Skip to main content
Download PDF
Download ePub

Association between myosteatosis or sarcopenia based on abdominal CT and hypertension in systemic lupus erythematosus patients

Lipids in Health and Disease volume 24, Article number: 105 (2025) Cite this article

A Correction to this article was published on 16 April 2025

This article has been updated

Abstract

Background

Hypertension and obesity are common in systemic lupus erythematosus (SLE) patients, with obesity-related changes potentially driving hypertension. However, the specific adiposity measures associated with hypertension in SLE patients remain unclear. This study assessed the association between myosteatosis and sarcopenia detected on abdominal CT and hypertension in SLE patients. Mediators of the association between myosteatosis and hypertension were also investigated.

Methods

This was a retrospective study involving SLE patients enrolled from January 2017 to August 2023 and who underwent abdominal CT at the L3 level to track myosteatosis and sarcopenia based on the skeletal muscle mean radiodensity (SMD) and skeletal muscle index considered as binary and continuous variables. The association between these body composition measures and hypertension was tested using logistic regression analyses, while mediation modeling was used to assess the mediators.

Results

A total of 279 adult SLE patients (median age, 41.00 [30.00, 51.00] years; 245 women) were included in this study. Hypertension was associated with myosteatosis (adjusted OR: 3.54; 95% CI: 1.18–10.61 for the binary variable and 1.31; 95% CI: 1.02–1.68 for the continuous variable). No statistically significant association was observed between hypertension and sarcopenia (adjusted OR: 0.48; 95%CI: 0.23–1.01 for the binary variable and 0.95; 95%CI: 0.78–1.16 for the continuous variable). Mediation analyses revealed eGFR could mediate the association between myosteatosis (considered as a continuous variable) and hypertension in SLE patients when taken alone (95% CI: 0.0177–0.2765) or in combination with the TyG index (95% CI: 0.0032–0.0614).

Conclusions

Myosteatosis was associated with hypertension in SLE patients. eGFR alone or in combination with the TyG index may mediate this association.

Introduction

Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease [1, 2]. Hypertension is common in patients with SLE and is associated with high cardiovascular disease burden [3, 4]. The use of corticosteroids and immunosuppressants to control inflammation may elevate blood pressure or exacerbate pre-existing essential hypertension. Lupus-associated kidney disease can also lead to secondary arterial hypertension [3]. Notably, patients with SLE have a high prevalence of obesity, estimated at 30–40% [5]. Alterations in hormones, inflammation, and endothelial cell levels that accompany obesity may contribute to hypertension and an increased incidence of cardiovascular disease through various mechanisms [6]. Moreover, obesity has been shown to be associated with both renal disease risk and hypertension in the general population [7]. However, few studies have investigated the effects of obesity on hypertension in patients with SLE.

Obesity is characterized by an excessive body fat accumulation and obesity is most commonly linked to hypertension based on the body mass index (BMI) [8]. However, the body mass index (BMI) fails to accurately quantify the body fat composition [9, 10]. Accordingly, abdominal Computed Tomography (CT) was developed as a standard method of assessment of body fat composition, with visceral adipose tissue (VAT) being the commonest body fat measure on abdominal CT [10, 11]. In both general and SLE populations, VAT has been linked with hypertension [6, 12]. Furthermore, muscle adiposity (myosteatossis) and sarcopenia (low skeletal muscle mass and function) which interplays with adiposity under certain circumstances, have also been linked with hypertension in the general population [10, 13,14,15,16]. However, to our knowledge, myosteatosis and sarcopenia have not been associated with hypertension in patients with SLE.

We previously found insulin resistance mediating an association between intermuscular adipose tissue (IMAT) area and renal impairment in patients with SLE [17]. This suggests a potential link between insulin resistance and body fat composition. Given the central role of kidneys in blood pressure regulation and hypertension pathogenesis as well as the established role of insulin resistance as an independent risk factor for cardiovascular disease, we hypothesized that insulin resistance and renal function may act as potential mediators in the relationship between abdominal fat-muscle composition and hypertension in patients with SLE [18,19,20].

Therefore, this study aimed [1] to investigate whether there is an epidemiological association between hypertension and abdominal CT-based measures of fat-muscle composition including mainly myosteatosis and sarcopenia in patients with SLE; and [2] to explore whether eventually insulin resistance renal impairment are mediators of the relationship between hypertension and abdomibnal CT-based measures of fat-muscle composition.

Methods

Study design

A retrospective, cross-sectional study was conducted. Findings were reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines to improve scientific rigor [21]. As this was a retrospective study, the ethics committee waived the requirement for written consent from all patients (KYLL-2021[KS]-226).

