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Chronic stress, social support, and domain-specific cognitive decline in HABS-HD.

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  1. [1] § METHODS › Cognitive assessment and diagnostic classification ↔ HABSHD_Github.Rmd, lines 70–84 · score 0.97 · Spanish English Verbal, Mini Mental State, WMS III, Logical Memory, Digit Symbol Substitution, Digit Span
  2. [2] § METHODS › Cognitive assessment and diagnostic classification ↔ HABSHD_Github.Rmd, lines 70–84 · score 0.94 · WMS III Logical, WMS III Digit, Delayed Recall, Immediate Recall, Digit Symbol Substitution, Digit Span

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The authors' code

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  1. ---
  2. title: "HABS-HD_2026 - Github"
  3. output: html_document
  4. date: "2026-05-01"
  5. ---
  7. ######CLEANING STEPS#######
  8. #Removing those with dementia at baseline (N=243) and all their subsequent visits
  9. ```{r}
  10. library(dplyr)
  12. # Find Med_IDs with CDX_Cog == 2 at Visit_ID == 1
  13. ids_to_remove <- df %>%
  14. filter(Visit_ID == 1, CDX_Cog == 2) %>%
  15. pull(Med_ID)
  17. # Remove all rows for these Med_IDs (all visits)
  18. df_nodementia <- df %>%
  19. filter(!Med_ID %in% ids_to_remove)
  20. #View(df_nodementia)
  21. #N=6360
  22. ```
  24. #Setting factors for comorbidities
  25. ```{r}
  26. # Convert gender to factor and relevel
  27. #IMH_Diabetes
  28. df_nodementia$Baseline_IMH_Diabetes <- factor(
  29. df_nodementia$Baseline_IMH_Diabetes,
  30. levels = c(0, 1),
  31. labels = c("No", "Yes")
  32. )
  33. df_nodementia$Baseline_IMH_Diabetes <- relevel(
  34. df_nodementia$Baseline_IMH_Diabetes, ref = "No"
  35. )
  37. #IMH_HeartAttack
  38. df_nodementia$Baseline_IMH_HeartAttack<- factor(
  39. df_nodementia$Baseline_IMH_HeartAttack,
  40. levels = c(0, 1),
  41. labels = c("No", "Yes")
  42. )
  43. df_nodementia$Baseline_IMH_HeartAttack <- relevel(
  44. df_nodementia$Baseline_IMH_HeartAttack, ref = "No"
  45. )
  47. #IMH_KidneyDisease
  48. df_nodementia$Baseline_IMH_KidneyDisease<- factor(
  49. df_nodementia$Baseline_IMH_KidneyDisease,
  50. levels = c(0, 1),
  51. labels = c("No", "Yes")
  52. )
  53. df_nodementia$Baseline_IMH_KidneyDisease <- relevel(
  54. df_nodementia$Baseline_IMH_KidneyDisease, ref = "No"
  55. )
  57. #IMH_Stroke
  58. df_nodementia$Baseline_IMH_Stroke<- factor(
  59. df_nodementia$Baseline_IMH_Stroke,
  60. levels = c(0, 1),
  61. labels = c("No", "Yes")
  62. )
  63. df_nodementia$Baseline_IMH_Stroke<- relevel(
  64. df_nodementia$Baseline_IMH_Stroke, ref = "No"
  65. )
  67. #View(df_nodementia)
  68. ```
  70. #Creating Cognitive Domains
  71. ```{r}
  72. #Global: Mini-Mental State Examination (MMSE)
  73. #Attention: WMS-III Digit Span, Trail Making Test A
  74. #Memory: Spanish-English Verbal Learning Test (SEVLT, Immediate Recall & Delayed Recall); WMS-III Logical Memory (Immediate & Delayed)
  75. #Executive: Digit Symbol Substitution, Trail Making Test B
  76. #Language: Animal Naming, FAS
  78. df_nodementia$AttentionDomain <- rowMeans(df_nodementia[, c("DS_ZScore", "Trails_A_ZScore")], na.rm = TRUE)
  79. df_nodementia$MemoryDomain <- rowMeans(df_nodementia[, c("SEVLT_T1235_ZScore", "SEVLT_DR_ZScore" , "LM1_AB_ZScore", "LM2_AB_ZScore")], na.rm = TRUE)
  80. df_nodementia$ExecutiveDomain <- rowMeans(df_nodementia[, c("Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore")], na.rm = TRUE)
  81. df_nodementia$LanguageDomain <- rowMeans(df_nodementia[, c("FAS_ZScore" , "Animal_ZScore")], na.rm = TRUE)
  83. #View(df_nodementia)
  84. ```
  86. #Creating Baseline Dataset
  87. ```{r}
  88. Baseline_df_nodementia <- df_nodementia %>%
  89. filter(Visit_ID == 1)
  90. #View(Baseline_df_nodementia )
  91. #3875
  92. ```
  94. #Downloading and Saving BaselineSheet
  95. ```{r}
  96. library(writexl)
  97. write_xlsx(Baseline_df_nodementia, "Baseline_df_nodementia.xlsx")
  98. ```
  100. #Renaming Baseline Variables
  101. ```{r}
  102. Baseline_df_nodementia <- Baseline_df_nodementia %>%
  103. # Add Baseline_ prefix to a long list of variables
  104. rename_with(.fn = ~paste0("Baseline_", .),
  105. .cols = c(
  106. Visit_ID, Visit_Date, Ethnicity, Age, ID_Gender, ID_Education, CDX_Cog, PSWQ_Total,
  107. SocialSupport_Total, ChronicStress_Total, GDS_Total,
  108. SEVLT_T1235_ZScore, SEVLT_DR_ZScore, LM1_AB_ZScore, LM2_AB_ZScore,
  109. Digit_Symbol_Substitution_ZScore, Trails_B_ZScore,
  110. FAS_ZScore, Animal_ZScore,
  111. CDP_Myocardial, CDP_HeartDisease, CDP_Stroke, CDP_MiniStroke, CDR_Sum, CDR_Global, MMSE_Total,
  112. PSWQ_1, PSWQ_2, PSWQ_3, PSWQ_4, PSWQ_5, PSWQ_6, PSWQ_7, PSWQ_8, PSWQ_9, PSWQ_10, PSWQ_11, PSWQ_12, PSWQ_13, PSWQ_14, PSWQ_15, PSWQ_16,
  113. SocialSupport_1, SocialSupport_2, SocialSupport_3, SocialSupport_4, SocialSupport_5, SocialSupport_6, SocialSupport_7, SocialSupport_8, SocialSupport_9,
  114. SocialSupport_10, SocialSupport_11, SocialSupport_12,
  115. ChronicStress_1, ChronicStress_1a, ChronicStress_1b, ChronicStress_2, ChronicStress_2a, ChronicStress_2b, ChronicStress_3, ChronicStress_3a, ChronicStress_3b,
  116. ChronicStress_4, ChronicStress_4a, ChronicStress_4b, ChronicStress_5, ChronicStress_5a, ChronicStress_5b, ChronicStress_6, ChronicStress_6a, ChronicStress_6b,
  117. ChronicStress_7, ChronicStress_7a, ChronicStress_7b, ChronicStress_8, ChronicStress_8a, ChronicStress_8b, ChronicStress_8c,
  118. SEVLT_T1_Total, SEVLT_T2_Total, SEVLT_T3_Total, SEVLT_T5_Total, SEVLT_T1235_Total, SEVLT_DR_Total,
  119. LM1_A_Total, LM1_B1_Total, LM1_B2_Total, LM1_AB_Total, LM2_A_Total, LM2_B_Total, LM2_AB_Total,
  120. DSF_Total, DSB_Total, DS_Total, DS_ZScore,
  121. Trails_A_Time, Trails_A_Errors, Trails_A_ZScore, Trails_B_Time, Trails_B_Errors, FAS_Total,
  122. GDS_Category, GDS_Score_D, GDS_Score_M, GDS_Score_A, GDS_Score_C,
  123. Animal_Total, Digit_Symbol_Substitution,
  124. CDX_Hypertension, CDX_Dyslipidemia, CDX_Diabetes, CDX_Hyperthyroid, CDX_Hypothyroid, CDX_CVD, CDX_Anemia, CDX_VitaminB12, CDX_Depression,
  125. CDX_Anxiety, CDX_Alcohol, CDX_Tobacco,
  126. AttentionDomain, MemoryDomain, ExecutiveDomain, LanguageDomain
  127. )
  128. )
  129. #View(Baseline_df_nodementia)
  130. ```
  132. #Merging back Baselinesheet with Full longitudinal sheet
  133. ```{r}
  134. library(dplyr)
  135. # Step 1: Merge baseline data into longitudinal follow-up dataset by Med_ID
  136. FULLMERGE_df_nodementia <- df_nodementia %>%
  137. left_join(
  138. Baseline_df_nodementia,
  139. by = "Med_ID"
  140. )
  141. #View(FULLMERGE_RP_HD_7_Clinical_Cleaned_071025_nodementia)
  142. #6354
  143. ```
  145. #Creating the time variable
  146. ```{r}
  147. FULLMERGE_df_nodementia <- FULLMERGE_df_nodementia %>%
  148. mutate(Time = Age - Baseline_Age)
  149. #View(FULLMERGE_df_nodementia)
  150. ```
  152. ##Table 1 - Baseline Demographics of Participants
  153. ```{r}
  154. # Open a file to save the output
  155. #sink("Table1_Cognition_Release7_Baseline_080525.txt")
  157. # Load the required package
  158. library(tableone)
  160. # Create the table for Visit_ID 1 (Baseline)
  161. Table1_Cognition_Release7_Baseline_080525 <- CreateTableOne(
  162. data = Baseline_df_nodementia, # Use the filtered data for baseline
  163. vars = c("Med_ID", "Baseline_Age", "Baseline_Ethnicity", "Baseline_ID_Gender", "Baseline_ID_Education",
  164. "Baseline_PSWQ_Total", "Baseline_GDS_Total", "Baseline_CDX_Cog", "Baseline_SocialSupport_Total",
  165. "Baseline_ChronicStress_Total",
  166. "Baseline_IMH_Diabetes", "Baseline_IMH_HeartAttack", "Baseline_IMH_KidneyDisease", "Baseline_IMH_Stroke", "Baseline_OM_BMI",
  167. "Baseline_AttentionDomain", "Baseline_MemoryDomain", "Baseline_LanguageDomain", "Baseline_ExecutiveDomain"),
  168. test = TRUE,
  169. includeNA = TRUE
  170. )
  172. # Print the table to the output file
  173. print(Table1_Cognition_Release7_Baseline_080525)
  175. # Close the sink (finish writing to the file)
  176. #sink()
  177. ```
  179. #eTable 1 - Baseline Demographics of participants; Stratified by Gender
  180. ```{r}
  181. # Table 1 Stratified by Gender --------------------------------------------
  182. # Set the output file name
  183. output_file <- "Table1_BaselineDemographics_strgender_Release7_CognitiveDomains_UPDATED080525.txt"
  184. #sink(output_file)
  185. table1_STRGender_UPDATED080525 <- CreateTableOne(
  186. data = Baseline_df_nodementia,
  187. vars = c("Med_ID", "Baseline_Age", "Baseline_Ethnicity", "Baseline_ID_Education",
  188. "Baseline_PSWQ_Total", "Baseline_GDS_Total", "Baseline_CDX_Cog", "Baseline_SocialSupport_Total",
  189. "Baseline_ChronicStress_Total",
  190. "Baseline_IMH_Diabetes", "Baseline_IMH_HeartAttack", "Baseline_IMH_KidneyDisease", "Baseline_IMH_Stroke", "Baseline_OM_BMI",
