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The microdialysis-derived lactate-pyruvate gradient indicates anaerobic activity after brain injury.

Code ↔ Paper

15 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 15 matches
  1. [1] § Results › Substrate delivery and LPR fidelity ↔ 8_substrate_lpr_fidelity.R, lines 520–572 · score 0.87 · LPR fidelity, nested random intercepts, median glucose, LPR perfectly tracked, Patient random intercepts, hyperbolic LPR glucose
  2. [2] § Results › Outcome association ↔ 9_outcome.R, lines 1–81 · score 0.83 · LME models, GOSE categories, segment median, Linear contrasts, 5–6, 7–8
  3. [3] § Results › Linear parameters and the hyperbolic consequence › Characterisation ↔ 5_linear_LP_models.R, lines 1073–1131 · score 0.76 · Combined overlay comparing, Negative intercepts produce, Positive intercepts produce, cohort median trend, Dashed horizontal, LPR hyperbolae
  4. [4] § Results › Linear parameters and the hyperbolic consequence › Within-segment LPR error ↔ 6_hyperbolic_LPR_error.R, lines 1137–1214 · score 0.74 · relative LPR error, pyruvate concentration, maximum pyruvate, zero intercept, minimum pyruvate, LPR range
  5. [5] § Materials and methods › In vitro model ↔ 10_in_vitro.R, lines 1–60 · score 0.73 · electron transport chain, mitochondrial dysfunction, vitro, inhibitor, rotenone, model
  6. [6] § Results › Substrate delivery and LPR fidelity ↔ 8_substrate_lpr_fidelity.R, lines 61–118 · score 0.70 · higher glucose, median glucose, anaerobic activity, median LPR, physiological, substrate
  7. [7] § Results › Independent Uppsala cohort ↔ 8_substrate_lpr_fidelity.R, lines 520–572 · score 0.68 · residual SD, median glucose, LPR perfectly tracked, hyperbolic LPR glucose, median LPR, substrate
  8. [8] § Results › Explaining metabolic polarisation ↔ 7_never_always_LPR25.R, lines 1–78 · score 0.68 · hyperbolic LPR pyruvate, higher intercepts, linear parameters, S3, threshold, pyruvate relationships
  9. [9] § Results › In vitro validation ↔ 10_in_vitro.R, lines 228–263 · score 0.67 · vitro lactate pyruvate, OLS regression fits, 1–24 h, rotenone treated, ANCOVA, R2
  10. [10] § Materials and methods › Data integration and pre-processing ↔ 4_segmentation_stats.R, lines 1–72 · score 0.60 · pre processed, Segmentation pipeline, S4, S1, patient, linear
  11. [11] § Results › In vitro validation ↔ 10_in_vitro.R, lines 1–60 · score 0.59 · mitochondrial dysfunction, linear lactate pyruvate, inhibitor, ANCOVA, rotenone, shift
  12. [12] § Results › Linear parameters and the hyperbolic consequence › Characterisation ↔ 5_linear_LP_models.R, lines 987–1026 · score 0.54 · median R2, negative intercept, positive intercept, Stratification, IQR, linear
  13. [13] § Results › Explaining metabolic polarisation ↔ 7_never_always_LPR25.R, lines 1–78 · score 0.54 · discarded segments, linear parameters, threshold, retained segments, filtering, datapoints
  14. [14] § Results › Outcome association ↔ 9_outcome.R, lines 1–81 · score 0.53 · worse outcome, linear contrast, steeper, Death, CI, patients
  15. [15] § Results › Substrate delivery and LPR fidelity ↔ 8_substrate_lpr_fidelity.R, lines 61–118 · score 0.52 · anaerobic activity, LPR glucose, substrate, fidelity, tracks, validating

