TYPE Original Research PUBLISHED 30 September 2022 DOI 10.3389/fnut.2022.986190 Longitudinal association of OPEN ACCESS dietary acid load with kidney function decline in an older EDITED BY Sorayya Kheirouri, Tabriz University of Medical Sciences, Iran REVIEWED BY adult population with metabolic syndrome Golaleh Asghari, Shahid Beheshti University of Medical Sciences, Iran Fabiana Baggio Nerbass, Fundacão Pró-Rim, Brazil Elnaz Daneshzad, Cristina Valle-Hita1,2,3† , Nerea Becerra-Tomás1,4† , Tehran University of Medical Sciences, Iran Andrés Díaz-López 1,2,3,5*, Zenaida Vázquez-Ruiz3,6 , *CORRESPONDENCE Isabel Megías1 , Dolores Corella3,7 , Albert Goday3,8 , Andrés Díaz-López J. Alfredo Martínez3,9,10 , Ángel M. Alonso-Gómez3,11 , andres.diaz@urv.cat Julia Wärnberg3,12 , Jesús Vioque13,14 , Dora Romaguera3,15 , † These authors have contributed equally to this work José López-Miranda3,16 , Ramon Estruch3,17 , SPECIALTY SECTION Francisco J. Tinahones3,18 , José Lapetra3,19 , This article was submitted to Nutritional Epidemiology, Lluís Serra-Majem3,20 , Aurora Bueno-Cavanillas14,21 , a section of the journal Josep A. Tur3,15,22 , Vicente Martín-Sánchez14,23 , Frontiers in Nutrition Xavier Pintó3,24,25 , José J. Gaforio14,26 , Pilar Matía-Martín27 , RECEIVED 04 July 2022 ACCEPTED 02 September 2022 Josep Vidal28,29 , Angela Amengual-Galbarte30 , Lidia Daimiel31 , PUBLISHED 30 September 2022 Emilio Ros3,28 , Ana García-Arellano3,6,32 , Rocío Barragán3,7 , Montse Fitó3,8 , Patricia J. Peña-Orihuela3,16 , Alberto Asencio-Aznar33 , Enrique Gómez-Gracia34,35 , Diego Martinez-Urbistondo36 , Marga Morey3,15 , Rosa Casas3,17 , Eva María Garrido-Garrido37 , Lucas Tojal-Sierra3,11 , Miguel Damas-Fuentes3,18 , Estibaliz Goñi9 , Carolina Ortega-Azorín3,7 , Olga Castañer3,8 , Antonio Garcia-Rios3,16 , Cristina Gisbert-Sellés38 , Carmen Sayón-Orea3,6 , Helmut Schröder8,14 , Jordi Salas-Salvadó1,2,3 and Nancy Babio1,2,3 1 Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Unitat de Nutrició Humana, Reus, Spain, 2 Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain, 3 Consorcio CIBER (Centro de Investigación Biomédica en Red), Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain, 4 Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, St Mary’s Campus, London, United Kingdom, 5 Serra Hunter Programme, Universitat Rovira i Virgili, Nutrition and Mental Health Research Group (NUTRISAM), Reus, Spain, 6 Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain, 7 Department of Preventive Medicine, University of Valencia, Valencia, Spain, 8 Cardiovascular Risk and Nutrition Research Group (CARIN), Hospital del Mar Research Institute (IMIM), Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain, 9 Department of Nutrition, University of Navarra, Food Science and Physiology, IdiSNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain, 10 Precision Nutrition Program, IMDEA (Institutos Madrileño de Estudios Avanzados) Food, CEI UAM (Comité de Ética para la Investigación-Universidad Autónoma de Madrid) + CSIC (Consejo Superior de Investigaciones Científicas), Madrid, Spain, 11 Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country/Euskal Herriko Unibertsitatea UPV/EHU, Vitoria-Gasteiz, Spain, Frontiers in Nutrition 01 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 12 Department of Nursing, University of Málaga, Institute of Biomedical Research in Malaga (IBIMA), Málaga, Spain, 13 Instituto de Investigación Sanitaria y Biomédica de Alicante, Miguel Hernandez University (ISABIAL-UMH), Alicante, Spain, 14 CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain, 15 Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain, 16 Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain, 17 Department of Internal Medicine, Institutd’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain, 18 Department of Endocrinology, Virgen de la Victoria Hospital, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga, Málaga, Spain, 19 Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain, 20 Preventive Medicine Service, Research Institute of Biomedical and Health Sciences (IUIBS), Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service, University of Las Palmas de Gran Canaria, Las Palmas, Spain, 21 Department of Preventive Medicine, University of Granada, Granada, Spain, 22 Research Group on Community Nutrition and Oxidative Stress, University of Balearic Islands, Palma de Mallorca, Spain, 23 Institute of Biomedicine (IBIOMED), University of León, León, Spain, 24 Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge-IDIBELL (Instituto de Investigación Biomédica de Bellvitge), Hospitalet de Llobregat, Barcelona, Spain, 25 University of Barcelona, Barcelona, Spain, 26 Department of Health Sciences, Instituto Universitario de Investigación en Olivar y Aceites de Oliva, University of Jaén, Jaén, Spain, 27 Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), Universidad Complutense, Madrid, Spain, 28 Departament of Endocrinology, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, University of Barcelona, Barcelona, Spain, 29 CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain, 30 Department of Endocrinology, Rey Juan Carlos University Hospital, Móstoles, Spain, 31 Nutritional Control of the Epigenome Group, IMDEA (Institutos Madrileño de Estudios Avanzados) Food, CEI UAM (Comité de Ética para la Investigación-Universidad Autónoma de Madrid) + CSIC (Consejo Superior de Investigaciones Científicas), Madrid, Spain, 32 Emergency Department, Hospital Universitario de Navarra, Servicio Navarro de Salud-Osasunbidea, Pamplona, Spain, 33 Primary Health Care Center Muchamiel, Alicante, Spain, 34 Department of Preventive Medicine and Public Health, School of Medicine, University of Málaga, Málaga, Spain, 35 Institute of Biomedical Research in Malaga-IBIMA, Málaga, Spain, 36 Department of Internal Medicine, Clinic University of Navarra, Pamplona, Spain, 37 Primary Care Center Zaidín-Center, Servicio Andaluz de Salud, Granada, Spain, 38 Primary Health Care Center San Vicente del Raspeig, Alicante, Spain Background: Diets high in acid load may contribute to kidney function impairment. This study aimed to investigate the association between dietary acid load and 1-year changes in glomerular filtration rate (eGFR) and urine albumin/creatinine ratio (UACR). Methods: Older adults with overweight/obesity and metabolic syndrome (mean age 65 ± 5 years, 48% women) from the PREDIMED-Plus study who had available data on eGFR (n = 5,874) or UACR (n = 3,639) at baseline and after 1 year of follow-up were included in this prospective analysis. Dietary acid load was estimated as potential renal acid load (PRAL) and net endogenous