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. 2024 Jul 3;38(4):1917–1926. doi: 10.21873/invivo.13647

Body Composition in Patients With Metabolic Dysfunction-associated Steatotic Liver Disease: Impact of Body Surface Area

SAORI ONISHI 1, AKIRA FUKUDA 2, MASAHIRO MATSUI 1, KOSUKE USHIRO 1, TOMOHIRO NISHIKAWA 1, AKIRA ASAI 1, SOO KI KIM 3, HIROKI NISHIKAWA 1
PMCID: PMC11215608  PMID: 38936925

Abstract

Background/Aim

To examine the relationship between the body surface area (BSA) and body composition in patients with metabolic dysfunction-associated steatotic liver disease (MASLD, 2,141 men and 986 women).

Materials and Methods

BSA and body composition parameters were examined.

Results

The median body mass index (BMI) was 25.0 kg/m² for both men and women (p=0.7754). The median body surface area (BSA) was 1.854 m² for men and 1.618 m² for women (p<0.0001). In men, the median fat mass was 17.7 kg, whereas in women, it was 22.1 kg (p<0.0001). Additionally, the median fat-free mass was 55.4 kg in men and 39.3 kg in women (p<0.0001).). In male cases, BSA significantly correlated with fat mass (r=0.82, p<0.0001) and fat-free mass (r=0.95, p<0.0001). In female cases, BSA significantly correlated with fat mass (r=0.87, p<0.0001) and fat-free mass (r=0.94, p<0.0001).

Conclusion

BSA could be a useful marker for the estimation of body composition in patients with MASLD.

Keywords: Metabolic dysfunction-associated steatotic liver disease, body composition, fat mass, fat-free mass

Metabolic dysfunction-associated steatotic liver disease (MASLD) was proposed as a new definition of fatty liver disease (1). This is a new way of looking at fatty liver combined with metabolic abnormalities such as obesity and diabetes and was proposed for early detection of “fatty liver at risk” (1,2). MASLD is diagnosed when, in addition to fatty liver, overweight, abnormal glucose metabolism, hypertension, or dyslipidemia coexist and other causes such as excessive alcohol intake are excluded (1,3). However, lean (non-obese) non-alcoholic fatty liver disease (NAFLD) has been increasing in recent years and has attracted much attention (4-7). Approximately 20% of Japanese patients with NAFLD have been reported to have lean NAFLD (5). Lean NAFLD is prone to sarcopenia (8). Our previous study in metabolic dysfunction-associated fatty liver disease (MAFLD) also showed that subjects with low body mass index (BMI) have a high rate of reduction in skeletal muscle mass, highlighting the importance of research om body composition in MASLD or MAFLD cases (9).

Body surface area (BSA) is a measurement or calculation of the surface area of the human body and is a metric that has been used for many years (10-13). German physiologist M. Rubner (1854-1932) introduced the law of BSA, which states that the basal metabolic rate of homothermal animals is approximately proportional to their BSA, regardless of animal species or body weight. In other words, the idea is that even in humans, their basal metabolic rate is more closely proportional to their BSA than to their weight or height, and that the basal metabolic rate can be calculated from the measured BSA (10). The cardiac index is a measure of cardiac function that converts cardiac output per BSA to correct for individual body size differences (14). It is one of the indicators to ascertain hemodynamics. The unit is l/min/m2. Also in the area of cancer chemotherapy, the dosage of anticancer drugs is often set according to BSA (15). With regard to renal function, the estimated glomerular filtration rate (eGFR) proposed by the Japanese Society of Nephrology is commonly used in Japan. eGFR refers to GFR (ml/min/1.73 m2) when corrected for a standard body size with a BSA of 1.73 m2 (16).

BSA has been used for a variety of applications in the real-world clinical practice. However, to our knowledge, there have been no reports examining the relationship between BSA and body composition in patients with MASLD. The present study aimed to clarify these issues.

Patients and Methods

Patients. Between February 2022 and August 2023, the data of a total of 3,127 consecutive subjects with MASLD with results for body composition by bioelectrical impedance analysis were extracted from their medical records and were retrospectively analyzed. All study subjects were tested at the Osaka Medical and Pharmaceutical University (OMPU) Health Sciences Clinic (OMPU-attached facility). Radiological findings of fatty liver were confirmed in all study subjects using ultrasonography. The diagnosis of MASLD was made on the basis of the current guidelines (1). The definition of MASLD includes one or more of five cardiometabolic risk factors, including BMI / waist circumference (WC), blood glucose, blood pressure, triglycerides, and high-density lipoprotein cholesterol. Patients with no metabolic abnormalities and undetermined cause are considered to have cryptogenic SLD (1). The DuBois formula [height (cm) 0.725×weight (kg) 0.425×0.007184], currently the most widely used, was used to calculate BSA (17).

