Nutrition and gastrointestinal cancer are inextricably linked. The metabolic effects of cancer along with changes in dietary intake, the development of cancer cachexia and the presence of sarcopenia can influence changes in body composition. These have a negative impact on quality of life and tolerance to cancer treatment. Treatment for cancer presents some significant nutritional challenges as nutrition impact symptoms may develop, be exacerbated by treatment and may contribute to a worsening in nutritional status. Nutrition screening and assessment should be an integral part of holistic patient care. The provision of appropriate, evidence-based dietary advice should occur before, during and after cancer treatment. Appropriate and timely methods of nutritional support across the spectrum of gastrointestinal cancer are needed to ensure that people are adequately supported during courses of treatment that can span weeks and months. These can range from standard approaches of supplementing oral intake to complex interventions such as managing high output intestinal stomas. The gastrointestinal tract is particularly susceptible to impact from systemic anti-cancer treatments and radiotherapy. Gastrointestinal late effects of cancer treatment are now recognised to present particular challenges in terms of both medical and nutritional management. These late effects have a significant impact on the individual and their quality of life in addition to implications for the health service. Dietary intake following cancer treatment has an impact on quality of life and future research may demonstrate its influence on the risk of recurrence of gastrointestinal cancer.

The association between hyporexia/anorexia, reduced food intake and disease-related malnutrition at hospital admission is well established. However, information on fluid intake according to nutritional risk has never been provided. Thus, we assessed the attitude and adequacy of fluid intake among case-mix hospitalised patients according to nutritional risk. A sample of 559 non-critically ill patients randomly taken from medical and surgical wards was evaluated. Nutritional risk was diagnosed by the Nutritional Risk Screening 2002. Usual fluid consumption the week before admission was assessed and categorised as < 5 and ≥ 5 cups/d (1 cup = 240 ml), with the acceptable intake being ≥ 5 cups/d. Prevalence of nutritional risk was 57·2 %, and 46·2 % of the patients reported a fluid intake < 5 cups/d. Multiple-adjusted logistic regression revealed that age ≥ 65 years (OR: 1·88 (95 % CI: 1·03, 3·43); P < 0·04), energy intake (for every 25 % increase in food intake compared with estimated requirements, OR: 0·37 (95 % CI: 0·25, 0·55); P < 0·001) and the number of drugs taken (every three-drug increase, OR: 0·63 (95 % CI: 0·44, 0·90); P < 0·02) were independently associated with inadequate fluid intake ( < 5 cups/d). A significant independent association was also found with nutritional risk (OR: 0·64 (95 % CI: 0·43, 0·95); P < 0·03). Nutritional risk appears to be positively associated with greater fluid intake in non-acute hospitalised patients, but both the reasons and the consequences of this relationship, as well as the impact on clinical practice, need to be explored. However, water replacement by oral nutritional support should take advantage of the patients' attitude to assuming a greater fluid intake, limiting at the same time fluid overload during the refeeding phase.

The calculation of body composition using bioelectrical impedance analysis in sick and hospitalized patients is hampered due to altered hydration state. We wanted to investigate how disease-related malnutrition assessed by the Subjective Global Assessment (SGA) is reflected in the bioelectrical impedance vector analysis. Patients with benign gastrointestinal disease (n 242) were entered in the study. Nutritional status was assessed by SGA. Arm muscle and fat area were estimated by anthropometry, muscle function was determined by hand grip strength. Whole body impedance measurements were made at 50 kHz. Ninety-eight patients were considered well nourished (SGA A), ninety-four were classified moderately malnourished (SGA B) and fifty patients were classified severely malnourished (SGA C) according to the SGA. The mean vector was significantly displaced between SGA C and SGA A and B, showing comparable resistance with a significantly reduced reactance, indicating comparable hydration but loss of dielectrical mass of soft tissues. This distinctive vector migration was not seen when studying the patients grouped according to BMI. In conclusion, disease-related malnutrition as assessed by the SGA is associated with a distinctive bioelectrical vector migration, implying that abnormal tissue structure and not reduced body mass only occurs in disease-related malnutrition. These disturbances are not seen in underweight according to BMI. Bioelectrical impedance vector analysis appears to be an attractive tool to identify disease-related malnutrition and to monitor nutritional intervention.