OSCR

Blood DNA methylation signature of cognitive reserve moderates the association between CSF tau pathology and memory in prodromal Alzheimer's disease.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § METHODS › Identification of DNAm loci moderating the pTau–memory association ↔ code/markdown/00_get_target_samples.Rmd, lines 44–77 · score 0.69 · PHC_EXF, PHC_LAN, PHC_MEM, methylated
  2. [2] § RESULTS › Methylation‐based memory reserve biomarkers at baseline are significantly associated with subsequent longitudinal memory trajectories in MCI ↔ code/markdown/_ADNI-future-visits-PHC-MEM_MCI-Z-score-MRS_11-11-2025.Rmd, lines 167–198 · score 0.67 · mixed model, high MRS, low MRS, mid, tertiles, baseline
  3. [3] § METHODS › Computation of methylation reserve scores (MRS) and assessment of its association with future memory decline ↔ code/markdown/_compute_mrs_MCI_11-1-2025.Rmd, lines 39–116 · score 0.65 · methylation beta, CpGs, pTau, resilience, raw, MRS
  4. [4] § RESULTS › Methylation‐based memory reserve biomarkers at baseline are significantly associated with subsequent longitudinal memory trajectories in MCI ↔ code/markdown/_ADNI-future-visits-PHC-MEM_MCI-Z-score-MRS_11-11-2025.Rmd, lines 52–107 · score 0.63 · baseline age, PHC_MEM, pTau, visits, longitudinal, sex
  5. [5] § METHODS › Cognitive measures and CSF biomarkers ↔ code/markdown/_ADNI-future-visits-PHC-MEM_MCI-Z-score-MRS_11-11-2025.Rmd, lines 18–50 · score 0.57 · ADSP PHC, phc mem, Amyloid positive, Phenotype, scores, Cognitive
  6. [6] § RESULTS › Methylation‐based memory reserve biomarkers at baseline are significantly associated with subsequent longitudinal memory trajectories in MCI ↔ code/markdown/_compute_mrs_MCI_11-1-2025.Rmd, lines 39–116 · score 0.57 · methylation beta, CpGs, pTau, component, MRS, scores
  7. [7] § METHODS › Identification of DNAm loci moderating the pTau–memory association ↔ code/markdown/_DNAm_pTau_interaction_plots_MCI_12-14-2025.Rmd, lines 155–274 · score 0.53 · covariate adjusted, memory scores, fitted, residuals, phc, pTau
  8. [8] § METHODS › Identification of DNAm loci moderating the pTau–memory association ↔ code/markdown/04b_dmr_plots.Rmd, lines 159–272 · score 0.53 · covariate adjusted, memory scores, fitted, residuals, phc, pTau
  9. [9] § METHODS › Study participants ↔ code/markdown/_ADNI-future-visits-PHC-MEM_MCI-Z-score-MRS_11-11-2025.Rmd, lines 52–107 · score 0.52 · PHC_pTau, PHC_MEM, visit, scores, ADNI, MCI

