OSCR

Race and Ethnicity, Hypertension, and Neuroimaging Markers of Brain Aging: A Causal Mediation Analysis in the HABS-HD Study.

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The 3 matches
  1. [1] § Methods › Covariates of Interest ↔ 2. descriptive_11 18 25.Rmd, lines 220–333 · score 0.96 · Cardiovascular disease, Geriatric Depression, medical history, Body mass, intracranial volume, hemoglobin A1c
  2. [2] § Methods › Statistical Analysis › Generating Exposure and Mediator IPWs ↔ 2. descriptive_11 18 25.Rmd, lines 220–333 · score 0.73 · tau PET SUVR, eGFR, amyloid PET, physical activity, diagnosis, glucose
  3. [3] § Methods › Statistical Analysis › Marginal Structural Models ↔ 2. descriptive_11 18 25.Rmd, lines 334–447 · score 0.67 · eGFR, social support, physical activity, diagnoses, depressive, glucose

Paper

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The authors' code

R Markdown · 738 lines · 31 KB · no license · 3 matches

  1. ---
  2. title: "2. descriptive_02 27 25"
  3. output: html_document
  4. date: "2025-02-27"
  5. ---
  6. # Loading packages
  7. ```{r packages, include = FALSE}
  8. library(haven) # inputting data
  9. library(tidyverse) # data mgmt
  10. library(psych) # easy summary statistics
  11. library(DT) # data tables
  12. library(tableone) # easy table 1
  13. library(kableExtra) # format kable objects
  14. library(naniar) # for missingness
  15. library(survey) # for MSMs
  16. library(nnet) # multinormal regression
  17. library(cobalt) # examining covariate balance
  18. library(gt) # nice tables
  19. library(gtsummary) # nice tables
  20. library(broom) # making output nice
  21. library(sjPlot) # nice cross tabs
  22. library(openxlsx) # outputting excel sheets
  23. library(ggcorrplot) # correlation plots
  24. library(reshape2) # reshaping
  25. library(RColorBrewer) # color pallettes
  26. options(max.print=100000)
  27. ```
  28. ```{css scroll box for code, include = FALSE}
  29. pre {
  30. max-height: 300px;
  31. overflow-y: auto;
  32. }
  34. pre[class] {
  35. max-height: 100px;
  36. }
  37. ```
  39. # Loading data
  40. ```{r data, include = FALSE}
  41. load(file = "./analysis data/causmed.Rdata") # n=3592, v=68
  42. names(causmed) %>% view()
  43. ```
  45. ## Creating time difference variable
  46. With age1 and age2 as the main time variable.
  47. ```{r time diff, include = FALSE}
  48. causmed2 <- causmed %>%
  49. mutate(agediff12 = age_2 - age_1) %>%
  50. mutate(gender_fct = case_when(
  51. gender == 0 ~ "Men",
  52. gender == 1 ~ "Women",
  53. TRUE ~ NA)) %>%
  54. mutate(gender_fct = factor(gender_fct,
  55. levels = c("Men", "Women"))) %>%
  56. mutate(haswmhv2_fct = case_when(
  57. haswmhv2 == 1 ~ "Has WMHV at Visit 2",
  58. haswmhv2 == 0 ~ "No WMHV at Visit 2",
  59. TRUE ~ NA)) %>%
  60. mutate(haswmhv2_fct = factor(haswmhv2_fct,
  61. levels = c("Has WMHV at Visit 2",
  62. "No WMHV at Visit 2"))) %>%
  63. mutate(hasabv2_fct = case_when(
  64. hasabv2 == 1 ~ "Has Amyloid-PET at Visit 2",
  65. hasabv2 == 0 ~ "No Amyloid-PET at Visit 2",
  66. TRUE ~ NA)) %>%
  67. mutate(hasabv2_fct = factor(hasabv2_fct,
  68. levels = c("Has Amyloid-PET at Visit 2",
  69. "No Amyloid-PET at Visit 2"))) %>%
  70. mutate(hasmttau2_fct = case_when(
  71. hasmttauv2 == 1 ~ "Has MTL Tau-PET at Visit 2",
  72. hasmttauv2 == 0 ~ "No MTL Tau-PET at Visit 2",
  73. TRUE ~ NA)) %>%
  74. mutate(hasmttau2_fct = factor(hasmttau2_fct,
  75. levels = c("Has MTL Tau-PET at Visit 2",
  76. "No MTL Tau-PET at Visit 2"))) %>%
  77. mutate(hasctv2_fct = case_when(
  78. hasctv2 == 1 ~ "Has AD meta-ROI CT at Visit 2",
  79. hasctv2 == 0 ~ "No AD meta-ROI CT at Visit 2",
  80. TRUE ~ NA)) %>%
  81. mutate(hasctv2_fct = factor(hasctv2_fct,
  82. levels = c("Has AD meta-ROI CT at Visit 2",
  83. "No AD meta-ROI CT at Visit 2"))) %>%
  84. mutate(icv_1_cm3 = (icv_1/1000)) %>%
