RESUMEN
Daily use of opioid analgesics has significantly increased in recent years due to an increasing prevalence of conditions associated with chronic pain. Opioid-induced constipation (OIC) is one of the most common, under-recognized, and under-treated side effects of opioid analgesics. OIC significantly reduces the quality of life by causing psychological distress, lowering work productivity, and increasing access to healthcare facilities. The economic and social burden of OIC led to the development of precise strategies for daily clinical practice. Key aspects are the prevention of constipation through adequate water intake and fiber support, avoidance of sedentariness, and early recognition and treatment of cofactors that could worsen constipation. Recommended first-line therapy includes osmotic (preferably polyethylene glycol) and stimulant laxatives. Peripherally acting µ-opioid receptor antagonists, such as methylnaltrexone, naloxegol, or naldemedine, should be used in patients that have not responded to the first-line treatments. The bowel functional index is the main tool for assessing the severity of OIC and for monitoring the response. The paper discusses the recent literature on the pathophysiology, clinical evaluation, and management of OIC and provides a pragmatic approach for its assessment and treatment.
RESUMEN
BACKGROUND: Steatosis is now the most common liver disease in the world, present in approximately 25% of the global population. The aim of this study was to study the association between food intake and liver disease and evaluate the differences in blood parameters in age classes and steatosic condition. METHODS: The present study included 1483 participants assessed in the fourth recall of the MICOL study. Patients were subdivided by age (65 years) and administered a validated food frequency questionnaire (FFQ) with 28 food groups. RESULTS: The prevalence of steatosis was 55.92% in the adult group and 55.88% in the elderly group. Overall, the results indicated many statistically significant blood parameters and dietary habits. Analysis of food choices with a machine learning algorithm revealed that in the adult group, olive oil, grains, processed meat, and sweets were associated with steatosis, while the elderly group preferred red meat, dairy, seafood, and fruiting vegetables. Furthermore, the latter ate less as compared with the adult group. CONCLUSIONS: Many differences were found between the two age groups, both in blood parameters and food intake. The random forest also revealed different foods predicted steatosis in the two groups. Future analysis will be useful to understand the molecular basis of these differences and how different food intake causes steatosis in people of different ages.
