Introduction

In recent years, with the continuous improvement of people’s living standards and changes in unhealthy eating habits. The global prevalence of PCOS and MAFLD is also increasing. Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in women, affecting 4–19% of women of child-bearing age [1], and is associated with high hormone and metabolic changes as well as reproductive abnormalities [2]. PCOS can lead to various metabolic disorders. Studies have shown that most PCOS patients develop insulin resistance (IR), hyperandrogenism (HA), and metabolic syndrome (Mets) [3], which also increase the risk of MAFLD in PCOS patients [4]. Existing research has found that the prevalence of MAFLD in PCOS patients is significantly higher than in the normal population [3], and this phenomenon is more common in PCOS obese patients [5]. The characteristic of MAFLD is that when the severity of the condition is mild, simple fatty liver degeneration may occur, and patients can recover after discovering and eliminating the cause. About 20% of individuals with MAFLD will develop metabolic dysfunction-associated steatohepatitis (MASH), which is the active form of MAFLD. Once it develops into MASH, patients will progress to cirrhosis, liver failure, and hepatocellular carcinoma at a faster rate [6]. MAFLD is becoming an increasingly serious public health problem, and early detection and intervention have important clinical value in controlling the disease progression of MAFLD patients. At present, liver biopsy is the gold standard for diagnosing MAFLD, but its clinical application is limited due to its invasiveness. Magnetic resonance imaging proton density fat fraction (MRI-PDFF) is the imaging gold standard for evaluating the degree of liver steatosis, with good consistency and reproducibility in assessing liver fat content. However, due to the high cost of examination and numerous contraindications, the clinical application of MRI-PDFF is limited and cannot be routinely used for screening and monitoring. In recent years, the development of quantitative ultrasound techniques (including attenuation coefficient, backscatter coefficient, and sound velocity) for evaluating hepatic steatosis has attracted widespread attention [7]. UDFF is a novel quantitative assessment technique for liver fat content, which integrates attenuation coefficient and backscatter coefficient data into an ultrasound system for rapid analysis, ultimately obtaining liver fat content measured as a percentage [8]. The previous studies have demonstrated the potential of UDFF in the diagnosis and grading of hepatic steatosis. There have also been studies confirming the diagnostic value of UDFF for liver steatosis in children, obese populations, etc. [9], but there is little research on obese polycystic ovary syndrome patients, who are prone to MAFLD. This article focuses on obese patients with polycystic ovary syndrome, aiming to confirm the diagnostic value of UDFF for metabolic-related fatty liver disease in obese patients with polycystic ovary syndrome.

Methods and materials

Research object

This study is a prospective, single-center study that randomly enrolled 124 PCOS obese patients who visited the outpatient department of Qingdao University Affiliated Hospital from May 2023 to December 2024. During the same period, 106 women who matched their age and body mass index (BMI) were selected as the simple obesity group. This study was approved by the Medical Ethics Committee of Qingdao University Affiliated Hospital (review number: QYFY WZLL 28963).

Inclusion criteria for obese PCOS group:

  1. (1)

    Premenopausal women aged 18 and above who have been diagnosed with obese PCOS for the first time.

  2. (2)

    PCOS diagnosis reference Rotterdam diagnostic criteria 2003 [10]

  3. (3)

    The diagnosis of MAFLD follows the guidelines for the prevention and treatment of non-alcoholic fatty liver disease (2018 updated version) [11].

S ≥ 1 includes mild, moderate, and severe fatty liver, S ≥ 2 includes moderate and severe fatty liver, and S ≥ 3 is severe fatty liver.

Inclusion criteria for the simple obesity group:

  • Women over 18 years old who are premenopausal.

  • BMI ≥ 28 kg/m2.

Exclusion criteria:

  1. (1)

    Individuals with a history of excessive alcohol consumption (females ≥ 70 g/week).