Paper

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The authors' code

R Markdown · 454 lines · 14 KB · MIT · 4 matches

  1. ---
  2. title: "MRS × Time Mixed Models, Spaghetti Plots, Comparison of baseline vars by MRS tertiles"
  3. author: "LW"
  4. date: "`r format(Sys.Date(), '%Y-%m-%d')`"
  5. output:
  6. html_document:
  7. toc: true
  8. toc_float: true
  9. df_print: paged
  10. code_folding: show
  11. encoding: UTF-8
  12. ---
  14. ```{r setup, include=FALSE}
  15. knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
  16. ```
  18. # Paths & Libraries
  20. ```{r libraries-and-paths}
  21. # Libraries
  22. library(dplyr)
  23. library(readr)
  24. library(lme4)
  25. library(lmerTest)
  26. library(broom.mixed)
  27. library(ggplot2)
  28. library(tidyr)
  29. library(knitr)
  31. # ---- Define directories using file.path() ----
  32. base_dir <- file.path("C:", "Users", "lxw391", "Lily Wang")
  34. # AD_CR analysis folders
  35. ad_cr_dir <- file.path(base_dir, "AD_CR")
  36. analysis_dir <- file.path(ad_cr_dir, "analysis_results", "only_amyloid_positive_2_stage_approach")
  37. out_dir <- file.path(analysis_dir, "mrs")
  38. sig_probes_dir <- file.path(analysis_dir, "sig_probes")
  40. # ADSP PHC (cognition) folder
  41. adsp_phc_dir <- file.path(base_dir, "DATASETS", "ADNI", "Phenotype", "raw", "ADSP-PHC_8-2025")
  43. # ---- File paths ----
  44. pheno_path <- file.path(sig_probes_dir, "Phenotypes.csv")
  45. cogn_path <- file.path(adsp_phc_dir, "ADSP_PHC_COGN_22Aug2025.csv")
  46. mrs_scores_path <- file.path(out_dir, "MCI_mrs_scores.csv")
  48. # Ensure output directory exists
  49. if (!dir.exists(out_dir)) dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)
  50. ```
  52. # Load & Prepare Data
  54. ```{r load-and-prep}
  55. # --- Load & prep --------------------------------------------------------------
  56. pheno_df <- read_csv(pheno_path)
  57. pheno_df$PHASE <- pheno_df$Phase
  58. pheno_df$EXAMDATE <- pheno_df$Edate
  60. # One row per RID already (MCI visit with DNAm/biomarker/cognition date)
  61. mci <- subset(pheno_df, PHC_Diagnosis == 2,
  62. select = c(sample, RID, PHASE, EXAMDATE, age_at_visit, PHC_Age_Biomarker,
  63. APOE4, Gender, B, Mono, CD4T, Neutro, Eosino, NK, PHC_pTau)) %>%
  64. mutate(EXAMDATE = as.Date(EXAMDATE)) %>%
  65. rename(dnam_date = EXAMDATE) %>%
  66. rename(baseline_age = age_at_visit ) %>%
  67. mutate(Gran = Neutro + Eosino)
  69. # Cognition longitudinal file
  70. cogn <- read_csv(cogn_path) %>%
  71. select(RID, PHASE, EXAMDATE, PHC_Education, PHC_MEM,
  72. PHC_Age_Cognition, PHC_Diagnosis, PHC_Sex) %>%
  73. mutate(EXAMDATE = as.Date(EXAMDATE))
  75. # --- Keep only COGN visits AFTER the single MCI visit per RID -----------------
  76. both <- cogn %>%
  77. inner_join(mci, by = "RID") %>%
  78. filter(EXAMDATE > dnam_date)
  80. # Add time since MCI in YEARS
  81. both <- both %>%
  82. mutate(time = as.numeric(difftime(EXAMDATE, dnam_date, units = "days"))/365.25)
  84. # --- Get baseline PHC_MEM at the MCI date (baseline_MEM) ----------------------
  85. baseline_mem <- cogn %>%
  86. select(RID, EXAMDATE, PHC_MEM) %>%
  87. inner_join(mci %>% select(RID, dnam_date), by = "RID") %>%
  88. filter(EXAMDATE == dnam_date) %>%
  89. transmute(RID, baseline_MEM = PHC_MEM)
  91. # Attach baseline_MEM to longitudinal rows
  92. both <- both %>%
  93. left_join(baseline_mem, by = "RID")
  95. # -- add baseline pTau from the MCI visit (dnam_date)
  96. baseline_pTau <- mci %>%
  97. dplyr::select(RID, baseline_pTau = PHC_pTau)
  99. # attach baseline_pTau and baseline_MEM to longitudinal rows
  100. both <- both %>%
  101. dplyr::left_join(baseline_mem, by = "RID") %>%
  102. dplyr::left_join(baseline_pTau, by = "RID")
  104. # --- Merge MRS scores ---------------------------------------------------------
  105. mrs <- read_csv(mrs_scores_path)
  106. mrs_cogn <- merge(mrs, both, by.x = "sample_id", by.y = "sample")