Study setting and population

The study was conducted at Qilu Hospital, Shandong University, China. We retrospectively reviewed the electronic medical records of patients hospitalized for SLE between January 2017 and August 2023. SLE classification was based on the ICD-10 (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision) code M32 which indicates that these patients met either the 1997 American College of Rheumatology (ACR) revised SLE classification criteria, either the 2012 Systemic Lupus International Collaborating Clinics (SLICC) classification criteria or the 2019 European Alliance of Associations for Rheumatology (EULAR)/ACR SLE classification criteria [22]. The other inclusion criteria for participants were an age ≥ 18 years and the availability of L3 cross-sectional images of abdominal CT scans. The exclusion criteria encompassed a pregnancy or puerperium state and factors that could lead to an erroneous interpretation of abdominal CT images such as a history of malignancy before the abdominal CT scan, low-quality CT images, distortion of anatomical structures, significant edema of adipose tissues, or severe ascites interfering with body composition measurements.

Data collection

A team of two trained resident doctors conducted the data collection process using a preconceived data sheet. We collected data on blood pressure levels, kidney function, other traditional cardiovascular/obesity risk factors, abdominal CT images, demographic parameters, and SLE clinical and therapeutic characteristics. The demographic information collected included age, sex and BMI (BMI = weight (in kg)/height2 (in m2)). The SLE clinical and therapeutic characteristics collected included SLE disease duration, regular treatment regimens, disease activity and white blood cell (neutrophils, lymphocytes and monocytes) counts upon admission. The regular treatment regimens assessed involved glucocorticoids and immunosuppressants. Disease activity was assessed using the systemic lupus erythematosus disease activity index (SLEDAI)-2000 scores available in patients’ electronic medical records [23]. Traditional cardiovascular/obesity risk factors recorded included the albumin-globulin (A/G; which could be viewed as a reflect of the body’s nutritional and inflammatory status [24]) ratio, blood glucose (referred to here as GLU), triglyceride levels, low-density lipoprotein cholesterol (LDLc) levels, and high-density lipoprotein cholesterol (HDLc) levels. Insulin resistance was subsequently assessed using the triglyceride glucose (TyG) index calculated based on the formula:

TyG index = Ln [fasting blood glucose (mg/dL) × fasting triglyceride (mg/dL)/2] [25].

In addition, we evaluated patients’ renal functions using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation for the estimated glomerular filtration rate (eGFR) [26].

Abdominal CT measurements of body fat-muscle composition

A single axial CT image with a 5-mm slice thickness at the L3 level was analyzed using SliceOmatic software (version 5.0; Tomovision, Montreal, Quebec, Canada) to quantify fat and muscle distribution for each participant, as this level demonstrates the highest correlation with overall body tissue mass [27, 28]. SliceOmatic was first used to semi-automatically outline the body composition of each region based on radiological radiodensity thresholds. A trained radiologist (Wang B.W) examined and adjusted the regions of interest, which were then reviewed by an abdominal radiologist with > 30 years of experience (Yu D.X). Neither radiologist knew the participants’ clinical information. The skeletal muscle area (SMA) at the L3 level, encompassing the psoas, quadratus lumborum, erector spinae, transversus abdominis, external and internal obliques, and rectus abdominis muscles, was quantified by thresholding using attenuation values of − 29 to 150 HU [29]. The skeletal muscle index (SMI) was calculated to normalize SMA by height using the equation: SMI (cm2/m2) = SMA (cm2) / height2 (m2) [29]. VAT was quantified using attenuation values of − 150 to − 50 HU, while SAT and IMAT were identified using − 190 to − 30 HU [29] (Fig. 1). Finally, the area and mean radiodensity of the skeletal muscle, SAT, VAT, and IMAT were recorded.

Fig. 1
figure 1

Measurement of Body Composition and Definition of Myosteatosis and Sarcopenia. Cross-sectional axial CT images at the L3 level in patients with SLE were analyzed using semi-automated software (SliceOmatic version 5.0, Tomovision Inc., Montreal, Quebec, Canada). Blue, subcutaneous adipose tissue (SAT); yellow, visceral adipose tissue; red, skeletal muscle (SM); green, intermuscular adipose tissue (IMAT). Skeletal muscle area (SMA) was normalized by dividing by the square of height: SMI (cm2/m2) = SMA (cm2) / height2 (m2). Phenotypic classification of skeletal muscle abnormalities in patients with SLE according to SMD and SMI

Full size image

Definition of operational terms

Hypertension was defined as either a prior clinical diagnosis of hypertension, current use of antihypertensive medications, or newly identified hypertension during hospitalization based on the average of three consecutive blood pressure measurements showing systolic BP ≥ 140 mm Hg and/or diastolic BP ≥ 90 mm Hg [30].

