One-year longitudinal association between changes in dietary choline or betaine intake and cardiometabolic variables in the PREvención con DIeta MEDiterránea-Plus (PREDIMED-Plus) trial Laura Díez-Ricote,1 Rodrigo San-Cristobal,2 M José Concejo,3 Miguel Á Martínez-González,4,5,6 Dolores Corella,4,7 Jordi Salas-Salvadó,4,8,9,10,11 Albert Goday,4,12,13 J Alfredo Martínez,2,4,14 Ángel M Alonso-Gómez,4,15 Julia Wärnberg,4,16 Jesús Vioque,17,18 Dora Romaguera,4,19 José López-Miranda,4,20 Ramon Estruch,4,21 Francisco J Tinahones,4,22 José Lapetra,4,23 Lluís Serra-Majem,4,24 Aurora Bueno-Cavanillas,17,25 Josep A Tur,4,26 Vicente Martín Sánchez,17,27 Xavier Pintó,4,28 José J Gaforio,17,29 Pilar Matía-Martín,30 Josep Vidal,31,32 Sebastián Mas Fontao,4,33 Emilio Ros,4,34 Zenaida Vázquez-Ruiz,4,5,35 Carolina Ortega-Azorín,4,7 Jesús F García-Gavilán,4,8,9,10 Mireia Malcampo,4,12 Diego Martínez-Urbistondo,36 Lucas Tojal-Sierra,15 Antonio García Rodríguez,37 Nuria Gómez-Bellvert,38 Alice Chaplin,4,19 Antonio García-Ríos,20 Rosa M Bernal-López,4,22 José M Santos-Lozano,4,23 Javier Basterra-Gortari,4,5,35 José V Sorlí,4,7 Michelle Murphy,4,8,9,10 Griselda Gasulla,4,12 Víctor Micó,2 Itziar Salaverria-Lete,4,15 Estibaliz Goñi Ochandorena,4,5 Nancy Babio,4,8,9,10 Xavier Herraiz,4,12 José M Ordovás,4,39,40 and Lidia Daimiel1,4 1 NutritionalControl of the Epigenome Group, Precision Nutrition and Obesity Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain; 2 Cardiometabolic Health Group, Precision Nutrition and Cardiometabolic Health Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain; 3 Primary Care Center Ibiza, SERMAS, Madrid, Spain; 4 Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Institute of Health Carlos III, Madrid, Spain; 5 Department of Preventive Medicine and Public Health, Navarra Institute of Health Research (IdISNA), University of Navarra, Pamplona, Spain; 6 Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA; 7 Department of Preventive Medicine, University of Valencia, Valencia, Spain; 8 Universitat Rovira i Virgili, Departament of Biochemistry and Biotechnology, Human Nutrition Unit, Reus, Spain; 9 Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain; 10 Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain; 11 Unit of Preventive Medicine & Public Health, Faculty of Medicine & Health Sciences, Universitat Rovira i Virgili, Reus, Spain; 12 Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar d`Investigació Médica (IMIM), Barcelona, Spain; 13 Department of Medicine, Universitat Autonoma de Barcelona, Barcelona, Spain; 14 Center for Nutrition Research, Department of Nutrition, Food Sciences, and Physiology, University of Navarra, Pamplona, Spain; 15 Cardiovascular, Respiratory and Metabolic Area, Bioaraba Health Research Institute; Araba University Hospital, Osakidetza Basque Health Service; University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain; 16 Department of Nursing, University of Málaga, Institute of Biomedical Research in Malaga (IBIMA), Málaga, Spain; 17 CIBER de Epidemiología y Salud Pública (CIBERESP), Carlos III Institute of Health (ISCIII), Madrid, Spain; 18 Institute of Health and Biomedical Research of Alicante, Miguel Hernández University (ISABIAL-UMH), Alicante, Spain; 19 Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology, Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain; 20 Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain; 21 Department of Internal Medicine, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain; 22 Department of Endocrinology, Virgen de la Victoria Hospital, Málaga Biomedical Research Institute (IBIMA), University of Málaga, Málaga, Spain; 23 Research Unit, Department of Family Medicine, Sevilla Primary Care Health District, Sevilla, Spain; 24 Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria and Maternal and Child Insular University Hospital Center (CHUIMI), Canarian Health Service, Las Palmas de Gran Canaria, Spain; 25 Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain; 26 Research Group on Community Nutrition & Oxidative Stress, University of the Balearic Islands, Palma de Mallorca, Spain; 27 Institute of Biomedicine (IBIOMED), University of León, León, Spain; 28 Lipids and Vascular Risk Unit, Internal Medicine, Bellvitge University Hospital, Barcelona, Spain; 29 Department of Health Sciences, University Institute for Research on Olives and Olive Oils, University of Jaén, Jaén, Spain; 30 Department of Endocrinology and Nutrition, San Carlos Clinical Hospital Institute of Health Research (IdISSC), Madrid, Spain; 31 CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Carlos III Institute of Health (ISCIII), Madrid, Spain; 32 Department of Endocrinology, Institut d` Investigacions Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain; 33 Department of Endocrinology and Nutrition, Jimenez Díaz Foundation Hospital Biomedical Research Institute (IISFJD), Autonomous University of Madrid, Madrid, Spain; 34 Lipid Clinic, Department of Endocrinology and Nutrition, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain; 35 Endocrinology Service, Navarra Hospital Complex, Osasunbidea, Navarro Health Service, Pamplona, Spain; 36 Internal Medicine Department, HM Sanchinarro Hospital, HM Hospitals, Madrid, Spain; 37 Division of Preventive Medicine and Public Health, University of Malaga, Institute of Biomedical Research in Málaga (IBIMA-University of Malaga), Málaga, Spain; 38 San Vicent del Raspeig Health Center, Alicante, Spain; 39 Nutritional Genomics and Epigenomics Group, Precision Nutrition and Obesity Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain; and 40 Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA Am J Clin Nutr 2022;116:1565–1579. Printed in USA. © The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommon s.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 1565 1566 Díez-Ricote et al. ABSTRACT foods include fish, eggs, and meat, and it also can be Background: Choline and betaine intakes have been related to found in dairy products, cruciferous vegetables, legumes, and cardiovascular health. nuts (3). Objectives: We aimed to explore the relation between 1-y changes Choline can be metabolized by 4 main pathways (4). One in dietary intake of choline or betaine and 1-y changes in involves the synthesis of the neurotransmitter acetylcholine in cardiometabolic and renal function traits within the frame of the the cytosol of presynaptic cholinergic neurons. The second PREDIMED (PREvención con DIeta MEDiterránea)-Plus trial. is the production of phosphatidylcholine, the most abundant Methods: We used baseline and 1-y follow-up data from 5613 phospholipid in the body. The third involves its oxidation to participants (48.2% female and 51.8% male; mean ± SD age: generate betaine. Betaine is a methyl group donor involved 65.01 ± 4.91 y) to assess cardiometabolic traits, and 3367 in the remethylation of homocysteine to produce methionine. participants to assess renal function, of the Spanish PREDIMED- Lastly, choline can be metabolized in the intestine before being Plus trial. Participants met ≥3 criteria of metabolic syndrome and absorbed. Gut microbiota can produce trimethylamine (TMA) had overweight or obesity [BMI (in kg/m2 ) ≥27 and ≤40]. These from choline. TMA is then absorbed, and it is metabolized criteria were similar to those of the PREDIMED parent study. Dietary intakes of choline and betaine were estimated from the FFQ. PI16/01120, PI17/00764, PI17/01183, PI17/00855, PI17/01347, PI17/00525, Results: The greatest 1-y increase in dietary choline or betaine PI17/01827, PI17/00532, PI17/00215, PI17/01441, PI17/00508, PI17/01732, intake (quartile 4) was associated with improved serum glucose PI17/00926, PI19/00957, PI19/00386, PI19/00309, PI19/01032, PI19/00576, concentrations (−3.39 and −2.72 mg/dL for choline and betaine, PI19/00017, PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, PI20/00886, respectively) and HbA1c levels (−0.10% for