acid production (NEAP) at baseline from a food frequency questionnaire. Linear and logistic regression models were fitted to evaluate the associations between baseline tertiles of dietary acid load and kidney function outcomes. One year- changes in eGFR and UACR were set as the primary outcomes. We secondarily assessed ≥ 10% eGFR decline or ≥10% UACR increase. Results: After multiple adjustments, individuals in the highest tertile of PRAL or NEAP showed higher one-year changes in eGFR (PRAL, β: – 0.64 ml/min/1.73 m2 ; 95% CI: –1.21 to –0.08 and NEAP, β: –0.56 ml/min/1.73 m2 ; 95% CI: –1.13 to 0.01) compared to those in the lowest category. No associations with changes in UACR were found. Participants with higher levels of PRAL and NEAP had significantly higher odds of developing ≥10% eGFR decline (PRAL, OR: 1.28; 95% CI: 1.07–1.54 and NEAP, OR: 1.24; 95% CI: 1.03–1.50) and ≥10 % UACR increase (PRAL, OR: 1.23; 95% CI: 1.04–1.46) compared to individuals with lower dietary acid load. Frontiers in Nutrition 02 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 Conclusions: Higher PRAL and NEAP were associated with worse kidney function after 1 year of follow-up as measured by eGFR and UACR markers in an older Spanish population with overweight/obesity and metabolic syndrome. KEYWORDS kidney function, chronic kidney disease (CKD), glomerular filtration rate (GFR), net endogenous acid production (NEAP), potential renal acid load (PRAL), dietary acid load, albuminuria, renal nutrition Introduction acid load may be a useful preventive strategy against kidney dysfunction. Impaired renal function is a common condition in older To date, results from epidemiological studies focused on individuals with comorbidities as diabetes, hypertension or dietary acid load and kidney function or CKD development obesity that usually predicts the onset of Chronic Kidney Disease are inconsistent (9) and this relationship needs to be further (CKD) (1). In the last few years, there has been a growing explored. In some studies, an association between higher concern about this disease since it has a huge impact worldwide levels of PRAL and/or NEAP indexes and an estimated- affecting around 700 million people (2). In addition, CKD is glomerular filtration rate (eGFR) decline or higher risk of linked to several complications, such as cardiovascular events, incident CKD (14–18) has been reported, but others have hospitalization and/or premature death (2, 3). Consequently, observed no such associations (19, 20). Also, the quality of appropriate and affordable prevention measures are required evidence is moderate as most of the studies were mainly cross- to preserve renal function, especially in high-risk populations sectional (14–17, 21, 22), and only a few were longitudinal (1). Prevention measures could also reduce the severe impact of studies (18–20). Furthermore, since most research has been CKD on the wellbeing of individuals and health systems (3–5). conducted in healthy young or middle-aged individuals or Dietary habits appear to be one of the major modifiable in patients with advanced CKD, little is known about the risk factors markedly influencing renal impartment and potentially harmful association between dietary acid load its progression to CKD (5, 6). Additionally, the role of and kidney function of older populations with underlying diet in preserving the acid-base balance of the body has comorbid conditions. In addition, analyses assessing dietary recently become more relevant, given the emerging evidence acid load on kidney function have rarely been conducted linking dietary acid load with the development of different in Mediterranean populations at high cardiovascular risk. chronic diseases (7, 8), including CKD (9). It has been Hence, as more scientific evidence and longitudinal studies previously documented that healthy dietary patterns provide in this field are required, we prospectively investigated the an alkaline environment in the body (10, 11) since plant- association between PRAL and NEAP and 1-year changes based food such as vegetables, fruit and some nuts or in two markers of kidney function decline, eGFR and legumes have the capacity of inducing a basic environment Urine Albumin/Creatinine Ratio (UACR), in a large Spanish (12). However, red and processed meats as well as ultra- cohort of older adults with overweight/obesity and metabolic processed foods are acid-producing (9, 12). Thus, these foods syndrome (MetS). might be implied in the onset of a low-grade metabolic acidosis state, thereby, resulting in faster progression of kidney disease (11, 13). Overall, potential renal acid load (PRAL) Materials and methods and net endogenous acid production (NEAP) are the most Study population and design common and suitable indexes used to estimate the acid load of the diet (9, 11). Considering the aforementioned The present study is a prospective analysis of baseline evidence, following a healthy diet characterized by a low and 1-year data within the framework of the PREvención con DIeta MEDiterránea (PREDIMED)-Plus trial. Briefly, the Abbreviations: BMI, Body Mass Index; CKD, Chronic Kidney Disease; CI, PREDIMED-Plus is an ongoing, parallel-group, randomized Confidence Interval; E, Energy; FFQ, Food Frequency Questionnaire; GFR, and controlled clinical trial aiming to assess the effect of Glomerular Filtration Rate; MedDiet, Mediterranean Diet; MetS, Metabolic an intensive weight loss intervention on cardiovascular Syndrome; METS, Metabolic Equivalent Task; NEAP, Net Endogenous Acid disease (CVD) morbidity and mortality. An energy-restricted Production; PRAL, Potential Renal Acid Load; PREDIMED, Prevención Mediterranean diet (MedDiet), physical activity promotion con Dieta Mediterránea; SCr, Serum Creatinine; CKD-EPI, Chronic Kidney and behavioral support are compared to usual care advice Disease Epidemiology Collaboration equation for Caucasian individuals; in 6,874 older adults enrolled between 2013 and 2016 by OR, Odds Ratios; UACR, Urine Albumin/Creatinine Ratio. 