Skip to main content
Download PDF
Download ePub

Epicardial and liver fat implications in albuminuria: a retrospective study

Cardiovascular Diabetology volume 23, Article number: 308 (2024) Cite this article

Abstract

Background

Albuminuria is considered an early and sensitive marker of kidney dysfunction, but also an independent cardiovascular risk factor. Considering the possible relationship among metabolic liver disease, cardiovascular disease and chronic kidney disease, we aimed to evaluate the risk of developing albuminuria regarding the presence of epicardial adipose tissue and the steatotic liver disease status.

Methods

A retrospective long-term longitudinal study including 181 patients was carried out. Epicardial adipose tissue and steatotic liver disease were assessed by computed tomography. The presence of albuminuria at follow-up was defined as the outcome.

Results

After a median follow up of 11.2 years, steatotic liver disease (HR 3.15; 95% CI, 1.20–8.26; p = 0.02) and excess amount of epicardial adipose tissue (HR 6.12; 95% CI, 1.69–22.19; p = 0.006) were associated with an increased risk of albuminuria after adjustment for visceral adipose tissue, sex, age, weight status, type 2 diabetes, prediabetes, hypertriglyceridemia, hypercholesterolemia, arterial hypertension, and cardiovascular prevention treatment at baseline. The presence of both conditions was associated with a higher risk of developing albuminuria compared to having steatotic liver disease alone (HR 5.91; 95% CI 1.15–30.41, p = 0.033). Compared with the first tertile of visceral adipose tissue, the proportion of subjects with liver steatosis and abnormal epicardial adipose tissue was significantly higher in the second and third tertile. We found a significant correlation between epicardial fat and steatotic liver disease (rho = 0.43 [p < 0.001]).

Conclusions

Identification and management/decrease of excess adiposity must be a target in the primary and secondary prevention of chronic kidney disease development and progression. Visceral adiposity assessment may be an adequate target in the daily clinical setting. Moreover, epicardial adipose tissue and steatotic liver disease assessment may aid in the primary prevention of renal dysfunction.

Background

Chronic kidney disease (CKD) is a persistent condition defined as abnormalities of kidney structure or function, present for more than 3 months, with an increased morbidity and mortality risk, which is also associated with a high economic cost [1, 2]. Worldwide, CKD prevalence is approximately 10% and it is expected to become the fifth leading cause of death by 2040 [2, 3]. CKD has risen from 19 to 11th in rank among leading causes of death between 1990 and 2019 due to ageing of the population and an increasing burden of risk factors for CKD (including obesity, diabetes, and hypertension). In 2017, CKD and its effect on cardiovascular disease (CVD) resulted in 2.6 million deaths [2]. CKD is categorized based on cause, estimated glomerular filtration rate (GFR) (G1-G5), and albuminuria (A1-A3). Albuminuria is considered an early and sensitive marker of kidney dysfunction [4]. There is evidence that the assessment of GFR and albuminuria improve the cardiovascular risk assessment, with this improvement being greater with albuminuria than with GFR [5]. Albuminuria is also considered a predictor of subsequent outcomes in patients with established CVD [6].

Metabolic dysfunction-associated steatotic liver disease (MASLD) [7], formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common cause of liver disease worldwide [8]. In 2023, steatotic liver disease (SLD) was chosen as an all-encompassing term to comprise the various etiologies of steatosis [7]; and recently the EASLD-EASD-EASO guidelines have been launched and published [9]. MASLD encompasses patients who have SLD and at least one cardiometabolic risk factor. It is produced by fat accumulation in the liver that may lead to liver inflammation and liver fibrosis [10]. Several studies have evidenced that advanced stages of MASLD are associated with a higher prevalence of CKD [11] and CVD [12].

The presence and severity of MASLD is strongly associated with a higher mortality from any cause [13] but mainly cardiovascular death due to an increased risk of subclinical atherosclerosis and major cardiovascular events (MACE) [13, 14]. Similarly, the presence and severity of MASLD is associated with an increased risk of albuminuria [15] and CKD [16, 17]. The presence of cardiometabolic risk factors clearly influences the relationship of this triumvirate, nevertheless MASLD seems to be an additional independent risk factor for CVD and CKD [12, 18], although this still remains a subject of debate.

Epicardial adipose tissue (EAT) and intra-abdominal fat depots evolve from brown adipose tissue providing energy and heat to organs. Under physiological conditions, the brown fat-like properties of EAT rapidly decrease with age, from childhood to adulthood [19]. With ageing, epicardial adipocytes become more susceptible to environmental, metabolic, and haemodynamic factors, which gradually decrease the thermogenic function, with reciprocal increases in the expression of genes encoding profibrotic and pro-apoptotic factors. Thus, EAT has important physiological functions [20]. Nonetheless, excessive EAT leads to a proinflammatory state with adverse effects on the myocardium. EAT is considered a surrogate marker of coronary artery disease improving the cardiovascular risk classification in asymptomatic individuals [19, 21, 22]. Excessive EAT has also been related with adverse outcomes in patients living with CKD [23]. Local secretion of certain adipocytokines in inflamed peri-coronary adipose tissue may have adverse consequences on myocardial contractility and vascular calcification.

Moreover, there is interest in determining the mechanisms by which fat compartments may influence CVD and renal dysfunction [18]. We hypothesize that visceral fat and organ-specific fat, specifically liver fat and epicardial fat, plays an important role in cardiorenal dysfunction. Therefore, in this study, we aimed to describe the risk of developing albuminuria regarding the EAT and SLD status.

Patients and methods

Patient population

The study protocol (2019.080) was approved by the ethics committee of Universidad de Navarra. In this retrospective study, we reviewed the records of subjects who underwent a routine health check-up, had a computed tomography whole body scan (CT-WBS) and blood test in the same visit at the Clínica Universidad de Navarra in Pamplona, Spain, from July 1, 2003 to December 31, 2006 and had at least one follow-up control. In our Centre, CT-WBS and laboratory analysis are routinely performed on the same day or within a few days of the initial visit. Exclusion criteria included known CKD (including the presence of micro or macroalbuminuria); ischemic heart disease, heart failure, atrial fibrillation, pericarditis, valvular disease or other heart diseases; personal history of cerebrovascular diseases (including transient ischemic attack); excessive alcohol consumption (average of three or more drinks per day for women, four or more drinks per day for men); drug-induced hepatotoxicity; advanced liver disease and malignant disease (Fig. 1).

Fig. 1
figure 1

Flowchart of patient participation. CT-WBS, Computed Tomography Whole Body Scan

Full size image

Clinical data (age, gender, smoking status, alcohol consumption, active medication list, personal and family medical history, anthropometrics), laboratory and radiological data were obtained from patients’ records. Body mass index (BMI) was calculated using the following formula: weight (in kilograms)/height (in meters2). Weight categories were classified as follows: underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), obesity class 1 (30.0–34.9 kg/m2), obesity class 2 (35.0–39.9 kg/m2) and obesity class 3 (≥ 40.0 kg/m2). GFR assessment was performed following the KDIGO Guidelines with CKD being considered when the GFR was < 60 ml/min per 1.73 m2 in the CKD-EPI equation [2].

