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Alpha-linolenic acid associations with disability and brain volume in multiple sclerosis: a brief replication report.

Code ↔ Paper

5 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 5 matches
  1. [1] § Method ↔ analyse.R, lines 618–699 · score 0.62 · annual rate, T1w lesions, disease duration, mediating, longitudinal, mediation
  2. [2] § Results ↔ analyse.R, lines 618–699 · score 0.58 · annual relapses, annual rate, T1w lesions, brain volume, Baseline, PASAT
  3. [3] § Method ↔ data_prep.R, lines 1–45 · score 0.57 · Serum alpha linolenic, acid, scores, MRI, MS, PASAT
  4. [4] § Method ↔ analyse.R, lines 505–543 · score 0.56 · negative binomial model, linear models, intracranial volume, relapses, treatment, sex
  5. [5] § Method ↔ analyse.R, lines 1–33 · score 0.52 · Serum alpha linolenic, acid, MS, PASAT, EDSS

Paper

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The authors' code

R · 699 lines · 26 KB · no license · 4 matches

  1. # Replication of Serum Alpha-Linolenic Acid and Long-Term Multiple Sclerosis Activity and Progression
  2. #
  3. # Max Korbmacher, Jul 2025
  4. #
  5. # ------------------------------------------------------------ #
  6. # ---------------------- Contents ---------------------------- #
  7. # ------------------------------------------------------------ #
  8. # 0. Preparations -------------------------------------------- #
  9. # 1. Descriptives & Validation ------------------------------- #
  10. # 2. Analyse ------------------------------------------------- #
  11. # 2.1 Mixed linear models ------------------------------------ #
  12. # 2.2 Mixed negative binomial models ------------------------- #
  13. # 3. Simple linear models ------------------------------------ #
  14. # 3.1 T2w number of lesions --------------------------------- #
  15. # 3.2 Brain volume ------------------------------------------ #
  16. # 3.3 PASAT ------------------------------------------------- #
  17. # 3.4 EDSS -------------------------------------------------- #
  18. # 3.5 T1w new Lesions (Negative binomial models) ------------- #
  19. # 4. Mediation analyses -------------------------------------- #
  20. # 4.1 Brain vol > ALA > EDSS---------------------------------- #
  21. # 4.2 T1w lesions > ALA > EDSS-------------------------------- #
  22. # 5. Longitudinal predictions of ALA ------------------------- #
  23. # 6. Disease Duration instead of Age as Covariate------------- #
  24. # ------------------------------------------------------------ #
  25. # ------------------------------------------------------------ #
  26. #
  27. # 0. Preparations --------------------------------------------
  28. # clean up
  29. rm(list = ls(all.names = TRUE)) # clear all objects includes hidden objects.
  30. gc() #free up memory and report the memory usage.
  32. # define data path
  33. datapath = "/Users/max/Documents/Local/MS/ALA/data/"
  35. # read packages
  36. pacman::p_load(haven,dplyr,reshape2,lme4,lmerTest,VGAM,mediation, psych, MuMIn, reshape2, tidyr)
  37. # load data
  38. df = read.csv(paste(datapath,"clean.csv",sep=""))
  39. long = read.csv(paste(datapath,"rate_of_change_10yrs.csv",sep=""))
  40. relapse = read.csv(paste(datapath,"relapses_long.csv",sep=""))
  41. #
  42. # 1. Descriptives & Validation -------------------------------
  43. ICC(reshape(df%>%dplyr::select(eid,session,ALA),
  44. idvar = "eid", timevar = "session",
  45. direction = "wide") %>% dplyr::select(-eid))
  46. # Descriptive table
  47. relapse %>%
  48. group_by(session) %>%
  49. summarise(
  50. across(
  51. c(RELAPSENEW, DiseaseDuration),
  52. list(
  53. mean = ~mean(., na.rm = TRUE),
  54. sd = ~sd(., na.rm = TRUE),
  55. N = ~sum(!is.na(.))
  56. )
  57. )
  58. ) %>%
  59. pivot_longer(
  60. cols = -session,
  61. names_to = c("variable", "stat"),
  62. names_sep = "_"
  63. ) %>%
  64. pivot_wider(
  65. names_from = session,
  66. values_from = value
  67. )
  68. Table1 = df %>%
  69. group_by(session) %>%
  70. summarise(
  71. across(
  72. c(age, ALA, new_T1Gd_lesion, TotalVol, lesion_count, edss, PASAT),
  73. list(
  74. mean = ~mean(., na.rm = TRUE),
  75. sd = ~sd(., na.rm = TRUE),
  76. N = ~sum(!is.na(.))
  77. )
  78. ),
  79. .groups = "drop"
  80. ) %>%
  81. pivot_longer(
  82. cols = -session,
  83. names_to = c("variable", "stat"),
  84. names_pattern = "^(.*)_(mean|sd|N)$"
  85. ) %>%
  86. pivot_wider(
  87. names_from = session,
  88. values_from = value
  89. )
  90. print(Table1, n=100)
  91. range((df %>% filter(session==0))$ALA,na.rm = T)
  92. summarise_categorical <- function(data, vars) {
  93. data %>%
  94. pivot_longer(
  95. cols = {{ vars }},
  96. names_to = "variable",
  97. values_to = "category"
  98. ) %>%
  99. group_by(session, variable, category) %>%
  100. summarise(N = n(), .groups = "drop_last") %>%
  101. mutate(Percent = N / sum(N) * 100) %>%
  102. ungroup() %>%
  103. pivot_wider(
  104. names_from = session,
