Impact of nutritional and health indicators on chemotoxicity in elderly patients planned for chemotherapy

This study aims to evaluate how nutritional and health indicators, such as weight, muscle status, and malnutrition risk, can predict the likelihood and severity of chemotoxicity in elderly cancer patients. The expected outcome is to establish a link between these indicators and a patient’s readiness for chemotherapy, allowing for the identification of high-risk individuals. The findings could pave the way for future research into developing and testing targeted nutritional interventions before chemotherapy begins. Such research could explore the efficacy of specific dietary plans or nutritional supplements in improving treatment tolerance and quality of life for geriatric oncology patients.

This study will investigate whether a patient’s nutritional and health status can predict their tolerance to chemotherapy by examining geriatric cancer patients at Shirdi Sai Baba Cancer Hospital in Manipal. The selection criteria will focus on elderly individuals (60+) who are scheduled for chemotherapy and are able to participate fully in the study. The methodology involves a two-phase approach: a baseline assessment before treatment and a follow-up after 3 chemotherapy cycles. The initial assessment will collect extensive data, including anthropometric measurements (weight, height, MUAC, calf circumference), muscle strength via handgrip dynamometer, skinfold thickness via a skinfold caliper and validated nutritional assessments (MNA) and quality of life surveys (SF-36). Additionally, biochemical parameters will be taken to measure key serum proteins like albumin, globulin and total serum protein from the hospital report. The dietary assessment will be carried using 24 hour recall and a food frequency questionnaire. The follow-up will repeat these measurements and document the severity of any chemotoxicity using established clinical criteria, such as the CTCAE, to correlate the patient’s initial health with their treatment outcome. This comprehensive approach aims to create a predictive model for chemotoxicity risk, enabling healthcare providers to identify and intervene with at-risk patients before they begin treatment.