Assessing Obesogenic School Environments in Sibiu County, Romania: Adapting the ISCOLE School Environment Questionnaire

The impact of the school environment on childhood weight status has garnered significant attention in recent years. This study aimed to adapt and validate the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) School and Environment questionnaire in order to assess the potential obesogenic impact of school environments in Sibiu County, Romania. The ISCOLE questionnaire was chosen for its rigorous methodology. It was derived from a comprehensive study conducted across 12 countries which aimed to capture multifaceted influences on childhood weight while emphasizing educational settings in the collection of data. To guide the translation and adaptation of the questionnaire, a multidisciplinary committee was assembled which comprised experts in teaching and school administration to ensure target responder relevance, experts in clinical research to ensure methodological robustness, experts in language adaptation to preserve the original intent of the survey, and experts in public health to steer the interpretation of the results, with potential policy implications. The data were analyzed by distinguishing between urban and rural settings, and a two-step cluster analysis was implemented to identify potential intervention targets. To assess the validity of the adapted tool, the questionnaire's construct validity and internal consistency were explored. A response rate of 71.2% of the approached schools in Sibiu County was achieved. Of the 84 responding school representatives, 37 (44%) were from a rural setting. The rural schools had significantly more limited access to gymnasiums, secured lockers, showers, and bicycle racks, and exhibited more serious problems regarding the inadequate disposal of garbage in the school vicinity. A two-step cluster analysis revealed distinct school categories, providing opportunities for public policy interventions. One of these primarily concerned rural schools with limited infrastructure but with proactive practices and policies which were termed "unable but willing"; on the opposing spectrum, the category "able but unwilling" mainly comprised urban schools which had available facilities but lacked local proactive initiatives. The findings emphasize the urgent need for targeted measures to bridge these discrepancies by investing in infrastructure in rural schools and promoting active school practices and policies in urban settings. The assessment of obesogenic school environments in Sibiu County provides a pilot model for broader applications due to the diverse school landscape and supportive local authorities. The results, which were achieved using low-cost methods, can guide future educational policies, health promotion initiatives, and preventive interventions.

Interactions between Metabolic Syndrome, MASLD, and Arterial Stiffening: A Single-Center Cross-Sectional Study

Metabolic-associated steatotic liver disease (MASLD), previously termed non-alcoholic fatty liver disease (NAFLD), has emerged as a prominent global cause of chronic liver disease and is increasingly recognized as associated with atherosclerotic vascular illness, consolidating its position along traditional cardiovascular risk factors. Individuals with MASLD exhibit a combination of metabolic syndrome risk factors, carotid atherosclerosis, and increased arterial stiffness, hinting at shared pathogenesis. In this study, we aim to explore liver involvement and arterial stiffness within metabolic syndrome. We enrolled 75 patients (30 male and 45 female) with either liver steatosis on conventional ultrasound, altered liver function tests, or the presence of cardiometabolic risk factors after excluding liver pathology other than MASLD. Clinical evaluation, laboratory measurements, abdominal and carotid ultrasounds, vibration-controlled transient elastography (VCTE, Fibroscan), and assessment with the Arteriograph (Tensiomed) were performed. The 26 patients diagnosed with MetS had significantly higher liver involvement as quantified via the hepatic steatosis index (HSI), Fibrosis-4 (FIB4), aspartate aminotransferase to platelet ratio index (APRI) category, and VCTE measurements, as well as Agile 3+ and Agile 4 scores which use a combination of clinical and laboratory parameters together with results obtained from VCTE to reflect the probability of advanced liver fibrosis or cirrhosis. Patients with MetS also exhibited more pronounced vascular involvement as quantified via arterial stiffness measurements and CIMT (carotid intima-media thickness). We applied a two-step clustering algorithm to enhance our analysis, which gave us pertinent insight into the interplay between metabolic syndrome elements and typologies of hepatic steatosis and arterial stiffness degrees. Notably, of the three obtained clusters, the cluster showing increased levels of hepatic steatosis and arterial stiffness also exhibited the highest prevalence of metabolic syndrome and its constituting components. The results have significant clinical implications, advocating for a comprehensive diagnostic approach when MetS or MASLD is suspected.

Hepatic Involvement across the Metabolic Syndrome Spectrum: Non-Invasive Assessment and Risk Prediction Using Machine Learning

Metabolic-dysfunction-associated steatotic liver disease (MASLD) and metabolic syndrome (MetS) are inextricably linked conditions, both of which are experiencing an upward trend in prevalence, thereby exerting a substantial clinical and economic burden. The presence of MetS should prompt the search for metabolic-associated liver disease. Liver fibrosis is the main predictor of liver-related morbidity and mortality. Non-invasive tests (NIT) such as the Fibrosis-4 index (FIB4), aspartate aminotransferase-to-platelet ratio index (APRI), aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), hepatic steatosis index (HIS), transient elastography (TE), and combined scores (AGILE3+, AGILE4) facilitate the detection of liver fibrosis or steatosis. Our study enrolled 217 patients with suspected MASLD, 109 of whom were diagnosed with MetS. We implemented clinical and biological evaluations complemented by transient elastography (TE) to discern the most robust predictors for liver disease manifestation patterns. Patients with MetS had significantly higher values of FIB4, APRI, HSI, liver stiffness, and steatosis parameters measured by TE, as well as AGILE3+ and AGILE4 scores. Machine-learning algorithms enhanced our evaluation. A two-step cluster algorithm yielded three clusters with reliable model quality. Cluster 1 contained patients without significant fibrosis or steatosis, while clusters 2 and 3 showed a higher prevalence of significant liver fibrosis or at least moderate steatosis as measured by TE. A decision tree algorithm identified age, BMI, liver enzyme levels, and metabolic syndrome characteristics as significant factors in predicting cluster membership with an overall accuracy of 89.4%. Combining NITs improves the accuracy of detecting patterns of liver involvement in patients with suspected MASLD.