Baseline and Follow-Up Association between Adiposity and Renal Function: Data from the ILERVAS Cohort

Abstract

Introduction

Obesity is multifactorial and increasingly prevalent disease associated with chronic kidney disease (CKD). While cross-sectional studies support a link between adiposity and renal dysfunction, the long-term impact of total body fat on kidney function remains unclear. This study aims to evaluate the association between baseline adiposity, assessed by body mass index (BMI) and total body fat percentage, and renal function parameters, and to determine whether adiposity predicts CKD outcomes over 4 years in individuals without diabetes at low-to-moderate cardiovascular risk.

Methods

This retrospective, multicenter study included 8,153 participants from the ILERVAS project in Lleida, Spain. Adiposity was measured using BMI and estimated body fat percentage (CUN-BAE formula). Renal function was evaluated by estimated glomerular filtration rate (eGFR) and albumin-to-creatinine ratio (ACR). A subset of 3,222 individuals was reevaluated after 4 years. Multivariable regression models and ROC analysis were performed to explore associations and predictive performance.

Results

CKD prevalence was 15.1%, higher in individuals with obesity (17.2%) than in those with overweight (13.7%) or normal weight (13.9%, p < 0.001). Body fat percentage showed a stronger inverse correlation with eGFR than BMI and was independently associated with eGFR decline. Baseline body fat percentage and its increase over time were associated with eGFR decline. Neither adiposity measure independently predicted CKD presence nor albuminuria.

Conclusions

Higher total body fat is associated with impaired renal function and its progression over time, independently of traditional CKD risk factors. These findings support incorporating body fat assessment in CKD prevention beyond BMI-based strategies.

Plain Language Summary

Chronic kidney disease (CKD) is a common condition in which the kidneys gradually lose function. Obesity is a known risk factor for multiple diseases, but its impact on kidney function over time remains unclear. It is also uncertain whether total body fat provides more relevant information than body mass index (BMI). In this study, we analyzed data from more than 8,000 adults in the ILERVAS cohort in Spain, all without diabetes and with low-to-moderate cardiovascular risk. Body composition was assessed using BMI and estimated total body fat percentage. Kidney function was evaluated using standard blood and urine markers. A subgroup was followed for 4 years to assess changes over time. We found that higher body fat was associated with worse kidney function, with a stronger relationship than BMI. Individuals with higher or increasing body fat experienced greater decline over time. However, neither BMI nor body fat alone accurately identified CKD or proteinuria. These findings suggest that excess body fat may contribute to kidney function decline even in individuals without diabetes. Direct measures of body fat may be more informative than BMI. Incorporating more precise measures of body fat into clinical practice to improve early identification of individuals at risk and enhance prevention strategies.

Introduction

Obesity is a chronic, multifactorial and relapsing disease, whose prevalence is exponentially increasing, becoming a major global health concern [1]. Adiposity, its quantity, location and function, plays an important role in developing multiple comorbidities, such as diabetes, hypertension, and cancer that significantly shorten quality and life expectancy [2–5]. Among these, chronic kidney disease (CKD) is a less recognized but important obesity-related comorbidity [6–8].

CKD is an increasingly prevalent condition, characterized by a progressive decline in renal function that also involves multiple comorbidities such as anemia, bone disease and increased cardiovascular (CV) risk. Ultimately, CKD leads to end-stage kidney failure requiring dialysis, often accompanied by many other complications [9–13]. Like obesity, CKD also reduces quality and life expectancy. It affects over 10% of the general population worldwide and ranked as the 9th cause of death in 2021, with 95% higher mortality since 2000 [14, 15].

The main modifiable risk factors described for CKD are hypertension and diabetes [14, 16]. Early detection of CKD and effective control of these risk factors are essential, as it can reduce the incidence of CKD-related complications, delay the progression to kidney failure and reduce mortality [16–19]. Although early stages of CKD are often asymptomatic, clinical guidelines use albuminuria and glomerular filtration rate (GFR), to classify disease severity, estimate risk of progression, and guide management recommendations [20, 21].

