OSCR

Computational phenotyping of effort-based decision-making in type-2 diabetes on and off semaglutide.

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] § Results › Time since semaglutide injection did not affect effort-based decision-making ↔ code/analyses/5_primary_analyses.R, lines 247–306 · score 0.71 · Uncontrolled Eating, Emotional Eating, Cognitive Restrained, TFEQ
  2. [2] § Methods › Recruitment and data acquisition ↔ code/functions/screener_parsing_fun.R, lines 53–85 · score 0.62 · chronic disease, daily medication, neurological, GLP, treatment, diabetes
  3. [3] § Methods › Procedure › Self-report questionnaires ↔ code/analyses/5_primary_analyses.R, lines 247–306 · score 0.57 · factor Eating Questionnaire, Monetary, MCQ, TFEQ
  4. [4] § Methods › Procedure › Compliance checks and exclusion criteria ↔ code/analyses/1_screening.R, lines 45–83 · score 0.56 · severe neurological disorder, B2, English
  5. [5] § Results › Type-2 diabetes subjects show a reduced bias to accept effort for reward ↔ code/analyses/6_non_diabetic_comparison.R, lines 209–252 · score 0.56 · 18.5–25, BMI matched, acceptance bias, semaglutide
  6. [6] § Results ↔ code/analyses/3_task_model_based.R, lines 370–421 · score 0.56 · Posterior predictive checks, winning model, model comparison, parabolic, fit

