预估葡萄糖处理率与肝脏脂肪变性和肝纤维化的相关性分析

杨智, 仝巧云, 王振华, 刘伟

杨智, 仝巧云, 王振华, 刘伟. 预估葡萄糖处理率与肝脏脂肪变性和肝纤维化的相关性分析[J]. 实用临床医药杂志, 2025, 29(7): 50-57. DOI: 10.7619/jcmp.20250282
引用本文: 杨智, 仝巧云, 王振华, 刘伟. 预估葡萄糖处理率与肝脏脂肪变性和肝纤维化的相关性分析[J]. 实用临床医药杂志, 2025, 29(7): 50-57. DOI: 10.7619/jcmp.20250282
YANG Zhi, TONG Qiaoyun, WANG Zhenhua, LIU Wei. Associations of estimated glucose disposal rate with liver steatosis and fibrosis[J]. Journal of Clinical Medicine in Practice, 2025, 29(7): 50-57. DOI: 10.7619/jcmp.20250282
Citation: YANG Zhi, TONG Qiaoyun, WANG Zhenhua, LIU Wei. Associations of estimated glucose disposal rate with liver steatosis and fibrosis[J]. Journal of Clinical Medicine in Practice, 2025, 29(7): 50-57. DOI: 10.7619/jcmp.20250282

预估葡萄糖处理率与肝脏脂肪变性和肝纤维化的相关性分析

基金项目: 

国家自然科学基金项目 31600134

详细信息
    通讯作者:

    仝巧云

  • 中图分类号: R589.2; R575.5; R445

Associations of estimated glucose disposal rate with liver steatosis and fibrosis

  • 摘要:
    目的 

    探讨预估葡萄糖处理率(eGDR)与代谢功能障碍相关肝脏脂肪变性和肝纤维化的相关性, 并分析这种关联性在不同人群特征中的差异。

    方法 

    纳入2017—2020年美国国家健康与营养调查(NHANES)数据库中10 549例参与振动控制瞬时弹性成像技术(VCTE)检查的参与者为研究对象。通过腰围、高血压和糖化血红蛋白(HbA1c)计算eGDR。以受控衰减参数(CAP)≥248 dB/m作为肝脏脂肪变性诊断标准,以肝脏硬度(LSM)≥8.2 kPa作为肝纤维化的判定标准。采用多因素Logistic回归分析评估eGDR与肝脏脂肪变性和肝纤维化的关系,并进行亚组分析和交互作用检验。

    结果 

    调整混杂因素后, eGDR水平与肝脏脂肪变性和肝纤维化风险均呈显著负相关。将研究对象按eGDR水平三分位数(Q1Q2Q3)分为Q1组、Q2组和Q3组。与Q1组相比, Q2组和Q3组肝脏脂肪变性的风险比分别为0.485(95% CI: 0.394~0.597)和0.286(95% CI: 0.239~0.343), 差异均有统计学意义(P < 0.001);肝纤维化风险比分别为0.457(95% CI: 0.363~0.576)和0.162(95% CI: 0.100~0.263), 差异均有统计学意义(P < 0.001)。亚组分析结果显示, eGDR与肝脏脂肪变性的负相关性在不同年龄和吸烟状态人群中存在差异,交互作用检验差异有统计学意义(P交互 < 0.001);eGDR与肝纤维化的负相关性在不同体力活动人群中的交互作用检验中差异有统计学意义(P交互 < 0.001)。eGDR水平与肝脏脂肪变性和肝纤维化的相关性保持稳定(P交互>0.05)。

    结论 

    eGDR水平与肝脏脂肪变性和肝纤维化风险密切相关,可作为评估肝脏代谢异常的有效指标。这一发现为肝脏脂肪变性和肝纤维化早期筛查、风险分层和预后评估提供了新思路。

    Abstract:
    Objective 

    To investigate the association of estimated glucose disposal rate (eGDR) with metabolic dysfunction-associated liver steatosis and fibrosis, and to analyze the difference of the association in different population.