The association between obesity and CKD has been widely explored. In a cross-sectional study, 35.2% of patients with CKD were affected by obesity [22]. While in a USA cohort with end-stage kidney disease 39.2% had obesity, including 22.5% with severe obesity [23]. Obesity is associated with higher CKD prevalence (RR 1.47, 95% confidence interval [CI]: 1.31–1.65, p < 0.001) [24], and each unit increase in body mass index (BMI) increases kidney-related mortality risk by 15% (HR 1.11–1.19). Greater visceral adiposity has also been linked to a higher risk of CKD [25]. Although this consistent evidence, the underlying pathophysiological mechanism remains unclear, and current clinical guidelines do not yet consider obesity an independent CKD risk factor. However, previous studies included patients with type 2 diabetes, an independent contributor to CKD, limiting the evaluation of the impact of adiposity alone [26, 27]. Moreover, the long-term impact of adiposity on kidney function and structure remains unknown.

In this context, the aim of our study was to investigate the association between obesity, assessed through BMI and percentage of body fat, and CKD. Additionally, we aimed to evaluate the impact of obesity on renal function over a 4-year follow-up period in a large cohort of individuals at low to moderate CV risk.

Methods

Study Design and Participants

This was a retrospective, multicenter, observational, and exploratory analysis of baseline data from a cohort of 8,153 participants enrolled in the ILERVAS project (ClinTrials.gov Identifier: NCT03228459). This study was conducted and reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, and the corresponding checklist is provided as online supplementary material (for all online suppl. material, see https://doi.org/10.1159/000552362). The ILERVAS project is a prospective, longitudinal, and multicenter study conducted in primary care centers across the province of Lleida (Catalonia, Spain) between 2015 and 2017. Its main aim was to determine the prevalence of both subclinical atheromatous disease and hidden CKD in a population at low to moderate CV risk [28]. One-third of the participants, 3,222 individuals, were reassessed after a follow-up period of 4 years.

The ILERVAS project protocol was approved by the Ethics Committee of the Arnau de Vilanova University Hospital (First visit: CEIC-1410, December 19, 2014; Follow-up: CEIC-2015, December 20, 2018), and written informed consent was obtained from all participants. The study complied with the Declaration of Helsinki, the International Society for Pharmacoepidemiology Guidelines for Good Pharmacoepidemiology Practices, Good Clinical Practice guidelines and all applicable local regulations [29].

Inclusion criteria were: (1) aged 45–70 years attending primary care centers; (2) no history of CV disease, and (3) presence of at least one CV risk factor (dyslipidemia, elevated blood pressure, obesity, smoking habit, or first-degree relative with premature CV disease [defined as myocardial infarction, stroke, or peripheral arterial disease before <55 years of age in men or 65 in women]). Exclusion criteria included: (1) diagnosis of any type of diabetes, (2) known CKD, (3) active neoplasia, (4) life expectancy <18 months, and/or (5) pregnancy. To minimize selection bias, participants were consecutively recruited from primary care centers following predefined inclusion and exclusion criteria.

Outcomes

Data on demographic characteristics, comorbidities, and current smoking habits were collected from electronic medical records. Standardized protocols and trained personnel were used for all anthropometric and biochemical measurements to reduce information bias.

Renal function was assessed using fasting urine samples. Albuminuria was measured using Clinitek Microalbumin 2 Reagent Strips and a Siemens Clinitek Status^® analyzer, which automatically provides the albumin-to-creatinine ratio (ACR, mg/g). Creatinine levels (mg/dL) were determined using dried capillary blood testing. Participants were classified into three categories of albuminuria based to ACR values: ≤30 mg/g, 30–300 mg/g, and ≥300 mg/g [21]. The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI 2009 equation, taking into account sex and race [30]. CKD was defined according to KDIGO guidelines as abnormalities of kidney structure or function, specifically an eGFR <60 mL/min/1.73 m² and/or an ACR ≥30 mg/g.

Normoglycemia was defined as glycated hemoglobin (HbA1c) level <39 mmol/mol (<5.7%), in accordance with the American Diabetes Association guidelines [31]. Weight and height were recorded with participants wearing minimal clothing and no shoes, with a calibrated instrument (precision: 0.5 kg and 1.0 cm). Waist circumference was measured to 0.1 cm using a nonelastic tape at the midline between the iliac crest and the lower rib in standing position. All anthropometric measures were performed by trained nurses under standardized conditions. The relative technical error of intra-rater measurement was less than 1% for height, weight, and waist circumferences. BMI was calculated as weight (kg) divided by the square of height (m).

