Changes in Kidney Fat upon Dietary-Induced Weight Loss.

As the metabolic role of kidney fat remains unclear, we investigated the effects of dietary weight loss on kidney fat content (KFC) and its connection to kidney function and metabolism. Overweight or obese participants (n = 137) of a dietary intervention trial were classified into quartiles of weight loss in a post hoc manner. Kidney sinus (KSF) and cortex fat (KCF) were measured by magnetic resonance imaging at baseline, week 12 and week 50. Weight loss effects on KFC were evaluated by linear mixed models. Repeated measures correlations between KFC, other body fat measures and metabolic biomarkers were obtained. KSF, but not KCF, decreased significantly across weight loss quartiles at week 12 (quartile 4: -21.3%; p = 0.02) and 50 (-22.0%, p = 0.001), which remained significant after adjusting for VAT. There were smaller improvements regarding creatinine (-2.5%, p = 0.02) at week 12, but not week 50. KSF, but not KCF, correlated with visceral (rrm = 0.38) and subcutaneous fat volumes (rrm = 0.31) and liver fat content (rrm = 0.32), as well as diastolic blood pressure and biomarkers of lipid, glucose and liver metabolism. Dietary weight loss is associated with decreases in KSF, but not KCF, which suggests that KSF may be the metabolically relevant ectopic fat depot of the kidney. KSF may be targeted for obesity-related disease prevention.

1. Introduction

Overweight (Body Mass Index (BMI) ≥ 25–30 kg/m2) and obesity (BMI ≥ 30 kg/m2) are major health problems of the modern society, that are not restricted to industrial, wealthy countries, but also affect low- and middle-income countries [1,2]. Over the last decades, the prevalence of overweight and obesity have increased tremendously worldwide [3]. Health consequences of obesity are an increased risk for the metabolic syndrome [4], cardiovascular diseases [5] and also different types of cancer [6]. Worldwide, diseases caused by obesity lead to high costs, not just in the health care system [7,8], but also in the economy [9,10]. Therefore, a major interest of health politics is to support obese and overweight people losing weight to avoid medical and economic consequences [11].

Obesity causes ectopic accumulation of adipose tissue in physiologically not designated locations of the body after the physiological storage of fat such as the subcutaneous adipose tissue (SAT) is exceeded [12]. Organs and tissues such as the liver, the heart, the pancreas, the muscle, the bone marrow and also the kidneys account for ectopic fat depots [12,13]. Within the kidney there are two possible locations for fat depots: the kidney sinus (i.e., renal sinus) and the kidney cortex (i.e., renal-parenchymal) [13,14,15]. Moreover, the kidneys are surrounded by the retroperitoneal adipose tissue within the Gerota’s fascia, which is part of the visceral adipose tissue (VAT) compartment. The kidney sinus fat is located within the hilum of each kidney, with contact to the external renal capsule, and thus a peri-renal fat depot [14,16]. High amounts of kidney sinus fat have been associated with hypertension, atherosclerosis, visceral obesity and chronic kidney disease [14,16,17]. One possible mechanism for hypertension and its secondary diseases as a consequence of increased accumulation of kidney sinus fat might be the activation of the renin angiotensin aldosterone system (RAAS) caused by a compression of the hilar structures such as veins, arteries, lymphatics and ureters due to fat accumulation [16,18]. Regarding the kidney cortex fat, which is composed of fat accumulations within the kidney parenchyma, rare data exist with no clear pathological link in human studies, but hints in animal studies at causing hypertension and kidney damage [14,15,19,20].

While kidney fat, and especially kidney sinus fat, seems to be connected with the pathogenesis of obesity-related diseases, only few studies exist on the question whether pathophysiological consequences of renal fat accumulation are reversible, and only one weight loss trial has been carried out so far [14]. Therefore, it is of great importance to gain a better understanding of kidney fat and its connections with metabolic function and lipid accumulation in other metabolically active body fat depots. In particular, it is interesting if kidney fat is an independent metabolic risk factor from VAT, as it remains unclear whether ectopic fat accumulation generally exerts metabolic effects beyond those of the metabolically active VAT [21]. Moreover, it is important to assess whether specific risks associated with excess accumulation of kidney fat could be reduced via weight loss in early phases of overweight and obesity to avoid potentially irreversible long-term cardio-metabolic impairments.