quartile 4 of either and PI20/01158), and the Especial Action Project “Implementación y choline or betaine intake increase). Other significant changes evaluación de una intervención intensiva sobre la actividad física Cohorte associated with the greatest increase in choline or betaine intake PREDIMED-Plus” (to JS-S); and Recercaixa grant 2013ACUP00194 (to JS- were reduced body weight (−2.93 and −2.78 kg, respectively), BMI S). Moreover, JS-S acknowledges the financial support by ICREA (Catalan (−1.05 and −0.99, respectively), waist circumference (−3.37 and Institution for Research and Advanced Studies) under the ICREA Academia −3.26 cm, respectively), total cholesterol (−4.74 and −4.52 mg/dL, program; the SEMERGEN grant (to JL); Government of Navarra Department respectively), and LDL cholesterol (−4.30 and −4.16 mg/dL, of Health grant 61/2015 (to MÁM-G); Fundació La Marató de TV3 grant respectively). Urine creatinine was reduced in quartile 4 of 1-y 201630.10; the AstraZeneca Young Investigators Award in Category of increase in choline or betaine intake (−5.42 and −5.74 mg/dL, Obesity and T2D 2017 (to DC); Consejería de Salud de la Junta de Andalucía respectively). grants PI0458/2013, PS0358/2016, and PI0137/2018; Generalitat Valenciana grant PROMETEO/2017/017 (to DC); and Balearic Islands Government grant Conclusions: Increases in dietary choline or betaine intakes of support to research groups 35/2011 (FEDER funds) (to JAT). RS-C was were longitudinally related to improvements in cardiometabolic supported by Spanish Ministerio de Ciencia e Innovación y Ministerio de parameters. Markers of renal function were also slightly improved, Universidades Spanish State Research Agency Juan de la Cierva Program and they require further investigation. This trial was registered at training grant FJC2018-038168-I. https://www.isrctn.com/ as ISRCTN89898870. Am J Clin Nutr Author disclosures: J.S-S reports grants from CIBEROBN, ISCIII (Spain), 2022;116:1565–1579. during the conduct of the study; non-financial support from Nut and Dried Fruit Foundation, personal fees from Instituto Danone Spain, grants from Nut Keywords: choline, betaine, Mediterranean diet, cardiovascular and Dried Fruit Foundation to this Field Code Changed institution, grants disease risk, cardiometabolic parameters, renal variables from Eroski Distributors, personal fees from Nut and Dried Fruit Foundation, outside the submitted work. E.R reports grants, personal fees, non-financial support and other from California Walnut Commission, grants, personal fees, non-financial support and other from Alexion, personal fees, non-financial Introduction support and other from Ferrer International, personal fees from Amarin, Choline has been recognized as an essential nutrient since personal fees, non-financial support and other from Danone, outside the submitted work. J. L-M reports personal fees and non-financial support from 1998 (1). Still, the average consumption in the population is AMGEN, personal fees and non-financial support from SANOFI, personal below the recommended intake (2, 3). High-choline-content fees from MSD, personal fees from Laboratorios Dr. Esteve, personal fees from NOVO-NORDISK, outside the submitted work. The other authors Supported by Ministerio de Ciencia e Innovación through Proyectos de declare no conflict of interests. I+D+i “Retos Investigación” grant RTI2018-095569-B-I00 (to JMO) and Supplemental Tables 1–7 and Supplemental Figures 1 and 2 are available “Programación Conjunta Internacional” grant PCI2018-093009 (to JMO). from the “Supplementary data” link in the online posting of the article and The PREDIMED-Plus (Prevención con Dieta Mediterránea-Plus) trial was from the same link in the online table of contents at https://academic.oup.c supported by European Research Council Advanced Research grant 2014– om/ajcn/. 2019, agreement #340918 (to MÁM-G); the official Spanish institutions Address correspondence to LD (e-mail: lidia.daimiel@imdea.org). for funding scientific biomedical research, CIBER Fisiopatología de la Abbreviations used: BP, blood pressure; CVD, cardiovascular disease; Obesidad y Nutrición (CIBEROBN) and Instituto de Salud Carlos III eGFR, estimated glomerular filtration rate; er-MedDiet, energy-restricted tra- (ISCIII), through the Fondo de Investigación para la Salud (FIS) which ditional Mediterranean diet; FMO, flavin monooxygenase; Hb1Ac, glycated is cofunded by the European Regional Development Fund (coordinated haemoglobin; MedDiet, Mediterranean diet; MetS, metabolic syndrome; FIS projects led by JS-S and J Vidal, including the following projects: NHS, Nurses’ Health Study; PA, physical activity; PREDIMED, PREvención PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, con DIeta MEDiterránea; TMA, trimethylamine; TMAO, trimethylamine-N- PI13/02184, PI13/00728, PI13/01090, PI13/01056, PI14/01722, PI14/00636, oxide; T2D, type 2 diabetes; UACR, urine albumin:creatinine ratio. PI14/00618, PI14/00696, PI14/01206, PI14/01919, PI14/00853, PI14/01374, Received November 10, 2021. Accepted for publication September 9, 2022. PI14/00972, PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, First published online September 16, 2022; doi: https://doi.org/10.1093/ PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, PI16/01522, ajcn/nqac255. Choline/betaine intake and cardiovascular features 1567 to trimethylamine-N-oxide (TMAO) in the liver by flavin Lung, and Blood Institute; American Heart Association; World monooxygenase (FMO) enzymes, particularly FMO3. This same Heart Federation; International Atherosclerosis Society; and pathway is also followed by dietary betaine, contributing to TMA International Association for the Study of Obesity criteria production in the gut by the microbiota (5). (26). History of prevalent CVD, cancer, immunodeficiency High plasma TMAO concentrations may predict cardiovas- or HIV-positive status, cirrhosis or liver failure, psychiatric cular events, even when other cardiovascular biomarkers are disorders, any disease with <24 mo life expectancy, or acute not altered (6). Although several studies show an association infection or inflammation were exclusion criteria. Participants between greater plasma TMAO concentrations and CVD risk (7– with T2D were recruited with a limit of 25% of the total 9), greater plasma TMAO concentrations can also occur when population. following a healthy diet, rich in foods known to be high in choline The trial was approved according to the ethical standards of and betaine (10) and protective against CVD. the Declaration of Helsinki by the Institutional Review Boards Different studies have observed that greater choline and (IRBs) of all participating centers. All participants provided betaine intakes were associated with lower type 2 diabetes (T2D) written informed consent for their participation in the study. The risk (11–14). However, this relation has not been confirmed in trial was retrospectively registered at the International Standard other studies (15). Randomized Controlled Trial Registry as ISRCTN89898870 on The Mediterranean diet (MedDiet) is rich in fiber (from 24 July, 2014. fruits, vegetables, and legumes) and has been shown to reduce CVD risk (16). Furthermore, it has been observed that choline Study sample intake is the lowest in Mediterranean countries, compared The PREDIMED-Plus trial recruited 6874 participants be- with northern European countries, where a meat-rich diet such tween October 2013 and December 2016. Baseline and 1-y data as the Western diet is usually consumed (17). Furthermore, from the PREDIMED-Plus study data set dated 22 December, consumption of a MedDiet has been associated with lower 2020 were used. Participants with missing values for energy TMAO urine concentrations (18, 19) after 3 y of follow- intake and dietary intake at baseline and after 1 y of follow-up up in the PREDIMED (PREvención con DIeta MEDiterránea; were excluded (n = 265 and n = 696 participants excluded for PREvention