  107. ```
  109. # number of visits
  110. ```{r}
  111. # --- Post-MCI visits for subjects with MRS (use mrs_cogn) --------------------
  112. mrs_cogn_unique <- mrs_cogn %>% dplyr::distinct(RID, EXAMDATE, .keep_all = TRUE)
  114. visits_post_mrs <- mrs_cogn_unique %>%
  115. dplyr::group_by(RID) %>%
  116. dplyr::summarize(
  117. n_visits_post = dplyr::n(),
  118. dnam_date = min(dnam_date), # should be identical within RID
  119. first_post_date = min(EXAMDATE),
  120. last_post_date = max(EXAMDATE),
  121. # years since DNAm date to the (chronologically) last post visit:
  122. followup_years_post = max(time, na.rm = TRUE),
  123. # If you prefer to compute directly from dates instead of `time`, use:
  124. # followup_years_post = as.numeric(difftime(last_post_date, dnam_date, units = "days"))/365.25,
  125. .groups = "drop"
  126. )
  128. # Distribution table
  129. visits_post_mrs_dist <- visits_post_mrs %>%
  130. dplyr::count(n_visits_post, name = "n_subjects") %>%
  131. dplyr::arrange(n_visits_post)
  133. # Save
  134. readr::write_csv(visits_post_mrs, file.path(out_dir, "visit_counts_postMCI_MRSonly.csv"))
  135. readr::write_csv(visits_post_mrs_dist, file.path(out_dir, "visit_counts_postMCI_MRSonly_dist.csv"))
  136. ```
  138. # Mixed-Effects Models
  140. ```{r mixed-models}
  141. df <- mrs_cogn
  142. df$RID <- factor(df$RID)
  143. df$PHC_Sex <- factor(df$PHC_Sex)
  145. df$mrs_z <- as.numeric(scale(df$MRS_raw))
  146. df$baseline_age_z <- as.numeric(scale(df$baseline_age))
  148. f <- lmer(
  149. PHC_MEM ~ baseline_age_z + PHC_Sex + APOE4 + PHC_Education + mrs_z + baseline_pTau +
  150. mrs_z*time + baseline_pTau*time + mrs_z*baseline_pTau
  151. + (1 | RID),
  152. data = df,
  153. REML = TRUE
  154. )
  155. summary(f)
  157. g <- lmer(
  158. PHC_MEM ~ baseline_age_z + PHC_Sex + APOE4 + PHC_Education + mrs_z*time*baseline_pTau
  159. + (1 | RID),
  160. data = df,
  161. REML = TRUE
  162. )
  163. summary(g)
  165. ```
  167. ```{r}
  168. # --- Build tertiles and make High MRS the reference ---------------------------
  169. # df <- mrs_cogn %>%
  170. # dplyr::filter(!is.na(mrs_z), !is.na(PHC_MEM), !is.na(time))
  172. mrs_cutoffs <- stats::quantile(df$mrs_z, probs = c(1/3, 2/3), na.rm = TRUE)
  174. df <- df %>%
  175. mutate(
  176. MRS.group = cut(
  177. mrs_z,
  178. breaks = c(-Inf, mrs_cutoffs[1], mrs_cutoffs[2], Inf),
  179. labels = c("Low MRS", "Mid MRS", "High MRS"),
  180. include.lowest = TRUE, right = TRUE
  181. ),
  182. # >>> High as reference <<<
  183. MRS.group = factor(MRS.group, levels = c("High MRS","Mid MRS","Low MRS")),
  184. RID = factor(RID),
  185. PHC_Sex = factor(PHC_Sex)
  186. )
  188. # --- Mixed models (High MRS is the reference) ---------------------------------
  189. f_cat <- lmer(
  190. PHC_MEM ~ baseline_age_z + PHC_Sex + APOE4 + PHC_Education + MRS.group + baseline_pTau +
  191. MRS.group*time + baseline_pTau*time + MRS.group*baseline_pTau + (1 | RID),
  192. data = df, REML = TRUE
  193. )
  194. summary(f_cat)
  196. df %>% distinct(RID, MRS.group) %>% count(MRS.group)
  198. ```
  200. # Spaghetti Plot: PHC_MEM by MRS Tertile (3 groups)
  202. ```{r spaghetti-3groups, fig.width=8, fig.height=5.5}
  203. # Keep subjects with non-missing MRS and assign tertiles per RID
  204. subj_mrs <- df %>%
  205. distinct(RID, mrs_z) %>% # one MRS per subject
  206. filter(!is.na(mrs_z)) %>%
  207. mutate(
  208. MRS_cat_num = ntile(mrs_z, 3), # 1,2,3 tertiles
  209. MRS_cat = factor(MRS_cat_num,
  210. levels = c(1, 2, 3),
  211. labels = c("Low MRS", "Mid MRS", "High MRS"))
  212. ) %>%
  213. select(RID, MRS_cat)
  215. # Add category to all visits
  216. df_cat <- df %>%
  217. inner_join(subj_mrs, by = "RID")
  219. # Spaghetti plot colored by MRS category
  220. p <- ggplot(df_cat, aes(x = time, y = PHC_MEM, group = RID, color = MRS_cat)) +
  221. geom_line(alpha = 0.35, linewidth = 1.1) + # thicker spaghetti lines