Body composition and our current analysis. Our method of the measurement of body composition is as described elsewhere (9). Fat mass (kg) and fat-free mass (kg) were measured and analyzed in the current analysis. Fat mass index (F index) and fat-free mass index (FF index) were defined as fat mass divided by height squared (kg/m2) and fat-free mass divided by height squared (kg/m2), respectively. According to the previous reports, we defined subjects with an FF index <18 kg/m2 in men and an FF index <15 kg/m2 in women as having decreased skeletal muscle mass (18).

First, we examined the relationship between the BSA and body composition in patients with MASLD. Second, receiver operating characteristic curve (ROC) analyses of factors for the skeletal muscle mass decline were performed. This study conformed to the principles of the Declaration of Helsinki and was approved by the ethics committee of OMPU hospital (approval no. 2023-165, approval date: 26th December 2023). An opt-out approach was used to obtain informed consent from study subjects, and personal information was protected during data collection.

Statistics. In the two-group comparison (continuous variables), Student’s t-test, the Mann-Whitney U-test, or Pearson’s correlation coefficient r was used, as appropriate. In the multiple-group comparison (continuous variables), analysis of variance (ANOVA) or the Kruskal-Wallis test was used, as appropriate. In the group comparison (nominal variables), Fisher’s exact test was used. Unless otherwise mentioned, data are shown as number or median (range). A p-value less than 0.05 was considered as statistically significant. JMP 17.0.0 software (SAS Institute, Cary, NC, USA) was used to perform statistical analyses.

Results

Baseline characteristics. Baseline characteristics in this study are demonstrated in Table I. The median (range) age in men and women was 55 (27-88) years and 56 (25-83) years (p=0.0028). The median (range) BMI in men and women was 25.0 (16.2-48.6) kg/m2 and 25.0 (16.8-43.9) kg/m2 (p=0.7754). The median (range) BSA in men and women was 1.854 (1.428-2.678) m2 and 1.618 (1.269-2.177) m2 (p<0.0001). The median (range) fat mass in men and women was 17.7 (2.3-75.4) kg and 22.1 (7.5-66.7) kg (p<0.0001). The median (range) fat-free mass in men and women was 55.4 (36.4-87.9) kg and 39.3 (28.7-51.8) kg (p<0.0001). The median (range) FIB4 index in men and women was 1.037 (0.249-6.726) and 1.030 (0.301-3.890) (p=0.1758).

Table I. Baseline characteristics.

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Data are shown as number or median (range). AST: Aspartate aminotransferase; ALT: alanine aminotransferase; GGT: γ-glutamyl transpeptidase; eGFR: estimated glomerular filtration rate; HDL: high-density lipoprotein.

The correlation between BSA and body composition in male and female cases. In male cases (n=2,141), BSA significantly correlated with fat mass (r=0.82, p<0.0001, Figure 1A) and fat-free mass (r=0.95, p<0.0001, Figure 1B). In female cases (n=986), BSA significantly correlated with fat mass (r=0.87, p<0.0001, Figure 1C) and fat-free mass (r=0.94, p<0.0001, Figure 1D).

Figure 1. The correlation between BSA and fat mass (A) and fat-free mass (B) in men (n=2,141). The correlation between BSA and fat mass (C) and fat-free mass (D) in women (n=986).

Figure 1

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The correlation between BSA and body composition in male and female cases according to age. In male cases aged 65 years or more (n=530), the median (range) BSA, fat mass and fat-free mass were 1.776 (1.428-2.269) m2, 16.2 (5.6-46.3) kg and 52.1 (36.4-66.7) kg, respectively. BSA significantly correlated with fat mass (r=0.79, p<0.0001, Figure 2A) and fat-free mass (r=0.95, p<0.0001, Figure 2B). In male cases aged less than 65 years (n=1611), the median (range) BSA, fat mass and fat-free mass were 1.877 (1.444-2.678) m2, 18.3 (2.3-75.4) kg and 56.5 (36.9-87.9) kg, respectively. BSA significantly correlated with fat mass (r=0.83, p<0.0001, Figure 2C) and fat-free mass (r=0.95, p<0.0001, Figure 2D).