23 Spanish recruitment centers. Eligible participants were Frontiers in Nutrition 03 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 men aged 55–75 years and women aged 60–75 years with NEAP (mEq/day) = 54.5 × protein (g/day)/potassium overweight or obesity [Body Mass Index (BMI) 27–40 (mEq/day) – 10.2. kg/m2 ], who satisfied at least 3 criteria for the MetS (23). Further details of the inclusion and exclusion criteria and the study design have been described elsewhere (24). A Ascertainment of the outcome detailed explanation of the protocol is also available at https:// www.predimedplus.com. This trial was registered on the Serum creatinine (SCr) levels and urinary creatinine International Standard Randomized Controlled Trial registry and albumin concentrations were determined using routine (https://www.isrctn.com/ISRCTN89898870) with number laboratory methods from blood and spot morning urine samples 89898870 in July of 2014. The final study protocol and collected at baseline and 1-year following overnight fasting. For procedures were approved following the standards of the the current study, 1-year changes in eGFR and UACR were Declaration of Helsinki by the Institutional Review Boards considered our primary outcomes. We indirectly determined of participating centers and all participants provided written eGFR from SCr using the Chronic Kidney Disease Epidemiology informed consent. Collaboration equation for Caucasian individuals (CKD-EPI) For the current study, participants without completed (30) and the UACR was calculated by dividing urine albumin food frequency questionnaire (FFQ) information and reporting (mg/l) by urine creatinine concentrations (mg/l). UACR values implausible total energy intake (men < 800 and >4,000 were truncated at 500 mg/g to minimize the influence of outliers. kcal/day and women < 500 and >3,500 kcal/day) at baseline There were 21 observations > 500 mg/g at baseline and 24 at were excluded (n = 227) from the analyses (25). We also 1 year that were >500 mg/g and subsequently set to 500 mg/g. excluded participants who died (n = 11) or were lost One-year changes in both eGFR and UACR were calculated to follow-up (n = 16) during the first year. Moreover, by subtracting values at 1 year minus values at baseline. participants with missing data on eGFR (n = 746) or UACR Secondary outcomes were ≥10% eGFR decline and ≥10% UACR (n = 2,981) at baseline and/or at the 1-year assessment increase following a 1-year follow-up. These were estimated were excluded when eGFR or UACR were the outcomes, by applying the formula: [(1-year eGFR or UACR – baseline respectively. Therefore, a final sample of 5,874 participants eGFR or UACR)/baseline eGFR or UACR]∗ 100. Participants for eGFR and 3,639 participants for UACR were analyzed were categorized as those with a ≥10% or <10% eGFR decline (Supplementary Figure 1). (31) or with a ≥10% or <10% increase in UACR. Covariate assessment Assessment of dietary intake and dietary acid load At baseline, trained PREDIMED-Plus staff collected socio-demographic and lifestyle information including age, To evaluate dietary intake, trained dieticians administered sex, educational level, physical activity, smoking status, as a 143-item FFQ, based on a previously validated one for well as medication use and history of disease using several the Spanish population (26), in face-to-face interviews questionnaires or reviewing medical records. Moreover, at baseline. Each participant was asked about their adherence to the energy-reduced MedDiet was evaluated using a frequency of consumption during the preceding year of validated 17-item MedDiet questionnaire (32). Compliance with each specific item, which had nine possible answers ranging each item of the MedDiet questionnaire was scored with one from never to more than 6 times per day. The typical point and non-compliance with 0. Thereafter, a cut-off point portion size of each item was subsequently transformed based on the median of the score was determined by dividing into grams or milliliters per day, as appropriate. Two individuals into those with high adherence to a MedDiet (≥9 Spanish food composition databases were referenced points) or a low adherence (<9 points). Moreover, other cut-off to calculate total daily energy and nutrient intake points were tested arbitrarily and defined as the highest tertiles (27, 28). or quartiles (in both cases high adherence was observed to be Dietary acid load was estimated at baseline using ≥12 points). Total daily energy intake and sodium intake were individual nutritional data obtained from the FFQ. Previously estimated according to data from the FFQ. Anthropometric published methods proposed by Remer and Manz (29) variables were measured in duplicate and resting blood pressure and Frassetto et al. (8) were applied for the calculation of was measured in triplicate using an automated digital device PRAL and NEAP scores, respectively. PRAL (mEq/day) = (Omron-HEM297705C). BMI was calculated as weight in 0.4888 × protein intake (g/day) + 0.0366 × phosphorus kilograms divided by the square of height in meters. In our (mg/day) – 0.0205 × potassium (mg/day) – 0.0125 × analysis, white blood cell count was used to assess inflammation calcium (mg/day) – 0.0263 × magnesium (mg/day). (leucocytes > 10 × 109 /L). Frontiers in Nutrition 04 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 Statistical analyses sensitivity analysis, we repeated our main analysis investigating the association between PRAL and NEAP with 1-year changes For the present report, we used the PREDIMED-Plus in eGFR and UACR after excluding individuals with eGFR < database generated in December 2020. Participants were 60 ml/min/1.73 m2 or with UACR > 300 mg/g at baseline. categorized into tertiles of PRAL and NEAP. One-way ANOVA In addition, as a supplementary analysis, we evaluated the and chi-square tests were used to evaluate differences among association between dietary acid load and ≥5% eGFR decline tertiles of PRAL and NEAP for the baseline characteristics of the and ≥5% UACR increase following the same procedure study population. Descriptive data were expressed as means ± mentioned previously. Statistical analyses were conducted using SD for continuous variables and percentages (%) and numbers Stata/SE software, version 14.0 (StataCorp, College Station, TX) for categorical variables. and significance level was set at a 2-tailed p < 0.05. Multivariate linear regression models were performed to examine the associations between tertiles of PRAL and NEAP at baseline and 1-year changes in eGFR (ml/min/1.73 m2 ) Results and UACR (mg/g). For these associations, PRAL and NEAP were also analyzed as continuous variables (both for each 1-SD Table 1 shows the baseline characteristics of the study increase). β-coefficients and 95% confidence intervals (CIs) population according to tertiles of PRAL and NEAP. In were assessed using two different models: Model 1 was adjusted general, participants with higher values of PRAL and NEAP for sex and age; and Model 2 was further adjusted for study at baseline were more likely to be younger, men, have a center (categorized into quartiles by number of participants), higher BMI, smoke, have a higher educational level, and intervention group (treatment/control), BMI (kg/m2 ), were less likely