Many persons with spinal cord injury (SCI) have one or more preventable chronic diseases related to excessive energetic intake and poor eating patterns. Appropriate nutrient consumption relative to need becomes a concern despite authoritative dietary recommendations from around the world. These recommendations were developed for the non-disabled population and do not account for the injury-induced changes in body composition, hypometabolic rate, hormonal dysregulation and nutrition status after SCI. Because evidence-based dietary reference intake values for SCI do not exist, ensuring appropriate consumption of macronutrient and micronutrients for their energy requirements becomes a challenge. In this compressive review, we briefly evaluate aspects of energy balance and appetite control relative to SCI. We report on the evidence regarding energy expenditure, nutrient intake and their relationship after SCI. We compare these data with several established nutritional guidelines from American Heart Association, Australian Dietary Guidelines, Dietary Guidelines for Americans, Institute of Medicine Dietary Reference Intake, Public Health England Government Dietary Recommendations, WHO Healthy Diet and the Paralyzed Veterans of America (PVA) Clinical Practice Guidelines. We also provide practical assessment and nutritional recommendations to facilitate a healthy dietary pattern after SCI. Because of a lack of strong SCI research, there are currently limited dietary recommendations outside of the PVA guidelines that capture the unique nutrient needs after SCI. Future multicentre clinical trials are needed to develop comprehensive, evidence-based dietary reference values specific for persons with SCI across the care continuum that rely on accurate, individual assessment of energy need.

The present study was designed to develop a novel method of energy calculation for controlling energetic intake in patients with the metabolic syndrome. Demographics and dietary data were recorded for 2582 obese subjects. Nutritional education was applied to all the patients. One year later, the data on age, sex, activity intensity coefficient, waistline, environmental temperature and BMI in subjects who lost ≥ 5 % body weight were entered into a multivariate logistic regression analysis model. Energy requirement was calculated from the results of multivariate logistic regression. Four hundred and thirty-four metabolic syndrome patients were then randomly divided into the treated group (216) and the control group (218). The energetic intake in the experimental group was controlled based on the new energy requirement model. The traditional energy exchange method was used in the control group. The independent factors predicting metabolic syndrome prognosis, such as age, sex, activity intensity coefficient, waistline, environmental temperature and BMI, were identified by multivariate logistic regression analysis. The energy requirement model was then constructed by logistic regression analysis. After 6 months of energetic intake control based on the new model, the parameters of the experimental group were significantly different from those of the controls (all P < 0·05): waistline, 89·65 (sd 5·54) v. 91·97 (sd 4·78) cm; BMI, 24·67 (sd 3·54) v. 25·87 (sd 2·65) kg/m2; fasting blood glucose, 6·9 (sd 3·6) v. 8·7 (sd 4·6) mmol/l; 2 h PG, 8·7 (sd 5·7) v. 10·7 (sd 4·5) mmol/l; HbA1c, 7·7 (sd 1·6) v. 8·9 (sd 2·6) %; homoeostasis model insulin resistance index, 3·14 (sd 1·62) v. 4·32 (sd 2·25). The new energy requirement model can effectively improve the clinical outcomes of controlling energetic intake in metabolic syndrome patients.