with MEDiterranean Diet) cohort. missing values at baseline and after 1 y, respectively). We also The association between choline and betaine intakes and excluded participants with missing values for lifestyle variables cardiometabolic health is still under debate. Previous longitudinal at baseline (n = 288). Then, we imputed missing values for these studies have examined the association of choline or betaine intake covariates at 1 y and used the remaining sample. Missing values with body composition (20, 21), T2D (15), DNA methylation for covariates were imputed using a multivariable imputation (22), and TMAO production (23). However, these were prospec- model based on a regression using baseline data for the imputed tive and observational studies in infants and adult populations and variables as well as sociodemographic variables as imputing did not look at cardiometabolic health traits in elder populations variables. Participants with implausible energy intakes (<500 or with metabolic syndrome (MetS). For this reason, we aimed to >3500 kcal for females and <800 or >4000 kcal for males) at analyze the association between choline or betaine intake and baseline (n = 0) and after 1 y (n = 12) were also excluded (27). cardiometabolic and anthropometric parameters in a longitudinal The final analytic sample included data from 5613 participants 1-y follow-up interventional study, within the frame of the (48.2% females, mean ± SD age: 65.01 ± 4.91 y) (Figure 1) PREDIMED-Plus study. and 30.4% of participants had T2D. For renal function analysis, 3367 participants were included, after excluding participants with Methods urine albumin:creatinine ratio (UACR) >500 to avoid influences for outliers and participants with diminished renal function, as Study population previously described by Díaz-López et al. (28). The PREDIMED-Plus trial (ISRCTN89898870) is an ongoing 6-y, multicenter, controlled, randomized, parallel-group, and Assessment of choline/betaine intake primary prevention trial conducted in 23 research centers Food intake was assessed with a 143-item FFQ validated for of Spain that started in October 2013. The trial aims to the Spanish population administered by a trained dietitian in face- assess the effect of an intensive weight loss intervention to-face interviews (29). Participants were asked to define the program based on an energy-restricted traditional MedDiet (er- frequency of consumption of each food item within a usual year. MedDiet), physical activity (PA) promotion, and behavioral Nine options for frequency of consumption were given, ranging support on hard cardiovascular events (cardiovascular mortality, from “never or hardly ever” to “more than six times a day.” Food acute myocardial infarction, or stroke), in comparison with intake (g/d) was then estimated by multiplying the portion size usual dietary counseling with an energy-unrestricted MedDiet by the frequency of consumption and dividing by 7 or by 30 for without PA promotion (control group). The study protocol, weekly or monthly consumed food items, respectively. Nutrient including study design and data collection, has been published estimates were calculated from all items in the FFQ responses that (24, 25) and can be found at the PREDIMED-Plus website contributed to nutrient intake, by using Spanish food composition (https://www.predimedplus.com/en/). Eligible participants were tables (30). community-dwelling males (55–75 y old) and females (60–75 To estimate choline/betaine intake, we used USDA databases y old) with overweight or obesity (BMI ≥27 and ≤40 kg/m2 ) from 2015–2018 and the website https://nutritiondata.self.com/ and MetS, according to the International Diabetes Federation and multiplied grams per day of each food item by the amount of Task Force on Epidemiology and Prevention; National Heart, choline/betaine per gram of food. Assessment of choline intake 1568 Díez-Ricote et al. FIGURE 1 Participant flowchart. through an FFQ has been previously validated in the Framingham calculated as the weight in kilograms divided by the square Offspring Study (31). of height in meters (kg/m2 ). Waist and hip perimeters were measured with an anthropometric tape. Waist circumference was measured midway between the lowest rib and the iliac crest. Cardiometabolic, renal function, and anthropometric Trained dietitians collected anthropometric measures with the parameters participants in light clothing with no shoes or accessories and in Cardiometabolic parameters analyzed included plasma total duplicate. The mean of the 2 measurements was used in all cases. cholesterol, LDL cholesterol, HDL cholesterol, TGs, and glucose Blood pressure (BP) was measured in triplicate with an Omron concentrations, and HbA1c levels. The renal function param- HEM-705CP with participants in a seated position after 5 min rest eters analyzed included serum creatinine and uric acid and and with a 1-min rest between measurements. urine creatinine and albumin:creatinine ratio. These metabolites Covariate variables included lifestyle and sociodemographic were measured with standard analytic procedures in accredited variables. Trained dietitians administered a general questionnaire biochemistry analytics laboratories after an overnight fast. in face-to-face visits to collect data about sex; age (continuous); LDL cholesterol was calculated with the Friedewald formula. civil status (married or living alone); years of education Estimated glomerular filtration rate (eGFR) was calculated (continuous); smoking habits (current smoker, former smoker, or following the formula previously described (32). Briefly, eGFR never smoker); prevalence of hypertension, T2D, and depression; was indirectly estimated by using serum creatinine values and and regular use of medication. implementing them in the Chronic Kidney Disease Epidemiology Lifestyle parameters included the adherence to an er-MedDiet Collaboration equation for Caucasian individuals. assessed by the 17-item questionnaire developed and validated Weight and height were measured with standard calibrated for the PREDIMED-Plus study (33), PA level assessed by the scales and a wall-mounted stadiometer, respectively. BMI was REGICOR questionnaire (34), and sedentary lifestyle assessed Choline/betaine intake and cardiovascular features 1569 TABLE 1 Baseline sociodemographic and lifestyle characteristics of participants according to quartiles of dietary choline intake1 Q1 (128.0–404.7 Q2 (404.8–457.7 Q3 (457.8–509.4 Q4 (509.5–914.9 Characteristics mg/d) (n = 1404) mg/d) (n = 1403) mg/d) (n = 1403) mg/d) (n = 1403) P value Choline intake, mg/d 359.95 ± 37.66 431.64 ± 15.38 482.30 ± 14.92 563.81 ± 53.32 <0.001 Sex, female 476 (33.9) 639 (45.5) 744 (53.0) 848 (60.4) <0.001 Age, y 64.40 ± 4.95 64.96 ± 5.12 65.37 ± 4.79 65.30 ± 4.70 <0.001 Civil status, married 1088 (77.5) 1095 (78.0) 1074 (76.6) 1070 (76.3) 0.653 Education, y 11.77 ± 7.50 11.39 ± 7.49 11.73 ± 8.71 11.83 ± 8.77 0.483 Smoking habits <0.001 Smoker 215 (15.3) 189 (13.5) 172 (12.3) 129 (9.2) Former smoker 683 (48.6) 608 (43.3) 581 (41.4) 549 (39.1) Never smoker 506 (36.0) 606 (43.2) 650 (46.3) 725 (51.7) Body weight, kg 87.68 ± 12.79 86.60 ± 12.77 85.51 ± 13.18 85.50 ± 12.88 <0.001 BMI, kg/m2 32.30 ± 3.35 32.54 ± 3.44 32.42 ± 3.49 32.72 ± 3.47 0.009 Energy intake, kcal 2390.99 ± 582.36 2344.24 ± 556.95 2326.94 ± 533.18 2385.85 ± 516.88 0.003 Adherence to er-MedDiet 7.76 ± 2.64 8.22 ± 2.65 8.73 ± 2.63 9.25 ± 2.57 <0.001 (max: 17 points) PA, intensity 0.531 Sedentary 842 (60.0) 853 (60.8) 818 (58.3) 859 (61.2) Moderately active 268 (19.1) 249 (17.7) 273 (19.5) 270 (19.2) Active 294 (20.9) 301 (21.5) 312 (22.2) 274 (19.5) PA, MET · min/wk 704.66 ± 475.33 713.19 ± 465.10 716.29 ± 450.69 736.69 ± 454.42 0.303 Sedentary lifestyle, yes 638 (45.4) 650 (46.3) 648 (46.2) 587 (41.8) 0.056 Diabetes prevalence, yes 375 (26.7) 400 (28.5) 450 (32.1) 483 (34.4) <0.001 Diabetes medication, yes 327 (23.3) 353 (25.2) 383 (27.3) 430 (30.6) <0.001 Hypertension