修回日期: 2022-11-21
接受日期: 2022-12-21
在线出版日期: 2023-01-28
早期识别代谢相关脂肪性肝病(metabolic associated fatty liver disease, MAFLD)的发病风险非常重要, 通过安全、无创的生物电阻抗法(bioelectrical impedance analysis, BIA)测定人体成分来预测MAFLD可能是一种新途径.
通过BIA法测定人体组成成分, 探索体脂率(body fat ratio, BFR)、内脏脂肪面积(visceral fat area, VFA)及静息代谢率(resting metabolic rate, RMR)与MAFLD的关系.
根据是否患有MAFLD将509例研究对象分为MAFLD组和非MAFLD组, 收集人体测量、人体成分分析及实验室检测相关指标, 利用Spearman相关分析、绘制受试者工作特征曲线(receiver operating characteristic curves, ROC)及Logistic回归分析等方法探索BFR、VFA和RMR与MAFLD及其相关指标间的关系.
男性和女性的BFR、VFA和RMR在MAFLD组与非MAFLD的组间分布差异有统计学意义(P＜0.05), 且BFR、VFA和RMR与丙氨酸转氨酶(alanine aminotransferase, ALT)、总胆红素(total bilirubin, TBIL)、甘油三酯(triglyceride, TG)、总胆固醇(total cholesterol, TC)、空腹血糖(fasting blood-glucose, FBG)、餐后2小时血糖(postprandial blood glucose, PBG)等多个实验室检测指标显著相关(P＜0.05); BFR、VFA和RMR预测MAFLD的ROC曲线下面积(area under the ROC curve, AUC)分别为0.800(95%CI: 0.752-0.875)、0.894(95%CI: 0.847-0.941)和0.715(95%CI: 0.639-0.790), 其中VFA为最优指标, 其界值为87.10 cm2, 男性和女性VFA预测MAFLD的AUC分别为0.943(95%CI: 0.910-0.976)和0.907(95%CI: 0.850-0.965), 其界值分别为87.25 cm2和117.05 cm2; 调整性别、年龄、FBG、TG和HDL-C后做Logistic回归分析, BFR、VFA和RMR的OR值分别为1.357(95%CI: 1.214-1.517)、1.079(95%CI: 1.049-1.111)和1.009(95%CI: 1.005-1.014).
BIA法测定BFR、VFA和RMR可用于MAFLD的患病风险预测中, 其中VFA是BIA测定法中预测MAFLD的最优指标.
核心提要: 本研究表明, 代谢相关脂肪性肝病(metabolic associated fatty liver disease, MAFLD)的发病风险随体脂率、内脏脂肪面积及静息代谢率的增加而增加, 三者可较好的预测MAFLD的发生, 其中, 内脏脂肪面积预测MAFLD的受试者工作特征曲线下面积为0.894, 界值为87.10 cm2, 为最优指标.
引文著录: 贺登花, 张勇湛, 徐亮, 裴佳佳, 张颖, 闫忠芳. 生物电阻抗法测定体脂率、内脏脂肪面积及静息代谢率与代谢相关脂肪性肝病的关系. 世界华人消化杂志 2023; 31(2): 56-65
Revised: November 21, 2022
Accepted: December 21, 2022
Published online: January 28, 2023
It is very important to identify the risk of metabolic associated fatty liver disease (MAFLD) in early stage, and body components measured by bioelectrical impedance analysis (BIA) would be a new way to achieve the prediction safely and noninvasively.
To measure the body composition by BIA, and explore the association of body fat ratio (BFR), visceral fat area (VFA), and resting metabolic rate (RMR) with MAFLD.
A total of 509 subjects were divided into either a MAFLD group or a non-MAFLD group. Indicators of anthropo-metric measurements, BIA, and laboratory tests were collected. Spearman correlation analysis, receiver operating characteristic (ROC) curve analysis, and binary logistic regression analysis were used to explore the relationship of BFR, VFA, and RMR with MAFLD.
The differences in BFR, VFA, and RMR between the MAFLD group and the non-MAFLD group were statisti-cally significant both in male and female subjects (P < 0.05), and BFR, VFA, and RMR were significantly correlated with alanine aminotransferase, total bilirubin, triglyceride (TG), total cholesterol, fasting blood-glucose (FBG), and postprandial blood glucose (P < 0.05). The area under the ROC curve (AUC) values of BFR, VFA, and RMR were 0.800 (95% confidence interval [CI]: 0.752-0.875), 0.894 (95%CI: 0.847-0.941), and 0.715 (95%CI: 0.639-0.790), respectively, and VFA was the best indicator with a cutoff value of 87.10 cm2 (87.25 cm2 in males and 117.05 cm2 in females). The odds ratio values of BFR, VFA, and RMR were 1.357 (95%CI: 1.214-1.517), 1.079 (95%CI: 1.049-1.111), and 1.009 (95%CI: 1.005-1.014), respectively, after adjsuting for gender, age, FBG, and TG.