Paper

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The authors' code

R · 572 lines · 25 KB · MIT · 4 matches

  1. # ==============================================================================
  2. # Script: 8_substrate_lpr_fidelity.R
  3. # Manuscript relevance: 3.5, Fig. 5
  4. # ==============================================================================
  5. # PURPOSE:
  6. # Elucidate the relationship between substrate delivery (glucose), the linear
  7. # gradient (m), and the LPR at within-segment and between-segment levels.
  8. #
  9. # INPUT:
  10. # - 1_output/2_linear_segmentation/1_results.csv: Segment-annotated data
  11. #
  12. # OUTPUT:
  13. # - 1_output/8_substrate_lpr_fidelity/__execution_time.csv: Runtime log
  14. # - 1_output/8_substrate_lpr_fidelity/_1_model_summaries.txt: Full lmer summaries
  15. # - 1_output/8_substrate_lpr_fidelity/_1_stats.csv: Model coefficients and statistics
  16. # - 1_output/8_substrate_lpr_fidelity/0_notes.txt: Figure legend
  17. # - 1_output/8_substrate_lpr_fidelity/1_figure.png: 4-panel figure
  18. #
  19. # DATA FILTERS:
  20. # - Retained segments only: p6e_m > 0 (positive gradient)
  21. # - Observations with non-NA glucose
  22. #
  23. # ANALYSES:
  24. # ------------------------------------------------------------------------------
  25. # Seven LME models. All use Satterthwaite approximation for t-tests.
  26. # Models 1, 5 produce no figure panels.
  27. #
  28. # Model 1: Pyruvate ~ Glucose (POINT-LEVEL, no panel)
  29. # - Establishes pyruvate–glucose association; reported in text.
  30. # Unit: Point-level (individual datapoints)
  31. # Model: pyruvate ~ glucose + (1|patient/p6e_seg_index)
  32. # Weighting: None
  33. # Hypothesis: β_glucose > 0 (one-sided)
  34. # Test: Satterthwaite approximation
  35. #
  36. # Model 2: LPR ~ 1/Glucose in Type Pb segments (POINT-LEVEL → Panel A)
  37. # - Within-segment: LPR vs glucose when b > 0.
  38. # Modelled as lpr ~ inv_glucose to capture hyperbolic shape;
  39. # plotted on glucose x-axis.
  40. # Unit: Point-level, Type Pb segments only
  41. # Model: lpr ~ inv_glucose + (1|patient/p6e_seg_index)
  42. # Weighting: None
  43. # Hypothesis: β > 0 (one-sided; theory: b > 0 → LPR ↓ as glucose ↑)
  44. # Test: Satterthwaite approximation
  45. #
  46. # Model 3: LPR ~ 1/Glucose in Type Nb segments (POINT-LEVEL → Panel B)
  47. # - Within-segment: LPR vs glucose when b < 0.
  48. # Unit: Point-level, Type Nb segments only
  49. # Model: lpr ~ inv_glucose + (1|patient/p6e_seg_index)
  50. # Weighting: None
  51. # Hypothesis: β < 0 (one-sided; theory: b < 0 → LPR ↑ as glucose ↑)
  52. # Test: Satterthwaite approximation
  53. #
  54. # Model 4: LPR ~ m (SEGMENT-LEVEL → Panel C)
  55. # - How faithfully does LPR track the underlying gradient?
  56. # Unit: Segment-level (median LPR, gradient per segment)
  57. # Model: median_lpr ~ m + (1|patient)
  58. # Weighting: n_points (segment size)
  59. # Hypothesis: β > 0 (one-sided); theoretical β = 1 if LPR = m
  60. # Test: Satterthwaite approximation
  61. #
  62. # Model 5: b ~ m (SEGMENT-LEVEL, no panel)
  63. # - Exploratory: quantifies the m–b coupling
  64. # Unit: Segment-level
  65. # Model: b ~ m + (1|patient)
  66. # Weighting: n_points (segment size)
  67. # Hypothesis: Two-sided (exploratory)
  68. # Test: Satterthwaite approximation
  69. #
  70. # Model 6: m ~ Glucose (SEGMENT-LEVEL → Panel D)
  71. # - Is substrate availability associated with anaerobic activity?
  72. # Unit: Segment-level (gradient, median glucose per segment)
  73. # Model: m ~ glucose + (1|patient)
  74. # Weighting: n_points (segment size)
  75. # Hypothesis: β < 0 (higher glucose → lower m; one-sided)
  76. # Test: Satterthwaite approximation
  77. #
  78. # Model 7: LPR ~ Glucose (SEGMENT-LEVEL → Panel D)
  79. # - Between-segment: does LPR track glucose at the segment level?
  80. # Unit: Segment-level (median LPR, median glucose per segment)
  81. # Model: median_lpr ~ glucose + (1|patient)
  82. # Weighting: n_points (segment size)
  83. # Hypothesis: Two-sided (exploratory)
  84. # Test: Satterthwaite approximation
  85. #
  86. # Figure panels:
  87. # Panel A: Within-segment LPR vs glucose, Type Pb (Model 2; hyperbolic fit)
  88. # Panel B: Within-segment LPR vs glucose, Type Nb (Model 3; hyperbolic fit)
  89. # Panel C: Between-segment median LPR vs m (Model 4)
  90. # Panel D: Between-segment m (Model 6) and LPR (Model 7) vs median glucose
  91. #
  92. # MULTIPLE COMPARISONS CORRECTION:
  93. # Applied only to Models 2 and 3 (Benjamini-Hochberg FDR, n=2). These two models jointly test
  94. # the overarching hyperbolic hypothesis (LPR vs 1/Glucose) across two mutually
  95. # exclusive subsets (Pb and Nb). The remaining models are uncorrected because they
  96. # address distinct, independent physiological questions:
  97. # - Model 1: Validates the fundamental Pyruvate-Glucose link.
  98. # - Model 4: Independent assessment of LPR-m association.
  99. # - Model 5: Exploratory quantification of the m-b coupling.
  100. # - Model 6: Tests the primary hypothesis linking m to substrate availability.
  101. # - Model 7: Exploratory comparison of LPR tracking vs m tracking.
  102. # ==============================================================================
  104. suppressPackageStartupMessages({
  105. library(here)
  106. library(dplyr)
  107. library(readr)
  108. library(ggplot2)
  109. library(lme4)
  110. library(lmerTest)
  111. library(patchwork)
  112. })
  114. source(here::here("_shared", "notes.R"))