  105. values_from = c(N, Percent),
  106. names_glue = "{.value}_session{session}"
  107. )
  108. }
  109. # Categorical summary
  110. summarise_categorical(
  111. df,
  112. c(sex)
  113. )
  115. # last time point summary
  116. continuous_vars <- c(
  117. "Age_OFAMS10", "T1wLesions", "relapse_rate", "relapses_12mnths_before_baseline",
  118. "relapse_rate_prior", "Nb_of_relapses_FU", "EDSS_10_short", "TotalVol",
  119. "PASAT", "t2wLesions", "EDSS", "lesion_count_diff"
  120. )
  123. long %>%
  124. summarise(
  125. across(
  126. all_of(continuous_vars),
  127. list(
  128. mean = ~mean(., na.rm = TRUE),
  129. sd = ~sd(., na.rm = TRUE),
  130. N = ~sum(!is.na(.))
  131. )
  132. )
  133. ) %>%
  134. pivot_longer(
  135. everything(),
  136. names_to = c("variable", "stat"),
  137. names_pattern = "^(.*)_(mean|sd|N)$"
  138. ) %>%
  139. pivot_wider(
  140. names_from = stat,
  141. values_from = value
  142. )
  143. sum(na.omit(long$Nb_of_relapses_FU))
  144. sum(na.omit(long$T1wLesions))
  146. dd = ((merge(long, df%>%filter(session==0),by="eid"))$Age_OFAMS10-
  147. (merge(long, df%>%filter(session==0),by="eid"))$age.x+
  148. (merge(long, df%>%filter(session==0),by="eid"))$DiseaseDuration)
  149. paste("Disease duration at follow up is Mean = ",round(mean(na.omit(dd)),1),"±",round(sd(na.omit(dd)),1),sep="")
  150. # 2. Analyse -------------------------------------------------
  151. # 2.1 Mixed linear models ------------------------------------
  152. # 2.1.1 PASAT
  153. m = lmer(PASAT ~ log(ALA) + (1|eid),df)
  154. summary(m)
  155. m = lmer(PASAT ~ log(ALA) + age + sex + Treatment_OFAMS + (1|eid),df)
  156. summary(m)
  157. # 2.1.2 brain volume ***
  158. m = lmer(TotalVol ~ log(ALA) + EstimatedTotalIntraCranialVol + (1|eid),df)
  159. summary(m)
  160. effectsize::standardize_parameters(m)
  161. r.squaredGLMM(m)
  163. m = lmer(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  164. summary(m)
  165. effectsize::standardize_parameters(m)
  166. r.squaredGLMM(m)
  168. # # 2.1.2.1 GM volume *
  169. # m = lmer(TotalGrayVol ~ log(ALA) + EstimatedTotalIntraCranialVol + (1|eid),df)
  170. # summary(m)
  171. # effectsize::standardize_parameters(m)
  172. # m = lmer(TotalGrayVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  173. # summary(m)
  174. # effectsize::standardize_parameters(m)
  175. #
  176. # # 2.1.2.2 WM volume
  177. # m = lmer(TotalWMVol ~ log(ALA) + EstimatedTotalIntraCranialVol + (1|eid),df)
  178. # summary(m)
  179. # effectsize::standardize_parameters(m)
  180. # m = lmer(TotalWMVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  181. # summary(m)
  182. # effectsize::standardize_parameters(m)
  184. # 2.1.3 EDSS
  185. m = lmer(edss ~ log(ALA) + (1|eid),df)
  186. summary(m)
  187. effectsize::standardize_parameters(m)
  188. r.squaredGLMM(m)
  189. m = lmer(edss ~ log(ALA) + age + sex + Treatment_OFAMS + (1|eid),df)
  190. summary(m)
  191. effectsize::standardize_parameters(m)
  192. r.squaredGLMM(m)
  194. # 2.1.3 T2w number of lesions
  195. m = lmer(lesion_count ~ log(ALA) + (1|eid),df)
  196. summary(m)
  197. effectsize::standardize_parameters(m)
  198. # m = lmer(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS + (1|eid),df)
  199. # summary(m)
  200. # control also for intracranial volume
  201. m = lmer(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  202. summary(m)
  203. effectsize::standardize_parameters(m)
  205. # 2.2 Mixed negative binomial models --------------------------
  206. # 2.2.1 T1w new Lesions (Negative binomial models) ---------------------
  207. m = glmer(new_T1Gd_lesion ~ log(ALA) + (1|eid),df,family = binomial(link = cloglog))
  208. summary(m)
  209. m = glmer(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + (1|eid),df,family = binomial(link = cloglog))
  210. summary(m)
  211. # control also for intracranial volume
  212. m = glmer(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df,family = binomial(link = cloglog))
  213. summary(m)
  215. # 2.2.2 New relapse (Negative binomial models) ---------------------
  216. relapse = merge(relapse%>%dplyr::select(eid,session,RELAPSENEW),
  217. df%>%dplyr::select(eid,session,ALA,sex,age,Treatment_OFAMS),
  218. by=c("eid","session"))
  219. relapse$RELAPSENEW = ifelse(is.na(relapse$RELAPSENEW) == T, 0,relapse$RELAPSENEW)
  220. m = glmer(RELAPSENEW ~ log(ALA) + (1|eid),relapse,family = binomial(link = cloglog))
  221. summary(m)
  222. m = glmer(RELAPSENEW ~ log(ALA) +age + sex +Treatment_OFAMS+ (1|eid),relapse,family = binomial(link = cloglog))
  223. summary(m)
  225. # 3. Simple linear models ------------------------------------
  227. # 3.1 T2w number of lesions -----------------------------------------
  228. # including ICV
  229. m = lm(lesion_count ~ log(ALA) + age + sex + EstimatedTotalIntraCranialVol,df%>%filter(session==0))
  230. summary(m)
  231. effectsize::standardize_parameters(m)
  232. m = lm(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==12))