  85. mutate(takes_BP_meds = factor(takes_BP_meds,
  86. levels = c(0, 1))) %>%
  87. mutate(has_img1 = case_when(
  88. (hasabv1 == 1 | hasmttauv1 == 1 | hasctv1 == 1 | haswmhv1 == 1) ~ 1,
  89. (hasabv1 == 0 & hasmttauv1 == 0 & hasctv1 == 0 & haswmhv1 == 0) ~ 0,
  90. TRUE ~ NA)) %>%
  91. mutate(has_img2 = case_when(
  92. (hasabv2 == 1 | hasmttauv2 == 1 | hasctv2 == 1 | haswmhv2 == 1) ~ 1,
  93. (hasabv2 == 0 & hasmttauv2 == 0 & hasctv2 == 0 & haswmhv2 == 0) ~ 0,
  94. TRUE ~ NA))
  96. # n=3592, v=76
  98. # checking variables
  99. summary(causmed2$haswmhv2_fct)
  100. summary(causmed2$hasabv2_fct)
  101. summary(causmed2$hasmttau2_fct)
  102. summary(causmed2$hasctv2_fct)
  103. summary(causmed2$icv_1_cm3)
  104. summary(as.factor(causmed2$has_img1))
  105. summary(as.factor(causmed2$has_img2))
  106. ```
  108. # Table 1, with any imaging, by race/ethnicity
  109. ```{r table 1, include = FALSE}
  110. tbl1_img <- causmed2 %>%
  111. filter(has_img2 == 1) %>%
  112. tbl_summary(include = c(# demographics
  113. age_1, gender_fct, agediff12,
  114. # social
  115. edu, hasnoinsurance, income, socsupptot,
  116. # clinical/behavioral variables
  117. smkever, bmi, sbpavg, dbpavg, rapa_1_total,
  118. rapa_2_total, gds_total, takes_BP_meds, has_htn,
  119. # medical history
  120. cdx_dep, cdx_cog, cdx_dyslipid, cdx_cvd, cdx_dm,
  121. # labs
  122. ldl, gluc, a1c, egfr_nonaa, choltot,
  123. apoe4_positivity,
  124. # imaging vars
  125. wmhv_2, absuvr_2, taumedtempsuvr_2, ctmetaroi_2,
  126. icv_1_cm3),
  127. by = ethnicity,
  128. type = list(
  129. age_1 ~ "continuous",
  130. gender_fct ~ "categorical",
  131. edu ~ "continuous",
  132. agediff12 ~ "continuous",
  133. hasnoinsurance ~ "dichotomous",
  134. income ~ "continuous",
  135. socsupptot ~ "continuous",
  136. smkever ~ "dichotomous",
  137. bmi ~ "continuous",
  138. sbpavg ~ "continuous",
  139. dbpavg ~ "continuous",
  140. rapa_1_total ~ "continuous",
  141. rapa_2_total ~ "continuous",
  142. gds_total ~ "continuous",
  143. takes_BP_meds ~ "dichotomous",
  144. has_htn ~ "dichotomous",
  145. cdx_dep ~ "dichotomous",
  146. cdx_cog ~ "dichotomous",
  147. cdx_dyslipid ~ "dichotomous",
  148. cdx_cvd ~ "dichotomous",
  149. cdx_dm ~ "dichotomous",
  150. ldl ~ "continuous",
  151. gluc ~ "continuous",
  152. a1c ~ "continuous",
  153. egfr_nonaa ~ "continuous",
  154. choltot ~ "continuous",
  155. apoe4_positivity ~ "dichotomous",
  156. wmhv_2 ~ "continuous",
  157. absuvr_2 ~ "continuous",
  158. taumedtempsuvr_2 ~ "continuous",
  159. ctmetaroi_2 ~ "continuous",
  160. icv_1_cm3 ~ "continuous"),
  161. value = list(
  162. hasnoinsurance ~ 1,
  163. takes_BP_meds ~ 1,
  164. has_htn ~ 1,
  165. cdx_dep ~ 1,
  166. cdx_cog ~ 1,
  167. cdx_dyslipid ~ 1,
  168. cdx_cvd ~ 1,
  169. cdx_dm ~ 1,
  170. apoe4_positivity ~ 1),
  171. statistic = list(
  172. all_continuous() ~ "{mean} ({sd})",
  173. all_dichotomous() ~ "{n} ({p}%)",
  174. all_categorical() ~ "{n} ({p}%)",
  175. wmhv_2 ~ "{median} ({p25}, {p75})",
  176. absuvr_2 ~ "{median} ({p25}, {p75})",
  177. taumedtempsuvr_2 ~ "{median} ({p25}, {p75})"),
  178. label = list(
  179. age_1 = "Age At Baseline (Years)",
  180. ethnicity = "Race/Ethnicity",
  181. gender_fct = "Sex/Gender",
  182. edu = "Education (Years)",
  183. agediff12 = "Years Between Visit 1 and 2",
  184. hasnoinsurance = "Has No Insurance",
  185. income = "Income (Dollars)",
  186. socsupptot = "Social Support Score",
  187. smkever = "Ever Smoker",
  188. bmi = "Body Mass Index",
  189. sbpavg = "Systolic Blood Pressure (mmHg)",
  190. dbpavg = "Diastolic Blood Pressure (mmHg)",
  191. rapa_1_total = "Rapid Assessment of Physical Activity Score, Aerobic",
  192. rapa_2_total = "Rapid Assessment of Physical Activity Score, Strength and Flexibility",
  193. gds_total = "Geriatric Depression Score",
  194. takes_BP_meds = "Taking Blood Pressure Medications at Baseline",
