布朗族非酒精性脂肪性肝病筛查模型的构建

梁烨倍; 杨春光; 曾华东; 陶茹娓; 胡秋明; 唐小英; 史华祥; 吴伟; 侯旭宏; 贾伟平

doi:10.3969/j.issn.1001-5256.2021.12.025

布朗族非酒精性脂肪性肝病筛查模型的构建

DOI: 10.3969/j.issn.1001-5256.2021.12.025

1.
上海交通大学附属第六人民医院内分泌代谢科, 上海市糖尿病临床医学中心, 上海市糖尿病重点实验室, 上海市糖尿病研究所, 上海 200233
2a.
云南省西双版纳农垦医院院办, 云南西双版纳傣族自治州 666100
2b.
云南省西双版纳农垦医院检验科, 云南西双版纳傣族自治州 666100
3.
云南省西双版纳傣族自治州勐海县布朗山乡卫生院, 云南西双版纳傣族自治州 666213
4.
云南省西双版纳傣族自治州勐海县人民医院院办, 云南西双版纳傣族自治州 666200

基金项目:

上海市糖尿病重点实验室开放课题资助 (SHKLD-KF-1602);

上海市自然科学基金项目 (18ZR1429000)

详细信息

通信作者:
贾伟平, wpjia@jtu.edu.cn

中图分类号: R575.5
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出版历程
- 收稿日期: 2021-04-10
- 录用日期: 2021-06-10
- 出版日期: 2021-12-20

Establishment of screening models for nonalcoholic fatty liver disease in the adult Blang population

1.
Department of Endocrinology and Metabolism, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Diabetes Institute, Shanghai 200233, China
2a.
Executive Office, Xishuangbanna Agricultural Reclamation Hospital, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666100, China
2b.
Department of Clinical Laboratory, Xishuangbanna Agricultural Reclamation Hospital, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666100, China
3.
Blang Township Hospital of Menghai County, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666213, China
4.
Executive Office, Menghai County People's Hospital, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666200, China

Research funding:

Open Research Project of Shanghai Key Laboratory of Diabetes Mellitus (SHKLD-KF-1602);

Natural Science Foundation of Shanghai (18ZR1429000)