  2. (2)

    Patients with chronic HBV and HCV infection, autoimmune liver disease, alcoholic liver disease, inherited metabolic liver disease, drug-induced liver injury, hepatic vascular disease, and liver tumors; patients with liver, kidney, and spleen deformities;

  3. (3)

    Diagnosed with other endocrine disorders such as nephrotic syndrome, hypothyroidism, systemic lupus erythematosus, Cushing’s syndrome, etc.;

  4. (4)

    Suffering from malignant tumors;

  5. (5)

    History of pregnancy and lactation within the past 3 months.

Clinical data

Obtain the patient’s age, height, and weight, and calculate BMI (weight (kg)/height 2 (m2)) according to the formula. Blood pressure is the average of two pressure measurements taken while sitting, with a minimum interval of 10 min between each measurement.

Laboratory examinations

Measure triglyceride (TG) levels using deionization and enzymatic methods; measure the levels of low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) using chemically modified enzymatic methods; fasting blood glucose (FBG) levels were measured using the hexokinase/glucose-6-phosphate dehydrogenase method; fasting insulin (FIns) levels were measured using chemiluminescence (double-antibody sandwich) method, and steady-state model evaluation insulin resistance (HOMA-IR) was calculated using the following formula: HOMA-IR = FBG (mmol/L) × FIns (mU/L)/22.5 [12]; and use electrochemiluminescence immunoassay to evaluate testosterone (T) levels.

Main equipment

When using the Acuson Sequoia ultrasound diagnostic instrument produced by Siemens in Germany, a 5C1 convex array probe (1.0–5.0 MHz) is used for two-dimensional image acquisition, and a DAX convex array probe (1.0–3.5 MHz) is used for UDFF measurement.

Ultrasonic examination

The measurement of liver UDFF and shear wave velocity (SWV) was independently conducted by two physicians with more than 5 years of clinical experience in ultrasound, and the UDFF measurement results of the two ultrasound physicians showed strong consistency. When there is a disagreement in the ultrasound conclusion, discuss and make a decision with a third physician with over 20 years of clinical experience in ultrasound. Examination method: 1. Patient preparation and position: Fasting for at least 4 h, supine position, with the right arm placed near the head. 2. Sampling method: The probe is placed between the ribs, start the UDFF software, place the depth marker line of the ROI (region of interest) on the hepatic capsule, and keep the ROI perpendicular to the hepatic capsule. Select the hepatic segment V, avoiding large blood vessels, hepatic bile ducts, and rib shadow areas. During the collection process, the patient pauses breathing while still breathing; measure 15 UDFF values through a single collection and display their mean, standard deviation (SD), median, IQR, and IQR/median on the report page.

Obtain a set of mean, standard deviation (SD), median, IQR, and IQR/median from three measurements taken at the same location and list them on the report page. This study used the median of three measurements as the evaluation criterion for UDFF. The examiners of routine ultrasound and UDFF are not aware of each other’s examination results and the patient’s clinical information. UDFF, the interval between ultrasound-related examinations and laboratory tests, shall not exceed 1 week.

Statistical methods

Calculate the sample size using PASS 15. Choose tests for one ROC curve with a confidence of 0.90, α = 0.05, the expected area under the curve (AUC) is 0.90, and the null hypothesis AUC is 0.60. Considering that this study is a diagnostic trial that does not require relevant interventions or follow-up of patients, a dropout rate of 5% is set. The ratio of obese PCOS patients to simply obese patients is 1:1, and 21 subjects need to be included in each group.