  195. has_htn = "Hypertension",
  196. cdx_dep = "Depression",
  197. cdx_cog = "Mild Cognitive Impairment",
  198. cdx_dyslipid = "Dyslipidemia",
  199. cdx_cvd = "Cardiovascular Disease",
  200. cdx_dm = "Diabetes",
  201. ldl = "LDL (mg/dL)",
  202. gluc = "Glucose (mg/dL)",
  203. a1c = "Hemoglobin A1c (%)",
  204. egfr_nonaa = "eGFR (non-African American, mL/min/1.73m^2)",
  205. choltot = "Total Cholesterol (mg/dL)",
  206. apoe4_positivity = "APOE4 Allele Positive",
  207. wmhv_2 = "White Matter Hyperintensity Volume (cm^3)",
  208. absuvr_2 = "Global Amyloid-PET SUVR",
  209. taumedtempsuvr_2 = "MTL Tau-PET SUVR",
  210. ctmetaroi_2 = "AD meta-ROI Cortical Thickness (mm)",
  211. icv_1_cm3 = "Intracranial Volume (cm^3)"),
  212. missing = "no")
  213. tbl1_img
  215. tbl1_img %>%
  216. as_gt() %>%
  217. gtsave("./results/tbl1_img.docx")
  218. ```
  220. # Table 1, for those with WMHV at visit 2
  221. ```{r table 1 wmhv, include = FALSE}
  222. tbl1_wmhv <- causmed2 %>%
  223. filter(haswmhv2_fct == "Has WMHV at Visit 2") %>%
  224. tbl_summary(include = c(# demographics
  225. age_1, gender_fct, agediff12,
  226. # social
  227. edu, hasnoinsurance, income, socsupptot,
  228. # clinical/behavioral variables
  229. smkever, bmi, sbpavg, dbpavg, rapa_1_total,
  230. rapa_2_total, gds_total, takes_BP_meds, has_htn,
  231. # medical history
  232. cdx_dep, cdx_cog, cdx_dyslipid, cdx_cvd, cdx_dm,
  233. # labs
  234. ldl, gluc, a1c, egfr_nonaa, choltot,
  235. apoe4_positivity,
  236. # imaging vars
  237. wmhv_2, absuvr_2, taumedtempsuvr_2, ctmetaroi_2,
  238. icv_1_cm3),
  239. by = ethnicity,
  240. type = list(
  241. age_1 ~ "continuous",
  242. gender_fct ~ "categorical",
  243. edu ~ "continuous",
  244. agediff12 ~ "continuous",
  245. hasnoinsurance ~ "dichotomous",
  246. income ~ "continuous",
  247. socsupptot ~ "continuous",
  248. smkever ~ "dichotomous",
  249. bmi ~ "continuous",
  250. sbpavg ~ "continuous",
  251. dbpavg ~ "continuous",
  252. rapa_1_total ~ "continuous",
  253. rapa_2_total ~ "continuous",
  254. gds_total ~ "continuous",
  255. takes_BP_meds ~ "dichotomous",
  256. has_htn ~ "dichotomous",
  257. cdx_dep ~ "dichotomous",
  258. cdx_cog ~ "dichotomous",
  259. cdx_dyslipid ~ "dichotomous",
  260. cdx_cvd ~ "dichotomous",
  261. cdx_dm ~ "dichotomous",
  262. ldl ~ "continuous",
  263. gluc ~ "continuous",
  264. a1c ~ "continuous",
  265. egfr_nonaa ~ "continuous",
  266. choltot ~ "continuous",
  267. apoe4_positivity ~ "dichotomous",
  268. wmhv_2 ~ "continuous",
  269. absuvr_2 ~ "continuous",
  270. taumedtempsuvr_2 ~ "continuous",
  271. ctmetaroi_2 ~ "continuous",
  272. icv_1_cm3 ~ "continuous"),
  273. value = list(
  274. hasnoinsurance ~ 1,
  275. takes_BP_meds ~ 1,
  276. has_htn ~ 1,
  277. cdx_dep ~ 1,
  278. cdx_cog ~ 1,
  279. cdx_dyslipid ~ 1,
  280. cdx_cvd ~ 1,
  281. cdx_dm ~ 1,
  282. apoe4_positivity ~ 1),
  283. statistic = list(
  284. all_continuous() ~ "{mean} ({sd})",
  285. all_dichotomous() ~ "{n} ({p}%)",
  286. all_categorical() ~ "{n} ({p}%)",
  287. wmhv_2 ~ "{median} ({p25}, {p75})",
  288. absuvr_2 ~ "{median} ({p25}, {p75})",
  289. taumedtempsuvr_2 ~ "{median} ({p25}, {p75})"),
  290. label = list(
  291. age_1 = "Age At Baseline (Years)",
  292. gender_fct = "Sex/Gender",
  293. edu = "Education (Years)",
  294. agediff12 = "Years Between Visit 1 and 2",
  295. hasnoinsurance = "Has No Insurance",
  296. income = "Income (Dollars)",
  297. socsupptot = "Social Support Score",
  298. smkever = "Ever Smoker",
  299. bmi = "Body Mass Index",
  300. sbpavg = "Systolic Blood Pressure (mmHg)",
  301. dbpavg = "Diastolic Blood Pressure (mmHg)",
  302. rapa_1_total = "Rapid Assessment of Physical Activity Score, Aerobic",
  303. rapa_2_total = "Rapid Assessment of Physical Activity Score, Strength and Flexibility",
  304. gds_total = "Geriatric Depression Score",