Paper

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The authors' code

R · 436 lines · 16 KB · no license · 2 matches

  1. #################################################################################################
  2. #######################---------------- Primary analyses ----------------########################
  3. #################################################################################################
  5. ### In this script:
  6. # (1) Prepare data
  7. # (2) Task data - model based
  8. # (3) Questionnaire data
  10. # Set working directory
  11. here::i_am("github/semaglutide-study/code/analyses/5_primary_analyses.R")
  12. setwd(here::here())
  14. # source functions
  15. source("github/semaglutide-study/code/functions/helper_funs.R")
  16. source("github/semaglutide-study/code/functions/plot_funs.R")
  18. # source dataset
  19. main_data <- readRDS("data/processed_data/main_data.RDS")
  21. # source parameter estimates
  22. m3_para_treat_s1_params <- readRDS(here::here("github/semaglutide-study/data/model_fits/treatment_s1/m3_para_treat_s1_params.RDS"))
  23. m3_para_control_params <- readRDS(here::here("github/semaglutide-study/data/model_fits/controls/m3_para_control_params.RDS"))
  25. # load required packages
  26. librarian::shelf(ggplot2, ggpubr, tidyverse, dplyr, stringr, purrr, here, janitor, MatchIt, PupillometryR,
  27. writexl, lubridate, magrittr, pushoverr, nlme, gridExtra, cmdstanr, rstanarm, bayestestR, hms)
  29. # Color pallet
  30. color_pal <- c("#E94D36", "#5B9BD5", "#71AB48", "#FDC219", "#8456B8", "#FF7236", "#1FD5B3", "#F781BE")
  32. ### (1) Prepare data -----------------------------------------------
  34. # Merge datasets for analyses
  35. data <- main_data$demographic_data %>%
  36. select(subj_id, group, age, gender, bmi, antidepressant) %>%
  37. mutate(antidepressant = ifelse(is.na(antidepressant), 0, antidepressant)) %>%
  38. left_join(main_data$glp_data %>%
  39. filter(session == 1) %>%
  40. select(subj_id, start_date_glp, side_effects_glp, glp_dose_mg, hours_since_injection, testing_day, local_testing_time) %>%
  41. mutate(time_on_glp = abs(difftime(as_date(start_date_glp), testing_day, units="days"))),
  42. by = "subj_id") %>%
  43. left_join(main_data$questionnaire_data %>%
  44. filter(session == 1) %>%
  45. select(subj_id, aes_sumScore, bdi_sumScore, findrisc_sumScore, mcq_discounting_rate, mctq_MSF_SC, meq_sumScore,
  46. ocir_sumScore, daq_sumScore, shaps_sumScore, tfeq_cr_sumScore, tfeq_ue_sumScore, tfeq_ee_sumScore,
  47. ipaq_sumScore, last_meal_time, last_meal_size, snack, snack_time, hunger_rating, cgl, cgl_measure,cgl_unit, hunger_rating),
  48. by = "subj_id") %>%
  49. left_join(main_data$task_meta_data %>%
  50. filter(session == 1) %>%
  51. select(subj_id, start_time)) %>%
  52. left_join(rbind(m3_para_treat_s1_params$individual_params %>%
  53. pivot_wider(id_cols = subj_id, names_from = parameter,
  54. values_from = c(estimate, hdi_lower, hdi_upper)),
  55. m3_para_control_params$individual_params %>%
  56. pivot_wider(id_cols = subj_id, names_from = parameter,
  57. values_from = c(estimate, hdi_lower, hdi_upper))),
  58. by = "subj_id") %>%
  59. # make MCTQ result numeric (minutes since 00:00)
  60. add_column(mctq_continuous = period_to_seconds(hm(.$mctq_MSF_SC))/60,
  61. .before = "mctq_MSF_SC")
  63. # Parameters
  64. # Scale parameters to be between 0 and 1
  65. data %<>%
  66. ungroup %>%
  67. mutate(across(c(estimate_kE, estimate_kR, estimate_a), rescale))
  69. # Visualize distributions
  70. ggplot(gather(data %>% select(c(estimate_kE:estimate_a))) %>% na.omit(),
  71. aes(value)) +
  72. geom_histogram(bins = 10) +
  73. facet_wrap(~key, scales = 'free_x')
  75. # Questionnaires
  76. # Scale parameters to be between 0 and 1
  77. data %<>%
  78. ungroup %>%
  79. mutate_at(colnames(data)[c(14:18, 21:27)], rescale)
  81. # Visualize distributions
  82. ggplot(gather(data %>% select(colnames(data)[c(5, 14:18, 21:27)])) %>% na.omit(),
  83. aes(value)) +
  84. geom_histogram(bins = 10) +
  85. facet_wrap(~key, scales = 'free_x')
  87. data %<>%
  88. # positively skewed
  89. mutate_at(c("bdi_sumScore", "daq_sumScore",
  90. "mcq_discounting_rate", "mctq_continuous",