The definitions of myosteatosis and sarcopenia as binary variables were based on SMD and SMI cut-off values, respectively (see Fig. 1). Accordingly, myosteatosis was defined as SMD values < 41 HU (BMI values < 25 kg/m2) or < 33 HU (BMI values ≥ 25 kg/m2) [31], while sarcopenia was defined as SMI value < 50 cm2/m2 (males) or < 39 cm2/m2 (females) [29]. The definitions of myosteatosis and sarcopenia as continuous variables were based on linear reductions of SMD and SMI values, respectively.

Statistical analysis

Categorical variables were expressed as frequencies and percentages, and continuous variables were described by means and standard deviations or median with the interquartile range (IQR) (Q25, Q75). We compared the demographic and clinical characteristics between the SLE- non-hypertension group and the SLE-hypertension group using the Pearson χ2 test for categorical variables, the test Independent Samples t test for normally distributed continuous variables, and the Mann–Whitney U test for non-normally distributed continuous variables.

We first compared the differences in body fat-muscle composition variables between SLE patients with and those without hypertension. We then used multivariate logistic regression to analyze the association between hypertension and fat-muscle metrics including myosteatosis, sarcopenia, IMAT, VAT, SAT, VAT/SAT, and BMI. During this analysis, the odds ratios (OR) of these different variables were compared. We fitted three regression models with incremental adjustments for potential confounders. The model 1 adjusted for demographic covariates (age, sex). The model 2 further adjusted for SLE-related parameters including disease duration, SLEDAI, history of immunosuppressant and glucocorticoid use and white blood cell counts including neutrophils, lymphocytes and monocytes. The model 3 which was the priority model, adjusted for the A/G ratio, GLU, LDLc, and HDLc levels on top of covariates in models 1 and 2. Because myosteatosis and sarcopenia were the main body fat-muscle composition measures of this study, they were both treated as binary (yes or no) and continuous (linear reductions of SMD and SMI values, respectively) variables. SMD, SMI, and BMI values were divided into 5 units, and VAT and SAT were divided into 20 units to make the results easily interpretable. To ensure that the SMD and SMI thresholds of myosteatosis and sarcopenia identified in other populations were relevant to our study population, we also estimated the nonlinear risk of hypertension in patients with SLE based on SMD and SMI using 3-knotted restricted cubic spline regression [29, 31].

Next, we exploratively analyzed the factors potentially underlying the association of myosteatosis with hypertension. To quantify the association between myosteatosis and those factors, myosteatosis was only considered as a continuous variable using SMD values in this analysis. Specifically, in this analysis, we first used Pearson and Spearman tests to determine whether there was a correlation between hypertension, SMD, TyG, and eGFR values. We then employed a Hayes’ SPSS Process Macro Model 6 to quantify potential serial multiple mediation effects of TyG index and eGFR [32]. Given that the renal function is a variable of the SLEDAI tool, only covariates other than SLEDAI were adjusted for in the mediation model.

All statistical analyses were performed using the IBM SPSS 26.0 software (Chicago, Illinois, United States) and R statistical (version 4.3.2, The R Foundation for statistical computing, Vienna, Austria) softwares. A two-tailed p-value of less than 0.05 was used to define statistical significance for hypothesis testing, except for mediation effects, where statistical significance was determined based on 95% bootstrap CI not including zero, with 5000 bootstrap samples obtained.

Results

Demographic and clinical characteristics of the SLE patients

Of the 2,174 patients with SLE who received inpatient care between January 2017 and August 2023, we ultimately included 279 patients with SLE of China who met the inclusion and exclusion criteria (Fig. 2). A total of 79 (28.3%, 95%CI: 21.5-35.17%) SLE patients were diagnosed with comorbid hypertension. The median age of these patients was 41.00 (IQR 30.00, 51.00) years. The SLE-hypertension group showed significantly higher age (Z=-3.30, P = 0.001), BMI (Z=-2.22, P = 0.026), and disease duration (Z=-1.98, P = 0.047) but lower eGFR (Z=-7.48, P < 0.001) compared to the SLE-non-hypertension group. The detailed characteristics of the participants are shown in Table 1.

