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Background <p>Non-alcoholic fatty liver disease (NAFLD), recently redefined as metabolic dysfunction-associated steatotic liver disease (MASLD), is strongly associated with metabolic dysfunction and altered body fat distribution. However, MASLD-based reclassification was not performed due to incomplete cardiometabolic data. Transient elastography non-invasively assesses hepatic steatosis and stiffness, but its metabolic associations remain unclear.</p> Methods <p>In this retrospective cross-sectional study, 238 NAFLD patients and 165 non-NAFLD controls were analyzed. Clinical, biochemical, and body composition data (bioelectrical impedance analysis) were collected. Liver steatosis (CAP) and stiffness (E value) were measured using FibroScan<sup>®</sup>. Group comparisons, correlation analyses, and multivariable regression models were performed. Logistic regression identified independent factors associated with NAFLD, and model performance was evaluated using ROC analysis.</p> Results <p>NAFLD patients showed significantly higher BMI, blood pressure, triglycerides, total cholesterol, LDL-C, and regional fat mass (including trunk and limb compartments) compared with controls (all <i>P</i> &lt; 0.05). CAP was positively correlated with HbA1c, total cholesterol, and LDL-C (all <i>P</i> &lt; 0.05). In multivariable analysis, total cholesterol (<i>β</i> = 9.26, <i>P</i> = 0.018) and LDL-C (<i>β</i> = 6.55, <i>P</i> = 0.021) were independently associated with CAP. Logistic regression identified sex, hypertension, BMI, LDL-C, left upper-limb fat mass, and liver stiffness as independent factors associated with NAFLD. The combined clinical model achieved the highest discriminatory performance (AUC = 0.91, 95% CI 0.88–0.94). However, retrospective reclassification according to the MASLD criteria was not feasible because key variables were not systematically recorded, including standardized waist circumference, fasting insulin, HOMA-IR, oral glucose tolerance test-derived indices, and detailed medication histories (statins, antihypertensives, and glucose-lowering agents). Therefore, these findings should be interpreted as exploratory associations rather than causal inferences.</p> Conclusion <p>NAFLD is associated with systemic metabolic alterations and regional adiposity changes, particularly increased trunk and upper-limb fat mass. CAP is independently associated with lipid parameters, especially total cholesterol and LDL-C. The combined metabolic–adiposity model demonstrates strong discriminatory ability for NAFLD classification. Importantly, detailed information on lipid-lowering, antihypertensive, and glucose-lowering medications was not available; therefore, the observed metabolic associations may be influenced by unmeasured pharmacological confounding and should be interpreted as exploratory.</p>

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Association of liver stiffness, metabolic parameters, and regional fat distribution in NAFLD: a retrospective cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Qian Zhang,
  • Xiang-dong Hu,
  • Yu-Min Wang,
  • Xue-Song Wang,
  • Xia Cao,
  • Pan Li,
  • Yi-Min Wang,
  • Li Cao,
  • Xian-Quan Shi

摘要

Background

Non-alcoholic fatty liver disease (NAFLD), recently redefined as metabolic dysfunction-associated steatotic liver disease (MASLD), is strongly associated with metabolic dysfunction and altered body fat distribution. However, MASLD-based reclassification was not performed due to incomplete cardiometabolic data. Transient elastography non-invasively assesses hepatic steatosis and stiffness, but its metabolic associations remain unclear.

Methods

In this retrospective cross-sectional study, 238 NAFLD patients and 165 non-NAFLD controls were analyzed. Clinical, biochemical, and body composition data (bioelectrical impedance analysis) were collected. Liver steatosis (CAP) and stiffness (E value) were measured using FibroScan®. Group comparisons, correlation analyses, and multivariable regression models were performed. Logistic regression identified independent factors associated with NAFLD, and model performance was evaluated using ROC analysis.

Results

NAFLD patients showed significantly higher BMI, blood pressure, triglycerides, total cholesterol, LDL-C, and regional fat mass (including trunk and limb compartments) compared with controls (all P < 0.05). CAP was positively correlated with HbA1c, total cholesterol, and LDL-C (all P < 0.05). In multivariable analysis, total cholesterol (β = 9.26, P = 0.018) and LDL-C (β = 6.55, P = 0.021) were independently associated with CAP. Logistic regression identified sex, hypertension, BMI, LDL-C, left upper-limb fat mass, and liver stiffness as independent factors associated with NAFLD. The combined clinical model achieved the highest discriminatory performance (AUC = 0.91, 95% CI 0.88–0.94). However, retrospective reclassification according to the MASLD criteria was not feasible because key variables were not systematically recorded, including standardized waist circumference, fasting insulin, HOMA-IR, oral glucose tolerance test-derived indices, and detailed medication histories (statins, antihypertensives, and glucose-lowering agents). Therefore, these findings should be interpreted as exploratory associations rather than causal inferences.

Conclusion

NAFLD is associated with systemic metabolic alterations and regional adiposity changes, particularly increased trunk and upper-limb fat mass. CAP is independently associated with lipid parameters, especially total cholesterol and LDL-C. The combined metabolic–adiposity model demonstrates strong discriminatory ability for NAFLD classification. Importantly, detailed information on lipid-lowering, antihypertensive, and glucose-lowering medications was not available; therefore, the observed metabolic associations may be influenced by unmeasured pharmacological confounding and should be interpreted as exploratory.