  91. "ocir_sumScore", "shaps_sumScore"), sqrt) %>%
  92. # negatively skewed
  93. mutate_at(c("aes_sumScore"), norm_neg_skew)
  95. ### (2) Task data - model based -----------------------------------------------
  97. ### Effort sensitivity
  98. data %>%
  99. group_by(group) %>%
  100. summarise(mean_kE = mean(estimate_kE),
  101. sd_kE = sd(estimate_kE))
  102. # Check if variances are equal
  103. var.test(data$estimate_kE ~ data$group)
  104. # => variances are equal -> t test
  105. t.test(data$estimate_kE ~ data$group, var.equal = TRUE)
  106. kE_glm <- stan_glm(estimate_kE ~ group, data = data,
  107. iter = 10000, seed = 123)
  108. kE_glm$coefficients
  109. hdi(kE_glm)
  110. kE_plot <- raincloud_plot(dat = data, title = "",
  111. xlab = " ", ylab = "Effort sensitivity",
  112. predictor_var = "group", outcome_var = "estimate_kE",
  113. predictor_tick_lab = c("control", "treatment"), col = c(color_pal[1], color_pal[2]),
  114. include_grouping = FALSE, direction = "horizontal", scale_seq = c(-0, 1,0.25)) +
  115. theme(legend.position = "none") +
  116. ggtitle("Effort sensitivity")
  118. ### Reward sensitivity
  119. data %>%
  120. group_by(group) %>%
  121. summarise(mean_kR = mean(estimate_kR),
  122. sd_kR = sd(estimate_kR))
  123. # Check if variances are equal
  124. var.test(data$estimate_kR ~ data$group)
  125. # => variances are not equal -> welch test
  126. t.test(data$estimate_kR ~ data$group, var.equal = FALSE)
  127. kR_glm <- stan_glm(estimate_kR ~ group, data = data,
  128. iter = 10000, seed = 123)
  129. kR_glm$coefficients
  130. hdi(kR_glm)
  131. kR_plot <- raincloud_plot(dat = data, title = "",
  132. xlab = " ", ylab = "Reward sensitivity",
  133. predictor_var = "group", outcome_var = "estimate_kR",
  134. predictor_tick_lab = c("control", "treatment"), col = c(color_pal[1], color_pal[2]),
  135. include_grouping = FALSE, direction = "horizontal", scale_seq = c(-0, 1,0.25)) +
  136. theme(legend.position = "none") +
  137. ggtitle("Reward sensitivity")
  139. ### Choice bias
  140. data %>%
  141. group_by(group) %>%
  142. summarise(mean_a = mean(estimate_a),
  143. sd_a = sd(estimate_a))
  144. # Check if variances are equal
  145. var.test(data$estimate_a ~ data$group)
  146. # => variances are equal -> t test
  147. t.test(data$estimate_a ~ data$group, var.equal = FALSE)
  148. # Bayesian GLM
  149. a_glm <- stan_glm(estimate_a ~ group, data = data,
  150. iter = 100000, seed = 123)
  151. a_glm$coefficients
  152. hdi(a_glm)
  153. a_plot <- raincloud_plot(dat = data, title = "",
  154. xlab = " ", ylab = "Choice bias",
  155. predictor_var = "group", outcome_var = "estimate_a",
  156. predictor_tick_lab = c("control", "treatment"), col = c(color_pal[1], color_pal[2]),
  157. include_grouping = FALSE, direction = "horizontal", scale_seq = c(-0, 1, 0.25)) +
  158. theme(legend.position = "none") +
  159. ggtitle("Choice bias")
  162. ### (3) Questionnaire data -----------------------------------------------
  164. ### Psychiatric questionnaires ----
  165. # Correlation between questionnaires
  166. psych_cor <- data %>%
  167. select(bmi, aes_sumScore, bdi_sumScore, ocir_sumScore, daq_sumScore, shaps_sumScore) %>%
  168. rename(aes = aes_sumScore, bdi = bdi_sumScore, ocir = ocir_sumScore, daq = daq_sumScore, shaps = shaps_sumScore) %>%
  169. cor()
  170. corrplot::corrplot(psych_cor, method="circle")
  172. # Group comparison
  173. # AES
  174. # frequentist
  175. data %>%
  176. group_by(group) %>%
  177. summarise(mean_aes = mean(aes_sumScore),
  178. sd_aes = sd(aes_sumScore))
  179. t.test(aes_sumScore ~ group, data = data)
  180. aes_f_glm <- glm(aes_sumScore ~ group, data = data, family = "gaussian")
  181. summary(aes_f_glm)
  182. # bayesian
  183. aes_b_glm <- stan_glm(aes_sumScore ~ group, data = data,
  184. iter = 10000, seed = 123)
  185. aes_b_glm$coefficients
  186. hdi(aes_b_glm)
  188. # BDI
  189. data %>%
  190. group_by(group) %>%
  191. summarise(mean_bdi = mean(bdi_sumScore),
  192. sd_bdi = sd(bdi_sumScore))
  193. t.test(bdi_sumScore ~ group, data = data)
  194. bdi_f_glm <- glm(bdi_sumScore ~ group, data = data, family = "gaussian")
  195. summary(bdi_f_glm)
  196. # bayesian
  197. bdi_b_glm <- stan_glm(bdi_sumScore ~ group, data = data,