  233. summary(m)
  234. m = lm(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==24))
  235. summary(m)
  236. # not including ICV
  237. m = lm(lesion_count ~ log(ALA) + age + sex ,df%>%filter(session==0))
  238. summary(m)
  239. m = lm(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==12))
  240. summary(m)
  241. m = lm(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==24))
  242. summary(m)
  244. # 3.2 Brain volume -----------------------------------------
  245. # including ICV
  246. m = lm(TotalVol ~ log(ALA) + age + sex + EstimatedTotalIntraCranialVol,df%>%filter(session==0))
  247. summary(m)
  248. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==12))
  249. summary(m)
  250. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==24))
  251. summary(m)
  252. # not including ICV
  253. m = lm(TotalVol ~ log(ALA) + age + sex ,df%>%filter(session==0))
  254. summary(m)
  255. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==12))
  256. summary(m)
  257. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==24))
  258. summary(m)
  261. # 3.3 PASAT -----------------------------------------
  262. m = lm(PASAT ~ log(ALA),df%>%filter(session==0))
  263. summary(m)
  264. m = lm(PASAT ~ log(ALA) + age + sex,df%>%filter(session==0))
  265. summary(m)
  266. m = lm(PASAT ~ log(ALA),df%>%filter(session==12))
  267. summary(m)
  268. m = lm(PASAT ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==12))
  269. summary(m)
  270. m = lm(PASAT ~ log(ALA),df%>%filter(session==24))
  271. summary(m)
  272. m = lm(PASAT ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==24))
  273. summary(m)
  275. # 3.4 EDSS -----------------------------------------
  276. m = lm(edss ~ log(ALA),df%>%filter(session==0))
  277. summary(m)
  278. m = lm(edss ~ log(ALA) + age + sex,df%>%filter(session==0))
  279. summary(m)
  280. m = lm(edss ~ log(ALA),df%>%filter(session==12))
  281. summary(m)
  282. effectsize::standardize_parameters(m)
  283. m = lm(edss ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==12))
  284. summary(m)
  285. m = lm(edss ~ log(ALA),df%>%filter(session==24))
  286. summary(m)
  287. effectsize::standardize_parameters(m)
  288. m = lm(edss ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==24))
  289. summary(m)
  290. effectsize::standardize_parameters(m)
  291. # 3.5 T1w new Lesions (Negative binomial models) ---------------------
  292. # BL
  293. m = glm(new_T1Gd_lesion ~ log(ALA),df%>%filter(session==0),family = binomial(link = cloglog))
  294. summary(m)
  295. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS ,df%>%filter(session==0),family = binomial(link = cloglog))
  296. summary(m)
  297. # control also for intracranial volume
  298. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==0),family = binomial(link = cloglog))
  299. summary(m)
  300. # 12 months
  301. m = glm(new_T1Gd_lesion ~ log(ALA),df%>%filter(session==12),family = binomial(link = cloglog))
  302. summary(m)
  303. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS ,df%>%filter(session==12),family = binomial(link = cloglog))
  304. summary(m)
  305. # control also for intracranial volume
  306. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==12),family = binomial(link = cloglog))
  307. summary(m)
  308. # 24 months
  309. m = glm(new_T1Gd_lesion ~ log(ALA),df%>%filter(session==24),family = binomial(link = cloglog))
  310. summary(m)
  311. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS ,df%>%filter(session==24),family = binomial(link = cloglog))
  312. summary(m)
  313. # control also for intracranial volume
  314. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==24),family = binomial(link = cloglog))
  315. summary(m)
  317. # 4. Mediation analyses --------------------------------------
  318. detach(package:lmerTest,unload = T) # lmerTest needs to leave for these functions to work
  319. # 4.1 Brain vol > ALA > EDSS----------------------------------
  320. # select and log transform
  321. df2 = df %>% dplyr::select(eid,age,sex,Treatment_OFAMS,ALA,edss,TotalVol,lesion_count) #%>% na.omit
  322. df2$ALA = log(df2$ALA)
  323. # models
  324. fit.mediator = lmer(ALA ~ age + sex + Treatment_OFAMS + TotalVol + (1|eid),df2)
  325. fit.dv = lmer(edss ~ age + sex + Treatment_OFAMS + ALA + TotalVol + (1|eid),df2)
  326. # mediation
  327. results1 <- mediation::mediate(fit.mediator, fit.dv, mediator='ALA', treat = 'TotalVol')
  328. summary(results1)
  330. # 4.2 T1w lesions > ALA > EDSS--------------------------------
  331. # models
  332. fit.mediator = lmer(ALA ~ age + sex + Treatment_OFAMS + lesion_count + (1|eid),df2)
  333. fit.dv = lmer(edss ~ age + sex + Treatment_OFAMS + ALA + lesion_count + (1|eid),df2)
  334. # mediation
  335. results <- mediation::mediate(fit.mediator, fit.dv, mediator='ALA', treat = 'lesion_count')
  336. summary(results)
  338. # 5. Longitudinal predictions of ALA -------------------------
  339. # prep df