SLD was defined as the presence of hepatic steatosis evaluated by computed tomography when no other causes of secondary liver fat accumulation were present (i.e. alcoholic liver disease, autoimmune or viral hepatitis, drug-induced liver disease, cholestatic liver disease, genetic liver disease, endocrinological disorders, inborn errors of metabolism or nutritional disorders, systematic inflammatory disease and malignant disease). Steatosis was diagnosed by a reduced liver attenuation. Severe steatosis was predicted by a liver attenuation 10 Hounsfield units (HU) less than that of spleen and/or liver attenuation lower than 40 HU. We used the BAAT Score [24] as non-invasive liver fibrosis serum marker. The BAAT Score was calculated adding the following variables: BMI ≥ 28 kg/m2 (1 point), age ≥ 50 years (1 point), ALT ≥ 2 times the normal value (1 point), and TG ≥ 150 mg/dl (1.70 mmol/l) (1 point). In MASLD, a BAAT score ≤ 1 is considered as low likelihood of fibrosis and a BAAT score ≥ 4 has a high likelihood of liver fibrosis. A score between 2–3 is considered as an indeterminate score.

Whole-body scan computed tomography protocol

All CT-WBS were performed using a sixty-four-row multidetector CT (SOMATOM Definition and SOMATOM Sensation-64 from Siemens Healthcare; Forchheim, Germany). All images were stored in Picture Archiving and Communication System (PACS). The protocol of CT-WBS includes a low-dose chest CT (120 kV and 40 mA/s) without contrast material, coronary artery calcium (CAC) measurement through Agatston Score (120 kV and 138 mA/s), abdominopelvic CT (120 kV and 180 mA/s) performed after intravenous injection of 120-ml iodinated contrast medium at 2 ml/s (Omnipaque TM 300 [iohexol], 300 mg I/ml from GE Healthcare Bio-Sciences; Madrid, Spain); portal phase was acquired at 65 s.

From January 2020 to December 2021, CT-WBS images were reobtained from PACS to measure EAT, liver and spleen attenuation, subcutaneous adipose tissue (SCAT) and visceral adipose tissue (VAT) by two radiologists, blinded to clinical data (A.E., F.J.M.). EAT, VAT and SCAT were semiautomatically quantified in a research prototype software (Syngo.via Frontier-Cardiac risk assessment application; Siemens, AG; Healthcare Sector, Germany). EAT was defined as all cardiac adipose tissue, including the epi- and pericardial fat. EAT was semi-automatically quantified including voxels with attenuation values between − 45 and − 190 HU. Adjusting for body surface area, indexed epicardial adipose tissue (EATi) was also calculated [25]. The normal limit of EATi was 68.1 cm3/m2. The Du Bois method was used (0.20247 × height (m)0.725 × weight (kg)0.425) to calculate the body surface area [26]. The overall abdominopelvic VAT and SCAT volumes were obtained with the attenuation-based method. The outer contour of the abdominal muscular wall was manually traced to differentiate VAT (inner) and SCAT (outer). On the longitudinal axis the analysed region ranged from the upper abdomen (adrenal gland level) to the L5/S1 intervertebral disc. Default thresholds (− 150 to 50 HU) obtained from the total volume were employed to semiautomatically quantify VAT and SCAT. The VAT/SCAT ratio was calculated due to its known correlation to cardiovascular risk, beyond BMI and VAT [27]. CAC through the Agatston Score was subdivided into 4 categories according to the degree of calcification (0: minimal risk; 0–99: mild risk; 100–399: moderate risk; > 400: severe risk).

Outcome and follow-up assessment

The outcome was defined as the presence of persistent albuminuria at follow-up. Micro-albuminuria and macro-albuminuria were defined as urinary albumin-creatinine ratio (UACR) > 30 mg/g Cr and > 300 mg/g Cr in spot urines, respectively. None of the volunteers presented albuminuria at baseline. Follow-up was calculated as the time between the date of the first visit and the date in which albuminuria was diagnosed for the first time or the date of last contact, which ever came first.

Statistical analysis

Demographic and clinical characteristics of patients were summarized using mean and standard deviation (SD) for continuous variables and percentages for categorical variables. Between groups comparisons were performed with Student’s t test or ANOVA for quantitative variables and chi-square test for categorical ones.

In the main analysis multivariable adjusted hazard ratio (HR) and 95% confident intervals (CI) for albuminuria were calculated using the Cox proportional hazards regression model. The model was adjusted for the following baseline variables using the inverse probability weighting (IPW) method: VAT (tertiles); sex (male vs. female); age (continuous), weight status (normal weight, overweight, obesity); type 2 diabetes (T2D), prediabetes or treatment for these conditions (dichotomous); hypertriglyceridemia, hypercholesterolemia or treatment for these conditions (dichotomous); hypertension or treatment for this condition (dichotomous); cardiovascular disease or treatment for this condition (dichotomous).

The marginal effect or the adjusted proportion of subjects with abnormal EATi and SLD in each tertile of VAT was calculated using a logistic regression model adjusted for the same covariates as above. To perform a joint analysis volunteers were classified in three groups as follows: (1) patients with normal EATi and no SLD, (2) patients with either abnormal EATi or SLD, and (3) patients with both abnormal EATi and SLD. All analyses were performed with Stata/SE 15.1 (StataCorp. College Station, TX: StataCorp LP). Two tailed p < 0.05 was considered statistically significant.

Results

A total of 181 patients from 547 patients fulfilled criteria to be included in the analysis.

Figure 1 summarizes exclusion criteria. Mean age was 55.9 ± 8.5 years and 76.8% were men. One hundred sixteen (64%) patients had SLD, of which 87 (75%) had a BAAT Score ≥ 2. A hundred and four (57%) patients had abnormal EATi. Table 1 displays the main demographic, clinical and laboratory characteristics of all the patients included. Mean duration of diabetes was 8 ± 7,8 years.

Table 1 Clinical and analytical characteristics of the participants included in the study
Full size table

Table 2 displays the main demographic, clinical and laboratory characteristics of patients regarding the SLD status. Compared with patients without SLD, patients with SLD (and especially those with indeterminate or high risk of fibrosis) were predominantly men, and had a higher glycemia, a more detrimental lipid profile, hyperuricemia, and higher ALT (p < 0.05). Additionally, a higher prevalence of diabetes and overweight was detected in patients with SLD (p < 0.05). Indices of adiposity (BMI, VAT, VAT/SCAT ratio) were higher in participants with SLD compared with patients without SLD (p < 0.05), especially in those with suspected fibrosis. Regarding subclinical cardiovascular disease, patients with SLD had a higher CAC Score.

Table 2 Clinical characteristics of the participants according to liver steatosis status
Full size table

Table 3 displays the main demographic, clinical and laboratory characteristics with normal and abnormal EATi. Compared with patients with normal EATi, patients with abnormal EATi were predominantly men, were older and had a higher glycemia, insulinemia, HOMA-IR, a more detrimental lipid profile (atherogenic dyslipidemia), hyperuricemia, and higher liver enzymes (p < 0.05). Additionally, a higher prevalence of metabolic disorders (impaired fasting glucose/diabetes, hypertension, dyslipidemia, hyperuricemia, overweight) was detected in patients with elevated EATi (p < 0.05). Indices of adiposity (BMI, SLD, VAT, SCAT, VAT/SCAT ratio) were higher in participants with abnormal EATi compared with patients with normal EATi (p < 0.05). Regarding subclinical cardiovascular disease, patients with abnormal EATi had higher CAC Score and BAAT Score.