The aim of this trial was to examine whether kidney fat content decreases after weight loss in a dietary intervention study among metabolically healthy adult obese or overweight individuals. Additionally, we analysed if the kidney fat content is associated with other body fat depots such as VAT, SAT, liver fat content (LFC) and pancreatic fat content (PFC) as well as metabolic biomarkers. To this end, we used data originating from a randomized controlled trial (RCT), initially designed to assess the metabolic effects of intermittent vs. continuous calorie restriction, in a post hoc manner, using overall weight loss achieved during the trial to create a model on the effects of weight loss on kidney fat and related biomarkers. Whole body Magnetic Resonance Imaging (MRI)-examinations were performed at baseline, after 12 weeks of intervention and after 50 weeks to measure the kidney fat content as well as the aforementioned fat depots.

2. Material and Methods

2.1. Study Population

For this study, data from the HELENA-Trial (trial registration number: NCT02449148 ClinicalTrials.gov), a randomized dietary intervention study, which had been undertaken at the German Cancer Research Centre (DKFZ), Heidelberg, and at the Heidelberg University Hospital from May 2015 to May 2017, was used to investigate the effects of continuous vs. intermittent calorie restriction in a post hoc manner.

The study included 150 obese or overweight individuals (50% female), who were non-smokers and aged between 35 and 65 years. Exclusion criteria were severe chronic diseases, which comprised kidney or liver dysfunction, major cardiovascular diseases and cancer. Additionally, diabetes mellitus, HbA1c levels ≥ 6.5% and/or fasting glucose levels ≥ 126 mg/dL measured at screening were defined as exclusion criteria. At baseline, the participants were assigned either to the group with intermittent calorie restriction, to that with continuous calorie restriction or to the control group. The study consisted of a 12-week intervention phase, a 12-week maintenance phase and a 26-week follow-up phase. At the beginning, all participants had given their written informed consent, and the study was approved by the responsible ethics committee at Heidelberg University Hospital, Germany (Vote S-299/2014).

Each participant received three MRI examinations, the first at baseline, the second after 12 weeks of intervention and the last one after 50 weeks. The exclusion criteria of the MRI examinations included claustrophobia, cardiac pacemakers or defibrillators, non-removable, medical and/or electronic foreign bodies, which were not approved for 1.5 Tesla-MR, joint protheses or metallic foreign bodies. Out of the initial 150 participants, six did not participate in the intervention phase and in seven participants, MRI analyses for kidney fat content were not available. Thus, for the present study, 137 participants were included.

At baseline, after 12 weeks, after 24 weeks, and after 50 weeks, participants filled out questionnaires, underwent medical examinations and provided blood samples. Biweekly phone calls by dietary assistants during the intervention phase were performed to record side effects and the compliance of the participants.

Further details on the rationale and design of the HELENA-Trial can be found in previous publications [22,23].

2.2. Laboratory Methods

The blood-based biomarkers glucose, HDL (high-density lipoprotein), cholesterol, triglycerides and HbA1c were measured in the central laboratory of Heidelberg University Hospital, Heidelberg, Germany. Serum biomarkers were analysed by electrochemiluminiscence on a Quickplex SQ 120 (Meso Scale Discoveries, Rockville, MD, USA) in the Division of Cancer Epidemiology at the German Cancer Research Centre (DKFZ), Heidelberg, Germany. Estimated GFR (glomerular filtration rate) was calculated by the CKD-EPI formula [24], which is the most accurate equation in respect of obese people [25]. A detailed description of the laboratory analyses can be found in a previous publication [22].

