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Pregnancy diet based on ancestral patterns increases growth in subcortical fetal brain regions.

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The authors' code

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  1. ---
  2. title: "Mikhuna Analysis"
  3. author: "Gifty Aboagye-Mensah"
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. ---
  8. #Dataset upload
  9. ```{r}
  10. library(openxlsx)
  11. Mikhunawide <- read.xlsx(
  12. file.path("data", "Mikhuna data_OSF.xlsx")
  13. )
  14. ```
  17. #Subsetting dataset
  18. ```{r}
  19. library(tidyverse)
  20. Mikhunafood<- Mikhunawide%>%
  21. dplyr::select( "record_id", w12_grupo, w35_arroz_blanco, w35_pan_tienda, w35_pan_dulce,
  22. w35_pan_agua, w35_pan_rodajas_blanco, w35_pan_rodajas_integral,
  23. w35_fideos_tallerin, w35_fideos_queso, w35_lasana, w35_tortilla,
  24. w35_granola, w35_quinoa, w35_arroz_cebada, w35_papas_cocinadas,
  25. w35_papas_fritas, w35_papas_locro, w35_yuca_frita, w35_yuca_cocinada,
  26. w35_mellocos, w35_oca, w35_camote, w35_choclo, w35_mote, w35_morocho,
  27. w35_canguil, w35_tostado, w35_platano_verde, w35_platano_maduro,
  28. w35_sambo, w35_zapallo, w35_menestra_frejol, w35_menestra_lentejas,
  29. w35_habas_tierna, w35_habas_seca, w35_habas_fritas, w35_chocho,
  30. w35_garbanzo, w35_alverjas_tierna, w35_alverjas_seca, w35_huevo_cocinado,
  31. w35_huevo_frito, w35_huevo_revuelto, w35_leche_vaca_entera,
  32. w35_leche_funda_entera, w35_leche_semidescremada, w35_leche_descremada,
  33. w35_leche_polvo, w35_nata, w35_cremas_leche, w35_batida_leche,
  34. w35_yogurt_frutas, w35_yogurt_natural, w35_yogurt_descremada,
  35. w35_empanada_queso, w35_queso_fresco, w35_queso_mozzarella,
  36. w35_queso_maduro, w35_mantequilla_sal, w35_mantequilla_sin_sal,
  37. w35_margarina, w35_res_frita, w35_res_asada_plancha, w35_res_jugo,
  38. w35_carne_molida, w35_sopa_legumbres_carne, w35_caldo_patas,
  39. w35_caldo_huesos, w35_pollo_frito, w35_pollo_asado_plancha, w35_pollo_jugo,
  40. w35_caldo_pollo, w35_pato_pavo, w35_cerdo, w35_borrego, w35_ternero,
  41. w35_menudencia, w35_higado_animal, w35_cerebro_animal, w35_sangre_frita,
  42. w35_salchicha, w35_mortadela_jamon, w35_pescado_frito, w35_pescado_asado,
  43. w35_pescado_envuelto, w35_sopa_pescado, w35_atun_aceite, w35_atun_agua,
  44. w35_sardinas, w35_camarones, w35_conchas, w35_cangrejo, w35_calamar,
  45. w35_pulpo, w35_empanada_carne, w35_empanada_pollo,
  46. w35_pescado_frito_tipo___1, w35_pescado_frito_tipo___2, w35_pescado_frito_tipo___3,
  47. w35_pescado_frito_tipo___4, w35_pescado_frito_tipo___5, w35_pescado_frito_tipo___77,
  48. w35_pescado_frito_especifique, w35_pescado_asado_tipo___1, w35_pescado_asado_tipo___2,
  49. w35_pescado_asado_tipo___3, w35_pescado_asado_tipo___4, w35_pescado_asado_tipo___5,
  50. w35_pescado_asado_especifique, w35_pescado_envuelto_tipo___1, w35_pescado_envuelto_tipo___2,
  51. w35_pescado_envuelto_tipo___3, w35_pescado_envuelto_tipo___4, w35_pescado_envuelto_tipo___5,
  52. w35_pescado_envuelto_tipo___77, w35_pescado_envuelto_especif, w35_guineo,
  53. w35_manzana, w35_pera, w35_pina, w35_papaya, w35_melon, w35_sandia, w35_naranja,
  54. w35_mandarina, w35_limon, w35_lima, w35_toronja, w35_uvas, w35_durazno,
  55. w35_kiwi, w35_mora, w35_arandano, w35_mortino, w35_frutillas_fresas,
  56. w35_uvilla, w35_mango, w35_tomate_arbol, w35_frutas_secas, w35_pasas,
  57. w35_ciruela, w35_aguacate, w35_taxo, w35_granadilla, w35_tuna, w35_naranjilla,
  58. w35_maracuya, w35_guanabana, w35_chirimoya, w35_guayaba, w35_guaba, w35_babaco,
  59. w35_pepinillo_dulce, w35_tamarindo, w35_pitahaya, w35_zapote, w35_nueces,
  60. w35_almendra, w35_mani, w35_mani_tostado, w35_mani_pasta, w35_tocte,
  61. w35_tomate_rinon, w35_tomate_cherry, w35_zanahoria, w35_rabanos, w35_brocoli,