摘要

摘要: 目的构建布朗族成人非酒精性脂肪性肝病(NAFLD)简易筛查模型。方法基于2017年布朗族18岁及以上居民的代谢性疾病的调查数据, 将2993例调查对象按性别和年龄组分层(5岁1组)后, 随机分为建模组(n=1497)和验模组(n=1496)。收集患者一般人口学、吸烟、饮酒、疾病家族史及个人疾病史, 身高、体质量、腰围和血压等, 测定空腹血浆血糖(FPG)、餐后或糖负荷后2 h血糖、甘油三酯、总胆固醇、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇、ALT、AST和GGT等。计数资料2组间比较采用χ²检验。应用logistic回归分析建立筛查模型, 并构建受试者工作特征曲线(ROC曲线), 采用ROC曲线下面积(AUC)、灵敏度、特异度、阳性似然比、阴性似然比、阳性预测值和阴性预测值评估构建的筛查模型与已有模型在研究人群中的筛查效能, AUC比较采用DeLong法。结果基于布朗族成人的体格测量指标及实验室检测指标构建3个NAFLD模型, 分别为简易无创模型1(包括年龄、BMI和腰围); 增加血压指标的无创模型2和联合血液检测指标(糖尿病和ALT/AST)的模型3。3个模型对判别NAFLD的AUC(95%CI)在建模组分别为0.881(0.864~0.897)、0.892(0.875~0.907) 和0.894(0.877~0.909), 模型1与模型2、3比较差异均有统计学意义(P值分别为0.004 0、＜0.001); 在验模组分别为0.891(0.874~0.906)、0.892(0.875~0.907)和0.893(0.876~0.908), 3个模型AUC之间无统计学差异(P值均＞0.05)。综合考虑模型筛查效能、检查方法有无创伤性和检查费用, 简易无创模型1被认为是该人群的最佳NAFLD筛查模型。当模型1得分为5分时, 该切点的约登指数最大, 选择得分≥5作为NAFLD的判别标准时, 其灵敏度、特异度、阳性预测值和阴性预测值在建模组和验模组中分别为86.5%和85.6%、79.7%和80.6%、50.3%和51.7%、96.1%和95.8%。结论基于年龄和肥胖指标构建的布朗族成人NAFLD筛查模型有较高的灵敏度、特异度和阴性预测值, 对该人群NAFLD及其密切相关的其他代谢性疾病的人群干预具有重要的实际意义。
- 非酒精性脂肪性肝病 /
- Logistic模型 /
- 筛查
Abstract: Objective To establish simple screening models for nonalcoholic fatty liver disease (NAFLD) in the adult Blang population. Methods Based on the survey data of metabolic diseases in the Blang people aged 18 years or above in 2017, 2993 respondents were stratified by sex and age (at an interval of 5 years) and then randomly divided into modeling group with 1497 respondents and validation group with 1496 respondents. Related information was collected, including demographic data, smoking, drinking, family history of diseases and personal medical history, body height, body weight, waist circumference, and blood pressure, and related markers were measured, including fasting plasma glucose, 2-hour postprandial plasma glucose or blood glucose at 2 hours after glucose loading, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transpeptidase. The chi-square test was used for comparison of categorical data between two groups. Logistic regression analysis was used to establish the screening model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, and negative predictive value were used to evaluate the screening performance of established models versus existing models in the study population, and the DeLong method was used for comparison of AUC. Results Three screening models for NAFLD were established based on physical and biochemical measurements, i.e., simple noninvasive model 1 (age, body mass index, and waist circumference), noninvasive model 2 with the addition of blood pressure, and model 3 with the combination of hematological parameters (diabetes and ALT/AST). In the modeling group, the three models had an AUC of 0.881 (95% confidence interval [CI]: 0.864-0.897), 0.892 (95%CI: 0.875-0.907), and 0.894 (95%CI: 0.877-0.909), respectively, and there was a significant difference between model 1 and models 2/3 (P=0.004 0 and P < 0.001); in the validation group, the three models had an AUC of 0.891 (95%CI: 0.874-0.906), 0.892 (95%CI: 0.875-0.907), and 0.893 (95%CI: 0.876-0.908), respectively, and there was no significant difference between the three groups (P > 0.05). Based on the overall consideration of screening performance, invasiveness, and cost, the simple noninvasive model 1 was considered the optimal screening model for NAFLD in this population. Model 1 had the highest Youden index at the cut-off value of 5 points, and when the score of ≥5 points was selected as the criteria for NAFLD, the model had a sensitivity of 86.5%, a specificity of 79.7%, a positive predictive value of 50.3%, and a negative predictive value of 96.1% in the modeling group and a sensitivity of 85.6%, a specificity of 80.6%, a positive predictive value of 51.7%, and a negative predictive value of 95.8% in the validation group. Conclusion The NAFLD screening models established for the adult Blang population based on age and obesity indicators have relatively higher sensitivity, specificity, and negative predictive value, and this tool is of important practical significance for the intervention of NAFLD and its closely related metabolic diseases in this population.
- Non-alcoholic Fatty Liver Disease /
- Logistic Models /
- Screening