  198. iter = 10000, seed = 123)
  199. bdi_b_glm$coefficients
  200. hdi(bdi_b_glm)
  202. # OCIR
  203. # frequentist
  204. data %>%
  205. group_by(group) %>%
  206. summarise(mean_ocir = mean(ocir_sumScore),
  207. sd_ocir = sd(ocir_sumScore))
  208. t.test(ocir_sumScore ~ group, data = data)
  209. ocir_f_glm <- glm(ocir_sumScore ~ group, data = data, family = "gaussian")
  210. summary(ocir_f_glm)
  211. # bayesian
  212. ocir_b_glm <- stan_glm(ocir_sumScore ~ group, data = data,
  213. iter = 10000, seed = 123)
  214. ocir_b_glm$coefficients
  215. hdi(ocir_b_glm)
  217. # DAQ
  218. # frequentist
  219. data %>%
  220. group_by(group) %>%
  221. summarise(mean_daq = mean(daq_sumScore),
  222. sd_daq = sd(daq_sumScore))
  223. t.test(daq_sumScore ~ group, data = data)
  224. daq_f_glm <- glm(daq_sumScore ~ group, data = data, family = "gaussian")
  225. summary(daq_f_glm)
  226. # bayesian
  227. daq_b_glm <- stan_glm(daq_sumScore ~ group, data = data,
  228. iter = 10000, seed = 123)
  229. daq_b_glm$coefficients
  230. hdi(daq_b_glm)
  232. # SHAPS
  233. # frequentist
  234. data %>%
  235. group_by(group) %>%
  236. summarise(mean_shaps = mean(shaps_sumScore),
  237. sd_shaps = sd(shaps_sumScore))
  238. t.test(shaps_sumScore ~ group, data = data)
  239. shaps_f_glm <- glm(shaps_sumScore ~ group, data = data, family = "gaussian")
  240. summary(shaps_f_glm)
  241. # bayesian
  242. shaps_b_glm <- stan_glm(shaps_sumScore ~ group, data = data,
  243. iter = 10000, seed = 123)
  244. shaps_b_glm$coefficients
  245. hdi(shaps_b_glm)
  247. # Monetary discounting
  248. # frequentist
  249. data %>%
  250. group_by(group) %>%
  251. summarise(mean_mcq = mean(mcq_discounting_rate),
  252. sd_mcq = sd(mcq_discounting_rate))
  253. t.test(mcq_discounting_rate ~ group, data = data)
  254. mcq_glm <- glm(mcq_discounting_rate ~ group, data = data, family = "gaussian")
  255. summary(mcq_glm)
  256. # bayesian
  257. mcq_glm <- stan_glm(mcq_discounting_rate ~ group, data = data,
  258. iter = 10000, seed = 123)
  259. mcq_glm$coefficients
  260. hdi(mcq_glm)
  262. ### Three factor eating questionnaire ----
  263. # Correlation between questionnaires
  264. eat_cor <- data %>%
  265. select(tfeq_cr_sumScore, tfeq_ue_sumScore, tfeq_ee_sumScore) %>%
  266. rename(cognitive_restraint = tfeq_cr_sumScore, uncontrolled_eating = tfeq_ue_sumScore, emotional_eating = tfeq_ee_sumScore) %>%
  267. cor()
  268. corrplot::corrplot(eat_cor, method="circle")
  270. # Cognitive restrained
  271. # frequentist
  272. data %>%
  273. group_by(group) %>%
  274. summarise(mean_cr = mean(tfeq_cr_sumScore),
  275. sd_cr = sd(tfeq_cr_sumScore))
  276. t.test(tfeq_cr_sumScore ~ group, data = data)
  277. tfeq_cr_f_glm <- glm(tfeq_cr_sumScore ~ group, data = data, family = "gaussian")
  278. summary(tfeq_cr_f_glm)
  279. # bayesian
  280. tfeq_cr_glm <- stan_glm(tfeq_cr_sumScore ~ group, data = data,
  281. iter = 10000, seed = 123)
  282. tfeq_cr_glm$coefficients
  283. hdi(tfeq_cr_glm)
  285. # Uncontrolled eating
  286. # frequentist
  287. data %>%
  288. group_by(group) %>%
  289. summarise(mean_ue = mean(tfeq_ue_sumScore),
  290. sd_ue = sd(tfeq_ue_sumScore))
  291. t.test(tfeq_ue_sumScore ~ group, data = data)
  292. tfeq_ue_f_glm <- glm(tfeq_ue_sumScore ~ group, data = data, family = "gaussian")
  293. summary(tfeq_ue_f_glm)
  294. # bayesian
  295. tfeq_ue_glm <- stan_glm(tfeq_ue_sumScore ~ group, data = data,
  296. iter = 10000, seed = 123)
  297. tfeq_ue_glm$coefficients
  298. hdi(tfeq_ue_glm)
  300. tfeq_ue_plot <- raincloud_plot(dat = data, title = "",
  301. xlab = " ", ylab = "Uncontrolled Eating",
  302. predictor_var = "group", outcome_var = "tfeq_ue_sumScore",
  303. predictor_tick_lab = c("control", "treatment"), col = c(color_pal[1], color_pal[2]),
  304. include_grouping = FALSE, direction = "horizontal", scale_seq = c(-0, 1,0.25)) +
  305. theme(legend.position = "none") +
  306. ggtitle("Uncontrolled Eating")
  308. # Emotional eating
  309. # frequentist
  310. data %>%
  311. group_by(group) %>%
  312. summarise(mean_ee = mean(tfeq_ee_sumScore),
  313. sd_ee = sd(tfeq_ee_sumScore))
  314. t.test(tfeq_ee_sumScore ~ group, data = data)