  62. w35_coliflor, w35_pepinillo, w35_remolacha, w35_vainitas, w35_berenjenas,
  63. w35_esparragos, w35_pepino, w35_pimiento, w35_cebolla_paitina, w35_cebolla_perla,
  64. w35_cebolla_larga, w35_cebollin, w35_col_blanca, w35_col_morada, w35_lechuga,
  65. w35_nabo, w35_rucula, w35_kale, w35_acelga, w35_espinaca, w35_alcachofa,
  66. w35_hongos_ostra, w35_ajo, w35_apio, w35_cilantro, w35_perejil, w35_albahaca_fresca,
  67. w35_aji, w35_hierba_buena, w35_jengibre, w35_avena_frutas, w35_avena_leche,
  68. w35_avena_agua, w35_machica_leche, w35_machina_sin_leche, w35_higos_queso,
  69. w35_salsa_tomate, w35_hamburguesa, w35_pizza, w35_humitas_tamales_quimbolito,
  70. w35_arepa, w35_mermelada
  71. )
  72. ```
  74. ```{r}
  75. head(Mikhunafood)
  76. ```
  78. #Converting character values to numeric
  79. ```{r}
  80. food_cols <- names(Mikhunafood)[!(names(Mikhunafood) %in% c("record_id", "w12_grupo"))]
  82. Mikhunafood[food_cols] <- lapply(Mikhunafood[food_cols], function(x) as.numeric(as.character(x)))
  84. ```
  86. #Viewing first few rows
  87. ```{r}
  88. head(Mikhunafood)
  89. ```
  92. #Converting from wide to long
  93. ```{r}
  94. library(tidyr)
  95. library(dplyr)
  97. food_long <- Mikhunafood %>%
  98. pivot_longer(
  99. cols = -c(record_id, w12_grupo), # keep group column out
  100. names_to = "food_var",
  101. values_to = "consumed"
  102. )
  103. ```
  105. #Viewing long dataset
  106. ```{r}
  107. head(food_long)
  108. ```
  110. #Importing MDD-W categories as food_map
  111. ```{r}
  112. library(tidyr)
  113. library(dplyr)
  114. library(readxl)
  115. food_map <- read_excel("data", "MDD-W food groups")
  116. ```
  118. #Viewing food_map
  119. ```{r}
  120. head(food_map)
  121. ```
  123. ```{r}
  124. colnames(food_map)
  125. ```
  127. #Converting food_map from wide to long
  128. ```{r}
  129. food_map_long <- food_map %>%
  130. pivot_longer(
  131. cols = starts_with("Category"), # Category 1 … Category 10
  132. names_to = "cat_number",
  133. values_to = "MDDW_category"
  134. ) %>%
  135. filter(MDDW_category != "") %>% # remove empty category cells
  136. select(food_var, MDDW_category)
  138. ```
  140. ```{r}
  141. head(food_map_long)
  142. ```
  144. #Merging food_map_long and food_long into food_long1
  145. ```{r}
  147. food_long1 <- food_long %>%
  148. left_join(food_map_long, by = "food_var", relationship = "many-to-many")
  150. ```
  152. ```{r}
  153. colnames(food_long1)
  154. ```
  156. #Counting participants per category sorted by group. 1- control, 2-intervention
  157. ```{r}
  158. category_counts <- food_long1 %>%
  159. filter(consumed == 1) %>%
  160. group_by(w12_grupo, MDDW_category) %>%
  161. summarise(n_participants = n_distinct(record_id), .groups = "drop")
  162. ```
  164. ```{r}
  165. head(category_counts)
  166. ```
  168. #Calculating percentages of participants per group who consumes each MDD-W category
  169. ```{r}
  170. total_per_group <- food_long1 %>%
  171. select(record_id, w12_grupo) %>%
  172. distinct() %>%
  173. group_by(w12_grupo) %>%
  174. summarise(total_participants = n())
  176. category_percent <- category_counts %>%
  177. left_join(total_per_group, by = "w12_grupo") %>%
  178. mutate(percent = 100 * n_participants / total_participants)
  179. ```
  184. #How many participants consumed each specific food item per group
  185. #Filtering to leave only foods consumed
  186. ```{r}
  187. food_long_consumed <- food_long1 %>%
  188. filter(consumed == 1)
  189. ```
  192. #Counting participants per food group
  193. ```{r}
  194. food_counts <- food_long_consumed %>%
  195. group_by(w12_grupo, food_var) %>%
  196. summarise(n_participants = n_distinct(record_id), .groups = "drop")
  197. ```
  199. #Mapping foods to their MDD-W categories