Figure 2. The correlation between BSA and fat mass (A) and fat-free mass (B) in men aged 65 years or more (n=530). The correlation between BSA and fat mass (C) and fat-free mass (D) in men aged less than 65 years (n=1,611). The correlation between BSA and fat mass (E) and fat-free mass (F) in women aged 65 years or more (n=245). The correlation between BSA and fat mass (G) and fat free-mass (H) in women aged less than 65 years (n=741).

Figure 2

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In female cases aged 65 years or more (n=245), the median (range) BSA, fat mass and fat-free mass were 1.537 (1.316-2.023) m2, 20.0 (8.4-55.6) kg and 36.9 (28.7-47.3) kg, respectively. BSA significantly correlated with fat mass (r=0.84, p<0.0001, Figure 2E) and fat-free mass (r=0.90, p<0.0001, Figure 2F). In female cases aged less than 65 years (n=741), the median (range) BSA, fat mass and fat-free mass were 1.651 (1.269-2.177) m2, 23.0 (7.5-66.7) kg and 40.0 (29.2-51.8) kg, respectively. BSA significantly correlated with fat mass (r=0.88, p<0.0001, Figure 2G) and fat-free mass (r=0.94, p<0.0001, Figure 2H).

The correlation between BSA and body composition in male and female cases according to BMI. In male cases with BMI ≥23 kg/m2 (n=1,700), the median (range) BSA, fat mass and fat-free mass were 1.885 (1.491-2.678) m2, 19.2 (2.3-75.4) kg and 56.6 (36.9-87.9) kg, respectively. BSA significantly correlated with fat mass (r=0.80, p<0.0001, Figure 3A) and fat-free mass (r=0.94, p<0.0001, Figure 3B). In male cases with BMI <23 kg/m2 (n=441), the median (range) BSA, fat mass and fat-free mass were 1.725 (1.428-2.048) m2, 12.3 (5.0-19.6) kg and 50.5 (36.4-64.7) kg, respectively. BSA significantly correlated with fat mass (r=0.82, p<0.0001, Figure 3C) and fat-free mass (r=0.95, p<0.001, Figure 3D).

Figure 3. The correlation between BSA and fat mass (A) and fat-free mass (B) in men with BMI ≥23 kg/m2 (n=1,700). The correlation between BSA and fat mass (C) and fat- free mass (D) in men with BMI <23 kg/m2 (n=441). The correlation between BSA and fat mass (E) and fat-free mass (F) in women with BMI ≥23 kg/m2 (n=740). The correlation between BSA and fat mass (G) and fat-free mass (H) in women with BMI <23 kg/m2 (n=246).

Figure 3

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In female cases with BMI ≥23 kg/m2 (n=740), the median (range) BSA, fat mass and fat-free mass were 1.667 (1.343-2.177) m2, 25.25 (15.7-66.7) kg and 40.2 (28.7-51.8) kg, respectively. BSA significantly correlated with fat mass (r=0.85, p<0.0001, Figure 3E) and fat-free mass (r=0.93, p<0.0001, Figure 3F). In female cases with BMI <23 kg/m2 (n=246), the median (range) BSA, fat mass and fat-free mass were 1.494 (1.269-1.751) m2, 15.8 (7.5-22.2) kg and 36.5 (29.2-44.3) kg, respectively. BSA significantly correlated with fat mass (r=0.72, p<0.0001, Figure 3G) and fat-free mass (r=0.94, p<0.0001, Figure 3H).

The correlation between BSA and body composition in male and female cases according to FIB4 index. In male cases with FIB4 index ≥1.3 (n=660) (19), the median (range) BSA, fat mass and fat-free mass were 1.813 (1.428-2.520) m2, 17.0 (5.0-72.3) kg and 53.55 (36.4-75.0) kg, respectively. BSA significantly correlated with fat mass (r=0.83, p<0.0001, Figure 4A) and fat-free mass (r=0.96, p<0.0001, Figure 4B). In male cases with FIB4 index <1.3 (n=1,481), the median (range) BSA, fat mass and fat-free mass were 1.871 (1.507-2.678) m2, 18.2 (2.3-75.4) kg and 56.2 (36.9-87.9) kg, respectively. BSA significantly correlated with fat mass (r=0.82, p<0.0001, Figure 4C) and fat-free mass (r=0.95, p<0.0001, Figure 4D).

