Clinical risk scoring for predicting non‐alcoholic fatty liver disease in metabolic syndrome patients (NAFLD‐MS score)

Saokaew, Surasak; Kanchanasuwan, Shada; Apisarnthanarak, Piyaporn; Charoensak, Aphinya; Charatcharoenwitthaya, Phunchai; Phisalprapa, Pochamana; Chaiyakunapruk, Nathorn

doi:10.1111/liv.13413

Cited by 21 publications

(17 citation statements)

References 67 publications

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“…Study has shown that NAFLD is closed related to obesity, diabetes, hypertension, hyperlipidemia, and other metabolic syndromes. [ 30 ] Patients with metabolic syndrome have a higher risk of NAFLD in 4 to 11 times than normal people. [ 7 ] Therefore, the American Society of Clinical Endocrinologists lists NAFLD as one of the components of metabolic syndrome in 2003.…”

Section: Discussionmentioning

confidence: 99%

Risk factors and community intervention for nonalcoholic fatty liver disease in community residents of Urumqi, China

Lin¹,

Xian²,

Liu

et al. 2018

Medicine

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This study is to investigate the prevalence and risk factors of nonalcoholic fatty liver disease (NAFLD) and to analyze the effect of comprehensive community intervention on NAFLD in community residents in Urumqi, China.Cluster sampling method with street community as a unit was adopted in this study. Questionnaire survey, body measurement, blood biochemistry (including liver function, fasting blood glucose [FPG], and uric acid [UA]) examination as well as liver B ultrasound were performed. Then, comprehensive intervention was conducted in NAFLD patients.A total of 1000 people were enrolled, including 344 men and 656 women, with an average age of 51.79 ± 4.28 years. Of them, 660 were Han Chinese, 327 were Uygur, and 13 were Hui. The overall prevalence rate of NAFLD was 54.3%. The prevalence rate of NAFLD is higher in middle-aged population and is higher in ethnic minority than that in Han. NAFLD was associated with the past medical history of metabolic diseases. The factors of body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), waist circumference, hip circumference, neck circumference, subcutaneous fat thickness, FPG, alanine aminotransferase, and aspartate aminotransferase were identified as risk factors for NFALD. Neck circumference predicts the occurrence of NAFLD in female better, whereas subcutaneous fat predicts the occurrence of NAFLD in male better. After 8 months of community intervention in NAFLD patients, the changes of BMI, SBP, DBP, waist circumference, neck circumference, subcutaneous fat thickness, and UA were statistically significant (P < .05).The prevalence rate of NAFLD is high in Urumqi, China. Community intervention is effective in reducing the degree of NAFLD and promoting the overall health of NAFLD patients.

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Section: Discussionmentioning

confidence: 99%

Risk factors and community intervention for nonalcoholic fatty liver disease in community residents of Urumqi, China

Lin¹,

Xian²,

Liu

et al. 2018

Medicine

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“…In recent years, MRI-PDFF had been used to diagnose the hepatic steatosis in several countries and the reliable accuracy, sensitivity and specificity had been verified [14,15,25,29]. Besides the imaging and pathological diagnosis, some molecular prediction models based on the clinical parameters, genetics information, and serum biochemical factors had been constructed for the prediction of the risk of NAFLD frequently [30][31][32], but the sensitivity and specificity of them were remain worth discussed based on the most accurate NAFLD diagnostic method such as MRI-PDFF in different ethnics and regions. In this study, we performed the MRI-PDFF examination as the NAFLD diagnostic gold standard to verify the diagnostic value of NAFLD molecular prediction model FLI, NAFLD LFS and Liver fat (%) in Chinese Han population that had been reported previously [17,18].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy assessment of molecular prediction model for the risk of NAFLD based on MRI-PDFF diagnosed Chinese Han population

Zhang

Zhu

et al. 2019

Preprint

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Background: Several molecular prediction models based on the clinical parameters had been constructed to predict and diagnosis the risk of NAFLD, but the accuracy of these molecular prediction models remain need to be verified based on the most accurate NAFLD diagnostic method. The aims of this study was to verify the accuracy of three molecular prediction models Fatty liver index (FLI), NAFLD liver fat score system (NAFLD LFS), and Liver fat (%) in the prediction and diagnosis of NAFLD in MRI-PDFF diagnosed Chinese Han population. Patients and Methods: MRI-PDFF was used to diagnose the hepatic steatosis of all the subjects. Information such as name, age, lifestyle, and major medical histories were collected and the clinical parameters were measured by the standard clinical laboratory techniques. The cut-off values of each model for the risk of NAFLD were calculated based on the MRI-PDFF results. All data were analyzed using the statistical analysis software SPSS 23.0. Results: A total of 98 subjects were recruited with the matched sex and age. The ROC curves of FLI, NAFLD LFS, and Liver fat (%) models were plotted based on the results of MRI-PDFF. We founded that the accuracy of FLI, NAFLD LFS, and Liver fat (%) models for the prediction and diagnosis of NAFLD were comparative available in Chinese Han population as well as the validity of them in other ethnics and regions. Conclusions: The molecular prediction models FLI, NAFLD LFS, and Liver fat (%) were comparative available for the prediction and diagnosis of NAFLD in Chinese Han population. MRI-PDFF could be used as the golden standard to develop the new molecular prediction models for the prediction and diagnosis of NAFLD.

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“…In order to build our proposed predictive model, we have exploited the capabilities of logistic regression. As the general formula of logistic regression in terms of sigmoid function can be expressed as follows [19]:…”

Section: The Framework Of the Predictive Modelmentioning

confidence: 99%

A Predictive model for liver disease progression based on logistic regression algorithm

Abdalrada

Yahya

Alaidi

et al. 2019

PEN

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Liver disease counts to be one of the most prevalent diseases in the worldwide. Therefore, this paper is aim to address the problem of predicting liver disease progression. As the existing predictive models focus on predicting the label of disease; the probability of developing the disease is still obscure. This paper, therefore, has proposed a model to predict the probability occurrence of liver diseases. The proposed predictive model used logistic regression abilities to predict the probability of liver disease occurrence. ILPD dataset was used to analyze the performance of the model. The predictive model has shown outstanding performance with a prediction accuracy rate of 72.4%, the sensitivity of 90.3%, the specificity of 78.3 %, Type I Error of 9.7 %, Type II Error of 21.7 %, and ROC of 0.758%. The model has furthermore confirmed the feasibility of the laboratory tests such as as (Age; Direct Bilirubin (DB), Alamine_Aminotransferase (SGPT), Total_Protiens (TP), Albumin (ALB)) to predict the disease progression. The predictive model will be helpful to patients and doctors to realize the progression of the disease and make a suitable timely intervention.

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Clinical risk scoring for predicting non‐alcoholic fatty liver disease in metabolic syndrome patients (NAFLD‐MS score)

Abstract: A simple and non-invasive scoring scheme of five predictors provides good prediction indices for NAFLD in MetS patients. This scheme may help clinicians in order to take further appropriate action.

Cited by 21 publications

References 67 publications

Risk factors and community intervention for nonalcoholic fatty liver disease in community residents of Urumqi, China

Risk factors and community intervention for nonalcoholic fatty liver disease in community residents of Urumqi, China

Diagnostic accuracy assessment of molecular prediction model for the risk of NAFLD based on MRI-PDFF diagnosed Chinese Han population

A Predictive model for liver disease progression based on logistic regression algorithm

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