BFR, VFA, and RMR estimated by BIA method can be well applied in the risk prediction of MAFLD, with VFA being the best indicator.
- Citation: He DH, Zhang YZ, Xu L, Pei JJ, Zhang Y, Yan ZF. Relationship between metabolic associated fatty liver disease and body fat ratio, visceral fat area, and resting metabolic rate estimated by bioelectrical impedance analysis. Shijie Huaren Xiaohua Zazhi 2023; 31(2): 56-65
- URL: https://www.wjgnet.com/1009-3079/full/v31/i2/56.htm
- DOI: https://dx.doi.org/10.11569/wcjd.v31.i2.56
既往的研究表明, 非酒精性脂肪性肝病(nonalcoholic fatty liver disease, NAFLD)已经发展为最常见的慢性肝病在全球广泛流行. 在全球范围内, NAFLD的患病率为25.24%[1], 中国NAFLD的患病率为6.3%-27.0%[2]. 2020年, 由22个国家30位专家组成的国际专家小组发表国际专家共识重新定义该疾病为代谢相关脂肪性肝病(metabolic associated fatty liver disease, MAFLD)[3], MAFLD更加突出了代谢异常在此类脂肪肝发生过程中的核心地位, 且不排除其它已知因素(如遗传因素、年龄、性别、饮食结构、少中等量酒精摄入、肠道菌群等)在其发生发展过程中的作用[4], 使得该疾病的预测更准确[5]. 脂肪堆积尤其是内脏脂肪堆积是MAFLD的主要危险因素[6], 内脏脂肪堆积与葡萄糖、甘油三酯和丙氨酸氨基转移酶之前显著相关[7], 并在肝脏脂肪变性和肝脏纤维化的过程中发挥重要作用[8-10]. 生物电阻抗分析法(bioelectrical impedance analysis, BIA)可通过无创的方法快速测量人体成分, 如体脂肪、体水分、骨骼肌等身体成分的含量, 并计算体脂率(body fat ratio, BFR)、内脏脂肪面积(visceral fat area, VFA)及静息代谢率(resting metabolic rate, RMR)等指标, 本研究即通过BIA测定MAFLD患者人体成分, 探索BFR、VFA及RMR与MAFLD的关系.
回顾性分析2020-11/2021-04于天津市第二人民医院诊治并行人体成分分析检查的患者, 排除患有肝性脑病、严重感染、肾病综合征、恶性肿瘤、甲状腺功能异常、严重精神疾病及长期卧床等疾病的患者, 共纳入患者509例(男性339例, 女性170例), 按照是否诊断为MAFLD分为MAFLD组(男性319例, 女性154例, 合计473例)和非MAFLD组(男性20例, 女性16例, 合计36例). 本研究经天津市第二人民医院伦理委员会审批通过.
(1)人体测量: 晨起空腹测量患者身高、体重(SHENGYUAN超声波体重秤HGM-700), 并计算体质指数(body mass index, BMI), BMI = 体重(kg)/身高2(m2); BIA法测定人体成分(InBody S10, 韩国), 患者晨起空腹, 测量前停止剧烈活动, 脱鞋袜并静躺5分钟后开始测量, 测量过程中保持安静、均匀呼吸, 测量结束后记录患者的腰围(waist circumference, WC)、上臂围(arm circumference, AC)、上臂肌围(arm muscle circumference, AMC)、骨骼肌含量(muscle weight, Mw)、蛋白质含量(protein weight, Pw)、体脂肪含量(fat weight, Fw)、BFR、VFA和RMR等指标; (2)实验室检测: 患者晨起空腹采血, 测定丙氨酸转氨酶(alanine aminotransferase, ALT)、天冬氨酸转氨酶(aspartate aminotransferase, AST)、总胆红素(total bilirubin, TBIL)、尿素氮(blood urea nitrogen, BUN)、肌酐(creatinine, CRE)、尿酸(uric acid, UA), 总胆固醇(total cholesterol, TC)、甘油三酯(triglyceride, TG)、低密度脂蛋白胆固醇(low density lipoprotein cholesterol, LDL-C)、高密度脂蛋白胆固醇(high density lipoprotein cholesterol, HDL-C)等生化指标; 测定空腹血糖(fasting blood-glucose, FBG)、空腹胰岛素(fasting insulin, FINS)、餐后2小时血糖(postprandial blood glucose, PBG)、糖化血红蛋白(glycated hemoglobin, HbA1C)等糖代谢指标, 并计算稳态模型的胰岛素抵抗指数(homeostasis model assessment of insulin resistance, HOMA-IR), HOMA-IR = FBG×FINS/22.5; 收集总蛋白(total protein, TP)、白蛋白(albumin, ALB)、前白蛋白(prealbumin, PA)、运铁蛋白(transferrin, TRF)及血红蛋白(hemoglobin, Hb)等蛋白质营养评价指标; 记录钾(kalium, K)、钠(natrium, Na)、钙(calcium, Ca)、磷(phosphorus, P)、镁(magnesium, Mg)、铁(ferrum, Fe)及维生素D[25 hydroxyvitamin D, 25-(OH)D]等微量营养素水平指标.
