Risk factors of nonalcoholic fatty liver disease in lean body mass population: A systematic review and meta‐analysis

Alam, Shahinul; Eslam, Mohammad; Hasan, Nazmul; Anam, Kamrul; Chowdhury, Muhammad Abdul Baker; Khan, Abdullah; Hasan, Mohammad Jahid; Mohamed, Rosmawati

doi:10.1002/jgh3.12658

Cited by 13 publications

(12 citation statements)

References 63 publications

(446 reference statements)

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“…No significant differences were observed between these two groups with respect to diabetes prevalence, age, and levels of total cholesterol and low-density lipoprotein-cholesterol (LDL-C), suggesting that lean/ nonobese NAFLD patients may have a risk for developing metabolic diseases similar to NAFLD patients with obesity. Alam et al observed that lean and non-lean NAFLD patients had similar characteristics and shared common risk factors [35].…”

Section: Clinical Characteristicsmentioning

confidence: 99%

NAFLD in normal weight individuals

DiStefano

Gerhard

2022

Diabetol Metab Syndr

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Nonalcoholic fatty liver disease (NAFLD) can develop in lean individuals. Despite a better metabolic profile, the risk of disease progression to hepatic inflammation, fibrosis, and decompensated cirrhosis in the lean is similar to that in obesity-related NAFLD and lean individuals may experience more severe hepatic consequences and higher mortality relative to those with a higher body mass index (BMI). In the absence of early symptoms and abnormal laboratory findings, lean individuals are not likely to be screened for NAFLD or related comorbidities; however, given the progressive nature of the disease and the increased risk of morbidity and mortality, a clearer understanding of the natural history of NAFLD in lean individuals, as well as efforts to raise awareness of the potential health risks of NAFLD in lean individuals, are warranted. In this review, we summarize available data on NAFLD prevalence, clinical characteristics, outcomes, and mortality in lean individuals and discuss factors that may contribute to the development of NAFLD in this population, including links between dietary and genetic factors, menopausal status, and ethnicity. We also highlight the need for greater representation of lean individuals in NAFLD-related clinical trials, as well as more studies to better characterize lean NAFLD, develop improved screening algorithms, and determine specific treatment strategies based on underlying etiology.

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Section: Clinical Characteristicsmentioning

confidence: 99%

NAFLD in normal weight individuals

DiStefano

Gerhard

2022

Diabetol Metab Syndr

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“…Lean NAFLD subjects may have clinically similar metabolic dysfunction to obese or overweight individuals [ 45 ], even if they are at a much higher risk of developing metabolic dysfunction [ 46 ]. The pathogenesis of NAFLD in lean and obese individuals is not exclusively congruent.…”

Section: Discussionmentioning

confidence: 99%

Unique genetic variants of lean nonalcoholic fatty liver disease: a retrospective cohort study

Yang

et al. 2023

BMC Endocr Disord

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We investigated the prevalence and clinical metabolic characteristics of lean nonalcoholic fatty liver disease (NAFLD) in an elderly Chinese population and assessed the relevance of lipid markers and genetic variation. All 5,338 community subjects underwent detailed clinical and laboratory examinations and were divided into three groups: lean (Body mass index (BMI) < 23 kg/m2, n = 2,012), overweight (BMI = 23–24.9 kg/m2, n = 1,354), and obese (BMI ≥ 25 kg/m2, n = 1,972). Single nucleotide polymorphisms were selected based on those reported in previous NAFLD or obesity genome-wide association studies. The frequencies of alleles and genotypes were calculated and statistically analyzed with Pearson’s χ2 tests. One-way ANCOVA was used to test the association between positive SNPs and metabolic parameters in lean NAFLD individuals. Our results showed that the C allele frequency of rs2279026, the G allele of rs2279028, the C allele of rs780093, and the C allele frequency of rs1260326 were higher in obese NAFLD than in lean NAFLD (P < 0.05). In addition, we observed an association between the CC of rs1421085, TT of rs3751812, AA of rs8050136, and AA of rs9939609 genotypes in the FTO gene and low-density lipoprotein levels (P < 0.05). In conclusion, our findings provide a unique perspective on the prevalence, genetic characteristics, and metabolic profile of NAFLD in older lean individuals in China. This is the first study to examine the association between genetic variants in the FTO, TFAP2B and GCKR genes and NAFLD in a cohort of lean individuals.

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“…Age is also an important risk of NAFLD in lean people. According to a meta-analysis, lean individuals with NAFLD were older than lean controls, with a mean difference of 2.87 years [ 21 ]. Sex differences have also been noted in NAFLD, such that a higher prevalence of NAFLD was found among males than among females.…”

Section: Discussionmentioning

confidence: 99%

“…Second, some clinical parameters were not included in the present study, such as uric acid, HbA1C, C-reactive protein and homeostasis model assessment for insulin resistance (HOMA-IR). These parameters have been shown to be associated with lean NAFLD [ 21 , 33 ]. Third, we did not collect histological data from liver biopsies or other non-invasive imaging techniques, such as the controlled attenuation parameter measured by FibroScan ® or magnetic resonance imaging-derived proton density fat fraction to confirm the degree of the steatosis.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver

Chen

Lin³

et al. 2023

Diagnostics

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The reported prevalence of non-alcoholic fatty liver disease in studies of lean individuals ranges from 7.6% to 19.3%. The aim of the study was to develop machine-learning models for the prediction of fatty liver disease in lean individuals. The present retrospective study included 12,191 lean subjects with a body mass index < 23 kg/m2 who had undergone a health checkup from January 2009 to January 2019. Participants were divided into a training (70%, 8533 subjects) and a testing group (30%, 3568 subjects). A total of 27 clinical features were analyzed, except for medical history and history of alcohol or tobacco consumption. Among the 12,191 lean individuals included in the present study, 741 (6.1%) had fatty liver. The machine learning model comprising a two-class neural network using 10 features had the highest area under the receiver operating characteristic curve (AUROC) value (0.885) among all other algorithms. When applied to the testing group, we found the two-class neural network exhibited a slightly higher AUROC value for predicting fatty liver (0.868, 0.841–0.894) compared to the fatty liver index (FLI; 0.852, 0.824–0.81). In conclusion, the two-class neural network had greater predictive value for fatty liver than the FLI in lean individuals.

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Risk factors of nonalcoholic fatty liver disease in lean body mass population: A systematic review and meta‐analysis

Cited by 13 publications

References 63 publications

NAFLD in normal weight individuals

NAFLD in normal weight individuals

Unique genetic variants of lean nonalcoholic fatty liver disease: a retrospective cohort study

Comparison of Machine Learning Models and the Fatty Liver Index in Predicting Lean Fatty Liver

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