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表 1 建模组和验模组一般特征描述

变量	建模组(n=1497)	验模组(n=1496)	χ²值	P值
NAFLD[例(%)]			0.038	0.846
无	1209(80.8)	1204(80.5)
有	288(19.2)	292(19.5)
性别[例(%)]			0.001	0.981
男	551(36.8)	550(36.8)
女	946(63.2)	946(63.2)
年龄分组[例(%)]			0.000 1	0.991
18~29岁	312(20.8)	311(20.8)
30~39岁	344(23.0)	345(23.1)
40~49岁	369(24.7)	368(24.6)
50~59岁	277(18.5)	277(18.5)
≥60岁	195(13.0)	195(13.0)
文化程度[例(%)]			4.765	0.029
文盲	991(66.2)	1046(69.9)
小学及以上	506(33.8)	450(30.1)
目前吸烟[例(%)]	359(24.0)	322(21.5)	2.570	0.109
目前饮酒[例(%)]	447(29.9)	443(29.6)	0.022	0.882
BMI[例(%)]			0.106	0.745
＜24 kg/m²	974(65.1)	992(66.3)
24~＜28 kg/m²	395(26.4)	369(24.7)
≥28 kg/m²	128(8.6)	135(9.0)
腰围[例(%)]			0.642	0.423
男: ＜85 cm/女: ＜80 cm	1191(79.6)	1183(79.1)
男: 85 cm~＜90 cm/	164(11.0)	150(10.0)
女: 80 cm~＜85 cm
男: ≥90 cm/女: ≥85 cm	142(9.5)	163(10.9)
血压[例(%)]			5.540	0.019
正常	692(46.2)	710(47.5)
正常高值血压	491(32.8)	551(36.8)
高血压	314(21.0)	235(15.7)
糖尿病[例(%)]			0.227	0.634
无	1379(92.1)	1385(92.6)
有	118(7.9)	111(7.4)
ALT/AST[例(%)]¹⁾			0.539	0.463
＜0.96	1067(71.3)	1048(70.1)
≥0.96	430(28.7)	448(29.9)
高甘油三酯血症[例(%)]			5.417	0.020
无	1103(73.7)	1157(77.3)
有	394(26.3)	339(22.7)
注：1)ALT/AST比值分组，根据建模组中筛查NAFLD的AUC的最佳切点(约登指数最大)分为＜0.96组和≥0.96组。

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表 2 建模组NAFLD和非NAFLD一般特征描述

变量	非NAFLD组(n=1209)	NAFLD组(n=288)	χ²值	P值	OR(95%CI)
性别[例(%)]			1.184	0.277
男	453(37.5)	98(34.0)			1.00(参照)
女	756(62.5)	190(66.0)			1.18(0.90~1.55)
年龄分组[例数(%)]			18.338	＜0.001
18~29岁	284(23.5)	28(9.7)			1.00(参照)
30~39岁	284(23.5)	60(20.8)			2.15(1.33~3.47)
40~49岁	269(22.2)	100(34.7)			3.77(2.40~5.92)
50~59岁	221(18.3)	56(19.4)			2.55(1.57~4.16)
≥60岁	151(12.5)	44(15.3)			2.98(1.78~4.99)
文化程度[例(%)]			0.416	0.519
文盲	805(66.6)	186(64.6)			1.00(参照)
小学及以上	404(33.4)	102(35.4)			1.56(1.15~2.13)
目前吸烟[例(%)]	308(25.5)	51(17.7)	7.697	0.006	0.56(0.39~0.82)
目前饮酒[例(%)]	355(29.4)	92(31.9)	0.740	0.390	1.98(1.35~2.90)
BMI[例(%)]			466.650	＜0.001
＜24 kg/m²	933(77.2)	41(14.2)			1.00(参照)
24 kg/m²~＜28 kg/m²	239(19.8)	156(54.2)			15.69(10.73~22.93)
≥28 kg/m²	37(3.1)	91(31.6)			63.54(38.20~105.68)
腰围[例(%)]			429.460	＜0.001
男: ＜85 cm/女: ＜80 cm	1088(90.0)	103(35.8)			1.00(参照)
男: 85 cm~＜90 cm/	80(6.6)	84(29.2)			10.91(7.54~15.77)
女: 80 cm~＜85 cm
男: ≥90 cm/女: ≥85 cm	41(3.4)	101(35.1)			25.20(16.60~38.24)
血压[例(%)]			85.038	＜0.001
正常	621(51.4)	71(24.7)			1.00(参照)
正常高值血压	382(31.6)	109(37.8)			2.64(1.88~3.69)
高血压	206(17.0)	108(37.5)			5.24(3.55~7.73)
糖尿病[例(%)]			47.417	＜0.001
无	1142(94.5)	237(82.3)			1.00(参照)
有	67(5.5)	51(17.7)			3.20(2.13~4.81)
ALT/AST[例(%)]			86.754	＜0.001
＜0.96	926(76.6)	141(49.0)			1.00(参照)
≥0.96	283(23.4)	147(51.0)			4.23(3.18~5.63)
高甘油三酯血症[例(%)]			106.170	＜0.001
无	960(79.4)	143(49.7)			1.00(参照)
有	249(20.6)	145(50.3)			3.79(2.88~4.99)
注：年龄OR值仅调整性别，性别OR值仅调整年龄，其余变量OR值均调整年龄和性别。