Figure 4. The correlation between BSA and fat mass (A) and fat-free mass (B) in men with FIB4 index ≥1.3 (n=660). The correlation between BSA and fat mass (C) and fat-free mass (D) in men with FIB4 index <1.3 (n=1,481). The correlation between BSA and fat mass (E) and fat-free mass (F) in women with FIB4 index ≥1.3 (n=291). The correlation between BSA and fat mass (G) and fat free-mass (H) in women withFIB4 index <1.3 (n=695).

Figure 4

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In female cases with FIB4 index ≥1.3 (n=291), the median (range) BSA, fat mass and fat-free mass were 1.557 (1.316-2.081) m2, 20.0 (8.4-66.7) kg and 37.4 (28.7-48.9) kg, respectively. BSA significantly correlated with fat mass (r=0.86, p<0.0001, Figure 4E) and fat-free mass (r=0.92, p<0.0001, Figure 4F). In female cases with FIB4 index <1.3 (n=695), the median (range) BSA, fat mass and fat-free mass were 1.643 (1.269-2.177) m2, 23.4 (7.5-53.8) kg and 39.9 (29.2-51.8) kg, respectively. BSA significantly correlated with fat mass (r=0.87, p<0.0001, Figure 4G) and fat-free mass (r=0.94, p<0.0001, Figure 4H).

The correlation between BSA and body composition in male and female cases according to WC. In male cases with WC ≥85 cm [essential factor for metabolic syndrome in men (20), n=1,549], the median (range) BSA, fat mass and fat-free mass were 1.897 (1.491-2.678) m2, 19.8 (2.3-75.4) kg and 57.0 (42.1-87.9) kg, respectively. BSA significantly correlated with fat mass (r=0.79, p<0.0001, Figure 5A) and fat-free mass (r=0.94, p<0.0001, Figure 5B). In male cases with WC <85 cm (n=592), the median (range) BSA, fat mass and fat-free mass were 1.731 (1.428-2.048) m2, 13.2 (5.0-21.5) kg and 51.1 (36.4-64.7) kg, respectively. BSA significantly correlated with fat mass (r=0.50, p<0.0001, Figure 5C) and fat-free mass (r=0.94, p<0.0001, Figure 5D).

Figure 5. The correlation between BSA and fat mass (A) and fat-free mass (B) in men with waist circumference (WC) ≥85 cm (n=1,549). The correlation between BSA and fat mass (C) and fat-free mass (D) in men with FIB4 index <85 cm (n=592). The correlation between BSA and fat mass (E) and fat-free mass (F) in women with WC ≥90 cm (n=399). The correlation between BSA and fat mass (G) and fat free-mass (H) in women with WC <90 cm (n=587).

Figure 5

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In female cases with WC ≥90 cm [essential factor for metabolic syndrome in women (28), n=399], the median (range) BSA, fat mass and fat-free mass were 1.726 (1.343-2.177) m2, 29.5 (15.9-66.7) kg and 41.4 (28.7-51.8) kg, respectively. BSA significantly correlated with fat mass (r=0.82, p<0.0001, Figure 5E) and fat-free mass (r=0.92, p<0.0001, Figure 5F). In male cases with WC <90 cm (n=587), the median (range) BSA, fat mass and fat-free mass were 1.561 (1.269-1.912) m2, 19.3 (7.5-33.0) kg and 37.8 (29.2-50.3) kg, any speculatively. BSA significantly correlated with fat mass (r=0.79, p<0.0001, Figure 5G) and fat-free mass (r=0.95, p<0.0001, Figure 5H).

ROC analysis for the muscle mass decline. BSA index was defined as BSA divided by height squared (m2). As described before, the FF index (fat-free mass divided by height squared (m2)) <18 kg/m2 in men and FF index <15 kg/m2 in women was defined as having muscle mass decline (18). Muscle mass decline was found in 456 (21.3%) men and 153 (15.5%) women. Results of ROC analysis of factors for the muscle mass decline are presented in Figure 6 and Table II. Included variables were BSA index, BMI, age, FIB4 index and WC. In men, BMI had the highest area under the ROC curve (AUC=0.95) for the muscle mass decline followed by BSA index (AUC=0.93). In women, BMI had the highest AUC (0.97) for the muscle mass decline followed by BSA index (AUC=0.96). The corresponding AUC, sensitivity (%), specificity (%) and optimal cut off point for each factor is shown in Table II.

Figure 6. ROC analysis of factors for the skeletal muscle decline. (A), (B), (C), (D), and (E) in upper row are for men. (F), (G), (H), (I), and (J) in lower row are for women.