  116. cat("\n\nStarting 8_substrate_lpr_fidelity.R\n")
  118. start_time <- Sys.time()
  120. save_plot_portable <- function(filename, plot, ...) {
  121. if (isTRUE(capabilities("cairo"))) {
  122. ggsave(filename = filename, plot = plot, type = "cairo", ...)
  123. } else {
  124. warning(sprintf("Cairo backend unavailable; saving %s with the default device.", basename(filename)))
  125. ggsave(filename = filename, plot = plot, ...)
  126. }
  127. }
  129. safe_divide <- function(numerator, denominator, tol = 1e-12, fill = NA_real_) {
  130. result <- numerator / denominator
  131. invalid <- !is.finite(result) | !is.finite(denominator) | abs(denominator) <= tol
  132. result[invalid] <- fill
  133. result
  134. }
  136. get_lmer_pred_se <- function(model, newdata) {
  137. fit <- predict(model, newdata = newdata, re.form = NA, allow.new.levels = TRUE)
  138. X <- model.matrix(delete.response(terms(model)), newdata)
  139. vcov_mat <- as.matrix(vcov(model))
  140. se <- sqrt(rowSums((X %*% vcov_mat) * X))
  141. list(fit = fit, se = se)
  142. }
  144. extract_coef <- function(model, predictor, one_sided = FALSE, expected_sign = 1) {
  145. coefs <- summary(model)$coefficients
  146. beta <- coefs[predictor, "Estimate"]
  147. se <- coefs[predictor, "Std. Error"]
  148. t_val <- coefs[predictor, "t value"]
  149. p_two <- coefs[predictor, "Pr(>|t|)"]
  150. if (one_sided) {
  151. p_val <- if ((expected_sign > 0 && beta > 0) || (expected_sign < 0 && beta < 0)) p_two / 2 else 1 - p_two / 2
  152. } else {
  153. p_val <- p_two
  154. }
  155. list(beta = beta, se = se, t_value = t_val, p_value = p_val)
  156. }
  158. format_p_text <- function(p) if (p < 0.001) "p < 0.001" else sprintf("p = %.3f", p)
  160. # ==============================================================================
  161. # Input / Output
  162. # ==============================================================================
  163. INPUT_FILE <- here::here("1_output", "2_linear_segmentation", "1_results.csv")
  165. if (!file.exists(INPUT_FILE)) {
  166. cat("\n\nError: Results file not found. Run 2_linear_segmentation.py first.\n")
  167. stop()
  168. }
  170. OUTPUT_DIR <- here::here("1_output", "8_substrate_lpr_fidelity")
  172. if (!dir.exists(OUTPUT_DIR)) dir.create(OUTPUT_DIR, recursive = TRUE)
  174. # ==============================================================================
  175. # Data Loading and Preparation
  176. # ==============================================================================
  178. raw_df <- read_csv(INPUT_FILE, show_col_types = FALSE, progress = FALSE)
  180. retained_df <- raw_df %>%
  181. filter(is.finite(p6e_m), p6e_m > 0)
  183. # Point-level data
  184. point_df <- retained_df %>%
  185. filter(!is.na(glucose)) %>%
  186. select(patient, p6e_seg_index, m = p6e_m, b = p6e_b, glucose, pyruvate, lpr) %>%
  187. mutate(
  188. type_b = case_when(b > 0 ~ "Pb", b < 0 ~ "Nb", TRUE ~ "Zb"),
  189. inv_glucose = safe_divide(1, glucose)
  190. )
  192. point_pb <- point_df %>% filter(type_b == "Pb")
  193. point_nb <- point_df %>% filter(type_b == "Nb")
  195. # Segment-level data
  196. segment_df <- point_df %>%
  197. group_by(patient, p6e_seg_index) %>%
  198. summarise(
  199. median_lpr = median(lpr, na.rm = TRUE),
  200. m = first(m),
  201. b = first(b),
  202. glucose = median(glucose, na.rm = TRUE),
  203. n_points = n(),
  204. .groups = "drop"
  205. )
  207. n_points_total <- nrow(point_df)
  208. n_segments <- nrow(segment_df)
  209. n_patients <- n_distinct(segment_df$patient)
  211. n_points_pb <- nrow(point_pb)
  212. n_seg_pb <- n_distinct(paste(point_pb$patient, point_pb$p6e_seg_index))
  213. n_pat_pb <- n_distinct(point_pb$patient)
  215. n_points_nb <- nrow(point_nb)
  216. n_seg_nb <- n_distinct(paste(point_nb$patient, point_nb$p6e_seg_index))
  217. n_pat_nb <- n_distinct(point_nb$patient)
  219. # ==============================================================================
  220. # Statistical Models
  221. # ==============================================================================
  223. # --- Model 1: Pyruvate (mM) ~ Glucose (POINT-LEVEL, no panel) ---
  224. model_1 <- lmer(pyruvate ~ glucose + (1|patient/p6e_seg_index), data = point_df)
  225. stats_1 <- extract_coef(model_1, "glucose", one_sided = TRUE, expected_sign = 1)
  226. resid_sd_pyr <- sigma(model_1)
  228. # --- Model 2: LPR ~ 1/Glucose in Type Pb (POINT-LEVEL → Panel A) ---
  229. model_2 <- lmer(lpr ~ inv_glucose + (1|patient/p6e_seg_index), data = point_pb)
  230. stats_2 <- extract_coef(model_2, "inv_glucose", one_sided = TRUE, expected_sign = 1)
  232. # --- Model 3: LPR ~ 1/Glucose in Type Nb (POINT-LEVEL → Panel B) ---
  233. model_3 <- lmer(lpr ~ inv_glucose + (1|patient/p6e_seg_index), data = point_nb)
  234. stats_3 <- extract_coef(model_3, "inv_glucose", one_sided = TRUE, expected_sign = -1)
  236. # Apply Benjamini-Hochberg FDR correction for Models 2 & 3 (family of 2 tests for hyperbolic hypothesis)
  237. p_adj_23 <- p.adjust(c(stats_2$p_value, stats_3$p_value), method = "BH")
  238. stats_2$p_value <- p_adj_23[1]
  239. stats_3$p_value <- p_adj_23[2]
  241. # --- Model 4: LPR ~ m (SEGMENT-LEVEL → Panel C) ---
  242. model_4 <- lmer(median_lpr ~ m + (1|patient), data = segment_df, weights = n_points,
  243. control = lmerControl(check.conv.singular = .makeCC(action = "ignore", tol = 1e-4)))
  244. stats_4 <- extract_coef(model_4, "m", one_sided = TRUE, expected_sign = 1)
  246. # --- Model 5: b ~ m (SEGMENT-LEVEL, no panel) ---