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表 3 模型1逻辑回归系数及得分

变量	OR(95%CI)	β值	得分
年龄分组
18~29岁	1.00(参照)	-	0
30~39岁	1.74(0.97~3.11)	0.553	1
40~49岁	2.97(1.71~5.16)	1.088	2
50~59岁	1.99(1.08~3.64)	0.686	2
≥60岁	4.11(2.16~7.83)	1.413	3
BMI
＜24 kg/m²	1.00(参照)	-	0
24 kg/m²~＜28 kg/m²	8.80(5.80~13.34)	2.174	4
≥28 kg/m²	17.89(9.76~32.81)	2.884	5
腰围
男: ＜85 cm/女: ＜80 cm	1.00(参照)	-	0
男: 85 cm~＜90 cm/	3.29(2.15~5.01)	1.190	2
女: 80 cm~＜85 cm
男: ≥90 cm/女: ≥85 cm	5.84(3.50~9.75)	1.765	3
注：模型1公式=年龄(岁)+BMI(kg/m²)+腰围(cm)。

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表 4 模型2逻辑回归系数及得分

变量	OR(95%CI)	β值	得分
年龄分组
18~29岁	1.00(参照)	-	0
30~39岁	1.60(0.89~2.89)	0.471	1
40~49岁	2.53(1.43~4.45)	0.927	3
50~59岁	1.42(0.75~2.69)	0.351	1
≥60岁	2.84(1.44~5.60)	1.045	3
BMI
＜24 kg/m²	1.00(参照)	-	0
24kg/m²~＜28 kg/m²	8.57(5.62~13.06)	2.148	6
≥28 kg/m²	16.77(9.07~30.99)	2.820	8
腰围
男: ＜85 cm/女: ＜80 cm	1.00(参照)	-	0
男: 85 cm~＜90 cm/	3.03(1.97~4.65)	1.109	3
女: 80 cm~＜85 cm
男: ≥90 cm/女: ≥85 cm	5.33(3.16~8.98)	1.673	5
血压
正常	1.00(参照)	-	0
正常高值血压	1.72(1.15~2.57)	0.543	2
高血压	2.57(1.63~4.03)	0.942	3
注：模型2公式=年龄+BMI(kg/m²)+腰围(cm)+血压。

