Perirenal Fat and Chronic Kidney Disease: A Systematic Review and Meta-Analysis Open Access

With the economic and technological developments of society, the global prevalence of obesity has considerably increased and is continuing to rise. Traditionally, obesity has been classified by the degree of body mass index (BMI) elevation. However, the role of BMI in predicting obesity-related risk diseases is limited [1]. Extensive research has found that visceral adipose tissues are metabolically active and are the source of humoral and cellular inflammation in obese patients [2]. Obesity has a close link with many diseases such as atherosclerosis [3], esophageal adenocarcinoma [4], and diabetic nephropathy [5].

Kidney diseases are among the most common diseases in the world. It has been shown that the total number of patients with chronic kidney disease (CKD), acute kidney injury, and renal replacement therapy exceeds 850 million [6, 7]. Worldwide, 1.2 million people died from CKD in 2017, especially in Oceania, sub-Saharan Africa, and Latin America; the burden of CKD is much higher than expected for development levels [8]. Generally, glomerular filtration rate (GFR) and albuminuria are used to assess the severity of kidney diseases [9]. The perirenal fat tissue surrounding the kidneys was originally thought to provide only mechanical support to the kidneys. However, an increasing number of studies have shown that perirenal fat tissue is more closely associated with kidney disease than other visceral fat deposits [10]. Furthermore, current studies propose that perirenal fat may have a higher predictive value for CKD in type 2 diabetes mellitus [11‒13]. The interaction of adipose tissue with the kidney is critical for normal kidney function and the kidney’s response to injury [14], and it may be related to inflammatory cytokines [15] and cardiorenal dysfunction [16].

To our knowledge, there are no previous systematic reviews and meta-analyses examining the relationship between perirenal fat and CKD. Therefore, we conducted a comprehensive systematic review and meta-analysis to investigate the association between perirenal fat and prediction of CKD.

The search period is from the establishment of the database to September 10, 2024. Six English electronic databases including PubMed, Scopus, Web of science, Ovid, Embase, and the Cochrane Library were searched for relevant publications on perirenal fat and CKD. The search strategies are as shown ((((((((((((((perirenal fat[Title/Abstract]) OR (perinephric fat[Title/Abstract])) OR (perirenal fat[Title/Abstract])) OR (perinephric adipose[Title/Abstract])) OR (perirenal fatty[Title/Abstract])) OR (perinephric fatty[Title/Abstract])) OR (perirenal fat Tissue[Title/Abstract])) OR (perinephric adipose tissues[Title/Abstract])) OR (perirenal fatty tissue[Title/Abstract])) OR (perinephric fatty tissue[Title/Abstract])) OR (perirenal fat tissue[Title/Abstract])) OR (perinephric fat tissue[Title/Abstract])) OR (perirenal fat thickness[Title/Abstract])) OR (perinephric fat thickness[Title/Abstract])) OR (perirenal fat pad[Title/Abstract]).

We set the following inclusion and exclusion criteria according to the PICO(S) (Participants, Intervention, Comparators, Outcomes [Study design]) model. The study protocol was previously registered at PROSPERO (CRD42024514011). Participants: patients with a diagnosis of CKD. Intervention: not applicable. Comparators: non-CKD patients. Outcomes: primary indicators of perirenal fat thickness.

Studies were excluded for the following reasons: (1) full text could not be obtained; (2) case reports, letters, reviews, conference abstracts, animal experiments, and expert opinions; and (3) studies were not published in English.

The literature management software NoteExpress was used to filter and eliminate literature. First, two researchers (P.F.K. and B.J.S.) screened the titles to eliminate duplicates, reviews, and conference papers. Then, both researchers read the literature abstracts to further determine their inclusion or exclusion based on the association between perirenal fat and CKD. Finally, they read the full text of the selected articles to confirm their inclusion in the meta-analysis. During this process, both researchers conducted independent screenings and compared the remaining literature. Disagreements were resolved by consensus where possible and/or by a third reviewer when necessary. The flowchart of the study is shown as a PRISMA flowchart in Figure 1.

The Newcastle-Ottawa Scale (NOS) is designed to assess the quality of these studies.

