OSCR

Strenuous physical activity is associated with a younger age of amyotrophic lateral sclerosis onset in two independent cohorts.

Code ↔ Paper

6 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 6 matches
  1. [1] § Materials and methods › Logistic regression ↔ MND Register and Case Control Analysis Script.R, lines 246–333 · score 0.94 · binomial logistic regression, Hosmer Lemeshow, Box Tidwell, quasi complete separation, detect_separation, goodness
  2. [2] § Materials and methods › Statistical analysis ↔ Post-Mortem Cohort Analysis Script.R, lines 42–94 · score 0.73 · Kruskal Wallis, post hoc, disease duration, Tukey, Levene, Shapiro
  3. [3] § Materials and methods › Statistical analysis ↔ Post-Mortem Cohort Analysis Script.R, lines 42–94 · score 0.68 · eta squared, Kruskal Wallis, way ANOVA
  4. [4] § Materials and methods › Logistic regression ↔ MND Register and Case Control Analysis Script.R, lines 246–333 · score 0.67 · Binomial logistic regression, disease status, MND Register, yes, female, highly active
  5. [5] § Materials and methods › Statistical analysis ↔ MND Register and Case Control Analysis Script.R, lines 53–105 · score 0.66 · Kruskal Wallis, post hoc, Levene, Shapiro, Dunn, variance
  6. [6] § Results › Age at symptom onset is decreased in highly active individuals with ALS ↔ Post-Mortem Cohort Analysis Script.R, lines 1–40 · score 0.53 · post hoc, post mortem cohort, adj, Cramer, Fisher, Bonferroni