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表 5 模型3逻辑回归系数及得分

变量	OR(95%CI)	β值	得分
年龄分组
18~29岁	1.00(参照)	-	0
30~39岁	1.62(0.89~2.96)	0.484	1
40~49岁	2.51(1.41~4.46)	0.918	2
50~59岁	1.45(0.75~2.80)	0.374	1
≥60岁	2.89(1.44~5.83)	1.063	3
BMI
＜24 kg/m²	1.00(参照)	-	0
24 kg/m²~＜28 kg/m²	7.93(5.18~12.15)	2.071	6
≥28 kg/m²	16.32(8.78~30.32)	2.792	7
腰围
男: ＜85 cm/女: ＜80 cm	1.00(参照)	-	0
男: 85 cm~＜90 cm/	2.74(1.77~4.25)	1.008	3
女: 80 cm~＜85 cm
男: ≥90 cm/女: ≥85 cm	4.69(2.76~7.98)	1.545	4
血压
正常	1.00(参照)	-	0
正常高值血压	1.69(1.13~2.54)	0.527	1
高血压	2.26(1.43~3.60)	0.818	2
糖尿病
无	1.00(参照)	-	0
有	2.08(1.21~3.57)	0.732	2
ALT/AST
＜0.96	1.00(参照)	-	0
≥0.96	1.67(1.17~2.39)	0.512	1
注：模型3公式=年龄(岁)+BMI(kg/m²)+腰围(cm)+血压+糖尿病+ALT/AST。

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表 6 建模组NAFLD筛查模型效能比较

模型	AUC(95%CI)	切点	灵敏度(%)	特异度(%)	阳性似然比	阴性似然比	阳性预测值(%)	阴性预测值(%)	约登指数
1	0.881(0.864~0.897)	≥5	86.5	79.7	4.3	0.2	50.3	96.1	0.661 1
2	0.892(0.875~0.907)	≥8	84.4	81.8	4.6	0.2	52.5	95.6	0.661 7
3	0.894(0.877~0.909)	≥7	88.9	77.3	3.9	0.1	48.3	96.7	0.662 3

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表 7 验模组不同NAFLD筛查模型效能比较

模型	危险因素	AUC(95%CI)	切点	灵敏度(%)	特异度(%)	阳性似然比	阴性似然比	阳性预测值(%)	阴性预测值(%)	约登指数
模型1	年龄、BMI、腰围	0.891(0.874~0.906)	≥5	85.6	80.6	4.4	0.2	51.7	95.8	0.661 8
模型2	年龄、BMI、腰围、血压	0.892(0.875~0.907)	≥7	87.0	78.7	4.1	0.2	49.8	96.1	0.657 3
模型3	年龄、BMI、腰围、血压、糖尿病、ALT/AST比值	0.893(0.876~0.908)	≥8	84.6	81.9	4.7	0.2	53.1	95.6	0.664 8
ZJU模型^[15]	BMI、TG、ALT/AST比值、FPG	0.894(0.877~0.909)	＞35.63	86.0	80.0	4.3	0.2	51.0	95.9	0.659 4
HSI模型^[12]	BMI、ALT/AST比值、性别、糖尿病	0.871(0.853~0.887)	＞32.98	82.2	79.2	3.9	0.2	48.9	94.8	0.613 4
TyG模型^[24]	TG、FPG	0.694(0.670~0.717)	＞8.88	66.8	64.6	1.9	0.5	31.4	88.9	0.314 0
LAP模型^[25]	TG、腰围	0.850(0.831~0.867)	＞20.85	88.0	68.4	2.8	0.2	40.3	95.9	0.563 7
FSI模型^[13]	年龄、性别、BMI、TG、高血压、糖尿病、ALT/AST比值	0.846(0.826~0.863)	＞-2.3	86.3	68.3	2.7	0.2	39.7	95.4	0.545 7
FLI模型^[11]	BMI、TG、腰围、GGT	0.846(0.827~0.864)	＞1.72	81.5	74.3	3.2	0.3	43.4	94.3	0.557 6

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参考文献(36)