  340. long.df = merge(df %>% dplyr::filter(session == 0)%>%dplyr::select(eid,ALA,EstimatedTotalIntraCranialVol,Treatment_OFAMS),long, id.vars = "eid")
  341. # check associations of ALA with THE ANNUAL RATE OF CHANGE in ...
  342. ## EDSS
  343. m=lm(EDSS_diff~log(ALA),data=long.df)
  344. summary(m)
  345. m=lm(EDSS_diff ~log(ALA)+age + sex + Treatment_OFAMS,data=long.df)
  346. summary(m)
  347. effectsize::standardize_parameters(m)
  349. ## PASAT
  350. m=lm(PASAT_diff~log(ALA),data=long.df)
  351. summary(m)
  352. m=lm(PASAT_diff ~log(ALA)+age + sex + Treatment_OFAMS,data=long.df)
  353. summary(m)
  355. ## T2w lesions
  356. m=lm(lesion_count_diff~log(ALA),data=long.df)
  357. summary(m)
  358. m=lm(lesion_count_diff ~log(ALA)+age + sex + Treatment_OFAMS+EstimatedTotalIntraCranialVol,data=long.df)
  359. summary(m)
  361. ## Brain volume
  362. m=lm(TotalVol_diff~log(ALA),data=long.df)
  363. summary(m)
  364. m=lm(TotalVol_diff ~log(ALA)+age + sex + Treatment_OFAMS+EstimatedTotalIntraCranialVol,data=long.df)
  365. summary(m)
  367. ## [[total]] T1w lesions
  368. m=lm(T1wLesions~log(ALA),data=long.df)
  369. summary(m)
  370. m=lm(T1wLesions ~log(ALA)+age + sex + Treatment_OFAMS+EstimatedTotalIntraCranialVol,data=long.df)
  371. summary(m)
  373. ## relapse rate / annual relapses during 12 study years
  374. m=lm(relapse_rate~log(ALA),data=long.df)
  375. summary(m)
  376. effectsize::standardize_parameters(m)
  377. m=lm(relapse_rate ~log(ALA)+age + sex + Treatment_OFAMS,data=long.df)
  378. summary(m)
  379. effectsize::standardize_parameters(m)
  381. ## non-log transformed
  382. m=lm(relapse_rate~(ALA),data=long.df)
  383. summary(m)
  384. m=lm(relapse_rate ~(ALA)+age + sex + Treatment_OFAMS,data=long.df)
  385. summary(m)
  387. ## relapse rate / annual relapses during 12 study years + the year before
  388. m=lm(relapse_rate_prior~log(ALA),data=long.df)
  389. summary(m)
  390. m=lm(relapse_rate_prior ~log(ALA)+age + sex + Treatment_OFAMS,data=long.df)
  391. summary(m)
  392. effectsize::standardize_parameters(m)
  394. ## relapses prior baseline
  395. m=lm(relapses_12mnths_before_baseline~log(ALA),data=long.df)
  396. summary(m)
  397. m=lm(relapses_12mnths_before_baseline ~log(ALA)+age + sex + Treatment_OFAMS,data=long.df)
  398. summary(m)
  409. # 6. Disease Duration instead of Age as Covariate-------------
  418. # 2. Analyse -------------------------------------------------
  419. # 2.1 Mixed linear models ------------------------------------
  420. # 2.1.1 PASAT
  421. library(lmerTest)
  422. m = lmer(PASAT ~ log(ALA) + (1|eid),df)
  423. summary(m)
  424. m = lmer(PASAT ~ log(ALA) + age + sex + Treatment_OFAMS + (1|eid),df)
  425. summary(m)
  426. m = lmer(PASAT ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS + (1|eid),df)
  427. summary(m)
  428. m = lmer(PASAT ~ log(ALA) +age + DiseaseDuration + sex + Treatment_OFAMS + (1|eid),df)
  429. summary(m)
  431. # 2.1.2 brain volume ***
  432. m = lmer(TotalVol ~ log(ALA) + EstimatedTotalIntraCranialVol + (1|eid),df)
  433. summary(m)
  434. effectsize::standardize_parameters(m)
  435. r.squaredGLMM(m)
  437. m = lmer(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  438. summary(m)
  439. effectsize::standardize_parameters(m)
  440. r.squaredGLMM(m)
  442. m = lmer(TotalVol ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  443. summary(m)
  444. effectsize::standardize_parameters(m)
  445. r.squaredGLMM(m)
  448. # # 2.1.2.1 GM volume *
  449. # m = lmer(TotalGrayVol ~ log(ALA) + EstimatedTotalIntraCranialVol + (1|eid),df)
  450. # summary(m)
  451. # effectsize::standardize_parameters(m)
  452. # m = lmer(TotalGrayVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  453. # summary(m)
  454. # effectsize::standardize_parameters(m)
  455. #
  456. # # 2.1.2.2 WM volume
  457. # m = lmer(TotalWMVol ~ log(ALA) + EstimatedTotalIntraCranialVol + (1|eid),df)
  458. # summary(m)
  459. # effectsize::standardize_parameters(m)
  460. # m = lmer(TotalWMVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  461. # summary(m)
  462. # effectsize::standardize_parameters(m)
  464. # 2.1.3 EDSS
  465. m = lmer(edss ~ log(ALA) + (1|eid),df)
  466. summary(m)
  467. effectsize::standardize_parameters(m)
  468. r.squaredGLMM(m)
  469. m = lmer(edss ~ log(ALA) + age + sex + Treatment_OFAMS + (1|eid),df)
  470. summary(m)
  472. m = lmer(edss ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS + (1|eid),df)
  473. summary(m)
  474. effectsize::standardize_parameters(m)
  475. r.squaredGLMM(m)
  477. # 2.1.3 T2w number of lesions
  478. m = lmer(lesion_count ~ log(ALA) + (1|eid),df)
  479. summary(m)
  480. effectsize::standardize_parameters(m)
  482. m = lmer(lesion_count ~ log(ALA) + DiseaseDuration+ (1|eid),df)
  483. summary(m)
  484. effectsize::standardize_parameters(m)
  486. # m = lmer(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS + (1|eid),df)
  487. # summary(m)
  488. # control also for intracranial volume
  489. m = lmer(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  490. summary(m)