prevalence, yes 1174 (83.6) 1184 (84.4) 1189 (84.7) 1179 (84.0) 0.864 Hypertension medication, yes 1091 (77.7) 1112 (79.3) 1095 (78.0) 1109 (79.0) 0.630 Cholesterol medication, yes 695 (49.5) 720 (51.3) 730 (52.0) 732 (52.2) 0.415 Heart medication, yes 116 (8.3) 111 (7.9) 106 (7.6) 107 (7.6) 0.465 Depression prevalence, yes 286 (20.4) 276 (19.7) 281 (20.0) 312 (22.2) 0.338 Depression medication, yes 304 (21.7) 309 (22.0) 359 (25.6) 360 (25.7) 0.027 1 Total n = 5613. Values are mean ± SD or n (%) unless otherwise indicated. Choline intake was adjusted for residuals of energy intake. Characteristics among quartiles were compared by using 1-factor ANOVA or chi-square test for continuous and categoric variables, respectively. er-MedDiet, energy-restricted traditional Mediterranean diet; MET, metabolic equivalent of task; PA, physical activity; Q, quartile. by the Nurses’ Health Study (NHS) questionnaire (35). Partic- For the longitudinal analysis, participants were classified ipants were classified according to their PA level as sedentary, according to quartiles of 1-y changes in choline/betaine intake moderately active, or active. adjusted by energy residuals. Statistically significant differences between quartiles of 1-y changes in choline/betaine consumption for 1-y changes in cardiometabolic, renal, and anthropometric Statistical analysis parameters were assessed by 1-factor ANCOVA test. Three Data analysis was conducted using R version 4.04 (R adjustment models were applied. The minimal adjustment model Foundation for Statistical Computing). The database used for this 1 included age, sex, and center as covariates. Adjustment model analysis was 202012220958_PREDIMEDplus_2anys_2020-12- 2 included those covariates from model 1 plus civil status, 22. Participants were classified according to quartiles of choline education, smoking habits, sedentary lifestyle, and choline or or betaine intake, adjusted by residuals of energy intake. betaine intake at baseline. Finally, adjustment model 3 included For each variable, data shown in tables are presented as all covariates from model 2 plus energy intake and changes mean ± SD or percentages. Missing values for covariates were in energy intake, adherence to an er-MedDiet and changes in imputed using a multivariable regression imputation method adherence to an er-MedDiet, and PA and changes in PA. We with the mice R package. Statistically significant differences in also calculated the P for linear trend using the quartile median general characteristics at baseline (P < 0.05) among quartiles values of changes in choline or betaine intake as a continuous of choline/betaine consumption were compared using a 1- independent variable in a linear regression test adjusted by age, factor ANOVA or chi-square test. Significant differences in sex, center, baseline BMI, and baseline energy intake. Sensitivity cardiometabolic, renal, and anthropometric parameters among analyses in addition adjusting for consumption of food groups choline/betaine consumption quartiles at baseline were compared (baseline consumption of fruits, vegetables, animal protein, fish, by 1-factor ANCOVA test adjusted by age, sex, study center, eggs, red meat, and dairy products and change in the consumption energy intake, smoking habits, adherence to an er-MedDiet, of these food groups after 1 y of follow-up), nutrients (baseline sedentary lifestyle, T2D prevalence, and medication for diabetes, intake of carbohydrates, fiber, saturated fats, and unsaturated hypertension, high cholesterol concentrations, heart conditions, fats and changes in these nutrients’ intake after 1 y of follow- and depression. up), medication (lipid-lowering drugs, antihypertensive drugs, 1570 Díez-Ricote et al. TABLE 2 Baseline sociodemographic and lifestyle characteristics of participants according to quartiles of dietary betaine intake1 Q1 (0–104.3 mg/d) Q2 (104.4–129.9 Q3 (130.0–160.6 Q4 (160.7–390.0 Characteristics (n = 1404) mg/d) (n = 1403) mg/d) (n = 1403) mg/d) (n = 1403) P value Betaine intake, mg/d 83.64 ± 17.18 117.36 ± 7.22 144.19 ± 8.66 191.71 ± 30.18 <0.001 Sex, female 583 (41.5) 737 (52.5) 694 (49.5) 693 (49.4) <0.001 Age, y 64.71 ± 4.98 65.06 ± 4.80 65.16 ± 4.99 65.11 ± 4.84 0.058 Civil status, married 1101 (78.4) 1058 (75.4) 1080 (77.0) 1088 (77.5) 0.281 Education, y 11.96 ± 8.79 11.43 ± 8.21 11.63 ± 8.09 11.71 ± 7.41 0.380 Smoking habits 0.008 Smoker 208 (14.8) 170 (12.1) 165 (11.8) 162 (11.5) Former smoker 627 (44.7) 578 (41.2) 595 (42.4) 621 (44.3) Never smoker 569 (40.5) 655 (46.7) 643 (45.8) 620 (44.2) Body weight, kg 87.04 ± 12.95 85.50 ± 12.72 86.21 ± 13.19 86.55 ± 12.84 0.015 BMI, kg/m2 32.35 ± 3.41 32.47 ± 3.39 32.47 ± 3.46 32.68 ± 3.51 0.084 Energy intake, kcal 2458.01 ± 526.04 2246.19 ± 557.55 2335.37 ± 543.81 2408.39 ± 543.11 <0.001 Adherence to er-MedDiet 8.64 ± 2.75 8.70 ± 2.71 8.35 ± 2.63 8.28 ± 2.61 <0.001 (max: 17 points) PA, intensity 0.159 Sedentary 859 (61.2) 841 (59.9) 859 (61.2) 813 (57.9) Moderately active 262 (18.7) 243 (17.3) 268 (19.1) 287 (20.5) Active 283 (20.2) 319 (22.7) 276 (19.7) 303 (21.6) PA, MET · min/wk 741.27 ± 464.89 714.59 ± 462.78 708.74 ± 460.20 706.19 ± 457.80 0.163 Sedentary lifestyle, yes 616 (43.9) 608 (43.3) 647 (46.1) 652 (46.5) 0.239 Diabetes prevalence, yes 390 (27.8) 412 (29.4) 435 (31.0) 471 (33.6) 0.007 Diabetes medication, yes 337 (24.0) 367 (26.2) 369 (26.3) 420 (29.9) 0.005 Hypertension prevalence, yes 1137 (81.0) 1196 (85.2) 1188 (84.7) 1205 (85.9) 0.002 Hypertension medication, yes 1078 (76.8) 1099 (78.3) 1107 (78.9) 1123 (80.0) 0.27 Cholesterol medication, yes 720 (51.3) 706 (50.3) 722 (51.5) 722 (51.5) 0.622 Heart medication, yes 125 (8.9) 112 (8.0) 111 (7.9) 92 (6.6) 0.105 Depression prevalence, yes 297 (21.2) 321 (22.9) 273 (19.5) 264 (18.8) 0.036 Depression medication, yes 329 (23.4) 335 (23.9) 317 (22.6) 351 (25.0) 0.292 1 Total n = 5613. Values are mean ± SD or n (%) unless otherwise indicated. Betaine intake was adjusted for residuals of energy intake. Characteristics among quartiles were compared using 1-factor ANOVA or chi-square test for continuous and categoric variables, respectively. er-MedDiet, energy-restricted traditional Mediterranean diet; MET, metabolic equivalent of task; PA, physical activity; Q, quartile. antidiabetic medication, and heart medication), and body weight Results and BMI (baseline and 1-y change) were carried out. Each food group, nutrient, and medication was added alone to the full Baseline characteristics adjustment model and 1 adjustment was made including all foods, The baseline characteristics of the 5613 participants are shown nutrients, or medications. in Tables 1 and 2 according to quartiles of baseline choline Sensitivity analyses were made for renal function parameters (Table 1) and betaine (Table 2) intake, adjusted by energy by excluding participants with T2D. residuals. Based on the US Institute of Medicine guidelines, The association between a 20-mg (approximately the mean choline intake was considered low when the consumption of choline content of 60 g dry legumes) change in choline/betaine choline was <400 mg/d, adequate for 400–600 mg/d, and high consumption (independent or “predictor” variable) and car- when intake was >600 mg/d (1). Average choline intake in diometabolic, renal, and anthropometric variables (dependent or quartile 1 was 359.95 ± 37.66 mg/d, and the intake range in “outcome” variables) was analyzed with linear regression models this quartile was 128.0–404.7 mg/d, meaning that most of the using 2 adjustments. The minimal adjustment model or model participants in quartile 1 did not meet the choline adequate 1 included age, gender, choline/betaine intake at baseline, and intake (Table 1) and the rest of the participants presented an study center as cofactors. The complete adjustment model or adequate intake. Subjects with greater choline intake were mainly model 2 included schooling (y), civil status, smoking habits, females, older, and nonsmokers. They showed