Table 3 Clinical characteristics of the participants according to epicardial adipose tissue status
Full size table

We found an important proportion of subjects with abnormal EATi and SLD in the higher tertiles of VAT. Regarding liver steatosis, we observed 38.1% (95% CI: 23.5–52.7), 73.2% (95% CI: 62.3–84.0) and 79.1% (95% CI: 67.9–90.3) of subjects with SLD in each tertile of VAT, respectively (Fig. 2). The proportion of subjects with liver steatosis was significantly higher in the third tertile (p =  < 0.01) and in the second tertile of VAT (p = 0.01) compared with the first tertile. Regarding EATi, we observed 33.1% (95% CI: 17.7–48.5), 56.3% (95% CI: 44.2–68.4) and 86.7% (95% CI: 75.9–97.6) of subjects with abnormal EATi in each tertile of VAT, respectively (Fig. 2). Compared to the first tertile, the proportion of subjects with abnormal EATi was significantly higher in the second (p = 0.02) and third tertile of VAT (p =  < 0.001). A significant positive correlation between EATi and SLD was also observed (rho = 0,43; p =  < 0.001).

Fig. 2
figure 2

Adjusted proportion of participants with abnormal EATi and with SLD in each tertile of visceral adipose tissue. 95% CI, 95% confidence interval. T1: First tertile of VAT (median: 1847.41 [interquartilic range: 1135.21–2427.55]); T2: Second tertile of VAT (median: 3802.65 [interquartilic range: 3241.47–4128.8]); T3: Third tertile of VAT (median: 6007.045 [interquartilic range: 5221.755- 6719.845]). VAT, visceral adipose tissue

Full size image

Follow up

After a median follow up of 11.2 years (25th percentile: 4.7; 75th percentile: 14.9), 32 events (17.7%) of albuminuria were registered. The mean albuminuria of those subjects was 242 ± 769 mg/gCr. Eighteen patients (9.9%) developed a GFR < 60 ml/min/1.73m2, six of whom presented albuminuria (33.3%). The overall incidence of albuminuria development in the cohort was estimated to be 21 per 1000 person-years in patients with SLD versus 8 per 1000 person-years in patients without SLD. Patients with SLD had an increased risk of albuminuria after adjustment for VAT, sex, age, weight status, T2D, prediabetes or treatment for these conditions, hyperuricemia, hypertriglyceridemia, hypercholesterolemia or treatment for these conditions, hypertension or treatment for this condition and cardiovascular prevention treatment at baseline using the IPW method: HR (95% CI) 3.15 (1.20–8.26) (p = 0.02). Figure 3 displays the incidence rate of albuminuria at follow-up regarding the presence or not of SLD.

Fig. 3
figure 3

Multivariable adjusted incidence rate of albuminuria at follow-up by the presence or not of SLD at baseline. SLD: steatotic liver disease

Full size image

In the studied cohort, the overall incidence rate of albuminuria was estimated to be 23 per 1000 person-years in patients with high EATi levels versus 4 per 1000 person-years in patients with normal EATi within the normal range. Patients with high levels of EATi had an increased risk of albuminuria after adjustment for VAT, sex, age, weight status, T2D, prediabetes or treatment for these conditions, hypertriglyceridemia, hypercholesterolemia or treatment for these conditions, hypertension or treatment for this condition and cardiovascular prevention treatment at baseline using the IPW method: HR 6.12 (95% CI 1.69–22.19) (p = 0.006). Figure 4 displays the incidence rate of albuminuria at follow-up by level of EATi.

Fig. 4
figure 4

Multivariable adjusted incidence rate of albuminuria at follow-up by level of epicardial adipose tissue. EAT: Epicardial adipose tissue

Full size image

No significant interaction was found for high EATi and SLD on the risk of albuminuria at follow up. Table 4 displays the risk of developing albuminuria regarding EATi and SLD status. Compared to patients with normal EAT and no SLD, those who presented both SLD and high EATi presented an increased risk of developing albuminuria [HR 5.91 (95% CI 1.15–30.41) (p = 0.033)] after adjustment for VAT, sex, age, weight status, T2D, prediabetes or treatment for these conditions, hypertriglyceridemia, hypercholesterolemia or treatment for these conditions, hypertension or treatment for this condition and cardiovascular prevention treatment at baseline using the IPW method.

Table 4 Risk of developing albuminuria regarding EATi and SLD status
Full size table

Discussion

In our cohort, 17.7% of patients developed albuminuria and 9.9% a GFRe < 60 ml/min/1.73 m2 after a mean follow-up of 11.2 years. Our results are similar to the prevalence previously described in a Spanish population [28]. However, our study provides evidence that the prevalence of albuminuria is clearly higher in patients with orthotopic adiposity, such as those with SLD (HR 3.15, 95% CI 1.20–8.26, p = 0.02) or excess EATi (HR 6.12, 95% CI 1.69–22.19, p = 0.006). Nevertheless, those with SLD alone or excess EATi alone did not show an increased risk for albuminuria, compared to those patients with both conditions (SLD and altered EATi) in which the risk of developing albuminuria was statistically significant (HR 5.91, 95%CI 1.15–30.41, p = 0.033). In addition, the increase in CKD risk was independent of VAT, sex, age, weight status, T2D, prediabetes or treatment for these conditions, hypertriglyceridemia, hypercholesterolemia or treatment for these conditions, hypertension or treatment for this condition and cardiovascular prevention treatment. Taken together, adiposopathy evaluation including orthotopic fat accumulation status may thus improve the discriminatory capacity of identifying patients at higher risk of developing CKD. This is in line with the latest new framework for the diagnosis, staging, and management of obesity in adults [29].

Multiple meta-analysis including several hospital and community-based studies have evidenced an independent association of MASLD with an increased risk of incident CKD in patients with and without T2D [16, 30,31,32,33,34]. Albuminuria is an early marker of CKD, therefore, it is considered a potential therapeutic target in a primary prevention setting [35]. A study that used data from NHANES 1999–2016 (n = 19,617 adults) identified a higher risk of CKD, albuminuria, and cardiovascular events in patients with MASLD [36]. A recent meta-analysis that explored 7 cross-sectional studies found that elevated liver stiffness is associated with increased odds of CKD among patients with MASLD (OR 1.98, 95% CI: 1.29–3.05; test for overall effect z = 3.113, p = 0.002) [11], suggesting that the screening for advanced fibrosis might help identify patients at risk of CKD. Nonetheless, the SLD and CKD association is apparently mediated by metabolic abnormalities, such as T2D, hypertension, and hyperuricemia. In that sense, our findings throw light to clarify the independent association between orthotopic fat accumulation and CKD progression.

