Baseline Characteristics by LMR Exposure Categories
Baseline_expo_lmr_cat_results <- read_csv("./csv/Baseline_expo_lmr_cat.csv")
Baseline_expo_lmr_cat_cleaned <- Baseline_expo_lmr_cat_results %>%
select(-test) %>%
rename(
` ` = ...1,
Level1 = `1`,
Level2 = `2`,
Level3 = `3`
)
knitr::kable(
Baseline_expo_lmr_cat_cleaned,
digits = 2,
caption = "Baseline Characteristics by LMR Exposure Categories",
align = "c"
) %>%
kable_styling(full_width = FALSE, position = "center")
Baseline Characteristics by LMR Exposure Categories
|
|
Overall
|
Level1
|
Level2
|
Level3
|
p
|
|
n
|
413146
|
105227
|
204634
|
103279
|
NA
|
|
sex = 2 (%)
|
190849 (46.2)
|
69149 (65.7)
|
93068 (45.5)
|
28629 (27.7)
|
<0.001
|
|
age (mean (SD))
|
56.54 (8.09)
|
57.70 (8.07)
|
56.32 (8.10)
|
55.79 (7.96)
|
<0.001
|
|
income (%)
|
NA
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
40844 (9.9)
|
9912 (9.4)
|
20134 (9.9)
|
10798 (10.5)
|
NA
|
|
2
|
17370 (4.2)
|
3874 (3.7)
|
8290 (4.1)
|
5206 (5.1)
|
NA
|
|
3
|
79972 (19.4)
|
21680 (20.7)
|
37894 (18.6)
|
20398 (19.8)
|
NA
|
|
4
|
89874 (21.8)
|
23863 (22.8)
|
44107 (21.6)
|
21902 (21.3)
|
NA
|
|
5
|
92455 (22.4)
|
23555 (22.5)
|
46634 (22.8)
|
22263 (21.6)
|
NA
|
|
6
|
72283 (17.5)
|
17494 (16.7)
|
37205 (18.2)
|
17583 (17.1)
|
NA
|
|
7
|
19249 (4.7)
|
4513 (4.3)
|
9872 (4.8)
|
4864 (4.7)
|
NA
|
|
townsend (mean (SD))
|
-1.33 (3.08)
|
-1.39 (3.04)
|
-1.39 (3.04)
|
-1.14 (3.18)
|
<0.001
|
|
total_met (mean (SD))
|
1619.08 (2015.70)
|
1645.29 (2084.82)
|
1625.33 (2008.34)
|
1578.85 (1955.34)
|
<0.001
|
|
diet_quality (mean (SD))
|
3.29 (1.57)
|
3.12 (1.57)
|
3.31 (1.57)
|
3.42 (1.56)
|
<0.001
|
|
sleep_hour (mean (SD))
|
7.15 (1.10)
|
7.17 (1.12)
|
7.15 (1.09)
|
7.14 (1.13)
|
<0.001
|
|
smoke_cat (%)
|
NA
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
32255 (7.8)
|
6743 (6.4)
|
15030 (7.3)
|
10481 (10.1)
|
NA
|
|
2
|
11334 (2.7)
|
2843 (2.7)
|
5572 (2.7)
|
2919 (2.8)
|
NA
|
|
3
|
96062 (23.3)
|
27859 (26.5)
|
47078 (23.0)
|
21122 (20.5)
|
NA
|
|
4
|
47431 (11.5)
|
12141 (11.5)
|
23827 (11.6)
|
11463 (11.1)
|
NA
|
|
5
|
60211 (14.6)
|
15267 (14.5)
|
30436 (14.9)
|
14507 (14.0)
|
NA
|
|
6
|
165853 (40.1)
|
40374 (38.4)
|
82691 (40.4)
|
42787 (41.4)
|
NA
|
|
total_alcohol (mean (SD))
|
12.55 (15.17)
|
15.16 (17.37)
|
12.50 (14.77)
|
9.98 (12.92)
|
<0.001
|
|
non_hdl (mean (SD))
|
359.49 (78.74)
|
345.15 (77.61)
|
361.10 (77.88)
|
371.09 (79.37)
|
<0.001
|
|
tg (mean (SD))
|
92.17 (22.17)
|
92.24 (22.59)
|
91.90 (21.60)
|
92.66 (22.85)
|
<0.001
|
|
bp_cat (mean (SD))
|
49.88 (28.78)
|
47.57 (28.26)
|
50.05 (28.71)
|
51.91 (29.25)
|
<0.001
|
Baseline Characteristics by SII Exposure Categories
Baseline_expo_sii_cat_results <- read_csv("./csv/Baseline_expo_sii_cat.csv")