MAFLD的诊断采用2020年国际专家共识[3], 即MAFLD的诊断基于肝脏脂肪积聚(肝细胞脂肪变性)的组织学(肝活检)、影像学及血液生物标志物证据(本研究以腹部超声检查结果为基础, 具备以下异常表现2项以上者可诊断为脂肪肝: (1)肝脏近场回声增强, 远场回声减弱; (2)肝脏实质回声致密, 强于肾脏实质; (3)肝内血管和胆道结构显示不清[11]). 同时满足以下三项条件之一: (1)超重/肥胖: 亚洲人群BMI＞23 kg/m2; (2)2型糖尿病: 糖尿病典型症状(多饮、多尿、多食及体重下降)加上随机血糖≥11.1 mmol/L或加上空腹血糖≥7.0 mmol/L或加上糖负荷后2 小时血糖≥11.1 mmol/L; (3)代谢功能障碍: (以下代谢风险因素至少存在两项): ①腰围: 男性WC≥90 cm,女性WC≥85 cm; ②血压: BP≥130/85 mmHg或接受特异性药物治疗; ③血浆甘油三酯: TG≥150 mg/dL(1.7 mmol/L)或接受特异性药物治疗; ④血浆高密度脂蛋白-胆固醇: 男性HDL-C＜40 mg/dL(1.0 mmol/L)及女性HDL-C＜50 mg/dL(1.3 mmol/L), 或接受特异性药物治疗; ⑤糖尿病前期: 空腹血糖为100-125 mg/dL(5.6-6.9 mmol/L)或餐后2 h血糖为140-199 mg/dL(7.8-11.0 mmol/L)或糖化血红蛋白为5.7%-6.4%(39-47 mmol/L); ⑥稳态模型评估-胰岛素抵抗指数: HOMA-IR≥2.5; ⑦血浆超敏C反应蛋白水平: CRP＞2 mg/L.
统计学处理 连续变量用均数±标准差(mean±SD)表示, 两组间比较采用t检验; VFA与各实验室检测指标之间的相关性采用Spearman相关分析; 绘制以BFR、VFA和RMR指标预测MAFLD的受试者工作特征(receiver operating characteristic, ROC)曲线, 计算ROC曲线下面积(area under the ROC curve, AUC), 以最大约登指数(约登指数 = 灵敏度+特异度-1)对应切点为界值; 应用二元Logistic回归分析探索BFR、VFA及RMR对MAFLD患病风险的影响. 数据分析均通过IBM SPSS Statistics 19.0完成, 以P＜0.05(双侧)作为显著性标准.
509例患者的平均年龄为(44.94±12.58)岁, 不同性别患者的年龄分布在MAFLD组和非MAFLD组间的分布差异无统计学意义. 对各人体测量指标进行组间分布差异的比较(t检验), 除身高外, 不同性别患者的体重、BMI、WC、AC、AMC、Mw、Pw、Fw、BFR、VFA、RMR在MAFLD组和非MAFLD组间的分布差异均有统计学意义(P＜0.05), MAFLD组各项指标均高于非MAFLD组. 对实验室检测指标进行组间分布差异比较(t检验), 不同性别患者的TG、FBG、FINS及HOMA-IR在MAFLD组和非MAFLD组间的分布差异均有统计学意义(P＜0.05), 但UA、LDL-C、PBG、TP仅在男性患者的组间分布差异有统计学意义(P＜0.05), 且除HDL-C外, MAFLD组各项指标均高于非MAFLD组, 详见表1.