  222. geom_point(alpha = 0.25, size = 1.0) + # slightly larger points
  223. geom_smooth(aes(group = MRS_cat, color = MRS_cat),
  224. method = "lm", formula = y ~ x, se = FALSE, linewidth = 1.8) + # thicker trends
  225. labs(
  226. title = "PHC_MEM trajectories by MRS tertile with linear trends",
  227. x = "Time since baseline (years)",
  228. y = "PHC_MEM",
  229. color = "MRS category"
  230. ) +
  231. theme_minimal(base_size = 12)
  233. p
  235. # Save PDF
  236. ggsave(
  237. filename = file.path(out_dir, "PHC_MEM_spaghetti_by_MRS_3groups.pdf"),
  238. plot = p,
  239. device = pdf,
  240. width = 8, height = 5.5, units = "in"
  241. )
  242. ```
  245. # Change-from-Baseline Plot (ΔPHC_MEM)
  247. ```{r change-from-baseline, fig.width=8, fig.height=5.5}
  248. # Start from df_cat which has baseline_MEM.x and baseline_MEM.y
  249. df_cat2x <- df_cat %>%
  250. mutate(baseline_MEM = dplyr::coalesce(baseline_MEM.x, baseline_MEM.y))
  252. # 1) Use only rows with a real baseline and valid time/MEM; compute change
  253. df_change <- df_cat2x %>%
  254. filter(!is.na(baseline_MEM), !is.na(PHC_MEM), !is.na(time)) %>%
  255. mutate(dMEM = PHC_MEM - baseline_MEM)
  257. # 2) Add one explicit baseline row per subject: time = 0, Δ = 0
  258. base_pts <- df_cat2x %>%
  259. distinct(RID, MRS_cat, baseline_MEM) %>%
  260. filter(!is.na(baseline_MEM)) %>%
  261. transmute(RID, MRS_cat, time = 0, dMEM = 0)
  263. # 3) Combine and plot
  264. df_plot <- bind_rows(df_change, base_pts) %>% arrange(RID, time)
  266. r <- ggplot(df_plot, aes(x = time, y = dMEM, group = RID, color = MRS_cat)) +
  267. geom_hline(yintercept = 0, linetype = "dashed") +
  268. geom_line(alpha = 0.35, linewidth = 1.1) +
  269. geom_point(alpha = 0.25, size = 1.0) +
  270. geom_smooth(aes(group = MRS_cat,color = MRS_cat),
  271. method = "lm", formula = y ~ x, se = FALSE, linewidth = 1.8) +
  272. labs(
  273. title = "Change in PHC_MEM from baseline by MRS tertile",
  274. x = "Time since baseline (years)",
  275. y = "Δ PHC_MEM from baseline",
  276. color = "MRS category"
  277. ) +
  278. theme_minimal(base_size = 12)
  280. r
  282. ggsave(
  283. filename = file.path(out_dir, "change_in_PHC_MEM_spaghetti_by_MRS_3groups.pdf"),
  284. plot = r,
  285. device = pdf,
  286. width = 8, height = 5.5, units = "in"
  287. )
  288. ```
  290. # Baseline Comparisons by MRS
  292. ```{r}
  293. # Helper: first non-missing value
  294. first_non_na <- function(x) {
  295. x <- x[!is.na(x)]
  296. if (length(x) == 0) return(NA)
  297. x[1]
  298. }
  300. # One row per subject (RID) with baseline covariates + max follow-up time (years)
  301. subject_level <- df %>%
  302. group_by(RID) %>%
  303. summarise(
  304. baseline_age = first_non_na(baseline_age),
  305. PHC_Sex = first_non_na(as.character(PHC_Sex)),
  306. APOE4 = first_non_na(APOE4),
  307. PHC_Education = first_non_na(PHC_Education),
  308. baseline_pTau = first_non_na(baseline_pTau),
  309. followup_years = { mx <- suppressWarnings(max(time, na.rm = TRUE));
  310. if (is.infinite(mx)) NA_real_ else mx },
  311. .groups = "drop"
  312. ) %>%
  313. inner_join(subj_mrs, by = "RID") %>%
  314. mutate(
  315. PHC_Sex = factor(PHC_Sex),
  316. APOE4 = factor(APOE4, levels = sort(unique(APOE4)))
  317. )
  319. ## =========================
  320. ## Continuous (wide, pretty)
  321. ## =========================
  323. cont_vars <- c("baseline_age", "PHC_Education", "baseline_pTau", "followup_years")
  325. cont_long <- subject_level %>%
  326. select(MRS_cat, all_of(cont_vars)) %>%
  327. pivot_longer(cols = all_of(cont_vars),
  328. names_to = "Variable", values_to = "value")
  330. # Kruskal–Wallis p per variable
  331. kw_df <- cont_long %>%
  332. group_by(Variable) %>%
  333. summarise(`Kruskal-Wallis p` = tryCatch(
  334. kruskal.test(value ~ MRS_cat)$p.value, error = function(e) NA_real_
  335. ), .groups = "drop")
  337. # Total N (non-missing across all groups) per variable