[1]	RINELLA ME. Nonalcoholic fatty liver disease: A systematic review[J]. JAMA, 2015, 313(22): 2263-2273. DOI: 10.1001/jama.2015.5370.
[2]	DIEHL AM, DAY C. Cause, pathogenesis, and treatment of nonalcoholic steatohepatitis[J]. N Engl J Med, 2017, 377(21): 2063-2072. DOI: 10.1056/NEJMra1503519.
[3]	ESTES C, ANSTEE QM, ARIAS-LOSTE MT, et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016-2030[J]. J Hepatol, 2018, 69(4): 896-904. DOI: 10.1016/j.jhep.2018.05.036.
[4]	YOUNOSSI ZM, KOENIG AB, ABDELATIF D, et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes[J]. Hepatology, 2016, 64(1): 73-84. DOI: 10.1002/hep.28431.
[5]	WILLIAMSON RM, PRICE JF, GLANCY S, et al. Prevalence of and risk factors for hepatic steatosis and nonalcoholic fatty liver disease in people with type 2 diabetes: The edinburgh type 2 diabetes study[J]. Diabetes Care, 2011, 34(5): 1139-1144. DOI: 10.2337/dc10-2229.
[6]	TARGHER G, BERTOLINI L, PADOVANI R, et al. Prevalence of nonalcoholic fatty liver disease and its association with cardiovascular disease among type 2 diabetic patients[J]. Diabetes Care, 2007, 30(5): 1212-1218. DOI: 10.2337/dc06-2247.
[7]	PETIT JM, GUIU B, TERRIAT B, et al. Nonalcoholic fatty liver is not associated with carotid intima-media thickness in type 2 diabetic patients[J]. J Clin Endocrinol Metab, 2009, 94(10): 4103-4106. DOI: 10.1210/jc.2009-0541.
[8]	MANTOVANI A, PETRACCA G, BEATRICE G, et al. Non-alcoholic fatty liver disease and risk of incident diabetes mellitus: An updated meta-analysis of 501 022 adult individuals[J]. Gut, 2021, 70(5): 962-969. DOI: 10.1136/gutjnl-2020-322572.
[9]	BALLESTRI S, ZONA S, TARGHER G, et al. Nonalcoholic fatty liver disease is associated with an almost twofold increased risk of incident type 2 diabetes and metabolic syndrome. Evidence from a systematic review and meta-analysis[J]. J Gastroenterol Hepatol, 2016, 31(5): 936-944. DOI: 10.1111/jgh.13264.
[10]	TARGHER G, BYRNE CD, LONARDO A, et al. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta-analysis[J]. J Hepatol, 2016, 65(3): 589-600. DOI: 10.1016/j.jhep.2016.05.013.
[11]	BEDOGNI G, BELLENTANI S, MIGLIOLI L, et al. The fatty liver index: A simple and accurate predictor of hepatic steatosis in the general population[J]. BMC Gastroenterol, 2006, 6: 33. DOI: 10.1186/1471-230X-6-33.
[12]	LEE JH, KIM D, KIM HJ, et al. Hepatic steatosis index: A simple screening tool reflecting nonalcoholic fatty liver disease[J]. Dig Liver Dis, 2010, 42(7): 503-508. DOI: 10.1016/j.dld.2009.08.002.
[13]	LONG MT, PEDLEY A, COLANTONIO LD, et al. Development and validation of the framingham steatosis index to identify persons with hepatic steatosis[J]. Clin Gastroenterol Hepatol, 2016, 14(8): 1172-1180. e1172. DOI: 10.1016/j.cgh.2016.03.034.
[14]	FUYAN S, JING L, WENJUN C, et al. Fatty liver disease index: A simple screening tool to facilitate diagnosis of nonalcoholic fatty liver disease in the Chinese population[J]. Dig Dis Sci, 2013, 58(11): 3326-3334. DOI: 10.1007/s10620-013-2774-y.