  247. model_5 <- lmer(b ~ m + (1|patient), data = segment_df, weights = n_points,
  248. control = lmerControl(check.conv.singular = .makeCC(action = "ignore", tol = 1e-4)))
  249. stats_5 <- extract_coef(model_5, "m", one_sided = FALSE)
  251. # --- Model 6: m ~ Glucose (SEGMENT-LEVEL → Panel D) ---
  252. model_6 <- lmer(m ~ glucose + (1|patient), data = segment_df, weights = n_points,
  253. control = lmerControl(check.conv.singular = .makeCC(action = "ignore", tol = 1e-4)))
  254. stats_6 <- extract_coef(model_6, "glucose", one_sided = TRUE, expected_sign = -1)
  256. # --- Model 7: LPR ~ Glucose (SEGMENT-LEVEL → Panel D) ---
  257. model_7 <- lmer(median_lpr ~ glucose + (1|patient), data = segment_df, weights = n_points,
  258. control = lmerControl(check.conv.singular = .makeCC(action = "ignore", tol = 1e-4)))
  259. stats_7 <- extract_coef(model_7, "glucose", one_sided = FALSE)
  261. p_text_1 <- format_p_text(stats_1$p_value)
  262. p_text_2 <- format_p_text(stats_2$p_value)
  263. p_text_3 <- format_p_text(stats_3$p_value)
  264. p_text_4 <- format_p_text(stats_4$p_value)
  265. p_text_5 <- format_p_text(stats_5$p_value)
  266. p_text_6 <- format_p_text(stats_6$p_value)
  267. p_text_7 <- format_p_text(stats_7$p_value)
  269. # Save model summaries
  270. sink(file.path(OUTPUT_DIR, "_1_model_summaries.txt"))
  271. cat("=======================================================================\n")
  272. cat("Model 1: Pyruvate (mM) ~ Glucose [No panel] (POINT-LEVEL)\n")
  273. cat("Establishes pyruvate–glucose association; reported in text.\n")
  274. cat(sprintf("Residual SD = %.5f mM (%.1f μM).\n", resid_sd_pyr, resid_sd_pyr * 1000))
  275. cat(sprintf("N points = %d, N segments = %d, N patients = %d\n",
  276. n_points_total, n_segments, n_distinct(point_df$patient)))
  277. cat("=======================================================================\n")
  278. print(summary(model_1))
  279. cat("\n\n")
  280. cat("=======================================================================\n")
  281. cat("Model 2: LPR ~ 1/Glucose in Type Pb [Panel A] (POINT-LEVEL)\n")
  282. cat("Within-segment: LPR–glucose association when b > 0.\n")
  283. cat("Modelled as lpr ~ inv_glucose; plotted on glucose axis (hyperbolic shape).\n")
  284. cat(sprintf("N points = %d, N segments = %d, N patients = %d\n",
  285. n_points_pb, n_seg_pb, n_pat_pb))
  286. cat(sprintf("Benjamini-Hochberg FDR corrected p-value (n=2): %g\n", stats_2$p_value))
  287. cat("=======================================================================\n")
  288. print(summary(model_2))
  289. cat("\n\n")
  290. cat("=======================================================================\n")
  291. cat("Model 3: LPR ~ 1/Glucose in Type Nb [Panel B] (POINT-LEVEL)\n")
  292. cat("Within-segment: LPR–glucose association when b < 0.\n")
  293. cat("Modelled as lpr ~ inv_glucose; plotted on glucose axis (hyperbolic shape).\n")
  294. cat(sprintf("N points = %d, N segments = %d, N patients = %d\n",
  295. n_points_nb, n_seg_nb, n_pat_nb))
  296. cat(sprintf("Benjamini-Hochberg FDR corrected p-value (n=2): %g\n", stats_3$p_value))
  297. cat("=======================================================================\n")
  298. print(summary(model_3))
  299. cat("\n\n")
  300. cat("=======================================================================\n")
  301. cat("Model 4: Median LPR ~ m [Panel C] (SEGMENT-LEVEL)\n")
  302. cat("How faithfully does LPR track the underlying gradient?\n")
  303. cat("Theoretical β = 1 if LPR perfectly tracked m.\n")
  304. cat(sprintf("N segments = %d, N patients = %d\n", n_segments, n_patients))
  305. cat("=======================================================================\n")
  306. print(summary(model_4))
  307. cat("\n\n")
  308. cat("=======================================================================\n")
  309. cat("Model 5: b ~ m [No panel] (SEGMENT-LEVEL)\n")
  310. cat("Exploratory: quantifies the m–b coupling.\n")
  311. cat(sprintf("N segments = %d, N patients = %d\n", n_segments, n_patients))
  312. cat("=======================================================================\n")
  313. print(summary(model_5))
  314. cat("\n\n")
  315. cat("=======================================================================\n")
  316. cat("Model 6: m ~ Glucose [Panel D] (SEGMENT-LEVEL)\n")
  317. cat("Between-segment: is substrate availability associated with gradient?\n")
  318. cat(sprintf("N segments = %d, N patients = %d\n", n_segments, n_patients))
  319. cat("=======================================================================\n")
  320. print(summary(model_6))
  321. cat("\n\n")
  322. cat("=======================================================================\n")
  323. cat("Model 7: Median LPR ~ Glucose [Panel D] (SEGMENT-LEVEL)\n")
  324. cat("Between-segment: does segment-level LPR track glucose?\n")
  325. cat(sprintf("N segments = %d, N patients = %d\n", n_segments, n_patients))
  326. cat("=======================================================================\n")
  327. print(summary(model_7))
  328. sink()
  330. # ==============================================================================
  331. # Visualization (4-Panel Figure)
  332. # ==============================================================================
  334. common_theme <- theme_minimal() +
  335. theme(
  336. text = element_text(size = 8, colour = "black"),
  337. axis.text = element_text(size = 6, colour = "black"),
  338. axis.title = element_text(size = 7, colour = "black"),