    Methods 

    A total of 10, 549 participants from 2017 to 2020 in National Health and Nutrition Examination Survey (NHANES) database who underwent vibration-controlled transient elastography (VCTE) were included. eGDR was calculated based on waist circumference, hypertension, and glycated hemoglobin (HbA1c). The controlled attenuation parameter (CAP) ≥ 248 dB/m was used as diagnostic criterion for liver steatosis, and liver stiffness measurement (LSM) ≥ 8.2 kPa was used as criterion for liver fibrosis. Multivariable Logistic regression analysis was conducted to assess the relationship between eGDR and liver steatosis and fibrosis, with subgroup analysis and interaction tests performed.

    Results 

    After adjusting for confounding factors, eGDR level was significantly negatively associated with the risks of both liver steatosis and fibrosis. The participants were divided into tertiles based on eGDR levels (Q1, Q2, Q3). Compared with the Q1 group, the risk ratios for liver steatosis in the Q2 and Q3 groups were 0.485 (95%CI, 0.394 to 0.597) and 0.286 (95%CI, 0.239 to 0.343), respectively(P < 0.001); the risk ratios for liver fibrosis were 0.457 (95%CI, 0.363 to 0.576) and 0.162 (95%CI, 0.100 to 0.263), respectively (P < 0.001). Subgroup analysis showed that the negative association between eGDR and liver steatosis differed among populations of different ages and smoking statuses, with statistically significant differences in interaction tests (P for interaction < 0.001); similarly, the negative association between eGDR and liver fibrosis differed among populations with different levels of physical activity, with statistically significant differences in interaction tests (P for interaction < 0.001). The associations of eGDR levels with liver steatosis and fibrosis remained stable (P for interaction>0.05).

    Conclusion 

    eGDR levels are closely associated with the risks of liver steatosis and fibrosis and can serve as an effective indicator for assessing liver metabolic abnormalities. This finding provides new insights into early screening, risk stratification, and prognosis assessment for liver steatosis and fibrosis.

  • 图  1   研究对象纳入与排除流程图

    图  2   eGDR与肝脏脂肪变性和肝纤维化的非线性关系

    表  1   研究对象基线资料比较(Mean±SD)[n(%)]