Paper

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The authors' code

R · 333 lines · 15 KB · CC-BY-4.0 · 3 matches

  1. install.packages("rstatix")
  2. install.packages("tidyverse")
  3. install.packages("dplyr")
  4. install.packages("ggplot2")
  5. install.packages("effectsize")
  6. install.packages("vcd")
  7. install.packages("rcompanion")
  8. install.packages("survival")
  9. install.packages("survminer")
  10. install.packages("car")
  11. install.packages("glmtoolbox")
  12. install.packages("pscl")
  13. install.packages("pROC")
  14. install.packages("broom")
  15. install.packages("detectseparation")
  16. library(rstatix)
  17. library(tidyverse)
  18. library(dplyr)
  19. library(ggplot2)
  20. library(effectsize)
  21. library(vcd)
  22. library(rcompanion)
  23. library(survival)
  24. library(survminer)
  25. library(car)
  26. library(glmtoolbox)
  27. library(pscl)
  28. library(pROC)
  29. library(broom)
  30. library(detectseparation)
  33. #Demographics, Categorical Variables
  34. gender_table <- data.frame("male" = c(27, 63, 9), "female" = c(9, 44, 14), row.names = c("Highly Active", "Active", "Inactive"))
  35. chi_result_gender <- chisq.test(gender_table)
  36. print(chi_result_gender)
  37. cramers_v(gender_table, ci = 0.95) #Effect size
  39. gender_data <- matrix(c(27,9,
  40. 63,44,
  41. 9,14),
  42. nrow = 3, byrow = TRUE)
  43. dimnames(gender_data) <- list(Group = c("A", "B","C"), Outcome = c("X", "Y"))
  44. chisq.test(gender_data)
  45. pairwiseNominalIndependence(gender_data, fisher = FALSE, gtest = FALSE, chisq = TRUE, method = "fdr")
  47. onset_table <- data.frame("limbic" = c(29, 82, 14), "bulbar" = c(7, 20, 9), "unknown" = c(0, 5, 0), row.names = c("Highly Active", "Active", "Inactive"))
  48. fisher_result_onset <- fisher.test(onset_table)
  49. print(fisher_result_onset)
  51. cramers_v(onset_table, ci = 0.95) #Effect size
  53. #Demographics, Continuous Variables
  54. MND_Register_Data_FINAL_Feb_25_AOforSurvAna %>% mutate(
  55. AGEONSET = as.factor(AGEONSET),
  56. LPA_LEVEL_GROUP = as.factor(LPA_LEVEL_GROUP),
  57. C9STATUS = as.factor(C9STATUS)
  58. ) #Set variables as factor
  60. MND_Register_Data_FINAL_Feb_25_AOforSurvAna %>%
  61. group_by(LPA_LEVEL_GROUP) %>%
  62. shapiro_test(AGEONSET) #Test Normality
  64. MND_Register_Data_FINAL_Feb_25_AOforSurvAna %>%
  65. levene_test(AGEONSET ~ LPA_LEVEL_GROUP) #Test equal variance
  67. MND_Register_Data_FINAL_Feb_25_AOforSurvAna %>%
  68. group_by(LPA_LEVEL_GROUP) %>%
  69. get_summary_stats(AGEONSET, type = "mean_sd")
  71. MND_Register_Data_FINAL_Feb_25_AOforSurvAna %>%
  72. group_by(LPA_LEVEL_GROUP) %>%
  73. get_summary_stats(AGEONSET, type = "median_iqr")
  75. kruskal_result_ageonset <- kruskal_test(MND_Register_Data_FINAL_Feb_25_AOforSurvAna, AGEONSET ~ LPA_LEVEL_GROUP)
  76. print(kruskal_result_ageonset)
  77. MND_Register_Data_FINAL_Feb_25_AOforSurvAna %>%
  78. kruskal_effsize(AGEONSET ~ LPA_LEVEL_GROUP, ci = TRUE) #Kruskal-Wallis Test
  80. MND_Register_Data_FINAL_Feb_25_AOforSurvAna %>%
  81. dunn_test(AGEONSET ~ LPA_LEVEL_GROUP, p.adjust.method = "bonferroni") #Dunn Bonferroni Post-Hoc
  84. #Cox Proportional Hazard regression, Age Onset
  86. #Set variables as factor and set level
  87. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$C9STATUS <- factor(
  88. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$C9STATUS,
  89. levels = c("normal", "expanded", "not tested"))
  91. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$LPA_LEVEL_GROUP <- factor(
  92. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$LPA_LEVEL_GROUP,
  93. levels = c("Not Active", "Active", "Highly Active"))
  95. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$GENDER <- factor(
  96. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$GENDER,
  97. levels = c("Female", "Male"))
  99. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$SITEONSET <- factor(
  100. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$SITEONSET,
  101. levels = c("Limb", "Bulbar", "Unknown"))
  103. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$HEADINJURY <- factor(
  104. MND_Register_Data_FINAL_Feb_25_AOforSurvAna$HEADINJURY,
  105. levels = c("No", "Yes"))
  107. #Cox models - Age Onset
  108. #Age Onset - by group
  109. cox_model_ao <- coxph(
  110. Surv(AGEONSET, EVENT) ~ LPA_LEVEL_GROUP + GENDER + C9STATUS + HEADINJURY,
  111. data = MND_Register_Data_FINAL_Feb_25_AOforSurvAna)
  112. summary(cox_model_ao)
  114. cox.zph(cox_model_ao) #Test proportional hazard assumption
  116. #Plot cumulative hazard
  117. plot_data_cox_model_ao <- with(MND_Register_Data_FINAL_Feb_25_AOforSurvAna,
  118. data.frame(LPA_LEVEL_GROUP=levels(LPA_LEVEL_GROUP),
  119. GENDER = levels(GENDER)[1],
  120. C9STATUS = levels(C9STATUS)[1],
  121. HEADINJURY = levels(HEADINJURY)[1]))
  122. fit_ao <- survfit(cox_model_ao, plot_data_cox_model_ao)
  123. cox_model_ao_ggplot <- ggsurvplot(fit_ao, data = plot_data_cox_model_ao,