Figure 6

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Table II. ROC analyses of factors for the muscle mass decline in men and women.

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BSA: Body surface area; BMI: body mass index; WC: waist circumference; AUC: area under the receiver operating characteristic curve.

The correlation between the cross-sectional areas (cm2) of skeletal muscle at the third lumbar vertebra in men and women. Yoshizumi et al. proposed the formula to calculate skeletal muscle area (SMA, cm2) at the third lumbar vertebra using the data for BSA (126.9×BSA - 66.2 in men, and 125.6×BSA - 81.1 in women) (21). In that study, BSA was calculated by the following formula: square root [BW (kg)×height (cm)/3,600]. Using this formula, we calculated SMA at the third lumbar vertebra (cm2) in each sex. In men, the median (range) SMA at the third lumbar vertebra was 170.9 (113.0-288.1) cm2, and significantly correlated with fat-free mass (r=0.94, p<0.0001, Figure 7A). In women, the median (range) SMA at the third lumbar vertebra was 124.6 (77.1-201.0) cm2, and significantly correlated with fat-free mass (r=0.91, p<0.0001, Figure 7B).

Figure 7. The correlation between calculated skeletal muscle area (SMA) at the third lumbar vertebra level and fat-free mass in men (A) and in women (B).

Figure 7

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Discussion

The primary function of the liver is to metabolize carbohydrates, lipids, proteins, and drugs. Fatty liver diseases that are associated with metabolic diseases such as diabetes mellitus and others have come to be called MASLD. The Japan Society of Hepatology has also expressed its support for a new disease name and classification system for the fatty liver disease (22). Viral hepatitis is being eradicated worldwide, and the causative disease of cirrhosis and hepatocellular carcinoma is changing from viral liver disease to MASLD and alcoholic liver disease (23,24). It is also known that the most frequent cause of death in diabetic patients is liver disease, including liver cancer and cirrhosis (25,26). However, sarcopenia is currently being recognized as an important prognostic factor in liver disease (27), and how BSA, which is frequently used in daily life, relates to body composition in patients with MASLD is a very important issue in the daily clinical practice. To our knowledge, this is the largest clinical study to examine the association between BSA and body composition in patients with MASLD.

In our study, BSA was strongly correlated with fat mass and lean mass in all cases and in almost all analyses stratified by age, BMI, FIB4 index and WC. These results indicate that BSA is extremely useful for estimating body composition in MASLD patients. These results shed some light on the better understanding between BSA and body composition in patients with MASLD. The significantly higher fat mass in women despite no significant difference in BMI between sex indicates a marked difference in body composition between sexes, but it is also an interesting fact that BSA and body composition showed a strong correlation regardless of sex. The results showed that BSA increased with increasing body weight, and both fat mass and lean mass increased; however, increasing fat mass raises the risk of cardiovascular disease-related events (28). Conversely, guidelines recommend a weight loss of 3-10% to improve fatty liver (19); however, this can be also accompanied by a decrease in skeletal muscle mass. In this study, 441 men (20.6%) and 246 women (24.9%) had MASLD with a BMI of less than 23 kg/m2. One in five Japanese patients with NAFLD have a BMI of less than 23 kg/m2, which is almost consistent with the results of our study (4). In patients with MASLD/NAFLD and obesity, it is recommended to lose weight without losing muscle mass by resistance training and diet restriction. In non-obese patients with MASLD/NAFLD, it is recommended to maintain weight without losing muscle mass by resistance training and nutritional interventions (19). It also should be noted that 592 (27.7%) men and 587 (59.5%) women with WC less than 85 cm and 90 cm (essential items in the diagnosis of metabolic syndrome), respectively, did not meet the criteria for metabolic syndrome in the current analysis (20). Metabolic syndrome is characterized by excessive accumulation of visceral fat (20), but may be viewed as a clinical entity different from MASLD. We would like to mention that MASLD is a disease concept of SLD involving metabolic abnormalities.

Based on the results of ROC analysis, BSA appears to be useful for identifying cases with low skeletal muscle mass. Asian guidelines for sarcopenia recommend the SARC-F for diagnosing sarcopenia, but including BSA and BMI as screening tools for sarcopenia may need to be considered (29). NAFLD guidelines recommend consideration of liver biopsy when the FIB4 index is greater than 1.3 and liver biopsy is recommended when the FIB4 index is greater than 2.67 to evaluate the degree of liver fibrosis (19). Based on the results of the present ROC analysis, evaluation of sarcopenia should also be considered when the FIB4 index is greater than 1.3. It should be noted that 660 (30.8%) men and 291 (29.5%) women had an FIB4 index of 1.3 or greater in this study, while only 51 (2.4%) men and 8 (8.1%) had an FIB4 index of 2.67 or greater.

