Last updated: 2023-03-11
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Knit directory: paperscripts/
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Here we plot table 1
library(table1)
Attaching package: 'table1'The following objects are masked from 'package:base':
units, units<-df=readRDS("output/amit_df.rds")
dat=df
dat$age=df$phenos.enrollment
dat$dm <- factor(dat$prev_disease_dm, levels=0:1, labels=c("None", "Diabetes Mellitus"))
dat$sex <- factor(dat$sex, levels=c("male", "female"), labels=c("Male", "Female"))
dat$prscat <- factor(dat$prscat, labels=c("Low","Intermediate", "High"))
dat$ascvdcat <- factor(dat$ascvdcat_all,
labels=c("<7.5",
"7.5-20",
"20+"))
dat$smoke=factor(dat$old_smoke,levels=c(-3,0,1,2),
labels=c("Never","Never","Former",
"Current"))
dat$bp_med=factor(dat$bp_med,levels=c(0,1),labels=c("No","Yes"))
label(dat$age) <- "Age (y)"
label(dat$sex) <- "Sex"
label(dat$prscat) <- "Polygenic score category"
label(dat$dm) <- "Diabetes status"
label(dat$sbp) <- "Systolic blood pressure (mm Hg) "
label(dat$choladj) <- "Total cholesterol (ng/ml)"
label(dat$hdladj) <- "High-density lipoprotein (ng/ml)"
label(dat$smoke) <- "Smoking Status"
label(dat$ldladj) <- "Low-density lipoprotein (ng/ml)"
label(dat$bp_med) <- "Anti-hypertensive"
label(dat$ascvdcat) <- "Pooled cohort equation category (10 year risk)"
table1(~ age + sex + prscat + sbp + choladj + hdladj + smoke + ascvdcat+
ldladj + bp_med , data=dat)| Overall (N=327837) |
|
|---|---|
| Age (y) | |
| Mean (SD) | 56.1 (8.08) |
| Median [Min, Max] | 56.9 [38.9, 73.7] |
| Sex | |
| Male | 141330 (43.1%) |
| Female | 186507 (56.9%) |
| Polygenic score category | |
| Low | 65696 (20.0%) |
| Intermediate | 196750 (60.0%) |
| High | 65391 (19.9%) |
| Systolic blood pressure (mm Hg) | |
| Mean (SD) | 140 (20.4) |
| Median [Min, Max] | 138 [62.0, 279] |
| Total cholesterol (ng/ml) | |
| Mean (SD) | 229 (41.4) |
| Median [Min, Max] | 226 [69.6, 598] |
| High-density lipoprotein (ng/ml) | |
| Mean (SD) | 57.2 (14.8) |
| Median [Min, Max] | 55.3 [8.74, 170] |
| Smoking Status | |
| Never | 186591 (56.9%) |
| Former | 107377 (32.8%) |
| Current | 33869 (10.3%) |
| Pooled cohort equation category (10 year risk) | |
| <7.5 | 207150 (63.2%) |
| 7.5-20 | 96775 (29.5%) |
| 20+ | 23912 (7.3%) |
| Low-density lipoprotein (ng/ml) | |
| Mean (SD) | 144 (31.9) |
| Median [Min, Max] | 142 [31.1, 476] |
| Anti-hypertensive | |
| No | 309420 (94.4%) |
| Yes | 18417 (5.6%) |
###
table1(~ age + sex + prscat + sbp + choladj + hdladj + smoke + ascvdcat+
ldladj + bp_med |ascvdcat, data=dat)| <7.5 (N=207150) |
7.5-20 (N=96775) |
20+ (N=23912) |
Overall (N=327837) |
|
|---|---|---|---|---|
| Age (y) | ||||
| Mean (SD) | 52.5 (7.08) | 61.4 (5.83) | 65.4 (3.79) | 56.1 (8.08) |
| Median [Min, Max] | 52.2 [38.9, 70.3] | 62.2 [40.2, 71.1] | 66.1 [40.4, 73.7] | 56.9 [38.9, 73.7] |
| Sex | ||||