to exercise. Participants in the highest tertiles smoking status (never/current/former smoker), educational of PRAL and NEAP also had higher levels of creatinine level (primary education/secondary education/graduate), and eGFR than those in the lowest tertile. In terms of leisure-time physical activity (METs/min/week, tertiles), mediations, participants in the highest tertiles of PRAL diabetes prevalence (yes/no), hypertension prevalence (yes/no), and NEAP were more likely to have used insulin, ACEis hypercholesterolemia prevalence (yes/no), angiotensin- treatment, and took less antihypertensive and ARB drugs. converting enzyme inhibitors (ACEis) (yes/no), angiotensin Furthermore, individuals in the highest tertile of NEAP were II receptor blockers (ARBs) (yes/no), MedDiet adherence more likely to have type 2 diabetes. However, no significant (high/low adherence), energy intake (kcal/day, tertiles), sodium differences were observed between tertiles of PRAL nor intake (mg/day, tertiles) and high leukocyte levels (yes/no). NEAP regarding the UACR or CKD. Concerning dietary Moreover, odds ratios (OR) and their 95% CIs were calculated assessment, adherence to an energy-reduced MedDiet was for the association between tertiles of NEAP and PRAL and lower in individuals with higher dietary acid load levels than ≥10% eGFR decline and ≥10% UACR increase at 1-year of those in the lowest tertile of PRAL and NEAP. Moreover, follow-up adjusting for the same confounders as mentioned in participants in the highest tertile of PRAL and NEAP had a model 2. The first tertile was used as a reference category in lower intake of vegetable/animal protein ratio, carbohydrates all regression models. Additionally, linear regression models and fiber while they had a higher energy, protein and fat were further adjusted for baseline eGFR (ml/min/1.73 m2 ) consumption than those with low values of both dietary acid or baseline UACR (mg/g) depending on the main outcome. load indexes. Similar trends were observed when baseline Variance inflation factors (VIFs) were used to assess collinearity consumption of food groups across tertiles of PRAL and NEAP for the multivariable models and, as VIFs were <2.5, none were analyzed (Supplementary Table 1). Supplementary Table 2 of the covariates needed to be removed. All analyses were presents further information regarding macronutrient and conducted with robust estimates of the variance to correct for micronutrient intake, especially those related to dietary acid possible intra-cluster correlation. Intra-cluster was defined as load, at 1-year of follow-up. Baseline characteristics according the participants who shared the same household. To assess the to included and excluded participants from the eGFR or UACR linear trend, the median value of each tertile of PRAL and NEAP analyses are described in Supplementary Table 3. were modeled as continuous variables. The association (β-coefficient; 95% CI) between tertiles of We also conducted subgroup analyses for the 1-year changes PRAL and NEAP and 1-year changes in eGFR and UACR in eGFR and UACR stratifying by baseline categories of are displayed in Table 2. In the most adjusted model, PRAL eGFR (≥90; 60–90; <60 ml/min/1.73 m2 ) and UACR (<30; showed a significant inverse association with 1-year changes ≥30 mg/g). Interaction between tertiles of PRAL and NEAP in eGFR (β: −0.17 ml/min/1.73 m2 ; 95% CI: −0.71 to 0.36 with categories of eGFR, UACR, and energy-reduced MedDiet for T2 vs. T1, β: −0.64 ml/min/1.73 m2 ; 95% CI: −1.21 to adherence (high/low), as well as the intervention/control group −0.08 for T3 vs. T1). We found similar results when PRAL were checked in the fullest multivariable model using likelihood and NEAP were analyzed as continuous variables (PRAL: β: ratio tests and non-significant results were observed. In a −0.25 ml/min/1.73 m2 ; 95% CI: −0.47 to −0.03 for each Frontiers in Nutrition 05 frontiersin.org Frontiers in Nutrition Valle-Hita et al. TABLE 1 Baseline characteristics of the study population with data on eGFR at 1-year follow-up by tertiles of PRAL and NEAP (n = 5,874). PRAL (mEq/d) NEAP (mEq/d) Total T1 T2 T3 T1 T2 T3 n = 5,874 n = 1,958 n = 1,958 n = 1,958 p-value n = 1,958 n = 1,958 n = 1,958 p-value PRAL, mEq/day −5.4 ± 15.6 – – – – −21.4 ± 11.3 −5.0 ± 5.4 10.1 ± 8.7 <0.01 NEAP, mEq/day 36.9 ± 8.1 29.0 ± 3.8 36.4 ± 2.8 45.6 ± 6.0 <0.01 – – – – Age, years 65.0 ± 4.9 65.7 ± 4.7 65.1 ± 5.0 64.2 ± 4.9 <0.01 65.7 ± 4.7 65.3 ± 4.9 64.1 ± 4.9 <0.01 Women, % (n) 48.0 (2,818) 52.7 (1, 31) 49.1 (961) 42.2 (826) <0.01 56.0 (1,097) 48.9 (957) 39.0 (764) <0.01 Intervention group, % (n) 49.4 (2,901) 49.3 (966) 50.3 (984) 48.6 (951) 0.57 50.0 (978) 49.9 (976) 48.4 (947) 0.54 BMI, kg/m2 32.5 ± 3.4 32.4 ± 3.4 32.4 ± 3.4 32.8 ± 3.5 <0.01 32.3 ± 3.4 32.5 ± 3.4 32.7 ± 3.5 <0.01 PA, METS/min/week 2,528.0 ± 2,350.4 2,740.2 ± 2,483.6 2,526.2 ± 2,342.2 2,317.7 ± 2,198.8 <0.01 2,681.7 ± 2,434.1 2,547.4 ± 2,373.8 2,355.1 ± 2,228.2 <0.01 Smoking status, % (n) <0.01 <0.01 Never smoked 44.4 (2,605) 47.9 (939) 45.5 (891) 39.6 (775) 49.9 (976) 44.7 (875) 38.5 (754) Former smoker 43.0 (2,528) 40.3 (789) 42.3 (828) 46.5 (911) 38.6 (756) 43.0 (842) 47.5 (930) 06 Current smoker 12.6 (741) 11.8 (230) 12.2 (239) 13.9 (272) 11.5 (226) 12.3 (241) 14.0 (274) Education level, % (n) <0.01 <0.01 Primary education 49.22 (2,891) 54.9 (1,075) 49.2 (963) 43.6 (853) 54.0 (1,058) 50.0 (979) 44.6 (854) Secondary education 29.18 (1,714) 25.2 (494) 28.9 (565) 33.4 (655) 25.6 (501) 28.3 (555) 33.6 (658) College/university 21.60 (1,269) 19.9 (374) 22.0 (430) 23.0 (450) 20.4 (399) 21.7 (424) 22.8 (446) Creatinine 0.8 ± 0.2 0.8 ± 0.2 0.8 ± 0.2 0.9 ± 0.2 <0.01 0.8 ± 0.2 0.8 ± 0.2 0.9 ± 0.2 <0.01 eGFR, ml/min/1.73 m2 84.2 ± 13.9 83.6 ± 13.6 84.7 ± 13.9 84.3 ± 14.4 0.04 83.5 ± 13.6 84.9 ± 13.7 84.1 ± 14.5 <0.01 UACR, mg/g 16.8 ± 48.9 16.2 ± 45.0 16.8 ± 50.0 17.5 ± 51.5 0.78 16.4 ± 46.4 15.9 ± 47.0 18.2 ± 53.1 0.42 CKD, % (n) 4.4 ± 3.7 6.5 (126) 6.5 (128) 6.8 (133) 0.79 6.6 (129) 5.8 (113) 7.6 (145) 0.10 Type 2 diabetes, % (n) 30.6 (1,797) 28.9 (567) 30.7 (601) 32.1 (629) 0.10 28.8 (564) 30.4 (595) 32.6 (638) 0.04 Hypertension, % (n) 84.1 (4,941) 85.1 (1,666) 84.5 (1,654) 82.8 (1,621) 0.13 85.1 (1,667) 84.4 (1,653) 82.8 (1,621) 0.12 Hypercholesterolemia, % (n) 69.7 (4,096) 69.4 (1,359) 69.2 (1,356) 70.5 (1,381) 0.63 70.2 (1,375) 69.3 (1,356) 69.7 (1,365) 0.80 Hypertriglyceridemia, % (n)* 39.7 (2,327) 40.7 (795) 39.1 (763) 42.5 (831) 0.10 40.8 (795) 39.2 (765) 42.4 (829) 0.13 10.3389/fnut.2022.986190 y Low HDL, % (n) 40.8 (2,389) 38.5 (751) 39.4 (768) 41.3 (808) 0.18 39.1 (763) 39.0 (760) 41.1 (804) 0.30 (Continued) frontiersin.org Frontiers in Nutrition Valle-Hita et