Poor Sensitivity of the Fatty Liver Index Among Lean Individuals

医学 虚假陈述 内科学 灵敏度(控制系统) 脂肪肝 内分泌学 疾病 体质指数 索引(排版) 胃肠病学 情感(语言学) 肝病 病例对照研究 生理学
作者
Mohammed Rifat Shaik,Yaron Rotman
出处
期刊:The American Journal of Gastroenterology [Lippincott Williams & Wilkins]
被引量:1
标识
DOI:10.14309/ajg.0000000000003883
摘要

INTRODUCTION: Fatty liver index (FLI) is widely used for detection of steatotic liver disease (SLD) in population studies. While frequently used to study SLD in lean individuals, its performance in this subgroup has not been validated. We hypothesized that FLI, which incorporates body-size measures, is not suitable for studies focusing on lean SLD. METHODS: Data from the National Health and Nutrition Examination Survey 2017-20 were used. Adults with available imaging and variables for FLI calculation were included. SLD was defined by controlled attenuation parameter ≥263 dB/m. Lean status was determined using ethnicity-adjusted body mass index cutoffs. The diagnostic performance of FLI was assessed overall and in lean participants using standard rule-in (≥60) and rule-out (<30) thresholds. RESULTS: The study population included 7,191 individuals, of whom 3,602 (50%) had SLD. SLD was seen in 238 of 1,739 lean individuals (13.7%). While FLI had adequate performance in the overall population, it performed poorly in lean participants, in whom FLI ≥60 detected only 15 of 238 SLD cases (sensitivity 6.3%, positive predictive value 46%) with only modest improvement using the rule-out threshold. Lean individuals predicted to have SLD by FLI markedly differed from imaging-confirmed lean SLD; they were older and had higher body mass index, waist circumference, gamma-glutamyl transferase, and triglyceride levels and a greater burden of cardiometabolic comorbidities. DISCUSSION: With its inherent dependence on body-size measures, FLI does not identify lean SLD accurately. Moreover, applying FLI to lean individuals overestimates disease severity. The misclassification and misrepresentation biases imply that FLI should not be used in population-based studies of lean SLD.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ly完成签到,获得积分20
1秒前
pywangsmmu92发布了新的文献求助30
1秒前
2秒前
赫鲁晓楠发布了新的文献求助10
2秒前
有魅力的水杯完成签到,获得积分10
2秒前
2秒前
ohh发布了新的文献求助10
2秒前
CipherSage应助逺書采纳,获得10
3秒前
传奇3应助陈陈陈采纳,获得30
3秒前
顾矜应助逺書采纳,获得10
3秒前
FashionBoy应助逺書采纳,获得10
3秒前
3秒前
干净寻冬发布了新的文献求助10
4秒前
1234发布了新的文献求助50
5秒前
ly发布了新的文献求助10
5秒前
小蘑菇应助yuyan2001采纳,获得10
5秒前
疯狂的戴夫完成签到 ,获得积分10
6秒前
称心映寒完成签到 ,获得积分10
6秒前
OCDer完成签到,获得积分0
7秒前
7秒前
8秒前
小杜在此完成签到,获得积分20
8秒前
8秒前
9秒前
pywangsmmu92完成签到,获得积分10
9秒前
bai123发布了新的文献求助10
10秒前
11秒前
12秒前
12秒前
南窗下完成签到 ,获得积分10
13秒前
OCDer发布了新的文献求助10
15秒前
15秒前
15秒前
17秒前
犹豫的迎梦完成签到 ,获得积分10
18秒前
滴滴滴滴发布了新的文献求助10
18秒前
19秒前
酷波er应助do0采纳,获得10
19秒前
平常心完成签到 ,获得积分20
19秒前
19秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Pediatric Dermoscopy Trichoscopy & Onychoscopy 2030
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Handbuch Trainingswissenschaft – Trainingslehre 500
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7577632
求助须知:如何正确求助?哪些是违规求助? 9157392
关于积分的说明 19591178
捐赠科研通 7161430
什么是DOI,文献DOI怎么找? 3265401
关于科研通互助平台的介绍 2430314
邀请新用户注册赠送积分活动 2256079