| 合计(n = 509) | 男性 | 女性 | |||||
| MAFLD(n = 319) | 非MAFLD(n = 20) | P | MAFLD(n = 154) | 非MAFLD(n = 16) | P | ||
| 年龄(岁) | 44.94±12.58 | 41.01±11.28 | 42.50±11.56 | 0.582 | 52.34±11.29 | 55.00±13.65 | 0.462 |
| 身高(cm) | 167.64±9.21 | 172.26±6.92 | 174.18±6.06 | 0.187 | 158.10±5.08 | 159.19±5.20 | 0.436 |
| 体重(kg) | 78.02±15.04 | 84.40±13.56b | 64.27±6.57b | ＜0.001 | 69.14±10.38b | 53.34±5.51b | ＜0.001 |
| BMI(kg/m2) | 27.60±4.01 | 28.35±3.62b | 20.7±2.10b | ＜0.001 | 27.62±3.65b | 21.01±1.55b | ＜0.001 |
| WC(cm) | 95.92±12.90 | 99.02±12.43b | 80.10±5.89b | ＜0.001 | 93.4±11.30b | 77.15±6.75b | ＜0.001 |
| AC(cm) | 33.28±3.90 | 34.39±2.91b | 28.72±1.89b | ＜0.001 | 32.27±2.87b | 26.77±1.96b | ＜0.001 |
| AMC(cm) | 26.55±2.58 | 27.82±1.95b | 24.46±1.09b | ＜0.001 | 24.64±1.80b | 21.65±1.59b | ＜0.001 |
| Mw(kg) | 28.69±6.74 | 32.43±5.24b | 27.90±3.10b | ＜0.001 | 22.01±2.91b | 19.33±2.20b | 0.001 |
| Pw(kg) | 10.18±2.23 | 11.43±1.71b | 9.90±1.04b | ＜0.001 | 7.95±0.96b | 7.06±0.74b | 0.001 |
| Fw(kg) | 26.24±8.29 | 26.50±7.94b | 13.55±3.14b | ＜0.001 | 28.34±7.70b | 16.76±3.55b | ＜0.001 |
| BFR(%) | 33.45±7.62 | 30.95±5.71b | 20.98±3.67b | ＜0.001 | 40.47±6.14b | 31.24±4.71b | ＜0.001 |
| VFA(cm2) | 125.66±46.24 | 120.01±41.81b | 62.14±14.96b | ＜0.001 | 150.05±44.98b | 84.70±22.82b | ＜0.001 |
| RMR(kcal/d) | 1501.71±357.17 | 1640.28±376.83a | 1464.95±106.06a | 0.039 | 1253.34±107.67b | 1160.06±77.24b | 0.001 |
| ALT(U/L) | 101.63±242.63 | 113.36±267.70 | 126.79±338.15 | 0.882 | 77.72±177.31 | 68.84±100.64 | 0.764 |
| AST(U/L) | 64.39±157.98 | 62.79±163.75 | 80.04±184.61 | 0.727 | 66.36±150.48 | 62.63±74.14 | 0.869 |
| TBIL(μmol/L) | 21.50±29.69 | 22.01±28.95 | 22.43±16.72 | 0.929 | 20.92±33.55 | 16.23±12.15 | 0.262 |
| BUN(mmol/L) | 5.56±5.61 | 5.90±6.45 | 5.81±4.39 | 0.956 | 4.91±3.76 | 4.66±1.98 | 0.686 |
| CRE(μmol/L) | 83.71±133.82 | 98.54±163.04 | 101.48±132.34 | 0.933 | 53.13±12.07 | 54.59±12.54 | 0.675 |
| UA(μmol/L) | 340.46±97.90 | 373.84±93.54b | 298.73±84.79b | 0.003 | 280.93±72.87 | 267.93±83.83 | 0.561 |
| TC(mmol/L) | 5.00±1.22 | 4.84±1.12 | 4.76±1.10 | 0.780 | 5.26±1.18 | 6.03±2.32 | 0.226 |
| TG(mmol/L) | 1.47±1.12 | 1.60±1.28a | 0.91±0.35a | 0.032 | 1.31±0.72b | 0.90±0.33b | ＜0.001 |
| HDL-C(mmol/L) | 1.48±0.42 | 1.38±0.33a | 1.61±0.37a | 0.030 | 1.61±0.41 | 2.13±1.10 | 0.143 |
| LDL-C(mmol/L) | 2.91±0.93 | 2.86±0.93 | 2.54±0.80 | 0.137 | 3.03±0.87 | 3.32±1.23 | 0.322 |
| FBG(mmol/L) | 6.26±1.90 | 6.26±1.97b | 5.32±0.60b | ＜0.001 | 6.48±1.89b | 5.20±0.56b | ＜0.001 |
| PBG(mmol/L) | 10.18±4.77 | 10.07±4.75b | 5.32±0.70b | ＜0.001 | 11.99±4.76 | 7.94±4.21 | 0.379 |
| FINS(μIU/ml) | 20.32±13.31 | 22.02±13.21b | 7.41±4.02b | 0.008 | 19.40±13.22b | 6.19±3.38b | ＜0.001 |