  200. ```{r}
  201. food_counts_with_cat <- food_counts %>%
  202. left_join(
  203. food_map_long %>% select(food_var, MDDW_category),
  204. by = "food_var",
  205. relationship = "many-to-many"
  206. )
  207. ```
  209. #Total number and percentage of participants that consumed each food per group
  210. ```{r}
  211. food_percent <- food_counts %>%
  212. left_join(total_per_group, by = "w12_grupo") %>%
  213. mutate(percent = 100 * n_participants / total_participants)
  214. ```
  216. #Adding MDD-W category to food_percent
  217. ```{r}
  218. food_percent_with_cat <- food_percent %>%
  219. left_join(
  220. food_map_long %>% select(food_var, MDDW_category),
  221. by = "food_var",
  222. relationship = "many-to-many"
  223. )
  224. ```
  226. #Fixing "grains roots and tubers" variable
  228. ```{r}
  229. food_map_long <- food_map_long %>%
  230. mutate(
  231. MDDW_category = MDDW_category %>%
  232. tolower() %>% # lower case
  233. trimws() %>% # remove leading/trailing spaces
  234. gsub(",", "", .) %>% # remove commas
  235. gsub("\\s+", " ", .) %>% # fix multiple spaces
  236. gsub(" +and +", " and ", .) # normalize 'and'
  237. )
  238. ```
  241. ```{r}
  242. food_map_long <- food_map_long %>%
  243. mutate(
  244. MDDW_category = case_when(
  245. grepl("grains.*roots.*tubers", MDDW_category) ~ "grains roots and tubers",
  246. TRUE ~ MDDW_category
  247. )
  248. )
  249. ```
  251. ```{r}
  252. food_long_with_cat <- food_long %>%
  253. left_join(
  254. food_map_long,
  255. by = "food_var",
  256. relationship = "many-to-many"
  257. )
  258. ```
  260. ```{r}
  261. category_percent <- food_long_with_cat %>%
  262. group_by(w12_grupo, MDDW_category) %>%
  263. summarise(
  264. n_consumed = sum(consumed == 1, na.rm = TRUE),
  265. total = n_distinct(record_id),
  266. percent = 100 * n_consumed / total,
  267. .groups = "drop"
  268. )
  269. ```
  271. #Above results counts foods as numerator and not participants.
  272. #Fixing so that numerator is number of participants
  274. ```{r}
  275. category_binary <- food_long_with_cat %>%
  276. filter(consumed == 1) %>%
  277. group_by(record_id, w12_grupo, MDDW_category) %>%
  278. summarise(any_consumed = 1, .groups = "drop")
  279. ```
  281. ```{r}
  282. category_counts <- category_binary %>%
  283. count(w12_grupo, MDDW_category, name = "n_consumed")
  284. ```
  286. ```{r}
  287. total_per_group <- food_long_with_cat %>%
  288. distinct(record_id, w12_grupo) %>%
  289. count(w12_grupo, name = "total_per_group")
  290. ```
  292. ```{r}
  293. category_percents <- category_counts %>%
  294. left_join(total_per_group, by = "w12_grupo") %>%
  295. mutate(percent = 100 * n_consumed / total_per_group)
  296. ```
  301. #Exporting tables
  302. ```{r}
  303. library(openxlsx)
  305. # Save to Desktop (change username if needed)
  306. write.xlsx(
  307. list(
  308. "Food_Percent_With_Categories" = food_percent_with_cat,
  309. "Category_Percents" = category_percents
  310. ),
  311. file = "C:/Users/Gifty Aboagye-Mensah/Desktop/Mikhuna_Food_Results.xlsx"
  312. )
  313. ```
  315. #Calculating chi-squares
  317. ```{r}
  318. class(category_percents)
  319. ```
  321. ```{r}
  322. class(category_percents)
  323. class(group_by)
  324. class(mutate)
  325. ```
  328. ```{r}
  329. results <- category_percents %>%
  330. mutate(
  331. not_consumed = total_per_group - n_consumed
  332. ) %>%
  333. group_by(MDDW_category) %>%
  334. group_modify(~{
  336. # extract values for each group
  337. c_yes <- .x$n_consumed[.x$w12_grupo == 1]
  338. c_no <- .x$not_consumed[.x$w12_grupo == 1]
  339. i_yes <- .x$n_consumed[.x$w12_grupo == 2]
  340. i_no <- .x$not_consumed[.x$w12_grupo == 2]
  342. # construct contingency table
  343. tbl <- matrix(c(c_yes, c_no, i_yes, i_no), nrow = 2, byrow = TRUE)