2.3. Imaging

Kidney fat content (KFC), i.e., fat content of kidney sinus and kidney cortex, liver fat content (LFC), pancreas fat content (PFC), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were measured with a 1.5 Tesla MR scanner (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) by generating a thoracoabdominal 2-point DIXON sequence and an abdominal multi-echo GRE sequence. Hardware, software and MR protocol remained constant for all MRI examinations. Details of imaging and MR protocol are given in the study design paper by Schübel et al. [23].

Fat content of cortex and sinus of both kidneys was measured by a multi-echo GRE (gradient echo) technique (Siemens LiverLab, Siemens Healthcare, Erlangen, Germany) [23,26,27]. Regions of interest (ROI; area = 0.5 cm2) were positioned manually in the cortex and sinus of the right and left kidney on the proton density fat fraction (PDFF) map using a post-processing software (OsiriX, Pixmeo SARL, Bernex, Switzerland) [27]. The placement of the ROIs avoided the borders of the organ, vessels, possible lesions, artefacts and lymph nodes. The right kidney is often positioned at a lower abdominal level than the left kidney. Therefore, in some cases, the ROIs of the right kidney needed to be placed at a lower slice than those of the left kidney. The MRI measurements of each participant were performed at baseline, after 12 weeks, and after 50 weeks with a constant positioning of each ROI for each time point.

Intra- and inter-reader coefficients were assessed in 40 examinations and were 0.93 and 0.89, respectively (Readers MS and YJ).

Liver fat content was assessed with three regions of interest of 4 cm2 each, which were placed separately in the dorsal, the anterior-medial, and the anterior-lateral right liver lobe directly above the porta hepatis on a PDFF map with the same post-processing software as for the KFC (OsiriX, Pixmeo SARL, Bernex, Switzerland). Details of LFC measurements are published previously [28,29].

Pancreas fat content was manually measured by placing 3 ROIs (each 0.785 cm2) in the pancreas head, body, and tail within the PDFF map derived from the multi-echo GRE sequence by using the same post-processing software tool (OsiriX, Pixmeo SARL, Bernex, Switzerland). Details of PFC measurements have been published previously [21].

The measurements of VAT and SAT were performed with a 2-point Dixon sequence from neck to the upper part of the legs and a semi-automatic quantification with an in-house developed software based on the Medical Imaging Interaction Toolkit (MITK, DKFZ, Heidelberg, Germany) was applied [30].

2.4. Statistical Analyses

The primary hypothesis of the HELENA trial was that intermittent calorie restriction is superior to continuous calorie restriction with respect to metabolism, adipose tissue gene expression, and body composition, which was disproved by previously published results showing no significant differences between both calorie restriction types across primary, secondary, and exploratory endpoints [22,31]. Therefore, the study cohort was re-classified in a post hoc manner into weight loss quartiles irrespective of the weight loss method. Weight loss quartiles were constructed based on the amount of weight lost after the 12-week intervention phase as follows: Quartile 1 (Q1) (≤2%, n = 35), quartile 2 (Q2) (>2 and ≤4.5%, n = 34), quartile 3 (Q3) (>4.5% and ≤7.5%, n = 35), and quartile 4 (Q4) (>7.5%, n = 33). These equally large quartiles were utilized to obtain a larger statistical power in comparison to a priori determined cut-off points. Importantly, there were no statistically significant differences between the study groups at baseline with respect to kidney fat content and other body composition and metabolic parameters used for the present post analyses on overall weight loss.

To determine the associations between kidney fat parameters and body composition, anthropometric parameters as well as metabolic parameters over all time points, repeated measures correlations (rmcorr) with the R package (The R foundation for Statistical Computing, Vienna, Austria) were used [32]. Rmcorr is a statistical technique for analysing the common within-subject association for paired variables assessed at two or more time points for a group of individuals [32]. The technique produces an rmcorr coefficient (rrm) between −1 and 1, similar to that produced by the Pearson correlation coefficient which indicates the strength of the linear association between two variables, while simultaneously accounting for non-independence of the repeated measurements. The kidney fat parameters and all other parameters were log-transformed prior to the repeated measures correlation analyses. The p values from the repeated measures correlation analyses were corrected for multiple testing using the Bonferroni correction method [33] and p values less than 0.05 after Bonferroni correction (for three tests, i.e., kidney cortex fat, kidney sinus fat and total kidney fat) were considered statistically significant. Thus, only p values less than 0.0167 (i.e., 0.05/3) prior to Bonferroni correction remained significant after correction.