  191. "Baseline_AttentionDomain", "Baseline_MemoryDomain", "Baseline_LanguageDomain", "Baseline_ExecutiveDomain"),
  192. strata = "Baseline_ID_Gender",
  193. test = TRUE,
  194. includeNA = TRUE
  195. )
  197. # Print the updated table
  198. print(table1_STRGender_UPDATED080525)
  200. #sink()
  201. ```
  203. #eTable 2 - Baseline Demographics of participants; Stratified by Race/Ethnicity
  204. ```{r}
  205. # Table 1 Stratified by Race --------------------------------------------
  206. # Set the output file name
  207. output_file <- "Table1_BaselineDemographics_strrace_Release7_CognitiveDomains_UPDATED080525.txt"
  208. sink(output_file)
  209. table1_STRRace_UPDATED080525 <- CreateTableOne(
  210. data = Baseline_df_nodementia,
  211. vars = c("Med_ID", "Baseline_Age", "Baseline_ID_Gender", "Baseline_ID_Education",
  212. "Baseline_PSWQ_Total", "Baseline_GDS_Total", "Baseline_CDX_Cog", "Baseline_SocialSupport_Total",
  213. "Baseline_ChronicStress_Total",
  214. "Baseline_IMH_Diabetes", "Baseline_IMH_HeartAttack", "Baseline_IMH_KidneyDisease", "Baseline_IMH_Stroke", "Baseline_OM_BMI",
  215. "Baseline_AttentionDomain", "Baseline_MemoryDomain", "Baseline_LanguageDomain", "Baseline_ExecutiveDomain"),
  216. strata = "Baseline_Ethnicity",
  217. test = TRUE,
  218. includeNA = TRUE
  219. )
  221. # Print the updated table
  222. print(table1_STRRace_UPDATED080525)
  223. sink()
  224. ```
  226. ##Setting reference groups for gender & race/ethnicity
  227. ```{r}
  228. # Convert to an unordered factor
  229. FULLMERGE_df_nodementia$Baseline_ID_Gender <- as.factor(FULLMERGE_df_nodementia$Baseline_ID_Gender)
  231. # Relevel to make "Non-Hispanic White" the reference group
  232. FULLMERGE_df_nodementia$Baseline_ID_Gender <- relevel(FULLMERGE_df_nodementia$Baseline_ID_Gender, ref = "Males")
  234. FULLMERGE_df_nodementia$Baseline_Ethnicity <- as.factor(FULLMERGE_df_nodementia$Baseline_Ethnicity)
  236. # Relevel to make "Non-Hispanic White" the reference group
  237. FULLMERGE_df_nodementia$Baseline_Ethnicity <- relevel(FULLMERGE_df_nodementia$Baseline_Ethnicity, ref = "White")
  240. ```
  242. ###Mixed Effects Model###
  243. ####CHRONIC STRESS#####
  245. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Unadjusted)
  246. #eTable 4 - Longitudinal Associations of Baseline Chronic Stress and Domain-Specific Cognitive Z-scores (Unadjusted)
  247. ##Chronic Stress - Unadjusted
  248. ```{r}
  250. library(dplyr)
  251. #library(lme4)
  252. library(lmerTest) #this package prints p values
  254. #sink("Mixed_Effect_Models_071225_ChronicStress_lme4_pvaluesUPDATED072125.txt")
  256. # Define the cognitive domain variables to loop through
  257. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  259. # Create an empty list to store the models
  260. models_chronicstress_unadjusted <- list()
  262. # Loop through the domains and fit the linear mixed-effects model for each
  263. for (domain in domains) {
  265. # Define the formula dynamically for each domain
  266. formula <- as.formula(paste(domain, "~ Baseline_ChronicStress_Total + Time + Baseline_ChronicStress_Total*Time + (1 | Med_ID)"))
  268. # Fit the linear mixed-effects model
  269. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  271. # Store the model in the list (use the correct list name here)
  272. models_chronicstress_unadjusted [[domain]] <- model
  274. # Optionally, print the summary of the model
  275. print(paste("Model for", domain))
  276. print(summary(model))
  277. }
  279. #sink()
  280. ```
  283. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Partial adjusted)
  284. #eTable 4 - Longitudinal Associations of Baseline Chronic Stress and Domain-Specific Cognitive Z-scores (Partial Adjusted)
  285. ##Chronic Stress - Partial Adjusted
  286. ```{r}
  288. library(dplyr)
  289. library(lme4)
  290. library(lmerTest) #this package prints p values
  292. #sink("Mixed_Effect_Models_071225_ChronicStress_adjusted_w_depression_lme4_pvalues_UPDATED072725.txt")
  294. # Define the cognitive domain variables to loop through
  295. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  297. # Create an empty list to store the models
  298. models_chronicstress_partialadjusted <- list()
  300. # Loop through the domains and fit the linear mixed-effects model for each
  301. for (domain in domains) {
  303. # Define the formula dynamically for each domain
  304. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_ChronicStress_Total + Time + Baseline_ChronicStress_Total*Time + (1 | Med_ID)"))
  306. # Fit the linear mixed-effects model
  307. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  309. # Store the model in the list (use the correct list name here)
  310. models_chronicstress_partialadjusted [[domain]] <- model
  312. # Optionally, print the summary of the model
  313. print(paste("Model for", domain))
  314. print(summary(model))
  315. }
  317. #sink()
  318. ```
  321. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Fully adjusted)
  322. #eTable 4 - Longitudinal Associations of Baseline Chronic Stress and Domain-Specific Cognitive Z-scores (Fully Adjusted)
  323. ##Chronic Stress - Fully Adjusted
  324. ```{r}
  326. library(dplyr)
  327. library(lme4)
  328. library(lmerTest) #this package prints p values
  330. #sink("Mixed_Effect_Models_071225_ChronicStress_fullyadjusted_lme4_pvalues_UPDATED072725.txt")
  332. # Define the cognitive domain variables to loop through
  333. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  335. # Create an empty list to store the models
  336. models_chronicstress_fullyadjusted <- list()
  338. # Loop through the domains and fit the linear mixed-effects model for each
  339. for (domain in domains) {
  341. # Define the formula dynamically for each domain
  342. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Time + Baseline_ChronicStress_Total*Time + (1 | Med_ID)"))
  344. # Fit the linear mixed-effects model
  345. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  347. # Store the model in the list (use the correct list name here)
  348. models_chronicstress_fullyadjusted[[domain]] <- model
  350. # Optionally, print the summary of the model
  351. print(paste("Model for", domain))
  352. print(summary(model))
  353. }
  355. #sink()
  356. ```
  359. ###Mixed Effects Model###
  360. ####SOCIAL SUPPORT#####
  362. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Unadjusted)
  363. #eTable 7 - Longitudinal Associations of Baseline Chronic Stress and Domain-Specific Cognitive Z-scores (Unadjusted)
  364. ##Social Support- Unadjusted
  365. ```{r}
  367. library(dplyr)
  368. library(lme4)
  369. library(lmerTest) #this package prints p values
  371. #sink("Mixed_Effect_Models_071225_SocialSupport_lme4_pvalues_UPDATED071625.txt")
  373. # Define the cognitive domain variables to loop through
  374. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  376. # Create an empty list to store the models
  377. models_socialsupport_unadjusted<- list()
  379. # Loop through the domains and fit the linear mixed-effects model for each
  380. for (domain in domains) {
  382. # Define the formula dynamically for each domain
  383. formula <- as.formula(paste(domain, "~ Baseline_SocialSupport_Total + Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  385. # Fit the linear mixed-effects model
  386. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  388. # Store the model in the list (use the correct list name here)
  389. models_socialsupport_unadjusted[[domain]] <- model
  391. # Optionally, print the summary of the model
  392. print(paste("Model for", domain))
  393. print(summary(model))
  394. }
  396. #sink()
  397. ```
  401. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Partial adjusted)
  402. #eTable 7 - Longitudinal Associations of Baseline Chronic Stress and Domain-Specific Cognitive Z-scores (Partial adjusted)
  403. ##Social Support- Partial Adjusted
  404. ```{r}
  406. library(dplyr)
  407. library(lme4)
  408. library(lmerTest) #this package prints p values
  410. sink("Mixed_Effect_Models_071225_SocialSupport_adjusted_w_depression_lme4_pvalues_UPDATED072725.txt")
  412. # Define the cognitive domain variables to loop through
  413. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  415. # Create an empty list to store the models
  416. models_socialsupport_partialadjusted <- list()
  418. # Loop through the domains and fit the linear mixed-effects model for each
  419. for (domain in domains) {
  421. # Define the formula dynamically for each domain
  422. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_SocialSupport_Total + Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  424. # Fit the linear mixed-effects model
  425. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  427. # Store the model in the list (use the correct list name here)