al. TABLE 1 (Continued) PRAL (mEq/d) NEAP (mEq/d) Total T1 T2 T3 T1 T2 T3 n = 5,874 n = 1,958 n = 1,958 n = 1,958 p-value n = 1,958 n = 1,958 n = 1,958 p-value Medication use, % (n) 51.8 (3, 42) 52.6 (1, 30) 51.7 (1, 13) 51.0 (999) 0.66 52.8 (1, 35) 51.9 (1, 17) 50.6 (990) 0.35 Lipid-lowering drugs Oral blood glucose-lowering drugs 26.0 (1,528) 25.2 (494) 26.3 (516) 26.5 (518) 0.62 25.3 (495) 25.7 (504) 27.0 (529) 0.44 Insulin treatment 4.1 (239) 3.5 (68) 3.7 (73) 5.0 (98) 0.03 3.2 (64) 4.1 (81) 4.8 (94) 0.05 Antihypertensive drugs 78.7 (4,625) 81.7 (1,599) 77.9 (1,525) 76.7 (1,501) <0.01 81.0 (1,585) 79.1 (1,549) 76.2 (1,491) <0.01 ARBs 36.3 (2,131) 39.6 (776) 34.9 (683) 34.3 (672) <0.01 39.5 (774) 35.2 (689) 34.1 (668) <0.01 ACEis 30.2 (1,775) 28.6 (559) 31.2 (611) 30.9 (605) 0.11 27.9 (546) 32.0 (624) 30.9 (605) 0.02 Dietary assessment 8.5 ± 2.7 9.2 ± 2.6 8.4 ± 2.7 7.9 ± 2.5 <0.01 9.3 ± 2.6 8.6 ± 2.6 7.7 ± 2.5 <0.01 erMedDiet score, 17-points Energy intake, kcal/d 2,370.5 ± 548.9 2,366.1 ± 537.9 2,303.1 ± 531.8 2,442.3 ± 567.8 <0.01 2,277.5 ± 531.2 2,278.6 ± 533.1 2,455.7 ± 562.5 <0.01 07 Protein intake, % energy 16.7 ± 2.8 16.1 ± 2.6 16.7 ± 2.7 17.4 ± 3.0 <0.01 16.1 ± 2.7 16.9 ± 2.7 17.3 ± 2.9 <0.01 Vegetal /animal protein ratio, g/d 0.5 ± 0.2 0.67 ± 0.27 0.56 ± 0.19 0.48 ± 0.17 <0.01 0.68 ± 0.28 0.56 ± 0.19 0.49 ± 0.17 <0.01 Fat intake, % energy 39.6 ± 6.5 38.4 ± 6.4 39.7 ± 6.4 40.8 ± 6.5 <0.01 38.5 ± 6.5 39.5 ± 6.3 40.9 ± 6.5 <0.01 Carbohydrate intake, % energy 40.5 ± 6.8 42.4 ± 6.6 40.4 ± 6.5 38.7 ± 6.8 <0.01 42.4 ± 6.8 40.5 ± 6.4 38.6 ± 6.8 <0.01 Fiber intake, g/day 26.1 ± 8.7 30.4 ± 9.1 25.2 ± 7.8 22.8 ± 7.5 <0.01 29.9 ± 9.5 26.4 ± 7.9 22.2 ± 7.0 <0.01 Potassium intake, mg/day 4,477.0 ± 1,079.6 5,108.6 ± 1,124.3 4,313.1 ± 898.2 4,009.2 ± 884.4 <0.01 4,953.4 ± 1,189.3 4,501.7 ± 924.5 3,975.8 ± 866.0 <0.01 Calcium intake, mg/day 1,034.0 ± 347.0 1,062.8 ± 353.6 999.2 ± 327.9 1,040.1 ± 355.9 <0.01 1,030.0 ± 350.5 1,049.6 ± 337.1 1,022.5 ± 352.7 0.04 Magnesium intake, mg/day 420.4 ± 108.2 457.7 ± 112.5 407.6 ± 102.2 396.0 ± 99.3 <0.01 446.2 ± 117.8 425.2 ± 102.5 389.8 ± 95.6 <0.01 Phosphorus intake, mg/day 1,759.1 ± 419.9 1,750.1 ± 429.1 1,703.8 ± 401.9 1,823.5 ± 419.7 <0.01 1,713.1 ± 438.3 1,783.5 ± 403.4 1,780.8 ± 413.6 <0.01 Sodium intake, mg/day 2,430.0 ± 774.8 2,272.5 ± 736.8 23,183.0 ± 679.8 2,699.4 ± 828.6 <0.01 2,187.8 ± 712.4 2,412.4 ± 689.3 2,689.6 ± 832.1 <0.01 ACEis, Angiotensin-Converting Enzyme Inhibitors; ARBs, Angiotensin II receptor blockers; eGFR, Estimated Glomerular Filtration Rate; erMedDiet, energy-restricted Mediterranean diet; HDL, High-Density Lipoprotein; NEAP, Net Endogenous Acid Production; MET, Metabolic Equivalent of Task T, tertile; BMI, Body Mass Index; PRAL, Potential Renal Acid Load; PA, Physical activity; eGFR, estimated Glomerular Filtration Rate; CKD, Chronic Kidney Disease (eGFR< 60 ml/min/1.73 m2 ); UACR, 10.3389/fnut.2022.986190 Urine Albumin/Creatinine Ratio. Values are presented as percentages (n) for categorical variables and means ± standard deviations for continuous variables. P-value was calculated by chi-square or one-way analysis of variance test for categorical and continuous variables, respectively. * Fasting triglyceride concentration ≥150 mg/dL or specific treatment for lipid abnormality. y HDL concentration <40 mg/dL in men and <50 mg/dL in women or specific treatment for lipid abnormality. frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 1-SD increment. NEAP: β: −0.28 ml/min/1.73 m2 ; 95% CI: load had higher odds of a ≥10% eGFR decline, and those in the −0.51 to −0.05 for each 1-SD increment). Results remained highest tertile of PRAL had 23% higher odds of a ≥10% UACR essentially the same after adding 1-year BMI change to the increase. GFR and albuminuria are the main complementary most adjusted model (data not shown). PRAL and NEAP were biomarkers used in epidemiological studies to assess kidney not significantly associated with UACR changes after 1-year of function (3). As far as we know, this is the first study to follow-up after modeling them as tertiles, nor as continuous prospectively evaluate the association between dietary acid load variables. In the sensitivity analyses, excluding individuals with and kidney function concurrently assessing eGFR and UACR in <60 ml/min/1.73 m2 of eGFR or with >300 mg/g of UACR a population of older adults with underlying comorbidities. did not modify the main findings for both outcomes (data not A large body of evidence has linked dietary acid load shown). When we repeated the principal analyses, stratifying with kidney outcomes in several studies (9). However, to the by baseline categories of eGFR (≥90; 60–90; <60 ml/min/1.73 best of our knowledge, there are only four cross-sectional m2 ) and UACR (<30; ≥30 mg/g), the results presented a studies and one longitudinal study investigating the potential similar tendency (Supplementary Table 4). In participants with relationship of dietary acid load with renal function defined eGFR ≥ 90 ml/min/1.73 m2 , significant associations were by eGFR and/or CKD in older adults without CKD. These observed with eGFR changes when both dietary acid load cross-sectional studies conducted in different cohorts of adults indexes were modeled as continuous variables (PRAL: β: −0.28 reported that higher dietary acid load was associated with higher ml/min/1.73 m2 ; 95% CI: −0.56 to −0.01 for each 1-SD odds of CKD and/or impaired kidney function as indicated increment. NEAP: β: −0.31 ml/min/1.73 m2 ; 95% CI: −0.58 by low eGFR after adjusting for multiple confounders (14, 16, to −0.03 for each 1-SD increment). The main analysis was 17, 33). Our observations are in accordance with these cross- repeated using other cut-offs points for the MedDiet score sectional studies since we observed a greater eGFR decline at 1 confounding factor (i.e., ≥12 points for high adherence) and year with higher PRAL and NEAP scores, even after adjusting similar results were found (Supplementary Table 5). We also for baseline eGFR and other essential confounding factors. explored the interactions between tertiles of PRAL and NEAP Interestingly, our supplementary stratified analyses according and the adherence to energy-reduced MedDiet, categories of to categories of eGFR, which have seldom been performed in eGFR and UACR, as well as intervention/control group, and no previous studies, revealed a similar non-significant tendency statistically significant findings were observed (all interactions, p to worsen kidney function with increased dietary acid load. > 0.05). Consistent with our findings, the prospective analysis from the Figure 1 depicts the OR and 95% CI for ≥10% eGFR decline