  345. # chi-squared test
  346. test <- chisq.test(tbl, correct = FALSE)
  348. tibble(
  349. control_percent = .x$percent[.x$w12_grupo == 1],
  350. intervention_percent = .x$percent[.x$w12_grupo == 2],
  351. p_value = test$p.value
  352. )
  353. }) %>%
  354. ungroup()
  355. ```
  358. #Calculating MDD_W scores per participant
  359. ```{r}
  360. category_binary <- food_long_with_cat %>%
  361. filter(consumed == 1) %>% # keep only foods consumed
  362. group_by(record_id, w12_grupo, MDDW_category) %>%
  363. summarise(any_consumed = 1, .groups = "drop")
  365. mddw_score <- category_binary %>%
  366. group_by(record_id, w12_grupo) %>%
  367. summarise(
  368. MDDW_score = sum(any_consumed),
  369. .groups = "drop"
  370. )
  371. ```
  373. #For all participants even those who consumed 0 food groups
  374. ```{r}
  375. all_participants <- food_long_with_cat %>%
  376. distinct(record_id, w12_grupo)
  378. mddw_score_full <- all_participants %>%
  379. left_join(mddw_score, by = c("record_id", "w12_grupo")) %>%
  380. mutate(MDDW_score = replace_na(MDDW_score, 0))
  381. ```
  383. #Adding 0/1 code for MDD-W
  384. ```{r}
  385. mddw_score_full <- mddw_score_full %>%
  386. mutate(
  387. MDDW_indicator = if_else(MDDW_score >= 5, 1, 0)
  388. )
  389. ```
  392. #Summary statistics for each group
  393. ```{r}
  394. mddw_summary <- mddw_score_full %>%
  395. group_by(w12_grupo) %>%
  396. summarise(
  397. mean_score = mean(MDDW_score),
  398. sd_score = sd(MDDW_score),
  399. n_participants = n(),
  400. n_meeting_MDDW = sum(MDDW_indicator),
  401. percent_meeting_MDDW = 100 * mean(MDDW_indicator),
  402. .groups = "drop"
  403. )
  404. ```
  406. #Crearing 2x2 contingency table
  407. ```{r}
  408. # Contingency table: rows = group, columns = MDDW indicator
  409. table_mddw <- table(
  410. Group = mddw_score_full$w12_grupo,
  411. MDDW = mddw_score_full$MDDW_indicator
  412. )
  414. table_mddw
  415. ```
  416. #Chi square test
  417. ```{r}
  418. chi_result <- chisq.test(table_mddw, correct = FALSE) # no Yates' correction
  419. chi_result
  420. ```
  423. #Calculating species richness
  424. ```{r}
  425. species_richness <- food_long_with_cat %>%
  426. filter(consumed == 1) %>% # keep only foods eaten
  427. group_by(record_id, w12_grupo) %>%
  428. summarise(
  429. richness = n_distinct(food_var),
  430. .groups = "drop"
  431. )
  433. head(species_richness)
  434. ```
  436. #Merging for all participants (include NA for participants with no data)
  437. ```{r}
  438. species_richness_full <- all_participants %>%
  439. left_join(species_richness, by = c("record_id", "w12_grupo")) %>%
  440. mutate(richness = replace_na(richness, 0))
  441. ```
  443. #Adding species richness to MDD-W score data
  444. ```{r}
  445. mddw_with_richness <- mddw_score_full %>%
  446. left_join(species_richness_full, by = c("record_id", "w12_grupo"))
  448. head(mddw_with_richness)
  449. ```
  452. #Testing for equality of variances
  453. ```{r}
  454. library(car)
  455. leveneTest(richness ~ as.factor(w12_grupo), data = species_richness_full)
  456. ```
  459. #Welch's t-test
  460. ```{r}
  461. t_test_richness <- t.test(
  462. richness ~ w12_grupo,
  463. data = species_richness_full,
  464. var.equal = FALSE # Welch t-test
  465. )
  466. t_test_richness
  467. ```
  469. #Equal variance (pooled) t-test
  470. ```{r}
  471. t_test_richness_equalvar <- t.test(
  472. richness ~ w12_grupo,
  473. data = species_richness_full,
  474. var.equal = TRUE
  475. )
  476. t_test_richness_equalvar
  477. ```
  479. ```{r}
  480. species_richness_full %>%
  481. group_by(w12_grupo) %>%
  482. summarise(
  483. n = n(),
  484. mean_richness = mean(richness, na.rm = TRUE),
  485. sd_richness = sd(richness, na.rm = TRUE)
  486. )
  487. ```