    项目 分类 总体(n=10 549) Q1组(n=3 533) Q2组(n=3 522) Q3组(n=3 494) P
    年龄/岁 47.74±17.00 56.25±15.05 49.22±16.73 39.94±15.06 < 0.001
    20~ < 40岁 3 280(31.09) 415(16.57) 990(32.60) 1 875(55.38) < 0.001
    40~ < 60岁 3 570(35.60) 1 181(37.07) 1 297(39.12) 1 092(31.50)
    60~80岁 3 699(27.80) 1 937(46.36) 1 235(28.28) 527(13.11)
    性别 5 342(50.66) 1 622(43.92) 1 750(49.88) 1 970(56.51) < 0.001
    5 207(49.34) 1 911(56.08) 1 772(50.12) 1 524(43.49)
    种族 非西班牙裔白人 3 767(63.92) 1 345(67.19) 1 289(62.78) 1 133(62.37) < 0.001
    非西班牙裔黑人 2 547(10.54) 1 091(13.22) 775(10.03) 681(8.92)
    墨西哥裔美国人 1 361(8.76) 390(6.63) 505(10.36) 466(9.03)
    其他西班牙裔 1 061(7.06) 321(5.50) 333(6.57) 407(8.69)
    其他种族 1 813(9.72) 386(7.46) 620(10.25) 807(11.00)
    教育水平 高中以下 1 863(10.05) 691(11.25) 621(10.42) 551(8.81) < 0.001
    高中 2 523(26.80) 878(28.27) 911(29.57) 734(23.32)
    大学肄业 3 502(30.90) 1 230(34.91) 1 175(30.41) 1 097(28.24)
    大学及以上 2 661(32.25) 734(25.56) 815(29.60) 1 112(39.63)
    婚姻状况 未婚 4 668(41.67) 1 541(38.10) 1 533(40.20) 1 594(45.67) 0.001
    已婚 5 881(58.33) 1 992(61.90) 1 989(59.80) 1 900(54.33)
    吸烟状况 从不吸烟 6 194(58.08) 1 847(52.30) 2 019(56.30) 2 328(64.04) < 0.001
    有吸烟史 2 517(25.73) 1 144(34.36) 825(24.73) 548(19.95)
    现在吸烟 1 838(16.18) 542(13.34) 678(18.97) 618(16.01)
    饮酒情况 不饮酒 978(6.86) 280(6.50) 374(7.32) 324(6.74) 0.576
    饮酒 9 571(93.14) 3 253(93.50) 3 148(92.68) 3 170(93.26)
    体力活动 剧烈体力活动 2 660(30.05) 499(15.52) 788(28.22) 1 373(42.77) < 0.001
    中等体力活动 2 553(26.39) 920(29.57) 904(26.74) 729(23.65)
    无体力活动 5 336(43.56) 2 114(54.91) 1 830(45.04) 1 392(33.58)
    BMI/(kg/m2) 29.74±7.13 35.06±7.20 30.77±6.18 24.78±3.65 < 0.001
    腰围/cm 100.76±17.23 115.91±14.43 103.60±13.61 86.71±8.88 < 0.001
    身高/cm 168.18±9.98 169.42±10.40 167.98±10.13 167.39±9.41 < 0.001
    空腹血糖/(mmol/L) 6.09±1.75 7.08±2.66 5.95±1.25 5.52±0.65 < 0.001
    胰岛素/(μIU/mL) 13.45±19.51 21.53±32.36 13.44±11.87 7.74±5.34 < 0.001
    HbA1c/% 5.66±0.92 6.21±1.30 5.62±0.69 5.28±0.36 < 0.001
    ALT/(U/L) 22.69±15.94 25.91±17.11 23.83±17.02 19.25±13.15 < 0.001
    AST/(U/L) 21.60±11.29 22.36±10.46 21.72±11.94 20.93±11.31 0.005
    GGT/(IU/L) 29.08±37.93 36.93±43.27 29.82±33.92 22.40±35.48 < 0.001
    Cr/(μmol/L) 77.71±30.04 83.17±43.29 77.26±26.88 73.90±16.43 < 0.001
    UA/(μmol/L) 318.81±84.88 353.35±87.42 320.85±81.69 290.51±74.84 < 0.001
    BUN/(mg/mL) 14.88±5.21 16.38±6.23 14.87±5.04 13.72±4.09 < 0.001
    TG/(mg/mL) 112.07±97.21 135.36±97.23 123.36±121.95 86.28±61.93 < 0.001
    TC/(mg/mL) 188.48±40.25 187.90±41.61 193.24±40.30 184.90±38.72 < 0.001
    HDL/(mg/mL) 53.52±15.48 48.46±13.38 53.09±15.89 57.79±15.41 < 0.001
    hs-CRP/(mg/L) 3.70±7.11 5.31±8.35 3.81±7.39 2.37±5.35 < 0.001
    HOMA-IR 3.96±7.42 7.14±12.63 3.68±3.98 1.93±1.47 < 0.001
    LSM/kPa 5.79±4.87 7.30±6.82 5.67±4.60 4.73±2.32 < 0.001
    CAP/(dB/m) 265.16±62.84 307.44±57.28 270.88±57.67 227.79±46.83 < 0.001
    eGDR/[mg/(kg·min)] 7.66±2.78 4.13±1.33 7.59±0.97 10.44±0.84 < 0.001
    糖尿病 8 386(84.80) 2 025(62.70) 2 974(88.73) 3 387(98.48) < 0.001
    2 163(15.20) 1 508(37.30) 548(11.27) 107(1.52)
    高血压 5 807(61.70) 198(7.02) 2 115(66.13) 3 494(100.00) < 0.001
    4 742(38.30) 3 335(92.98) 1 407(33.87) 0
    心血管疾病 9 540(92.22) 2 902(84.24) 3 232(92.76) 3 406(97.89) < 0.001
    1 009(7.78) 631(15.76) 290(7.24) 88(2.11)
    慢性肾脏病 8 692(86.22) 2 455(73.74) 2 980(88.37) 3 257(93.99) < 0.001
    1 857(13.78) 1 078(26.26) 542(11.63) 237(6.01)
    肿瘤 9 535(89.72) 3 071(86.64) 3 141(87.61) 3 323(93.87) < 0.001
    1 014(10.28) 462(13.36) 381(12.39) 171(6.13)
    肝脂肪变性 4 269(42.90) 605(14.62) 1 307(36.17) 2 357(70.35) < 0.001
    6 280(57.10) 2 928(85.38) 2 215(63.83) 1 137(29.65)
    肝纤维化 9 530(91.33) 2 866(80.81) 3 239(92.51) 3 425(98.42) < 0.001
    1 019(8.67) 667(19.19) 283(7.49) 69(1.58)
    空腹子样本权重通过加权算法进行统计分析; 计数资料采用未加权频数和加权百分比表示。BMI: 体质量指数;
    WBC: 白细胞; RBC: 红细胞; Hb: 血红蛋白; PLT: 血小板; HbA1c: 糖化血红蛋白; ALT: 谷丙转氨酶;
    AST: 谷草转氨酶; GGT: 谷酰转肽酶; Cr: 肌酐; UA: 尿酸; BUN: 尿素; TG: 甘油三酯; TC: 总胆固醇; HDL: 高密度脂蛋白;
    hs-CRP: 超敏C反应蛋白; HOMA-IR: 胰岛素抵抗; LSM: 肝脏硬度; CAP: 受控衰减参数; eGDR: 估算葡萄糖处理率。
    下载: 导出CSV