cohort of the Atherosclerosis Risk in Communities (ARIC) and ≥10% UACR increase according to tertiles of PRAL and study of 15,055 apparently healthy middle-aged participants NEAP. After multiple adjustments, participants in the highest with preserved kidney function showed that higher levels of tertile of PRAL and NEAP were significantly more likely to have PRAL were associated with a 13% higher risk of CKD incidence a ≥10% eGFR decline after 1 year of follow up compared to over 21 years of follow-up (18). those in the lowest tertile, with ORs of 1.28 (95% CI: 1.07–1.54) Regarding albuminuria, which is considered a reliable for PRAL and 1.24 (95% CI: 1.03–1.0) for NEAP. When PRAL marker of kidney damage (3), preceding studies have assessed and NEAP were modeled as continuous variables (per each 1- its cross-sectional association with dietary acid load obtaining SD increment) higher ORs were also observed. Compared to inconclusive findings. In The Jackson Heart Study, there was participants with low PRAL values at baseline, participants with no association between estimated Net Acid Excretion (NAEes ) the highest levels had a 23% (95% CI: 1.04–1.46) higher odds of and albuminuria (16). In contrast, the NHANES study reported ≥10% UACR increase after 1 year of follow-up after adjusting for a positive association between dietary acid load and albuminuria potential confounders. No significant associations were found in 12,293 healthy American adults (17). Additionally, the between NEAP and the odds of ≥10% UACR increase or for 1- researchers from The Uonuma CKD Cohort Study also found SD increment of PRAL and NEAP. When a ≥5% eGFR decline that higher NEAP was associated with a higher UACR and and a ≥5% UACR increase were assessed, the same results were risk of albuminuria among 6,684 middle-aged Japanese adults found (Supplementary Table 6). (21). To date, no large prospective cohort study has focused on the relationship between dietary acid load and albuminuria in vulnerable older adults. In the current study, we report Discussion no association between PRAL and NEAP scores and 1-year changes in UACR. This could suggest that high dietary acid load The results of this prospective study conducted in older may promote tubule-interstitial injury rather than glomerular Spanish adults at high cardiovascular disease risk suggest that damage. Nevertheless, we were not able to check this tubular PRAL and NEAP are inversely associated with 1-year changes damage hypothesis since spot/24 h total proteinuria data were in eGFR, but not with 1-year UACR changes. Furthermore, not available in our dataset (34). However, it is worthwhile to participants with higher levels of both estimates of dietary acid mention that when UACR was also assessed as an increase ≥ 10% Frontiers in Nutrition 08 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 TABLE 2 Multivariable-adjusted β-coefficients and 95% CI of 1-year changes in eGFR (ml/min/1.73 m2 ) or in UACR (mg/g) across tertiles and per 1-SD increment of baseline PRAL and NEAP. PRAL (mEq/d) T1 T2 T3 p for trend Continuous (1 SD**) n = 1,958 n = 1,958 n = 1,958 n = 5,874 1 in eGFR, ml/min/1.73 m2 −0.69 (−1.07 to −0.31) −0.86 (−1.24 to −0.49) −1.34 (−1.72 to −0.95) Model 1 0 (Ref.) −0.16 (−0.70 to 0.37) −0.52 (−1.06 to 0.03) 0.062 −0.21 (−0.42 to 0.01) Model 2 0 (Ref.) −0.17 (−0.71 to 0.36) −0.64 (−1.21 to −0.08)* 0.026 −0.25 (−0.47 to −0.03)* n = 1,213 n = 1,213 n = 1,213 n = 3,639 1 in UACR, mg/g 4.37 (1.96 to 6.78) 2.74 (0.60 to 4.88) 1.39 (−0.62 to 3.39) Model 1 0 (Ref.) −1.20 (−4.32 to 1.93) −2.31 (−5.28 to 0.66) 0.128 −0.88 (−2.00 to 0.25) Model 2 0 (Ref.) −1.63 (−4.84 to 1.58) −2.99 (−6.34 to 0.37) 0.082 −1.22 (−2.51 to 0.08) NEAP (mEq/d) n = 1,958 n = 1,958 n = 1,958 n = 5,874 1 in eGFR, ml/min/1.73 m2 −0.68 (−1.06 to −0.30) −0.97 (−1.35 to −0.60) −1.24 (−1.63 to −0.84) Model 1 0 (Ref.) −0.28 (−0.81 to 0.25) −0.44 (−0.99 to 0.11) 0.116 −0.22 (−0.44 to −0.01)* Model 2 0 (Ref.) −0.30 (−0.83 to 0.24) −0.56 (−1.13 to 0.01) 0.056 −0.28 (−0.51 to −0.05)* n = 1,213 n = 1,213 n = 1,213 n = 3,639 1 in UACR, mg/g 3.92 (1.49 to 6.34) 3.09 (1.14 to 5.03) 1.49 (−0.54 to 3.53) Model 1 0 (Ref.) −0.81 (−3.82 to 2.20) −1.96 (−5.07 to 1.15) 0.214 −0.93 (−2.13 to 0.28) Model 2 0 (Ref.) −0.83 (−3.87 to 2.21) −2.42 (−5.79 to 0.95) 0.154 −1.26 (−2.63 to 0.10) eGFR, Estimated glomerular filtration rate; NEAP, Net Endogenous Acid Production; T, tertile; PRAL, Potential Renal Acid Load; UACR, Urine albumin/creatinine ratio. Model 1: adjusted for age (years), sex and baseline eGFR or baseline UACR (in continuous, depending on the main outcome). Model 2: additionally adjusted for participating center (categorized into quartiles by number of participants), intervention group (treatment/control), body mass index (kg/m2 ), smoking habits (never, current or former smoker), educational level (primary, secondary education or graduate), leisure-time physical activity (METS/min/week in tertiles), diabetes prevalence (yes/no), hypertension prevalence (yes/no) and hypercholesterolemia prevalence (yes/no), ARBs (yes/no), ACEis (yes/no), Mediterranean diet adherence (high/low adherence), energy intake (kcal/day in tertiles), sodium intake (mg/g in tertiles) and high leukocytes levels (yes/no). *p-value < 0.05. **One SD = 15.6 mEq/d in PRAL and 8.1 mEq/d in NEAP. after 1 year of follow-up, which is a more clinical approach, we medullary ammonia concentrations (40–42). There is also a found a significant association with PRAL. Consequently, future high probability that acid retention increases the production of longitudinal studies and clinical trials would be helpful to clarify oxygen-free radicals and oxidative stress (43, 44). Consequently, these observations related to albuminuria and dietary acid load. it is crucial for kidney health to maintain appropriate levels of Overall, our findings in conjunction with the evidence acid load, and diet may play an important role in this respect available to date, suggests that following a diet with a low acid (11). It should be noted that in our study individuals with high load could be an appropriate measure to improve renal function levels of dietary acid load reported higher intakes of some food and, accordingly, decrease the risk of CKD development and groups which have been directly or indirectly implicated in progression among older individuals from middle-aged to kidney function damage, such as total and animal protein intake elderly with underlying comorbid conditions. (33, 45) or sugar and sweetened products (46). By contrast, as The potential mechanisms by which high dietary acid dietary acid load increased there was a lower consumption of load may