  305. takes_BP_meds = "Taking Blood Pressure Medications at Baseline",
  306. has_htn = "Hypertension",
  307. cdx_dep = "Depression",
  308. cdx_cog = "Mild Cognitive Impairment",
  309. cdx_dyslipid = "Dyslipidemia",
  310. cdx_cvd = "Cardiovascular Disease",
  311. cdx_dm = "Diabetes",
  312. ldl = "LDL (mg/dL)",
  313. gluc = "Glucose (mg/dL)",
  314. a1c = "Hemoglobin A1c (%)",
  315. egfr_nonaa = "eGFR (mL/min/1.73m^2)",
  316. choltot = "Total Cholesterol (mg/dL)",
  317. apoe4_positivity = "APOE4 Allele Positive",
  318. wmhv_2 = "White Matter Hyperintensity Volume (cm^3)",
  319. absuvr_2 = "Global Amyloid-PET SUVR",
  320. taumedtempsuvr_2 = "MTL Tau-PET SUVR",
  321. ctmetaroi_2 = "AD meta-ROI Cortical Thickness (mm)",
  322. icv_1_cm3 = "Intracranial Volume (cm^3)"),
  323. missing = "no")
  324. tbl1_wmhv
  326. # Cross-tab of race/ethnicity and HTN diagnosis among those with WMHV at visit 2
  327. wmhv <- causmed2 %>% filter(haswmhv2 == 1)
  328. tab_xtab(var.row = as.factor(wmhv$has_htn),
  329. var.col = as.factor(wmhv$ethnicity),
  330. show.row.prc = TRUE,
  331. show.col.prc = TRUE,
  332. show.na = TRUE)
  333. ```
  334. # Table 1, for those with amyloid PET at visit 2
  335. ```{r table 1 ab, include = FALSE}
  336. tbl1_ab <- causmed2 %>%
  337. filter(hasabv2_fct == "Has Amyloid-PET at Visit 2") %>%
  338. tbl_summary(include = c(# demographics
  339. age_1, gender_fct, agediff12,
  340. # social
  341. edu, hasnoinsurance, income, socsupptot,
  342. # clinical/behavioral variables
  343. smkever, bmi, sbpavg, dbpavg, rapa_1_total,
  344. rapa_2_total, gds_total, takes_BP_meds, has_htn,
  345. # medical history
  346. cdx_dep, cdx_cog, cdx_dyslipid, cdx_cvd, cdx_dm,
  347. # labs
  348. ldl, gluc, a1c, egfr_nonaa, choltot,
  349. apoe4_positivity,
  350. # imaging vars
  351. wmhv_2, absuvr_2, taumedtempsuvr_2, ctmetaroi_2,
  352. icv_1_cm3),
  353. by = ethnicity,
  354. type = list(
  355. age_1 ~ "continuous",
  356. gender_fct ~ "categorical",
  357. edu ~ "continuous",
  358. agediff12 ~ "continuous",
  359. hasnoinsurance ~ "dichotomous",
  360. income ~ "continuous",
  361. socsupptot ~ "continuous",
  362. smkever ~ "dichotomous",
  363. bmi ~ "continuous",
  364. sbpavg ~ "continuous",
  365. dbpavg ~ "continuous",
  366. rapa_1_total ~ "continuous",
  367. rapa_2_total ~ "continuous",
  368. gds_total ~ "continuous",
  369. takes_BP_meds ~ "dichotomous",
  370. has_htn ~ "dichotomous",
  371. cdx_dep ~ "dichotomous",
  372. cdx_cog ~ "dichotomous",
  373. cdx_dyslipid ~ "dichotomous",
  374. cdx_cvd ~ "dichotomous",
  375. cdx_dm ~ "dichotomous",
  376. ldl ~ "continuous",
  377. gluc ~ "continuous",
  378. a1c ~ "continuous",
  379. egfr_nonaa ~ "continuous",
  380. choltot ~ "continuous",
  381. apoe4_positivity ~ "dichotomous",
  382. wmhv_2 ~ "continuous",
  383. absuvr_2 ~ "continuous",
  384. taumedtempsuvr_2 ~ "continuous",
  385. ctmetaroi_2 ~ "continuous",
  386. icv_1_cm3 ~ "continuous"),
  387. value = list(
  388. hasnoinsurance ~ 1,
  389. takes_BP_meds ~ 1,
  390. has_htn ~ 1,
  391. cdx_dep ~ 1,
  392. cdx_cog ~ 1,
  393. cdx_dyslipid ~ 1,
  394. cdx_cvd ~ 1,
  395. cdx_dm ~ 1,
  396. apoe4_positivity ~ 1),
  397. statistic = list(
  398. all_continuous() ~ "{mean} ({sd})",
  399. all_dichotomous() ~ "{n} ({p}%)",
  400. all_categorical() ~ "{n} ({p}%)",
  401. wmhv_2 ~ "{median} ({p25}, {p75})",
  402. absuvr_2 ~ "{median} ({p25}, {p75})",
  403. taumedtempsuvr_2 ~ "{median} ({p25}, {p75})"),
  404. label = list(
  405. age_1 = "Age At Baseline (Years)",
  406. gender_fct = "Sex/Gender",
  407. edu = "Education (Years)",
  408. agediff12 = "Years Between Visit 1 and 2",
  409. hasnoinsurance = "Has No Insurance",
  410. income = "Income (Dollars)",
  411. socsupptot = "Social Support Score",
  412. smkever = "Ever Smoker",
  413. bmi = "Body Mass Index",