| Male | 55003 (26.6%) | 63116 (65.2%) | 23211 (97.1%) | 141330 (43.1%) |
| Female | 152147 (73.4%) | 33659 (34.8%) | 701 (2.9%) | 186507 (56.9%) |
| Polygenic score category | ||||
| Low | 42166 (20.4%) | 18892 (19.5%) | 4638 (19.4%) | 65696 (20.0%) |
| Intermediate | 123405 (59.6%) | 58686 (60.6%) | 14659 (61.3%) | 196750 (60.0%) |
| High | 41579 (20.1%) | 19197 (19.8%) | 4615 (19.3%) | 65391 (19.9%) |
| Systolic blood pressure (mm Hg) | ||||
| Mean (SD) | 133 (17.1) | 148 (18.4) | 166 (19.1) | 140 (20.4) |
| Median [Min, Max] | 131 [62.0, 247] | 147 [83.0, 268] | 164 [97.0, 279] | 138 [62.0, 279] |
| Total cholesterol (ng/ml) | ||||
| Mean (SD) | 224 (40.3) | 237 (41.9) | 235 (42.4) | 229 (41.4) |
| Median [Min, Max] | 222 [69.6, 511] | 234 [79.2, 598] | 232 [80.6, 586] | 226 [69.6, 598] |
| High-density lipoprotein (ng/ml) | ||||
| Mean (SD) | 59.7 (14.8) | 54.0 (13.7) | 48.0 (11.6) | 57.2 (14.8) |
| Median [Min, Max] | 58.1 [8.82, 170] | 52.0 [8.74, 156] | 46.4 [10.7, 158] | 55.3 [8.74, 170] |
| Smoking Status | ||||
| Never | 129984 (62.7%) | 47864 (49.5%) | 8743 (36.6%) | 186591 (56.9%) |
| Former | 63295 (30.6%) | 34113 (35.2%) | 9969 (41.7%) | 107377 (32.8%) |
| Current | 13871 (6.7%) | 14798 (15.3%) | 5200 (21.7%) | 33869 (10.3%) |
| Pooled cohort equation category (10 year risk) | ||||
| <7.5 | 207150 (100%) | 0 (0%) | 0 (0%) | 207150 (63.2%) |
| 7.5-20 | 0 (0%) | 96775 (100%) | 0 (0%) | 96775 (29.5%) |
| 20+ | 0 (0%) | 0 (0%) | 23912 (100%) | 23912 (7.3%) |
| Low-density lipoprotein (ng/ml) | ||||
| Mean (SD) | 139 (30.8) | 152 (31.8) | 154 (33.1) | 144 (31.9) |
| Median [Min, Max] | 137 [31.1, 369] | 150 [37.0, 377] | 151 [44.4, 476] | 142 [31.1, 476] |
| Anti-hypertensive | ||||
| No | 205365 (99.1%) | 89074 (92.0%) | 14981 (62.7%) | 309420 (94.4%) |
| Yes | 1785 (0.9%) | 7701 (8.0%) | 8931 (37.3%) | 18417 (5.6%) |
table1(~ age + sex + prscat + sbp + choladj + hdladj + smoke + ascvdcat+
ldladj + bp_med |prscat, data=dat)| Low (N=65696) |
Intermediate (N=196750) |
High (N=65391) |
Overall (N=327837) |
|
|---|---|---|---|---|
| Age (y) | ||||
| Mean (SD) | 56.1 (8.14) | 56.2 (8.07) | 55.7 (8.05) | 56.1 (8.08) |
| Median [Min, Max] | 56.8 [39.2, 71.1] | 57.0 [38.9, 73.7] | 56.3 [40.0, 71.2] | 56.9 [38.9, 73.7] |
| Sex | ||||
| Male | 28757 (43.8%) | 85194 (43.3%) | 27379 (41.9%) | 141330 (43.1%) |
| Female | 36939 (56.2%) | 111556 (56.7%) | 38012 (58.1%) | 186507 (56.9%) |
| Polygenic score category | ||||
| Low | 65696 (100%) | 0 (0%) | 0 (0%) | 65696 (20.0%) |
| Intermediate | 0 (0%) | 196750 (100%) | 0 (0%) | 196750 (60.0%) |
| High | 0 (0%) | 0 (0%) | 65391 (100%) | 65391 (19.9%) |
| Systolic blood pressure (mm Hg) | ||||
| Mean (SD) | 139 (20.3) | 140 (20.4) | 141 (20.4) | 140 (20.4) |
| Median [Min, Max] | 137 [62.0, 279] | 138 [69.0, 261] | 139 [78.0, 245] | 138 [62.0, 279] |
| Total cholesterol (ng/ml) | ||||