  491. effectsize::standardize_parameters(m)
  493. m = lmer(lesion_count ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df)
  494. summary(m)
  496. # 2.2 Mixed negative binomial models --------------------------
  497. # 2.2.1 T1w new Lesions (Negative binomial models) ---------------------
  498. m = glmer(new_T1Gd_lesion ~ log(ALA) + (1|eid),df,family = binomial(link = cloglog))
  499. summary(m)
  500. m = glmer(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + (1|eid),df,family = binomial(link = cloglog))
  501. summary(m)
  502. m = glmer(new_T1Gd_lesion ~ log(ALA) +DiseaseDuration + sex + Treatment_OFAMS + (1|eid),df,family = binomial(link = cloglog))
  503. summary(m)
  505. # control also for intracranial volume
  506. m = glmer(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df,family = binomial(link = cloglog))
  507. summary(m)
  508. m = glmer(new_T1Gd_lesion ~ log(ALA) +DiseaseDuration + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol + (1|eid),df,family = binomial(link = cloglog))
  509. summary(m)
  511. # 2.2.2 New relapse (Negative binomial models) ---------------------
  512. relapse = merge(relapse%>%dplyr::select(eid,session,RELAPSENEW),
  513. df%>%dplyr::select(eid,session,ALA,sex,age,Treatment_OFAMS),
  514. by=c("eid","session"))
  515. relapse$RELAPSENEW = ifelse(is.na(relapse$RELAPSENEW) == T, 0,relapse$RELAPSENEW)
  516. m = glmer(RELAPSENEW ~ log(ALA) + (1|eid),relapse,family = binomial(link = cloglog))
  517. summary(m)
  518. m = glmer(RELAPSENEW ~ log(ALA) +age + sex +Treatment_OFAMS+ (1|eid),relapse,family = binomial(link = cloglog))
  519. summary(m)
  521. # 3. Simple linear models ------------------------------------
  523. # 3.1 T2w number of lesions -----------------------------------------
  524. # including ICV
  525. m = lm(lesion_count ~ log(ALA) + DiseaseDuration + sex + EstimatedTotalIntraCranialVol,df%>%filter(session==0))
  526. summary(m)
  527. effectsize::standardize_parameters(m)
  528. m = lm(lesion_count ~ log(ALA) + age + DiseaseDuration + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==0))
  529. summary(m)
  530. m = lm(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==0))
  531. summary(m)
  533. m = lm(lesion_count ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==12))
  534. summary(m)
  535. m = lm(lesion_count ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==24))
  536. summary(m)
  537. # not including ICV
  538. m = lm(lesion_count ~ log(ALA) + DiseaseDuration + sex ,df%>%filter(session==0))
  539. summary(m)
  540. m = lm(lesion_count ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS,df%>%filter(session==12))
  541. summary(m)
  542. m = lm(lesion_count ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==24))
  543. summary(m)
  545. # 3.2 Brain volume -----------------------------------------
  546. # including ICV
  547. m = lm(TotalVol ~ log(ALA) + age + sex + EstimatedTotalIntraCranialVol,df%>%filter(session==0))
  548. summary(m)
  549. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==12))
  550. summary(m)
  551. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==24))
  552. summary(m)
  553. # not including ICV
  554. m = lm(TotalVol ~ log(ALA) + age + sex ,df%>%filter(session==0))
  555. summary(m)
  556. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==12))
  557. summary(m)
  558. m = lm(TotalVol ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==24))
  559. summary(m)
  562. # 3.3 PASAT -----------------------------------------
  563. m = lm(PASAT ~ log(ALA),df%>%filter(session==0))
  564. summary(m)
  565. m = lm(PASAT ~ log(ALA) + age + sex,df%>%filter(session==0))
  566. summary(m)
  567. m = lm(PASAT ~ log(ALA),df%>%filter(session==12))
  568. summary(m)
  569. m = lm(PASAT ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==12))
  570. summary(m)
  571. m = lm(PASAT ~ log(ALA),df%>%filter(session==24))
  572. summary(m)
  573. m = lm(PASAT ~ log(ALA) + age + sex + Treatment_OFAMS,df%>%filter(session==24))
  574. summary(m)
  576. # 3.4 EDSS -----------------------------------------
  577. m = lm(edss ~ log(ALA),df%>%filter(session==0))
  578. summary(m)
  579. m = lm(edss ~ log(ALA) + DiseaseDuration + sex,df%>%filter(session==0))
  580. summary(m)
  581. m = lm(edss ~ log(ALA),df%>%filter(session==12))
  582. summary(m)
  583. effectsize::standardize_parameters(m)
  584. m = lm(edss ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS,df%>%filter(session==12))
  585. summary(m)
  586. m = lm(edss ~ log(ALA),df%>%filter(session==24))
  587. summary(m)
  588. effectsize::standardize_parameters(m)
  589. m = lm(edss ~ log(ALA) + DiseaseDuration + sex + Treatment_OFAMS,df%>%filter(session==24))
  590. summary(m)
  591. effectsize::standardize_parameters(m)
  592. # 3.5 T1w new Lesions (Negative binomial models) ---------------------
  593. # BL
  594. m = glm(new_T1Gd_lesion ~ log(ALA),df%>%filter(session==0),family = binomial(link = cloglog))