A seven-item standardized and preselected data extraction form was used to record the data included in the study, with the following headings: (1) Author, (2) Year of publication, (3) Country, (4) Sample size, (5) Detection method, (6) Perirenal fat thickness, (7) Glomerular filtration rate.

Stata (version 16 SE; College Station, TX, USA) software was used for statistical analysis of this meta-analysis. Measurement data are presented as mean ± standard deviation. The effective rate using relative risk and the 95% confidence interval (CI) are the effect indicators. Heterogeneity between groups was measured using I², when I² ≤ 50% and p ≥ 0.10, multiple similar studies can be considered to have low heterogeneity, when I² > 50% or p < 0.10, multiple similar studies can be considered to have high heterogeneity. Different heterogeneity was matched to different effect models (low, fixed; high, random) [17]. Further analysis of differences was carried out, such as sensitivity analysis and subgroup analysis, to reduce within-group heterogeneity.

As shown in Figure 1, there were 9,719 articles matching our search strategy, and a total of 4,216 articles remained after removing duplicate articles. After reviewing the titles and abstracts, 4,183 irrelevant articles were further excluded. Finally, after reading the full texts, 8 articles were included in this meta-analysis.

The total number of participants in our meta-analysis was 2,576 from eight studies. All included studies were retrospective cohorts [11, 18‒24]. Among these included studies, the most commonly used method to measure perirenal fat thickness was computed tomography (55.6%), while the remainder used ultrasound. Online supplementary Table S1 shows the characteristics of the studies included in the analysis (for all online suppl. material, see https://doi.org/10.1159/000543989).

Most of the included studies were of good quality and had an NOS score of >6 points. The minimum score for inclusion studies was 6 points. Online supplementary Table S2 describes the detailed quality assessment of these studies.

We analyzed eight studies with 2,576 participants to assess the relationship between perirenal fat thickness and CKD. A combined analysis of these studies showed a significant association between perirenal fat thickness and CKD (95% CI: 0.48–0.65, p = 0.00), and no heterogeneity was detected between these two groups (I² = 31.06%, p = 0.18) (Fig. 2).

Two articles analyzed the relationship between perirenal fat and CKD secondary to cardiac dysfunction, these studies demonstrated that pericardial fat was significantly associated with CKD Secondary to Cardiac Dysfunction (95% CI: 0.49–0.92, p < 0.00001). Five studies examined the relationship between pericardial fat and diabetic nephropathy, and the pooled analysis of these articles demonstrated a significant association between pericardial fat and the presence of diabetic nephropathy (95% CI: 0.39–0.60, p < 0.00001) (Fig. 3).

The initial analysis of publication bias was conducted through visual inspection, which indicated a potential presence of bias due to observed asymmetry. Consequently, we performed the Egger test, revealing a one-tailed p value of 0.204 for the regression intercept, suggesting an absence of publication bias. Figure 4 illustrates funnel plots for both observational and estimated studies.

In this systematic review and meta-analysis, we found that increased perirenal fat thickness is closely associated with the development of CKD, and the results of our subgroup analysis showed that regardless of whether CKD was caused by cardiac dysfunction, type 2 diabetes, or primary CKD, the perirenal fat thickness may significantly affect the progression of them.

Based on the above data, we performed a further review and analyzed why perirenal fat affects CKD.

The distance between the perirenal fat and kidneys is small enough for the two organs to interact. When perirenal fat increases, Gerota’s fascia can limit the expansion of adipose tissue, which may exert physical pressure on the renal vessels and renal parenchyma. Compression of the renal parenchyma may result in changes in the renal hemodynamics, including increased interstitial hydrostatic pressure and decreased renal blood flow and tubular flow velocity [25, 26]. Studies have found that with increasing perirenal fat volume, these surrounding adipose tissues can compress the renal vasculature, leading to pathological activation of the renin-angiotensin-aldosterone system (RAAS) and reduced renal perfusion, as well as venous compression and internal hemodynamic changes, which could lead to decreased kidney function [27‒29]^.