  428. models_socialsupport_partialadjusted[[domain]] <- model
  430. # Optionally, print the summary of the model
  431. print(paste("Model for", domain))
  432. print(summary(model))
  433. }
  435. sink()
  436. ```
  439. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Fully adjusted)
  440. #eTable 7 - Longitudinal Associations of Baseline Chronic Stress and Domain-Specific Cognitive Z-scores (Fully adjusted)
  441. ##Social Support- Fully Adjusted
  442. ```{r}
  444. library(dplyr)
  445. library(lme4)
  446. library(lmerTest) #this package prints p values
  448. sink("Mixed_Effect_Models_071225_SocialSupport_fullyadjusted_lme4_pvalues_UPDATED072725.txt")
  450. # Define the cognitive domain variables to loop through
  451. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  453. # Create an empty list to store the models
  454. models_socialsupport_fullyadjusted <- list()
  456. # Loop through the domains and fit the linear mixed-effects model for each
  457. for (domain in domains) {
  459. # Define the formula dynamically for each domain
  460. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_SocialSupport_Total + Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  462. # Fit the linear mixed-effects model
  463. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  465. # Store the model in the list (use the correct list name here)
  466. models_socialsupport_fullyadjusted[[domain]] <- model
  468. # Optionally, print the summary of the model
  469. print(paste("Model for", domain))
  470. print(summary(model))
  471. }
  473. sink()
  474. ```
  477. ####INTERACTION TERMS###
  478. #####CHRONIC STRESS######
  480. ##GENDER##
  481. #eTable 5- Interaction between Baseline Chronic Stress and Gender in relation to Domain-Specific Cognitive Z-scores (Unadjusted)
  482. #CS*GENDER*Time (Unadjusted)
  483. ```{r}
  484. ##Chronic Stress - Unadjusted - Interaction with Gender
  485. #_UPDATED071625
  486. library(dplyr)
  487. library(lmerTest) #this package prints p values
  489. sink("Mixed_Effect_Models_071225_Release7_ChronicStress_unadjustedgender_UPDATED071625.txt")
  491. # Define the cognitive domain variables to loop through
  492. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  494. # Create an empty list to store the models
  495. models_chronicstress_unadjusted_interactiongender <- list()
  497. # Loop through the domains and fit the linear mixed-effects model for each
  498. for (domain in domains) {
  500. # Define the formula dynamically for each domain
  501. formula <- as.formula(paste(domain, "~ Baseline_ChronicStress_Total + Baseline_ID_Gender + Time + Baseline_ChronicStress_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  503. # Fit the linear mixed-effects model
  504. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  506. # Store the model in the list (use the correct list name here)
  507. models_chronicstress_unadjusted_interactiongender[[domain]] <- model
  509. # Optionally, print the summary of the model
  510. print(paste("Model for", domain))
  511. print(summary(model))
  512. }
  514. sink()
  515. ```
  517. #eTable 5- Interaction between Baseline Chronic Stress and Gender in relation to Domain-Specific Cognitive Z-scores (Partial Adjusted)
  518. #CS*GENDER*Time (Partial Adjusted)
  519. ```{r}
  520. ##Chronic Stress - Adjusted - Interaction with Gender
  522. library(dplyr)
  523. library(lmerTest) #this package prints p values
  525. sink("Mixed_Effect_Models_071225_Release7_ChronicStress_adjustedgender_partialadjusted_UPDATED073025.txt")
  527. # Define the cognitive domain variables to loop through
  528. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  530. # Create an empty list to store the models
  531. models_chronicstress_partialadjusted_interactiongender <- list()
  533. # Loop through the domains and fit the linear mixed-effects model for each
  534. for (domain in domains) {
  536. # Define the formula dynamically for each domain
  537. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_ChronicStress_Total + Baseline_ID_Gender + Time + Baseline_ChronicStress_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  539. # Fit the linear mixed-effects model
  540. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  542. # Store the model in the list (use the correct list name here)
  543. models_chronicstress_partialadjusted_interactiongender[[domain]] <- model
  545. # Optionally, print the summary of the model
  546. print(paste("Model for", domain))
  547. print(summary(model))
  548. }
  550. sink()
  551. ```
  553. #eTable 5- Interaction between Baseline Chronic Stress and Gender in relation to Domain-Specific Cognitive Z-scores (Fully Adjusted)
  554. #CS*GENDER*Time (Fully Adjusted)
  555. ```{r}
  556. ##Chronic Stress - Adjusted - Interaction with Gender
  558. library(dplyr)
  559. library(lmerTest) #this package prints p values
  561. sink("Mixed_Effect_Models_071225_Release7_ChronicStress_adjustedgender_fullyadjusted_UPDATED073025.txt")
  563. # Define the cognitive domain variables to loop through
  564. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  566. # Create an empty list to store the models
  567. models_chronicstress_fullyadjusted_interactiongender <- list()
  569. # Loop through the domains and fit the linear mixed-effects model for each
  570. for (domain in domains) {
  572. # Define the formula dynamically for each domain
  573. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Baseline_ID_Gender + Time + Baseline_ChronicStress_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  575. # Fit the linear mixed-effects model
  576. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  578. # Store the model in the list (use the correct list name here)
  579. models_chronicstress_fullyadjusted_interactiongender[[domain]] <- model
  581. # Optionally, print the summary of the model
  582. print(paste("Model for", domain))
  583. print(summary(model))
  584. }
  586. sink()
  587. ```
  589. ##RACE##
  590. #Setting White as the reference group
  591. ```{r}
  592. # Convert to an unordered factor
  593. FULLMERGE_df_nodementia$Baseline_Ethnicity <- as.factor(FULLMERGE_df_nodementia$Baseline_Ethnicity)
  595. # Relevel to make "Non-Hispanic White" the reference group
  596. FULLMERGE_df_nodementia$Baseline_Ethnicity <- relevel(FULLMERGE_df_nodementia$Baseline_Ethnicity, ref = "White")
  598. ```
  600. #eTable 6 - Interaction between Baseline Chronic Stress and Race/Ethnicity in relation to Domain-Specific Cognitive Z-scores (Unadjusted)
  601. #CS*Race*Time (unadjusted)
  602. ```{r}
  603. ##Chronic Stress - Unadjusted - Interaction with Race
  605. library(dplyr)
  606. library(lmerTest) #this package prints p values
  608. sink("Mixed_Effect_Models_071225_ChronicStress_unadjustedrace_r7_UPDATED071625.txt")
  610. # Define the cognitive domain variables to loop through
  611. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  613. # Create an empty list to store the models
  614. models_chronicstress_unadjusted_interactionrace <- list()
  616. # Loop through the domains and fit the linear mixed-effects model for each
  617. for (domain in domains) {
  619. # Define the formula dynamically for each domain
  620. formula <- as.formula(paste(domain, "~ Baseline_ChronicStress_Total + Baseline_Ethnicity + Time + Baseline_ChronicStress_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  622. # Fit the linear mixed-effects model
  623. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  625. # Store the model in the list (use the correct list name here)
  626. models_chronicstress_unadjusted_interactionrace[[domain]] <- model
  628. # Optionally, print the summary of the model
  629. print(paste("Model for", domain))
  630. print(summary(model))
  631. }
  633. sink()
  634. ```
  636. #eTable 6 - Interaction between Baseline Chronic Stress and Race/Ethnicity in relation to Domain-Specific Cognitive Z-scores (Partial Adjusted)
  637. #CS*Race*Time (Partial Adjusted)
  638. ```{r}
  639. ##Chronic Stress - Partially Adjusted w. Depression - Interaction with Race
  640. library(dplyr)
  641. library(lmerTest) #this package prints p values
  643. sink("Mixed_Effect_Models_073025_ChronicStress_partialadjustedrace_r7_UPDATED073025.txt")
  645. # Define the cognitive domain variables to loop through
  646. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  648. # Create an empty list to store the models
  649. models_chronicstress_partialadjusted_interactionrace <- list()
  651. # Loop through the domains and fit the linear mixed-effects model for each
  652. for (domain in domains) {
  654. # Define the formula dynamically for each domain