Additionally, Spearman’s coefficients were calculated to assess the association of KFC with anthropometric parameters, blood pressure, blood biochemistry markers (markers of glucose and lipid metabolism, kidney and liver function test, inflammation and adipokines), and body composition parameters (VAT, SAT, LFC and PFC) for each time point separately (see Supplementary Materials Table S1).

The effect of overall weight loss on changes in kidney cortex fat, kidney sinus fat, total kidney fat and kidney function parameters, i.e., serum creatinine and estimated glomerular filtration rate (eGFR) was evaluated using linear mixed models. Trends for linear associations between weight loss and changes in each of the aforementioned variables, i.e., kidney fat and kidney function parameters were modelled with weight loss (weight change) as a continuous parameter. The linear mixed model included age, sex, time, weight change, and a time-by-weight change interaction term, with participants’ identifier or subject ID set as the random effect. Associations with a time-by-weight change interaction term less than 0.05, i.e., p-interaction < 0.05 were considered significant. The kidney fat and kidney function parameters were log-transformed prior to the linear mixed models analyses. Additionally, the linear mixed model was adjusted for VAT at both time points.

SAS 9.4 (Cary, NC, USA) was used for the statistical analyses.

3. Results

3.1. Characteristics of the Study Population at Baseline

The characteristics of the study population at baseline are shown in Table 1. Parts of the baseline data have already been published by this group [21,34]. Overall, kidney cortex fat contents, kidney sinus fat and total kidney fat contents were similarly distributed across the four quartiles at baseline, while the absolute values were higher in the kidney total and kidney sinus fat content than in the kidney cortex fat content (see Table 1). Height, weight, BMI, SAT, and VAT were similarly distributed across all quartiles at baseline. Participants in Q4 were slightly younger (47.3 ± 8.5 years) than the participants in Q1, Q2 and Q3 (50.9 ± 6.4 years, 50.8 ± 8.4 years, 51.2 ± 7.9 years). Notably, however, neither this difference nor differences in any other parameters at baseline were statistically significant according to ANOVA tests.

Table 1

Characteristics of the four weight loss groups (Q1 to Q4) at baseline, n = 137.