  338. totals_cont <- cont_long %>%
  339. group_by(Variable) %>%
  340. summarise(`Total N` = sum(!is.na(value)), .groups = "drop")
  342. # Group summaries and pretty string (use actual ±, not \u escapes)
  343. cont_summ <- cont_long %>%
  344. group_by(Variable, MRS_cat) %>%
  345. summarise(
  346. N = sum(!is.na(value)),
  347. Mean = mean(value, na.rm = TRUE),
  348. SD = sd(value, na.rm = TRUE),
  349. Median = median(value, na.rm = TRUE),
  350. .groups = "drop"
  351. ) %>%
  352. mutate(
  353. grp = dplyr::recode(as.character(MRS_cat),
  354. "Low MRS" = "Low", "Mid MRS" = "Mid", "High MRS" = "High"),
  355. pretty = sprintf("%.2f ± %.2f (%.2f)", Mean, SD, Median)
  356. )
  358. # Wide: one row per variable; columns like "Low N", "Low pretty", etc.
  359. cont_table_wide <- cont_summ %>%
  360. select(Variable, grp, N, pretty) %>%
  361. pivot_wider(
  362. names_from = grp,
  363. values_from = c(N, pretty),
  364. names_glue = "{grp} {.value}"
  365. ) %>%
  366. left_join(totals_cont, by = "Variable") %>%
  367. left_join(kw_df, by = "Variable") %>%
  368. relocate(`Total N`, .after = Variable)
  370. # Save & show
  371. write.csv(cont_table_wide,
  372. file = file.path(out_dir, "baseline_group_comparison_continuous_wide.csv"),
  373. row.names = FALSE)
  375. kable(cont_table_wide, digits = 3,
  376. caption = "Continuous variables by MRS tertile (wide): N and mean±SD (median), with Total N and Kruskal–Wallis p.")
  378. ## =========================
  379. ## Categorical (wide, pretty)
  380. ## =========================
  381. cat_long <- subject_level %>%
  382. select(MRS_cat, PHC_Sex, APOE4) %>%
  383. pivot_longer(cols = c(PHC_Sex, APOE4),
  384. names_to = "Variable", values_to = "Level") %>%
  385. mutate(Level = factor(Level))
  387. # Counts/percents within each MRS group
  388. cat_counts <- cat_long %>%
  389. count(Variable, Level, MRS_cat, name = "Count") %>%
  390. group_by(Variable, MRS_cat) %>%
  391. mutate(Percent = 100 * Count / sum(Count)) %>%
  392. ungroup()
  394. # Fisher’s exact p across Low/Mid/High (one p per variable)
  395. fisher_df <- cat_long %>%
  396. group_by(Variable) %>%
  397. summarise(`Fisher p` = tryCatch(
  398. fisher.test(table(MRS_cat, Level))$p.value, error = function(e) NA_real_
  399. ), .groups = "drop")
  401. # Total N across groups per Variable–Level
  402. totals_cat <- cat_counts %>%
  403. group_by(Variable, Level) %>%
  404. summarise(`Total N` = sum(Count), .groups = "drop")
  406. # Pretty wide table with n(%) and Total N
  407. cat_table_wide <- cat_counts %>%
  408. mutate(
  409. grp = dplyr::recode(as.character(MRS_cat),
  410. "Low MRS" = "Low", "Mid MRS" = "Mid", "High MRS" = "High"),
  411. `n(%)` = sprintf("%d (%.1f%%)", Count, Percent)
  412. ) %>%
  413. select(Variable, Level, grp, `n(%)`) %>%
  414. pivot_wider(names_from = grp, values_from = `n(%)`) %>%
  415. left_join(totals_cat, by = c("Variable", "Level")) %>%
  416. left_join(fisher_df, by = "Variable") %>%
  417. relocate(`Total N`, .after = Level)
  419. # Add group denominators (Ns) to the categorical wide table
  420. group_Ns <- cat_long %>%
  421. group_by(Variable, MRS_cat) %>%
  422. summarise(Group_N = sum(!is.na(Level)), .groups = "drop") %>%
  423. mutate(col = dplyr::recode(as.character(MRS_cat),
  424. "Low MRS"="Low_N","Mid MRS"="Mid_N","High MRS"="High_N")) %>%
  425. select(-MRS_cat) %>%
  426. tidyr::pivot_wider(names_from = col, values_from = Group_N)
  428. cat_table_wide <- cat_table_wide %>%
  429. dplyr::left_join(group_Ns, by = "Variable")
  431. # Save & show
  432. write.csv(cat_table_wide,
  433. file = file.path(out_dir, "baseline_group_comparison_categorical_wide.csv"),
  434. row.names = FALSE)
  435. kable(cat_table_wide, digits = 3,
  436. caption = "Categorical variables by MRS tertile (wide): n(%) with Total N and Fisher’s exact p.")
  438. ```
  450. # Session Info
  452. ```{r}
  453. sessionInfo()
  454. ```