| HbA1C(%) | 6.26±1.15 | 6.10±1.03 | 5.00±1.21 | 0.295 | 6.72±1.31 | 5.28±1.02 | 0.282 |
| HOMA-IR | 5.78±4.39 | 6.27±4.19b | 1.71±0.87b | ＜0.001 | 5.61±4.86b | 1.36±0.78b | ＜0.001 |
| TP(g/L) | 74.00±6.61 | 74.47±6.06a | 66.48±14.11a | 0.046 | 73.93±5.97 | 72.66±7.72 | 0.532 |
| ALB(g/L) | 45.89±5.88 | 46.92±5.52 | 44.33±5.51 | 0.096 | 44.27±6.03 | 42.39±7.06 | 0.318 |
| PA(mg/L) | 159.44±70.81 | 168.84±77.91 | 174.85±50.65 | 0.731 | 141.91±61.52 | 155.02±53.60 | 0.478 |
| TRF(g/L) | 2.65±1.96 | 2.48±1.10 | 2.26±0.58 | 0.362 | 2.57±0.49 | 5.02±7.09 | 0.329 |
| Hb(g/L) | 142.14±21.74 | 149.17±20.95 | 140.00±22.98 | 0.168 | 130.42±15.79 | 123.21±22.47 | 0.265 |
| K(mmol/L) | 3.95±0.45 | 4.00±0.45 | 4.12±0.50 | 0.418 | 3.83±0.40 | 3.85±0.51 | 0.908 |
| Na(mmol/L) | 138.03±12.76 | 139.08±2.14 | 139.96±1.47 | 0.092 | 135.33±22.56 | 140.09±2.56 | 0.118 |
| Ca(mmol/L) | 2.37±0.17 | 2.35±0.16 | 2.39±0.16 | 0.421 | 2.40±0.13 | 2.44±0.40 | 0.786 |
| P(mmol/L) | 1.17±0.38 | 1.18±0.47 | 1.19±0.50 | 0.920 | 1.15±0.15 | 1.15±0.11 | 0.893 |
| Mg(mmol/L) | 0.98±0.13 | 0.98±0.14 | 0.97±0.05 | 0.778 | 0.97±0.13 | 0.98±0.16 | 0.873 |
| Fe(μmol/L) | 24.04±12.56 | 27.45±12.51 | 24.50±12.76 | 0.525 | 18.60±10.19 | 25.33±16.03 | 0.253 |
| 25-(OH)D(μg/L) | 34.45±3.12 | 34.43±3.91 | 35.80±0.99 | 0.299 | 34.39±2.20 | 33.60±2.12 | 0.656 |
分别对BFR、VFA和RMR与各实验室检测指标之间做Spearman相关分析, 结果显示BFR与ALT、TBIL、CRE、ALB、Hb及Fe的水平呈负相关(rsBFR＜0, P＜0.05), BFR与TC、TG、HDL-C、LDL-C、FBG、FINS、HbA1C、HOMA-IR及Ca的水平呈正相关(rsBFR＞0, P＜0.05); VFA与TBIL、CRE及Fe的水平呈负相关(rsVFA＜0, P＜0.05), VFA与TC、TG、LDL-C、FBG、FINS、HbA1C及HOMA-IR的水平呈正相关(rsVFA＞0, P＜0.05); RMR与TC、HDL-C、LDL-C及TRF的水平呈负相关(rsRMR＜0, P＜0.05), RMR与ALT、TBIL、CRE、UA、TG、FINS、HOMA-IR、ALB、Hb及Fe的水平呈正相关(rsRMR＞0, P＜0.05)详见表2.
| rs-BFR | P | rs-VFA | P | rs-RMR | P | |
| ALT(U/L) | -0.101 | 0.008 | -0.056 | 0.225 | 0.191 | ＜0.001 |
| AST(U/L) | -0.011 | 0.808 | -0.026 | 0.572 | -0.017 | 0.706 |
| TBIL(μmol/L) | -0.126 | 0.006 | -0.094 | 0.039 | 0.099 | 0.030 |
| BUN(mmol/L) | -0.093 | 0.054 | -0.071 | 0.142 | 0.049 | 0.312 |
| CRE(μmol/L) | -0.368 | ＜0.001 | -0.204 | ＜0.001 | 0.486 | ＜0.001 |
| UA(μmol/L) | -0.090 | 0.057 | 0.051 | 0.285 | 0.418 | ＜0.001 |
| TC(mmol/L) | 0.209 | ＜0.001 | 0.139 | 0.004 | -0.224 | ＜0.001 |
| TG(mmol/L) | 0.126 | 0.008 | 0.230 | ＜0.001 | 0.178 | ＜0.001 |
| HDL-C(mmol/L) | 0.157 | 0.001 | 0.042 | 0.390 | -0.283 | ＜0.001 |
| LDL-C(mmol/L) | 0.194 | ＜0.001 | 0.145 | 0.003 | -0.135 | 0.006 |
| FBG(mmol/L) | 0.239 | ＜0.001 | 0.257 | ＜0.001 | -0.066 | 0.171 |
| PBG(mmol/L) | 0.144 | 0.274 | 0.215 | 0.099 | -0.032 | 0.809 |
| FINS(μIU/ml) | 0.166 | 0.019 | 0.037 | ＜0.001 | 0.390 | ＜0.001 |
| HbA1C(%) | 0.238 | 0.030 | 0.247 | 0.023 | -0.164 | 0.136 |