SPSS 26.0 and GraphPad Prism 9.5.0 statistical software were used to analyze the data. Kappa test was used to analyze the consistency of two physicians in categorical data, and Bland–Altman scatter plot was used to analyze the consistency of two physicians in continuous data. The Shapiro–Wilk test is used to test the normality of the metric data. The metric data that conform to a normal distribution are expressed as mean ± standard deviation. Independent sample t-test is used for comparison between two groups, and one-way analysis of variance is used for comparison of means between multiple groups; individuals who do not conform to the normal distribution are represented by M (P25, P75)/M (IQR). Mann–Whitney U-test is used for comparison between two groups, and rank-sum test is used for comparison between multiple groups; count data is presented as an example (%), and Fisher’s exact test is used for intergroup comparisons. Spearman correlation analysis was used to investigate the correlation between UDFF and various variables, with MAFLD as the dependent variable and statistical differences in univariate analysis as the independent variables. Binary logistic regression was applied to analyze the independent risk factors affecting MAFLD occurrence and establish a regression model to obtain independent predictive factors. Spearman correlation analysis was used to investigate the relationship between UDFF and MAFLD severity. Based on the ultrasound results, GraphPad Prism was used to plot the receiver operating characteristic curve (ROC) of UDFF for diagnosing the degree of hepatic steatosis in obese PCOS patients, and the area under the ROC curve (AUC) was calculated. The cutoff value for UDFF diagnosis of liver steatosis degree is taken as the maximum point of the Youden index (sensitivity + species-1) (Figs. 1 and 2).

Fig. 1
figure 1

Ultrasound schematic diagram of UDFF quantitative detection of liver steatosis degree in enrolled women

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Fig. 2
figure 2

UDFF values measured by enrolled women, including their mean, standard deviation, median, interquartile range, and interquartile range/median

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Results

Clinical and ultrasound examination results

The abdominal circumference, T, TG, LDL, and UDFF of the obese PCOS group were higher than those of the simple obesity group, and the differences were statistically significant (P < 0.05). The HDL of the obese PCOS group was lower than the simple obesity group, and the differences were statistically significant (P < 0.05). There was no statistically significant difference in age, blood pressure, BMI, fasting blood glucose, fasting insulin, IR, ALT, and AST between the two groups (P > 0.05) (Table 1).

Table 1 Clinical data and UDFF comparison between obese PCOS group and simple
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The prevalence of MAFLD in the obese PCOS group and the simple obesity group was 51.61% (64/124) and 40.57% (43/106), respectively, and the prevalence of moderate-to-severe fatty liver was higher in the obese PCOS group than in the simple obesity group (Table 2).

Table 2 Classification of fatty liver in obese PCOS group and simple obese group
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Analysis of related factors of MAFLD in obese PCOS patients

A total of 124 obese PCOS patients were divided into MAFLD group (n = 64) and no MAFLD group (n = 60), and clinical data and UDFF were compared between the two groups (Table 3).

Table 3 Comparison between MAFLD and MAFLD free groups
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Using the presence of MAFLD in obese PCOS patients as the dependent variable; select diagnostic indicators with diagnostic value as independent variables through single-factor analysis. According to binary logistic regression analysis, the independent risk factor for MAFLD is UDFF (Table 4). According to the likelihood ratio test, Cox&Snell R2 = 0.646, Nagelkerke R2 = 0.861, and Hosmer–Lemeshow test results: χ2 = 4.080, P = 0.850 (> 0.05). UDFF is positively correlated with the severity of MAFLD (r = 0.603, P < 0.01), with UDFF values in the order of mild MAFLD < moderate MAFLD < severe MAFLD (P < 0.001) (Fig. 3).

Table 4 Binary logistic regression analysis results
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Fig. 3
figure 3

Comparative analysis of UAFF among MAFLD groups at different degrees

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The correlation between UDFF and BMI, abdominal circumference, serum, and other indicators in obese PCOS patients: UDFF is positively correlated with BMI, fasting insulin, HOMA-IR, T, TG, and negatively correlated with HDL, all P < 0.01. There was no significant correlation between UDFF and LDL (P > 0.05) (Table 5).

Table 5 The correlation between UDFF and various indicators
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Efficiency analysis of UDFF diagnosis for MAFLD

Use the Youden index maximization method, i.e., maximizing (sensitivity + species-1), to determine the optimal cutoff value.

Based on the ultrasound results, patients were divided into three groups according to the degree of fat degeneration: S ≥ 1, S ≥ 2, and S = 3. The efficiency of using area analysis under ROC curve for UDFF diagnosis of liver steatosis degree S ≥ 1, S ≥ 2, and S = 3 was evaluated. The results showed that the AUC of UDFF diagnosis of liver steatosis degree S ≥ 1, S ≥ 2, and S = 3 was 0.935, 0.951, and 0.916, respectively, with cutoff points of 4.5%, 6.5%, and 13.0%, and sensitivity and specificity were (92.0%, 85.0%), (93.8%, 80.3%), and (97.1%, 85.0%), respectively (Fig. 4 and Table 6).