  339. plot.tag = element_text(size = 9, face = "bold", colour = "black", hjust = 1, vjust = -1),
  340. plot.tag.position = c(0.02, 0.99),
  341. panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.5),
  342. panel.grid.minor = element_blank(),
  343. plot.background = element_rect(fill = "white", color = NA),
  344. panel.background = element_rect(fill = "white", color = NA),
  345. legend.position = "none",
  346. plot.margin = margin(t = 3, r = 3, b = 0, l = 3, unit = "mm")
  347. )
  349. # Viewports (point-level for A–B, segment-level for C–D)
  350. glucose_range_pb <- quantile(point_pb$glucose, c(0.025, 0.975), na.rm = TRUE)
  351. glucose_range_nb <- quantile(point_nb$glucose, c(0.025, 0.975), na.rm = TRUE)
  352. glucose_range_seg <- quantile(segment_df$glucose, c(0.025, 0.975), na.rm = TRUE)
  353. m_range <- quantile(segment_df$m, c(0.025, 0.975), na.rm = TRUE)
  355. glucose_seq_pb <- seq(glucose_range_pb[1], glucose_range_pb[2], length.out = 100)
  356. glucose_seq_nb <- seq(glucose_range_nb[1], glucose_range_nb[2], length.out = 100)
  357. glucose_seq_seg <- seq(glucose_range_seg[1], glucose_range_seg[2], length.out = 100)
  358. m_seq <- seq(m_range[1], m_range[2], length.out = 100)
  360. # --- Panel A: LPR ~ 1/Glucose within Pb (Model 2, plotted on glucose axis) ---
  361. pred_a <- data.frame(inv_glucose = safe_divide(1, glucose_seq_pb))
  362. res_a <- get_lmer_pred_se(model_2, pred_a)
  363. pred_a$glucose <- glucose_seq_pb
  364. pred_a$y <- res_a$fit
  365. pred_a$ymin <- pred_a$y - 1.96 * res_a$se
  366. pred_a$ymax <- pred_a$y + 1.96 * res_a$se
  368. ylim_a <- range(c(pred_a$ymin[1], pred_a$ymax[1],
  369. pred_a$ymin[nrow(pred_a)], pred_a$ymax[nrow(pred_a)]))
  371. p_a <- ggplot() +
  372. geom_ribbon(data = pred_a, aes(x = glucose, ymin = ymin, ymax = ymax),
  373. fill = "#44AA99", alpha = 0.2) +
  374. geom_line(data = pred_a, aes(x = glucose, y = y),
  375. color = "#44AA99", linewidth = 1) +
  376. coord_cartesian(xlim = glucose_range_pb, ylim = ylim_a) +
  377. scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
  378. scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
  379. labs(tag = "A", x = "Glucose (mM)", y = "LPR") +
  380. common_theme
  382. # --- Panel B: LPR ~ 1/Glucose within Nb (Model 3, plotted on glucose axis) ---
  383. pred_b <- data.frame(inv_glucose = safe_divide(1, glucose_seq_nb))
  384. res_b <- get_lmer_pred_se(model_3, pred_b)
  385. pred_b$glucose <- glucose_seq_nb
  386. pred_b$y <- res_b$fit
  387. pred_b$ymin <- pred_b$y - 1.96 * res_b$se
  388. pred_b$ymax <- pred_b$y + 1.96 * res_b$se
  390. ylim_b <- range(c(pred_b$ymin[1], pred_b$ymax[1],
  391. pred_b$ymin[nrow(pred_b)], pred_b$ymax[nrow(pred_b)]))
  393. p_b <- ggplot() +
  394. geom_ribbon(data = pred_b, aes(x = glucose, ymin = ymin, ymax = ymax),
  395. fill = "#DDCC77", alpha = 0.2) +
  396. geom_line(data = pred_b, aes(x = glucose, y = y),
  397. color = "#DDCC77", linewidth = 1) +
  398. coord_cartesian(xlim = glucose_range_nb, ylim = ylim_b) +
  399. scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
  400. scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
  401. labs(tag = "B", x = "Glucose (mM)", y = "LPR") +
  402. common_theme
  404. # --- Panel C: LPR ~ m (Model 4, segment-level) ---
  405. pred_c <- data.frame(m = m_seq)
  406. res_c <- get_lmer_pred_se(model_4, pred_c)
  407. pred_c$y <- res_c$fit
  408. pred_c$ymin <- pred_c$y - 1.96 * res_c$se
  409. pred_c$ymax <- pred_c$y + 1.96 * res_c$se
  411. ref_c <- data.frame(m = m_range, lpr = m_range)
  413. ylim_c <- range(c(pred_c$ymin[1], pred_c$ymax[1],
  414. pred_c$ymin[nrow(pred_c)], pred_c$ymax[nrow(pred_c)],
  415. m_range))
  417. p_c <- ggplot() +
  418. geom_line(data = ref_c, aes(x = m, y = lpr),
  419. linetype = "dashed", color = "black", linewidth = 0.6) +
  420. geom_ribbon(data = pred_c, aes(x = m, ymin = ymin, ymax = ymax),
  421. fill = "#CC3311", alpha = 0.2) +
  422. geom_line(data = pred_c, aes(x = m, y = y),
  423. color = "#CC3311", linewidth = 1) +
  424. coord_cartesian(xlim = m_range, ylim = ylim_c) +
  425. scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
  426. scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
  427. labs(tag = "C", x = expression(italic(m)), y = "LPR") +
  428. common_theme
  430. # --- Panel D: m AND LPR ~ Glucose (Models 6 & 7, segment-level) ---
  431. pred_d_m <- data.frame(glucose = glucose_seq_seg)
  432. res_d_m <- get_lmer_pred_se(model_6, pred_d_m)
  433. pred_d_m$y <- res_d_m$fit
  434. pred_d_m$ymin <- pred_d_m$y - 1.96 * res_d_m$se
  435. pred_d_m$ymax <- pred_d_m$y + 1.96 * res_d_m$se
  437. pred_d_lpr <- data.frame(glucose = glucose_seq_seg)
  438. res_d_lpr <- get_lmer_pred_se(model_7, pred_d_lpr)
  439. pred_d_lpr$y <- res_d_lpr$fit
  440. pred_d_lpr$ymin <- pred_d_lpr$y - 1.96 * res_d_lpr$se
  441. pred_d_lpr$ymax <- pred_d_lpr$y + 1.96 * res_d_lpr$se
  443. ylim_d <- range(c(pred_d_m$ymin[1], pred_d_m$ymax[1],
  444. pred_d_m$ymin[nrow(pred_d_m)], pred_d_m$ymax[nrow(pred_d_m)],
  445. pred_d_lpr$ymin[1], pred_d_lpr$ymax[1],
  446. pred_d_lpr$ymin[nrow(pred_d_lpr)], pred_d_lpr$ymax[nrow(pred_d_lpr)]))
  448. p_d <- ggplot() +
  449. geom_ribbon(data = pred_d_lpr, aes(x = glucose, ymin = ymin, ymax = ymax),
  450. fill = "#E69F00", alpha = 0.2) +
  451. geom_line(data = pred_d_lpr, aes(x = glucose, y = y),
  452. color = "#E69F00", linewidth = 1) +
  453. geom_ribbon(data = pred_d_m, aes(x = glucose, ymin = ymin, ymax = ymax),
  454. fill = "#0072B2", alpha = 0.2) +
  455. geom_line(data = pred_d_m, aes(x = glucose, y = y),