The relationship of this triumvirate (EAT-SLD-CKD) has been explored in patients with T2D. In 2019, Mantovani et al. [37] found a higher proportion of patients with chronic vascular complications in patients with non-insulin-treated T2D and increased liver stiffness. The presence of CKD increased significantly as liver stiffness increased, after adjusting for other well-known established risk factors. Similarly, a recent prospective study in 238 patients living with T2D followed during a median period of 7.6 years [38], evidenced a higher proportion of patients developing incident acute myocardial infarction, cerebrovascular events or CKD in patients with more steatosis or fibrosis (determined by controlled attenuation parameter [CAP] and liver stiffness measurements, respectively). Recently, data from the Korean National Health Insurance Service (n = 1,607,232 patients with T2D) revealed the highest risk for myocardial infarction and stroke in patients with CKD and MASLD after adjusting for conventional risk factors and during a mean follow-up of 6.9 years [39]. The combination of CKD and MASLD was associated with an increased risk of CVD and mortality in patients with T2D. Interestingly, another recent study found that, EAT volume measured using computed tomography, was an independent predictor of reduced GFR in patients with a youth onset of T2D [40]. In that sense, our study supports the relation of SLD and pathological accumulation of EATi and CKD even after adjusting for the presence of prediabetes/T2D.

Regarding CVD, and in line with our findings, a recent prospective study (including 18,073 participants with CKD with a median follow-up of 13 years) found that MASLD remained as an independent risk factor for cardiovascular events even after adjustment for age, sex, ethnicity, smoking, baseline kidney function and T2D (p < 0.0001) [41]. The NAFLD fibrosis score (NFS) was associated with an elevated risk of cardiovascular events and worse survival. Regarding EAT, epicardial fat accumulation is associated with increased cardiovascular morbidity and mortality in stages 3–5 of CKD [42, 43]. Undoubtfully, close monitoring and appropriate management of SLD together with assessment of orthotopic fat accumulation, more specifically, abnormal quality or quantity of peri-organ or intra-organ fat, should be warranted to prevent CVD and CKD in these patients [44, 45]. Interestingly, isolated liver or epicardial adiposity was not associated to an increased risk of albuminuria, probably reflecting that albuminuria is associated to generalized adiposopathy. Similarly, a recent Japanese study including 14,141 adults found that the coexistence of MASLD and CKD, but not MASLD or CKD alone, was a significant risk factor for ischemic CVD during a mean follow-up of 6.9 years. These results remained significant after adjustment for age, sex, smoking, family history of ischemic CVD, and presence of obesity, diabetes, hypertension, or dyslipidemia (adjusted-HR 1.51, 95%CI 1.02–2.22) [46].

Common pathophysiology

It is reasonable to believe that SLD, CVD, and CKD share common risk factors (such as visceral obesity, insulin resistance, dyslipidemia, and hypertension) [47, 48], making it challenging to establish causative relationships between conditions. Metabolic, genetic, and environmental risk factors are common pathophysiologic entities surrounding these three pathologies [49]. Our study provides novel data regarding the association of progressive orthotopic fat accumulation and the development of kidney disease, independently from metabolic risk factors including insulin resistance, atherogenic dyslipidemia, and hypertension [1, 18].

Adipose tissue (AT) produces a variety of molecules called adipokines to maintain homeostasis (i.e. thermoregulation, energy storage, insulin sensitivity, and immunity, among others) [50,51,52]. Chronic overfeeding, genetic susceptibilities, sedentarism, gut dysbiosis, and an imbalanced diet may contribute to abnormal peri-organ or intra-organ fat accumulation (intrahepatic, epicardial/pericardial, perivascular, intramuscular, peripancreatic and perirenal fat) and AT dysfunction which is also associated to cardiometabolic risk [45]. These fat depots can disturb the nearby anatomic organs through lipid accumulation and cytokine secretion.

The different phenotypes of obesity exhibit inflammatory cytokine levels that reflect the dysfunctional AT continuum implicated in systemic low-grade inflammation [53,54,55]. AT dysfunction underlies the mechanisms linking obesity and the development of metabolic comorbidities [50, 56, 57], as hypertension, atherogenic dyslipidemia, and dysglycaemia [49]. Chronic inflammation contributes to the decline in GFR in CKD [58]. Orthotopic fat intensifies the pro-inflammatory cytokine activity favoring the development of lipotoxicity via oxidative stress, activation of platelets, elevated renin–angiotensin–aldosterone system activity, cellular senescence, and dysfunction of the endothelium, eventually underlying obesity-related diseases [17, 59,60,61]. The Adiponectin/Leptin ratio (Adpn/Lep) is a suitable indicator of AT dysfunction, thus it may be a useful estimator of cardiometabolic risk [62]. A recent study of 2646 Koreans evidenced that higher plasma leptin concentrations were predictive of incident CKD after a 2.8-year mean follow-up [63]. On the other hand, adiponectin may have renoprotective effects by ameliorating renal inflammation, oxidative stress, and fibrosis [47].

EAT is a rich source of free fatty acids and is capable of secreting inflammatory cytokines that promote atherosclerosis through a local paracrine effect. Perirenal fat is potentially related to EAT as both exhibit mesothelial layers enriched in white AT progenitors [64]. Both tissues are linked to proven cardiovascular indicators such as carotid intima media thickness and vascular calcifications. Accordingly, these forms of organ-specific fat deposits may act as a link between vascular and cardiorenal disease. The term Obesity-related Adipose tissue Disease (OrAD) collectively englobes the diverse pathologies related to adiposopathy [10, 65]. Subclinical portal hypertension, or hepatorenal reflux, may be involved in these association through increased intra-hepatic vascular resistance [17, 66]. Moreover, obesity is considered an independent risk factor for the development of CKD [67], through increased sodium ingestion, an insulin resistant effect over podocytes and low grade inflammation [67]. Overweight is associated with increased tubular sodium reabsorption and volume overload that eventually may lead to hypertension [68]. Increased blood pressure leads to hyperfiltration due to glomerulomegaly, mesangial expansion, and renin-angiotensin system (RAS) activation, which gradually lead to albuminuria and progressive kidney injury through the production of proinflammatory cytokines [67]. Our results show that EAT enlargement and SLD severity may be a proxy for overall visceral adiposity that could help identify high-risk patients for the development or progression of CKD [42] (Fig. 5).

Fig. 5
figure 5

Adipose tissue dysfunction continuum. Chronic overfeeding, sedentarism, intestinal dysbiosis and genetic susceptibilities may contribute to adipose tissue dysfunction hampering lipid metabolism and, thus, favoring visceral and liver fat accumulation. Liver fat accumulation and progression may lead to a pro-inflammatory state characterized by lipotoxicity, oxidative stress, elevated renin–angiotensin–aldosterone-system activity, and eventually endothelial and organ dysfunction. Epicardial adipose tissue and perirenal fat have mesothelial layers enriched in white adipose tissue progenitors thus adipose tissue dysfunction enhance a proinflammatory state in organs eventually leading to fibrosis and apoptosis. AT: adipose tissue; FFA: free fatty acid; RAS: renin-angiotensin system

Full size image

Clinical translation

Both MASLD and CKD are two underdiagnosed pathologies and yet have important consequences for health. Both can be diagnosed with simple tests and its prevention and treatment may aid in the prevention of major health problems, like CVD. To prevent CKD and/or enhance transplant prognosis, clinicians should actively treat obesity and its associated comorbidities. While MASLD is currently gaining prominence worldwide, systematic measures for renal and cardiovascular prevention should be indicated [69]. Consequently, a multidisciplinary approach for managing and treating obesity is relevant to prevent MASLD, a high amount of EATi and CKD [10, 70, 71]. Lifestyle modifications, weight loss and control of cardiovascular risk factors (including T2D, hypertension and dyslipidemia), are essential to control the different pathologies that englobe the metabolic syndrome [61, 72]. Short-term weight loss of at least 7% could decrease CKD risk [73]. Weight loss may be achieved by lifestyle modifications, pharmacological agents, endoscopical procedures, or bariatric surgery [74].