| HOMA-IR | 0.195 | 0.006 | 0.405 | ＜0.001 | 0.393 | ＜0.001 |
| TP(g/L) | 0.067 | 0.141 | 0.052 | 0.261 | 0.034 | 0.456 |
| ALB(g/L) | -0.096 | 0.036 | -0.039 | 0.390 | 0.237 | ＜0.001 |
| PA(mg/L) | -0.033 | 0.662 | -0.018 | 0.814 | 0.073 | 0.332 |
| Hb(g/L) | -0.252 | ＜0.001 | -0.064 | 0.250 | 0.506 | ＜0.001 |
| TFR(g/L) | 0.036 | 0.664 | -0.084 | 0.311 | -0.234 | 0.004 |
| K(mmol/L) | -0.083 | 0.249 | -0.024 | 0.734 | 0.153 | 0.032 |
| Na(mmol/L) | 0.045 | 0.532 | -0.012 | 0.873 | -0.089 | 0.216 |
| Ca(mmol/L) | 0.147 | 0.034 | 0.093 | 0.180 | -0.106 | 0.128 |
| P(mmol/L) | 0.122 | 0.075 | 0.119 | 0.083 | -0.706 | 0.270 |
| Mg(mmol/L) | 0.001 | 0.990 | -0.023 | 0.775 | -0.002 | 0.978 |
| Fe(μmol/L) | -0.303 | ＜0.001 | -0.187 | 0.022 | 0.274 | 0.001 |
| 25-(OH)D(μg/L) | -0.284 | 0.143 | -0.297 | 0.125 | 0.197 | 0.315 |
分别以BFR、VFA和RMR作为预测MAFLD的指标做ROC曲线, 结果表明BFR、VFA和RMR均有预测MAFLD的价值(P＜0.05), 三个指标对应的AUC分别为0.800(95%CI: 0.752-0.875)、0.894(95%CI: 0.847-0.941)和0.715(95%CI: 0.639-0.790), BFR预测MAFLD的界值为28.55%, VFA预测MAFLD的界值为87.10 cm2, RMR预测MAFLD的界值为1497.50 kcal/d, 三个指标中VFA的灵敏度最高(0.850), RMR的特异度最高(0.861). 按照性别分组分别做BFR、VFA和RMR预测MAFLD的ROC曲线, 三者同样具有预测MAFLD的价值(P＜0.05), 男性BFR、VFA和RMR预测MAFLD的AUC分别为0.934(95%CI: 0.895-0.972)、0.943(95%CI: 0.910-0.976)和0.782(95%CI: 0.696-0.867), 对应的界值分别为26.55%、87.25 cm2和1579.50 kcal/d, 其中VFA的灵敏度(0.815)和特异度(0.968)均最高; 女性BFR、VFA和RMR预测MAFLD的AUC分别为0.893(95%CI: 0.824-0.963)、0.907(95%CI: 0.850-0.965)和0.751(95%CI: 0.644-0.859), 对应的界值分别为33.85%、117.05 cm2和1277.50 kcal/d, 其中BFR的灵敏度最高(0.876), RMR的特异度最高(0.970), 详见图1-3和表3.
| 指标 | AUC(95%CI) | P | 界值 | 灵敏度 | 特异度 | |
| 合计 | BFR | 0.800(0.752-0.875) | ＜0.001 | 28.55 | 0.771 | 0.694 |
| VFA | 0.894(0.847-0.941) | ＜0.001 | 87.10 | 0.850 | 0.833 | |
| RMR | 0.715(0.639-0.790) | ＜0.001 | 1497.50 | 0.511 | 0.861 | |
| 男性 | BFR | 0.934(0.895-0.972) | ＜0.001 | 26.55 | 0.790 | 0.950 |
| VFA | 0.943(0.910-0.976) | ＜0.001 | 87.25 | 0.815 | 0.968 | |
| RMR | 0.782(0.696-0.867) | ＜0.001 | 1579.50 | 0.602 | 0.900 | |
| 女性 | BFR | 0.893(0.824-0.963) | ＜0.001 | 33.85 | 0.876 | 0.812 |
| VFA | 0.907(0.850-0.965) | ＜0.001 | 117.05 | 0.771 | 0.937 | |
| RMR | 0.751(0.644-0.859) | 0.001 | 1277.50 | 0.399 | 0.970 |
以是否为MAFLD为因变量做二元Logistic回归分析, 未调整影响因素时, MAFLD的患病风险随着BFR、VFA和RMR的增加而增加(P＜0.05), BFR、VFA和RMR的OR值分别为1.203(95%CI: 1.133-1.277)、1.063(95%CI: 1.043-1.082)和1.004(95%CI: 1.002-1.005); 在调整了性别、年龄后(模型Ⅰ), BFR、VFA和RMR的OR值分别为1.378(95%CI: 1.261-1.505)、1.077(95%CI: 1.054-1.101)和1.008(95%CI: 1.004-1.011), 调整性别、年龄、FBG、TG和HDL-C后(模型Ⅱ), BFR、VFA和RMR的OR值分别为1.357(95%CI: 1.214-1.517)、1.079(95%CI: 1.049-1.111)和1.009(95%CI: 1.005-1.014). 按照年龄分组后做二元Logistic回归分析, 分别在未调整影响因素、仅调整年龄(模型Ⅰ)及调整年龄、FBG、TG和HDL-C(模型Ⅱ)时, 仍然存在BFR、VFA和RMR增加则MAFLD的患病风险增加(P＜0.05)的结果, 详见表4.
