scholarly journals Combined effects of lifestyle risk factors on fatty liver index

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ulla Nivukoski ◽  
Markus Niemelä ◽  
Aini Bloigu ◽  
Risto Bloigu ◽  
Mauri Aalto ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fatty Liver ◽  
Combined Effects ◽  
Lifestyle Risk Factors ◽  
Liver Index ◽  
Fatty Liver Index ◽  
Lifestyle Risk

scholarly journals Combined Effects of Lifestyle Risk Factors on Fatty Liver Index

2020 ◽  
Author(s):  
Ulla Nivukoski ◽  
Markus Niemelä ◽  
Aini Bloigu ◽  
Risto Bloigu ◽  
Mauri Aalto ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fatty Liver ◽  
Hepatic Steatosis ◽  
Linear Trend ◽  
Total Risk ◽  
Cross Sectional ◽  
Lifestyle Risk Factors ◽  
Liver Index ◽  
Fatty Liver Index ◽  
Lifestyle Risk

Abstract Background: Factors of lifestyle may have a major impact on liver-related morbidity and mortality.We examined independent and joint effects of lifestyle risk factors on fatty liver index (FLI), a biomarker of hepatic steatosis, in a population-based cross-sectional national health survey. Methods: The study included 12,368 participants (5,784 men, 6,584 women) aged 25–74 years. Quantitative estimates of alcohol use, smoking, adiposity and physical activity were used to establish a total score of risk factors, with higher scores indicating an unhealthier lifestyle. FLI was calculated based on an algorithm including body mass index, waist circumference, serum gamma-glutamyltransferase and triglycerides.Results: The occurrence of FLI ≥ 60% indicating fatty liver increased from 2.4% in men with zero risk factors to 81.9% in those with a total risk score of 7–8 (p < 0.0005 for linear trend) and in women from 0% to 73.5% (p < 0.0005). The most striking individual impacts on the likelihood for FLI above 60% were observed for physical inactivity (p < 0.0005 for both genders) and alcohol consumption (p < 0.0005 for men). Interestingly, coffee consumption was also found to increase with increasing risk factor scores (p < 0.0005 for linear trend in both genders).Conclusions: The data indicates that unfavorable combinations of lifestyle risk factors lead to a high likelihood of hepatic steatosis. Use of FLI as a diagnostic tool may benefit the assessment of interventions aimed at maintaining a healthy lifestyle and prevention of liver-related morbidity.

scholarly journals Combined Effects of Lifestyle Risk Factors on Fatty Liver Index

2020 ◽  
Author(s):  
Ulla Nivukoski ◽  
Markus Niemelä ◽  
Aini Bloigu ◽  
Risto Bloigu ◽  
Mauri Aalto ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fatty Liver ◽  
Hepatic Steatosis ◽  
Linear Trend ◽  
Survey Methods ◽  
Total Risk ◽  
Lifestyle Risk Factors ◽  
Liver Index ◽  
Fatty Liver Index ◽  
Lifestyle Risk

Abstract Background Factors of lifestyle may have a major impact on liver-related morbidity and mortality. We examined independent and joint effects of lifestyle risk factors on fatty liver index (FLI), a biomarker of hepatic steatosis, in a population-based cross-sectional national health survey. Methods The study included 12,368 participants (5,784 men, 6,584 women) aged 25–74 years. Quantitative estimates of alcohol use, smoking, adiposity and physical activity were used to establish a total score of risk factors, with higher scores indicating an unhealthier lifestyle. FLI was calculated based on an algorithm including body mass index, waist circumference, serum gamma-glutamyltransferase and triglycerides. Results The occurrence of FLI ≥ 60% indicating fatty liver increased from 2.4% in men with zero risk factors to 81.9% in those with a total risk score of 7–8 (p < 0.0005 for linear trend) and in women from 0% to 73.5% (p < 0.0005). The most striking individual impacts on the likelihood for FLI above 60% were observed for physical inactivity (p < 0.0005 for both genders) and alcohol consumption (p < 0.0005 for men). Interestingly, coffee consumption was also found to increase with increasing risk factor scores (p < 0.0005 for linear trend in both genders). Conclusions The data indicates that unfavorable combinations of lifestyle risk factors lead to a high likelihood of hepatic steatosis. Use of FLI as a diagnostic tool may benefit the assessment of interventions aimed at maintaining a healthy lifestyle and prevention of liver-related morbidity.

Abstract P216: Fatty Liver Index and Stroke Risk: the Reasons for Geographic and Racial Differences in Stroke Study

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Kristine S Alexander ◽  
Neil A Zakai ◽  
Steven D Lidofsky ◽  
Peter W Callas ◽  
Suzanne E Judd ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Fatty Liver ◽  
Racial Differences ◽  
Stroke Risk ◽  
Stroke Risk Factors ◽  
Liver Index ◽  
Fatty Liver Index ◽  
Negative Score ◽  
Positive Score