Fig. 2
figure 2

Flowchart highlighting the selection of included SLE Patients

Full size image
Table 1 Overall characteristics of participants with SLE
Full size table

Association between body fat-muscle composition measures and hypertension: focus on myosteatosis and sarcopenia

Overall, 203 (72.8%, 95%CI: 66.03-79.57%) as having myosteatosis and 79 (58.8%, 95%CI: 51.3-66.3%) patients were defined as having sarcopenia (Table 2). We found that the proportion of myosteatosis in the SLE-hypertension group (68 of 79 [86.1%]) was significantly higher than in the SLE-non-hypertension group (135 of 200 [67.5%]) (χ²=9.86, P = 0.002). Correspondingly, SMD was lower in the SLE-hypertension group than in the SLE-non-hypertension group (31.31 [26.03, 35.74] vs. 34.45 [29.33, 40.39], t = 3.52, P < 0.001). However, no significant differences were observed between the two groups in terms of the proportion of sarcopenia (χ²=3.02 P = 0.082) and SMI (Z=-0.31, P = 0.759). A detailed comparison of the body fat-muscle composition measures can be found in Table 2. Furthermore, we found that in patients with SLE, SMD values were linearly and negatively associated with hypertension (Fig. 3A) while SMI was not associated with hypertension (Fig. 3B).

Table 2 Abdominal CT-Based body fat-muscle composition measures and BMI in SLE patients with hypertension and SLE patients without hypertension
Full size table
Fig. 3
figure 3

Nonlinear Associations of CT-Based SMD and SMI with Hypertension in SLE Patients. Histograms and line graphs show unadjusted, nonlinear associations between abdominal CT-based (A) skeletal muscle radiodensity (SMD) and (B) skeletal muscle index (SMI) and hypertension among SLE patients

Full size image

When considered as a binary variable, myosteatosis was independently associated with a higher odds for hypertension in SLE patients (adjusted OR: 3.54; 95%CI: 1.18–10.61). We did not find any association between hypertension and sarcopenia as a binary variable (adjusted OR: 0.48; 95%CI: 0.23–1.01). When myosteatosis and sarcopenia were considered as continuous variables, the adjusted ORs for their association with hypertension were 1.31 (95%CI: 1.02–1.68) per 5-HU of SMD reduction for myosteatosis, and 0.95 (95%CI: 0.78–1.16) per 5-cm2/m of SMI reduction. In addition, the VAT was also associated with hypertension in patients with SLE (adjusted OR: 1.14; 95%CI: 1.02–1.28). There was no association between hypertension and IMAT (adjusted OR: 1.04; 95%CI: 0.82–1.30), SAT (adjusted OR: 1.08; 95%CI: 1.00-1.16), VAT/SAT (adjusted OR: 1.91; 95%CI: 0.68–5.37), and BMI (adjusted OR: 1.33; 95%CI: 0.91–1.94) values (Table 3).

Table 3 Univariate and multivariate logistic regression analysis for the association between abdominal CT-Based body composition measures, BMI and hypertension in patients with SLE
Full size table

Correlation between SMD, TyG index, eGFR, and hypertension

A correlation analysis between SMD, TyG index, eGFR, and hypertension is shown in Table S1. SMD values were correlated with TyG index (r = 0.196, P = 0.003) and hypertension (r = 0.216, P < 0.001), and inversely correlated with eGFR (r=-0.356, P < 0.001). In contrast, eGFR was negatively correlated with TyG (r=-0.189, P = 0.003) and hypertension (r=-0.457, P < 0.001).

Mediating effect of TyG index and eGFR in the relationship between myosteatosis and hypertension

Table 4 shows the mediating effect values of TyG index and eGFR between SMD and hypertension in patients with SLE. The bootstrap 95% of CI (0.0354, 0.3272) for the total indirect effect (0.1403) of TyG index and eGFR had a significant mediating effect on the association between SMD and hypertension. The total indirect effect results from the sum of three indirect effects: [1] indirect effects of TyG index (SMD–TyG index–hypertension; effect size = 0.0071); [2] indirect effect of eGFR (SMD–eGFR–hypertension; effect size = 0.1121); and [3] indirect effect of both TyG index and eGFR (SMD–TyG index–eGFR-hypertension; effect size = 0.0211). The indirect effect 1 (95% CI: -0.0576, 0.0759) was not significant. In contrast, the indirect effects 2 (95% CI: 0.0177, 0.2765) and 3 (95% CI: 0.0032, 0.0614) were statistically significant (Table 4). Based on the above analysis, the chain intermediary model is shown in Figure S1.

Table 4 Mediating effect of TyG index and eGFR in the relationship between SMD and hypertension in patients with SLE
Full size table

Discussion

In this study, we found that myosteatosis as a binary and a continuous variable was significantly associated with high odds of hypertension in patients with SLE. Furthermore, our findings suggest that the relationship between myosteatosis and hypertension may be mediated by the eGFR either alone or in combination with the TyG index. By contrast, sarcopenia as a binary and a continuous variable was not statistically associated with hypertension in participants with SLE.