  315. tfeq_ee_f_glm <- glm(tfeq_ee_sumScore ~ group, data = data, family = "gaussian")
  316. summary(tfeq_ee_f_glm)
  317. # bayesian
  318. tfeq_ee_glm <- stan_glm(tfeq_ee_sumScore ~ group, data = data,
  319. iter = 10000, seed = 123)
  320. tfeq_ee_glm$coefficients
  321. hdi(tfeq_ee_glm)
  323. # Controlling for antidepressants
  324. tfeq_cr_f_glm <- glm(tfeq_cr_sumScore ~ group + antidepressant, data = data, family = "gaussian")
  325. summary(tfeq_cr_f_glm)
  327. tfeq_ue_f_glm <- glm(tfeq_ue_sumScore ~ group + antidepressant, data = data, family = "gaussian")
  328. summary(tfeq_ue_f_glm)
  330. tfeq_ee_f_glm <- glm(tfeq_ee_sumScore ~ group + antidepressant, data = data, family = "gaussian")
  331. summary(tfeq_ee_f_glm)
  333. # Effect of time on medication in Ozempic group?
  334. time_on_glp_ue_f_glm <- glm(tfeq_ue_sumScore ~ time_on_glp,
  335. data = data %>% filter(group == "treatment"),
  336. family = "gaussian")
  337. summary(time_on_glp_ue_f_glm)
  339. time_on_glp_ue_glm <- stan_glm(tfeq_ue_sumScore ~ time_on_glp, data = data %>% filter(group == "treatment"),
  340. iter = 10000, seed = 123)
  341. time_on_glp_ue_glm$coefficients
  342. hdi(time_on_glp_ue_glm)
  344. ggplot(data %>% filter(group == "treatment"),
  345. aes(time_on_glp, tfeq_ue_sumScore)) +
  346. geom_point() +
  347. stat_smooth(method = "lm",
  348. formula = y ~ x,
  349. geom = "smooth")
  352. ### Circadian questionnaires ----
  353. # Correlation between questionnaires
  354. circ_cor <- data %>%
  355. select(mctq_continuous, meq_sumScore) %>%
  356. rename(MCTQ = mctq_continuous, MEQ = meq_sumScore) %>%
  357. cor()
  358. corrplot::corrplot(circ_cor, method="circle")
  360. # MCTQ
  361. # frequentist
  362. data %>%
  363. group_by(group) %>%
  364. summarise(mean_mctq = mean(mctq_continuous),
  365. sd_mctq = sd(mctq_continuous))
  366. t.test(mctq_continuous ~ group, data = data)
  367. mctq_f_glm <- glm(mctq_continuous ~ group, data = data, family = "gaussian")
  368. summary(mctq_f_glm)
  369. # bayesian
  370. mctq_glm <- stan_glm(mctq_continuous ~ group, data = data,
  371. iter = 10000, seed = 123)
  372. mctq_glm$coefficients
  373. hdi(mctq_glm)
  375. # MEQ
  376. # frequentist
  377. data %>%
  378. group_by(group) %>%
  379. summarise(mean_meq = mean(meq_sumScore),
  380. sd_meq = sd(meq_sumScore))
  381. t.test(meq_sumScore ~ group, data = data)
  382. meq_f_glm <- glm(meq_sumScore ~ group, data = data, family = "gaussian")
  383. summary(meq_f_glm)
  384. # bayesian
  385. meq_glm <- stan_glm(meq_sumScore ~ group, data = data,
  386. iter = 10000, seed = 123)
  387. meq_glm$coefficients
  388. hdi(meq_glm)
  390. # Make chronotype
  391. data %<>%
  392. mutate(chronotype = case_when(meq_sumScore > 58 & hm(mctq_MSF_SC) < hm("02:30") ~ "early",
  393. meq_sumScore < 42 & hm(mctq_MSF_SC) > hm("05:30") ~ "late",
  394. is.na(mctq_MSF_SC) ~ "NA",
  395. .default = "intermediate"))
  397. data %>%
  398. tabyl(group, chronotype) %>%
  399. chisq.test()
  401. # Test relationship between chronotype and time of testing
  402. data %<>%
  403. mutate(local_testing_time = as.POSIXct(paste("01jan2000 ", local_testing_time, ":00", sep = ""),
  404. format = "%d%b%Y %H:%M:%S")) %>%
  405. mutate(local_testing_time = case_when(as.POSIXlt(local_testing_time)$hour < 4 ~ .$local_testing_time + days(1),
  406. .default = .$local_testing_time ))
  408. chronotype_testing_time <- glm(data$local_testing_time %>% as.numeric() ~
  409. data$chronotype, family = "gaussian")
  410. summary(chronotype_testing_time)
  412. time_chrono_plot <- raincloud_plot(dat = data, title = "",
  413. xlab = " ", ylab = "Testing Time",
  414. predictor_var = "chronotype", outcome_var = "local_testing_time",
  415. predictor_tick_lab = c("early", "intermediate", "late"), col = c(color_pal[1], color_pal[2], color_pal[3]),
  416. include_grouping = FALSE, direction = "horizontal", scale_seq = NULL) +
  417. theme(legend.position = "none") +
  418. ggtitle("Time of testing by Chronotype")
  420. time_meq_plot <- ggplot(data = data,
  421. aes_string(y = "meq_sumScore", x = "local_testing_time")) +
  422. geom_point(aes(color = color_pal[3])) +
  423. stat_smooth(method = "lm",
  424. formula = y ~ x,
  425. geom = "smooth",
  426. aes(color = color_pal[3]))