	Q1 (n = 35)	Q2 (n = 34)	Q3 (n = 35)	Q4 (n = 33)
	≤2%	>2% to ≤4.5%	>4.5% to ≤7.5%	>7.5%
Demographics
Women (n (%))	19 (54.3)	14 (41.2)	18 (51.4)	17 (51.5)
Age (years)	50.9 ± 6.4	50.8 ± 8.4	51.2 ± 7.9	47.3 ± 8.5
Anthropometrics
Waist circumference (cm)	106.1 ± 12.0	104.9 ± 10.7	103.1 ± 10.8	103.5 ± 12.2
BMI (kg/m2)	32.2 ± 4.1	30.8 ± 3.6	31.0 ± 3.4	31.6 ± 3.6
Blood pressure
Systolic BP (mmHg)	139.5 ± 11.2	132.7 ± 14.2	136.5 ± 14.6	140.4 ± 22.8
Diastolic BP (mmHg)	90.0 ± 8.2	85.6 ± 8.2	87.2 ± 7.8	87.0 ± 10.2
Fat depots
VAT (L)	5.3 ± 2.2	4.8 ± 2.1	4.8 ± 2.0	4.7 ± 2.1
SAT (L)	13.1 ± 4.6	11.2 ± 2.9	12.1 ± 3.9	12.9 ± 4.1
Total kidney fat content (%)	58.9 ± 18.5	52.3 ± 18.4	52.0 ± 16.8	52.8 ± 13.3
Kidney cortex fat content (%)	3.6 ± 1.8	3.2 ± 1.2	2.9 ± 1.2	3.4 ± 1.8
Kidney sinus fat content (%)	55.3 ± 18.3	49.2 ± 18.4	49.1 ± 16.8	49.4 ± 13.2
Liver fat content (%)	7.1 ± 4.4	8.8 ± 7.8	7.9 ± 6.5	7.4 ± 4.9
Liver function
ALT (U/L)	25.0 ± 7.3	30.8 ± 13.9	26.6 ± 12.2	24.8 ± 9.9
AST (U/L)	21.7 ± 4.0	25.4 ± 6.8	22.3 ± 3.9	22.4 ± 5.2
GGT (U/L)	29.4 ± 14.0	25.5 ± 16.2	29.8 ± 19.7	24.3 ± 12.6
Lipid metabolism
Triglycerides (mg/dL)	138.9 ± 65.7	135.0 ± 91.8	145.3 ± 94.2	109.4 ± 55.0
Cholesterol (mg/dL)	210.3 ± 33.9	201.9 ± 36.9	214.5 ± 36.5	203.3 ± 32.0
HDL (mg/dL)	53.5 ± 14.8	53.1 ± 14.5	56.3 ± 13.5	53.4 ± 15.3
LDL (mg/dL)	129.1 ± 26.2	120.2 ± 25.7	129.2 ± 26.8	128.0 ± 27.9
Glucose metabolism
Glucose (mg/dL)	93.5 ± 8.0	93.2 ± 7.0	94.9 ± 6.9	91.9 ± 8.3
Insulin (mU/L)	14.9 ± 7.8	12.0 ± 6.9	10.9 ± 5.1	11.4 ± 5.6
HbA1c (%)	5.4 ± 0.4	5.5 ± 0.3	5.5 ± 0.3	5.5 ± 0.3
HOMA-IR	3.5 ± 1.9	2.8 ± 1.8	2.6 ± 1.2	2.6 ± 1.4
Kidney function
Creatinine (mg/dL)	0.8 ± 0.1	0.8 ± 0.1	0.8 ± 0.1	0.8 ± 0.1
GFR (ml/min)	100.6 ± 7.0	99.4 ± 6.4	100.2 ± 12.6	103.1 ± 8.2
Albumin (g/L)	44.0 ± 2.0	43.7 ± 2.2	43.2 ± 2.2	43.7 ± 2.4
Inflammation
CRP (ng/pL)	7.0 ± 8.7	4.1 ± 5.5	3.8 ± 2.8	3.9 ± 3.8
IFN-γ (ng/µL)	16.6 ± 16.1	12.9 ± 12.9	17.5 ± 16.7	11.1 ± 7.8
TNF-α (ng/µL)	4.3 ± 2.7	4.0 ± 2.5	5.0 ± 2.6	4.2 ± 2.5
IL-6 (ng/µL)	2.0 ± 1.7	1.8 ± 3.5	1.3 ± 0.8	1.3 ± 1.1
IL-8 (ng/µL)	10.6 ± 4.4	14.2 ± 23.7	9.8 ± 4.8	10.6 ± 5.3
LDH (U/L)	197.3 ± 30.1	197.4 ± 26.8	192.7 ± 31.8	200.2 ± 28.3
Adipokines
Adiponectin (ng/mL)	15.6 ± 8.4	18.7 ± 11.4	16.9 ± 11.4	19.9 ± 13.7
Leptin (ng/mL)	29.2 ± 25.3	19.8 ± 20.3	21.5 ± 15.2	29.7 ± 29.4
Resistin (ng/mL)	5.7 ± 2.5	5.4 ± 2.1	5.3 ± 1.5	6.2 ± 3.3

Data are shown as mean ± SD. Abbreviations: ALT, alanine aminotransferase; AST, aspartat aminotransferase; BMI, body mass index; CRP, C-reactive protein; GFR, glomerular filtration rate; GGT, gamma glutamyl transferase; IFN-γ, interferon gamma; IL6, interleukin 6; IL8, interleukin 8; HbA1c, hemoglobin A1c; HDL, high density lipoprotein; HOMA-IR, homeostatic model assessment for insulin resistance; LDH, lactate dehydrogenase; LDL, low density lipoprotein, SAT, subcutaneous adipose tissue; TNF-α, tumour necrosis factor-alpha; VAT, visceral adipose tissue.