| 未调整 | 模型Ⅰ | 模型Ⅱ | |||||
| OR(95%CI) | P | OR(95%CI) | P | OR(95%CI) | P | ||
| 合计 | BFR | 1.203(1.133-1.277) | ＜0.001 | 1.378(1.261-1.505) | ＜0.001 | 1.357(1.214-1.517) | ＜0.001 |
| VFA | 1.063(1.043-1.082) | ＜0.001 | 1.077(1.054-1.101) | ＜0.001 | 1.079(1.049-1.111) | ＜0.001 | |
| RMR | 1.004(1.002-1.005) | ＜0.001 | 1.008(1.004-1.011) | ＜0.001 | 1.009(1.005-1.014) | ＜0.001 | |
| 男性 | BFR | 1.453(1.279-1.651) | ＜0.001 | 1.458(1.282-1.658) | ＜0.001 | 1.427(1.209-1.685) | ＜0.001 |
| VFA | 1.095(1.058-1.132) | ＜0.001 | 1.100(1.062-1.140) | ＜0.001 | 1.120(1.059-1.185) | ＜0.001 | |
| RMR | 1.006(1.003-1.010) | ＜0.001 | 1.007(1.003-1.010) | ＜0.001 | 1.010(1.004-1.015) | ＜0.001 | |
| 女性 | BFR | 1.281(1.143-1.435) | ＜0.001 | 1.292(1.148-1.455) | ＜0.001 | 1.292(1.106-1.510) | ＜0.001 |
| VFA | 1.058(1.032-1.086) | ＜0.001 | 1.058(1.032-1.086) | ＜0.001 | 1.054(1.020-1.088) | ＜0.001 | |
| RMR | 1.010(1.004-1.017) | ＜0.001 | 1.011(1.004-1.018) | ＜0.001 | 1.010(1.001-1.020) | ＜0.001 | |
本研究入选的研究对象中男性平均年龄小于女性(男性约41岁, 女性约53岁), 但MAFLD诊断分组中不同性别研究对象的年龄差异没有统计学意义, 进一步对年龄进行分析发现在MAFLD组中, 男性平均年龄为(41.01±11.28)岁, 女性平均年龄为(52.34±11.29)岁, 差异有统计学意义(P＜0.001), 以上结果与以往研究结果一致[12], 在绝经后年龄(50-60)岁, 女性比男性更易罹患MAFLD, 这可能与激素水平有关, 有研究表明雌激素可以预防MAFLD[13], 通过激素替代治疗可以降低绝经后女性MAFLD的患病风险[14], 而在绝经前女性受到脂肪储存过多带来的不利影响较少[13]. 因此, 对于有MAFLD相关代谢风险的男性患者更宜早期干预, 对于绝经后的女性应警惕机体激素水平变化所带来的代谢功能改变, 以降低MAFLD的患病风险.
MAFLD诊断分组的比较中, BMI、WC、AMC、Mw、Pw、Fw、BFR、VFA、RMR等人体测量指标的组间差异均有统计学意义, 且MAFLD组高于非MAFLD组. 以往的研究结果表明MAFLD患者的肌肉减少并不常见[15], 一项比利时的研究中NAFLD患者的肌肉含量同样高于非NAFLD患者[16]. 本研究中MAFLD的平均BMI为28.11 kg/cm2, 平均Fw为27.10 kg, 平均Mw为29.04 kg, 平均RMR为1514.85 kcal/d, 低于巴西的研究结果[17], 可能与人种、环境、饮食及文化等差异有关. 另外, 本研究中BFR和VFA高于一项中国台湾的研究结果(男性BFR为24.2%, 女性的BFR为31.2%; 男性VFA为71.6 cm2, 女性VFA为71.3 cm2)[18], WC及ALT、TC、TG、HDL-C、LDL-C、FBG等生化指标也高于一项中国广西的研究结果[6], 此差异可能与样本选择有关, 本研究以医院就诊患者为研究对象, 所以研究对象的肝功能、血糖、血脂、胰岛素抵抗等情况可能相对更为严重. 除此之外, 本研究中MAFLD组和非MAFLD组在K、Ca、P、Mg、Fe等微量营养素的含量上没有差异, 但MAFLD组的Na含量低于非MAFLD组, 以上结果可能提示MAFLD患者处于营养过剩、营养不均衡的状态.
在代谢相关疾病的发生发展过程中, 脂肪发挥着非常重要的作用. 对于代谢综合征(metabolic syndrome, MS)人群, 内脏脂肪型(V型)MS的患者其TG、UA、ALT、AST、糖耐量异常及脂肪肝的发生显著高于皮下脂肪型(S型)MS患者[19]. 在MAFLD患者中, ALT随着骨骼肌-体脂肪比值增加而下降[20], 体脂肪和内脏脂肪与NAFLD相关[18,21]. 提示对于MAFLD患者而言降低体脂率并保持骨骼肌含量有利于改善肝功能. 本研究的相关性分析显示, BFR与ALT及TBIL分别呈负相关(rs-BFR＜0, P＜0.05), BFR与TC、TG、HDL-C、LDL-C、FBG、FINS、HbA1C及HOMA-IR的水平呈正相关(rs-BFR＞0, P＜0.05); VFA与TBIL呈负相关(rs-VFA＜0, P＜0.05)VFA与TC、TG、LDL-C、FBG、FINS、HbA1C及HOMA-IR的水平呈正相关(rs-VFA＞0, P＜0.05), 且在VFA对MAFLD的Logistic分析结果显示, 无论是否调整相关因素, BFR和VFA的升高均可增加MAFLD的发病风险(OR＞0, P＜0.05), 因此早期进行体脂肪尤其是内脏脂肪的管理和干预有助于改善肝功能并降低MAFLD等代谢相关疾病的发生风险.