We found a very strong correlation between the calculated SMA at the third lumbar vertebra level and total fat-free mass. Many studies have used SMA at the third lumbar vertebra level on computed tomography to determine skeletal muscle mass decline (27,30,31), and our results demonstrate its validity. Skeletal muscle is an important metabolic organ responsible for 80% of glucose metabolism (32). Decreased skeletal muscle mass reduces the uptake of glucose into skeletal muscle, increasing release glucose into the bloodstream, and thus worsening glucose tolerance and elevating blood glucose (32). In other words, sarcopenia is a risk factor for the exacerbation of diabetes (32).

A major limitation of this study is that it is a single-center, retrospective study. In addition, this study was limited to Japanese MASLD patients, and it is not known if it applies to other ethnic groups. Thus, the current data should be interpreted cautiously. However, the sample size was large and thus we would like to emphasize one last time that the results obtained in this study show how useful BSA is in estimating the body composition of MASLD patients. In conclusion, BSA could be a helpful marker for the estimation of body composition in patients with MASLD.

Conflicts of Interest

None of the Authors have any conflicts of interest to declare in relation to this study.

Authors’ Contributions

Data collection: all Authors. Statistical analysis: SO and HN. Writing original draft: SO and HN.

Review and editing: all Authors. Supervision: AF. Final approval: all Authors.

Acknowledgements

The Authors gratefully thank all medical staff in their department for their significant help.