  595. summary(m)
  596. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS ,df%>%filter(session==0),family = binomial(link = cloglog))
  597. summary(m)
  598. # control also for intracranial volume
  599. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==0),family = binomial(link = cloglog))
  600. summary(m)
  601. # 12 months
  602. m = glm(new_T1Gd_lesion ~ log(ALA),df%>%filter(session==12),family = binomial(link = cloglog))
  603. summary(m)
  604. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS ,df%>%filter(session==12),family = binomial(link = cloglog))
  605. summary(m)
  606. # control also for intracranial volume
  607. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==12),family = binomial(link = cloglog))
  608. summary(m)
  609. # 24 months
  610. m = glm(new_T1Gd_lesion ~ log(ALA),df%>%filter(session==24),family = binomial(link = cloglog))
  611. summary(m)
  612. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS ,df%>%filter(session==24),family = binomial(link = cloglog))
  613. summary(m)
  614. # control also for intracranial volume
  615. m = glm(new_T1Gd_lesion ~ log(ALA) +age + sex + Treatment_OFAMS + EstimatedTotalIntraCranialVol,df%>%filter(session==24),family = binomial(link = cloglog))
  616. summary(m)
  618. # 4. Mediation analyses --------------------------------------
  619. detach(package:lmerTest,unload = T) # lmerTest needs to leave for these functions to work
  620. # 4.1 Brain vol > ALA > EDSS----------------------------------
  621. # select and log transform
  622. df2 = df %>% dplyr::select(eid,age,sex,DiseaseDuration, Treatment_OFAMS,ALA,edss,TotalVol,lesion_count) #%>% na.omit
  623. df2$ALA = log(df2$ALA)
  624. # models
  625. fit.mediator = lmer(ALA ~ DiseaseDuration + sex + Treatment_OFAMS + TotalVol + (1|eid),df2)
  626. fit.dv = lmer(edss ~ DiseaseDuration + sex + Treatment_OFAMS + ALA + TotalVol + (1|eid),df2)
  627. # mediation
  628. results <- mediation::mediate(fit.mediator, fit.dv, mediator='ALA', treat = 'TotalVol')
  629. summary(results)
  631. # 4.2 T1w lesions > ALA > EDSS--------------------------------
  632. # models
  633. fit.mediator = lmer(ALA ~ DiseaseDuration + sex + Treatment_OFAMS + lesion_count + (1|eid),df2)
  634. fit.dv = lmer(edss ~ DiseaseDuration + sex + Treatment_OFAMS + ALA + lesion_count + (1|eid),df2)
  635. # mediation
  636. results <- mediation::mediate(fit.mediator, fit.dv, mediator='ALA', treat = 'lesion_count')
  637. summary(results)
  639. # 5. Longitudinal predictions of ALA -------------------------
  640. # prep df
  641. long.df = merge(df %>% dplyr::filter(session == 0)%>%dplyr::select(eid,ALA,EstimatedTotalIntraCranialVol,Treatment_OFAMS,DiseaseDuration),long, id.vars = "eid")
  642. # check associations of ALA with THE ANNUAL RATE OF CHANGE in ...
  643. ## EDSS
  644. m=lm(EDSS_diff~log(ALA),data=long.df)
  645. summary(m)
  646. m=lm(EDSS_diff ~log(ALA)+ DiseaseDuration + sex + Treatment_OFAMS,data=long.df)
  647. summary(m)
  648. effectsize::standardize_parameters(m)
  650. ## PASAT
  651. m=lm(PASAT_diff~log(ALA),data=long.df)
  652. summary(m)
  653. m=lm(PASAT_diff ~log(ALA)+DiseaseDuration + sex + Treatment_OFAMS,data=long.df)
  654. summary(m)
  656. ## T2w lesions
  657. m=lm(lesion_count_diff~log(ALA),data=long.df)
  658. summary(m)
  659. m=lm(lesion_count_diff ~log(ALA)+DiseaseDuration + sex + Treatment_OFAMS+EstimatedTotalIntraCranialVol,data=long.df)
  660. summary(m)
  662. ## Brain volume
  663. m=lm(TotalVol_diff~log(ALA),data=long.df)
  664. summary(m)
  665. m=lm(TotalVol_diff ~log(ALA)+DiseaseDuration + sex + Treatment_OFAMS+EstimatedTotalIntraCranialVol,data=long.df)
  666. summary(m)
  668. ## [[total]] T1w lesions
  669. m=lm(T1wLesions~log(ALA),data=long.df)
  670. summary(m)
  671. m=lm(T1wLesions ~log(ALA)+DiseaseDuration + sex + Treatment_OFAMS+EstimatedTotalIntraCranialVol,data=long.df)
  672. summary(m)
  674. ## relapse rate / annual relapses during 12 study years
  675. m=lm(relapse_rate~log(ALA),data=long.df)
  676. summary(m)
  677. effectsize::standardize_parameters(m)
  678. m=lm(relapse_rate ~log(ALA)+DiseaseDuration + sex + Treatment_OFAMS,data=long.df)
  679. summary(m)
  680. effectsize::standardize_parameters(m)
  682. ## non-log transformed
  683. m=lm(relapse_rate~(ALA),data=long.df)
  684. summary(m)
  685. m=lm(relapse_rate ~(ALA)+DiseaseDuration + sex + Treatment_OFAMS,data=long.df)
  686. summary(m)
  688. ## relapse rate / annual relapses during 12 study years + the year before
  689. m=lm(relapse_rate_prior~log(ALA),data=long.df)
  690. summary(m)
  691. m=lm(relapse_rate_prior ~log(ALA)+DiseaseDuration + sex + Treatment_OFAMS,data=long.df)
  692. summary(m)
  693. effectsize::standardize_parameters(m)
  695. ## relapses prior baseline
  696. m=lm(relapses_12mnths_before_baseline~log(ALA),data=long.df)
  697. summary(m)
  698. m=lm(relapses_12mnths_before_baseline ~log(ALA)+DiseaseDuration + sex + Treatment_OFAMS,data=long.df)
  699. summary(m)