  124. legend.labs = c("Inactive", "Active", "Highly Active"),
  125. xlab = "Age at Symptom Onset (Years)",
  126. fun = "cumhaz",
  127. ylab = "Cumulative Hazard",
  128. xlim = c(50,90), break.time.by = 10,
  129. ylim = c(0,10),
  130. risk.table = FALSE, conf.int = FALSE,
  131. legend.title = "LPA Group",
  132. palette = c("#0072B2", "#E69F00", "#009E73"), #colourblind friendly
  133. ggtheme = theme_survminer())
  134. cox_model_ao_ggplot
  136. #Age Onset - by score
  137. cox_model_ao_LPAScore <- coxph(
  138. Surv(AGEONSET, EVENT) ~ LPA_SCORE + GENDER + C9STATUS + HEADINJURY,
  139. data = MND_Register_Data_FINAL_Feb_25_AOforSurvAna)
  140. summary(cox_model_ao_LPAScore)
  142. cox.zph(cox_model_ao_LPAScore) #Test proportional hazard assumption
  144. #Checking for multicolinearity
  145. multico_check_ao <- lm(
  146. AGEONSET ~ LPA_LEVEL_GROUP + GENDER + SITEONSET + C9STATUS + HEADINJURY,
  147. data = MND_Register_Data_FINAL_Feb_25_AOforSurvAna)
  148. vif(multico_check_ao)
  150. #Cox Proportional Hazard regression, Survival
  152. #Set variables as factor and set level
  153. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$C9STATUS <- factor(
  154. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$C9STATUS,
  155. levels = c("normal", "expanded", "not tested"))
  157. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$LPA_LEVEL_GROUP <- factor(
  158. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$LPA_LEVEL_GROUP,
  159. levels = c("Not Active", "Active", "Highly Active"))
  161. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$GENDER <- factor(
  162. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$GENDER,
  163. levels = c("Female", "Male"))
  165. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$SITEONSET <- factor(
  166. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$SITEONSET,
  167. levels = c("Limb", "Bulbar", "Unknown"))
  169. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$HEADINJURY <- factor(
  170. MND_Register_Data_FINAL_Feb_25_DDforSurvAna$HEADINJURY,
  171. levels = c("No", "Yes"))
  173. #Cox models - Survival
  174. #Survival - by group
  175. cox_model_dd <- coxph(
  176. Surv(TIMEDEATH_OR_LASTCHECK, SURVIVALSTATUS_NUM) ~ LPA_LEVEL_GROUP + GENDER + SITEONSET + C9STATUS + HEADINJURY + AGEONSET,
  177. data = MND_Register_Data_FINAL_Feb_25_DDforSurvAna)
  178. summary(cox_model_dd)
  180. cox.zph(cox_model_dd) #Test proportional hazard assumption
  182. #Plot cumulative survival
  183. plot_data_cox_model_dd <- with(MND_Register_Data_FINAL_Feb_25_DDforSurvAna,
  184. data.frame(LPA_LEVEL_GROUP=levels(LPA_LEVEL_GROUP),
  185. GENDER = levels(GENDER)[1],
  186. SITEONSET = levels(SITEONSET)[1],
  187. C9STATUS = levels(C9STATUS)[1],
  188. HEADINJURY = levels(HEADINJURY)[1],
  189. AGEONSET = mean(AGEONSET, na.rm = TRUE)))
  190. fit_dd <- survfit(cox_model_dd, plot_data_cox_model_dd)
  191. cox_model_dd_ggplot <- ggsurvplot(fit_dd, data = plot_data_cox_model_dd,
  192. legend.labs = c("Inactive", "Active", "Highly Active"),
  193. xlab = "Disease Duration (Years)",
  194. ylab = "Survival Probability",
  195. xlim = c(0,10), break.time.by = 2.5,
  196. ylim = c(0,1),
  197. risk.table = FALSE, conf.int = FALSE,
  198. legend.title = "LPA Group",
  199. palette = c("#0072B2", "#E69F00", "#009E73"), #colourblind friendly
  200. censor = FALSE,
  201. ggtheme = theme_survminer())
  202. cox_model_dd_ggplot
  204. #Survival - by score
  205. cox_model_dd_LPAScore <- coxph(
  206. Surv(TIMEDEATH_OR_LASTCHECK, SURVIVALSTATUS_NUM) ~ LPA_SCORE + GENDER + SITEONSET + C9STATUS + HEADINJURY + AGEONSET,
  207. data = MND_Register_Data_FINAL_Feb_25_DDforSurvAna)
  208. summary(cox_model_dd_LPAScore)
  210. cox.zph(cox_model_dd_LPAScore) #Test proportional hazard assumption
  212. #Checking for multicolinearity
  213. multico_check_dd <- lm(
  214. TIMEDEATH_OR_LASTCHECK ~ LPA_LEVEL_GROUP + GENDER + SITEONSET + C9STATUS + HEADINJURY + AGEONSET,
  215. data = MND_Register_Data_FINAL_Feb_25_DDforSurvAna)
  216. vif(multico_check_dd)
  218. #Linear Regression, Age Onset
  219. #Age onset - by group
  220. lm_ao <- lm(formula = AGEONSET ~ LPA_LEVEL_GROUP + HEADINJURY + C9STATUS + GENDER,
  221. data = MND_Register_Data_FINAL_Feb_25_AOforSurvAna)
  222. summary(lm_ao)
  224. #Age onset - by score
  225. lm_ao_LPAScore <- lm(formula = AGEONSET ~ LPA_SCORE + HEADINJURY + C9STATUS + GENDER,
  226. data = MND_Register_Data_FINAL_Feb_25_AOforSurvAna)
  227. summary(lm_ao_LPAScore)
  229. #Linear regression age onset plot
  230. lm_plot_ao <- ggplot(lm(formula = AGEONSET ~ LPA_SCORE + HEADINJURY + C9STATUS + GENDER,
  231. data = MND_Register_Data_FINAL_Feb_25_AOforSurvAna),aes(x=AGEONSET, y=LPA_SCORE)) +geom_point()+geom_smooth(method = lm)
  232. print(lm_plot_ao)
  234. C9orf72_statusAO <- MND_Register_Data_FINAL_Feb_25_AOforSurvAna$C9STATUS
  235. HI_AO <- MND_Register_Data_FINAL_Feb_25_AOforSurvAna$HEADINJURY
  236. lm_plot_ao_HIC9 <- ggplot(lm(formula = AGEONSET ~ LPA_SCORE + HEADINJURY + C9STATUS + GENDER,