References

  • 1.Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, Romero D, Abdelmalek MF, Anstee QM, Arab JP, Arrese M, Bataller R, Beuers U, Boursier J, Bugianesi E, Byrne CD, Castro Narro GE, Chowdhury A, Cortez-Pinto H, Cryer DR, Cusi K, El-Kassas M, Klein S, Eskridge W, Fan J, Gawrieh S, Guy CD, Harrison SA, Kim SU, Koot BG, Korenjak M, Kowdley KV, Lacaille F, Loomba R, Mitchell-Thain R, Morgan TR, Powell EE, Roden M, Romero-Gómez M, Silva M, Singh SP, Sookoian SC, Spearman CW, Tiniakos D, Valenti L, Vos MB, Wong VW, Xanthakos S, Yilmaz Y, Younossi Z, Hobbs A, Villota-Rivas M, Newsome PN, NAFLD Nomenclature consensus group A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J Hepatol. 2023;79(6):1542–1556. doi: 10.1016/j.jhep.2023.06.003. [DOI] [PubMed] [Google Scholar]
  • 2.Lee HH, Lee HA, Kim EJ, Kim HY, Kim HC, Ahn SH, Lee H, Kim SU. Metabolic dysfunction-associated steatotic liver disease and risk of cardiovascular disease. Gut. 2023;73(3):533–540. doi: 10.1136/gutjnl-2023-331003. [DOI] [PubMed] [Google Scholar]
  • 3.Hutchison AL, Tavaglione F, Romeo S, Charlton M. Endocrine aspects of metabolic dysfunction-associated steatotic liver disease (MASLD): Beyond insulin resistance. J Hepatol. 2023;79(6):1524–1541. doi: 10.1016/j.jhep.2023.08.030. [DOI] [PubMed] [Google Scholar]
  • 4.Xu R, Pan J, Zhou W, Ji G, Dang Y. Recent advances in lean NAFLD. Biomed Pharmacother. 2022;153:113331. doi: 10.1016/j.biopha.2022.113331. [DOI] [PubMed] [Google Scholar]
  • 5.Ito T, Ishigami M, Zou B, Tanaka T, Takahashi H, Kurosaki M, Maeda M, Thin KN, Tanaka K, Takahashi Y, Itoh Y, Oniki K, Seko Y, Saruwatari J, Kawanaka M, Atsukawa M, Hyogo H, Ono M, Ogawa E, Barnett SD, Stave CD, Cheung RC, Fujishiro M, Eguchi Y, Toyoda H, Nguyen MH. The epidemiology of NAFLD and lean NAFLD in Japan: a meta-analysis with individual and forecasting analysis, 1995–2040. Hepatol Int. 2021;15(2):366–379. doi: 10.1007/s12072-021-10143-4. [DOI] [PubMed] [Google Scholar]
  • 6.Kawanaka M, Nishino K, Kawada M, Ishii K, Tanikawa T, Katsumata R, Urata N, Nakamura J, Suehiro M, Haruma K, Kawamoto H. Lean nonalcoholic fatty liver disease: Age-dependent differences in pathology, prognosis, and liver-related events. Hepatol Res. 2023;53(9):829–843. doi: 10.1111/hepr.13911. [DOI] [PubMed] [Google Scholar]
  • 7.De A, Bhagat N, Mehta M, Taneja S, Duseja A. Metabolic dysfunction-associated steatotic liver disease (MASLD) definition is better than MAFLD criteria for lean patients with NAFLD. J Hepatol. 2024;80(2):e61–e62. doi: 10.1016/j.jhep.2023.07.031. [DOI] [PubMed] [Google Scholar]
  • 8.Chen M, Cao Y, Ji G, Zhang L. Lean nonalcoholic fatty liver disease and sarcopenia. Front Endocrinol (Lausanne) 2023;14:1217249. doi: 10.3389/fendo.2023.1217249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Onishi S, Fukuda A, Matsui M, Ushiro K, Nishikawa T, Asai A, Kim SK, Nishikawa H. Body composition analysis in patients with metabolic dysfunction-associated fatty liver disease. Nutrients. 2023;15(18):3878. doi: 10.3390/nu15183878. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.McMahon T. Size and shape in biology. Science. 1973;179(4079):1201–1204. doi: 10.1126/science.179.4079.1201. [DOI] [PubMed] [Google Scholar]
  • 11.Yu CY, Lin CH, Yang YH. Human body surface area database and estimation formula. Burns. 2010;36(5):616–629. doi: 10.1016/j.burns.2009.05.013. [DOI] [PubMed] [Google Scholar]
  • 12.Wang J, Hihara E. Human body surface area: a theoretical approach. Eur J Appl Physiol. 2004;91(4):425–428. doi: 10.1007/s00421-003-1011-3. [DOI] [PubMed] [Google Scholar]
  • 13.Wang J, Hihara E. A unified formula for calculating body surface area of humans and animals. Eur J Appl Physiol. 2004;92(1-2):13–17. doi: 10.1007/s00421-004-1074-9. [DOI] [PubMed] [Google Scholar]
  • 14.Martinez-Naharro A, Hawkins PN, Fontana M. Cardiac amyloidosis. Clin Med (Lond) 2018;18(Suppl 2):s30–s35. doi: 10.7861/clinmedicine.18-2-s30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Gao B, Klumpen HJ, Gurney H. Dose calculation of anticancer drugs. Expert Opin Drug Metab Toxicol. 2008;4(10):1307–1319. doi: 10.1517/17425255.4.10.1307. [DOI] [PubMed] [Google Scholar]
  • 16.Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, Yamagata K, Tomino Y, Yokoyama H, Hishida A, Collaborators developing the Japanese equation for estimated GFR Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53(6):982–992. doi: 10.1053/j.ajkd.2008.12.034. [DOI] [PubMed] [Google Scholar]