Obesity status was classified by BMI as 18.5–24.9 kg/m², 25.0–29.9 kg/m², and ≥30 kg/m², for normal weight, overweight and obesity, respectively, following clinical guidelines [32]. The percentage of total body fat was estimated by Body Adiposity Estimator of the Clínica Universidad de Navarra (CUN-BAE), calculated form age, sex, and BMI, as previously described [33].

Endpoint

The primary objectives of this study were: (i) to evaluate the association of the presence of CKD, ACR classification, and eGFR with adiposity measures (BMI, waist circumference, and percentage of body fat); (ii) to assess whether baseline adiposity is independently associated with CKD, increased ACR, or eGFR decline; and (iii) to explore the relationship between baseline adiposity and 4-year adiposity change with kidney function outcomes (eGFR, ACR).

Statistical Methods

Given the large sample size, the study was considered sufficiently powered to detect clinically meaningful associations. Descriptive statistics were used to summarize key variables. Categorical variables are reported as counts and percentages, while continuous variables are expressed as medians with interquartile ranges (Q1–Q3). Participants with missing eGFR or ACR data or undiagnosed diabetes were excluded.

Group comparisons were performed using the Chi-square test for categorical variables and Student’s t test or ANOVA for normally distributed variables, and the Mann-Whitney U test or Kruskal-Wallis test otherwise. Normality was assessed using the Shapiro-Wilk test.

Pearson’s correlation were used to evaluate associations between eGFR and adiposity measures (BMI, waist circumference, percentage of total body fat) at baseline, after 4 years, and as delta change. Univariate linear regression models were used to estimate the association slopes and to explore the relationship between CKD prognosis categories and body fat percentage.

For multivariable analysis, logistic regression models were used for the binary outcomes CKD (yes/no) and elevated ACR (yes/no), and linear regression models for eGFR as continuous outcome. To fit these models, Least Absolute Shrinkage and Selection Operator (LASSO) method was chosen for its ability to efficiently select relevant variables, handle multicollinearity, and reduce overfitting, resulting in a more robust and interpretable model for complex clinical data. Predictors included age, sex, race, hypertension, dyslipidemia, smoking habit, obesity, and body fat percentage; albuminuria (ACR >30 mg/g) was additionally included in the eGFR regression model. A 10-fold cross-validation approach was used to determine the optimal penalization parameter (lambda). Variables selected by LASSO method (including potential confounders such as age, sex, hypertension, smoking status, and dyslipidemia) were then refitted using standard regression models to obtain odds ratios, 95% CIs, and p values.

Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive performance of CUN-BAE for CKD. The area under the curve with 95% CI was reported.

All p values were two-sided, and statistical significance was set at p < 0.05. Analyses used R software version 4.4.1 (R Core Team, 2024), LASSO models used the “glmnet” package.

Results

Clinical Characterization of Participants and Prevalence of CKD

The study included 8,153 participants, of whom 1,228 (15.1%) met the criteria for CKD. The main clinical and metabolic characteristics of the participants, according to the presence of CKD, are summarized in Table 1. Individuals with CKD were predominantly women and exhibited a more adverse CV risk profile, including older age and a higher prevalence of hypertension and obesity, compared to participants without CKD. Both BMI and total body fat percentage were significantly higher in participants with CKD than in those without the disease (both p < 0.001). Furthermore, CKD prevalence was highest among individuals with obesity (17.2%), compared to those with normal weight (13.9%) or overweight (13.7%) (p < 0.001).

Table 1.