| Mean (SD) | 222 (40.7) | 229 (41.1) | 234 (42.1) | 229 (41.4) |
| Median [Min, Max] | 220 [69.6, 506] | 226 [70.7, 586] | 232 [79.2, 598] | 226 [69.6, 598] |
| High-density lipoprotein (ng/ml) | ||||
| Mean (SD) | 57.6 (14.9) | 57.1 (14.7) | 56.8 (14.7) | 57.2 (14.8) |
| Median [Min, Max] | 55.7 [8.74, 158] | 55.3 [8.82, 170] | 54.9 [14.5, 153] | 55.3 [8.74, 170] |
| Smoking Status | ||||
| Never | 37823 (57.6%) | 111650 (56.7%) | 37118 (56.8%) | 186591 (56.9%) |
| Former | 21143 (32.2%) | 64919 (33.0%) | 21315 (32.6%) | 107377 (32.8%) |
| Current | 6730 (10.2%) | 20181 (10.3%) | 6958 (10.6%) | 33869 (10.3%) |
| Pooled cohort equation category (10 year risk) | ||||
| <7.5 | 42166 (64.2%) | 123405 (62.7%) | 41579 (63.6%) | 207150 (63.2%) |
| 7.5-20 | 18892 (28.8%) | 58686 (29.8%) | 19197 (29.4%) | 96775 (29.5%) |
| 20+ | 4638 (7.1%) | 14659 (7.5%) | 4615 (7.1%) | 23912 (7.3%) |
| Low-density lipoprotein (ng/ml) | ||||
| Mean (SD) | 139 (31.4) | 144 (31.7) | 149 (32.4) | 144 (31.9) |
| Median [Min, Max] | 137 [31.1, 349] | 142 [32.6, 476] | 147 [37.5, 379] | 142 [31.1, 476] |
| Anti-hypertensive | ||||
| No | 62038 (94.4%) | 185760 (94.4%) | 61622 (94.2%) | 309420 (94.4%) |
| Yes | 3658 (5.6%) | 10990 (5.6%) | 3769 (5.8%) | 18417 (5.6%) |
rm(dat)
fh=readRDS("output/fh_full.rds")
dat=fh
dat=na.omit(dat)
dat$age=dat$enroll_age
dat$dm <- factor(dat$DMRX1, levels=c("NA","0","1"), labels=c("None","None","Diabetes Mellitus"))
dat$sex <- factor(dat$SEX, levels=c(1, 2), labels=c("Male", "Female"))
# dat$prscat <- factor(dat$prscat, labels=c("Low","Intermediate", "High"))
# dat$ascvdcat <- factor(dat$ascvdcat_all,
# labels=c("<7.5",
# "7.5-20",
# "20+"))
dat$smoke=factor(dat$CURRSMK1,levels=c(0,1),
labels=c("Never",
"Current"))
dat$bp_med=factor(dat$HRX1,levels=c(0,"NA",1),labels=c("No","No","Yes"))
label(dat$AGE1) <- "Age (y)"
label(dat$sex) <- "Sex"
label(dat$dm) <- "Diabetes status"
label(dat$SBP1) <- "Systolic blood pressure (mm Hg) "
label(dat$TC1) <- "Total cholesterol (ng/ml)"
label(dat$HDL1) <- "High-density lipoprotein (ng/ml)"
label(dat$smoke) <- "Smoking Status"
label(dat$CALC_LDL1) <- "Low-density lipoprotein (ng/ml)"
label(dat$bp_med) <- "Anti-hypertensive"
table1(~ AGE1 + sex + SBP1 + TC1 + HDL1 + smoke +
CALC_LDL1 + bp_med , data=dat)| Overall (N=3660) |
|
|---|---|
| Age (y) | |
| Mean (SD) | 35.9 (10.3) |
| Median [Min, Max] | 35.0 [9.00, 70.0] |
| Sex | |
| Male | 1817 (49.6%) |
| Female | 1843 (50.4%) |
| Systolic blood pressure (mm Hg) | |
| Mean (SD) | 122 (16.5) |
| Median [Min, Max] | 120 [78.0, 240] |
| Total cholesterol (ng/ml) | |
| Mean (SD) | 198 (39.2) |
| Median [Min, Max] | 193 [100, 457] |
| High-density lipoprotein (ng/ml) | |
| Mean (SD) | 51.9 (16.0) |
| Median [Min, Max] | 50.0 [14.0, 132] |
| Smoking Status | |
| Never | 2048 (56.0%) |
| Current | 1612 (44.0%) |
| Low-density lipoprotein (ng/ml) | |