  • 17.Verbraecken J, Van de Heyning P, De Backer W, Van Gaal L. Body surface area in normal-weight, overweight, and obese adults. A comparison study. Metabolism. 2006;55(4):515–524. doi: 10.1016/j.metabol.2005.11.004. [DOI] [PubMed] [Google Scholar]
  • 18.Kawakami R, Tanisawa K, Ito T, Usui C, Miyachi M, Torii S, Midorikawa T, Ishii K, Muraoka I, Suzuki K, Sakamoto S, Higuchi M, Oka K. Fat-free mass index as a surrogate marker of appendicular skeletal muscle mass index for low muscle mass screening in sarcopenia. J Am Med Dir Assoc. 2022;23(12):1955–1961.e3. doi: 10.1016/j.jamda.2022.08.016. [DOI] [PubMed] [Google Scholar]
  • 19.Tokushige K, Ikejima K, Ono M, Eguchi Y, Kamada Y, Itoh Y, Akuta N, Yoneda M, Iwasa M, Yoneda M, Otsuka M, Tamaki N, Kogiso T, Miwa H, Chayama K, Enomoto N, Shimosegawa T, Takehara T, Koike K. Evidence-based clinical practice guidelines for nonalcoholic fatty liver disease/nonalcoholic steatohepatitis 2020. J Gastroenterol. 2021;56(11):951–963. doi: 10.1007/s00535-021-01796-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Yamagishi K, Iso H. The criteria for metabolic syndrome and the national health screening and education system in Japan. Epidemiol Health. 2017;39:e2017003. doi: 10.4178/epih.e2017003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Yoshizumi T, Shirabe K, Nakagawara H, Ikegami T, Harimoto N, Toshima T, Yamashita Y, Ikeda T, Soejima Y, Maehara Y. Skeletal muscle area correlates with body surface area in healthy adults. Hepatol Res. 2014;44(3):313–318. doi: 10.1111/hepr.12119. [DOI] [PubMed] [Google Scholar]
  • 22.NAFLD, NASH. Available at: https://dx-mice.jp/jsh_cms/files/info/1328/20230929%E3%80%80oshirase62.pdf. [Last accessed on February 22, 2024]
  • 23.Enomoto H, Ueno Y, Hiasa Y, Nishikawa H, Hige S, Takikawa Y, Taniai M, Ishikawa T, Yasui K, Takaki A, Takaguchi K, Ido A, Kurosaki M, Kanto T, Nishiguchi S, Japan Etiology of Liver Cirrhosis Study Group in the 54th Annual Meeting of JSH Transition in the etiology of liver cirrhosis in Japan: a nationwide survey. J Gastroenterol. 2020;55(3):353–362. doi: 10.1007/s00535-019-01645-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Enomoto H, Ueno Y, Hiasa Y, Nishikawa H, Hige S, Takikawa Y, Taniai M, Ishikawa T, Yasui K, Takaki A, Takaguchi K, Ido A, Kurosaki M, Kanto T, Nishiguchi S, Japan Etiology of Liver Cirrhosis Study Group in the 54th Annual Meeting of JSH The transition in the etiologies of hepatocellular carcinoma-complicated liver cirrhosis in a nationwide survey of Japan. J Gastroenterol. 2021;56(2):158–167. doi: 10.1007/s00535-020-01748-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Castera L, Cusi K. Diabetes and cirrhosis: Current concepts on diagnosis and management. Hepatology. 2023;77(6):2128–2146. doi: 10.1097/HEP.0000000000000263. [DOI] [PubMed] [Google Scholar]
  • 26.Li X, Wang X, Gao P. Diabetes mellitus and risk of hepatocellular carcinoma. Biomed Res Int. 2017;2017:5202684. doi: 10.1155/2017/5202684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Nishikawa H, Shiraki M, Hiramatsu A, Moriya K, Hino K, Nishiguchi S. Japan Society of Hepatology guidelines for sarcopenia in liver disease (1st edition): Recommendation from the working group for creation of sarcopenia assessment criteria. Hepatol Res. 2016;46(10):951–963. doi: 10.1111/hepr.12774. [DOI] [PubMed] [Google Scholar]
  • 28.Dwivedi AK, Dubey P, Cistola DP, Reddy SY. Association between obesity and cardiovascular outcomes: updated evidence from meta-analysis studies. Curr Cardiol Rep. 2020;22(4):25. doi: 10.1007/s11886-020-1273-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, Kojima T, Kuzuya M, Lee JSW, Lee SY, Lee WJ, Lee Y, Liang CK, Lim JY, Lim WS, Peng LN, Sugimoto K, Tanaka T, Won CW, Yamada M, Zhang T, Akishita M, Arai H. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21(3):300–307.e2. doi: 10.1016/j.jamda.2019.12.012. [DOI] [PubMed] [Google Scholar]
  • 30.Jin Y, Ma X, Yang Z, Zhang N. Low L3 skeletal muscle index associated with the clinicopathological characteristics and prognosis of ovarian cancer: a meta-analysis. J Cachexia Sarcopenia Muscle. 2023;14(2):697–705. doi: 10.1002/jcsm.13175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Gadducci A, Cosio S. The prognostic relevance of computed tomography-assessed skeletal muscle index and skeletal muscle radiation attenuation in patients with gynecological cancer. Anticancer Res. 2021;41(1):9–20. doi: 10.21873/anticanres.14747. [DOI] [PubMed] [Google Scholar]
  • 32.DeFronzo RA. The Triumvirate: β-cell, muscle, liver: a collusion responsible for NIDDM. Diabetes. 1988;37(6):667–687. doi: 10.2337/diab.37.6.667. [DOI] [PubMed] [Google Scholar]

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