  456. color = "#0072B2", linewidth = 1) +
  457. coord_cartesian(xlim = glucose_range_seg, ylim = ylim_d) +
  458. scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
  459. scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
  460. labs(tag = "D", x = "Glucose (mM)", y = "Value") +
  461. common_theme
  463. # Assemble 4-panel figure
  464. fig <- p_a + p_b + p_c + p_d +
  465. plot_layout(nrow = 1)
  467. save_plot_portable(
  468. filename = file.path(OUTPUT_DIR, "1_figure.png"),
  469. plot = fig,
  470. width = 185, height = 55, units = "mm", dpi = 300
  471. )
  473. # ==============================================================================
  474. # Summary Statistics CSV
  475. # ==============================================================================
  477. summary_stats <- data.frame(
  478. Metric = c(
  479. "N_points", "N_segments", "N_patients",
  480. "N_points_Pb", "N_segments_Pb", "N_patients_Pb",
  481. "N_points_Nb", "N_segments_Nb", "N_patients_Nb",
  482. "Model_1_beta_glucose_mM_per_mM", "Model_1_beta_glucose_uM_per_mM",
  483. "Model_1_SE_uM_per_mM", "Model_1_p_value", "Model_1_resid_SD_mM",
  484. "Model_2_beta_inv_glucose_Pb", "Model_2_SE_Pb", "Model_2_p_value_bh_Pb",
  485. "Model_3_beta_inv_glucose_Nb", "Model_3_SE_Nb", "Model_3_p_value_bh_Nb",
  486. "Model_4_beta_m", "Model_4_SE", "Model_4_p_value",
  487. "Model_5_beta_m_bm", "Model_5_SE_bm", "Model_5_p_value_bm",
  488. "Model_6_beta_glucose", "Model_6_SE", "Model_6_p_value",
  489. "Model_7_beta_glucose_lpr", "Model_7_SE_lpr", "Model_7_p_value_lpr"
  490. ),
  491. Value = c(
  492. n_points_total, n_segments, n_patients,
  493. n_points_pb, n_seg_pb, n_pat_pb,
  494. n_points_nb, n_seg_nb, n_pat_nb,
  495. stats_1$beta, stats_1$beta * 1000,
  496. stats_1$se * 1000, stats_1$p_value, resid_sd_pyr,
  497. stats_2$beta, stats_2$se, stats_2$p_value,
  498. stats_3$beta, stats_3$se, stats_3$p_value,
  499. stats_4$beta, stats_4$se, stats_4$p_value,
  500. stats_5$beta, stats_5$se, stats_5$p_value,
  501. stats_6$beta, stats_6$se, stats_6$p_value,
  502. stats_7$beta, stats_7$se, stats_7$p_value
  503. )
  504. )
  506. write_csv(summary_stats, file.path(OUTPUT_DIR, "_1_stats.csv"))
  508. # ==============================================================================
  509. # Completion Log
  510. # ==============================================================================
  511. end_time <- Sys.time()
  512. execution_time <- as.numeric(difftime(end_time, start_time, units = "secs"))
  513. write_csv(
  514. data.frame(execution_time_seconds = execution_time),
  515. file.path(OUTPUT_DIR, "__execution_time.csv")
  516. )
  518. cat("\n\n8_substrate_lpr_fidelity.R complete.\n\n")
  520. # ==============================================================================
  521. # Figure Legend
  522. # ==============================================================================
  523. legends <- list(
  524. list(
  525. target = "Figure 1 (1_figure.png)",
  526. caption = sprintf(
  527. paste0(
  528. "Glucose associations with LPR and gradient (m). ",
  529. "(A) Within-segment LPR vs glucose for Type Pb segments (b > 0; %d segments, %d patients; ",
  530. "β_1/Glucose = %.2f mM, %s). ",
  531. "(B) Within-segment LPR vs glucose for Type Nb segments (b < 0; %d segments, %d patients; ",
  532. "β_1/Glucose = %.2f mM, %s). ",
  533. "Within-segment models use 1/Glucose as predictor to capture the hyperbolic LPR–glucose ",
  534. "relationship (LPR = m + b/P); predictions are plotted on the glucose x-axis. ",
  535. "P-values for within-segment models (A, B) are Benjamini-Hochberg FDR corrected (n=2). ",
  536. "(C) Segment-level median LPR vs gradient m (β = %.3f, %s); dashed black line indicates the ",
  537. "theoretical β = 1 relationship expected if LPR perfectly tracked the gradient. ",
  538. "(D) Segment-level gradient (m, blue; β = %.3f mM⁻¹, %s) and median LPR (orange; β = %.3f mM⁻¹, %s) ",
  539. "vs median glucose. ",
  540. "Lines = LME fixed effects; shaded = 95%% CI. ",
  541. "A–B: nested random intercepts (segment within patient). ",
  542. "C–D: patient random intercepts, weighted by segment size. ",
  543. "Sample: %d segments, %d patients."
  544. ),
  545. n_seg_pb, n_pat_pb, stats_2$beta, p_text_2,
  546. n_seg_nb, n_pat_nb, stats_3$beta, p_text_3,
  547. stats_4$beta, p_text_4,
  548. stats_6$beta, p_text_6,
  549. stats_7$beta, p_text_7,
  550. n_segments, n_patients
  551. ),
  552. abbreviations = "b, linear intercept; BH, Benjamini-Hochberg; CI, confidence interval; FDR, false discovery rate; LME, linear mixed-effects model; LPR, lactate/pyruvate ratio; m, linear gradient; Nb, negative intercept (b < 0); Pb, positive intercept (b > 0)"
  553. ),
  554. list(
  555. target = "Text-only results (no panel)",
  556. caption = sprintf(
  557. paste0(
  558. "Model 1 (pyruvate ~ glucose, point-level): β = %.2f μM/mM (%s); ",
  559. "residual SD = %.1f μM. %d datapoints, %d segments, %d patients. ",
  560. "Model 5 (b ~ m, segment-level): β = %.4f mM (%s). ",
  561. "%d segments, %d patients."
  562. ),
  563. stats_1$beta * 1000, p_text_1,
  564. resid_sd_pyr * 1000, n_points_total, n_segments, n_patients,
  565. stats_5$beta, p_text_5,
  566. n_segments, n_patients
  567. ),
  568. abbreviations = "b, linear intercept; LPR, lactate/pyruvate ratio; m, linear gradient; SD, standard deviation"
  569. )
  570. )
  572. writeLines(build_notes(legends, title = "8_substrate_lpr_fidelity"), file.path(OUTPUT_DIR, "0_notes.txt"))