  655. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_PSWQ_Total + Baseline_ID_Gender + Baseline_GDS_Total + Baseline_ChronicStress_Total + Baseline_Ethnicity + Time + Baseline_ChronicStress_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  657. # Fit the linear mixed-effects model
  658. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  660. # Store the model in the list (use the correct list name here)
  661. models_chronicstress_partialadjusted_interactionrace[[domain]] <- model
  663. # Optionally, print the summary of the model
  664. print(paste("Model for", domain))
  665. print(summary(model))
  666. }
  668. sink()
  669. ```
  671. #eTable 6 - Interaction between Baseline Chronic Stress and Race/Ethnicity in relation to Domain-Specific Cognitive Z-scores (Fully Adjusted)
  672. #CS*Race*Time (Fully Adjusted)
  673. ```{r}
  674. ##Chronic Stress - Fully Adjusted - Interaction with Race
  676. library(dplyr)
  677. library(lmerTest) #this package prints p values
  679. sink("Mixed_Effect_Models_073025_ChronicStress_fullyadjustedrace_r7_UPDATED073025.txt")
  681. # Define the cognitive domain variables to loop through
  682. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  684. # Create an empty list to store the models
  685. models_chronicstress_fullyjusted_interactionrace <- list()
  687. # Loop through the domains and fit the linear mixed-effects model for each
  688. for (domain in domains) {
  690. # Define the formula dynamically for each domain
  691. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_PSWQ_Total + Baseline_ID_Gender + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Baseline_Ethnicity + Time + Baseline_ChronicStress_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  693. # Fit the linear mixed-effects model
  694. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  696. # Store the model in the list (use the correct list name here)
  697. models_chronicstress_fullyjusted_interactionrace[[domain]] <- model
  699. # Optionally, print the summary of the model
  700. print(paste("Model for", domain))
  701. print(summary(model))
  702. }
  704. sink()
  705. ```
  707. ####INTERACTION TERMS###
  708. #####SOCIAL SUPPORT######
  710. ##GENDER##
  711. #eTable 8 - Interaction between Baseline Social Support and Gender in relation to Domain-Specific Cognitive Z-scores (Unadjusted)
  712. #SS*GENDER*Time (unadjusted)
  713. ```{r}
  714. ##Social Support - Unadjusted - Interaction with Gender
  716. library(dplyr)
  717. library(lmerTest) #this package prints p values
  719. sink("Mixed_Effect_Models_071225_Release7_SocialSupport_unadjustedgender_UPDATED071625.txt")
  721. # Define the cognitive domain variables to loop through
  722. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  724. # Create an empty list to store the models
  725. models_socialsupport_unadjusted_interactiongender<- list()
  727. # Loop through the domains and fit the linear mixed-effects model for each
  728. for (domain in domains) {
  730. # Define the formula dynamically for each domain
  731. formula <- as.formula(paste(domain, "~ Baseline_SocialSupport_Total + Baseline_ID_Gender + Time + Baseline_SocialSupport_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  733. # Fit the linear mixed-effects model
  734. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  736. # Store the model in the list (use the correct list name here)
  737. models_socialsupport_unadjusted_interactiongender[[domain]] <- model
  739. # Optionally, print the summary of the model
  740. print(paste("Model for", domain))
  741. print(summary(model))
  742. }
  744. sink()
  745. ```
  747. #eTable 8 - Interaction between Baseline Social Support and Gender in relation to Domain-Specific Cognitive Z-scores (Partial Adjusted)
  748. #SS*GENDER*Time (Partial Adjusted)
  749. ```{r}
  750. ##Social Support - Adjusted - Interaction with Gender
  752. library(dplyr)
  753. library(lmerTest) #this package prints p values
  755. sink("Mixed_Effect_Models_Release7_SocialSupport_partialadjustedgender_UPDATED080525.txt")
  757. # Define the cognitive domain variables to loop through
  758. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  760. # Create an empty list to store the models
  761. models_socialsupport_partialadjusted_interactiongender <- list()
  763. # Loop through the domains and fit the linear mixed-effects model for each
  764. for (domain in domains) {
  766. # Define the formula dynamically for each domain
  767. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_SocialSupport_Total + Baseline_ID_Gender + Time + Baseline_SocialSupport_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  769. # Fit the linear mixed-effects model
  770. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  772. # Store the model in the list (use the correct list name here)
  773. models_socialsupport_partialadjusted_interactiongender[[domain]] <- model
  775. # Optionally, print the summary of the model
  776. print(paste("Model for", domain))
  777. print(summary(model))
  778. }
  780. sink()
  781. ```
  783. #eTable 8 - Interaction between Baseline Social Support and Gender in relation to Domain-Specific Cognitive Z-scores (Fully Adjusted)
  784. #SS*GENDER*Time (Fully Adjusted)
  785. ```{r}
  786. ##Social Support - Adjusted - Interaction with Gender
  788. library(dplyr)
  789. library(lmerTest) #this package prints p values
  791. sink("Mixed_Effect_Models_Release7_SocialSupport_fullyadjustedgender_UPDATED080525.txt")
  793. # Define the cognitive domain variables to loop through
  794. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  796. # Create an empty list to store the models
  797. models_socialsupport_fullyadjusted_interactiongender <- list()
  799. # Loop through the domains and fit the linear mixed-effects model for each
  800. for (domain in domains) {
  802. # Define the formula dynamically for each domain
  803. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_SocialSupport_Total + Baseline_ID_Gender + Time + Baseline_SocialSupport_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  805. # Fit the linear mixed-effects model
  806. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  808. # Store the model in the list (use the correct list name here)
  809. models_socialsupport_fullyadjusted_interactiongender[[domain]] <- model
  811. # Optionally, print the summary of the model
  812. print(paste("Model for", domain))
  813. print(summary(model))
  814. }
  816. sink()
  817. ```
  819. ##RACE###
  821. #eTable 9 - Interaction between Baseline Social Support and Race/Ethnicity in relation to Domain-Specific Cognitive Z-scores (unadjusted)
  822. #SS*Race*Time (unadjusted)
  823. ```{r}
  824. ##Social Support - Unadjusted - Interaction with Race
  826. library(dplyr)
  827. library(lmerTest) #this package prints p values
  829. sink("Mixed_Effect_Models_071225_SocialSupport_unadjustedrace_r7_UPDATED071625.txt")
  831. # Define the cognitive domain variables to loop through
  832. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  834. # Create an empty list to store the models
  835. models_socialsupport_unadjusted_interactionrace <- list()
  837. # Loop through the domains and fit the linear mixed-effects model for each
  838. for (domain in domains) {
  840. # Define the formula dynamically for each domain
  841. formula <- as.formula(paste(domain, "~ Baseline_SocialSupport_Total + Baseline_Ethnicity + Time + Baseline_SocialSupport_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  843. # Fit the linear mixed-effects model
  844. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  846. # Store the model in the list (use the correct list name here)
  847. models_socialsupport_unadjusted_interactionrace[[domain]] <- model
  849. # Optionally, print the summary of the model
  850. print(paste("Model for", domain))
  851. print(summary(model))
  852. }
  854. sink()
  855. ```
  857. #eTable 9 - Interaction between Baseline Social Support and Race/Ethnicity in relation to Domain-Specific Cognitive Z-scores (Partial Adjusted)
  858. #SS*Race*Time (Partial Adjusted)
  859. ```{r}
  860. ##Social Support - Unadjusted - Interaction with Race
  862. library(dplyr)
  863. library(lmerTest) #this package prints p values
  865. sink("Mixed_Effect_Models_SocialSupport_partiallyadjustedrace_r7_UPDATED080525.txt")
  867. # Define the cognitive domain variables to loop through
  868. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  870. # Create an empty list to store the models
  871. models_socialsupport_partiallyadjusted_interactionrace <- list()
  873. # Loop through the domains and fit the linear mixed-effects model for each
  874. for (domain in domains) {
  876. # Define the formula dynamically for each domain
  877. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_ID_Gender + Baseline_SocialSupport_Total + Baseline_Ethnicity + Time + Baseline_SocialSupport_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  879. # Fit the linear mixed-effects model