[15]	WANG J, XU C, XUN Y, et al. ZJU index: A novel model for predicting nonalcoholic fatty liver disease in a Chinese population[J]. Sci Rep, 2015, 5: 16494. DOI: 10.1038/srep16494.
[16]	LIANG YB, HOU XH, WU W, et al. Prevalence of diabetes and its associated factors in Blang ethnic adults[J]. Chin J Intern Med, 2019, 58(1): 27-32. DOI: 10.3760/cma.j.issn.0578-1426.2019.01.005. 梁烨倍, 侯旭宏, 吴伟, 等. 布朗族成人糖尿病患病率及其影响因素研究[J]. 中华内科杂志, 2019, 58(1): 27-32. DOI: 10.3760/cma.j.issn.0578-1426.2019.01.005.
[17]	Fatty Liver Expert Committee, Chinese Medical Doctor Association, National Workshop on Fatty Liver and Alcoholic Liver Disease, Chinese Society of Hepatology, Chinese Medical Association. Guidelines of prevention and treatment for alcoholic liver disease: A 2018 update[J]. J Clin Hepatol, 2018, 34(5): 939-946. DOI: 10.3969/j.issn.1001-5256.2018.05.006 中国医师协会脂肪性肝病专家委员会, 中华医学会肝病学分会脂肪肝和酒精性肝病学组. 酒精性肝病防治指南(2018年更新版)[J]. 临床肝胆病杂志, 2018, 34(5): 939-946. DOI: 10.3969/j.issn.1001-5256.2018.05.006
[18]	National Workshop on Fatty Liver and Alcoholic Liver Disease, Chinese Society of Hepatology, Chinese Medical Association, Fatty Liver Expert Committee, Chinese Medical Doctor Association. Guidelines of prevention and treatment for nonalcoholic fatty liver disease: A 2018 update[J]. J Clin Hepatol, 2018, 34(5): 947-957. DOI: 10.3969/j.issn.1001-5256.2018.05.007. 中华医学会肝病学分会脂肪肝和酒精性肝病学组, 中国医师协会脂肪性肝病专家委员会. 非酒精性脂肪性肝病防治指南(2018年更新版)[J]. 临床肝胆病杂志, 2018, 34(5): 947-957. DOI: 10.3969/j.issn.1001-5256.2018.05.007.
[19]	Cooperative Meta-analysis Group of China Obesity Task Force. Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population[J]. Chin J Epidemiol, 2002, 23(1): 5-10. DOI: 10.3760/j.issn:0254-6450.2002.01.003. 中国肥胖问题工作组数据汇总分析协作组. 我国成人体重指数和腰围对相关疾病危险因素异常的预测价值: 适宜体重指数和腰围切点的研究[J]. 中华流行病学杂志, 2002, 23(1): 5-10. DOI: 10.3760/j.issn:0254-6450.2002.01.003.
[20]	ALBERTI KG, ZIMMET PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation[J]. Diabet Med, 1998, 15(7): 539-553. DOI: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S.
[21]	Chinese Guidelines for the Management of Hypertension Writing Group; Chinese Society of Cardiovascular Diseases, Chinese Medical Association, Hypertension League; Hypertension Professional Committee, Chinese Medical Doctor Association. Chinese guidelines for the management of hypertensionWriting Group of 2018[J]. Chin J Cardiovasc Med, 2019, 24(1): 24-56. DOI: 10.3969/j.issn.1007-5410.2019.01.002. 中国高血压防治指南修订委员会, 高血压联盟中华医学会心血管病学分会, 中国医师协会高血压专业委员会. 中国高血压防治指南(2018年修订版)[J]. 中国心血管杂志, 2019, 24(1): 24-56. DOI: 10.3969/j.issn.1007-5410.2019.01.002.
[22]	Joint Committee for Revision of Chinese Guidelines for Prevention and Treatment of Dyslipidemia in Adults. 2016 Chinese guideline for the management of dyslipidemia in adults[J]. Chin J Cardiol, 2016, 44(10): 833-853. DOI: 10.3760/cma.j.issn.0253-3758.2016.10.005. 中国成人血脂异常防治指南修订联合委员会. 中国成人血脂异常防治指南(2016年修订版)[J]. 中华心血管病杂志, 2016, 44(10): 833-853. DOI: 10.3760/cma.j.issn.0253-3758.2016.10.005.