5_primary_analyses.R at commit 24265e3, no license · at the source

Overview

Authors: Sara Z Mehrhof1, Hugo Fleming1, Camilla L Nord1,2
ORCID iDs: Sara Z Mehrhof, Hugo Fleming, Camilla L Nord
  1. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  2. Department of Psychiatry, University of Cambridge, Cambridge, UK
Institutions: MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom)
Journal: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, volume 51, issue 11, pages 1924-1931
Dates: received 23 December 2025; accepted 17 April 2026; published online 6 May 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41386-026-02433-y · PMID 42086986 · PMCID PMC13597482 · OpenAlex W7160301471
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), depression (population), cognitive (subfield)
Methods: Statistics, Machine learning, Connectivity
Keywords: Motivation, Human behaviour, Decision, Depression
MeSH: Decision Making*, Diabetes Mellitus, Type 2*, Glucagon-Like Peptides*, Hypoglycemic Agents*, Motivation*, Aged, Female, Humans, Male, Middle Aged, Phenotype, Reward, Semaglutide (* major topic)
Topic: Diabetes Treatment and Management (Endocrinology, Diabetes and Metabolism, Medicine), according to OpenAlex
Funding: AXA Research Fund (Le Fonds AXA pour la Recherche) (G102329); Wellcome Trust (226490/Z/22/Z); National Institute for Health Research (NIHR) (BRC-1215-20014); Medical Research Council (MC_UU_00030/12)
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