  414. sbpavg = "Systolic Blood Pressure (mmHg)",
  415. dbpavg = "Diastolic Blood Pressure (mmHg)",
  416. rapa_1_total = "Rapid Assessment of Physical Activity Score, Aerobic",
  417. rapa_2_total = "Rapid Assessment of Physical Activity Score, Strength and Flexibility",
  418. gds_total = "Geriatric Depression Score",
  419. takes_BP_meds = "Taking Blood Pressure Medications at Baseline",
  420. has_htn = "Hypertension",
  421. cdx_dep = "Depression",
  422. cdx_cog = "Mild Cognitive Impairment",
  423. cdx_dyslipid = "Dyslipidemia",
  424. cdx_cvd = "Cardiovascular Disease",
  425. cdx_dm = "Diabetes",
  426. ldl = "LDL (mg/dL)",
  427. gluc = "Glucose (mg/dL)",
  428. a1c = "Hemoglobin A1c (%)",
  429. egfr_nonaa = "eGFR (mL/min/1.73m^2)",
  430. choltot = "Total Cholesterol (mg/dL)",
  431. apoe4_positivity = "APOE4 Allele Positive",
  432. wmhv_2 = "White Matter Hyperintensity Volume (cm^3)",
  433. absuvr_2 = "Global Amyloid-PET SUVR",
  434. taumedtempsuvr_2 = "MTL Tau-PET SUVR",
  435. ctmetaroi_2 = "AD meta-ROI Cortical Thickness (mm)",
  436. icv_1_cm3 = "Intracranial Volume (cm^3)"),
  437. missing = "no")
  438. tbl1_ab
  440. # Cross-tab of race/ethnicity and HTN diagnosis among those with WMHV at visit 2
  441. ab <- causmed2 %>% filter(hasabv2 == 1)
  442. tab_xtab(var.row = as.factor(ab$has_htn),
  443. var.col = as.factor(ab$ethnicity),
  444. show.row.prc = TRUE,
  445. show.col.prc = TRUE,
  446. show.na = TRUE)
  447. ```
  448. # Table 1, for those with MTL Tau-PET at visit 2
  449. ```{r table 1 wmhv, include = FALSE}
  450. tbl1_mttau <- causmed2 %>%
  451. filter(hasmttau2_fct == "Has MTL Tau-PET at Visit 2") %>%
  452. tbl_summary(include = c(# demographics
  453. age_1, gender_fct, agediff12,
  454. # social
  455. edu, hasnoinsurance, income, socsupptot,
  456. # clinical/behavioral variables
  457. smkever, bmi, sbpavg, dbpavg, rapa_1_total,
  458. rapa_2_total, gds_total, takes_BP_meds, has_htn,
  459. # medical history
  460. cdx_dep, cdx_cog, cdx_dyslipid, cdx_cvd, cdx_dm,
  461. # labs
  462. ldl, gluc, a1c, egfr_nonaa, choltot,
  463. apoe4_positivity,
  464. # imaging vars
  465. wmhv_2, absuvr_2, taumedtempsuvr_2, ctmetaroi_2,
  466. icv_1_cm3),
  467. by = ethnicity,
  468. type = list(
  469. age_1 ~ "continuous",
  470. gender_fct ~ "categorical",
  471. edu ~ "continuous",
  472. agediff12 ~ "continuous",
  473. hasnoinsurance ~ "dichotomous",
  474. income ~ "continuous",
  475. socsupptot ~ "continuous",
  476. smkever ~ "dichotomous",
  477. bmi ~ "continuous",
  478. sbpavg ~ "continuous",
  479. dbpavg ~ "continuous",
  480. rapa_1_total ~ "continuous",
  481. rapa_2_total ~ "continuous",
  482. gds_total ~ "continuous",
  483. takes_BP_meds ~ "dichotomous",
  484. has_htn ~ "dichotomous",
  485. cdx_dep ~ "dichotomous",
  486. cdx_cog ~ "dichotomous",
  487. cdx_dyslipid ~ "dichotomous",
  488. cdx_cvd ~ "dichotomous",
  489. cdx_dm ~ "dichotomous",
  490. ldl ~ "continuous",
  491. gluc ~ "continuous",
  492. a1c ~ "continuous",
  493. egfr_nonaa ~ "continuous",
  494. choltot ~ "continuous",
  495. apoe4_positivity ~ "dichotomous",
  496. wmhv_2 ~ "continuous",
  497. absuvr_2 ~ "continuous",
  498. taumedtempsuvr_2 ~ "continuous",
  499. ctmetaroi_2 ~ "continuous",
  500. icv_1_cm3 ~ "continuous"),
  501. value = list(
  502. hasnoinsurance ~ 1,
  503. takes_BP_meds ~ 1,
  504. has_htn ~ 1,
  505. cdx_dep ~ 1,
  506. cdx_cog ~ 1,
  507. cdx_dyslipid ~ 1,
  508. cdx_cvd ~ 1,
  509. cdx_dm ~ 1,
  510. apoe4_positivity ~ 1),
  511. statistic = list(
  512. all_continuous() ~ "{mean} ({sd})",
  513. all_dichotomous() ~ "{n} ({p}%)",
  514. all_categorical() ~ "{n} ({p}%)",
  515. wmhv_2 ~ "{median} ({p25}, {p75})",
  516. absuvr_2 ~ "{median} ({p25}, {p75})",
  517. taumedtempsuvr_2 ~ "{median} ({p25}, {p75})"),
  518. label = list(
  519. age_1 = "Age At Baseline (Years)",
  520. gender_fct = "Sex/Gender",