The finding of a strong association between myosteatosis and hypertension in patients with SLE is consistent with findings from studies on the general population. For example, an analysis from the Framingham Heart Study [33] reported that lower SMD values in the two paraspinal muscles was correlated with a higher prevalence of hypertension. In a cross-sectional study conducted in Korea, researchers segmented SMA from abdominal CT scans at the L3 level into normal attenuated muscle area and low attenuated muscle area based on SMD [14]. The results demonstrated that individuals with hypertension exhibited more advanced profiles of myosteatosis compared to normotensive counterparts, and a lower normal attenuated muscle area/BMI ratio was significantly associated with a higher risk of hypertension [14]. Compared to the general population, patients with SLE are more prone to developing muscle-related complications, including myositis (an inflammatory muscle disease) and drug-induced myopathy (a complication associated with long-term steroid use) [34]. These conditions can lead to type II muscle fiber atrophy and increased intramuscular fat infiltration [34, 35]. Therefore, the association between myosteatosis and hypertension may hold greater clinical significance in the context of SLE.

The chain-mediation model identified renal impairment, either alone or combined with insulin resistance, as a potential mediator in the association between myosteatosis and hypertension in SLE patients, though underlying mechanisms remain unexplored. Myosteatosis, characterized by ectopic fat accumulation in skeletal muscle due to exceeded adipocyte storage capacity, may share mechanisms with adiposity-induced hypertension, including insulin resistance, inflammation, leptin-mediated arterial stiffness, sympathetic nervous system activation, and renin-angiotensin-aldosterone system stimulation with increased sodium reabsorption [8, 36]. Further research is needed to explore the roles of adipokines and oxidative stress, particularly their interactions with renal impairment and insulin resistance, to better elucidate the mechanisms linking myosteatosis to hypertension. Current research indicates that increased adipokines can stimulate renal interstitial cell proliferation, thickening of the basement membrane, and expansion of the extracellular matrix, leading to glomerulosclerosis, loss of nephron function, and elevated arterial pressure [37,38,39]. Furthermore, increasing evidence indicates that when lipids and their derivatives accumulate in skeletal muscle, it induces mitochondrial dysfunction, and interferes with fatty acid β-oxidation, thereby leading to impaired insulin signaling. Intramuscular fat accumulation also induces changes in muscle metabolism and insulin sensitivity through secretion of inflammatory adipokines [40, 41]. The consequent hyperinsulinemia exerts direct effects on multiple segments of the renal tubule, enhancing sodium reabsorption while suppressing urinary sodium excretion which play an important role in initiating hypertension associated with obesity [42]. While our study highlights these associations in SLE patients, these mechanistic insights informed by existing research require further exploration to establish causality.

However, no statistically significant association between sarcopenia and hypertension was found in patients with SLE. A cross-sectional study using data from the Fourth and Fifth Korean National Health and Nutrition Examination Survey found that elderly individuals with sarcopenia had a significantly higher prevalence of hypertension compared to those without sarcopenia [43]. In contrast, another cohort study based on a Chinese middle-aged and elderly community population showed that sarcopenia was not associated with hypertension [44]. Thus, the association between sarcopenia and hypertension may vary in different countries. Han et al. [35] conducted a large prospective cohort study that demonstrated low relative skeletal muscle mass was independently associated with the prevalence of hypertension in Korean males, but this association was not significant in females, suggesting that there may be a sex-dependent relationship between muscle mass and hypertension [35]. Notably, these studies were conducted in general populations, whereas our study specifically focused on SLE patients, a population predominantly comprising young women (nearly 90% of our participants were female) [45]. This distinct demographic characteristic may contribute to the observed differences. Future studies incorporating gender-stratified analyses could provide further insights into the relationship between sarcopenia and hypertension in SLE patients.

This study had several limitations. First, it included a population receiving care in a tertiary referral center, which is therefore less representative of the community’s SLE population. Second, we did not assess the Chinese Visceral Adiposity Index, which is increasingly recognized as an important tool for predicting cardiovascular disease in China, notably when combined with the insulin resistance marker TyG index [46, 47]. However, we used VAT measures that may also apply to SLE patients from other parts of the world [27,28]. Third, there is currently no internationally standardized SMD cut-off value for defining myosteatosis based on abdominal CT, and the SMD cut-off value we employed was developed in studies involving cancer patients [10, 31, 48]. These limitations call for large multicenter prospective cohorts or case-control studies to further assess the association between hypertension and both myosteatosis and sarcopenia given that prospective cohorts are the gold standard for demonstrating causality among observational primary studies, whereas case-control studies may be the most efficient observational primary studies for assessing causality in relatively uncommon diseases such as SLE [49].