3.2. Correlations of Kidney Fat Content with Blood Biomarkers, Blood Pressure, Anthropometrics and Body Fat Depots

Correlations of total kidney fat content, renal cortex fat content and renal sinus fat content with anthropometrics (waist circumference and BMI), blood pressure, body fat parameters, and blood biomarkers over all three time points combined (baseline, after 12 and 50 weeks) are given in Table 2 and in Figure 1. Spearman‘s correlations for the aforementioned parameters at each time point (baseline, after 12 and 50 weeks) separately are given in Supplementary Materials Table S1.

Table 2

Repeated measures correlations of kidney cortex fat, kidney sinus fat and kidney total fat over all time points (time points T0, T1 and T3 combined).

	Kidney Cortex			Kidney Sinus Fat			Total Kidney Fat
	rrm	p	Adjusted p	rrm	p	Adjusted p	rrm	p	Adjusted p
Anthropometrics
BMI	0.03	0.66	1.00	0.35	<0.0001 *	<0.0001 *	0.36	<0.0001 *	<0.0001 *
Waist	−0.02	0.73	1.00	0.26	<0.0001 *	<0.0001 *	0.26	<0.0001 *	<0.0001 *
Fat depots
VAT	0.02	0.70	1.00	0.38	<0.0001 *	<0.0001 *	0.39	<0.0001 *	<0.0001 *
SAT	0.01	0.89	1.00	0.31	<0.0001 *	<0.0001 *	0.33	<0.0001 *	<0.0001 *
Liver fat content	0.00	0.96	1.00	0.32	<0.0001 *	<0.0001 *	0.32	<0.0001 *	<0.0001 *
Pancreas fat content	0.02	0.76	1.00	0.07	0.25	0.74	0.08	0.19	0.58
Blood pressure
Systolic BP	0.05	0.41	1.00	0.07	0.26	0.78	0.08	0.20	0.60
Diastolic BP	0.01	0.93	1.00	0.15	0.01 *	0.04 *	0.16	0.01 *	0.03 *
Adipokine
Leptin	0.02	0.75	1.00	0.23	<0.001 *	<0.001 *	0.24	<0.0001 *	<0.001 *
Adiponectin	−0.01	0.88	1.00	0.02	0.79	1.00	0.03	0.76	1.00
Resistin	−0.16	0.01 *	0.03 *	0.01	0.84	1.00	−0.02	0.80	1.00
Glucose metabolism
HOMA-IR	0.05	0.38	1.00	0.12	0.06	0.17	0.13	0.04 *	0.11
Insulin	0.06	0.37	1.00	0.10	0.12	0.36	0.11	0.08	0.25
Glucose	0.01	0.87	1.00	0.19	0.002 *	0.01 *	0.20	<0.002 *	<0.004 *
IGF-1	0.02	0.70	1.00	−0.11	0.07	0.21	−0.12	0.06	0.18
HbA1c	−0.07	0.26	0.77	0.11	0.08	0.24	0.11	0.09	0.27
Lipid metabolism
Triglycerides	0.00	0.97	1.00	0.13	0.04 *	0.12	0.14	0.03 *	0.08
Cholesterol	−0.05	0.42	1.00	0.23	<0.001 *	<0.001 *	0.23	<0.001 *	<0.001 *
LDL	−0.08	0.21	0.64	0.16	0.01 *	0.03 *	0.15	0.02 *	0.06
HDL	−0.01	0.85	1.00	0.18	<0.003 *	0.01 *	0.18	<0.003 *	0.01 *
Liver function tests
ALT	0.04	0.55	1.00	0.05	0.42	1.00	0.07	0.24	0.72
AST	0.00	0.97	1.00	0.01	0.89	1.00	0.02	0.73	1.00
GGT	−0.01	0.92	1.00	0.31	<0.0001 *	<0.0001 *	0.32	<0.0001 *	<0.0001 *
Inflammation
CRP	0.01	0.90	1.00	0.09	0.16	0.49	0.09	0.13	0.39
TNF α	−0.02	0.77	1.00	−0.03	0.68	1.00	−0.03	0.66	1.00
IFN	−0.04	0.50	1.00	−0.02	0.76	1.00	−0.02	0.69	1.00
IL6	−0.05	0.45	1.00	−0.01	0.88	1.00	−0.02	0.79	1.00
IL8	−0.13	0.04 *	0.11	0.00	1.00	1.00	−0.01	0.88	1.00
LDH	−0.01	0.92	1.00	0.21	0.02 *	0.05 *	0.23	0.01 *	0.03 *
Kidney function tests
Creatinine	−0.07	0.25	0.76	−0.05	0.38	1.00	−0.08	0.22	0.66
GFR	0.06	0.33	0.99	0.03	0.58	1.00	0.05	0.42	1.00
Albumin	0.01	0.90	1.00	0.19	<0.002 *	0.01 *	0.20	<0.001 *	<0.003 *