    表  2   eGDR与肝脏脂肪变性和肝纤维化的关系

    模型 分类 肝脏脂肪变性 肝纤维化
    OR(95% CI) P OR(95% CI) P
    模型1 eGDR 0.623(0.605~0.643) < 0.001 0.666(0.640~0.693) < 0.001
    Q1 参照 参照
    Q2 0.301(0.252~0.359) < 0.001 0.331(0.275~0.398) < 0.001
    Q3 0.076(0.064~0.091) < 0.001 0.067(0.043~0.103) < 0.001
    模型2 eGDR 0.762(0.743~0.781) < 0.001 0.721(0.684~0.761) < 0.001
    Q1 参照 参照
    Q2 0.500(0.401~0.622) < 0.001 0.424(0.342~0.527) < 0.001
    Q3 0.298(0.249~0.358) < 0.001 0.151(0.094~0.243) < 0.001
    模型3 eGDR 0.753(0.735~0.772) < 0.001 0.733(0.695~0.773) < 0.001
    Q1 参照 参照
    Q2 0.485(0.394~0.597) < 0.001 0.457(0.363~0.576) < 0.001
    Q3 0.286(0.239~0.343) < 0.001 0.162(0.100~0.263) < 0.001
    下载: 导出CSV

    表  3   eGDR与肝脏脂肪变性关系的亚组分析

    项目 分类 Q2 Q3 P趋势 P交互
    OR(95%CI) P OR(95%CI) P
    年龄 20~ < 40岁 0.259(0.182~0.370) < 0.001 0.126(0.091~0.174) < 0.001 < 0.001 < 0.001
    40~ < 60岁 0.474(0.378~0.594) < 0.001 0.334(0.228~0.490) < 0.001 < 0.001
    60~80岁 0.599(0.430~0.835) 0.004 0.379(0.293~0.489) < 0.001 < 0.001
    性别 0.482(0.355~0.653) < 0.001 0.258(0.164~0.406) < 0.001 < 0.001 0.892
    0.445(0.306~0.646) < 0.001 0.263(0.173~0.400) < 0.001 < 0.001
    种族 非西班牙裔白人 0.452(0.328~0.622) < 0.001 0.251(0.194~0.324) < 0.001 < 0.001 0.790
    非西班牙裔黑人 0.532(0.360~0.786) 0.003 0.217(0.131~0.358) < 0.001 < 0.001
    墨西哥裔美国人 0.407(0.188~0.883) 0.027 0.244(0.117~0.510) 0.001 < 0.001
    其他西班牙裔 0.411(0.272~0.620) < 0.001 0.234(0.136~0.402) < 0.001 < 0.001
    其他种族 0.483(0.287~0.813) 0.008 0.287(0.173~0.478) < 0.001 < 0.001
    教育水平 高中以下 0.627(0.392~1.002) 0.051 0.239(0.139~0.410) < 0.001 < 0.001 0.198
    高中毕业生 0.449(0.327~0.617) < 0.001 0.302(0.224~0.407) < 0.001 < 0.001
    大学肄业 0.373(0.259~0.537) < 0.001 0.186(0.129~0.267) < 0.001 < 0.001
    大学及以上 0.512(0.313~0.837) 0.010 0.299(0.228~0.393) < 0.001 < 0.001
    婚姻状况 未婚 0.560(0.417~0.753) < 0.001 0.277(0.192~0.400) < 0.001 < 0.001 0.299
    已婚 0.398(0.269~0.588) < 0.001 0.232(0.164~0.328) < 0.001 < 0.001
    吸烟状况 从不吸烟 0.637(0.457~0.887) 0.010 0.426(0.267~0.679) 0.001 < 0.001 0.044
    有吸烟史 0.358(0.257~0.498) < 0.001 0.199(0.141~0.280) < 0.001 < 0.001
    吸烟 0.584(0.342~0.997) 0.049 0.245(0.128~0.468) < 0.001 < 0.001
    饮酒情况 不饮酒 0.512(0.253~1.034) 0.061 0.314(0.150~0.659) 0.004 0.004 0.835
    饮酒 0.457(0.369~0.566) < 0.001 0.246(0.204~0.296) < 0.001 < 0.001
    体力活动 剧烈体力活动 0.363(0.207~0.638) 0.001 0.173(0.101~0.295) < 0.001 < 0.001 0.793