Conclusions

Among all abdominal CT–based body fat-muscle composition variables measured in this cross-sectional study, myosteatosis is the one that was statistically significantly associated with hypertension in patients with SLE. This association may be indirectly mediated by eGFR isolated or combined with the TyG index. Sarcopenia was not linked to hypertension. Timely identification of muscle adiposity in patients with SLE using abdominal CT could be a useful tool for hypertension prevention in patients with SLE if future diagnostic test accuracy and cost-effectiveness studies prove it to be useful for measuring myosteatosis in SLE patients and if future case-control/cohort studies confirm the epidemiological association between myosteatosis and hypertension in SLE. Future well-designed large-scale case control/cohort studies are warranted to further assess whether there is an epidemiological link between hypertension and other adiposity measures or sarcopenia in SLE patients.

Data availability

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

Change history

Abbreviations

A/G:

Albumin-globulin ratio

BMI:

Body mass index

CI:

Confidence interval

CT:

Computed Tomography

eGFR:

Estimated glomerular filtration rate

GLU:

Blood glucose

HDLc:

High-density lipoprotein cholesterol

HU:

Hounsfield units

IMAT:

Intermuscular adipose tissue

IQR:

Interquartile range

LDLc:

Low-density lipoprotein cholesterol

OR:

Odds ratio

SAT:

Subcutaneous adipose tissue

SLE:

Systemic lupus erythematosus

SLEDAI:

Systemic lupus erythematosus disease activity index

SMA:

Skeletal muscle area

SMI:

Skeletal muscle index

TyG index:

Triglyceride-glucose index

VAT:

Visceral adipose tissue

References

  1. Fanouriakis A, Kostopoulou M, Andersen J, Aringer M, Arnaud L, Bae SC, et al. EULAR recommendations for the management of systemic lupus erythematosus: 2023 update. Ann Rheum Dis. 2024;83(1):15–29.

    Article  CAS  PubMed  Google Scholar 

  2. Gavilán-Carrera B, Ruiz-Cobo A, Amaro-Gahete FJ, Soriano-Maldonado A, Vargas-Hitos JA. No changes in body composition and adherence to the mediterranean diet after a 12-Week aerobic training intervention in women with systemic lupus erythematosus: the EJERCITA-LES study. Nutrients. 2023;15(20).

  3. Munguia-Realpozo P, Mendoza-Pinto C, Sierra Benito C, Escarcega RO, Garcia-Carrasco M, Mendez Martinez S, et al. Systemic lupus erythematosus and hypertension. Autoimmun Rev. 2019;18(10):102371.

    Article  PubMed  Google Scholar 

  4. Lu X, Wang Y, Zhang J, Pu D, Hu N, Luo J, et al. Patients with systemic lupus erythematosus face a high risk of cardiovascular disease: A systematic review and Meta-analysis. Int Immunopharmacol. 2021;94:107466.

    Article  CAS  PubMed  Google Scholar 

  5. Kono M, Nagafuchi Y, Shoda H, Fujio K. The impact of obesity and a High-Fat diet on clinical and immunological features in systemic lupus erythematosus. Nutrients. 2021;13(2).

  6. Seravalle G, Grassi G. Obesity and hypertension. Pharmacol Res. 2017;122:1–7.

    Article  CAS  PubMed  Google Scholar 

  7. Ahmed B, Sultana R, Greene MW. Adipose tissue and insulin resistance in obese. Biomed pharmacotherapy = Biomedecine Pharmacotherapie. 2021;137:111315.

    Article  CAS  Google Scholar 

  8. General. Abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants. Lancet (London England). 2024;404(10455):851–63.

    Article  Google Scholar 

  9. Mazzoccoli G. Body composition: where and when. Eur J Radiol. 2016;85(8):1456–60.

    Article  PubMed  Google Scholar 

  10. Gómez-Ambrosi J, Catalán V, Frühbeck G. The evolution of the Understanding of obesity over the last 100 years. Int J Obes. 2025;49(2):168–76.

    Article  Google Scholar 

  11. Pishgar F, Shabani M, Quinaglia ACST, Bluemke DA, Budoff M, Barr RG, et al. Quantitative analysis of adipose depots by using chest CT and associations with All-Cause mortality in chronic obstructive pulmonary disease: longitudinal analysis from mesarthritis ancillary study. Radiology. 2021;299(3):703–11.

    Article  PubMed  Google Scholar 

  12. Li Z, Shang J, Zeng S, Wu H, Zhou Y, Xu H. Altered body composition and increased visceral adipose tissue in premenopausal and late postmenopausal patients with SLE. Clin Rheumatol. 2019;38(11):3117–27.

    Article  PubMed  Google Scholar 

  13. Henin G, Loumaye A, Leclercq IA, Lanthier N, Myosteatosis. Diagnosis, pathophysiology and consequences in metabolic dysfunction-associated steatotic liver disease. JHEP Reports: Innov Hepatol. 2024;6(2):100963.