  237. data = MND_Register_Data_FINAL_Feb_25_AOforSurvAna),aes(x=AGEONSET, y=LPA_SCORE)) +geom_point(aes(colour = HI_AO, shape = C9orf72_statusAO))+geom_smooth(method = lm)
  238. print(lm_plot_ao_HIC9)
  240. #Linear Regression, Disease Duration
  241. #Survival - by group
  242. lm_dd <- lm(formula = TIMEDEATH_OR_LASTCHECK ~ LPA_LEVEL_GROUP + HEADINJURY + C9STATUS + GENDER + SITEONSET + AGEONSET,
  243. data = MND_Register_Data_FINAL_Feb_25_DDforSurvAna)
  244. summary(lm_dd)
  246. #Survival - by score
  247. lm_dd_LPAScore <- lm(formula = TIMEDEATH_OR_LASTCHECK ~ LPA_SCORE + HEADINJURY + C9STATUS + GENDER+ SITEONSET + AGEONSET,
  248. data = MND_Register_Data_FINAL_Feb_25_DDforSurvAna)
  249. summary(lm_dd_LPAScore)
  251. #Linear regression survival plot
  252. lm_plot_dd <- ggplot(lm(formula = TIMEDEATH_OR_LASTCHECK ~ LPA_SCORE + HEADINJURY + C9STATUS + GENDER + SITEONSET + AGEONSET,
  253. data = MND_Register_Data_FINAL_Feb_25_DDforSurvAna),aes(x=TIMEDEATH_OR_LASTCHECK, y=LPA_SCORE)) +geom_point()+geom_smooth(method = lm)
  254. print(lm_plot_dd)
  256. C9orf72_statusDD <- MND_Register_Data_FINAL_Feb_25_DDforSurvAna$C9STATUS
  257. HI_DD <- MND_Register_Data_FINAL_Feb_25_DDforSurvAna$HEADINJURY
  258. lm_plot_dd_HIC9 <- ggplot(lm(formula = TIMEDEATH_OR_LASTCHECK ~ LPA_SCORE + HEADINJURY + C9STATUS + GENDER + SITEONSET + AGEONSET,
  259. data = MND_Register_Data_FINAL_Feb_25_DDforSurvAna),aes(x=TIMEDEATH_OR_LASTCHECK, y=LPA_SCORE)) +geom_point(aes(colour = HI_DD, shape = C9orf72_statusDD))+geom_smooth(method = lm)
  260. print(lm_plot_dd_HIC9)
  262. #Case Control Analysis
  264. #Set variables as factor and set level
  265. Case_vs_Control_Data_MND_Register$LPA_LEVEL_GROUP <- factor(
  266. Case_vs_Control_Data_MND_Register$LPA_LEVEL_GROUP,
  267. levels = c("Not Active", "Active", "Highly Active"))
  269. Case_vs_Control_Data_MND_Register$GENDER <- factor(
  270. Case_vs_Control_Data_MND_Register$GENDER,
  271. levels = c("Female", "Male"))
  273. Case_vs_Control_Data_MND_Register$HEADINJURY <- factor(
  274. Case_vs_Control_Data_MND_Register$HEADINJURY,
  275. levels = c("No", "Yes"))
  277. Case_vs_Control_Data_MND_Register$DISEASE_STATUS <- as.numeric(
  278. as.character(Case_vs_Control_Data_MND_Register$DISEASE_STATUS))
  280. #Case Control Binomial Logistic Regression
  281. CaseControl_LogReg <- glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  282. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit"))
  284. summary(glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  285. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit")))
  287. exp(coef(glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  288. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit")))) #Generate odds ratios
  290. confint(glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  291. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit"))) #Profile likelihood confidence intervals
  293. exp(confint(glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  294. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit")))) #Convert confidence intervals to odds ratio scale
  296. hltest(glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  297. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit")), verbose = TRUE) #Hosmer-Lemeshow test for goodness of fit
  299. pR2(glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  300. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit"))) #Pseudo r squared
  302. roc(Case_vs_Control_Data_MND_Register$DISEASE_STATUS, fitted(glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  303. data = Case_vs_Control_Data_MND_Register, family = binomial(link = "logit")))) #ROC curve measure of discrimination
  304. pvals <- coef(summary(CaseControl_LogReg))[, 4] #Extract p values
  305. print(pvals)
  307. #Case Control test logistic regression assumptions
  308. #Linearity of the logit
  309. model <- glm(DISEASE_STATUS ~ AGE, family = binomial,
  310. data = Case_vs_Control_Data_MND_Register)
  311. boxTidwell(DISEASE_STATUS ~ AGE, data = Case_vs_Control_Data_MND_Register)
  313. Case_vs_Control_Data_MND_Register$logit <- log(model$fitted.values / (AGE = model$fitted.values))
  314. ggplot(Case_vs_Control_Data_MND_Register, aes(x = AGE, y = logit)) +
  315. geom_point() +
  316. geom_smooth(method = "loess")
  318. #Checking for multicolinearity
  319. multico_check_CaseControl <- lm(
  320. DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  321. data = Case_vs_Control_Data_MND_Register)
  322. vif(multico_check_CaseControl)
  324. #Checking for complete or quasi complete separation
  325. CaseControl_detectsep <- glm(DISEASE_STATUS ~ LPA_LEVEL_GROUP + AGE + GENDER + HEADINJURY,
  326. data = Case_vs_Control_Data_MND_Register,
  327. family = binomial("logit"),
  328. method = "detect_separation")
  329. CaseControl_detectsep
  331. #Checking influential observations and outliers
  332. plot(CaseControl_LogReg, which = 1)
  333. plot(CaseControl_LogReg, which = 4)