  521. edu = "Education (Years)",
  522. agediff12 = "Years Between Visit 1 and 2",
  523. hasnoinsurance = "Has No Insurance",
  524. income = "Income (Dollars)",
  525. socsupptot = "Social Support Score",
  526. smkever = "Ever Smoker",
  527. bmi = "Body Mass Index",
  528. sbpavg = "Systolic Blood Pressure (mmHg)",
  529. dbpavg = "Diastolic Blood Pressure (mmHg)",
  530. rapa_1_total = "Rapid Assessment of Physical Activity Score, Aerobic",
  531. rapa_2_total = "Rapid Assessment of Physical Activity Score, Strength and Flexibility",
  532. gds_total = "Geriatric Depression Score",
  533. takes_BP_meds = "Taking Blood Pressure Medications at Baseline",
  534. has_htn = "Hypertension",
  535. cdx_dep = "Depression",
  536. cdx_cog = "Mild Cognitive Impairment",
  537. cdx_dyslipid = "Dyslipidemia",
  538. cdx_cvd = "Cardiovascular Disease",
  539. cdx_dm = "Diabetes",
  540. ldl = "LDL (mg/dL)",
  541. gluc = "Glucose (mg/dL)",
  542. a1c = "Hemoglobin A1c (%)",
  543. egfr_nonaa = "eGFR (mL/min/1.73m^2)",
  544. choltot = "Total Cholesterol (mg/dL)",
  545. apoe4_positivity = "APOE4 Allele Positive",
  546. wmhv_2 = "White Matter Hyperintensity Volume (cm^3)",
  547. absuvr_2 = "Global Amyloid-PET SUVR",
  548. taumedtempsuvr_2 = "MTL Tau-PET SUVR",
  549. ctmetaroi_2 = "AD meta-ROI Cortical Thickness (mm)",
  550. icv_1_cm3 = "Intracranial Volume (cm^3)"),
  551. missing = "no")
  552. tbl1_mttau
  554. # Cross-tab of race/ethnicity and HTN diagnosis among those with tau at visit 2
  555. tau <- causmed2 %>% filter(hasmttauv2 == 1)
  556. tab_xtab(var.row = as.factor(tau$has_htn),
  557. var.col = as.factor(tau$ethnicity),
  558. show.row.prc = TRUE,
  559. show.col.prc = TRUE,
  560. show.na = TRUE)
  561. ```
  563. # Table 1, for those with AD meta-ROI CT at visit 2
  564. ```{r table 1 wmhv, include = FALSE}
  565. tbl1_adct <- causmed2 %>%
  566. filter(hasctv2_fct == "Has AD meta-ROI CT at Visit 2") %>%
  567. tbl_summary(include = c(# demographics
  568. age_1, gender_fct, agediff12,
  569. # social
  570. edu, hasnoinsurance, income, socsupptot,
  571. # clinical/behavioral variables
  572. smkever, bmi, sbpavg, dbpavg, rapa_1_total,
  573. rapa_2_total, gds_total, takes_BP_meds, has_htn,
  574. # medical history
  575. cdx_dep, cdx_cog, cdx_dyslipid, cdx_cvd, cdx_dm,
  576. # labs
  577. ldl, gluc, a1c, egfr_nonaa, choltot,
  578. apoe4_positivity,
  579. # imaging vars
  580. wmhv_2, absuvr_2, taumedtempsuvr_2, ctmetaroi_2,
  581. icv_1_cm3),
  582. by = ethnicity,
  583. type = list(
  584. age_1 ~ "continuous",
  585. gender_fct ~ "categorical",
  586. edu ~ "continuous",
  587. agediff12 ~ "continuous",
  588. hasnoinsurance ~ "dichotomous",
  589. income ~ "continuous",
  590. socsupptot ~ "continuous",
  591. smkever ~ "dichotomous",
  592. bmi ~ "continuous",
  593. sbpavg ~ "continuous",
  594. dbpavg ~ "continuous",
  595. rapa_1_total ~ "continuous",
  596. rapa_2_total ~ "continuous",
  597. gds_total ~ "continuous",
  598. takes_BP_meds ~ "dichotomous",
  599. has_htn ~ "dichotomous",
  600. cdx_dep ~ "dichotomous",
  601. cdx_cog ~ "dichotomous",
  602. cdx_dyslipid ~ "dichotomous",
  603. cdx_cvd ~ "dichotomous",
  604. cdx_dm ~ "dichotomous",
  605. ldl ~ "continuous",
  606. gluc ~ "continuous",
  607. a1c ~ "continuous",
  608. egfr_nonaa ~ "continuous",
  609. choltot ~ "continuous",
  610. apoe4_positivity ~ "dichotomous",
  611. wmhv_2 ~ "continuous",
  612. absuvr_2 ~ "continuous",
  613. taumedtempsuvr_2 ~ "continuous",
  614. ctmetaroi_2 ~ "continuous",
  615. icv_1_cm3 ~ "continuous"),
  616. value = list(
  617. hasnoinsurance ~ 1,
  618. takes_BP_meds ~ 1,
  619. has_htn ~ 1,
  620. cdx_dep ~ 1,
  621. cdx_cog ~ 1,
  622. cdx_dyslipid ~ 1,
  623. cdx_cvd ~ 1,
  624. cdx_dm ~ 1,
  625. apoe4_positivity ~ 1),
  626. statistic = list(
  627. all_continuous() ~ "{mean} ({sd})",
  628. all_dichotomous() ~ "{n} ({p}%)",