Clinical and metabolic characteristics of patients from ILERVAS cohort according to the presence of CKD

​	No CKD (N = 6,925)	CKD (N = 1,228)	p value
Women, n (%)	3,416 (49.3)	721 (58.7)	<0.001
Age, years	57.0 [52.0–62.0]	59.0 [53.0–64.0]	<0.001
Current smoking habit, n (%)	2,048 (29.6)	357 (29.1)	0.748
High blood pressure, n (%)	2,629 (38.0)	619 (50.4)	<0.001
Pulse pressure, mm Hg	47.0 [41.0–56.0]	50.0 [42.0–60.0]	<0.001
Dyslipidemia, n (%)	3,569 (51.5)	632 (51.5)	0.988
Total cholesterol, mg/dL	204 [180–229]	204 [179–231]	0.920
Obesity, n (%)	2,480 (35.8)	516 (42.1)	<0.001
Body mass index, kg/m2	28.3 [25.5–31.6]	29.0 [25.8–32.4]	<0.001
CUN-BAE, %	35.6 [29.8–42.1]	38.0 [32.1–43.7]	<0.001

Numerical variables summarized with median [Q1–Q3]. p values from Mann-Whitney’s test. Categorical variables’ p values from Chi-square test.

Association between Adiposity Measures and Renal Function

Given the increased prevalence of CKD among participants with obesity, we investigated association between renal function and damage and adiposity. eGFR correlated inversely with body fat percentage (r = −0.177, p < 0.001), BMI (r = −0.079, p < 0.001) and waist circumference (r = −0.054, p < 0.001) (Fig. 1). Specifically, each percentage-point increase in body fat, BMI unit, and waist centimeter were associated with decreases of 0.312, 0.223, and 0.065 eGFR units, respectively, in eGFR. Total body fat percentage and BMI increased progressively with higher levels of ACR, reaching highest values in participants with ACR >300 mg/g (median CUN-BAE: 39.6% [34.2–46.2]; BMI 30.1% [26.6–33. 0] and p < 0.001 and p = 0.005, respectively). Obesity prevalence was also higher in this group reaching 53.1% (online suppl. Table 1). Moreover, body fat percentage increased by 1.79% (95% CI: 1.36–2.22) with each successive CKD risk category (online suppl. Fig. 1).

Fig. 1.

Correlation between eGFR and different adiposity measurements. Scatter plots showing the association between eGFR and percentage of total body fat (CUN-BAE) (a); BMI (b) and waist circumference (c). Pearson correlation coefficients (r) and p values are indicated. The lines represent fitted univariate linear regressions.

Identification of Predictors for CKD, Albuminuria, and Reduced eGFR

Total body fat percentage was independently associated with lower eGFR (Estimate: −0.11; p = 0.010), whereas BMI was not. Other significant predictors of lower eGFR included older age (estimate: −0.68 mL/min per year; p < 0.001), hypertension (estimate: −1.55; p < 0.001), and dyslipidemia (estimate: −0.60; p = 0.051). In contrast, female sex (estimate: 1.18; p = 0.044), non-white race (estimate: 7.31; p = 0.002) and current smoking habit (estimate: 1.20; p < 0.001) were associated with higher eGFR (Table 2). However, neither total body fat percentage nor BMI were identified as significant independent predictors of ACR or the presence of CKD (online suppl. Tables 2, 3). Additionally, ROC curve analysis showed that total body fat percentage had low discriminatory power for identifying CKD, with an area under the curve of 0.57 (online suppl. Fig. 2).

Table 2.

Multivariate linear regression model for eGFR

Term	Estimate	CI lower	CI upper	p value
Age, years	−0.68	−0.73	−0.62	<0.001
Sex (female)	1.18	0.03	2.33	0.044
Race (non-White)	7.31	2.72	11.90	0.002
Hypertension	−1.55	−2.19	−0.91	<0.001
Dyslipidemia	−0.60	−1.19	0.00	0.051
Current smoking habit	1.20	0.52	1.89	<0.001
Obesity	−0.02	−1.00	0.95	0.965
CUN-BAE	−0.11	−0.20	−0.03	0.010
ACR >30 mg/g	−0.57	−1.45	0.30	0.199

p values from multivariate linear regression LASSO model.