The Food and Drug Administration has recently approved resmetirom for the treatment of advanced stages of MASLD [75]. Its long-term effect over CVD and CKD must be evaluated. Nonetheless, additional therapeutic approaches with multiple mechanisms of action (including lipid, immune and metabolism modulation, among others) are developing rapidly [76]. The potential role of pioglitazone, glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors on hepatic fat content and cardiorenal outcomes, independently of the presence of T2D, is encouraging [1, 47]. Recently, a phase 2 trial demonstrated that tirzepatide (an agonist of the glucose-dependent insulinotropic polypeptide and GLP-1 receptors), was more effective than placebo with respect to resolution of steatohepatitis without worsening of fibrosis involving patients with moderate or severe fibrosis [77]. Current guidelines recommend SGLT2 inhibitors and/or GLP-1 receptor agonists for people with T2D and CKD [78]. In patients with T2D, metformin, pioglitazone, DPP-4 inhibitors, GLP-1 receptor agonists and statins have demonstrated a reduction in the quantity or improvement of the adipokine secretory pattern of EAT [79].

The strength of this study is that it is the first attempt to describe kidney function associated with liver steatotic status and EAT volume levels in a European population. Besides, our population is very well characterized. Nonetheless, our study has various limitations. First, a single evaluation may not entirely reflect a patient’s metabolic status since EAT and liver fat accumulation may change over time. Second, although the use of CT-WBS is recognized for the evaluation of hepatic steatosis in international guidelines, incipient SLD may be underdiagnosed by this imaging technique. Third, the fibrosis serum marker used in this study is not widely validated. Fourth, dietary intake and genetic predisposition (i.e. PNPLA3 polymorphism) was not analyzed in our cohort. Fifth, our study is limited by the small sample size. However, the patients included in our study are well-characterized individuals. Sixth, our results are derived from middle-aged Spanish adults, so it should be interpreted with caution when applied to different populations.

In conclusion, treatment of excess adiposity must be a target in the primary and secondary prevention of CKD development and progression. More specifically, SLD and EATi can actively become a target for the primary prevention of CVD and CKD. Vice versa, patients with SLD should have an early screening of CKD and CVD with the aim of minimizing long-term renal and cardiovascular complications. Further large and prospective studies are required, focusing on preventing renal, cardiovascular, and liver complications in people living with obesity.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CKD:

Chronic kidney disease

GFR:

Glomerular filtration rate

CVD:

Cardiovascular disease

SLD:

Steatotic liver disease

MASLD:

Metabolic dysfunction-associated steatotic liver disease

NAFLD:

Non-alcoholic fatty liver disease

EAT:

Epicardial adipose tissue

CT-WBS:

Computed tomography whole body scan

BMI:

Body mass index

PACS:

Picture Archiving and Communication System

SCAT:

Subcutaneous adipose tissue

VAT:

Visceral adipose tissue

HU:

Hounsfield units (HU)

EATi:

Indexed epicardial adipose tissue

UACR:

Urinary albumin-creatinine ratio

SD:

Standard deviation

HR:

Hazard ratio

CI:

Confidence interval

IPW:

Inverse probability weighting

T2D:

Type 2 diabetes

ALT:

Alanine aminotransferase

ALP:

Alkaline phosphatase

CAC:

Coronary artery calcium

CKD-EPI:

Chronic kidney disease epidemiology collaboration equation

GGT:

Glutamyl transferase

HDL:

High density lipoprotein

HOMA-IR:

Homeostasis model assessment for insulin resistance

LDL:

Low density lipoprotein

TGL:

HDL ratio: triglycerides to HDL-cholesterol ratio

NFS:

NAFLD fibrosis score

AT:

Adipose tissue

Adpn/Lep:

Adiponectin/leptin ratio

OrAD:

Obesity-related adipose tissue disease

RAS:

Renin-angiotensin system

References

  1. Mantovani A, Lombardi R, Cattazzo F, Zusi C, Cappelli D, Dalbeni A. MAFLD and CKD: an updated narrative review. Int J Mol Sci. 2022;23:1–11.

    Article  Google Scholar 

  2. Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group. KDIGO 2022 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 2022;102(5S):S1–127.

    Google Scholar 

  3. Kalantar-Zadeh K, Jafar TH, Nitsch D, Neuen BL, Perkovic V. Chronic kidney disease. Lancet. 2021;398(10302):786–802.

    Article  CAS  PubMed  Google Scholar 

  4. Lezaic V. Albuminuria as a biomarker of the renal disease. Biomarkers Kidney Dis. 2015;81:1–18.

    Google Scholar 

  5. Matsushita K, Coresh J, Sang Y, Chalmers J, Fox C, Guallar E, et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol. 2015;3:514–25.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Mok Y, Ballew SH, Sang Y, Grams ME, Coresh J, Evans M, et al. Albuminuria as a predictor of cardiovascular outcomes in patients with acute myocardial infarction. J Am Heart Assoc. 2019;8(8): e010546.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multi-society Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78:1966–86.

    Article  PubMed  Google Scholar 

  8. Younossi ZM. Non-alcoholic fatty liver disease—a global public health perspective. J Hepatol. 2019;70:531–44.

    Article  PubMed  Google Scholar 

  9. European Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO); European Association for the Study of the Liver (EASL). EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024. https://doi.org/10.1016/j.jhep.2024.04.031. (Epub ahead of print)

    Article  Google Scholar 

  10. Perdomo CM, Avilés-Olmos I, Dicker D, Frühbeck G. Towards an adiposity-related disease framework for the diagnosis and management of obesities. Rev Endocr Metab Disord. 2023;24(5):795–807.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ciardullo S, Ballabeni C, Trevisan R, Perseghin G. Liver stiffness, albuminuria and chronic kidney disease in patients with NAFLD: a systematic review and meta-analysis. Biomolecules. 2022;12:1–10.

    Article  Google Scholar 

  12. Muzurović E, Peng CC, Belanger MJ, Sanoudou D, Mikhailidis DP, Mantzoros CS. Nonalcoholic fatty liver disease and cardiovascular disease: a review of shared cardiometabolic risk factors. Hypertension. 2022;79(7):1319–26.

    Article  PubMed  Google Scholar 

  13. Targher G, Byrne CD, Lonardo A, Zoppini G, Barbui C, Targher G, et al. Nonalcoholic fatty liver disease and risk of incident cardiovascular disease: a meta-analysis of observational studies. J Hepatol. 2016;65(3):589–600.

