The Role of Diet in the Treatment of Chronic Diseases Case Study

Abstract

In the context of chronic diseases, drug treatment is often long-term, perhaps even lifelong. Although in therapeutic terms the hygienic-dietary regime is very important, the importance of diet is still neglected by the patient. Diet and lifestyle have a special role in maintaining health, longevity and treating various ailments. It is known that a large part of adults between the ages of 20-70 suffer from obesity and that according to the data provided by the WHO, the main causes of mortality in the world are related to unhealthy eating, 14 million deaths are caused annually by an unbalanced diet globally. In this context, the population of Romania mainly has an unhealthy diet based on an increased intake of saturated fats, carbohydrates and protein and an insufficient intake of vitamins, minerals and enzymes from fruits, vegetables and seeds. The 62-year-old CV patient from Constanţa presented herself at the nutrition clinic for being overweight, having several chronic conditions. Through an appropriate and customized diet low in sodium, hypoprotein and hypocaloric, he managed to return to normal weight and significantly alleviates all the chronic conditions initially presented, together with the return of biochemical analysis results to normal levels, values maintained over time.

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

“…The typical decades-long disease progression of ME/CFS makes it challenging for our four-year longitudinal design to capture stable temporal signals -although separating our short-term (<4 years) and long-term (>10 years) provided valuable insightsideally, tracking the same patients over a longer period would likely yield more accurate trends 95 , 96 . Long disease history also increases the likelihood of exposure to various diets and medications 97 , which could influence biomarker identification, particularly in metabolomics. Finally, model-wise, BioMapAI was trained on < 500 samples with fivefold cross-validation, which is relatively small given the complexity of the outcome matrix; expanding the training dataset and incorporating more independent validation sets could potentially enhance its performance and generalizability 98 , 99 .…”