    中等体力活动 0.523(0.429~0.637) < 0.001 0.260(0.207~0.327) < 0.001 < 0.001
    无体力活动 0.433(0.258~0.727) 0.003 0.303(0.176~0.521) < 0.001 < 0.001
    体质量指数分类 正常 0.225(0.080~0.632) 0.007 0.132(0.051~0.340) < 0.001 < 0.001 0.077
    超重 0.454(0.312~0.658) < 0.001 0.308(0.225~0.422) < 0.001 < 0.001
    肥胖 0.423(0.311~0.574) < 0.001 0.158(0.097~0.257) < 0.001 < 0.001
    下载: 导出CSV

    表  4   eGDR与肝纤维化关系的亚组分析

    项目 分类 Q2 Q3 P趋势 P交互
    OR(95%CI) P OR(95%CI) P
    年龄 20~ < 40岁 0.459(0.293~0.720) 0.002 0.112(0.056~0.226) < 0.001 < 0.001 0.057
    40~ < 60岁 0.404(0.256~0.639) < 0.001 0.201(0.083~0.489) 0.001 < 0.001
    60~80岁 0.643(0.440~0.941) 0.025 0.418(0.202~0.863) 0.021 0.008
    性别 女性 0.331(0.224~0.488) < 0.001 0.076(0.023~0.253) < 0.001 < 0.001 0.052
    男性 0.587(0.445~0.774) < 0.001 0.285(0.199~0.408) < 0.001 < 0.001
    种族 非西班牙裔白人 0.433(0.311~0.603) < 0.001 0.188(0.094~0.375) < 0.001 < 0.001 0.135
    非西班牙裔黑人 0.455(0.251~0.827) 0.012 0.337(0.182~0.624) 0.001 < 0.001
    墨西哥裔美国人 0.580(0.278~1.214) 0.133 0.156(0.049~0.499) 0.005 0.008
    其他西班牙裔 0.722(0.428~1.219) 0.209 0.110(0.038~0.319) < 0.001 < 0.001
    其他种族 0.405(0.219~0.748) 0.006 0.089(0.033~0.242) < 0.001 < 0.001
    教育水平 高中以下 0.459(0.249~0.845) 0.015 0.236(0.089~0.624) 0.006 0.003 0.059
    高中毕业生 0.480(0.329~0.700) < 0.001 0.131(0.073~0.236) < 0.001 < 0.001
    大学肄业 0.298(0.195~0.454) < 0.001 0.096(0.058~0.159) < 0.001 < 0.001
    大学及以上 0.820(0.413~1.629) 0.553 0.328(0.110~0.978) 0.046 0.048
    婚姻状况 未婚 0.572(0.368~0.887) 0.015 0.275(0.093~0.808) 0.022 0.008 0.359
    已婚 0.412(0.298~0.569) < 0.001 0.120(0.075~0.191) < 0.001 < 0.001
    吸烟状况 从不吸烟 0.586(0.375~0.916) 0.021 0.141(0.056~0.353) < 0.001 < 0.001 0.459
    有吸烟史 0.418(0.287~0.607) < 0.001 0.172(0.092~0.322) < 0.001 < 0.001
    吸烟 0.447(0.203~0.985) 0.046 0.173(0.046~0.649) 0.012 0.012
    饮酒情况 不饮酒 0.301(0.131~0.694) 0.007 0.199(0.029~1.355) 0.094 0.035 0.752
    饮酒 0.473(0.364~0.615) < 0.001 0.169(0.104~0.275) < 0.001 < 0.001
    体力活动 剧烈体力活动 0.287(0.178~0.462) < 0.001 0.034(0.011~0.109) < 0.001 < 0.001 0.011
    中等体力活动 0.480(0.333~0.691) < 0.001 0.178(0.077~0.412) < 0.001 < 0.001
    无体力活动 0.718(0.374~1.381) 0.303 0.339(0.145~0.796) 0.016 0.027
    体质量指数分类 正常 0.570(0.155~2.087) 0.376 0.388(0.094~1.605) 0.179 0.129 0.070
    超重 0.844(0.470~1.517) 0.552 0.366(0.142~0.941) 0.038 0.020
    肥胖 0.407(0.303~0.545) < 0.001 0.037(0.010~0.136) < 0.001 < 0.001
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  • [1]