Fig. 4
figure 4

ROC curve of UDFF diagnosis for different degrees of MAFLD

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Table 6 Efficiency analysis of UDFF in diagnosing MAFLD of different degrees
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Consistency check

Twenty obese PCOS patients were randomly selected, and two physicians measured UDFF, respectively. Bland–Altman scatter plot analysis showed that UDFF had good repeatability within and between observers (Fig. 5), with measurement differences of − 0.250 ± 0.123 (95% CI − 0.507 ~ 0.007) and − 0.400 ± 0.234 (95% CI − 0.590 ~ 0.090), respectively.

Fig. 5
figure 5

Repeatability detection results of UDFF within (a) and between (b) observers

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Discussion

The increase of liver fat content is considered to be the key early factor of MAFLD liver injury and progress. In addition, diagnosis of liver steatosis and measurement of liver fat content can be used to predict the potential development of cardiovascular disease and diabetes in the future. There are many methods for evaluating liver fat content, among which conventional ultrasound (CUS) is the most common imaging examination for assessing hepatic steatosis [13]. Due to its economy and non-invasiveness, it is the most widely used diagnostic tool for metabolic-related fatty liver disease. CUS has high sensitivity and specificity in detecting moderate-to-severe fatty liver. However, in mild steatosis, the sensitivity of CUS is significantly reduced [14], and CUS can only qualitatively detect fatty liver and cannot be quantitatively evaluated as continuous data. Quantifying liver steatosis is crucial, and the use of magnetic resonance imaging (MRI) to measure liver fat content overcomes these limitations [15]. MRI proton density fat fraction (MRI-PDFF) has good consistency and reproducibility with liver biopsy in evaluating liver fat content and is the gold standard for quantitative evaluation of liver fat content. However, due to its high examination cost, the clinical application of MRI-PDFF is limited. Ultrasound-derived fat fraction (UDFF), as an emerging imaging technique, is a deep algorithm developed by combining BSC and AC. Its clinical application in evaluating liver fat content is gradually receiving attention. Studies by scholars have shown that the measurement results of UDFF and PDFF are highly consistent [16], and Labyed et al. [17] reported a Pearson correlation coefficient of 0.87 between UDFF and MRI-PDFF. Dillman et al. [18] reported an average bias of 4.0% between UDFF and MRI-PDFF, indicating a significant positive correlation between the two detection methods (ρ = 0.82; P < 0.001). This indicates that the accuracy of UDFF in evaluating liver fat content is comparable to MRI [19]. In addition, UDFF is relatively easy to operate and can be directly obtained during routine ultrasound examinations, reducing the burden on patients. UDFF has high repeatability and reliability in clinical applications [20]. Gao et al. [21] prospectively evaluated 21 subjects and reported high repeatability (> 0.85) of UDFF measurement in observers. Given the repeatability, safety, and cost-effectiveness of UDFF, it is highly suitable as a screening tool for metabolic-associated fatty liver disease.

The previous studies have shown that women with PCOS have a 2.5-fold higher risk of MAFLD compared to the general population [22]. As is well known, obesity can lead to MAFLD. Therefore, in this study, we recruited an age- and BMI-matched obese group with no statistically significant difference in BMI, thereby reducing the potential outcome bias caused by obesity. The results showed that compared with the simple obesity group, the obese PCOS group had more severe lipid metabolism disorders (TG, LDL-C, and HDL-C), glucose metabolism disorders (fasting blood glucose), and sex hormone disorders (T), further confirming the correlation between PCOS and endocrine metabolism disorders. To our knowledge, this is the first study to include obese Chinese women with PCOS as UDFF test subjects, and also the first study to include a weight- and age-matched group of obese individuals to observe and analyze changes in liver UDFF.