  629. all_categorical() ~ "{n} ({p}%)",
  630. wmhv_2 ~ "{median} ({p25}, {p75})",
  631. absuvr_2 ~ "{median} ({p25}, {p75})",
  632. taumedtempsuvr_2 ~ "{median} ({p25}, {p75})"),
  633. label = list(
  634. age_1 = "Age At Baseline (Years)",
  635. gender_fct = "Sex/Gender",
  636. edu = "Education (Years)",
  637. agediff12 = "Years Between Visit 1 and 2",
  638. hasnoinsurance = "Has No Insurance",
  639. income = "Income (Dollars)",
  640. socsupptot = "Social Support Score",
  641. smkever = "Ever Smoker",
  642. bmi = "Body Mass Index",
  643. sbpavg = "Systolic Blood Pressure (mmHg)",
  644. dbpavg = "Diastolic Blood Pressure (mmHg)",
  645. rapa_1_total = "Rapid Assessment of Physical Activity Score, Aerobic",
  646. rapa_2_total = "Rapid Assessment of Physical Activity Score, Strength and Flexibility",
  647. gds_total = "Geriatric Depression Score",
  648. takes_BP_meds = "Taking Blood Pressure Medications at Baseline",
  649. has_htn = "Hypertension",
  650. cdx_dep = "Depression",
  651. cdx_cog = "Mild Cognitive Impairment",
  652. cdx_dyslipid = "Dyslipidemia",
  653. cdx_cvd = "Cardiovascular Disease",
  654. cdx_dm = "Diabetes",
  655. ldl = "LDL (mg/dL)",
  656. gluc = "Glucose (mg/dL)",
  657. a1c = "Hemoglobin A1c (%)",
  658. egfr_nonaa = "eGFR (mL/min/1.73m^2)",
  659. choltot = "Total Cholesterol (mg/dL)",
  660. apoe4_positivity = "APOE4 Allele Positive",
  661. wmhv_2 = "White Matter Hyperintensity Volume (cm^3)",
  662. absuvr_2 = "Global Amyloid-PET SUVR",
  663. taumedtempsuvr_2 = "MTL Tau-PET SUVR",
  664. ctmetaroi_2 = "AD meta-ROI Cortical Thickness (mm)",
  665. icv_1_cm3 = "Intracranial Volume (cm^3)"),
  666. missing = "no")
  667. tbl1_adct
  669. # Cross-tab of race/ethnicity and HTN diagnosis among those with adct at visit 2
  670. ct <- causmed2 %>% filter(hasctv2 == 1)
  671. tab_xtab(var.row = as.factor(ct$has_htn),
  672. var.col = as.factor(ct$ethnicity),
  673. show.row.prc = TRUE,
  674. show.col.prc = TRUE,
  675. show.na = TRUE)
  676. ```
  678. # Merging table 1 across all samples
  679. ```{r table 1 testing, echo = T, results = 'hide'}
  680. tbl1_everything <- tbl_merge(tbls =
  681. list(tbl1_ab,
  682. tbl1_mttau,
  683. tbl1_adct,
  684. tbl1_wmhv ),
  685. tab_spanner = c("**Amyloid PET (n=679)**",
  686. "**MTL-Tau (n=408)**",
  687. "**AD meta-ROI CT (n=1005)**",
  688. "**WMHV (n=1333)**"))
  689. tbl1_everything
  691. # saving output
  692. tbl1_everything %>%
  693. as_gt() %>%
  694. gtsave("./results/tbl1_everything.docx")
  696. # Cross-tab of race/ethnicity and HTN diagnosis among those with any imaging at visit 2
  697. any <- causmed2 %>% filter(haswmhv2 == 1 | hasabv2 == 1 | hasmttauv2 == 1 | hasctv2 == 1) #n=1347
  699. tab_xtab(var.row = as.factor(any$has_htn),
  700. var.col = as.factor(any$ethnicity),
  701. #show.row.prc = TRUE,
  702. show.col.prc = TRUE,
  703. show.na = TRUE)
  705. # SBP stratified by race ethnicity
  706. describeBy(any$sbpavg, any$ethnicity)
  707. ```
  709. # Heatmap of correlation matrix of covariates
  710. ```{r corr}
  711. # Taking a look at all the non-categorical variables
  712. cov <- causmed2 %>%
  713. select(# demographics
  714. age_1, agediff12,
  715. # social
  716. edu, income, socsupptot,
  717. # clinical/behavioral variables
  718. smkever, bmi, sbpavg, dbpavg, rapa_1_total,
  719. rapa_2_total, gds_total,
  720. # labs
  721. ldl, gluc, a1c, egfr_nonaa, choltot,
  722. # imaging vars
  723. wmhv_2, absuvr_2, taumedtempsuvr_2, ctmetaroi_2,
  724. icv_1_cm3)
  725. str(cov)
  726. cor_mat <- cor(cov, use = "complete.obs")
  728. cor_p <- ggcorrplot(cor_mat, hc.order = FALSE, type = "lower",
  729. outline.col = "white",
  730. ggtheme = ggplot2::theme_gray,
  731. colors = c("#af8dc3", "white", "#7fbf7b"))
  732. cor_p
  734. ggsave("./results/cor_plot.jpeg",
  735. dpi = 400,
  736. plot = cor_p)
  737. ```