MikhunaMDD-W.Rmd, no license · at the source

Overview

Authors: Lora L. Iannotti1, Gabriela Vintimilla Andrade2, Shirley Tipanquiza Piedra2, Rachel B. Zimmerman1, Elizabeth Hahn1, Pamela Camana2, Dessire Baldeon2, Paul Silva3, Karen Balladares4, Carlos Andres Gallegos-Riofrio5, Melissa Chapnick6, Gifty Aboagye-Mensah1, Timothy Zielonko7, Marcus Raichle8, Manu S. Goyal8, William F. Waters2, Iván Palacios León2, Jennifer Nicholas9
ORCID iDs: Gabriela Vintimilla Andrade, Carlos Andres Gallegos-Riofrio, Gifty Aboagye-Mensah, Marcus Raichle, Manu S. Goyal, William F. Waters, Jennifer Nicholas
  1. E3 Nutrition Lab, Bursky School of Public Health, Washington University in St. Louis, St. Louis, MO 63130
  2. Institute for Research in Health and Nutrition, Universidad San Francisco de Quito, Quito 170902, Ecuador
  3. Q*RA Medicina Especialidades, Quito 170184, Ecuador
  4. Amazonic Gardens, Quito 170902, Ecuador
  5. Institute for Agroecology, University of Vermont, Burlington, VT 05405
  6. Rollins School of Public Health, Emory University, Atlanta, GA 30322
  7. Bursky School of Public Health, Washington University in St. Louis, St. Louis, MO 63130
  8. Neuroimaging Labs Research Center, Mallinckrodt Institute of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO 63110
  9. Department of Radiology, University of Colorado School of Medicine, Aurora, CO 80045
Institutions: Washington University in St. Louis (United States); Universidad San Francisco de Quito (Ecuador); University of Vermont (United States); Emory University (United States); University of Colorado Anschutz (United States)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 36, article e2602218123
Dates: received 28 January 2026; accepted 17 July 2026; published online 31 August 2026; in print 8 September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2602218123 · PMID 42673450 · PMCID PMC13552917 · OpenAlex W7204789140
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), developmental (subfield)
Methods: Statistics, fMRI & imaging
Keywords: maternal nutrition, fetal and neonatal growth, fetal and neonatal brain development, dietary pattern intervention, sustainable foods
MeSH: Brain*, Diet*, Fetal Development*, Maternal Nutritional Physiological Phenomena*, Adult, Ecuador, Female, Humans, Infant, Newborn, Neurodevelopment, Pregnancy (* major topic)
Journal subjects: Social Sciences, Psychological and Cognitive Sciences
Topic: Birth, Development, and Health (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: Children’s Discovery Institute (CDI) (MI-II-2019-812)
Citations: cited by 1 paper (Europe PMC); 61 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above.

OSF qwap8

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 5 files, 1 script
Software Heritage: not checked
Found in: the references
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: car (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/qwap8

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

Tracing map

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  • 1 script, each with its path and the digest of its content;
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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1073/pnas.2602218123.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 5 keywords, 11 MeSH terms, 1 funder, 56 references.