Association between Baseline Body Fat and Renal Function after Four Years of Follow-Up

We analyzed the longitudinal association between baseline body fat percentage and eGFR at 4-year follow-up. Baseline body fat was inversely associated with eGFR after 4 years (r = −0.208, p = <0.001) (Fig. 2a), with each one percentage-point increase corresponding to a 0.40-unit eGFR decrease. Additionally, changes in body fat over the 4-year period were negatively correlated with eGFR at year four (r = −0.06, p = 0.001) (Fig. 2b), each 1.0% increase in body fat corresponded to a 0.408-unit decrease in eGFR (p < 0.001). This decline in renal function was more pronounced in specific subgroups: participants with obesity showed a mean reduction of 0.518 eGFR units, whereas those with a baseline eGFR <60 mL/min/1.73 m² experienced a greater decrease of 0.713 units.

Fig. 2.

Correlation between baseline adiposity measurements and eGFR at 4-year follow-up. Scatter plots showing the association between baseline percentage of total body fat (CUN-BAE) and eGFR at 4-year follow-up (a) and change in the percentage of total body fat (CUN-BAE) and change in eGFR after 4 years of follow-up (b). Pearson correlation coefficients (r) and p values are indicated. The lines represent fitted univariate linear regressions.

Discussion

Our study provides new evidence on the association between obesity and CKD. Participants with CKD, as well as those with evidence of renal functional or structural impairment, showed a higher prevalence of obesity and a greater percentage of total body fat compared to those without kidney dysfunction. Across the entire cohort, a progressive increase in BMI, total body fat percentage, and waist circumference was associated with a decline in eGFR. In addition, after adjusting for potential confounders, total body fat percentage, but not BMI, remained independently associated with reduced eGFR. Finally, both baseline body fat percentage and its variation over 4 years were associated with changes in renal function during follow-up.

Our findings are consistent with previous studies reporting a higher prevalence of CKD among people with obesity [34], suggesting a potential causal link between adiposity and kidney disease. A large meta-analysis involving 5.4 million healthy individuals further demonstrated an inverse association between increasing BMI above 25 kg/m² and eGFR, indicating that excess weight may have a detrimental impact on renal function [35]. Notably, our data were obtained in a large cohort of individuals without diabetes, highlighting the detrimental impact of excess adiposity in the absence of a major known cause of kidney failure. Our study builds upon these findings by showing that this inverse relationship is not limited to BMI but is also evident when using other adiposity measures, such as total body fat percentage and waist circumference. However, our data also underscore the limitations of BMI in fully capturing the renal impact of excess adiposity. In this context, the inability of BMI to distinguish between fat and muscle mass, together with the muscle dependency of creatinine-based eGFR, may obscure its association with renal function [30]. This may explain why, in our multivariable models, only total body fat percentage, and not BMI, was independently associated with lower eGFR. In line with this limitation, recent proposals from the European Association for the Study of Obesity and the Lancet Diabetes & Endocrinology Commission advocate moving beyond BMI-only definitions of obesity and incorporating a more comprehensive assessment of adiposity and its clinical consequences [36, 37]. Moreover, neither BMI-defined obesity nor estimated body fat percentage were independent predictors of CKD or albuminuria. This suggests that the deleterious effect of obesity on kidney health may be mediated primarily through functional rather than structural renal impairment.

Regarding the evolution of kidney function over time, a prospective cohort study with 5 years of follow-up and more than 42,000 participants found that higher BMI was associated with a greater decline in eGFR, even among individuals without metabolic comorbidities [38]. Similarly, in line with our longitudinal findings, a 13-year follow-up cohort study in adolescents showed that elevated BMI in late adolescence was associated with early onset CKD in young adulthood, even among those who did not develop diabetes or hypertension during the follow-up period [39]. Furthermore, a systematic review and meta-analysis of 39 cohort studies, with a median follow-up of 6.8 years, concluded that obesity predicts the development of CKD, through both eGFR decline and the incidence of albuminuria [40]. These findings are further reinforced by our study, which highlights a similar negative impact of excess body fat on kidney function in a population with low-to-moderate CV risk (a category that, by definition, excludes individuals with type 2 diabetes), in whom body composition and renal parameters were assessed both at baseline and after 4-years of follow-up. In addition, our results add new information and suggest that this adverse effect may be more pronounced in specific subgroups, such as individuals with obesity or those with already reduced eGFR at baseline.