analyse.R at commit dcb02fc, no license · at the source

Overview

Authors: Max Korbmacher1,2, Kjell-Morten Myhr1,3, Stig Wergeland1,3,4, Kristin Wesnes5, Øivind Torkildsen1,3
  1. Department of Clinical Medicine, University of Bergen, Bergen, Norway
  2. Department for Radiography, Western Norway University of Applied Sciences, Bergen, Norway
  3. Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
  4. Norwegian MS-Registry and Biobank, Helse Bergen, Haukeland University Hospital, Bergen, Norway
  5. Department of Neurology, St. Olav’s Hospital, Trondheim, Norway
Institutions: Western Norway University of Applied Sciences (Norway); University of Bergen (Norway); Haukeland University Hospital (Norway); St Olav's University Hospital (Norway)
Journal: Frontiers in neurology, volume 17, article 1796427
Dates: received 26 January 2026; accepted 25 March 2026; published online 27 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fneur.2026.1796427 · PMID 42124854 · PMCID PMC13159527 · OpenAlex W7155995891
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population), clinical / translational (subfield)
Keywords: alpha-linolenic acid (ALA), brain volume, magnetic resonance imaging (MRI), MS, multiple sclerosis, replication
Topic: Fatty Acid Research and Health (Nutrition and Dietetics, Nursing), according to OpenAlex
Citations: not cited yet (Europe PMC); 22 references in the paper