| Mean (SD) | 127 (37.2) |
| Median [Min, Max] | 124 [33.0, 394] |
| Anti-hypertensive | |
| No | 3538 (96.7%) |
| Yes | 122 (3.3%) |
fhp=readRDS("output/fh_prs.rds")
fhp$prs=scale(fhp$V2)
fhp$prs.r=rank(fhp$prs)/length(fhp$prs)
fhp$prscat <- cut(fhp$prs.r, breaks=c(0, 0.20,0.80,1), labels=c("low", "intermediate","high"))
dat=fhp
dat=na.omit(dat)
dat$age=dat$enroll_age
dat$dm <- factor(dat$DMRX1, levels=c("NA","0","1"), labels=c("None","None","Diabetes Mellitus"))
dat$sex <- factor(dat$SEX, levels=c(1, 2), labels=c("Male", "Female"))
dat$prscat <- factor(dat$prscat, labels=c("Low","Intermediate", "High"))
# dat$ascvdcat <- factor(dat$ascvdcat_all,
# labels=c("<7.5",
# "7.5-20",
# "20+"))
dat$smoke=factor(dat$CURRSMK1,levels=c(0,1),
labels=c("Never",
"Current"))
dat$bp_med=factor(dat$HRX1,levels=c(0,"NA",1),labels=c("No","No","Yes"))
label(dat$AGE1) <- "Age (y)"
label(dat$sex) <- "Sex"
label(dat$dm) <- "Diabetes status"
label(dat$SBP1) <- "Systolic blood pressure (mm Hg) "
label(dat$TC1) <- "Total cholesterol (ng/ml)"
label(dat$HDL1) <- "High-density lipoprotein (ng/ml)"
label(dat$smoke) <- "Smoking Status"
label(dat$CALC_LDL1) <- "Low-density lipoprotein (ng/ml)"
label(dat$bp_med) <- "Anti-hypertensive"
label(dat$prscat) <- "Polygenic score category"
table1(~ AGE1 + sex + SBP1 + TC1 + HDL1 + smoke +prscat+
CALC_LDL1 + bp_med , data=dat)| Overall (N=2656) |
|
|---|---|
| Age (y) | |
| Mean (SD) | 34.2 (9.57) |
| Median [Min, Max] | 33.0 [9.00, 65.0] |
| Sex | |
| Male | 1253 (47.2%) |
| Female | 1403 (52.8%) |
| Systolic blood pressure (mm Hg) | |
| Mean (SD) | 120 (14.5) |
| Median [Min, Max] | 118 [78.0, 209] |
| Total cholesterol (ng/ml) | |
| Mean (SD) | 194 (37.0) |
| Median [Min, Max] | 190 [103, 394] |
| High-density lipoprotein (ng/ml) | |
| Mean (SD) | 52.6 (15.9) |
| Median [Min, Max] | 51.0 [14.0, 126] |
| Smoking Status | |
| Never | 1589 (59.8%) |
| Current | 1067 (40.2%) |
| Polygenic score category | |
| Low | 535 (20.1%) |
| Intermediate | 1590 (59.9%) |
| High | 531 (20.0%) |
| Low-density lipoprotein (ng/ml) | |
| Mean (SD) | 124 (35.2) |
| Median [Min, Max] | 120 [33.0, 326] |
| Anti-hypertensive | |
| No | 2595 (97.7%) |
| Yes | 61 (2.3%) |
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] table1_1.4.3
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 Formula_1.2-4 rstudioapi_0.14 knitr_1.40
[5] magrittr_2.0.3 workflowr_1.7.0 R6_2.5.1 rlang_1.0.6
[9] fastmap_1.1.0 fansi_1.0.3 stringr_1.4.1 tools_4.2.1
[13] xfun_0.33 utf8_1.2.2 cli_3.4.1 git2r_0.30.1
[17] jquerylib_0.1.4 htmltools_0.5.3 rprojroot_2.0.3 yaml_2.3.5
[21] digest_0.6.29 tibble_3.1.8 lifecycle_1.0.3 later_1.3.0
[25] sass_0.4.2 vctrs_0.5.2 promises_1.2.0.1 fs_1.5.2
[29] cachem_1.0.6 glue_1.6.2 evaluate_0.17 rmarkdown_2.17
[33] stringi_1.7.8 bslib_0.4.0 compiler_4.2.1 pillar_1.8.1
[37] jsonlite_1.8.2 httpuv_1.6.6 pkgconfig_2.0.3