    Article  PubMed  Google Scholar 

  14. Schonmann Y, Yeshua H, Bentov I, Zelber-Sagi S. Liver fibrosis marker is an independent predictor of cardiovascular morbidity and mortality in the general population. Dig Liver Dis. 2021;53:79–85.

    Article  CAS  PubMed  Google Scholar 

  15. Wijarnpreecha K, Thongprayoon C, Boonpheng B, Panjawatanan P, Sharma K, Ungprasert P, Pungpapong S, Cheungpasitporn W. Nonalcoholic fatty liver disease and albuminuria: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol. 2018;30(9):986–94.

    Article  PubMed  Google Scholar 

  16. Mantovani A, Zaza G, Byrne CD, Lonardo A, Zoppini G, Bonora E, et al. Nonalcoholic fatty liver disease increases risk of incident chronic kidney disease: a systematic review and meta-analysis. Metabolism. 2017;79:64–76.

    Article  PubMed  Google Scholar 

  17. Bilson J, Mantovani A, Byrne CD, Targher G. Steatotic liver disease, MASLD and risk of chronic kidney disease. Diabetes Metab. 2024;50(1): 101506.

    Article  PubMed  Google Scholar 

  18. Perdomo CM, Garcia-Fernandez N, Escalada J. Diabetic kidney disease, cardiovascular disease and non-alcoholic fatty liver disease: a new triumvirate? J Clin Med. 2021;10(9):2040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Iacobellis G. Epicardial adipose tissue in contemporary cardiology. Nat Rev Cardiol. 2022;19:593–606.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Psychari SN, Rekleiti N, Papaioannou N, Varhalama E, Drakoulis C, Apostolou TS, Iliodromitis EK. Epicardial fat in nonalcoholic fatty liver disease: properties and relationships with metabolic factors, cardiac structure, and cardiac function. Angiology. 2016;67(1):41–8.

    Article  CAS  PubMed  Google Scholar 

  21. Petta S, Craxì A. Epicardial fat in patients with non-alcoholic fatty liver disease. J Hepatol. 2015;62:1215.

    Article  PubMed  Google Scholar 

  22. Kim BJ, Cheong ES, Kang JG, Kim BS, Kang JH. Relationship of epicardial fat thickness and nonalcoholic fatty liver disease to coronary artery calcification: from the CAESAR study. J Clin Lipidol. 2016;10(3):619–26.

    Article  PubMed  Google Scholar 

  23. Saritas T, Reinartz SD, Nadal J, Schmoee J, Schmid M, Marwan M, et al. Epicardial fat, cardiovascular risk factors and calcifications in patients with chronic kidney disease. Clin Kidney J. 2020;13:571–9.

    Article  CAS  PubMed  Google Scholar 

  24. Ratziu V, Giral P, Charlotte F, Bruckert E, Thibault V, Theodorou I, et al. Liver fibrosis in overweight patients. Gastroenterology. 2000;118:1117–23.

    Article  CAS  PubMed  Google Scholar 

  25. Shmilovich H, Dey D, Cheng VY, Rajani R, Nakazato R, Otaki Y, Nakanishi R, Slomka PJ, Thomson LE, Hayes SW, Friedman JD, Gransar H, Wong ND, Shaw LJ, Budoff M, Rozanski A, Berman DS. Threshold for the upper normal limit of indexed epicardial fat volume: derivation in a healthy population and validation in an outcome-based study. Am J Cardiol. 2011;108(11):1680–5.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mitchell D, Strydom NB, van Graan CH, Van Der Walt WH. Human surface area: comparison of the du bois formula with direct photometric measurement. Pflügers Arch Eur J Physiol. 1971;325:188–90.

    Article  CAS  Google Scholar 

  27. Kaess BM, Pedley A, Massaro JM, Murabito J, Hoffmann U, Fox CS. The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk. Diabetologia. 2012;55:2622–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Gorostidi M, Sánchez-Martínez M, Ruilope LM, Graciani A, de la Cruz JJ, Santamaría R, et al. Prevalencia de enfermedad renal crónica en España: impacto de la acumulación de factores de riesgo cardiovascular. Nefrologia. 2018;38(6):606–15.

    Article  PubMed  Google Scholar 

  29. Busetto L, Dicker D, Frühbeck G, Halford JCG, Sbraccia P, Yumuk V, Goossens GH. A nesw framework for the diagnosis staging and management of obesity in adults. Nat Med. 2024. https://doi.org/10.1038/s41591-024-03095-3. (Epub ahead of print).

    Article  PubMed  Google Scholar 

  30. Mantovani A, Petracca G, Beatrice G, Csermely A, Lonardo A, Schattenberg JM, et al. Non-alcoholic fatty liver disease and risk of incident chronic kidney disease: an updated meta-analysis. Gut. 2022;71:156–62.

    Article  PubMed  Google Scholar 

  31. Musso G, Gambino R, Tabibian JH, Ekstedt M, Kechagias S, Hamaguchi M, et al. Association of non-alcoholic fatty liver disease with chronic kidney disease: a systematic review and meta-analysis. PLoS Med. 2014;11(7): e1001680.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Byrne CD, Targher G. NAFLD as a driver of chronic kidney disease. J Hepatol. 2020;72:785–801.

    Article  PubMed  Google Scholar 

  33. Agustanti N, Soetedjo NNM, Damara FA, Iryaningrum MR, Permana H, Bestari MB, et al. The association between metabolic dysfunction-associated fatty liver disease and chronic kidney disease: a systematic review and meta-analysis. Diabetes Metab Syndr Clin Res Rev. 2023;17: 102780.

    Article  CAS  Google Scholar 

  34. Chen Y, Bai W, Mao D, Long F, Wang N, Wang K, et al. The relationship between non-alcoholic fatty liver disease and incidence of chronic kidney disease for diabetic and non-diabetic subjects: a meta-analysis. Adv Clin Exp Med. 2022;32:407–14.

    Article  Google Scholar 

  35. Ferris M, Hogan SL, Chin H, Shoham DA, Gipson DS, Gibson K, et al. Obesity, albuminuria, and urinalysis findings in US young adults from the Add Health Wave III study. Clin J Am Soc Nephrol. 2007;2:1207–14.

    Article  PubMed  Google Scholar 

  36. Zhang HJ, Wang YY, Chen C, Lu YL, Wang NJ, Guo LS. Cardiovascular and renal burdens of metabolic associated fatty liver disease from serial US national surveys, 1999–2016. Chin Med J (Engl). 2021;134:1593–601.

    Article  CAS  PubMed  Google Scholar 

  37. Mantovani A, Turino T, Lando MG, Gjini K, Byrne CD, Zusi C, et al. Screening for non-alcoholic fatty liver disease using liver stiffness measurement and its association with chronic kidney disease and cardiovascular complications in patients with type 2 diabetes. Diabetes Metab. 2020;46:296–303.

    Article  CAS  PubMed  Google Scholar 

  38. Mikolasevic I, Domislovic V, Ruzic A, Hauser G, Rahelic D, Klobucar-Majanovic S, et al. Elastographic parameters of liver steatosis and fibrosis predict independently the risk of incident chronic kidney disease and acute myocardial infarction in patients with type 2 diabetes mellitus. J Diabetes Complications. 2022;36: 108226.