Lipid metabolism-related factors (HDL, LDL, TG, TC) are significantly associated with the progression of CKD by affecting local lipid deposition around the kidney. Alla et al. [30] noted that TC, TG, and fatty acids contribute to the progression of CKD due to dysregulated in podocytes, endothelial cells, and tubular cells; moreover, kidney parenchymal TC accumulation also contributes to CKD progression. Lee et al. [31] pointed out that higher LDL-C levels are associated with an increased risk of adverse renal outcomes than lower LDL-C levels (70–99, 100–129, and ≥130 mg/dL vs. <70 mg/dL, respectively). Nam et al. [32] believed that both low and high serum HDL-C levels may be detrimental to patients with non-dialysis CKD. Li et al. [33] pointed out that the TC/HDL-C ratio had predictive value in the progression of CKD.

However, we found an interesting phenomenon wherein no association between BMI and the progression of CKD was noted, but WC significantly affects the progression of CKD. This is also similar to the findings of a prospective cohort study by Cejka et al. [34] who found that WC significantly affects the risk of death in patients with CKD, but BMI does not. A meta-analysis conducted by Betzler et al. [35] also showed that BMI was not significantly associated with CKD. In clinical research and practice, systemic adiposity is often assessed by BMI, while visceral fat is often more closely related to WC, and higher WC means increased visceral fat and cardio-metabolic risk [36]. From the above results, we can speculate that WC, rather than BMI, may be a higher reliable indicator of kidney function.

The kidney plays a crucial role in regulating blood pressure, and renal disease can lead to elevated blood pressure, while hypertension may further exacerbate the progression of renal impairment. Moreover, it is posited that alterations in vascular structures, glomeruli, and tubular compartments induced by hypertension contribute to the development of CKD [37]. Li et al. [38] identified perinephric fat as a specific target for both the onset and progression of hypertension; notably, resection of this adipose tissue can result in systemic arterial system expansion. Conversely, several studies have also reported a positive correlation between perirenal fat thickness and diastolic blood pressure levels [39‒41]. Our findings may be influenced by the limited number of studies included.

The mechanism through which increased perirenal fat elevates the risk of CKD remains unclear. Renal lipotoxicity is induced by multiple pathophysiological mechanisms, such as inflammation, oxidative stress generated by reactive oxygen species, insulin resistance, and the activation of the renin-angiotensin system [42, 43]. It is widely recognized that obesity is a chronic state of low-grade inflammation, the accumulation of triglycerides causes adipocyte hypertrophy, thereby leading to the secretion of free fatty acids, leptin, pro-inflammatory cytokines such as TNF-α, interleukin-1β, interleukin-6 [44], jointly promoting insulin resistance [45]. Reactive oxygen species play a vital role in the detrimental effects of renal adipose accumulation in obese patients. Lipid overload aggravates endoplasmic reticulum stress via oxidative pathways, forming a feedback loop that exacerbates renal injury [46]. Therefore, these studies have demonstrated that perirenal fat may intricately lead to the onset of CKD through mechanisms such as lipotoxicity, chronic inflammation, and oxidative stress.

This study initially demonstrated an association between perirenal fat and CKD, followed by a subgroup analysis. While it presents certain advantages over previous related studies, it also has notable limitations. First, unexplained heterogeneity exists due to the low methodological quality of the included studies, which precludes the attainment of high-strength evidence; thus, the evaluation results should be interpreted with caution in clinical practice. Second, the small sample size in each study affects the external validity of our findings. Third, although two reviewers conducted the entire review process independently, variations in defining perirenal fat may introduce selection bias. Finally, most data originate from Asia, resulting in a lack of representation from South America, Oceania, and Africa.

Our systematic review and meta-analysis showed that thickening of perirenal fat affects the progression of CKD. It is recommended that clinicians pay more attention to this indicator to evaluate the possibility of incorporating them into CKD scores.

Ethical approval and consent were not applicable because this study is based exclusively on published literature.

The authors declare that they have no competing interests.

This work was supported by the Project of the Department of Integrated Traditional Chinese and Western Medicine and Minority Medicine, National Administration of Traditional Chinese Medicine (No. 2023384).

P.K. and B.S. designed and conceptualized the research, wrote the manuscript, and contributed equally to this study and shared first authorship. J.H. and C.W. analyzed the data. X.C. revised it. All authors have read and agreed to the published version of the manuscript.

All data generated or analyzed during this study are included in this article and its online supplementary materials, and further inquiries can be directed to the corresponding author.