    PAN J, WU F, CHEN M, et al. Prevalence of nafld, mafld, and masld: nhanes 1999-2018[J]. Diabetes Metab, 2024, 50(6): 101562. doi: 10.1016/j.diabet.2024.101562

    [2]

    PAROLA M, PINZANI M. Liver fibrosis in NAFLD/NASH: from pathophysiology towards diagnostic and therapeutic strategies[J]. Mol Aspects Med, 2024, 95: 101231. doi: 10.1016/j.mam.2023.101231

    [3]

    WELLS R G. Liver fibrosis: our evolving understanding[J]. Clin Liver Dis: Hoboken, 2024, 23(1): e0243.

    [4]

    ZHANG W, SONG W J, CHEN W, et al. Metabolic dysfunction-associated steatotic liver disease-related hepatic fibrosis increases risk of insulin resistance, type 2 diabetes, and chronic kidney disease[J]. Eur J Gastroenterol Hepatol, 2024, 36(6): 802-810. doi: 10.1097/MEG.0000000000002767

    [5]

    FABRIS L, CAMPELLO E, CADAMURO M, et al. The evil relationship between liver fibrosis and cardiovascular disease in metabolic dysfunction-associated fatty liver disease (MAFLD): Looking for the culprit[J]. Biochim Biophys Acta BBA Mol Basis Dis, 2024, 1870(3): 166763. doi: 10.1016/j.bbadis.2023.166763

    [6] 沈颖筱, 施惠海, 罗家乐, 等. 非酒精性脂肪性肝病肝纤维化风险预测模型的应用与进展[J]. 实用临床医药杂志, 2023, 27(9): 131-136, 142. doi: 10.7619/jcmp.20222652
    [7]

    ZHANG Z, ZHAO L, LU Y, et al. Insulin resistance assessed by estimated glucose disposal rate and risk of incident cardiovascular diseases among individuals without diabetes: findings from a nationwide, population based, prospective cohort study[J]. Cardiovasc Diabetol, 2024, 23(1): 194. doi: 10.1186/s12933-024-02256-5

    [8]

    YI J, QU C, LI X, et al. Insulin resistance assessed by estimated glucose disposal rate and risk of atherosclerotic cardiovascular diseases incidence: the multi-ethnic study of atherosclerosis[J]. Cardiovasc Diabetol, 2024, 23(1): 349. doi: 10.1186/s12933-024-02437-2

    [9]

    JUNG I, KOO D J, LEE W Y. Insulin resistance, non-alcoholic fatty liver disease and type 2 diabetes mellitus: clinical and experimental perspective[J]. Diabetes Metab J, 2024, 48(3): 327-339. doi: 10.4093/dmj.2023.0350

    [10]

    JOHNSON C L, DOHRMANN S M, BURT V L, et al. National health and nutrition examination survey: sample design, 2011-2014[J]. Vital Health Stat 2, 2014(162): 1-33.