8_substrate_lpr_fidelity.R at commit 7cc6e72, under MIT · at the source

Overview

Authors: Michael S Baker1, Joshua M Heihre1, Tomasz Kuliński2, Claudia A Smith1, Berfin Barlas1,3, Chisomo Zimphango1, Ivan Timofeev1, Koby Baranes1,3, Keri L H Carpenter1, Mark Kotter1,3, Mathew R Guilfoyle1, Elham Rostami2,4, Peter J Hutchinson1, Adel Helmy1
ORCID iDs: Michael S Baker, Joshua M Heihre, Tomasz Kuliński, Claudia A Smith, Ivan Timofeev, Koby Baranes, Elham Rostami, Adel Helmy
  1. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
  2. Section of Neurosurgery, Department of Medical Sciences, Uppsala University Hospital, Uppsala, Sweden
  3. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
  4. Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
Institutions: University of Cambridge (United Kingdom); Uppsala University Hospital (Sweden); Wellcome/MRC Cambridge Stem Cell Institute (United Kingdom); Karolinska Institutet (Sweden)
Journal: Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, article 0271678X261485404
Dates: received 31 March 2026; accepted 20 August 2026; published online 26 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1177/0271678x261485404 · PMID 42644382 · PMCID PMC13558473 · OpenAlex W7204260038
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: Cerebral metabolism, changepoint detection, mitochondrial dysfunction, neuromonitoring, neurocritical care
Topic: Traumatic Brain Injury and Neurovascular Disturbances (Neurology, Medicine), according to OpenAlex
Funding: National Institute for Health Research (NIHR) (NIHR129748); Wellcome Trust
Citations: not cited yet (Europe PMC); 29 references in the paper

Abstract

Cerebral microdialysis (CMD) after traumatic brain injury (TBI) has focused on lactate/pyruvate ratio (LPR), overlooking lactate–pyruvate gradient (LPG, the linear regression slope within a time segment of CMD datapoints) as a proxy for anaerobic activity. We retrospectively applied changepoint detection to the linear lactate–pyruvate relationship (L = mP + b) over time within patients from two independent CMD-monitored TBI cohorts (Cambridge, n = 510; Uppsala, n = 81), estimating per-segment LPG (m) and intercept (b, capturing metabolic variance). In vitro, we tested whether rotenone-induced mitochondrial dysfunction increases LPG in hiPSC-derived neurons, as expected if LPG tracks anaerobic activity. In the Cambridge cohort, a median of two linear segments were identified per patient (median LPG = 19.83; median b = 0.54 mM). Between segments, LPR tracked LPG with heavy attenuation (β = 0.255). LPG was negatively associated with cerebral microdialysate glucose (p < 0.001) and 6-month GOSE (p = 0.039). Uppsala data confirmed pipeline robustness and replicated the LPG–glucose association (p = 0.022). LPG was higher in rotenone-treated neurons than controls (32.9 vs 14.2, p < 0.001). LPG calculated per time segment provides a more accurate, outcome-associated proxy for anaerobic activity than datapoint-level LPR.