_ADNI-future-visits-PHC-MEM_MCI-Z-score-MRS_11-11-2025.Rmd at commit fc972f8, under MIT · at the source

Overview

Authors: David Lukacsovich1, Juan I Young2,3, Lissette Gomez3, Brian W Kunkle2,3, Zhixin Mao1, Wei Zhang1, X Steven Chen1,4, Deirdre M O'Shea5, Tatjana Rundek5,6, Eden R Martin2,3, Lily Wang1,2,3,4,6, for the Alzheimer's Disease Neuroimaging Initiative
ORCID iDs: Lily Wang
  1. Division of Biostatistics, Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, Florida, USA
  2. Dr. John T Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, Florida, USA
  3. John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA
  4. Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, Florida, USA
  5. Department of Neurology, University of Miami Miller School of Medicine, Miami, Florida, USA
  6. Evelyn F. McKnight Brain Institute, University of Miami School of Medicine, Miami, Florida, USA
Institutions: University of Miami (United States); Dr. John T. Macdonald Foundation (United States); Sylvester Comprehensive Cancer Center (United States)
Journal: Alzheimer's & dementia (New York, N. Y.), volume 12, issue 2, article e70257
Dates: received 31 December 2025; accepted 25 March 2026; published online 1 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/trc2.70257 · PMID 42079999 · PMCID PMC13133550 · OpenAlex W7159963434
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), other (modality), human (organism), Alzheimer's / dementia (population)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: cognitive reserve, DNA methylation, CSF pTau181, mild cognitive impairment, Alzheimer's Disease Neuroimaging Initiative, ADNI
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS128145, RF1 NS128145, R61 NS135587); NIA NIH HHS (U01 AG024904)
Citations: not cited yet (Europe PMC); 24 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 9 matches between paragraphs and lines of code.