    [11]

    WANG S, ZHANG Q, QIN B. Association between remnant cholesterol and insulin resistance levels in patients with metabolic-associated fatty liver disease[J]. Sci Rep, 2024, 14(1): 4596. doi: 10.1038/s41598-024-55282-4

    [12]

    KARLAS T, PETROFF D, SASSO M, et al. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis[J]. J Hepatol, 2017, 66(5): 1022-1030. doi: 10.1016/j.jhep.2016.12.022

    [13]

    MIKOLASEVIC I, DOMISLOVIC V, KLAPAN M, et al. Accuracy of controlled attenuation parameter and liver stiffness measurement in patients with non-alcoholic fatty liver disease[J]. Ultrasound Med Biol, 2021, 47(3): 428-437. doi: 10.1016/j.ultrasmedbio.2020.11.015

    [14]

    KARAASLAN H, INAN H, TURKMEN A T, et al. Comparison of triglyceride-glucose index and anthropometric obesity indices in predicting severe grades of hepatic steatosis in nonalcoholic fatty liver disease among non-diabetic obese individuals[J]. Hepatol Forum, 2024, 5(3): 113-119.

    [15]

    SHI J, CHEN J, ZHANG Z, et al. Multi-dimensional comparison of abdominal obesity indices and insulin resistance indicators for assessing NAFLD[J]. BMC Public Health, 2024, 24(1): 2161. doi: 10.1186/s12889-024-19657-6

    [16]

    SONG J, MA R, YIN L. Associations between estimated glucose disposal rate and arterial stiffness and mortality among US adults with non-alcoholic fatty liver disease[J]. Front Endocrinol: Lausanne, 2024, 15: 1398265. doi: 10.3389/fendo.2024.1398265

    [17]

    CHEN Y, ZHAO X. The mediating role of insulin resistance in the association between inflammatory score and MAFLD: NHANES 2017-2018[J]. Immun Inflamm Dis, 2024, 12(10): e70035. doi: 10.1002/iid3.70035

    [18]

    TAUIL R B, GOLONO P T, DE LIMA E P, et al. Metabolic-associated fatty liver disease: the influence of oxidative stress, inflammation, mitochondrial dysfunctions, and the role of polyphenols[J]. Pharmaceuticals: Basel, 2024, 17(10): 1354. doi: 10.3390/ph17101354

    [19]

    ZHAO Y, ZHOU Y, WANG D, et al. Mitochondrial dysfunction in metabolic dysfunction fatty liver disease (MAFLD)[J]. Int J Mol Sci, 2023, 24(24): 17514. doi: 10.3390/ijms242417514

    [20]

    POPA S G, SIMION A M, SOARE M, et al. Insulin resistance and hepatic steatosis in type 1 diabetes mellitus and their association with diabetic chronic complications[J]. Minerva Endocrinol: Torino, 2023, 48(1): 27-34.

    [21]

    FAN X D, SONG Y F, ZHAO J J. Evolving liver disease insights from NAFLD to MASLD[J]. Trends Endocrinol Metab, 2024, 35(8): 683-686. doi: 10.1016/j.tem.2024.02.012

    [22]

    CHAN W K, WONG V W, ADAMS L A, et al. MAFLD in adults: non-invasive tests for diagnosis and monitoring of MAFLD[J]. Hepatol Int, 2024, 18(2): 909-921.

    [23]

    VIEIRA-LARA M A, DOMMERHOLT M B, ZHANG W X, et al. Age-related susceptibility to insulin resistance arises from a combination of CPT1B decline and lipid overload[J]. BMC Biol, 2021, 19(1): 154. doi: 10.1186/s12915-021-01082-5

    [24]

    BAJAJ M. Nicotine and insulin resistance: when the smoke clears[J]. Diabetes, 2012, 61(12): 3078-3080. doi: 10.2337/db12-1100

    [25]

    VAN DER VELDE J H P M, BOONE S C, WINTERS-VAN EEKELEN E, et al. Timing of physical activity in relation to liver fat content and insulin resistance[J]. Diabetologia, 2023, 66(3): 461-471. doi: 10.1007/s00125-022-05813-3

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  • 收稿日期:  2025-01-10
  • 修回日期:  2025-03-15
  • 刊出日期:  2025-04-14

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