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 15 matches between paragraphs and lines of code.

msb-jr/LPG

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7cc6e72aec21e4b971deb857b3e2f671a79b399e, 9 September 2026
Languages: R (9), Python (7)
Size: 44 files, 16 scripts
Software Heritage: not archived
Found in: “Statistical analysis and code availability”
Holds: README, license file, environment (renv.lock, requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (7 files), ggplot2 (6 files), patchwork (5 files), NumPy (4 files), lme4 (3 files), lmerTest (3 files), Matplotlib (3 files), pandas (3 files), emmeans (2 files), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
18 files
At the source: github.com/msb-jr/LPG

Statistical analysis and code availability

Multiple statistical approaches were used, including OLS regression, Fisher’s z-transformed correlation comparisons, model comparison via Akaike Information Criterion (AIC), linear mixed-effects (LME) models with nested random effects, nonparametric tests for group comparisons with Benjamini-Hochberg correction for multiple testing, linear contrasts via estimated marginal means (EMMs), and analysis of covariance (ANCOVA) for comparing in vitro regression lines. EMMs represent the model-estimated typical values within each group, adjusted for the model’s random effects and weighting structure. All analyses were performed using R (v4.5.2; https://www.r-project.org/) and Python (v3.9.6; https://www.python.org/). The complete, annotated source code of this project is publicly available at https://github.com/msb-jr/LPG. The relation of each script to each section of the manuscript and Supplementary materials (https://journals.sagepub.com/doi/suppl/10.1177/0271678X261485404) is specified in the repository. Where directional hypotheses were pre-specified based on theoretical expectation or established literature (e.g. higher LPR associated with worse outcomes), one-sided tests were used; all other tests were two-sided. The directionality of each test is specified in the published code. The Uppsala cohort was analysed using the same published pipeline and scripts; all statistical methods were identical to those applied to the Cambridge cohort.

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

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;
  • 16 scripts, each with its path and the digest of its content;
  • 15 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

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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, pages, dates, 14 authors, 5 keywords, 2 funders, 24 references.

Cite

This paper

Baker, M. S., Heihre, J. M., Kuliński, T., Smith, C. A., Barlas, B., Zimphango, C., Timofeev, I., Baranes, K., Carpenter, K. L. H., Kotter, M., Guilfoyle, M. R., Rostami, E., Hutchinson, P. J., & Helmy, A. (2026). The microdialysis-derived lactate-pyruvate gradient indicates anaerobic activity after brain injury. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 0271678X261485404. https://doi.org/10.1177/0271678x261485404

BibTeX

@article{baker2026microdialysis,
author = {Baker, Michael S and Heihre, Joshua M and Kuliński, Tomasz and Smith, Claudia A and Barlas, Berfin and Zimphango, Chisomo and Timofeev, Ivan and Baranes, Koby and Carpenter, Keri L H and Kotter, Mark and Guilfoyle, Mathew R and Rostami, Elham and Hutchinson, Peter J and Helmy, Adel},
title = {{The microdialysis-derived lactate-pyruvate gradient indicates anaerobic activity after brain injury}},
journal = {Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism},
year = {2026},
month = aug,
pages = {0271678X261485404},
publisher = {SAGE Publications},
issn = {0271-678X},
doi = {10.1177/0271678x261485404},
url = {https://doi.org/10.1177/0271678x261485404},
pmid = {42644382},
pmcid = {PMC13558473}
}

RIS

TY - JOUR
AU - Baker, Michael S
AU - Heihre, Joshua M
AU - Kuliński, Tomasz
AU - Smith, Claudia A
AU - Barlas, Berfin
AU - Zimphango, Chisomo
AU - Timofeev, Ivan
AU - Baranes, Koby
AU - Carpenter, Keri L H
AU - Kotter, Mark
AU - Guilfoyle, Mathew R
AU - Rostami, Elham
AU - Hutchinson, Peter J
AU - Helmy, Adel
TI - The microdialysis-derived lactate-pyruvate gradient indicates anaerobic activity after brain injury
T2 - Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
J2 - J Cereb Blood Flow Metab
PY - 2026
DA - 2026/08/26
SP - 0271678X261485404
SN - 0271-678X
PB - SAGE Publications
DO - 10.1177/0271678x261485404
UR - https://doi.org/10.1177/0271678x261485404
LA - en
ER -

CSL-JSON

{
"id": "10.1177/0271678x261485404",
"type": "article-journal",
"title": "The microdialysis-derived lactate-pyruvate gradient indicates anaerobic activity after brain injury",
"container-title": "Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism",
"author": [
{
"family": "Baker",
"given": "Michael S"
},
{
"family": "Heihre",
"given": "Joshua M"
},
{
"family": "Kuliński",
"given": "Tomasz"
},
{
"family": "Smith",
"given": "Claudia A"
},
{
"family": "Barlas",
"given": "Berfin"
},
{
"family": "Zimphango",
"given": "Chisomo"
},
{
"family": "Timofeev",
"given": "Ivan"
},
{
"family": "Baranes",
"given": "Koby"
},
{
"family": "Carpenter",
"given": "Keri L H"
},
{
"family": "Kotter",
"given": "Mark"
},
{
"family": "Guilfoyle",
"given": "Mathew R"
},
{
"family": "Rostami",
"given": "Elham"
},
{
"family": "Hutchinson",
"given": "Peter J"
},
{
"family": "Helmy",
"given": "Adel"
}
],
"container-title-short": "J Cereb Blood Flow Metab",
"page": "0271678X261485404",
"DOI": "10.1177/0271678x261485404",
"PMID": "42644382",
"PMCID": "PMC13558473",
"ISSN": "0271-678X",
"publisher": "SAGE Publications",
"URL": "https://doi.org/10.1177/0271678x261485404",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
26
]
]
}
}

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