2. descriptive_11 18 25.Rmd at commit 2b1ca73, no license · at the source

Overview

Authors: Michelle Caunca1, Sirena Gutierrez2, Koral Wheeler3, Meredith N Braskie3, Jacqueline Torres2, Kristine Yaffe1,2,4
ORCID iDs: Sirena Gutierrez, Jacqueline Torres, Kristine Yaffe
  1. Department of Neurology, University of California, San Francisco
  2. Department of Epidemiology and Biostatistics, University of California, San Francisco
  3. Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey; and
  4. Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
Institutions: University of California, San Francisco (United States); University of Southern California (United States)
Journal: Neurology, volume 107, issue 1, article e218164
Dates: received 26 June 2025; accepted 8 April 2026; published online 12 June 2026; in print 14 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1212/wnl.0000000000218164 · PMID 42284535 · PMCID PMC13312934 · OpenAlex W7164485887
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Machine learning, Statistics, Preprocessing, fMRI & imaging
MeSH: Aging*, Brain*, Hypertension*, Aged, Black or African American, Cohort Studies, Female, Hispanic or Latino, Humans, Magnetic Resonance Imaging, Male, Mediation Analysis, Middle Aged, Neuroimaging, White, White Matter (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (UE5 NS070680)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

michelle-caunca/HABS_CM_RaceEthHTNImg

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2b1ca7397e214766fa4914b23bdbd9bae0aed3bf, 19 December 2025
Languages: R (11)
Size: 12 files, 11 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, 11 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (11 files), psych (11 files), tidyverse (11 files), ggplot2 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/michelle-caunca/HABS_CM_RaceEthHTNImg

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1212/wnl.0000000000218164.

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Version 2, 28 September 2026

  • Publisher: n/a → Lippincott Williams & Wilkins

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 16 MeSH terms, 1 funder, 47 references.

Cite

This paper

Caunca, M., Gutierrez, S., Wheeler, K., Braskie, M. N., Torres, J., & Yaffe, K. (2026). Race and Ethnicity, Hypertension, and Neuroimaging Markers of Brain Aging: A Causal Mediation Analysis in the HABS-HD Study. Neurology, 107(1), e218164. https://doi.org/10.1212/wnl.0000000000218164

BibTeX

@article{caunca2026race,
author = {Caunca, Michelle and Gutierrez, Sirena and Wheeler, Koral and Braskie, Meredith N and Torres, Jacqueline and Yaffe, Kristine},
title = {{Race and Ethnicity, Hypertension, and Neuroimaging Markers of Brain Aging: A Causal Mediation Analysis in the HABS-HD Study}},
journal = {Neurology},
year = {2026},
month = jun,
volume = {107},
number = {1},
pages = {e218164},
publisher = {Lippincott Williams \& Wilkins},
issn = {0028-3878},
doi = {10.1212/wnl.0000000000218164},
url = {https://doi.org/10.1212/wnl.0000000000218164},
pmid = {42284535},
pmcid = {PMC13312934}
}

RIS

TY - JOUR
AU - Caunca, Michelle
AU - Gutierrez, Sirena
AU - Wheeler, Koral
AU - Braskie, Meredith N
AU - Torres, Jacqueline
AU - Yaffe, Kristine
TI - Race and Ethnicity, Hypertension, and Neuroimaging Markers of Brain Aging: A Causal Mediation Analysis in the HABS-HD Study
T2 - Neurology
J2 - Neurology
PY - 2026
DA - 2026/06/12
VL - 107
IS - 1
SP - e218164
SN - 0028-3878
PB - Lippincott Williams & Wilkins
DO - 10.1212/wnl.0000000000218164
UR - https://doi.org/10.1212/wnl.0000000000218164
LA - en
ER -

CSL-JSON

{
"id": "10.1212/wnl.0000000000218164",
"type": "article-journal",
"title": "Race and Ethnicity, Hypertension, and Neuroimaging Markers of Brain Aging: A Causal Mediation Analysis in the HABS-HD Study",
"container-title": "Neurology",
"author": [
{
"family": "Caunca",
"given": "Michelle"
},
{
"family": "Gutierrez",
"given": "Sirena"
},
{
"family": "Wheeler",
"given": "Koral"
},
{
"family": "Braskie",
"given": "Meredith N"
},
{
"family": "Torres",
"given": "Jacqueline"
},
{
"family": "Yaffe",
"given": "Kristine"
}
],
"container-title-short": "Neurology",
"volume": "107",
"issue": "1",
"page": "e218164",
"DOI": "10.1212/wnl.0000000000218164",
"PMID": "42284535",
"PMCID": "PMC13312934",
"ISSN": "0028-3878",
"publisher": "Lippincott Williams & Wilkins",
"URL": "https://doi.org/10.1212/wnl.0000000000218164",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
12
]
]
}
}

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