greater adherence sedentary lifestyle, PA and difference in PA, adherence to an to an er-MedDiet. Furthermore, there were more participants with er-MedDiet and difference in adherence to an er-MedDiet, and T2D among the greatest choline intake group, and consequently, energy intake and difference in energy intake (1 y–baseline), in there were more participants in quartile 4 of choline intake addition to the cofactors included in model 1. receiving antidiabetic medication. Although depression was not To determine to what extent different food sources contributed more frequent in this quartile, there were more participants to the individual variability in the differences in choline or betaine receiving medication for depression in quartiles 3 and 4 of choline intake after 1-y follow-up, stepwise regression was performed. intake (Table 1). Whereas, participants with greater betaine intake Dietary choline and betaine intakes were adjusted by energy showed greater hypertension and T2D prevalence but lower residuals. adherence to an er-MedDiet (Table 2). Medication for T2D TABLE 3 Changes in cardiometabolic and anthropometric parameters according to quartiles of 1-y changes in choline intake1 Q1 (−203.5 to −39.7 mg/d) Q2 (−39.6 to −4.3 Q3 (−4.2 to 35.1 Q4 (35.2–233.8 P-linear Variables Total n (n = 1404) mg/d) (n = 1403) mg/d) (n = 1403) mg/d) (n = 1403) P model 1 P model 2 P model 3 trend  Choline intake, mg/d 5613 − 70.46 ± 26.31 − 21.56 ± 10.22 14.61 ± 11.42 77.46 ± 34.05 <0.001 <0.001 <0.001 <0.001  Energy intake, kcal/d 5613 438.01 ± 305.64 12.61 ± 184.77 − 267.17 ± 184.80 − 783.34 ± 325.17 <0.001 <0.001 <0.001 <0.001  Adherence to er-MedDiet 5591 2.78 ± 3.03 3.05 ± 3.19 3.35 ± 3.23 3.77 ± 3.38 <0.001 <0.001 <0.001 0.126 (max: 17 points)  PA, MET · min/wk 5613 432.82 ± 2250.30 512.42 ± 2410.53 481.80 ± 2454.52 712.03 ± 2587.82 0.005 0.005 <0.001 0.017  BMI, kg/m2 5601 − 0.58 ± 1.52 − 0.76 ± 1.58 − 0.92 ± 1.50 − 1.05 ± 1.61 <0.001 <0.001 <0.001 0.293  Body weight, kg 5609 − 1.55 ± 3.88 − 2.01 ± 4.10 − 2.49 ± 4.02 − 2.93 ± 4.41 <0.001 <0.001 <0.001 <0.001  Waist circumference, cm 5593 − 1.72 ± 5.34 − 2.44 ± 4.92 − 3.06 ± 5.06 − 3.37 ± 5.09 <0.001 <0.001 <0.001 <0.001  Glucose, mg/dL 5492 − 0.59 ± 21.67 − 2.95 ± 22.65 − 3.39 ± 24.65 − 3.39 ± 20.76 0.001 0.001 0.001 0.162  HbA1c, % 4718 − 0.03 ± 0.50 − 0.08 ± 0.57 − 0.09 ± 0.60 − 0.10 ± 0.50 <0.001 <0.001 <0.001 0.047  TG, mg/dL 5472 − 4.06 ± 59.80 − 6.49 ± 64.08 − 7.30 ± 78.98 − 11.88 ± 71.38 0.003 0.003 0.002 0.07  Total cholesterol, mg/dL 5483 − 0.65 ± 30.51 − 1.88 ± 32.08 − 2.48 ± 34.52 − 4.74 ± 32.16 0.001 0.001 0.001 0.88  LDL cholesterol, mg/dL 5088 − 1.19 ± 26.92 − 2.00 ± 28.30 − 2.71 ± 25.91 − 4.30 ± 27.87 0.003 0.003 0.003 0.762  HDL cholesterol, mg/dL 5427 1.32 ± 7.71 1.54 ± 7.24 1.47 ± 7.36 1.41 ± 7.22 0.806 0.806 0.827 <0.001  Systolic BP, mm Hg 5613 − 3.27 ± 23.66 − 5.37 ± 23.73 − 4.93 ± 25.88 − 4.53 ± 22.80 0.234 0.234 0.211 0.247  Diastolic BP, mm Hg 5613 − 2.18 ± 13.52 − 3.32 ± 13.57 − 3.08 ± 15.26 − 2.94 ± 12.96 0.203 0.203 0.187 0.921 Choline/betaine intake and cardiovascular features 1 Total n = 5613. Values are mean ± SD unless otherwise indicated. Choline intake was adjusted by energy residuals. Characteristics among quartiles were compared using 1-factor ANOVA. Model 1: adjusted by sex, age, and center. Model 2: adjusted by sex, age, center, civil status, education, smoking habits, sedentary lifestyle, and choline intake at baseline. Model 3: adjusted by sex, age, center, civil status, education, smoking habits, sedentary lifestyle, choline intake at baseline, adherence to an er-MedDiet and changes in adherence to an er-MedDiet, energy intake and changes in energy intake, and PA and changes in PA. P-linear trend: adjusted by age, sex, center, BMI, and energy intake at baseline. BP, blood pressure; er-MedDiet, energy-restricted traditional Mediterranean diet; MET, metabolic equivalent of task; PA, physical activity; Q, quartile. 1571 1572 Díez-Ricote et al. was also more frequent in quartile 4, but not medication for or betaine intake after 1-y follow-up, the stepwise regression hypertension or depression (Table 2). analysis showed that choline intake reduction in quartile 1 Supplemental Tables 1 and 2 show the dietary intake of participants was mainly due to a change in the consumption participants according to quartiles of choline (Supplemental of dairy products, nuts, cereals (both refined- and whole-grain Table 1) and betaine (Supplemental Table 2) intake. Participants cereals), sweet-processed foods, and vegetables (Supplemental in quartile 4 of choline intake consumed greater quantities of Table 5). There was an increase in the consumption of these all food groups except for dietary fats and processed foods. foods in quartile 1 of change of choline intake over 1 year, Regarding betaine intake, participants in quartile 4 of betaine except for refined-grain cereals which showed a decrease in intake showed greater consumption of all food groups except for consumption (Supplemental Table 3). The increase in choline fruits and eggs. intake after 1 y of follow-up in quartile 4 of change in choline intake was mainly due to changes in the consumption Cross-sectional baseline and 1-y follow-up associations of refined-grain cereals, sweet-processed foods, fruits, and red between choline or betaine intake and cardiometabolic meat. Participants in quartile 4 of change in choline intake parameters reduced red meat and refined-grain cereals intake, whereas they increased fruits and sweet-processed food intake (Supplemental Participants with greater choline intake at baseline (Table 1) Table 3). exhibited greater BMI but lower body weight and waist circum- Changes in betaine intake after 1 y of follow-up were ference. However, participants with greater choline intake after mainly due to changes in the consumption of refined-grain 1 y of follow-up showed greater weight at that follow-up time cereals in both extreme quartiles of change in betaine intake (Supplemental Figure 1A). Baseline greater choline intake over 1 year (Supplemental Table 6). Quartile 1 decreased was associated with greater glucose, total cholesterol, and HDL consumption of refined-grain cereals by 4.19 g/d, whereas cholesterol concentrations and HbA1c levels, but with lower TGs. quartile 4 decreased its consumption by 56.71 g/d (Supplemental This trend was maintained after 1 y of follow-up (Supplemental Table 4). Figure 1A). Those participants with the greatest increase in choline intake Participants with a greater intake of betaine at baseline showed showed the greatest reductions in energy intake, body weight, greater BMI, glucose concentrations, and HbA1c levels. After 1 waist circumference, BMI, HbA1c level, TG, total cholesterol, y of follow-up, differences in BMI among quartiles of betaine and LDL-cholesterol concentrations (Table 3), independently of intake were not significant (Supplemental Figure 1B). choline intake at baseline. This quartile also showed the greatest increase in PA and adherence to an er-MedDiet. There was a Cross-sectional baseline and 1-y follow-up associations significant linear association between 1-y increase in choline between choline or betaine intake and renal function intake and 1-y decrease in energy intake, body weight, waist variables circumference, HbA1c, and TGs and 1-y increase in PA and Participants with greater choline intake at baseline ex- HDL cholesterol (Table 3). These same trends were observed hibited greater plasma urea and lower plasma creatinine, in participants with the greatest increase in betaine intake plasma uric acid, and urine creatinine concentrations (Sup- (Table 4). plemental Figure 2A). Similar trends were found in the To check if medication had an impact on the effect of cross-sectional analysis of 1-y follow-up data (Supplemental increase in choline or betaine intake on cardiometabolic traits, Figure 2A). we in addition adjusted by prevalent medication for hyper- Participants with