Different biological mechanisms may underlie the link between obesity and renal dysfunction. One of the most widely recognized is lipotoxicity, where excessive lipid accumulation within renal cells leads to cellular injury and functional decline [41–43]. This mechanism aligns with our findings, in which total body adiposity was independently associated with reduced eGFR. Another relevant pathway involves the pro-inflammatory state of adipose tissue, which promotes the release of cytokines and adipokines that increase insulin resistance and contribute to renal injury [44]. Obesity-induced hypertension and activation of the sympathetic nervous system also appear to be key mediators of renal dysfunction [45, 46]. This is clearly reflected in our results, where hypertension emerged as an independent risk factor for CKD, elevated ACR, and lower eGFR. Taken together, these mechanisms suggest that the impact of obesity on CKD risk is multifactorial, driven by both excess and dysfunctional adiposity, as well as by comorbidities such as hypertension, which may together initiate a self-perpetuating cycle of functional kidney decline.

A key contribution of our study is the demonstration that increased total body fat is not only a cross-sectional marker of renal risk but also a dynamic predictor of renal function deterioration over time, as reflected by the inverse association between changes in adiposity and changes in eGFR. Importantly, these results were observed in a cohort without type 2 diabetes or known CKD, and in the absence of any structured intervention, highlighting the potential intrinsic renal vulnerability to sustained or increasing adiposity. Therefore, this finding opens new perspectives regarding the preventive potential of adiposity reduction, and suggests that targeting fat mass, not just weight or BMI, could play a significant role in preserving kidney function. In line with our data, previous studies have shown that weight loss may improve renal outcomes, including dietary interventions such as the 6-month follow-up of patients with obesity-related glomerulopathy [47] or ketogenic very low-calorie diets with improved GFR [48]. Similarly, pharmacological strategies aimed at weight reduction have demonstrated renoprotective effects [49, 50]. However, once again, it remains challenging to disentangle the specific contributions of weight loss and glycemic improvement in this context.

Despite the consistency of our findings, some limitations must be considered. First, CUN-BAE, an indirect estimator of body fat, was used instead of gold-standard methods for assessing body composition, such as dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging, computerized tomography, or bioelectrical impedance analysis. However, the CUN-BAE formula has been widely validated and correlates well with DXA measurements, particularly in estimating body fat percentage in large epidemiological studies [33]. Its feasibility, low cost, and noninvasive nature make it a practical alternative for estimating adiposity at the population level. Nevertheless, because body composition was not directly measured, the observed associations between estimated body fat and eGFR decline should be interpreted at the population level rather than for any individual prediction. Second, as study participants presented one or more cardiometabolic risk factors, caution is needed when generalizing the results to the healthy population. However, the results are highly relevant for individuals at low-to-moderate CV risk, a population frequently encountered in clinical practice and in whom early detection of kidney function decline may have important clinical implications. Third, we did not assess mechanistic pathways, which may mediate the observed associations and clarify the underlying biological mechanisms. Lastly, the diagnosis of kidney dysfunction was based on a single measurement of eGFR and ACR, without confirmation in a subsequent determination, as recommended by current clinical guidelines. However, the observed CKD prevalence (15.1%) matches that reported in a nationwide survey involving 11,505 individuals representative of the Spanish adult population [51].

Nevertheless, this study also has several strengths. The large sample size enhances statistical power and makes the findings more robust and potentially generalizable to populations with similar risk profiles. The application of strict inclusion and exclusion criteria, including the exclusion of participants with diabetes, reduces the likelihood of selection bias. Furthermore, the comprehensive adjustment for key CKD risk factors strengthens the validity of the observed associations and minimizes confounding by other comorbidities.

In conclusion, increased adiposity may play a direct and independent role in the development of renal dysfunction. Our results support the inclusion of adiposity assessment and management in clinical guidelines and prevention strategies to combat CKD.