  880. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  882. # Store the model in the list (use the correct list name here)
  883. models_socialsupport_partiallyadjusted_interactionrace[[domain]] <- model
  885. # Optionally, print the summary of the model
  886. print(paste("Model for", domain))
  887. print(summary(model))
  888. }
  890. sink()
  891. ```
  893. #eTable 9 - Interaction between Baseline Social Support and Race/Ethnicity in relation to Domain-Specific Cognitive Z-scores (Fully Adjusted)
  894. #SS*Race*Time (Fully Adjusted)
  895. ```{r}
  896. ##Social Support - Unadjusted - Interaction with Race
  898. library(dplyr)
  899. library(lmerTest) #this package prints p values
  901. sink("Mixed_Effect_Models_SocialSupport_fullyadjustedrace_r7_UPDATED080525.txt")
  903. # Define the cognitive domain variables to loop through
  904. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  906. # Create an empty list to store the models
  907. models_socialsupport_fullyadjusted_interactionrace<- list()
  909. # Loop through the domains and fit the linear mixed-effects model for each
  910. for (domain in domains) {
  912. # Define the formula dynamically for each domain
  913. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ID_Gender + Baseline_SocialSupport_Total + Baseline_Ethnicity + Time + Baseline_SocialSupport_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  915. # Fit the linear mixed-effects model
  916. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  918. # Store the model in the list (use the correct list name here)
  919. models_socialsupport_fullyadjusted_interactionrace[[domain]] <- model
  921. # Optionally, print the summary of the model
  922. print(paste("Model for", domain))
  923. print(summary(model))
  924. }
  926. sink()
  927. ```
  931. #####CHRONIC STRESS & SOCIAL SUPPORT - COMBINATION MODEL######
  932. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Unadjusted)
  933. #eTable 10 - Interrelationship of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Unadjusted)
  934. ##Chronic Stress & Social Support (Combination Model) (Unadjusted)
  935. ```{r}
  936. library(dplyr)
  937. library(lmerTest) #this package prints p values
  939. sink("Mixed_Effect_Models_071225_CS_SS_samemodel_unadjusted_Updated072125.txt")
  941. # Define the cognitive domain variables to loop through
  942. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  944. options(scipen = 999)
  946. # Create an empty list to store the models
  947. models_cs_ss_unadjusted <- list()
  949. # Loop through the domains and fit the linear mixed-effects model for each
  950. for (domain in domains) {
  952. # Define the formula dynamically for each domain
  953. formula <- as.formula(paste(domain, "~ Baseline_ChronicStress_Total + Baseline_SocialSupport_Total + Time + Baseline_ChronicStress_Total*Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  955. # Fit the linear mixed-effects model
  956. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  958. # Store the model in the list (use the correct list name here)
  959. models_cs_ss_unadjusted[[domain]] <- model
  961. # Optionally, print the summary of the model
  962. print(paste("Model for", domain))
  963. print(summary(model))
  964. }
  966. sink()
  967. ```
  969. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Partial Adjusted)
  970. #eTable 10 - Interrelationship of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Partial Adjusted)
  971. ##Chronic Stress & Social Support (Combination Model) (Partial Adjusted)
  972. ```{r}
  973. library(dplyr)
  974. library(lmerTest) #this package prints p values
  976. sink("Mixed_Effect_Models_CS_SS_samemodel_adjusted_w_Depression_Updated072725.txt")
  978. # Define the cognitive domain variables to loop through
  979. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  981. options(scipen = 999)
  983. # Create an empty list to store the models
  984. models_cs_ss_partialadjusted <- list()
  986. # Loop through the domains and fit the linear mixed-effects model for each
  987. for (domain in domains) {
  989. # Define the formula dynamically for each domain
  990. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_ChronicStress_Total + Baseline_SocialSupport_Total + Time + Baseline_ChronicStress_Total*Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  992. # Fit the linear mixed-effects model
  993. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  995. # Store the model in the list (use the correct list name here)
  996. models_cs_ss_partialadjusted[[domain]] <- model
  998. # Optionally, print the summary of the model
  999. print(paste("Model for", domain))
  1000. print(summary(model))
  1001. }
  1003. sink()
  1004. ```
  1006. #Table 2 - Longitudinal Associations of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Fully Adjusted)
  1007. #eTable 10 - Interrelationship of Baseline Chronic Stress, Social Support, and Domain-Specific Cognitive Z-scores (Fully Adjusted)
  1008. ##Chronic Stress & Social Support (Combination Model) (Fully Adjusted)
  1009. ```{r}
  1010. library(dplyr)
  1011. library(lmerTest) #this package prints p values
  1013. sink("Mixed_Effect_Models_CS_SS_samemodel_fullyadjusted_Updated072725.txt")
  1015. # Define the cognitive domain variables to loop through
  1016. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  1018. options(scipen = 999)
  1020. # Create an empty list to store the models
  1021. models_cs_ss_fullyadjusted <- list()
  1023. # Loop through the domains and fit the linear mixed-effects model for each
  1024. for (domain in domains) {
  1026. # Define the formula dynamically for each domain
  1027. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Baseline_SocialSupport_Total + Time + Baseline_ChronicStress_Total*Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  1029. # Fit the linear mixed-effects model
  1030. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  1032. # Store the model in the list (use the correct list name here)
  1033. models_cs_ss_fullyadjusted[[domain]] <- model
  1035. # Optionally, print the summary of the model
  1036. print(paste("Model for", domain))
  1037. print(summary(model))
  1038. }
  1040. sink()
  1041. ```
  1043. #Table 3 - Interaction between Baseline Chronic Stress, Social Support, and Gender in relation to Domain-Specific Cognitive Z-scores
  1044. #CS*GENDER*Time + SS*Gender*Time (Fully Adjusted)
  1045. ```{r}
  1046. ##CS SS Combo - Adjusted - Interaction with Gender
  1048. library(dplyr)
  1049. library(lmerTest) #this package prints p values
  1051. sink("Mixed_Effect_Models_071225_Release7_CS_SS_Combo_adjustedgender_fullyadjusted_UPDATED08052025.txt")
  1053. # Define the cognitive domain variables to loop through
  1054. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  1056. # Create an empty list to store the models
  1057. models_cs_ss_combo_fullyadjusted_interactiongender <- list()
  1059. # Loop through the domains and fit the linear mixed-effects model for each
  1060. for (domain in domains) {
  1062. # Define the formula dynamically for each domain
  1063. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Baseline_SocialSupport_Total + Baseline_ID_Gender + Time + Baseline_ChronicStress_Total*Baseline_ID_Gender*Time + Baseline_SocialSupport_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  1065. # Fit the linear mixed-effects model
  1066. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  1068. # Store the model in the list (use the correct list name here)
  1069. models_cs_ss_combo_fullyadjusted_interactiongender[[domain]] <- model
  1071. # Optionally, print the summary of the model
  1072. print(paste("Model for", domain))
  1073. print(summary(model))
  1074. }
  1076. sink()
  1077. ```
  1079. #Table 4 - Interaction between Baseline Chronic Stress, Social Support, and Race/Ethnicity in relation to Domain-Specific Cognitive Z-scores
  1080. #CS*Ethnicity*Time + SS*Ethnicity*Time (Fully Adjusted)
  1081. ```{r}
  1082. ##CS SS Combo - Adjusted - Interaction with Gender
  1084. library(dplyr)
  1085. library(lmerTest) #this package prints p values
  1087. sink("Mixed_Effect_Models_071225_Release7_CS_SS_Combo_adjustedethnicity_fullyadjusted_UPDATED08052025.txt")
  1089. # Define the cognitive domain variables to loop through
  1090. domains <- c("AttentionDomain", "MemoryDomain", "ExecutiveDomain", "LanguageDomain")
  1092. # Create an empty list to store the models
  1093. models_cs_ss_combo_fullyadjusted_interactionethnicity <- list()
  1095. # Loop through the domains and fit the linear mixed-effects model for each
  1096. for (domain in domains) {
  1098. # Define the formula dynamically for each domain