    Article  CAS  PubMed  Google Scholar 

  39. Chung GE, Han K, Lee KN, Cho EJ, Bae JH, Yang SY, et al. Combined effects of chronic kidney disease and nonalcoholic fatty liver disease on the risk of cardiovascular disease in patients with diabetes. Biomedicines. 2022;10:1245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Reinhardt M, Cushman TR, Thearle MS, Krakoff J. Epicardial adipose tissue is a predictor of decreased kidney function and coronary artery calcification in youth- and early adult onset type 2 diabetes mellitus. J Endocrinol Invest. 2019;42:979–86.

    Article  CAS  PubMed  Google Scholar 

  41. Hydes T, Kennedy O, Buchanan R, Cuthbertson D, Parkes J, Fraser S, et al. The impact of non-alcoholic fatty liver disease and liver fibrosis on adverse clinical outcomes and mortality in patients with chronic kidney disease: a prospective study using UK Biobank data. BMC Med. 2023;21:185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cordeiro AC, Amparo FC, Oliveira MAC, Amodeo C, Smanio P, Pinto IMF, et al. Epicardial fat accumulation, cardiometabolic profile and cardiovascular events in patients with stages 3–5 chronic kidney disease. J Intern Med. 2015;278:77–87.

    Article  CAS  PubMed  Google Scholar 

  43. Turkmen K, Ozer H, Kusztal M. The relationship of epicardial adipose tissue and cardiovascular disease in chronic kidney disease and hemodialysis patients. J Clin Med. 2022;11:1308.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Katsiki N, Mikhailidis DP. Excessive “orthotopic” fat accumulation: links with cardiometabolic diseases and potential drug treatment. J Cell Physiol. 2020;235(9):6321–2.

    Article  CAS  PubMed  Google Scholar 

  45. Miyamori D, Tanaka M, Sato T, Endo K, Mori K, Mikami T, et al. Coexistence of metabolic dysfunction-associated fatty liver disease and chronic kidney disease is a more potent risk factor for ischemic heart disease. J Am Heart Assoc. 2023;12(14): e030269.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Theofilis P, Vordoni A, Kalaitzidis RG. Interplay between metabolic dysfunction-associated fatty liver disease and chronic kidney disease: epidemiology, pathophysiologic mechanisms, and treatment considerations. World J Gastroenterol. 2022;28:5691–706.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bonora E, Targher G. Increased risk of cardiovascular disease and chronic kidney disease in NAFLD. Nat Rev Gastroenterol Hepatol. 2012;9:372–81.

    Article  CAS  PubMed  Google Scholar 

  48. Wang TY, Wang RF, Bu ZY, Targher G, Byrne CD, Sun DQ, et al. Association of metabolic dysfunction-associated fatty liver disease with kidney disease. Nat Rev Nephrol. 2022;18:259–68.

    Article  PubMed  Google Scholar 

  49. Hamjane N, Benyahya F, Nourouti NG, Mechita MB, Barakat A. Cardiovascular diseases and metabolic abnormalities associated with obesity: What is the role of inflammatory responses? A systematic review. Microvasc Res. 2020;131: 104023.

    Article  CAS  PubMed  Google Scholar 

  50. Gómez-Ambrosi J, Salvador J, Páramo JA, Orbe J, De Irala J, Diez-Caballero A, et al. Involvement of leptin in the association between percentage of body fat and cardiovascular risk factors. Clin Biochem. 2002;35:315–20.

    Article  PubMed  Google Scholar 

  51. Frühbeck G, Gómez-Ambrosi J. Control of body weight: a physiologic and transgenic perspective. Diabetologia. 2003;46:143–72.

    Article  PubMed  Google Scholar 

  52. Katsiki N, Athyros VG, Mikhailidis DP. Abnormal Peri-Organ or Intra-organ Fat (APIFat) deposition: an underestimated predictor of vascular risk? Curr Vasc Pharmacol. 2016;14(5):432–41.

    Article  CAS  PubMed  Google Scholar 

  53. Ferreira FG, Reitz LK, Valmorbida A, Papini Gabiatti M, Hansen F, Faria Di Pietro P, et al. Metabolically unhealthy and overweight phenotypes are associated with increased levels of inflammatory cytokines: a population-based study. Nutrition. 2022;96: 111590.

    Article  CAS  PubMed  Google Scholar 

  54. Gómez-Ambrosi J, Salvador J, Rotellar F, Silva C, Catalán V, Rodríguez A, Jesús Gil M, Frühbeck G. Increased serum amyloid A concentrations in morbid obesity decrease after gastric bypass. Obes Surg. 2006;16(3):262–9.

    Article  PubMed  Google Scholar 

  55. Catalán V, Gómez-Ambrosi J, Rodrígue A, Ramírez B, Rotellar F, Valentí V, et al. Increased levels of calprotectin in obesity are related to macrophage content: impact on inflammation and effect of weight loss. Mol Med. 2011;17:1157–67.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Cypess AM. Reassessing human adipose tissue. N Engl J Med. 2022;386:768–79.

    Article  CAS  PubMed  Google Scholar 

  57. Sakers A, De Siqueira MK, Seale P, Villanueva CJ. Adipose-tissue plasticity in health and disease. Cell. 2022;185:419–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Stenvinkel P, Chertow GM, Devarajan P, Levin A, Andreoli SP, Bangalore S, et al. Chronic inflammation in chronic kidney disease progression: role of Nrf2. Kidney Int Reports. 2021;6:1775–87.

    Article  Google Scholar 

  59. De Fano M, Bartolini D, Tortoioli C, Vermigli C, Malara M, Galli F, et al. Adipose tissue plasticity in response to pathophysiological cues: a connecting link between obesity and its associated comorbidities. Int J Mol Sci. 2022;23:5511.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Nguyen Dinh Cat A, Montezano AC, Burger D, Touyz RM. Angiotensin II, NADPH oxidase, and redox signaling in the vasculature. Antioxidants Redox Signal. 2013;19:1110–20.

    Article  CAS  Google Scholar 

  61. Perdomo CM, Frühbeck G, Escalada J. Impact of nutritional changes on nonalcoholic fatty liver disease. Nutrients. 2019;11:1–25.

    Article  Google Scholar 

  62. Frühbeck G, Catalán V, Rodríguez A, Ramírez B, Becerril S, Salvador J, et al. Adiponectin-leptin ratio is a functional biomarker of adipose tissue inflammation. Nutrients. 2019;11:1–13.

    Article  Google Scholar 

  63. Park YC, Lee S, Kim YS, Park JM, Han K, Lee H, et al. Serum leptin level and incidence of CKD: a longitudinal study of adult enrolled in the Korean genome and epidemiology study(KoGES). BMC Nephrol. 2022;23:1–9.

    Article  Google Scholar 

  64. D’Marco L, Puchades MJ, Panizo N, Romero-Parra M, Gandía L, Giménez-Civera E, et al. Cardiorenal fat: a cardiovascular risk factor with implications in chronic kidney disease. Front Med. 2021;8:1–8.

