol; ALD: aldosterone; baPWV: arm-ankle pulse wave velocity
2.Correlation analysis (Table 2): SPSS was used to analyze the correlations between baPWV and sex, age, diabetes status, hypertension severity, hypertension duration, total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, aldosterone, and other factors. P < 0.05 indicated that the correlation was significant. According to the results shown in Table 2, there was a significant correlation between baPWV, diabetes age, NLR, and blood glucose (P≤0.05). The correlations of the other variables were weak (P > 0.05).
Table 2 Results of the correlation analysis between baPWV and various factors
Item
|
Correlation coefficient
|
P
|
sex
|
-0.043
|
0.362
|
diabetes
|
0.177
|
<0.001
|
smoke
|
0.044
|
0.352
|
age
|
0.403
|
<0.001
|
hypertension grading
|
0.086
|
0.068
|
Course of hypertension
|
0.01
|
0.829
|
BMI
WBC
NLR
Blood sugar
TC
TG
HDL-C
LDL-C
ALD
Renin activity
|
-0.081
0.079
0.155
0.132
-0.06
0.066
-0.03
-0.07
0.043
-0.006
|
0.088
0.095
0.001
0.005
0.209
0.160
0.53
0.139
0.367
0.904
|
Note: BMI: body mass index; WBC: white blood cell; NLR: ratio of neutrophils to lymphocytes; TC: total cholesterol; TG: triglyceride; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; ALD: aldosterone; baPWV: arm-ankle puls;
3.Multivariate regression analysis and t-test of two independent samples: Multiple linear regression analysis revealed a positive correlation between the NLR and baPWV in both the low-NLR group (adjusted for sex, age, BMI, blood pressure, blood glucose, and other factors, β = 0.9, P = 0.03;Table 3) and the high-NLR group (adjusted for sex, age, BMI, blood pressure, blood glucose, and other factors, β = 1.1, P = 0.03;Table 3). The results of multivariate logistic regression analysis showed that after adjusting for sex, age, BMI, blood lipids, blood glucose, and blood pressure, whether the NLR was used as a continuous variable (OR = 2.5, P < 0.001) or the classification variables of low NLR (OR = 0.4, P = 0.004) and high NLR (OR = 2.2, P = 0.004), baPWV was correlated with the NLR (Table 4). Furthermore, the results of the t-test of two independent samples between the low NLR group and the high NLR group showed that there was a difference in the effect of the low NLR group and the high NLR group on baPWV (P<0.05, Table 5).
Table 3 Multiple linear regression analysis of the effect of the NLR on baPWV in patients with primary aldosteronism
factor
|
β(95%CI)
|
P
|
Other factors are not corrected
NLR
NLR≥1.9
NLR<1.9
Model 1a
NLR
NLR≥1.9
NLR<1.9
Model 2b
NLR
NLR≥1.9
NLR<1.9
|
1.2(-6.4,8.8)
1.1(-3.4,5.6)
0.9(-3.6,5.4)
1.2(-6.1,8.5)
1.1(-4.2,6.4)
0.9(-4.4,6.2)
1.2(-5.9,8.3)
1.1(-3.2,5.4)
0.9(-3.4,5.2)
|
<0.001
0.02
0.02
<0.001
0.007
0.007
0.004
0.03
0.03
|
Note: NLR: the ratio of neutrophils to lymphocytes; baPWV: arm-ankle pulse wave propagation velocity. Model 1a: adjusted for sex, age, course of disease, grade, body mass index(BMI),and smoking status. Model 2b: adjusted for sex, age, course of hypertension, hypertension grade, BMI (body weight index), smoking status, white blood cell count, total cholesterol, triglyceride, low-density lipoprotein total cholesterol, high -density lipoprotein cholesterol, aldosterone, renin activity, the ARR, blood glucose,and diabetes.
Table 4 Multivariate logistic regression analysis of the effect of the NLR on baPWV in patients with primary aldosteronism
Item
|
OR(95%CI)
|
P
|
Other factors are not corrected
|
|
|
NLR
|
2.1(1.4,3.1)
|
<0.001
|
NLR≥1.9
NLR<1.9
|
2.2(1.3,3.6)
0.46(0.3,0.8)
|
0.002
0.002
|
Model 1a
|
|
|
NLR
|
2.6(1.3,5.5)
|
0.009
|
NLR≥1.9
NLR<1.9
|
2.4(1.4,4.1)
0.4(0.3,0.7)
|
0.001
0.001
|
Model 2b
NLR
|
2.5(1.5,4)
|
<0.001
|
NLR≥1.9
NLR<1.9
|
2.2(1.3,3.9)
0.4(0.3,0.8)
|
0.004
0.004
|
Note: NLR: the ratio of neutrophils to lymphocytes; baPWV: arm-ankle pulse wave propagation velocity. Model Ia: adjusted for sex, age, course of hypertension, grade of hypertension, BMI (body mass index), and smoking status. Model IIb: adjusted for sex, age, course of hypertension, hypertension grade, BMI (body mass index), smoking status, white blood cell count, total cholesterol, triglyceride, low -density lipoprotein cholesterol, high-density lipoprotein cholesterol, aldosterone, renin activity, ARR, blood glucose, and diabetes.
Table 5 Comparison of two independent samples for baPWV values between the low NLR group and the high NLR group
Average value of baPWV in low NLR group
1638.5
|
Average value of baPWV in high NLR group
1714.4
|
t
-2.34
|
P
0.02
|
Note: NLR: the ratio of neutrophils to lymphocytes; baPWV: arm-ankle pulse wave velocity;
4. Results of smooth curve fitting and threshold effect analysis: EmpowerStats software was used for smooth curve fitting between baPWV and the NLR. The results of smooth curve fitting showed that there was a nonlinear relationship between the NLR and baPWV, and the inflection point was located at NLR = 1.9, suggesting that the correlation between the NLR and baPW in the second half was steeper than that in the first half (Figure A). After adjusting for other confounding factors (sex, age, smoking status, BMI, course of hypertension, and grade of hypertension), the analysis revealed a positive linear correlation between the NLR and baPWV (Figure B). The results of the uncorrected threshold effect analysis showed that when the NLR was≥ 1.9, the NLR was positively correlated with baPWV (β = 115.3, 95% CI 68.8, 161.8; P < 0.001). There was no significant difference in the logarithmic likelihood ratio test between one linear model and a two-stage linear regression model (P > 0.002), suggesting that a linear model can replace a piecewise linear model.
5. Stratified analysis results: EmpowerStats software was used to evaluate the interactions between other diseases (glycosuria, hyperlipidemia, etc.) and the baPWV and NLR. According to the results of the stratified analysis, there was an interaction effect between the NLR and baPWV and hyperlipidemia (P = 0.002). In patients with hyperlipidemia, the correlation between the NLR and baPWV was stronger (β = 88.9, 95% CI: 32.2–145.5, P = 0.002). In patients without hyperlipidemia, the correlation between the NLR and baPWV was weak (β = 11.4; 95% CI: -46.4–69.1; P = 0.7). On the other hand, there was a strong correlation between the NLR and baPWV, regardless of diabetes status(P < 0.005).