Acknowledgments

We would like to thank to Virtudes María, Marta Elias, Teresa Molí, Cristina Domínguez, Noemí Nova, Alba Prunera, Núria Sans, Meritxell soria, Francesc Pons, Rebeca Senar, Pau Guix, Fundació Renal Jaume Arnó, and the Primary Care teams of the province of Lleida for recruiting participants and their efforts in the accurate development of the ILERVAS project. We would like to thank Ane Orrantia Robles, PhD and Laura Vilorio Marqués, PhD (Medical Science Consulting, Valencia, Spain) for technical and analytical support in writing this manuscript and data analysis. Samples were obtained with support from IRBLleida Biobank (B.0000682), Plataforma de Biobancos PT17/0015/0027. The ILERVAS Project teams includes Aitziber Izarra, Marcelino Bermúdez-López, José Manuel Valdivielso, María José Soler, Reinald Pamplona, Gerard Torres, Dídac Mauricio, Eva Castro-Boqué, Elvira Fernández, Marta Hernández, Rafael Simó, Cristina Hernández, Albert Lecube, Eva Miquel, Marta Ortega, Ferran Barbé, Jordi de Batlle, Jessica González, Manuel Portero-Otín, Mariona Jové, Josep Franch-Nadal, Esmeralda Castelblanco, Pere Godoy, Montse Martinez-Alonso, and Cristina Farràs. A complete list of collaborators and their affiliations is provided in the online supplementary material.

Statement of Ethics

The ILERVAS project protocol was approved by the Ethics Committee of the Arnau de Vilanova University Hospital (First visit: CEIC-1410, December 19, 2014; follow-up: CEIC-2015, December 20, 2018), and written informed consent was obtained from all participants.

Conflict of Interest Statement

A.I. is an employee of AstraZeneca and holds stocks in the company. A. L. has participated in clinical trials on kidney disease sponsored by AstraZeneca and has given lectures on obesity and kidney with the support of Boehringer-Ingelheim. M.B-L., J.M.V., M.J.S., R.P., G.T., D.M., E.C-B., E.F., M.H., R.S., and C.H. have no conflicts of interest to declare.

Funding Sources

This research was supported by Instituto de Salud Carlos III (ISCIII) through the project PI21/00462 and co-funded by the European Union. Other grants have contributed: Diputació de Lleida, PI21/01099, PMP21/00109, PMP22/00073, PI23/00237 and RD24/0004/0015 (Institute of Health Carlos III, European Regional Development Fund, “A way to build Europe), PID2022-141964OB-I00 (Ministerio de Ciencia, Innovación y Universidades, co-funded by the European Regional Development Fund, “A way to build Europe”), the Spanish Ministry of Science, Innovation, and Universities (PID2023-152233OB-100, co-funded by the European Regional Development Fund, “A way to build Europe”), and the Generalitat of Catalonia: Agency for Management of University and Research Grants (2021SGR00990) and Department of Health (SLT002/16/00250). Statistical analysis and medical writing support were funded by AstraZeneca.

Author Contributions

A.I.: conceptualization; investigation; writing – original draft; and writing – review and editing. M.B-L.: data curation; investigation; project administration; supervision; visualization. J.M.V.: data curation; investigation; project administration; supervision; and visualization. M.J.S: Writing – review and editing. R.P., D.M., and G.T.: investigation and methodology. E.C-B.: data curation. E.F.: data curation; investigation; project administration; supervision; and visualization. M.H.: investigation. R.S.: conceptualization. C.H.: conceptualization and methodology. A.L.: conceptualization; formal analysis; investigation; methodology; visualization; writing – original draft; and writing – review and editing. All authors approved the final version.

Funding Statement

This research was supported by Instituto de Salud Carlos III (ISCIII) through the project PI21/00462 and co-funded by the European Union. Other grants have contributed: Diputació de Lleida, PI21/01099, PMP21/00109, PMP22/00073, PI23/00237 and RD24/0004/0015 (Institute of Health Carlos III, European Regional Development Fund, “A way to build Europe), PID2022-141964OB-I00 (Ministerio de Ciencia, Innovación y Universidades, co-funded by the European Regional Development Fund, “A way to build Europe”), the Spanish Ministry of Science, Innovation, and Universities (PID2023-152233OB-100, co-funded by the European Regional Development Fund, “A way to build Europe”), and the Generalitat of Catalonia: Agency for Management of University and Research Grants (2021SGR00990) and Department of Health (SLT002/16/00250). Statistical analysis and medical writing support were funded by AstraZeneca.

Data Availability Statement

The data that support the findings of this study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author upon reasonable request.