    Article  Google Scholar 

  14. Jung HN, Cho YK, Kim HS, Kim EH, Lee MJ, Lee WJ, et al. Association between hypertension and myosteatosis evaluated by abdominal computed tomography. Hypertens Research: Official J Japanese Soc Hypertens. 2023;46(4):845–55.

    Article  Google Scholar 

  15. Sayer AA, Cooper R, Arai H, Cawthon PM, Ntsama Essomba MJ, Fielding RA, et al. Sarcopenia Nat Reviews Disease Primers. 2024;10(1):68.

    Article  PubMed  Google Scholar 

  16. Gao Q, Hu K, Yan C, Zhao B, Mei F, Chen F et al. Associated factors of sarcopenia in Community-Dwelling older adults: A systematic review and Meta-Analysis. Nutrients. 2021;13(12).

  17. Wang B, Zuo L, Fan J, Ji Y, Xu L, Xu M, et al. Association between higher intermuscular adipose tissue and decreased renal function in patients with systemic lupus erythematosus mediated by insulin resistance. Insights into Imaging. 2024;15(1):144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Granger JP, Abram S, Stec D, Chandler D, LaMarca B. Endothelin, the kidney, and hypertension. Curr Hypertens Rep. 2006;8(4):298–303.

    Article  CAS  PubMed  Google Scholar 

  19. Cui C, Liu L, Zhang T, Fang L, Mo Z, Qi Y, et al. Triglyceride-glucose index, renal function and cardiovascular disease: a National cohort study. Cardiovasc Diabetol. 2023;22(1):325.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ginsberg HN. Insulin resistance and cardiovascular disease. J Clin Investig. 2000;106(4):453–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4(10):e297.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Batu ED, Akca UK, Kısaarslan AP, Sağ E, Demir F, Demir S, et al. The performances of the ACR 1997, SLICC 2012, and EULAR/ACR 2019 classification criteria in pediatric systemic lupus erythematosus. J Rhuematol. 2021;48(6):907–14.

    Article  CAS  Google Scholar 

  23. Gladman DD, Ibañez D, Urowitz MB. Systemic lupus erythematosus disease activity index 2000. J Rhuematol. 2002;29(2):288–91.

    Google Scholar 

  24. Chen Z, Song C, Yao Z, Sun J, Liu W. Associations between albumin, Globulin, albumin to Globulin ratio and muscle mass in adults: results from the National health and nutrition examination survey 2011–2014. BMC Geriatr. 2022;22(1):383.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dang K, Wang X, Hu J, Zhang Y, Cheng L, Qi X, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23(1):8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Noumura Y, Kamishima T, Sutherland K, Nishimura H. Visceral adipose tissue area measurement at a single level: can it represent visceral adipose tissue volume? Br J Radiol. 2017;90(1077):20170253.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Schweitzer L, Geisler C, Pourhassan M, Braun W, Glüer CC, Bosy-Westphal A, et al. What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults? Am J Clin Nutr. 2015;102(1):58–65.

    Article  CAS  PubMed  Google Scholar 

  29. Shafaat O, Liu Y, Jackson KR, Motter JD, Boyarsky BJ, Latif MA, et al. Association between abdominal CT measurements of body composition before deceased donor liver transplant with posttransplant outcomes. Radiology. 2023;306(3):e212403.

    Article  PubMed  Google Scholar 

  30. Yin R, Yin L, Li L, Silva-Nash J, Tan J, Pan Z, et al. Hypertension in China: burdens, guidelines and policy responses: a state-of-the-art review. J Hum Hypertens. 2022;36(2):126–34.

    Article  PubMed  Google Scholar 

  31. Poltronieri TS, de Paula NS, Chaves GV. Assessing skeletal muscle radiodensity by computed tomography: an integrative review of the applied methodologies. Clin Physiol Funct Imaging. 2020;40(4):207–23.

    Article  PubMed  Google Scholar 

  32. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford; 2017.

  33. Therkelsen KE, Pedley A, Speliotes EK, Massaro JM, Murabito J, Hoffmann U, et al. Intramuscular fat and associations with metabolic risk factors in the Framingham heart study. Arterioscler Thromb Vasc Biol. 2013;33(4):863–70.

  34. Lim KL, Abdul-Wahab R, Lowe J, Powell RJ. Muscle biopsy abnormalities in systemic lupus erythematosus: correlation with clinical and laboratory parameters. Ann Rheum Dis. 1994;53(3):178–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Han JM, Lee MY, Lee KB, Kim H, Hyun YY. Low relative skeletal muscle mass predicts incident hypertension in Korean men: a prospective cohort study. J Hypertens. 2020;38(11):2223–9.

