Non-invasive Screening of Metabolic Associated Fatty Liver Disease and Affecting Factors in Primary Care.

OBJECTIVE: To identify the presence of MAFLD (metabolic associated fatty liver disease) with some non-invasive screening methods and the factors affecting in patients with metabolic dysfunction. STUDY DESIGN: A cross-sectional study. Place and Duration of the Study: University of Health Sciences, Kartal Dr. Lutfi Kirdar City Hospital, Istanbul, Turkiye, from March to June 2021. METHODOLOGY: This study included 233 participants with metabolic disease over the age of 18 who applied to family medicine clinics. The participants' sociodemographic data, chronic disease status, biochemical parameters, waist circumference, weight, height, body mass index, and presence of steatosis by ultrasonography were recorded. The risk of developing hepatic fibrosis and steatosis was calculated with the non-alcoholic fatty liver disease liver fat score (NAFLD-LFS), hepatic steatosis index (HSI), fatty liver index (FLI), fibrosis-4 index (FIB-4), NAFLD fibrosis score (NAFLD-FS), and aspartate aminotransaminase to platelet ratio index (APRI). The conclusions were evaluated with SPSS. RESULTS: According to the diagnostic criteria, MAFLD was detected in 58.4% of the participants. Statistically significant difference was found between FLI, HSI, NAFLD-LFS and MAFLD (p<0.001). According to the steatosis index risk groups of the participants, 64.4% - 89.7% were found to be high-risk. Steatosis was confirmed by ultrasonography in 63.6% - 77.8% of those at high-risk for index steatosis. The statistically significant difference was found between hypertension, diabetes mellitus, hyperlipidemia, metabolic syndrome, obesity, and MAFLD (p=0.039, p<0.001, p<0.001, p<0.001, and p=0.011, respectively). CONCLUSION: Using non-invasive screening methods for steatosis can be clinically useful in detecting patients at risk for steatosis, and these methods are applicable in predicting MAFLD. KEY WORDS: NAFLD, Fatty liver index, Hepatic steatosis index, MAFLD, Steatosis.

Change of pulmonary function tests in hospitalized COVID-19 patients at third and sixth month follow-up.

BACKGROUND: The effect of COVID-19 infection on pulmonary function is unknown. OBJECTIVE: This study aimed to evaluate pulmonary function tests (PFTs) of patients hospitalized with the diagnosis of COVID-19 pneumonia at 3 and 6 months post-discharge. METHODS: Patients aged 18 years and over who had positive COVID-19 PCR test results and were hospitalized in the pandemic service between 1 May 2020 and 31 October 2020, were included in the study. All patients were evaluated with PFTs FVC, FEV1, FEV1/FVC, and FEF25-75 at 3 and 6 months after discharge. RESULTS: The mean age of 34 patients included in the study was 47.7 ± 12.7 years. The FVC, FEV1, FEV1/FVC, and FEF25-75 measurements at 3 and 6 months post-discharge showed no significant difference (P = 0.765, P = 0.907, P = 0.707, and P = 0.674, respectively). There was no significant difference in any PFT measurements at the third month follow-up, regardless of the pharmacological treatment protocols applied during hospitalization (P > 0.05). However, FEV1/FVC and FEF25-75 levels were 83.1 [3.4]% and 91.0 [10.0]%, respectively, in those who received systemic steroid treatment, and 78.3 ± 8.5% and 72.5 ± 25.7% in those who did not (P = 0.019 and P = 0.048, respectively). In addition, FVC and FEV1 levels increased significantly from the third to the sixth month follow-up in patients who received systemic steroid therapy (P = 0.035 and P = 0.018, respectively). CONCLUSION: Although there is no significant difference in PFT measurements from 3 to 6 months in COVID-19 patients, systemic steroid therapy may have a beneficial effect on respiratory function in COVID-19 patients.

Body Shape Index and Cardiovascular Risk in Individuals With Obesity.

INTRODUCTION: Cardio-metabolic risks are tested to show various anthropometric measurements. This study aimed to evaluate a body shape index (ABSI) of individuals with obesity to determine the role of these measurements in cardiovascular risk prediction. METHODS: This cross-sectional study included 368 patients who were evaluated by the researcher in the polyclinic. Sociodemographic information was obtained, and anthropometric measurements were made. Body mass index (BMI), waist circumference, and ABSI were evaluated in all patients. The patient's risk of developing cardiovascular disease was calculated from the pooled cohort equations risk calculator (PRCAE), Framingham risk score, and systematic coronary risk evaluation (SCORE) risk calculation systems. RESULTS: Of the 368 patients in the study, 302 (82.1%) were females, and 66 (17.9%) were males. The average age of participants was 46.2 ± 12.0 years. The median BMI of participants was 37.5 (34.0-42.4) kg/m2. The median ABSI of participants was 0.0816 (0.0775-0.0849). A positive correlation was found between ABSI and Framingham risk score and PRCAE risk score (r = 0.297, p = 0.000 and r = 0.305, p = 0.000, respectively). A significant relationship was found between ABSI and Framingham, PRCAE, and SCORE risk groups (p = 0.000, p = 0.000, and p = 0.000, respectively). CONCLUSIONS: Our study results revealed a significant association of ABSI with Framingham, PRCAE, and SCORE risk calculation systems, which helps predict cardiovascular risk.

Predictive Value of Betatrophin for Determining the Cardiovascular Risks of Obese Individuals.

OBJECTIVE: To determine cardiovascular risk factors, calculate cardiovascular risk, and the value of betatrophin, a novel biomarker in predicting the risk of cardiovascular disease (CVD) in obese individuals. STUDY DESIGN: Cross-sectional and descriptive study. PLACE AND DURATION OF STUDY:  University of Health Sciences, Kartal Dr. Lutfi Kirdar City Hospital, Turkey, from August to November 2019. METHODOLOGY:  Three hundred and sixty-three patients sociodemographic information was obtained, anthropometric measurements were made, and routine laboratory examinations were taken for obesity. In addition, betatrophin was studied from the samples collected and stored under appropriate conditions. The risk of CVD development in the patients was calculated using Framingham and PRCAE risk calculation systems. RESULTS: The median betatrophin level of the participants was 745.8 (636.3-935.7) ng/L. A significant relationship was found between Framingham sub-risk groups and betatrophin level (p=0.049). Moreover, a significant relationship was also found, especially between the medium-risk group and the high-risk group (p=0.029). A significant correlation was found between the triglyceride and the betatrophin levels (p=0.001, r=-0.166). No relation was found between betatrophin level and risk score in the PRCAE sub-risk groups. CONCLUSION: Its significant relationship with the risk groups determined by the Framingham scoring system and triglyceride level is promising for betatrophin to be used as a new biomarker in predicting the CVD risk. Key Words: Betatrophin, Framingham score, Cardiovascular disease, Obesity, Risk.