Cite

This paper

Iannotti, L. L., Vintimilla Andrade, G., Tipanquiza Piedra, S., Zimmerman, R. B., Hahn, E., Camana, P., Baldeon, D., Silva, P., Balladares, K., Gallegos-Riofrio, C. A., Chapnick, M., Aboagye-Mensah, G., Zielonko, T., Raichle, M., Goyal, M. S., Waters, W. F., Palacios León, I., & Nicholas, J. (2026). Pregnancy diet based on ancestral patterns increases growth in subcortical fetal brain regions. Proceedings of the National Academy of Sciences of the United States of America, 123(36), e2602218123. https://doi.org/10.1073/pnas.2602218123

BibTeX

@article{iannotti2026pregnancy,
author = {Iannotti, Lora L. and Vintimilla Andrade, Gabriela and Tipanquiza Piedra, Shirley and Zimmerman, Rachel B. and Hahn, Elizabeth and Camana, Pamela and Baldeon, Dessire and Silva, Paul and Balladares, Karen and Gallegos-Riofrio, Carlos Andres and Chapnick, Melissa and Aboagye-Mensah, Gifty and Zielonko, Timothy and Raichle, Marcus and Goyal, Manu S. and Waters, William F. and Palacios León, Iván and Nicholas, Jennifer},
title = {{Pregnancy diet based on ancestral patterns increases growth in subcortical fetal brain regions}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = aug,
volume = {123},
number = {36},
pages = {e2602218123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2602218123},
url = {https://doi.org/10.1073/pnas.2602218123},
pmid = {42673450},
pmcid = {PMC13552917}
}

RIS

TY - JOUR
AU - Iannotti, Lora L.
AU - Vintimilla Andrade, Gabriela
AU - Tipanquiza Piedra, Shirley
AU - Zimmerman, Rachel B.
AU - Hahn, Elizabeth
AU - Camana, Pamela
AU - Baldeon, Dessire
AU - Silva, Paul
AU - Balladares, Karen
AU - Gallegos-Riofrio, Carlos Andres
AU - Chapnick, Melissa
AU - Aboagye-Mensah, Gifty
AU - Zielonko, Timothy
AU - Raichle, Marcus
AU - Goyal, Manu S.
AU - Waters, William F.
AU - Palacios León, Iván
AU - Nicholas, Jennifer
TI - Pregnancy diet based on ancestral patterns increases growth in subcortical fetal brain regions
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/08/31
VL - 123
IS - 36
SP - e2602218123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2602218123
UR - https://doi.org/10.1073/pnas.2602218123
LA - en
ER -

CSL-JSON

{
"id": "10.1073/pnas.2602218123",
"type": "article-journal",
"title": "Pregnancy diet based on ancestral patterns increases growth in subcortical fetal brain regions",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
{
"family": "Iannotti",
"given": "Lora L."
},
{
"family": "Vintimilla Andrade",
"given": "Gabriela"
},
{
"family": "Tipanquiza Piedra",
"given": "Shirley"
},
{
"family": "Zimmerman",
"given": "Rachel B."
},
{
"family": "Hahn",
"given": "Elizabeth"
},
{
"family": "Camana",
"given": "Pamela"
},
{
"family": "Baldeon",
"given": "Dessire"
},
{
"family": "Silva",
"given": "Paul"
},
{
"family": "Balladares",
"given": "Karen"
},
{
"family": "Gallegos-Riofrio",
"given": "Carlos Andres"
},
{
"family": "Chapnick",
"given": "Melissa"
},
{
"family": "Aboagye-Mensah",
"given": "Gifty"
},
{
"family": "Zielonko",
"given": "Timothy"
},
{
"family": "Raichle",
"given": "Marcus"
},
{
"family": "Goyal",
"given": "Manu S."
},
{
"family": "Waters",
"given": "William F."
},
{
"family": "Palacios León",
"given": "Iván"
},
{
"family": "Nicholas",
"given": "Jennifer"
}
],
"container-title-short": "Proc Natl Acad Sci U S A",
"volume": "123",
"issue": "36",
"page": "e2602218123",
"DOI": "10.1073/pnas.2602218123",
"PMID": "42673450",
"PMCID": "PMC13552917",
"ISSN": "0027-8424",
"publisher": "National Academy of Sciences",
"URL": "https://doi.org/10.1073/pnas.2602218123",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
31
]
]
}
}

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