greater baseline betaine intake showed tension, hypercholesterolemia, T2D, and depression. Observed significantly greater plasma urea concentrations (Supplemental associations did not change (data not shown). To test if the Figure 2B). Plasma urea concentrations were also greater change in consumption of certain food groups or changes in in quartile 4 of betaine intake after 1 y of follow-up the intake of different nutrients modified these longitudinal (Supplemental Figure 2B). After 1 y of follow-up, plasma associations, we in addition adjusted by food groups (baseline uric acid decreased with greater betaine intake (Supplemental consumption of fruits, vegetables, animal protein, fish, eggs, Figure 2B). red meat, and dairy products and change in the consumption of these food groups after 1 y of follow-up) and by nutrient Longitudinal association between 1-y changes in choline or intakes (baseline intakes of carbohydrates, fiber, saturated fats, betaine intake and changes in cardiometabolic parameters and unsaturated fats and changes in these nutrients’ intakes after 1 y of follow-up). Associations did not change (data not Participants with the greatest increase in their choline intake shown). To test if changes in body weight or BMI could affect showed the greatest increase in fruits, nuts, vegetables, saturated changes in cardiometabolic parameters far beyond changes in fats, and salty and sweet-processed foods consumption. However, choline or betaine intake, we carried out a sensitivity analysis they showed the smallest increase in fish, legumes, and whole- by further adjusting by baseline body weight or BMI and by 1-y grain cereals intakes. These participants also showed a greater changes in these parameters. The association between changes decrease in intakes of dairy and meat products (both red and in choline or betaine intake and changes in cardiometabolic white meat), and eggs, cereals, refined-grain cereals, dietary parameters did not change. We also added both traits (BMI and fats, and processed food (Supplemental Table 3). We found weight), both at baseline and 1-y changes, and association results similar trends for participants with the greatest increase in betaine did not change (data not shown). Postmenopausal females are intake (Supplemental Table 4). Regarding the food sources that less capable of synthesizing choline de novo, because the S- explained the individual variability in the change of choline adenosylmethionine that acts as the methyl donor and needs TABLE 4 Changes in cardiometabolic and anthropometric parameters according to quartiles of 1-y changes in betaine intake1 Q1 (−87.8 to −18.2 Q2 (−18.1 to −2.1 Q3 (−2.0 to 16.3 Q4 (16.4–99.0 P-linear Variables Total n mg/d) (n = 1404) mg/d) (n = 1403) mg/d) (n = 1403) mg/d) (n = 1403) P model 1 P model 2 P model 3 trend  Betaine intake, mg/d 5613 − 31.83 ± 11.15 − 9.99 ± 4.60 6.71 ± 5.29 35.13 ± 15.21 <0.001 <0.001 <0.001 <0.001  Energy intake, kcal/d 5613 392.43 ± 335.55 12.89 ± 242.54 − 261.31 ± 242.52 − 743.87 ± 372.17 <0.001 <0.001 <0.001 <0.001  Adherence to er-MedDiet 5591 2.89 ± 3.04 3.07 ± 3.16 3.31 ± 3.27 3.69 ± 3.38 <0.001 <0.001 <0.001 0.127 (max: 17 points)  PA, MET · min/wk 5613 416.68 ± 2229.72 561.58 ± 2374.00 486.55 ± 2530.31 674.29 ± 2568.02 0.015 0.015 <0.001 0.017  BMI, kg/m2 5601 − 0.68 ± 1.58 − 0.72 ± 1.51 − 0.93 ± 1.55 − 0.99 ± 1.58 <0.001 <0.001 <0.001 0.269  Body weight, kg 5609 − 1.79 ± 4.07 − 1.90 ± 3.90 − 2.52 ± 4.14 − 2.78 ± 4.35 <0.001 <0.001 <0.001 <0.001  Waist circumference, cm 5593 − 1.83 ± 5.48 − 2.34 ± 4.80 − 3.16 ± 5.09 − 3.26 ± 5.05 <0.001 <0.001 <0.001 <0.001  Glucose, mg/dL 5492 − 1.14 ± 21.14 − 2.22 ± 22.69 − 4.23 ± 25.28 − 2.72 ± 20.50 0.013 0.013 0.010 0.155  HbA1c, % 4718 − 0.04 ± 0.50 − 0.06 ± 0.55 − 0.10 ± 0.62 − 0.10 ± 0.50 0.002 0.002 0.002 0.017  TG, mg/dL 5472 − 4.16 ± 59.03 − 7.01 ± 67.49 − 7.34 ± 75.63 − 11.21 ± 72.49 0.010 0.010 0.007 0.028  Total cholesterol, mg/dL 5483 − 1.30 ± 29.99 − 1.42 ± 32.53 − 2.50 ± 34.56 − 4.52 ± 32.17 0.006 0.006 0.005 0.861  LDL cholesterol, mg/dL 5088 − 1.64 ± 26.59 − 1.43 ± 28.65 − 2.97 ± 26.45 − 4.16 ± 27.31 0.008 0.008 0.007 0.742  HDL cholesterol, mg/dL 5427 1.32 ± 7.69 1.51 ± 7.30 1.44 ± 7.35 1.47 ± 7.18 0.672 0.672 0.707 <0.001  Systolic BP, mm Hg 5613 − 3.83 ± 24.29 − 5.14 ± 23.33 − 4.69 ± 24.90 − 4.43 ± 23.66 0.624 0.624 0.593 0.379  Diastolic BP, mm Hg 5613 − 2.51 ± 13.68 − 3.13 ± 13.74 − 3.26 ± 14.34 − 2.63 ± 13.65 0.746 0.746 0.717 0.879 1 Total n = 5613. Values are mean ± SD unless otherwise indicated. Betaine intake was adjusted by energy residuals. Characteristics among quartiles were compared using 1-factor ANOVA. Model 1: Choline/betaine intake and cardiovascular features adjusted by sex, age, and center. Model 2: adjusted by sex, age, center, civil status, education, smoking habits, sedentary lifestyle, and choline intake at baseline. Model 3: adjusted by sex, age, center, civil status, education, smoking habits, sedentary lifestyle, choline intake at baseline, adherence to an er-MedDiet and changes in adherence to an er-MedDiet, energy intake and changes in energy intake, and PA and changes in PA. P-linear trend: adjusted by age, sex, center, BMI, and energy intake at baseline. BP, blood pressure; er-MedDiet, energy-restricted traditional Mediterranean diet; MET, metabolic equivalent of task; PA, physical activity; Q, quartile. 1573 1574 Díez-Ricote et al. FIGURE 2 Forest plots showing the association of 20-mg increments in dietary choline intake (A) and dietary betaine intake (B) with changes in cardiometabolic parameters, after 1 y of follow-up (n = 5613). Dietary choline and betaine intakes were adjusted by energy residuals. The model was adjusted by age, sex, center, years of schooling, civil status, smoking habits, sedentary lifestyle, choline and betaine intake at baseline, PA and difference in PA, adherence to an er-MedDiet and difference in adherence to an er-MedDiet, and energy intake and difference in energy intake (1 y–baseline). Values are represented as β ± 95% CI, and P value. BP, blood pressure; er-MedDiet, energy-restricted traditional Mediterranean diet; PA, physical activity. phosphatidylethanolamine-N-methyltransferase is activated by associations between greater intake of choline after 1 y of follow- estrogen, and therefore their choline requirements are higher up and greater reduction in total energy intake, BMI, body (36). Thus, to test if hormonal replacement therapy in females weight, and waist circumference. However, the significance in the could affect the association between changes in choline intake association between choline intake at 1 y and changes in glucose, and changes in cardiometabolic parameters, we carried out a HbA1c, and lipid traits was lost. sensitivity analysis by removing females receiving hormone replacement therapy (n = 115 at baseline and n = 127 at Longitudinal association between 1-y changes in choline or 1 y). The association between changes in choline or betaine betaine intake and changes in renal function parameters intake and changes in cardiometabolic parameters did not change (data not shown). A linear regression was performed to assess Participants with the greatest increase in choline intake showed changes in anthropometric and cardiometabolic parameters for a greater decrease in urine creatinine (Table 5). A similar every 20-mg increase in choline or betaine intake (Figure 2). For result was found in those participants with a greater increase in every increase in 20 mg of choline intake, body weight, BMI, betaine intake after 1 y (Table 6). Changes in the other renal waist circumference, glucose, HbA1c, total cholesterol, LDL function parameters were not significant. Among participants cholesterol, and systolic and diastolic BP decreased (Figure 2A). that increased their choline or betaine intake the most, a greater A trend toward a decrease in TGs was also observed although number of participants with a ≥10% decrease in eGFR was this association