This study investigated the prevalence of MAFLD in obese PCOS patients by measuring UDFF, providing valuable follow-up and management for obese PCOS patients. This study found that the UDFF in the obese PCOS group was significantly increased compared to the simple obese group, indicating that obese PCOS patients are more prone to metabolic-related fatty liver disease, which is consistent with the findings of Huffman AM et al. This study provides further evidence that UDFF is an accurate and simple ultrasound indicator for evaluating hepatic steatosis in obese individuals at high risk of PCOS. In Gu J et al.’s study, it was found that excessive androgen levels, insulin resistance, obesity, and other factors are risk factors for MAFLD in PCOS patients [23]. The results analyzed in this article also draw similar conclusions: abnormal glucose and lipid metabolism and sex hormone disorders are the main influencing factors of elevated UDFF in obese PCOS patients. This is manifested as a significant increase in UDFF, abdominal circumference, T, TG, and LDL levels in obese PCOS patients, as well as a significant decrease in HDL levels in the obese PCOS group. Further analysis shows that, BMI, elevated abdominal circumference, fasting blood glucose, T, and TG levels are associated with elevated liver UDFF in PCOS obese patients. BMI, as the ratio of height to weight, is mainly used to evaluate overall weight. However, the value of BMI in stratifying the risk of liver steatosis in PCOS patients is limited, as different patients with the same BMI index may exhibit varying levels of liver steatosis. Women with PCOS typically have a centripetal obese body shape, and measuring abdominal circumference has been shown to be a good predictor of blood glucose abnormalities and insulin resistance in PCOS women. However, measuring abdominal circumference cannot distinguish between visceral and subcutaneous fat, and the distinction between the two can affect the incidence of metabolic abnormalities. UDFF is an independent risk factor for PCOS obese patients with MAFLD. The correlation analysis of this study shows that UDFF is positively correlated with the severity of MAFLD, and as the severity of MAFLD increases, UDFF levels gradually increase. The reason for this may be related to the imaging principle of UDFF technology, which quantitatively evaluates the fat content in the liver by analyzing the propagation characteristics of ultrasound in liver tissue, combined with parameters such as echo attenuation and scattering coefficient [8]. The severity of hepatic steatosis increases, and the more severe the attenuation of ultrasound propagation in liver tissue, the higher the UDFF value.

In this study, the AUC of UDFF for diagnosing liver steatosis with S ≥ 1, S ≥ 2, and S = 3 was 0.935, 0.951, and 0.916, respectively. The cutoff points were 4.5%, 6.5%, and 13.0%, respectively. The sensitivity and specificity were (92.0%, 85.0%), (93.8%, 80.3%), and (97.1%, 85.0%), respectively. As the degree of liver steatosis worsened, the cutoff point of UDFF diagnosis gradually increased, indicating its good diagnostic efficacy for different degrees of liver steatosis. From this, it can be seen that UDFF, as a non-invasive ultrasound examination method for diagnosing liver steatosis, has good sensitivity and specificity, indicating that UDFF can be used as one of the non-invasive diagnostic techniques for diagnosing MAFLD and judging the degree of disease. It can be used as the preferred examination for regularly monitoring the degree of liver steatosis in patients with chronic liver disease.

There are also some limitations to this study: It is a single-center diagnostic trial with a relatively small sample size. However, as a prospective trial with randomized continuous enrollment, the sample size was strictly calculated, and the final number of subjects included was much larger than the required sample size. It can be considered that the conclusion drawn in this study that UDFF has strong diagnostic ability for MAFLD is relatively reliable. In the future, larger samples and more long-term follow-up data can be collected to verify the application value of UDFF in obese PCOS patients. In addition, standardization and further clinical validation of UDFF collection are also directions for future research.

Conclusion

In summary, UDFF has shown promising application prospects in the quantitative assessment of liver fat content in obese PCOS patients. Through in-depth analysis of this study, we have provided reliable scientific evidence for UDFF as a routine examination for liver health management in obese PCOS patients. The results of this study not only contribute to a deeper understanding of MAFLD in PCOS patients, but also provide new directions for early intervention and treatment.