Baseline_expo_sii_cat_cleaned <- Baseline_expo_sii_cat_results %>%
select(-test) %>%
rename(
` ` = ...1,
Level1 = `1`,
Level2 = `2`,
Level3 = `3`
)
knitr::kable(
Baseline_expo_sii_cat_cleaned,
digits = 2,
caption = "Baseline Characteristics by SII Exposure Categories",
align = "c"
) %>%
kable_styling(full_width = FALSE, position = "center")
Baseline Characteristics by SII Exposure Categories
|
|
Overall
|
Level1
|
Level2
|
Level3
|
p
|
|
n
|
413146
|
103286
|
206571
|
103286
|
NA
|
|
sex = 2 (%)
|
190849 (46.2)
|
50744 (49.1)
|
93870 (45.4)
|
46234 (44.8)
|
<0.001
|
|
age (mean (SD))
|
56.54 (8.09)
|
56.70 (7.93)
|
56.53 (8.04)
|
56.39 (8.34)
|
<0.001
|
|
income (%)
|
NA
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
40844 (9.9)
|
10185 (9.9)
|
20481 (9.9)
|
10178 (9.9)
|
NA
|
|
2
|
17370 (4.2)
|
4265 (4.1)
|
8296 (4.0)
|
4809 (4.7)
|
NA
|
|
3
|
79972 (19.4)
|
18085 (17.6)
|
38964 (18.9)
|
22923 (22.3)
|
NA
|
|
4
|
89874 (21.8)
|
22149 (21.5)
|
44783 (21.7)
|
22941 (22.3)
|
NA
|
|
5
|
92455 (22.4)
|
23315 (22.6)
|
47003 (22.8)
|
22135 (21.5)
|
NA
|
|
6
|
72283 (17.5)
|
19303 (18.7)
|
36803 (17.9)
|
16177 (15.7)
|
NA
|
|
7
|
19249 (4.7)
|
5716 (5.5)
|
9715 (4.7)
|
3818 (3.7)
|
NA
|
|
townsend (mean (SD))
|
-1.33 (3.08)
|
-1.30 (3.11)
|
-1.40 (3.03)
|
-1.21 (3.13)
|
<0.001
|
|
total_met (mean (SD))
|
1619.08 (2015.70)
|
1688.28 (2026.41)
|
1612.54 (2009.32)
|
1560.73 (2015.54)
|
<0.001
|
|
diet_quality (mean (SD))
|
3.29 (1.57)
|
3.42 (1.57)
|
3.29 (1.56)
|
3.14 (1.57)
|
<0.001
|
|
sleep_hour (mean (SD))
|
7.15 (1.10)
|
7.14 (1.08)
|
7.16 (1.09)
|
7.16 (1.15)
|
0.001
|
|
smoke_cat (%)
|
NA
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
32255 (7.8)
|
6875 (6.7)
|
15757 (7.6)
|
9622 (9.3)
|
NA
|
|
2
|
11334 (2.7)
|
3029 (2.9)
|
5568 (2.7)
|
2737 (2.6)
|
NA
|
|
3
|
96062 (23.3)
|
24195 (23.4)
|
47994 (23.2)
|
23871 (23.1)
|
NA
|
|
4
|
47431 (11.5)
|
12113 (11.7)
|
23927 (11.6)
|
11391 (11.0)
|
NA
|
|
5
|
60211 (14.6)
|
15265 (14.8)
|
30389 (14.7)
|
14557 (14.1)
|
NA
|
|
6
|
165853 (40.1)
|
41809 (40.5)
|
82936 (40.1)
|
41108 (39.8)
|
NA
|
|
total_alcohol (mean (SD))
|
12.55 (15.17)
|
12.58 (14.76)
|
12.56 (15.01)
|
12.49 (15.85)
|
0.349
|
|
non_hdl (mean (SD))
|
359.49 (78.74)
|
358.75 (79.08)
|
361.75 (78.52)
|
355.70 (78.68)
|
<0.001
|
|
tg (mean (SD))
|
92.17 (22.17)
|
91.58 (21.20)
|
91.87 (21.41)
|
93.38 (24.47)
|
<0.001
|
|
bp_cat (mean (SD))
|
49.88 (28.78)
|
51.13 (28.68)
|
50.01 (28.76)
|
48.38 (28.83)
|
<0.001
|
Baseline Characteristics by NPAR Exposure Categories
Baseline_expo_npar_cat_results <- read_csv("./csv/Baseline_expo_npar_cat.csv")
Baseline_expo_npar_cat_cleaned <- Baseline_expo_npar_cat_results %>%
select(-test) %>%
rename(
` ` = ...1,