induce kidney dysfunction are unclear, though fiber-rich foods, including fruits, vegetables, whole-grain cereals, possible mechanisms have been proposed for consideration. and nuts. Thus, the potential beneficial effects of fiber on the Acid retention has been proposed to activate the intracellular kidney (47) could be lacking in those individuals with high renin-angiotensin system, through the previous stimulation of dietary acid load. aldosterone production, which might be implicated in the onset This study has some limitations that deserve to be or progression of kidney damage (35, 36). Moreover, metabolic mentioned. First, the population consisted of older Spanish acidosis appears to contribute to endothelin-1 production, individuals at high cardiovascular risk, meaning the findings which in turn could be related to tubulointerstitial injury (37– may not be generalizable to other populations. Furthermore, 39). Besides, high dietary acid load would also induce tubular the Mediterranean lifestyle could imply healthier habits which, toxicity activating the complement pathway and increasing renal at the same time, may result in different macro- and Frontiers in Nutrition 09 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 FIGURE 1 (A) Multivariable-adjusted OR (95% CIs) for ≥10% eGFR decline by tertiles of baseline PRAL and NEAP and per 1-SD increment. (B) Multivariable-adjusted OR (95% CIs) for ≥10% UACR increase by tertiles of baseline PRAL and NEAP and per 1-SD increment. eGFR, Estimated glomerular filtration rate; NEAP, Net Endogenous Acid Production; T, tertile; PRAL, Potential Renal Acid Load; UACR, Urine albumin/creatinine ratio. Percentage of participants with eGFR decline (>10%): tertile 1 of PRAL (n = 296; % = 15.1), tertile 2 of PRAL (n = 304; % = 15.5), tertile 3 of PRAL (n = 346; % = 17.7); tertile 1 of NEAP (n = 297; % = 15.2), tertile 2 of NEAP (n = 312; % = 15.9). tertile 3 of NEAP (n = 337; % = 17.2). Percentage of participants with UACR increase (>10%): tertile 1 of PRAL (n = 539; % = 44.4), tertile 2 of PRAL (n = 547; % = 45.1), tertile 3 of PRAL (n = 597; % = 49.2); tertile 1 of NEAP (n = 550; % = 45.3), tertile 2 of NEAP (n = 539; % = 44.4), tertile 3 of NEAP (n = 594; % = 49.0). All models were adjusted for age (years), sex, participating center (categorized into quartiles by number of participants), intervention group (treatment/control), body mass index (kg/m2 ), smoking habits (never, current or former smoker), educational level (primary, secondary education or graduate), leisure-time physical activity (METS/min/week in tertiles), diabetes prevalence (yes/no), hypertension prevalence (yes/no) and hypercholesterolemia prevalence (yes/no), ARBs (yes/no), ACEis (yes/no), Mediterranean diet adherence (high/low adherence), energy intake (kcal/day in tertiles), sodium intake (mg/g in tertiles), and high leukocytes levels (yes/no). **one SD = 15.6 mEq/d in PRAL and 8.1 mEq/d in NEAP. micronutrients intake related to kidney function, such as is the sensitivity and supplementary analyses conducted which potassium-rich or low-sodium dietary intakes. Second, as gave robustness to the main results. PREDIMED-Plus is a randomized controlled trial, though, all the analyses were adjusted for the intervention group, the lifestyle advice that participants received could be affecting our Conclusion findings. Third, dietary acid load was calculated using PRAL In conclusion, the current study conducted in a population and NEAP from dietary nutrient intake information obtained of older Spanish adults with overweight/obesity and MetS shows from FFQ data. Although this questionnaire was validated that higher dietary acid load is associated with changes toward and carefully administered by trained dietitians, potential a worse eGFR and higher odds of ≥10% eGFR decline and measurement errors and reporting bias could be present. Fourth, ≥10% UACR increase. Nevertheless, further longitudinal and while SCr-based eGFR was used as a biomarker of kidney interventional studies are needed to clarify and confirm the function, as is common in most epidemiologic studies, there are consistency of these associations before considering a reduction other more optimal markers such as inulin, iothalamate or 24- in dietary acid load as part of strategies for preventing kidney h urinary creatinine clearance. Nevertheless, these procedures function decline. are expensive, time-consuming, and difficult measure in large populations. Finally, as in any observational study, although a substantial number of confounding factors were considered, Data availability statement confounding bias could not be completely ruled out and direct causality cannot be inferred. However, our study also has several There are restrictions on the availability of data for strengths. Analyses were conducted using data from a large the PREDIMED-Plus trial, due to the signed consent cohort, which has a wide selection of different variables to adjust agreements around data sharing, which only allow access the models for kidney function related-potential confounders. to external researchers for studies following the project Moreover, it is important to highlight the prospective design that purposes. Requestors wishing to access the PREDIMED- we performed and the joint assessment of two commonly used Plus trial data used in this study can make a request biomarkers of renal function. Another novel aspect of this study to the PREDIMED-Plus trial Steering Committee chair: Frontiers in Nutrition 10 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 predimed_plus_scommitte@googlegroups.com. The request entitled: Implementación y evaluación de una intervención will then be passed to members of the PREDIMED-Plus Steering intensiva sobre la actividad física Cohorte PREDIMED-Plus Committee for deliberation. grant to JS-S; the European Research Council (Advanced Research Grant 2014–2019; agreement #340918) granted to ÁA-Gó; the Recercaixa (Number: 2013ACUP00194) grant Ethics statement to JS-S; grants from the Consejería de Salud de la Junta de Andalucía (PI0458/2013, PS0358/2016, and PI0137/2018); the The studies involving human participants were PROMETEO/2017/017 and PROMETEO/2021/021 grants reviewed and approved by the ethical standards of the from the Generalitat Valenciana; Generalitat Valenciana Declaration of Helsinki by the Institutional Review AICO/2021/347 grant to JVid; the SEMERGEN grant; the Boards (IRBs). The patients/participants provided Boosting young talent call grant program for the development their written informed consent to participate in of IISPV research projects 2019–2021 (Ref.: 2019/IISPV/03 grant this study. to AD-L); the