了解不同患者的人体成分组成是开展MAFLD个体化防治及干预的前提, 目前测量人体成分的方法多种多样, 如人体测量法、计算机断层扫描法、核磁共振法、BIA法、双能X线吸收法、水下称重法、同位素稀释法、空气置换法等[22], 其中BIA法测定结果精确、安全无创、操作方便, 在慢性疾病的监测中广泛应用. 多项研究表明BIA法测定的RMR比较接近间接测热法(indirect calorimetry, IC)所获得的静息代谢能量(resting energy expenditure, REE), 可以作为无法利用代谢车获取REE时的替代测量方法[17], BIA法测量的肌肉含量(包括骨骼肌、四肢肌肉、躯干肌肉)和脂肪含量(包括总脂肪、皮下脂肪、内脏脂肪)与核磁共振(magnetic resonance imaging, MRI)测得的腰大肌表面积和脂肪含量之间分别具有强相关性, 提示BIA可以作为MRI测量体成分的无辐射替代方法[23], 且BIA法测量指标预测MAFLD的性能优于人体测量指标(如BMI)[24]. 本项研究中, 预测MAFLD的AUC: VFA＞BFR＞0.8＞RMR＞0.7, 可提示VFA和BFR在预测MAFLD的效果上要优于RMR, 最优的指标(VFA)预测MAFLD的AUC为0.894, 界值为87.10 cm2, 该结果高于中国台湾(70.5 cm2)的一项研究[18]. 在区分性别之后, VFA在男性和女性中预测MAFLD的AUC均＞0.9, 提示BIA测量指标VFA可能是一种预测MAFLD的潜在指标. Logistic回归分析显示, 无论是否调整影响因素, 随着VFA的升高, MAFLD的患病风险增加, 即VFA是MAFLD的危险因素之一. 以往的研究也表明内脏脂肪是肝脂肪变性的独立预测因子(OR = 1.031, 95%CI: 1.013-1.048)[25,26], 并与MAFLD的发生发展密切相关[27-29].
综上所述, BIA法测定BFR、VFA和RMR可用于MAFLD的患病风险预测中, 其中VFA是BIA测定法中预测MAFLD的最优指标, 这为无法实现核磁共振、双能X线吸收等测定的情况下提供了一种无创又相对精确的测量和预测方法.
代谢相关脂肪性肝病(metabolic associated fatty liver disease, MAFLD)的重新定义更突出了代谢异常在其发生发展过程中的核心地位, 人体成分的组成可反映机体各组分代谢情况, 通过生物电阻抗法(bioelectrical impedance analysis, BIA)测定人体成分可为预测MAFLD的患病风险提供新思路.
脂肪堆积尤其是内脏脂肪堆积在MAFLD的发生发展过程中起着重要的作用, BIA可快速、无创获取体脂率(body fat ratio, BFR)、内脏脂肪面积(visceral fat area, VFA)及静息代谢率(resting metabolic rate, RMR)等人体成分相关数据, 分析其与MAFLD之间的关系有助于预测疾病的发生风险.
通过BIA测定人体组成成分, 探索BFR、VFA及RMR预测MAFLD的临床应用价值.
分别收集MAFLD组和非MAFLD组的人体测量、人体成分分析及实验室检测相关指标, 利用Spearman相关分析、绘制受试者工作特征(receiver operating characteristic, ROC)曲线及Logistic回归分析等方法探索BFR、VFA和RMR与MAFLD及其相关指标间的关系.
BFR、VFA和RMR预测MAFLD的ROC曲线下面积(area under the ROC curve,AUC)分别为0.800(95%CI: 0.752-0.875)、0.894(95%CI: 0.847-0.941)和0.715(95%CI: 0.639-0.790), 其中VFA为最优指标, 其界值为87.10 cm2, 男性和女性VFA预测MAFLD的AUC分别为0.943(95%CI: 0.910-0.976)和0.907(95%CI: 0.850-0.965), 其界值分别为87.25 cm2和117.05 cm2.
BIA法测定BFR、VFA和RMR可用于MAFLD的患病风险预测中, 其中VFA是BIA测定法中预测MAFLD的最优指标, 可作为一种无创又相对精确的测量和预测方法.
BIA法测定人体成分快捷、无创且相对精确, 在MAFLD的预测中具有较好的应用价值, 今后也可广泛应用于其他代谢相关疾病中.
编辑: 张砚梁
学科分类: 胃肠病学和肝病学
手稿来源地: 天津市
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科学编辑:张砚梁 制作编辑:张砚梁
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