  1099. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Baseline_SocialSupport_Total + Baseline_Ethnicity + Time + Baseline_ChronicStress_Total*Baseline_Ethnicity*Time + Baseline_SocialSupport_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  1101. # Fit the linear mixed-effects model
  1102. model <- lmer(formula, data = FULLMERGE_df_nodementia)
  1104. # Store the model in the list (use the correct list name here)
  1105. models_cs_ss_combo_fullyadjusted_interactionethnicity[[domain]] <- model
  1107. # Optionally, print the summary of the model
  1108. print(paste("Model for", domain))
  1109. print(summary(model))
  1110. }
  1112. sink()
  1113. ```
  1115. #eTable 3. Distribution of Longitudinal Visits and Follow-Up Time
  1116. ```{r}
  1117. #Count visits per participant
  1118. visit_counts_012126 <- FULLMERGE_df_nodementia %>%
  1119. group_by(Med_ID) %>%
  1120. summarise(
  1121. n_visits = n_distinct(Visit_ID),
  1122. .groups = "drop"
  1123. )
  1124. print(visit_counts_012126)
  1126. visit_table_012126 <- visit_counts_012126 %>%
  1127. count(n_visits) %>%
  1128. arrange(n_visits) %>%
  1129. mutate(
  1130. percent = round(100 * n / sum(n), 1)
  1131. )
  1132. visit_table_012126
  1134. #1 Visit - 2191
  1135. #2 Visit - 1070
  1136. #3 Visit - 430
  1137. #4 Visit - 182
  1138. ```
  1141. #######
  1142. ##Running Individual tests
  1143. ```{r}
  1145. FULLMERGE_df_nodementia_individualtests <- FULLMERGE_df_nodementia %>%
  1146. select(
  1147. Visit_ID,
  1148. Med_ID,
  1149. AttentionDomain,
  1150. MemoryDomain,
  1151. ExecutiveDomain,
  1152. LanguageDomain,
  1153. DS_ZScore,
  1154. Trails_A_ZScore,
  1155. SEVLT_T1235_ZScore,
  1156. SEVLT_DR_ZScore,
  1157. LM1_AB_ZScore,
  1158. LM2_AB_ZScore,
  1159. Digit_Symbol_Substitution_ZScore,
  1160. Trails_B_ZScore,
  1161. FAS_ZScore,
  1162. Animal_ZScore
  1163. )
  1164. View(FULLMERGE_df_nodementia_individualtests)
  1166. library(dplyr)
  1167. FULLMERGE_df_nodementia_individualtests <-FULLMERGE_df_nodementia_individualtests %>%
  1168. dplyr::rename(
  1169. AttentionDomain_Check = AttentionDomain,
  1170. MemoryDomain_Check = MemoryDomain,
  1171. ExecutiveDomain_Check = ExecutiveDomain,
  1172. LanguageDomain_Check = LanguageDomain
  1173. )
  1175. View(FULLMERGE_df_nodementia_individualtests)
  1176. ```
  1179. ##Individual Tests
  1180. ##Chronic Stress - Fully Adjusted 05.07.2026
  1181. #eTable 11. Longitudinal Associations of Baseline Chronic Stress and Individual Cognitive Assessment Z-scores
  1182. ```{r}
  1184. library(dplyr)
  1185. library(lme4)
  1186. library(lmerTest) #this package prints p values
  1188. sink("Updatedreference_Mixed_Effect_IndividualTests_ChronicStress_Fullyadjusted_050726.txt")
  1190. # Define the cognitive domain variables to loop through
  1191. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1192. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1194. # Create an empty list to store the models
  1195. models_chronicstress_fullyadjusted_050726 <- list()
  1197. # Loop through the domains and fit the linear mixed-effects model for each
  1198. for (domain in domains) {
  1200. # Define the formula dynamically for each domain
  1201. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Time + Baseline_ChronicStress_Total*Time + (1 | Med_ID)"))
  1203. # Fit the linear mixed-effects model
  1204. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1206. # Store the model in the list (use the correct list name here)
  1207. models_chronicstress_fullyadjusted_050726[[domain]] <- model
  1209. # Optionally, print the summary of the model
  1210. print(paste("Model for", domain))
  1211. print(summary(model))
  1212. }
  1214. sink()
  1215. ```
  1217. ##Chronic Stress - Partial Adjusted 05.07.2026
  1218. #Individual Tests
  1219. #eTable 11. Longitudinal Associations of Baseline Chronic Stress and Individual Cognitive Assessment Z-scores
  1220. ```{r}
  1222. library(dplyr)
  1223. library(lme4)
  1224. library(lmerTest) #this package prints p values
  1226. sink("Mixed_Effect_IndividualTests_ChronicStress_partialadjusted_050726.txt")
  1228. # Define the cognitive domain variables to loop through
  1229. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1230. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1232. # Create an empty list to store the models
  1233. models_chronicstress_partialadjusted_050726 <- list()
  1235. # Loop through the domains and fit the linear mixed-effects model for each
  1236. for (domain in domains) {
  1238. # Define the formula dynamically for each domain
  1239. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_ChronicStress_Total + Time + Baseline_ChronicStress_Total*Time + (1 | Med_ID)"))
  1241. # Fit the linear mixed-effects model
  1242. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1244. # Store the model in the list (use the correct list name here)
  1245. models_chronicstress_partialadjusted_050726[[domain]] <- model
  1247. # Optionally, print the summary of the model
  1248. print(paste("Model for", domain))
  1249. print(summary(model))
  1250. }
  1252. sink()
  1253. ```
  1256. ##Chronic Stress - Unadjusted 05.07.2026
  1257. #Individual Tests
  1258. #eTable 11. Longitudinal Associations of Baseline Chronic Stress and Individual Cognitive Assessment Z-scores
  1259. ```{r}
  1261. library(dplyr)
  1262. library(lme4)
  1263. library(lmerTest) #this package prints p values
  1265. sink("Mixed_Effect_IndividualTests_ChronicStress_unadjusted_050726.txt")
  1267. # Define the cognitive domain variables to loop through
  1268. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1269. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1271. # Create an empty list to store the models
  1272. models_chronicstress_unadjusted_050726 <- list()
  1274. # Loop through the domains and fit the linear mixed-effects model for each
  1275. for (domain in domains) {
  1277. # Define the formula dynamically for each domain
  1278. formula <- as.formula(paste(domain, "~Baseline_ChronicStress_Total + Time + Baseline_ChronicStress_Total*Time + (1 | Med_ID)"))
  1280. # Fit the linear mixed-effects model
  1281. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1283. # Store the model in the list (use the correct list name here)
  1284. models_chronicstress_unadjusted_050726[[domain]] <- model
  1286. # Optionally, print the summary of the model
  1287. print(paste("Model for", domain))
  1288. print(summary(model))
  1289. }
  1291. sink()
  1292. ```
  1294. ##Individual Tests
  1295. ##Social Support - Fully Adjusted
  1296. #eTable 12. Longitudinal Associations of Baseline Social Support and Individual Cognitive Assessment Z-scores
  1297. ```{r}
  1299. library(dplyr)
  1300. library(lme4)
  1301. library(lmerTest) #this package prints p values
  1303. sink("Mixed_Effect_IndividualTests_SocialSupport_Fullyadjusted_050726.txt")
  1305. # Define the cognitive domain variables to loop through
  1306. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1307. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1309. # Create an empty list to store the models
  1310. models_socialsupport_fullyadjusted_050726 <- list()
  1312. # Loop through the domains and fit the linear mixed-effects model for each
  1313. for (domain in domains) {
  1315. # Define the formula dynamically for each domain
  1316. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_SocialSupport_Total + Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  1318. # Fit the linear mixed-effects model
  1319. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1321. # Store the model in the list (use the correct list name here)
  1322. models_socialsupport_fullyadjusted_050726[[domain]] <- model
  1324. # Optionally, print the summary of the model
  1325. print(paste("Model for", domain))
  1326. print(summary(model))
  1327. }
  1329. sink()
  1330. ```
  1332. ##Social - Partial Adjusted 05.07.2026
  1333. #Individual Tests
  1334. ##eTable 12. Longitudinal Associations of Baseline Social Support and Individual Cognitive Assessment Z-scores
  1335. ```{r}
  1337. library(dplyr)
  1338. library(lme4)
  1339. library(lmerTest) #this package prints p values
  1341. sink("Mixed_Effect_IndividualTests_SocialSupport_partialadjusted_050726.txt")
  1343. # Define the cognitive domain variables to loop through
  1344. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1345. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1347. # Create an empty list to store the models
  1348. models_socialsupport_partialadjusted_050726 <- list()
  1350. # Loop through the domains and fit the linear mixed-effects model for each
  1351. for (domain in domains) {
  1353. # Define the formula dynamically for each domain