Background: Nonalcoholic fatty liver disease (NAFLD) is a common condition driven by the obesity epidemic. It is associated with cardiometabolic risk factors including diabetes, obesity, and hyperlipidemia, but also cardiovascular disease events, independent of these factors. No prospective studies have investigated the association of NAFLD with stroke risk. Hypothesis: NAFLD is associated with the risk of stroke in the REasons for Geographic and Racial Differences in Stroke (REGARDS) case-cohort study. Methods: The REGARDS study recruited 30,239 participants from the contiguous U.S., in order to study the reasons for regional and racial differences in stroke mortality. The REGARDS case-cohort study consists of 569 cases of incident stroke with 5.4 years follow up and a cohort random sample of 1,104. The Fatty Liver Index (FLI) was used a surrogate marker for NAFLD. It is calculated as e X /(1 + e X ) x 100, where x = 0.953*log(triglycerides) + 0.139*BMI + 0.718*log(γ-glutamyltransferase) + 0.053*waist circumference - 15.745. An FLI >60 is considered a positive score, 20-60 an intermediate score, and <20 a negative score. After excluding 68 participants who reported heavy alcohol consumption and 87 with baseline stroke, Cox proportional hazards models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) of stroke for FLI category, adjusting for age, race, sex, and the Framingham stroke risk factors and stratified by body mass index (BMI). Results: In the cohort sample, 44% of participants had NAFLD based on the FLI and 19% had a negative score. Compared to those without NAFLD, individuals with a positive score were more likely to be male (51% vs. 28%), have hypertension (69% vs. 40%), dyslipidemia (68% vs. 37%) diabetes (35% vs. 8%), and higher BMI (mean 33.7 vs. 23.0 kg/m 2 ; all p<0.001). No participant with BMI < 20 kg/m 2 had NAFLD by FLI. NAFLD was not associated with risk of stroke in a model adjusted for age, race and sex; HR 1.00 (95% CI 0.69-1.46), or a model further adjusted for Framingham stroke risk factors; HR 0.71 (95% CI 0.45-1.11). Stratifying by BMI group (20-30 kg/m 2 ), there was no association between NAFLD and stroke risk in those with BMI 20-<25 or 25-30 kg/m 2 . We were unable to analyze NAFLD in the BMI >30 group, due to low number of negative scores. When analyzed as a continuous variable among those with BMI 30 kg/m 2 , the HRs for a 10 unit higher FLI score were 0.92 (95% CI 0.84-1.01) and 1.17 (95% CI 0.97-1.42), respectively, adjusted for age, sex, race, and stroke risk factors. Discussion: NAFLD, as determined by a positive FLI score, was not associated with risk of stroke although FLI score was borderline associated with stroke risk in those with a BMI >30. Results raise the possibility that NAFLD represents end organ damage from an adverse metabolic profile, and is not a mediator of stroke risk.

scholarly journals Lifestyle Risk Factors Associated With Non-Alcoholic Fatty Liver Disease (NAFLD) Among Mexican-Origin Men

2020 ◽  
Vol 52 (7S) ◽  
pp. 588-588
Author(s):  
David O. Garcia ◽  
Miryoung Lee ◽  
Kristin E. Morrill ◽  
Melissa Lopez-Pentecost ◽  
Belinda M. Reininger ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Mexican Origin ◽  
Alcoholic Fatty Liver ◽  
Factors Associated ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk ◽  
Alcoholic Fatty Liver Disease

scholarly journals Fatty liver index as a predictor for type 2 diabetes in subjects with normoglycemia in a nationwide cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
E. García-Escobar ◽  
S. Valdés ◽  
F. Soriguer ◽  
J. Vendrell ◽  
I. M. Urrutia-Etxebarria ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Cohort Study ◽  
Fatty Liver ◽  
Prediction Models ◽  
Oral Glucose ◽  
Liver Index ◽  
Fatty Liver Index ◽  
Significant Independent Risk Factor

AbstractOur aim was to evaluate whether fatty liver index (FLI) is associated with the risk of type 2 diabetes (T2DM) development within the Spanish adult population and according to their prediabetes status; additionally, to examine its incremental predictive value regarding traditional risk factors. A total of 2260 subjects (Prediabetes: 641 subjects, normoglycemia: 1619 subjects) from the [email protected] cohort study were studied. Socio-demographic, anthropometric, clinical data and survey on habits were recorded. An oral glucose tolerance test was performed and fasting determinations of glucose, lipids and insulin were made. FLI was calculated and classified into three categories: Low (< 30), intermediate (30–60) and high (> 60). In total, 143 people developed diabetes at follow-up. The presence of a high FLI category was in all cases a significant independent risk factor for the development of diabetes. The inclusion of FLI categories in prediction models based on different conventional T2DM risk factors significantly increase the prediction power of the models when all the population was considered. According to our results, FLI might be considered an early indicator of T2DM development even under normoglycemic condition. The data also suggest that FLI could provide additional information for the prediction of T2DM in models based on conventional risk factors.

1907-P: GLP-1RA and iSGLT2 Combination Treatment Effects over Fatty Liver Index

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1907-P
Author(s):  
JUANA CARRETERO GÓMEZ ◽  
JOSE CARLOS AREVALO LORIDO ◽  
RICARDO GÓMEZ-HUELGAS ◽  
JOSÉ MIGUEL SEGUÍ-RIPOLL ◽  
MANUEL SUAREZ TEMBRA ◽  
...  
Keyword(s):  
Fatty Liver ◽  
Combination Treatment ◽  
Treatment Effects ◽  
Liver Index ◽  
Fatty Liver Index

2306-PUB: Serum CXCL14 Levels Are Associated with Plasma C-Peptide and Fatty Liver Index in T2DM Patients: A Retrospective Analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2306-PUB
Author(s):  
YURIKO MATSUSHITA ◽  
YUTAKA HASEGAWA ◽  
NORIKO TAKEBE ◽  
YASUSHI ISHIGAKI
Keyword(s):  
Fatty Liver ◽  
Retrospective Analysis ◽  
C Peptide ◽  
Liver Index ◽  
Fatty Liver Index

Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility (Preprint)

2020 ◽  
Author(s):  
Neil Kale
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Insurance Coverage ◽  
Premature Mortality ◽  
Health Insurance Coverage ◽  
Heart Attacks ◽  
Lifestyle Risk Factors ◽  
The People ◽  
Potential Risk Factors ◽  
Lifestyle Risk

BACKGROUND Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. OBJECTIVE The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. METHODS I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. RESULTS Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. CONCLUSIONS With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

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