Societat Catalana d’Endocrinologia i Nutrició (SCEN) Clinical-Research Grant 2019 (IPs: JS-S and AD-L). Author contributions Collaborative Nutrition and/or Obesity Project for Young Researchers 2019 supported by CIBEROBN entitled: Lifestyle CV-H, NB-T, AD-L, ZV-R, IM, DC, AG, JM, ÁA-Gó, Interventions and Chronic Kidney Disease: Inflammation, JW, JVio, DR, JL-M, RE, FT, JL, LS-M, AB-C, JT, VM-S, Oxidative Stress and Metabolomic Profile (LIKIDI study) XP, JG, PM-M, JVid, AA-Ga, LD, ER, AG-A, RB, MF, PP- grant to AD-L. Jordi Salas-Salvadó, gratefully acknowledges O, AA-A, EG-G, DM-U, MM, RC, EMG-G, LT-S, MD-F, the financial support by ICREA under the ICREA Academia EG, CO-A, OC, AG-R, CG-S, CS-O, HS, JS-S, and NB programme. CV-H receives a predoctoral grant from the designed and conducted the research. CV-H and AD-L analyzed Generalitat de Catalunya (2022 FI_B100108). None of the the data. CV-H, NB-T, AD-L, and NB wrote the article. funding sources took part in the design, collection, analysis, CV-H and AD-L are the guarantors of this work and, as interpretation of the data, or writing the report, or in the such, had full access to all the data in the study and take decision to submit the manuscript for publication. responsibility for the integrity of the data and the accuracy of the data analysis. All authors revised the manuscript for Acknowledgments important intellectual content and read and approved the final manuscript. The authors would especially like to thank the PREDIMED- Plus participants for their enthusiastic collaboration, the PREDIMED-Plus personnel for their outstanding support and Funding the personnel of affiliated primary care centers for their exceptional effort. CIBEROBN, CIBERESP, and CIBERDEM This work was supported by the official Spanish Institutions are initiatives of ISCIII, Madrid, Spain. We also thank the for funding scientific biomedical research, CIBER Fisiopatología PREDIMED-Plus Biobank Network, part of the National de la Obesidad y Nutrición (CIBEROBN) and Instituto Biobank Platform of the ISCIII for storing and managing the de Salud Carlos III (ISCIII), through the Fondo de PREDIMED-Plus biological samples. Investigación para la Salud (FIS), which is co-funded by the European Regional Development Fund (six coordinated FIS projects leaded by JS-S and JVid, including the following Conflict of interest projects: PI13/00673, PI13/00492, PI13/00272, PI13/01123, PI13/00462, PI13/00233, PI13/02184, PI13/00728, PI13/01090, Author JS-S reported receiving research support from the PI13/01056, PI14/01722, PI14/00636, PI14/00618, PI14/00696, Instituto de Salud Carlos III (ISCIII), Ministerio de Educación PI14/01206, PI14/01919, PI14/00853, PI14/01374, PI14/00972, y Ciencia, Departament de Salut Pública de la Generalitat de PI14/00728, PI14/01471, PI16/00473, PI16/00662, PI16/01873, Catalunya, the European Commission, the California Walnut PI16/01094, PI16/00501, PI16/00533, PI16/00381, PI16/00366, Commission, Patrimonio Comunal Olivarero, La Morella Nuts, PI16/01522, PI16/01120, PI17/00764, PI17/01183, PI17/00855, and Borges S.A; receiving consulting fees or travel expenses PI17/01347, PI17/00525, PI17/01827, PI17/00532, PI17/00215, from California Walnut Commission, Eroski Foundation, PI17/01441, PI17/00508, PI17/01732, PI17/00926, PI19/00957, Instituto Danone, Abbott Laboratories and Mundifarma, PI19/00386, PI19/00309, PI19/01032, PI19/00576, PI19/00017, receiving non-financial support from Hojiblanca, Patrimonio PI19/01226, PI19/00781, PI19/01560, PI19/01332, PI20/01802, Comunal Olivarero, and Almond Board of California; serving PI20/00138, PI20/01532, PI20/00456, PI20/00339, PI20/00557, on the board of and receiving grant support through his PI20/00886, and PI20/01158); the Especial Action Project institution from the International Nut and Dried Foundation Frontiers in Nutrition 11 frontiersin.org Valle-Hita et al. 10.3389/fnut.2022.986190 and the Eroski Foundation; and grants and personal fees The remaining authors declare that the research was from Instituto Danone. Author ER reported receiving grants, conducted in the absence of any commercial or financial personal fees, and non-financial support from the California relationships that could be construed as a potential conflict Walnut Commission during the conduct of the study and of interest. grants, personal fees, non-financial support from Alexion; personal fees from Amarin; and non-financial support from the International Nut Council outside the submitted work. Publisher’s note Author RE reported receiving grants from Instituto de All claims expressed in this article are solely those Salud Carlos III and olive oil for the trial from Fundacion of the authors and do not necessarily represent those Patrimonio Comunal Olivarero/during the conduct of the of their affiliated organizations, or those of the publisher, study and personal fees from Brewers of Europe, Fundación the editors and the reviewers. Any product that may be Cerveza y Salud, Interprofesional del Aceite de Oliva, Instituto evaluated in this article, or claim that may be made by Cervantes, Pernaud Richar, Fundación Dieta Mediterránea, its manufacturer, is not guaranteed or endorsed by the Wine and Culinary International Forum; non-financial support publisher. from Sociedad Española de Nutrición and Fundación Bosch y Gimpera; and grants from Uriach Laboratories outside the submitted work. Author XP reported receiving grants Supplementary material from ISCIII during the conduct of the study; receiving consulting fees from Sanofi Aventis, Amgen, and Abbott The Supplementary Material for this article can be laboratories; receiving lecture personal fees from Esteve, Lacer found online at: https://www.frontiersin.org/articles/10.3389/ and Rubio laboratories. fnut.2022.986190/full#supplementary-material References 1. O’Sullivan ED, Hughes J, Ferenbach DA. Renal aging: causes and 11. Scialla JJ, Anderson CAM. 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Acid retention accompanies distribution or reproduction in other forums is permitted, provided the reduced GFR in humans and increases plasma levels of endothelin and aldosterone. original author(s) and the copyright owner(s) are credited and that the Am J Physiol Ren Physiol. (2011) 300:830–7. doi: 10.1152/ajprenal.00587.2010 original publication in this journal is cited, in accordance with accepted 39. Phisitkul S, Khanna A, Simoni J, Broglio K, Sheather S, Rajab MH, et al. academic practice. No use, distribution or reproduction is permitted Amelioration of metabolic acidosis in patients with low GFR reduced kidney which does not comply with these terms. Frontiers in Nutrition 13 frontiersin.org