was not significant. The same results were found. As mentioned, T2D prevalence was greater in participants obtained for every 20-mg increase in betaine intake. In this with greater choline or betaine intake, affecting eGFR values case, the association between increase in betaine intake and owing to diabetic complications. Therefore, we analyzed renal decrease in TG concentrations was significant (P = 0.05) function parameters in participants without diabetes only and a (Figure 2B). linear decrease in eGFR was not found (data not shown). Also, Automatic procedures for category formation in quartiles or although a greater percentage of participants who reduced eGFR quintiles, although very commonly used, can be suboptimal in by >10% in quartiles of greater increase of betaine and choline some applications and can sometimes be harmful to power, intake was found, differences across quartiles were not significant precision, and confounder control. Therefore, quartile categories (data not shown). Further adjustment by medications, nutrients, might blindly lump together disparate subgroups of subjects and food groups, and weight or BMI did not change results (data not may thereby hide important effects. For this reason, we repeated shown). However, when we categorized participants according to our longitudinal analyses by categorizing participants into groups 1-y intake of choline using estimated adequate levels, a greater according to validated thresholds for adequate choline intake percentage of participants who reduced eGFR by >10% was after 1 y of follow-up (Supplemental Table 7). Participants found in the high intake group (Supplemental Table 7). We in the low intake group at 1 y reduced their choline intake carried out sensitivity analyses by removing data from women (−39.22 ± 49.26 mg/d), whereas intake of choline increased receiving hormone replacement therapy, and the significance for significantly in the other groups (P < 0.001). We found similar the association between 1-y change in choline intake and urine Choline/betaine intake and cardiovascular features 1575 creatinine concentrations was lost (P = 0.057). The association civil status, education, smoking habits, sedentary lifestyle, choline intake at baseline, adherence to an er-MedDiet and changes in adherence to an er-MedDiet, energy intake and changes in energy intake, and PA P-linear Model 1: adjusted by sex, age, and center. Model 2: adjusted by sex, age, center, civil status, education, smoking habits, sedentary lifestyle, and choline intake at baseline. Model 3: adjusted by sex, age, center, and changes in PA. P-linear trend: adjusted by age, sex, center, BMI, and energy intake at baseline. eGFR, estimated glomerular filtration rate; er-MedDiet, energy-restricted traditional Mediterranean diet; PA, 0.195 0.360 0.293 0.805 0.004 0.617 0.300 trend with 1-y change in betaine intake remained significant (data not n = 3367. Values are mean ± SD or n (%) unless otherwise indicated. Choline intake was adjusted by energy residuals. Characteristics among quartiles were compared using 1-factor ANOVA. shown). Discussion P model 3 0.195 0.511 0.653 0.590 0.049 0.367 0.129 In our study, an increase in dietary choline or betaine intake over 1 y was associated with improved serum glucose concentrations and HbA1c levels, body weight, BMI, waist circumference, serum total cholesterol, serum LDL cholesterol, P model 2 0.203 0.516 0.690 0.594 0.094 0.316 0.144 and urine creatinine. Improvements in serum glucose concentrations and HbA1c levels when dietary choline was increased were among the most striking observations. Age has been shown to increase P model 1 the risk of insulin resistance (37). Exercise improves insulin 0.203 0.515 0.690 0.593 0.094 0.316 0.144 sensitivity (38). An increase in the adherence to a MedDiet has been shown to improve disease progression and to prevent it (39). And a decrease in calorie intake has been associated with better insulin sensitivity (40). However, the observed 56.24 1.02 ± 36.01 mg/d) (n = 859) Q4 (35.2–233.8 8.62 0.11 2.48 8.86 association between increase in choline or betaine intake and 133 (15.0) improvement in diabetes-related traits was independent of ± ± ± ± ± choline or betaine intake at baseline as well as of age, PA, increase 0.99 0.00 0.00 − 5.42 − 0.32 in the adherence to an er-MedDiet, and decrease in energy intake. Studies aiming to define the relation between choline intake and insulin sensitivity are controversial. An observational study Q3 (−4.2 to 35.1 10.71 53.71 − 0.33 ± 40.46 mg/d) (n = 836) 0.11 4.91 8.64 reported that serum choline concentrations were inversely asso- 120 (13.8) ciated with diabetes risk and its microangiopathy complications. ± ± ± ± ± However, serum betaine concentrations did not associate with 1.13 0.00 0.11 − 4.16 − 0.07 diabetes risk, but they did associate with diabetes complications TABLE 5 Changes in renal function parameters according to quartiles of 1-y changes in choline intake1 (41). In a large cohort of the CODING (Complex Diseases in the Newfoundland population: Environment and Genetics) study, dietary choline and betaine intakes were assessed and Q2 (−39.6 to −4.3 57.63 2.41 ± 40.55 mg/d) (n = 840) correlated with lower fasting glucose and insulin concentrations 9.99 0.12 2.94 9.15 112 (13.0) and lower insulin resistance (11). A more recent observational ± ± ± ± ± study examined dietary choline intake in the Kuopio Ischemic 0.42 0.00 0.04 − 0.82 0.12 Heart Disease Risk Factor Study cohort. After 19 y of follow- up, the authors concluded that greater dietary choline intake was associated with a lower risk of T2D (12). However, a prospective study that included results from the NHS I and II and from 10.10 53.21 3.14 ± 43.58 the Health Professionals Follow-up Study concluded that greater Q1 (−203.5 to 0.34 3.40 9.32 −39.7 mg/d) physical activity; Q, quartile; UACR, urine albumin:creatinine ratio. (n = 832) 96 (11.0) phosphatidylcholine intake was associated with increased risk ± ± ± ± ± of T2D (42). The PREDIMED trial found that greater plasma 0.88 0.01 0.12 − 1.81 0.07 TMAO concentrations were associated with lower risk of T2D incidence (43), whereas in China, the opposite was found (44). Taking these findings together, it could be suggested that greater amounts of dietary choline intake, within the adequate intake estimated limits, might help to reduce insulin resistance and to Total n 3349 3046 3349 3260 3332 3349 3367 improve glucose concentrations and HbA1c levels and, thus, to prevent T2D development. The mechanisms by which dietary choline may reduce insulin resistance remain to be elucidated. Dietary choline intake reduces inflammatory and oxidative stress markers, which play a role in eGFR, >10% decrease, yes  Urine creatinine, mg/dL T2D development (45, 46). In addition, betaine supplementation  eGFR, mL · min−1 ·  Creatinine, mg/dL in mice has been shown to improve carbohydrate metabolism by  Uric acid, mg/dL increasing insulin receptor substrate phosphorylation in the liver  UACR, mg/g  Urea, mg/dL (47). 