    Google Scholar 

  65. Pincu Y, Yoel U, Haim Y, Makarenkov N, Maixner N, Shaco-Levy R, et al. Assessing obesity-related adipose tissue disease (OrAD) to improve precision medicine for patients living with obesity. Front Endocrinol (Lausanne). 2022;13:1–17.

    Article  Google Scholar 

  66. Lonardo A, Mantovani A, Targher G, Baffy G. Nonalcoholic fatty liver disease and chronic kidney disease: epidemiology, pathogenesis, and clinical and research implications. Int J Mol Sci. 2022;23:13320.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. García-Carro C, Vergara A, Bermejo S, Azancot MA, Sellarés J, Soler MJ. A nephrologist perspective on obesity: from kidney injury to clinical management. Front Med. 2021;8: 655871.

    Article  Google Scholar 

  68. Zhang X, Lerman LO. Obesity and renovascular disease. Am J Physiol - Ren Physiol. 2015;309:F273–9.

    Article  CAS  Google Scholar 

  69. Roderburg C, Krieg S, Krieg A, Demir M, Luedde T, Kostev K, et al. Non - alcoholic fatty liver disease (NAFLD )is associated with an increased incidence of chronic kidney disease (CKD). Eur J Med Res. 2023. https://doi.org/10.1186/s40001-023-01114-6.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Radaelli MG, Martucci F, Perra S, Accornero S, Castoldi G, Lattuada G, Manzoni G, Perseghin G. NAFLD/NASH in patients with type 2 diabetes and related treatment options. J Endocrinol Invest. 2018;41(5):509–21.

    Article  CAS  PubMed  Google Scholar 

  71. Benlloch S, Moncho F, Górriz JL. Targeting metabolic-associated fatty liver disease in diabetic kidney disease: a call to action. Nefrologia (Engl Ed). 2024;44(2):129–38.

    Article  PubMed  Google Scholar 

  72. Perdomo AC, Ingianna PD, Escalada J, Petta S, Gómez R, Ampuero J. Nonalcoholic fatty liver disease and the risk of metabolic comorbidities: how to manage in clinical practice Pol. Arch Intern Med. 2020;130(11):975–85.

    Google Scholar 

  73. Hu S, Li X, Sun Y, Wu S, Lan Y, Chen S, Wang Y, Liao W, Wang X, Zhang D, Yuan X, Gao J, Wang L. Short-term weight loss decreased the risk of chronic kidney disease in men with incident nonalcoholic fatty liver disease. Obesity (Silver Spring). 2022;30(7):1495–506.

    Article  CAS  PubMed  Google Scholar 

  74. Perdomo CM, Cohen RV, Sumithran P, Clément K, Frühbeck G. Contemporary medical, device, and surgical therapies for obesity in adults. Lancet. 2023;401(10382):1116–30.

    Article  PubMed  Google Scholar 

  75. Harrison SA, Bedossa P, Guy CD, Schattenberg JM, Loomba R, Taub R, MAESTRO-NASH Investigators. A phase 3, randomized, controlled trial of resmetirom in NASH with liver fibrosis. N Engl J Med. 2024;390(6):497–509.

    Article  PubMed  Google Scholar 

  76. Kokkorakis M, Muzurović E, Volčanšek Š, Chakhtoura M, Hill MA, Mikhailidis DP, Mantzoros CS. Steatotic liver disease: pathophysiology and emerging pharmacotherapies. Pharmacol Rev. 2024;76(3):454–99.

    Article  PubMed  Google Scholar 

  77. Loomba R, Hartman ML, Lawitz EJ, Vuppalanchi R, Boursier J, Bugianesi E, Yoneda M, Behling C, Cummings OW, Tang Y, Brouwers B, Robins DA, Nikooie A, Bunck MC, Haupt A, Sanyal AJ, SYNERGY-NASH Investigators. Tirzepatide for metabolic dysfunction-associated steatohepatitis with liver fibrosis. N Engl J Med. 2024. https://doi.org/10.1056/NEJMoa2401943. (Epub ahead of print).

    Article  PubMed  Google Scholar 

  78. Gerdes C, Müller N, Wolf G, Busch M. Nephroprotective properties of antidiabetic drugs. J Clin Med. 2023;12:3377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Muzurović EM, Vujošević S, Mikhailidis DP. Can we decrease epicardial and pericardial fat in patients with diabetes? J Cardiovasc Pharmacol Ther. 2021;26(5):415–36.

    Article  PubMed  Google Scholar 

Download references

Funding

Spanish Institute of Health ISCIII (Subdirección General de Evaluación and Fondos FEDER Project PI22/00745), CIBEROBN and and CIBERehd.

Author information

Authors and Affiliations

  1. Department of Endocrinology and Nutrition, Clínica Universidad de Navarra, Pamplona, Spain

    Carolina M. Perdomo, Javier Escalada & Gema Frühbeck

  2. IdiSNA (Instituto de Investigación en la Salud de Navarra), Pamplona, Spain

    Carolina M. Perdomo, Nerea Martin-Calvo, Nuria Garcia-Fernandez, José I. Herrero, Javier Escalada & Gema Frühbeck

  3. CIBERObn (CIBER Fisiopatología de la Obesidad y Nutrición), Instituto de Salud Carlos III, Madrid, Spain

    Carolina M. Perdomo, Nerea Martin-Calvo, Javier Escalada & Gema Frühbeck

  4. Department of Preventive Medicine and Public Health, Universidad de Navarra, Pamplona, Spain

    Nerea Martin-Calvo

  5. Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain

    Ana Ezponda, Francisco J. Mendoza & Gorka Bastarrika

  6. Department of Nephrology, Clínica Universidad de Navarra, Pamplona, Spain

    Nuria Garcia-Fernandez

  7. Red de Investigación Renal (REDINREN) and RICORS2040, Instituto de Salud Carlos III, 28029, Madrid, Spain

    Nuria Garcia-Fernandez

  8. Liver Unit, Clínica Universidad de Navarra, Pamplona, Spain

    José I. Herrero

  9. CIBERehd (CIBER Enfermedades Hepáticas y Digestiva), Instituto de Salud Carlos III, Madrid, Spain

    José I. Herrero

  10. Department of Internal Medicine, Clínica Universidad de Navarra, Pamplona, Spain

    Inmaculada Colina

Authors
  1. Carolina M. Perdomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nerea Martin-Calvo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana Ezponda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francisco J. Mendoza
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gorka Bastarrika
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nuria Garcia-Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. José I. Herrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Inmaculada Colina
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Javier Escalada
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gema Frühbeck
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CMP and NMC analyzed data. CMP, NMC, NG, JIH, IC, JE and GF interpreted data. AE, FJM, GB performed the radiological evaluations. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Gema Frühbeck.

Ethics declarations

Ethics approval and consent to participate

The study protocol (2019.080) was approved by the ethics committee of Clínica Universidad de Navarra. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Carolina M. Perdomo and Nerea Martin-Calvo have co-first authorship.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Perdomo, C.M., Martin-Calvo, N., Ezponda, A. et al. Epicardial and liver fat implications in albuminuria: a retrospective study. Cardiovasc Diabetol 23, 308 (2024). https://doi.org/10.1186/s12933-024-02399-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12933-024-02399-5

Keywords

Cardiovascular Diabetology

ISSN: 1475-2840