  1354. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_ID_Gender + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_SocialSupport_Total + Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  1356. # Fit the linear mixed-effects model
  1357. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1359. # Store the model in the list (use the correct list name here)
  1360. models_socialsupport_partialadjusted_050726[[domain]] <- model
  1362. # Optionally, print the summary of the model
  1363. print(paste("Model for", domain))
  1364. print(summary(model))
  1365. }
  1367. sink()
  1368. ```
  1370. ##Social Support - Unadjusted 05.07.2026
  1371. #Individual Tests
  1372. ##eTable 12. Longitudinal Associations of Baseline Social Support and Individual Cognitive Assessment Z-scores
  1373. ```{r}
  1375. library(dplyr)
  1376. library(lme4)
  1377. library(lmerTest) #this package prints p values
  1379. sink("Mixed_Effect_IndividualTests_SocialSupport_unadjusted_050726.txt")
  1381. # Define the cognitive domain variables to loop through
  1382. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1383. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1385. # Create an empty list to store the models
  1386. models_socialsupport_unadjusted_050726 <- list()
  1388. # Loop through the domains and fit the linear mixed-effects model for each
  1389. for (domain in domains) {
  1391. # Define the formula dynamically for each domain
  1392. formula <- as.formula(paste(domain, "~Baseline_SocialSupport_Total + Time + Baseline_SocialSupport_Total*Time + (1 | Med_ID)"))
  1394. # Fit the linear mixed-effects model
  1395. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1397. # Store the model in the list (use the correct list name here)
  1398. models_socialsupport_unadjusted_050726[[domain]] <- model
  1400. # Optionally, print the summary of the model
  1401. print(paste("Model for", domain))
  1402. print(summary(model))
  1403. }
  1405. sink()
  1406. ```
  1409. ###Individual Tests####
  1410. ####Interactions####
  1412. ###Gender & Chronic Stress###
  1413. ##Individual Tests##
  1414. #CS*GENDER*Time (Fully Adjusted)
  1415. #eTable 13. Interaction between Baseline Chronic Stress, Social Support, and Gender in relation to Individual Cognitive Assessment Z-Scores
  1416. ```{r}
  1417. ##Chronic Stress - Adjusted - Interaction with Gender
  1419. library(dplyr)
  1420. library(lmerTest) #this package prints p values
  1422. sink("Mixed_Effect_IndividualTests_ChronicStress_adjustedgender_fullyadjusted_050826.txt")
  1424. # Define the cognitive domain variables to loop through
  1425. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1426. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1428. # Create an empty list to store the models
  1429. models_chronicstress_fullyadjusted_interactiongender_050726 <- list()
  1431. # Loop through the domains and fit the linear mixed-effects model for each
  1432. for (domain in domains) {
  1434. # Define the formula dynamically for each domain
  1435. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Baseline_ID_Gender + Time + Baseline_ChronicStress_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  1437. # Fit the linear mixed-effects model
  1438. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1440. # Store the model in the list (use the correct list name here)
  1441. models_chronicstress_fullyadjusted_interactiongender_050726[[domain]] <- model
  1443. # Optionally, print the summary of the model
  1444. print(paste("Model for", domain))
  1445. print(summary(model))
  1446. }
  1448. sink()
  1449. ```
  1451. ###Gender & Social Support###
  1452. ##Individual Tests##
  1453. #SS*GENDER*Time (Fully Adjusted)
  1454. #eTable 13. Interaction between Baseline Chronic Stress, Social Support, and Gender in relation to Individual Cognitive Assessment Z-Scores
  1455. ```{r}
  1456. ##Chronic Stress - Adjusted - Interaction with Gender
  1458. library(dplyr)
  1459. library(lmerTest) #this package prints p values
  1461. sink("Mixed_Effect_IndividualTests_SocialSupport_adjustedgender_fullyadjusted_050826.txt")
  1463. # Define the cognitive domain variables to loop through
  1464. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1465. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1467. # Create an empty list to store the models
  1468. models_socialsupport_fullyadjusted_interactiongender_050726 <- list()
  1470. # Loop through the domains and fit the linear mixed-effects model for each
  1471. for (domain in domains) {
  1473. # Define the formula dynamically for each domain
  1474. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_Ethnicity + Baseline_PSWQ_Total + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_SocialSupport_Total + Baseline_ID_Gender + Time + Baseline_SocialSupport_Total*Baseline_ID_Gender*Time + (1 | Med_ID)"))
  1476. # Fit the linear mixed-effects model
  1477. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1479. # Store the model in the list (use the correct list name here)
  1480. models_socialsupport_fullyadjusted_interactiongender_050726[[domain]] <- model
  1482. # Optionally, print the summary of the model
  1483. print(paste("Model for", domain))
  1484. print(summary(model))
  1485. }
  1487. sink()
  1488. ```
  1490. ##Interaction###
  1491. ###Race##
  1493. ##Individual Tests##
  1494. #CS*Race*Time (Fully Adjusted)
  1495. #eTable 14. Interaction between Baseline Chronic Stress, Social Support, and Race/Ethnicity in relation to Individual Cognitive Assessment Z-Scores
  1496. ```{r}
  1497. ##Chronic Stress - Fully Adjusted - Interaction with Race
  1499. library(dplyr)
  1500. library(lmerTest) #this package prints p values
  1502. sink("Mixed_Effect_IndividualTests_ChronicStress_adjustedrace_fullyadjusted_050826.txt")
  1504. # Define the cognitive domain variables to loop through
  1505. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1506. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1508. # Create an empty list to store the models
  1509. models_chronicstress_fullyadjusted_interactionrace_050726 <- list()
  1511. # Loop through the domains and fit the linear mixed-effects model for each
  1512. for (domain in domains) {
  1514. # Define the formula dynamically for each domain
  1515. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_PSWQ_Total + Baseline_ID_Gender + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_ChronicStress_Total + Baseline_Ethnicity + Time + Baseline_ChronicStress_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  1517. # Fit the linear mixed-effects model
  1518. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1520. # Store the model in the list (use the correct list name here)
  1521. models_chronicstress_fullyadjusted_interactionrace_050726[[domain]] <- model
  1523. # Optionally, print the summary of the model
  1524. print(paste("Model for", domain))
  1525. print(summary(model))
  1526. }
  1528. sink()
  1529. ```
  1532. ##Individual Tests##
  1533. #SS*Race*Time (Fully Adjusted)
  1534. #eTable 14. Interaction between Baseline Chronic Stress, Social Support, and Race/Ethnicity in relation to Individual Cognitive Assessment Z-Scores
  1535. ```{r}
  1536. ##Chronic Stress - Fully Adjusted - Interaction with Race
  1538. library(dplyr)
  1539. library(lmerTest) #this package prints p values
  1541. sink("Mixed_Effect_IndividualTests_SocialSupport_adjustedrace_fullyadjusted_050826.txt")
  1543. # Define the cognitive domain variables to loop through
  1544. domains <- c("DS_ZScore", "Trails_A_ZScore", "SEVLT_T1235_ZScore", "SEVLT_DR_ZScore", "LM1_AB_ZScore", "LM2_AB_ZScore",
  1545. "Digit_Symbol_Substitution_ZScore", "Trails_B_ZScore", "FAS_ZScore", "Animal_ZScore")
  1547. # Create an empty list to store the models
  1548. models_socialsupport_fullyadjusted_interactionrace_050726 <- list()
  1550. # Loop through the domains and fit the linear mixed-effects model for each
  1551. for (domain in domains) {
  1553. # Define the formula dynamically for each domain
  1554. formula <- as.formula(paste(domain, "~ Baseline_Age + Baseline_ID_Education + Baseline_PSWQ_Total + Baseline_ID_Gender + Baseline_GDS_Total + Baseline_IMH_Diabetes + Baseline_IMH_HeartAttack + Baseline_IMH_KidneyDisease + Baseline_IMH_Stroke + Baseline_OM_BMI + Baseline_SocialSupport_Total + Baseline_Ethnicity + Time + Baseline_SocialSupport_Total*Baseline_Ethnicity*Time + (1 | Med_ID)"))
  1556. # Fit the linear mixed-effects model
  1557. model <- lmer(formula, data = FULLMERGE_df_nodementia_individualtests)
  1559. # Store the model in the list (use the correct list name here)
  1560. models_socialsupport_fullyadjusted_interactionrace_050726[[domain]] <- model
  1562. # Optionally, print the summary of the model
  1563. print(paste("Model for", domain))
  1564. print(summary(model))
  1565. }
  1567. sink()
  1568. ```