Level1 = `1`,
Level2 = `2`,
Level3 = `3`
)
knitr::kable(
Baseline_expo_npar_cat_cleaned,
digits = 2,
caption = "Baseline Characteristics by NPAR Exposure Categories",
align = "c"
) %>%
kable_styling(full_width = FALSE, position = "center")
Baseline Characteristics by NPAR Exposure Categories
|
|
Overall
|
Level1
|
Level2
|
Level3
|
p
|
|
n
|
413146
|
103287
|
206574
|
103285
|
NA
|
|
sex = 2 (%)
|
190849 (46.2)
|
48556 (47.0)
|
95248 (46.1)
|
47045 (45.5)
|
<0.001
|
|
age (mean (SD))
|
56.54 (8.09)
|
55.91 (7.89)
|
56.54 (8.05)
|
57.16 (8.31)
|
<0.001
|
|
income (%)
|
NA
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
40844 (9.9)
|
10029 (9.7)
|
20412 (9.9)
|
10403 (10.1)
|
NA
|
|
2
|
17370 (4.2)
|
4084 (4.0)
|
8458 (4.1)
|
4828 (4.7)
|
NA
|
|
3
|
79972 (19.4)
|
16934 (16.4)
|
39005 (18.9)
|
24033 (23.3)
|
NA
|
|
4
|
89874 (21.8)
|
21489 (20.9)
|
44821 (21.8)
|
23564 (22.9)
|
NA
|
|
5
|
92455 (22.4)
|
23997 (23.3)
|
46929 (22.8)
|
21529 (20.9)
|
NA
|
|
6
|
72283 (17.5)
|
20577 (20.0)
|
36749 (17.8)
|
14957 (14.5)
|
NA
|
|
7
|
19249 (4.7)
|
5948 (5.8)
|
9688 (4.7)
|
3613 (3.5)
|
NA
|
|
townsend (mean (SD))
|
-1.33 (3.08)
|
-1.34 (3.10)
|
-1.40 (3.03)
|
-1.17 (3.14)
|
<0.001
|
|
total_met (mean (SD))
|
1619.08 (2015.70)
|
1671.02 (2018.22)
|
1617.55 (2012.63)
|
1568.39 (2018.03)
|
<0.001
|
|
diet_quality (mean (SD))
|
3.29 (1.57)
|
3.41 (1.57)
|
3.29 (1.57)
|
3.16 (1.57)
|
<0.001
|
|
sleep_hour (mean (SD))
|
7.15 (1.10)
|
7.13 (1.08)
|
7.15 (1.09)
|
7.18 (1.16)
|
<0.001
|
|
smoke_cat (%)
|
NA
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
32255 (7.8)
|
6618 (6.4)
|
15628 (7.6)
|
10009 (9.7)
|
NA
|
|
2
|
11334 (2.7)
|
3051 (3.0)
|
5572 (2.7)
|
2711 (2.6)
|
NA
|
|
3
|
96062 (23.3)
|
24055 (23.3)
|
48085 (23.3)
|
23922 (23.2)
|
NA
|
|
4
|
47431 (11.5)
|
12149 (11.8)
|
23838 (11.5)
|
11444 (11.1)
|
NA
|
|
5
|
60211 (14.6)
|
15418 (14.9)
|
30402 (14.7)
|
14391 (13.9)
|
NA
|
|
6
|
165853 (40.1)
|
41996 (40.7)
|
83049 (40.2)
|
40808 (39.5)
|
NA
|
|
total_alcohol (mean (SD))
|
12.55 (15.17)
|
12.98 (15.08)
|
12.63 (15.06)
|
11.95 (15.44)
|
<0.001
|
|
non_hdl (mean (SD))
|
359.49 (78.74)
|
368.07 (79.61)
|
361.33 (78.16)
|
347.34 (77.58)
|
<0.001
|
|
tg (mean (SD))
|
92.17 (22.17)
|
90.77 (18.16)
|
91.81 (21.11)
|
94.30 (27.18)
|
<0.001
|
|
bp_cat (mean (SD))
|
49.88 (28.78)
|
49.89 (28.39)
|
49.86 (28.70)
|
49.93 (29.30)
|
0.806
|
Baseline Characteristics by NPS Exposure Categories
Baseline_expo_nps_cat_results <- read_csv("./csv/Baseline_expo_nps_cat.csv")
Baseline_expo_nps_cat_cleaned <- Baseline_expo_nps_cat_results %>%
select(-test) %>%
rename(
` ` = ...1,
Level1 = `1`,
Level2 = `2`
)
knitr::kable(
Baseline_expo_nps_cat_cleaned,
digits = 2,
caption = "Baseline Characteristics by NPS Exposure Categories",