1 Total A greater increase in choline or betaine intake was also Variables 1.73m−2 associated with improvement in lipid and cardiometabolic traits. Observed improvements in cardiometabolic variables could be 1576 Díez-Ricote et al. n = 3367. Values are mean ± SD or n (%) unless otherwise indicated. Betaine intake was adjusted by energy residuals. Characteristics among quartiles were compared using 1-factor ANOVA. Model due to greater adherence to a MedDiet and reduced energy intake status, education, smoking habits, sedentary lifestyle, choline intake at baseline, adherence to an er-MedDiet and changes in adherence to an er-MedDiet, energy intake and changes in energy intake, and PA and P-linear 0.285 0.360 0.293 0.805 0.004 0.617 0.300 trend 1: adjusted by sex, age, and center. Model 2: adjusted by sex, age, center, civil status, education, smoking habits, sedentary lifestyle, and choline intake at baseline. Model 3: adjusted by sex, age, center, civil (25). HDL increase could also be due to an increase in PA changes in PA. P-linear trend: adjusted by age, sex, center, BMI, and energy intake at baseline. eGFR, estimated glomerular filtration rate; er-MedDiet, energy-restricted traditional Mediterranean diet; PA, performance (48). Nevertheless, these factors were considered in the analyses. Therefore, we suggest that choline or betaine P model 3 intake could improve cardiometabolic traits irrespective of improvements in other lifestyle factors. Dietary choline and 0.179 0.742 0.534 0.361 0.042 0.478 0.148 betaine food sources could have an impact on cardiometabolic variables. In this regard, after 1 y of follow-up, those participants who increased by more their choline and/or betaine intake P model 2 increased consumption of fruits and salty and sweet-processed 0.180 0.754 0.568 0.365 0.091 0.417 0.158 foods and decreased consumption of refined-grain cereals, red meat, and processed foods. The decreases in red meat and refined- grain cereals were the main contributors to the change in choline and betaine intakes. Meat consumption has been proven to P model 1 increase plasma TMAO concentrations, and, consequently, CVD 0.754 0.568 0.364 0.091 0.417 0.180 0.158 risk (49–51). However, egg consumption, also high in choline, has been reported to improve insulin sensitivity and lipoprotein profile (13, 14, 52–56). Fish, whole-grain cereals, and vegetables have been shown to increase plasma TMAO concentrations (10); 56.70 0.61 ± 36.85 mg/d) (n = 877) Q4 (16.4–99.0 8.66 0.11 2.45 8.48 however, they have also been proven to improve lipid profile and 133 (14.7) cardiometabolic parameters (16). Taking all these points together, ± ± ± ± ± it could be suggested that improvements in blood lipid profile and 0.85 0.00 − 0.01 − 5.74 − 0.17 cardiometabolic parameters may be due to a change in the main food source of choline and betaine that shows a shift toward a healthier diet, with an increase in consumption of fiber, omega-3 fatty acids, and polyphenols, which may act synergistically with Q3 (−2.0 to 16.3 10.99 52.67 0.37 ± 36.70 mg/d) (n = 823) 0.12 1.40 8.83 choline and betaine. 120 (14.4) Greater betaine concentrations in coronary artery disease ± ± ± ± ± subjects have been found to be correlated with lower BP and 1.16 0.00 − 0.08 − 4.16 − 0.20 HbA1c levels, and glucose, insulin, and TG concentrations TABLE 6 Changes in renal function parameters according to quartiles of 1-y changes in betaine intake1 (57). This is consistent with our results showing that those participants with a greater increase in betaine intake reduced HbA1c levels by more. Besides, we carried out sensitivity Q2 (−18.1 to −2.1 mg/d) (n = 855) 57.39 2.47 ± 42.02 analysis by further adjustment for baseline and 1-y changes in 8.99 0.12 6.12 9.29 118 (13.3) consumption of different foods and nutrients and the associations ± ± ± ± ± between changes in choline or betaine intake and cardiometabolic 0.50 0.00 0.33 0.19 0.06 and lipid parameters did not lose statistical significance. Taking these findings together, it could be suggested that betaine intake could be beneficial to prevent CVD and T2D. Lastly, a slight improvement in renal function was observed Q1 (−87.8 to −18.2 10.76 53.83 2.83 ± 44.90 mg/d) (n = 812) in those participants with the greatest increase in choline intake. 0.35 1.96 9.39 physical activity; Q, quartile; UACR, urine albumin:creatinine ratio. 90 (10.4) Although an increase in choline or betaine intake was associated ± ± ± ± ± with a decrease in urine creatinine, it was also associated with an 0.91 0.01 0.02 − 2.49 0.12 increase in the percentage of participants who decreased eGFR by >10%. Although this difference was not significant, this could be due to the greater prevalence of participants with T2D in quartiles 3 and 4 of changes in choline and betaine intake, thus increasing Total n the risk of renal failure due to diabetic complications. In fact, a 3349 3046 3349 3260 3332 3349 3367 decrease in eGFR >7.5% in the course of 1 y has been proposed as a good indicator of prognosis for renal failure (58). When we analyzed data without participants with T2D, a linear increase of participants that decreased their eGFR was not observed (data eGFR, >10% decrease, yes not shown). Therefore, it could be suggested that an increase in  Urine creatinine, mg/dL choline or betaine intake could lead to a decrease in eGFR in those  eGFR, mL · min−1 ·  Creatinine, mg/dL  Uric acid, mg/dL participants with diabetic complications. The relation between choline and betaine intake and renal  UACR, mg/g  Urea, mg/dL function has not been deeply studied yet. Some studies have 1 Total Variables concluded that there are both positive and negative effects (59). 1.73m−2 For instance, an er-MedDiet intervention has been reported to preserve kidney function and could delay chronic kidney Choline/betaine intake and cardiovascular features 1577 disease progression (28). That study also reported a decrease The authors’ responsibilities were as follows—MÁM-G, DC, JS-S, AG, in UACR, similar to our results. Thus, we could suggest that a JAM, ÁMA-G, JW, J Vioque, DR, JL-M, RE, FJT, JL, LS-M, AB-C, Mediterranean dietary pattern with an adequate intake of choline JAT, VMS, XP, JJG, PM-M, J Vidal, SMF, ER, ZV-R, CO-A, JFG-G, M and high intake of betaine, obtained from fish, eggs, vegetables, Malcampo, DM-U, LT-S, AGR, NG-B, AC, AG-R, RMB-L, JMS-L, JB-G, JVS, M Murphy, GG, VM, IS-L, EGO, NB, XH, JMO, RS-C, LD-R, and nuts, and whole-grain cereals, could improve renal parameters LD: designed and conducted the research; LD and LD-R: conceived the study and, in turn, could help to reduce CVD risk. idea and the analysis design; LD: supervised the research; LD-R: carried out In summary, here we demonstrate that an increase in choline the analysis procedures, bibliographic research, data preparation, statistical or betaine intake was related to improvements in lipoprotein analysis, and wrote the initial drafts; RS-C: assisted with statistical analysis profile, cardiometabolic parameters, glucose concentrations, and and R programming; MJC: collaborated with data collection and analysis; HbA1c levels, independently of age, sex, sedentary lifestyle, JMO and MJC: participated in the scientific discussion of experimental PA, adherence to an er-MedDiet, and smoking habits. However, results; and all authors: assisted in manuscript revision for intellectual content the impact of choline or betaine intake on renal function and read and approved the final manuscript. deserves further investigation because we found a decrease in urine creatinine with an increase in the intake of these compounds, but we also found an increase in the percentage Data availability of participants who decreased eGFR by >10% after 1 y of The PREDIMED-Plus trial imposed restrictions on data follow-up. availability because of the signed consent agreements around The present study has some strengths, such as the large data sharing. These only enable access to external researchers sample size and its longitudinal design. In addition, we used for studies following the project purposes. To request access validated questionnaires to assess nutritional characteristics to PREDIMED-Plus trial data used in this study, make a and lifestyle and we considered multiple confounders in our request to the PREDIMED-Plus trial Steering Committee chair analyses. at: jordi.salas@urv.cat. The request will then be passed to Nevertheless, our study has some limitations. First, it is members of the PREDIMED-Plus Steering Committee for based on elder subjects with overweight/obesity and with MetS; deliberation. therefore, these findings cannot be considered for the general population. Furthermore, the nutritional characteristics and lifestyle parameters are based on self-reported data, implying an References information bias. Food and nutrient intakes have been calculated 1. 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