TransBioInfoLab/ad-cr

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: fc972f85ac977e7480274ef8ff2586aecf656931, 17 January 2026
Languages: R (40)
Size: 46 files, 40 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, license file, 34 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (36 files), ggplot2 (6 files), broom (3 files), patchwork (2 files), rstatix (2 files), ggpubr (1 file), limma (1 file), lme4 (1 file), lmerTest (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
42 files
At the source: github.com/TransBioInfoLab/ad-cr

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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;
  • 40 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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.

Code and data availability statement

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

Read it in the paper: doi.org/10.1002/trc2.70257.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 6 keywords, 2 funders, 24 references.

Cite

This paper

Lukacsovich, D., Young, J. I., Gomez, L., Kunkle, B. W., Mao, Z., Zhang, W., Chen, X. S., O'Shea, D. M., Rundek, T., Martin, E. R., Wang, L., & for the Alzheimer's Disease Neuroimaging Initiative. (2026). Blood DNA methylation signature of cognitive reserve moderates the association between CSF tau pathology and memory in prodromal Alzheimer's disease. Alzheimer's & dementia (New York, N. Y.), 12(2), e70257. https://doi.org/10.1002/trc2.70257

BibTeX

@article{lukacsovich2026blood,
author = {Lukacsovich, David and Young, Juan I and Gomez, Lissette and Kunkle, Brian W and Mao, Zhixin and Zhang, Wei and Chen, X Steven and O'Shea, Deirdre M and Rundek, Tatjana and Martin, Eden R and Wang, Lily and {for the Alzheimer's Disease Neuroimaging Initiative}},
title = {{Blood DNA methylation signature of cognitive reserve moderates the association between CSF tau pathology and memory in prodromal Alzheimer's disease}},
journal = {Alzheimer's \& dementia (New York, N. Y.)},
year = {2026},
month = apr,
volume = {12},
number = {2},
pages = {e70257},
publisher = {Wiley},
issn = {2352-8737},
doi = {10.1002/trc2.70257},
url = {https://doi.org/10.1002/trc2.70257},
pmid = {42079999},
pmcid = {PMC13133550}
}

RIS

TY - JOUR
AU - Lukacsovich, David
AU - Young, Juan I
AU - Gomez, Lissette
AU - Kunkle, Brian W
AU - Mao, Zhixin
AU - Zhang, Wei
AU - Chen, X Steven
AU - O'Shea, Deirdre M
AU - Rundek, Tatjana
AU - Martin, Eden R
AU - Wang, Lily
AU - for the Alzheimer's Disease Neuroimaging Initiative
TI - Blood DNA methylation signature of cognitive reserve moderates the association between CSF tau pathology and memory in prodromal Alzheimer's disease
T2 - Alzheimer's & dementia (New York, N. Y.)
J2 - Alzheimers Dement (N Y)
PY - 2026
DA - 2026/04/01
VL - 12
IS - 2
SP - e70257
SN - 2352-8737
PB - Wiley
DO - 10.1002/trc2.70257
UR - https://doi.org/10.1002/trc2.70257
LA - en
ER -

CSL-JSON

{
"id": "10.1002/trc2.70257",
"type": "article-journal",
"title": "Blood DNA methylation signature of cognitive reserve moderates the association between CSF tau pathology and memory in prodromal Alzheimer's disease",
"container-title": "Alzheimer's & dementia (New York, N. Y.)",
"author": [
{
"family": "Lukacsovich",
"given": "David"
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{
"family": "Young",
"given": "Juan I"
},
{
"family": "Gomez",
"given": "Lissette"
},
{
"family": "Kunkle",
"given": "Brian W"
},
{
"family": "Mao",
"given": "Zhixin"
},
{
"family": "Zhang",
"given": "Wei"
},
{
"family": "Chen",
"given": "X Steven"
},
{
"family": "O'Shea",
"given": "Deirdre M"
},
{
"family": "Rundek",
"given": "Tatjana"
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{
"family": "Martin",
"given": "Eden R"
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{
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{
"literal": "for the Alzheimer's Disease Neuroimaging Initiative"
}
],
"container-title-short": "Alzheimers Dement (N Y)",
"volume": "12",
"issue": "2",
"page": "e70257",
"DOI": "10.1002/trc2.70257",
"PMID": "42079999",
"PMCID": "PMC13133550",
"ISSN": "2352-8737",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/trc2.70257",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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