align = "c"
) %>%
kable_styling(full_width = FALSE, position = "center")
Baseline Characteristics by NPS Exposure Categories
|
|
Overall
|
Level1
|
Level2
|
p
|
|
n
|
413146
|
365846
|
47294
|
NA
|
|
sex = 2 (%)
|
190849 (46.2)
|
164549 (45.0)
|
26297 (55.6)
|
<0.001
|
|
age (mean (SD))
|
56.54 (8.09)
|
56.45 (8.06)
|
57.22 (8.26)
|
<0.001
|
|
income (%)
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
40844 (9.9)
|
36184 (9.9)
|
4660 (9.9)
|
NA
|
|
2
|
17370 (4.2)
|
15368 (4.2)
|
2002 (4.2)
|
NA
|
|
3
|
79972 (19.4)
|
69618 (19.1)
|
10354 (22.0)
|
NA
|
|
4
|
89874 (21.8)
|
79112 (21.7)
|
10760 (22.8)
|
NA
|
|
5
|
92455 (22.4)
|
82188 (22.5)
|
10264 (21.8)
|
NA
|
|
6
|
72283 (17.5)
|
65052 (17.8)
|
7230 (15.3)
|
NA
|
|
7
|
19249 (4.7)
|
17374 (4.8)
|
1875 (4.0)
|
NA
|
|
townsend (mean (SD))
|
-1.33 (3.08)
|
-1.33 (3.08)
|
-1.31 (3.08)
|
0.219
|
|
total_met (mean (SD))
|
1619.08 (2015.70)
|
1620.03 (2009.37)
|
1611.68 (2064.33)
|
0.457
|
|
diet_quality (mean (SD))
|
3.29 (1.57)
|
3.31 (1.57)
|
3.14 (1.57)
|
<0.001
|
|
sleep_hour (mean (SD))
|
7.15 (1.10)
|
7.15 (1.10)
|
7.17 (1.14)
|
0.005
|
|
smoke_cat (%)
|
NA
|
NA
|
NA
|
<0.001
|
|
1
|
32255 (7.8)
|
29492 (8.1)
|
2762 (5.8)
|
NA
|
|
2
|
11334 (2.7)
|
10183 (2.8)
|
1151 (2.4)
|
NA
|
|
3
|
96062 (23.3)
|
84650 (23.1)
|
11409 (24.1)
|
NA
|
|
4
|
47431 (11.5)
|
41980 (11.5)
|
5451 (11.5)
|
NA
|
|
5
|
60211 (14.6)
|
53024 (14.5)
|
7186 (15.2)
|
NA
|
|
6
|
165853 (40.1)
|
146517 (40.0)
|
19335 (40.9)
|
NA
|
|
total_alcohol (mean (SD))
|
12.55 (15.17)
|
12.42 (14.99)
|
13.52 (16.42)
|
<0.001
|
|
non_hdl (mean (SD))
|
359.49 (78.74)
|
360.84 (78.79)
|
349.07 (77.57)
|
<0.001
|
|
tg (mean (SD))
|
92.17 (22.17)
|
91.95 (21.76)
|
93.91 (25.10)
|
<0.001
|
|
bp_cat (mean (SD))
|
49.88 (28.78)
|
50.14 (28.77)
|
47.86 (28.74)
|
<0.001
|
The baseline characteristics tables present a comprehensive summary
of the study population stratified by four key exposure categories:
Lymphocyte-to-Monocyte Ratio (LMR), Systemic Immune-Inflammation Index
(SII), Neutrophil-to-Albumin Ratio (NPAR), and Naples Prognostic Score
(NPS). These tables outline demographic variables (e.g., age, sex),
socioeconomic factors (e.g., income, Townsend index), lifestyle
behaviors (e.g., smoking, alcohol intake), and clinical markers (e.g.,
triglycerides, diet quality), while highlighting statistically
significant differences across exposure levels for most variables. These
differences underscore the potential influence of these covariates on
the outcomes, necessitating their adjustment in subsequent analyses,
such as Cox proportional hazards modeling, to mitigate confounding and
ensure robust evaluation of exposure-outcome relationships.