    Article  CAS  PubMed  Google Scholar 

  36. Shariq OA, McKenzie TJ. Obesity-related hypertension: a review of pathophysiology, management, and the role of metabolic surgery. Gland Surg. 2020;9(1):80–93.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hall JE, do Carmo JM, da Silva AA, Wang Z, Hall ME. Obesity-induced hypertension: interaction of neurohumoral and renal mechanisms. Circul Res. 2015;116(6):991–1006.

    Article  CAS  Google Scholar 

  38. Czaja-Stolc S, Potrykus M, Stankiewicz M, Kaska Ł, Małgorzewicz S. Pro-Inflammatory profile of adipokines in obesity contributes to pathogenesis, nutritional disorders, and cardiovascular risk in chronic kidney disease. Nutrients. 2022;14(7).

  39. Hall JE. The kidney, hypertension, and obesity. Hypertens (Dallas Tex: 1979). 2003;41(3 Pt 2):625–33.

  40. Ingram KH, Hill H, Moellering DR, Hill BG, Lara-Castro C, Newcomer B, et al. Skeletal muscle lipid peroxidation and insulin resistance in humans. J Clin Endocrinol Metab. 2012;97(7):E1182–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Vettor R, Milan G, Franzin C, Sanna M, De Coppi P, Rizzuto R, et al. The origin of intermuscular adipose tissue and its pathophysiological implications. Am J Physiol Endocrinol Metabolism. 2009;297(5):E987–98.

    Article  CAS  Google Scholar 

  42. Soleimani M. Insulin resistance and hypertension: new insights. Kidney Int. 2015;87(3):497–9.

    Article  PubMed  Google Scholar 

  43. Han K, Park YM, Kwon HS, Ko SH, Lee SH, Yim HW, et al. Sarcopenia as a determinant of blood pressure in older Koreans: findings from the Korea National health and nutrition examination surveys (KNHANES) 2008–2010. PLoS ONE. 2014;9(1):e86902.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Du R, Yuan J, Huang Y, Jiang G, Duan Z, Yang H, et al. Sarcopenia is not associated with hypertension, but sarcopenic obesity increases risk of hypertension: a 7-year cohort study. Front Public Health. 2024;12:1479169.

    Article  PubMed  Google Scholar 

  45. Barber MRW, Drenkard C, Falasinnu T, Hoi A, Mak A, Kow NY, et al. Global epidemiology of systemic lupus erythematosus. Nat Rev Rheumatol. 2021;17(9):515–32.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yang Y, Li S, Ren Q, Qiu Y, Pan M, Liu G, et al. The interaction between triglyceride-glucose index and visceral adiposity in cardiovascular disease risk: findings from a nationwide Chinese cohort. Cardiovasc Diabetol. 2024;23(1):427.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu L, Peng J, Zang D, Zhang Y, Wu Z, Song C, et al. The Chinese visceral adiposity index: a novel indicator more closely related to cardiovascular disease than other abdominal obesity indices among postmenopausal women. J Translational Med. 2024;22(1):855.

    Article  Google Scholar 

  48. Martin L, Birdsell L, Macdonald N, Reiman T, Clandinin MT, McCargar LJ, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncology: Official J Am Soc Clin Oncol. 2013;31(12):1539–47.

    Article  Google Scholar 

  49. Thiese MS. Observational and interventional study design types; an overview. Biochemia Med. 2014;24(2):199–210.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Author notes

  1. Bowen Wang and Liping Zuo were co-first author.

Authors and Affiliations

  1. Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China

    Bowen Wang, Liping Zuo, Jinlei Fan, Wei Zhao, Yueming An, Wangshu Cai & Dexin Yu

  2. Department of Radiology, Jinan Central Hospital, Jinan, Shandong, 250013, China

    Wangshu Cai

Authors
  1. Bowen Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Liping Zuo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jinlei Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Wei Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yueming An
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Wangshu Cai
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Dexin Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

W.B. and Z.L. performed the majority of the research and they analyzed the data; W.B, F.J. and Z.W. wrote the manuscript; A.Y and C.W. were involved in revising the paper. Y.D. supervised and organized the study and contributed to the critical revision. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Dexin Yu.

Ethics declarations

Ethics approval and consent to participate

This retrospective study was approved by our Ethics Committee and the requirement for informed consent was waived (KYLL-202307-043).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

The original online version of this article was revised: the authors reported that the corresponding author’s name was mistakenly listed as “Deixin Yu”. The correct spelling should be “Dexin Yu”.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

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

Wang, B., Zuo, L., Fan, J. et al. Association between myosteatosis or sarcopenia based on abdominal CT and hypertension in systemic lupus erythematosus patients. Lipids Health Dis 24, 105 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02530-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02530-9

Keywords

Lipids in Health and Disease

ISSN: 1476-511X