MND Register and Case Control Analysis Script.R, under CC-BY-4.0 · at the source

Overview

Authors: Jaimee Kennedy1,2, Ahmad Al Khleifat1, Andrew King2,3, Safa Al-Sarraj2,3, Ammar Al-Chalabi1,4, Jacqueline C Mitchell1, Claire Troakes1,2
ORCID iDs: Jaimee Kennedy, Ahmad Al Khleifat, Andrew King, Ammar Al-Chalabi, Claire Troakes
  1. Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King's College London, London SE5 9RT, UK
  2. London Neurodegenerative Diseases Brain Bank, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, London SE8 8AF, UK
  3. Clinical Neuropathology Department, Kings College Hospital NHS Trust, London SE5 9RS, UK
  4. Department of Clinical Neurosciences, King’s College Hospital NHS Trust, London SE5 9RS, UK
Institutions: King's College London (United Kingdom); King's College Hospital (United Kingdom)
Journal: Brain communications, volume 8, issue 4, article fcag272
Dates: received 3 December 2025; accepted 7 May 2026; published online 13 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag272 · PMID 42494493 · PMCID PMC13392466 · OpenAlex W7168182195
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Machine learning
Keywords: motor neuron disease, amyotrophic lateral sclerosis, physical activity, age onset, exercise
Topic: Amyotrophic Lateral Sclerosis Research (Neurology, Medicine), according to OpenAlex
Funding: Alzheimer’s Society and Alzheimer’s Research UK; Motor Neurone Disease Association; Betty Messenger Charitable Foundation
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disease characterized predominantly by degeneration of both upper and lower motor neurons, thought to occur due to an interplay between genetics and environmental factors. Physical activity has been suggested as a potential risk factor for ALS; however, the exact role of exercise in the onset and progression of the disease is still unclear.

We assessed lifetime physical activity in two independent ALS cohorts: post-mortem brain donors from the London Neurodegenerative Diseases Brain Bank (n = 139) and patients from the Motor Neurone Disease (MND) Register of England, Wales and Northern Ireland (n = 166 cases, 196 controls).