Abstract

Objective: To replicate and extend recent findings, suggesting that higher serum alpha-linolenic acid (ALA) levels are associated with reduced disease activity and progression in multiple sclerosis (MS).

Methods: We reanalysed clinical trial data from 85 people with MS who had serum ALA using magnetic resonance imaging (MRI) and clinical (EDSS, PASAT) assessments, collected for 2 years, with additional follow-up at 12-years. Linear and mixed models were used to assess the relationship between ALA and clinical and MRI outcomes. Mediation analyses tested whether ALA mediated associations between brain volume or T2 lesion load and disability.

Results: ALA measures were consistent over time (κ = 0.83). Higher ALA predicted lower EDSS (β = −0.41, 95% CI [−0.73, −0.08]) and larger brain volume (β = 0.22, 95% CI [0.09, 0.36]). ALA was a non-significant mediator of brain volume or lesion effects on EDSS and did not predict long-term clinical or cognitive changes.

Discussion: We replicate prior associations between higher serum ALA levels and reduced disability in MS and extend these by showing a beneficial association of serum ALA with brain volume. However, ALA did not predict long-term progression, limiting its prognostic value.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

MaxKorbmacher/ALA

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: dcb02fc4fc1c85ee221d04ddbf15d9b8bce45893, 12 August 2025
Languages: R (3)
Size: 4 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: reshape2 (3 files), tidyverse (3 files), easystats (2 files), lme4 (2 files), lmerTest (2 files), data.table (1 file), ggplot2 (1 file), ggpubr (1 file), ggseg (1 file), psych (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: github.com/MaxKorbmacher/ALA

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;
  • 3 scripts, each with its path and the digest of its content;
  • 5 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.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: Data access can be requested after obtaining ethics approval. Analysis code is available on GitHub: https://github.com/MaxKorbmacher/ALA. Requests to access these datasets should be directed to .

Reproduced under the paper's license (CC BY), from the paper cited above.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 22 references.

Cite

This paper

Korbmacher, M., Myhr, K.-M., Wergeland, S., Wesnes, K., & Torkildsen, Ø. (2026). Alpha-linolenic acid associations with disability and brain volume in multiple sclerosis: a brief replication report. Frontiers in neurology, 17, 1796427. https://doi.org/10.3389/fneur.2026.1796427

BibTeX

@article{korbmacher2026alpha,
author = {Korbmacher, Max and Myhr, Kjell-Morten and Wergeland, Stig and Wesnes, Kristin and Torkildsen, Øivind},
title = {{Alpha-linolenic acid associations with disability and brain volume in multiple sclerosis: a brief replication report}},
journal = {Frontiers in neurology},
year = {2026},
month = apr,
volume = {17},
pages = {1796427},
publisher = {Frontiers Media SA},
issn = {1664-2295},
doi = {10.3389/fneur.2026.1796427},
url = {https://doi.org/10.3389/fneur.2026.1796427},
pmid = {42124854},
pmcid = {PMC13159527}
}

RIS

TY - JOUR
AU - Korbmacher, Max
AU - Myhr, Kjell-Morten
AU - Wergeland, Stig
AU - Wesnes, Kristin
AU - Torkildsen, Øivind
TI - Alpha-linolenic acid associations with disability and brain volume in multiple sclerosis: a brief replication report
T2 - Frontiers in neurology
J2 - Front Neurol
PY - 2026
DA - 2026/04/27
VL - 17
SP - 1796427
SN - 1664-2295
PB - Frontiers Media SA
DO - 10.3389/fneur.2026.1796427
UR - https://doi.org/10.3389/fneur.2026.1796427
LA - en
ER -

CSL-JSON

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"id": "10.3389/fneur.2026.1796427",
"type": "article-journal",
"title": "Alpha-linolenic acid associations with disability and brain volume in multiple sclerosis: a brief replication report",
"container-title": "Frontiers in neurology",
"author": [
{
"family": "Korbmacher",
"given": "Max"
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{
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"given": "Kjell-Morten"
},
{
"family": "Wergeland",
"given": "Stig"
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{
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"given": "Kristin"
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{
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"given": "Øivind"
}
],
"container-title-short": "Front Neurol",
"volume": "17",
"page": "1796427",
"DOI": "10.3389/fneur.2026.1796427",
"PMID": "42124854",
"PMCID": "PMC13159527",
"ISSN": "1664-2295",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fneur.2026.1796427",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
27
]
]
}
}

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