Motivation plays a fundamental role in human behaviour. Dopaminergic pathways have long been implicated in individual differences in motivation. Emerging evidence suggests such neural mechanisms interact with metabolic processes to coordinate energy expenditure with energy resources, thereby linking motivation with metabolic health. We ask whether a cognitive-computational index of motivation—reliably linked to neuropsychiatric symptoms—is altered in the context of type-2 diabetes and treatment with a GLP-1 agonist (semaglutide). In a pre-registered experiment, we quantified computational effort-based decision-making parameters in participants with diabetes on (N = 58) or off (N = 54) semaglutide treatment, compared to two groups of matched controls without diabetes (N = 58 each). Subjects with type-2 diabetes showed a blunted acceptance bias, a computational parameter describing the bias to accept effort for reward. This effect was not driven by neuropsychiatric comorbidity or antidepressant use. Across all participants, we found that increasing diabetes risk linearly predicted reduced acceptance bias. Participants with diabetes treated with semaglutide did not show restored motivation. Metabolic ill-health is associated with reduced acceptance bias during motivational decision-making. This blunting mirrors—but is largely independent of—neuropsychiatric motivational deficits. This suggests metabolic ill-health is accompanied by a cognitive shift towards energy conservation, potentially contributing to comorbidity between metabolic ill-health and mental illness.

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

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OSF 7kmf5

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 0 files, 0 scripts
Software Heritage: not checked
Found in: the text, “Methods”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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At the source: osf.io/7kmf5

smehrhof/semaglutide-study

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Evidence: files inventoried
Commit: 24265e3830c49ab74ad2af6420ab1565961ab643, 23 June 2025
Languages: R (16), Stan (16)
Size: 83 files, 32 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Stan (19 files), ggplot2 (4 files), ggpubr (2 files), tidyverse (2 files), broom (1 file), cowplot (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
33 files
At the source: github.com/smehrhof/semaglutide-study

smehrhof/effort-study

License: MIT
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Commit: 80f3557d5fe4e159c46866d3259d05299d71660f, 16 June 2025
Languages: R (21), Stan (20), JavaScript (13)
Size: 136 files, 54 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Stan (28 files), ggplot2 (4 files), ggpubr (4 files), cowplot (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
56 files
At the source: github.com/smehrhof/effort-study

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

Tracing map

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  • 86 scripts, each with its path and the digest of its content;
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Data

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Data Availability Statement

The analysis code and data are openly available at github.com/smehrhof/semaglutide-study. The effort-expenditure task code is available at github.com/smehrhof/effort-study. A previous version of this article has been published as a pre-print on PsyArXiv: https://doi.org/10.31234/osf.io/4bkm9. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

The analysis code and data are openly available at github.com/smehrhof/semaglutide-study. The effort-expenditure task code is available at github.com/smehrhof/effort-study. A previous version of this article has been published as a pre-print on PsyArXiv: https://doi.org/10.31234/osf.io/4bkm9. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 13 MeSH terms, 4 funders, 40 references.

Cite

This paper

Mehrhof, S. Z., Fleming, H., & Nord, C. L. (2026). Computational phenotyping of effort-based decision-making in type-2 diabetes on and off semaglutide. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 51(11), 1924-1931. https://doi.org/10.1038/s41386-026-02433-y

BibTeX

@article{mehrhof2026computational,
author = {Mehrhof, Sara Z and Fleming, Hugo and Nord, Camilla L},
title = {{Computational phenotyping of effort-based decision-making in type-2 diabetes on and off semaglutide}},
journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology},
year = {2026},
month = may,
volume = {51},
number = {11},
pages = {1924--1931},
publisher = {Nature Publishing Group},
issn = {0893-133X},
doi = {10.1038/s41386-026-02433-y},
url = {https://doi.org/10.1038/s41386-026-02433-y},
pmid = {42086986},
pmcid = {PMC13597482}
}

RIS

TY - JOUR
AU - Mehrhof, Sara Z
AU - Fleming, Hugo
AU - Nord, Camilla L
TI - Computational phenotyping of effort-based decision-making in type-2 diabetes on and off semaglutide
T2 - Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
J2 - Neuropsychopharmacology
PY - 2026
DA - 2026/05/06
VL - 51
IS - 11
SP - 1924
EP - 1931
SN - 0893-133X
PB - Nature Publishing Group
DO - 10.1038/s41386-026-02433-y
UR - https://doi.org/10.1038/s41386-026-02433-y
LA - en
ER -

CSL-JSON

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