In both cohorts, highly active individuals developed ALS symptoms at a significantly younger age, 54.2 years (mean, standard deviation = 7.5) in the post-mortem cohort and 58.0 years (median, interquartile range = 15) in the MND Register, compared with 63.9 years (mean, standard deviation = 11.5) and 63.0 years (median, interquartile range = 17.5) in inactive individuals, respectively [one-way analysis of variance (ANOVA), F(2, 136) = 6.10, P = 0.003, η2 = 0.08, 95% confidence interval (CI) 0.02–1.00 and Kruskal–Wallis, H(2) = 7.39, P = 0.02, η2 = 0.03, 95% CI 0.003–0.12]. Cox regression showed a higher hazard of earlier onset in highly active patients [post-mortem: hazard ratio (HR) 2.84, 95% CI 1.55–5.26, P = 0.0008; MND Register: HR 2.34, 95% CI 1.30–4.23, P = 0.005].

Our findings suggest that strenuous physical activity may be associated with a significantly younger age of ALS onset, replicated in both the post-mortem and MND Register cohorts, but not with an increased risk of developing ALS. Logistic regression analysis confirmed that neither highly active [odds ratio (OR) 1.43, 95% CI 0.69–2.99, P = 0.333] nor being active (OR 1.30, 95% CI 0.72–2.37, P = 0.386) was significantly associated with ALS risk, whereas a history of head injury was (OR 1.72, 95% CI 1.03–2.88, P = 0.038). These results suggest that strenuous exercise may accelerate disease onset in predisposed individuals, while the role of head injury requires further study and the findings may in fact indicate reverse causality.

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

Repository

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Zenodo 18508813

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 2 files, 2 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: car (2 files), easystats (2 files), ggplot2 (2 files), rstatix (2 files), survival (2 files), tidyverse (2 files), broom (1 file), pROC (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: doi.org/10.5281/zenodo.18508813

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;
  • 2 scripts, each with its path and the digest of its content;
  • 6 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

Access to the MND register can be requested here https://mndregister.ac.uk and access to the MND Association MND Collections here https://www.mndassociation.org/research/our-research/uk-mnd-collections-samples. Data derived from the Brain Bank can be accessed by request to the authors. All R code generated for this manuscript has been uploaded to an online repository and can be accessed using the following link: https://doi.org/10.5281/zenodo.18508813.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 3 funders, 37 references.

Cite

This paper

Kennedy, J., Al Khleifat, A., King, A., Al-Sarraj, S., Al-Chalabi, A., Mitchell, J. C., & Troakes, C. (2026). Strenuous physical activity is associated with a younger age of amyotrophic lateral sclerosis onset in two independent cohorts. Brain communications, 8(4), fcag272. https://doi.org/10.1093/braincomms/fcag272

BibTeX

@article{kennedy2026strenuous,
author = {Kennedy, Jaimee and Al Khleifat, Ahmad and King, Andrew and Al-Sarraj, Safa and Al-Chalabi, Ammar and Mitchell, Jacqueline C and Troakes, Claire},
title = {{Strenuous physical activity is associated with a younger age of amyotrophic lateral sclerosis onset in two independent cohorts}},
journal = {Brain communications},
year = {2026},
month = jul,
volume = {8},
number = {4},
pages = {fcag272},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag272},
url = {https://doi.org/10.1093/braincomms/fcag272},
pmid = {42494493},
pmcid = {PMC13392466}
}

RIS

TY - JOUR
AU - Kennedy, Jaimee
AU - Al Khleifat, Ahmad
AU - King, Andrew
AU - Al-Sarraj, Safa
AU - Al-Chalabi, Ammar
AU - Mitchell, Jacqueline C
AU - Troakes, Claire
TI - Strenuous physical activity is associated with a younger age of amyotrophic lateral sclerosis onset in two independent cohorts
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/07/13
VL - 8
IS - 4
SP - fcag272
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag272
UR - https://doi.org/10.1093/braincomms/fcag272
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag272",
"type": "article-journal",
"title": "Strenuous physical activity is associated with a younger age of amyotrophic lateral sclerosis onset in two independent cohorts",
"container-title": "Brain communications",
"author": [
{
"family": "Kennedy",
"given": "Jaimee"
},
{
"family": "Al Khleifat",
"given": "Ahmad"
},
{
"family": "King",
"given": "Andrew"
},
{
"family": "Al-Sarraj",
"given": "Safa"
},
{
"family": "Al-Chalabi",
"given": "Ammar"
},
{
"family": "Mitchell",
"given": "Jacqueline C"
},
{
"family": "Troakes",
"given": "Claire"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "4",
"page": "fcag272",
"DOI": "10.1093/braincomms/fcag272",
"PMID": "42494493",
"PMCID": "PMC13392466",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag272",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
13
]
]
}
}

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

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