HABSHD_Github.Rmd at commit 130a796, no license · at the source

Overview

Authors: Jillian K. Lee1, Leigh Johnson2, James R. Hall2, James R. Bateman3, Michelle M. Mielke1
ORCID iDs: Michelle M. Mielke
  1. Department of Epidemiology and Prevention Wake Forest University School of Medicine Winston‐Salem North Carolina USA
  2. Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA
  3. Department of Neurology Virginia Commonwealth University Richmond Virginia USA
Institutions: Wake Forest University Health Sciences (United States); Wake Forest University (United States); University of North Texas Health Science Center (United States); Virginia Commonwealth University (United States); Virginia Commonwealth University Medical Center (United States)
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 8, article e71760
Dates: received 17 February 2026; accepted 15 July 2026; published online 25 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/alz.71760 · PMID 42642823 · PMCID PMC13507027 · OpenAlex W7204277785
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), human (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, Connectivity
Keywords: Alzheimer's disease, chronic stress, cognitive decline, gender, race/ethnicity, social support
MeSH: Cognitive Dysfunction*, Social Support*, Stress, Psychological*, Aged, Aged, 80 and over, Attention, Executive Function, Female, Humans, Longitudinal Studies, Male, Neuropsychological Tests (* major topic)
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: National Institute on Aging of the National Institutes of Health (F31AG087696, 5T32NS115704, R01AG054073, R01AG058533, R01AG070862, P41EB015922, U19AG078109)
Citations: not cited yet (Europe PMC); 66 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

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jkleeri/mielkelab_chronicstress_socialsupport_HABSHD

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 130a796426833dd30c393ba0956d41f1901fe7be, 20 May 2026
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “CODE AVAILABILITY”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lme4 (1 file), lmerTest (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
2 files
At the source: github.com/jkleeri/mielkelab_chronicstress_socialsupport_HABSHD.git

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Read it in the paper: doi.org/10.1002/alz.71760.

Tracing map

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  • 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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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1002/alz.71760.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 12 MeSH terms, 1 funder, 63 references.

Cite

This paper

Lee, J. K., Johnson, L., Hall, J. R., Bateman, J. R., & Mielke, M. M. (2026). Chronic stress, social support, and domain-specific cognitive decline in HABS-HD. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(8), e71760. https://doi.org/10.1002/alz.71760

BibTeX

@article{lee2026chronic,
author = {Lee, Jillian K. and Johnson, Leigh and Hall, James R. and Bateman, James R. and Mielke, Michelle M.},
title = {{Chronic stress, social support, and domain-specific cognitive decline in HABS-HD}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = aug,
volume = {22},
number = {8},
pages = {e71760},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/alz.71760},
url = {https://doi.org/10.1002/alz.71760},
pmid = {42642823},
pmcid = {PMC13507027}
}

RIS

TY - JOUR
AU - Lee, Jillian K.
AU - Johnson, Leigh
AU - Hall, James R.
AU - Bateman, James R.
AU - Mielke, Michelle M.
TI - Chronic stress, social support, and domain-specific cognitive decline in HABS-HD
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/08/01
VL - 22
IS - 8
SP - e71760
SN - 1552-5260
PB - Wiley
DO - 10.1002/alz.71760
UR - https://doi.org/10.1002/alz.71760
LA - en
ER -

CSL-JSON

{
"id": "10.1002/alz.71760",
"type": "article-journal",
"title": "Chronic stress, social support, and domain-specific cognitive decline in HABS-HD",
"container-title": "Alzheimer's & dementia : the journal of the Alzheimer's Association",
"author": [
{
"family": "Lee",
"given": "Jillian K."
},
{
"family": "Johnson",
"given": "Leigh"
},
{
"family": "Hall",
"given": "James R."
},
{
"family": "Bateman",
"given": "James R."
},
{
"family": "Mielke",
"given": "Michelle M."
}
],
"container-title-short": "Alzheimers Dement",
"volume": "22",
"issue": "8",
"page": "e71760",
"DOI": "10.1002/alz.71760",
"PMID": "42642823",
"PMCID": "PMC13507027",
"ISSN": "1552-5260",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/alz.71760",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
1
]
]
}
}

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