* p values were adjusted using Bonferroni correction, on the p-values generated for each bioclinical variables with all three kidney fat parameters. Thus, only p-values less than 0.0167, i.e., (0.05/3) remained significant after adjustment. Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BP, blood pressure; BMI, body mass index; CRP, C-reactive protein; GFR, glomerular filtration rate; GGT, gamma glutamyl transferase; IFN-γ, interferon gamma; IL6, interleukin 6; IL8, interleukin 8; HbA1c, hemoglobin A1c; HDL, high density lipoprotein; HOMA-IR, homeostatic model assessment for insulin resistance; LDH, lactate dehydrogenase; LDL, low density lipoprotein, SAT, subcutaneous adipose tissue; TNF-α, tumour necrosis factor-alpha; VAT, visceral adipose tissue.

Figure 1

Repeated measures correlations of kidney cortex fat, kidney sinus fat and kidney total fat over all time points (time points T0, T1, and T3 combined). The asterisks *, ** and *** represent p-values (after Bonferroni correction) of 0.05, 0.01 and 0.001, respectively. Abbreviations: LDL, low density lipoprotein, SAT, subcutaneous adipose tissue; BMI, body mass index; VAT, visceral adipose tissue, ALT, alanine aminotransferase; AST, aspartat aminotransferase, GGT, gamma glutamyl transferase, CRP, C-reactive protein, TNF, tumour necrosis factor, IFN, interferon, IL, Interleukin, LDH, lactate dehydrogenase.

Regarding BMI, a moderate positive correlation for kidney sinus fat and total kidney fat, with a rrm of 0.35 and 0.36, respectively, and a p < 0.0001 for both was observed. Regarding waist circumference, a correlation with a rrm of 0.26 and a p < 0.0001 both for kidney sinus fat and total kidney fat was found.

In correlation analyses on body fat parameters, a stronger significant correlation between kidney sinus fat as well as total kidney fat and VAT, with a rrm of 0.38 and 0.39, respectively, and a p < 0.0001 for each was found. For SAT a significant correlation with kidney sinus fat and total kidney fat (rrm: 0.31 and 0.33; p < 0.0001 both) was observed. For liver fat content also a significant correlation with kidney sinus fat and total kidney fat (rrm: 0.32 both; p < 0.0001 both) was seen. There was no significant correlation between kidney cortex fat and VAT, SAT and liver fat content. Pancreas fat content showed no significant correlation with any kidney fat content parameter.

With respect to blood pressure, a weak, but statistically significant correlation between diastolic blood pressure and both kidney sinus fat and total kidney fat was seen (rrm: 0.15 and 0.16). No significant correlation was seen for kidney cortex fat and blood pressure.

Of the liver function tests, only GGT showed a correlation with kidney sinus fat and total kidney fat (rrm: 0.31 and 0.32,