[23]	DELONG ER, DELONG DM, CLARKE-PEARSON DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach[J]. Biometrics, 1988, 44(3): 837-845.
[24]	ZHANG S, DU T, ZHANG J, et al. The triglyceride and glucose index (TyG) is an effective biomarker to identify nonalcoholic fatty liver disease[J]. Lipids Health Dis, 2017, 16(1): 15. DOI: 10.1186/s12944-017-0409-6.
[25]	DAI H, WANG W, CHEN R, et al. Lipid accumulation product is a powerful tool to predict non-alcoholic fatty liver disease in Chinese adults[J]. Nutr Metab (Lond), 2017, 14: 49. DOI: 10.1186/s12986-017-0206-2.
[26]	NALBANTOGLU IL, BRUNT EM. Role of liver biopsy in nonalcoholic fatty liver disease[J]. World J Gastroenterol, 2014, 20(27): 9026-9037. DOI: 10.3748/wjg.v20.i27.9026.
[27]	CASTERA L, VILGRAIN V, ANGULO P. Noninvasive evaluation of NAFLD[J]. Nat Rev Gastroenterol Hepatol, 2013, 10(11): 666-675. DOI: 10.1038/nrgastro.2013.175.
[28]	FENG RN, DU SS, WANG C, et al. Lean-non-alcoholic fatty liver disease increases risk for metabolic disorders in a normal weight Chinese population[J]. World J Gastroenterol, 2014, 20(47): 17932-17940. DOI: 10.3748/wjg.v20.i47.17932.
[29]	LIU CJ. Prevalence and risk factors for non-alcoholic fatty liver disease in Asian people who are not obese[J]. J Gastroenterol Hepatol, 2012, 27(10): 1555-1560. DOI: 10.1111/j.1440-1746.2012.07222.x.
[30]	FAN JG, ZHU J, LI XJ, et al. Prevalence of and risk factors for fatty liver in a general population of Shanghai, China[J]. J Hepatol, 2005, 43(3): 508-514. DOI: 10.1016/j.jhep.2005.02.042.
[31]	WEI JL, LEUNG JC, LOONG TC, et al. Prevalence and severity of nonalcoholic fatty liver disease in non-obese patients: A population study using proton-magnetic resonance spectroscopy[J]. Am J Gastroenterol, 2015, 110(9): 1306-1314; quiz 1315. DOI: 10.1038/ajg.2015.235.
[32]	KAWAMOTO R, KOHARA K, KUSUNOKI T, et al. Alanine aminotransferase/aspartate aminotransferase ratio is the best surrogate marker for insulin resistance in non-obese Japanese adults[J]. Cardiovasc Diabetol, 2012, 11: 117. DOI: 10.1186/1475-2840-11-117.
[33]	WANG YH, GAO Y. Research progress in diagnosis and treatment of non-alcoholic fatty liver disease combinated with type 2 diabetes mellitus[J]. J Jilin Univ(Med Edit), 2020, 46(6): 1324-1331. DOI: 10.13481/j.1671-587x.20200634. 王雨涵, 高影. 非酒精性脂肪性肝病并发2型糖尿病诊断和治疗的研究进展[J]. 吉林大学学报(医学版), 2020, 46(6): 1324-1331. DOI: 10.13481/j.1671-587x.20200634.
[34]	SAADEH S, YOUNOSSI ZM, REMER EM, et al. The utility of radiological imaging in nonalcoholic fatty liver disease[J]. Gastroenterology, 2002, 123(3): 745-750. DOI: 10.1053/gast.2002.35354.
[35]	FISHBEIN M, CASTRO F, CHERUKU S, et al. Hepatic MRI for fat quantitation: Its relationship to fat morphology, diagnosis, and ultrasound[J]. J Clin Gastroenterol, 2005, 39(7): 619-625. DOI: 10.1097/00004836-200508000-00012.
[36]	RYAN CK, JOHNSON LA, GERMIN BI, et al. One hundred consecutive hepatic biopsies in the workup of living donors for right lobe liver transplantation[J]. Liver Transpl, 2002, 8(12): 1114-1122. DOI: 10.1053/jlts.2002.36740.