METHODS: We recruited 54 abdominally obese subjects to participate in a prospective cross-over design, single-blind trial comparing isocaloric 2000 kcal MUFA or carbohydrate-enriched diet with SFA-enriched diet (control). The control diet consisted of 15E% protein, 53E% carbohydrate and 32E% fat (12E% SFA, 13E% MUFA). A total of ∼7E% of MUFA or refined carbohydrate was exchanged with SFA in the MUFA-rich and carbohydrate-rich diets respectively for 6-weeks. Blood samples were collected at fasting upon trial commencement and at week-5 and 6 of each dietary-intervention phase to measure levels of cytokines (IL-6, IL-1β), C-reactive protein (CRP), thrombogenic markers (E-selectin, PAI-1, D-dimer) and lipid subfractions. Radial pulse wave analysis and a 6-h postprandial mixed meal challenge were carried out at week-6 of each dietary intervention. Blood samples were collected at fasting, 15 and 30 min and hourly intervals thereafter till 6 h after a mixed meal challenge (muffin and milkshake) with SFA or MUFA (872.5 kcal, 50 g fat, 88 g carbohydrates) or CARB (881.3 kcal, 20 g fat, 158 g carbohydrates)- enrichment corresponding to the background diets.

METHODS: A cross-sectional study was conducted among 134 geriatric patients with a mean age of 68.9 ± 8.4 who stayed at acute care wards in Hospital Tuanku Ampuan Rahimah, Klang from July 2017 to August 2017. The SGA, MNA, and GNRI were administered through face-to-face interviews with all the participants who gave their consent. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the GNRI and MNA were analyzed against the SGA. Receiver-operating characteristic (ROC) curve analysis was used to obtain the area under the curve (AUC) and suitable optimal cutoff values for both the GNRI and MNA.

RESULTS: According to the SGA, MNA, and GNRI, 26.9%, 42.5%, and 44.0% of the participants were malnourished, respectively. The sensitivity, specificity, PPV, and NPV for the GNRI were 0.622, 0.977, 0.982, and 0.558, respectively, while those for the MNA were 0.611, 0.909, 0.932, and 0.533, respectively. The AUC of the GNRI was comparable to that of the MNA (0.831 and 0.898, respectively). Moreover, the optimal malnutrition cutoff value for the GNRI was 94.95.

METHODS: Medical records of all HSCR patients who underwent pull-through at the Dr. Sardjito Hospital, Indonesia between January 2010 and August 2016 were reviewed for their growth outcomes before and after the surgery.

RESULTS: We included 64 HSCR patients, 45 males and 19 females, of which 14, 17, and 33 patients underwent Duhamel, Soave, and TEPT respectively. There were no nutritional status differences in HSCR patients after Duhamel, Soave, and TEPT surgery (p=0.07, 0.17, and 0.79, respectively). Z-score average of weight-for-age did not differ between three surgical methods (p=0.77 and 0.15 for preoperative and postoperative, respectively). In addition, the improvement of nutritional status was achieved in 21.2% HSCR patients after TEPT, 14.3% post Duhamel and 5.9% following Soave procedure, but these differences did not reach a significant level (p=0.34).

PATIENTS AND METHODS: CGA data was collected from 249 Asian patients aged 70 years or older. Nutritional risk was assessed based on the Nutrition Screening Initiative (NSI) checklist. Univariate and multivariate logistic regression analyses were applied to assess the association between patient clinical factors together with domains within the CGA and moderate to high nutritional risk. Goodness of fit was assessed using Hosmer-Lemeshow test. Discrimination ability was assessed based on the area under the receiver operating characteristics curve (AUC). Internal validation was performed using simulated datasets via bootstrapping.

RESULTS: Among the 249 patients, 184 (74%) had moderate to high nutritional risk. Multivariate logistic regression analysis identified stage 3-4 disease (Odds Ratio [OR] 2.54; 95% CI, 1.14-5.69), ECOG performance status of 2-4 (OR 3.04; 95% CI, 1.57-5.88), presence of depression (OR 5.99; 95% CI, 1.99-18.02) and haemoglobin levels <12 g/dL (OR 3.00; 95% CI 1.54-5.84) as significant independent factors associated with moderate to high nutritional risk. The model achieved good calibration (Hosmer-Lemeshow test's p = 0.17) and discrimination (AUC = 0.80). It retained good calibration and discrimination (bias-corrected AUC = 0.79) under internal validation.

METHODS: This study includes 235,880 participants, 25-70 years old, recruited between 1992 and 2000 in 10 European countries. Intakes of 23 nutrients were estimated from country-specific validated dietary questionnaires using the harmonized EPIC Nutrient DataBase. Four nutrient patterns, explaining 67 % of the total variance of nutrient intakes, were previously identified from principal component analysis. Body weight was measured at recruitment and self-reported 5 years later. The relationship between nutrient patterns and annual weight change was examined separately for men and women using linear mixed models with random effect according to center controlling for confounders.

RESULTS: Mean weight gain was 460 g/year (SD 950) and 420 g/year (SD 940) for men and women, respectively. The annual differences in weight gain per one SD increase in the pattern scores were as follows: principal component (PC) 1, characterized by nutrients from plant food sources, was inversely associated with weight gain in men (-22 g/year; 95 % CI -33 to -10) and women (-18 g/year; 95 % CI -26 to -11). In contrast, PC4, characterized by protein, vitamin B2, phosphorus, and calcium, was associated with a weight gain of +41 g/year (95 % CI +2 to +80) and +88 g/year (95 % CI +36 to +140) in men and women, respectively. Associations with PC2, a pattern driven by many micro-nutrients, and with PC3, a pattern driven by vitamin D, were less consistent and/or non-significant.

Methods: Data from the National Health and Morbidity Survey (NHMS) 2018 was analysed. This survey applied a multistage stratified cluster sampling design to ensure national representativeness. Malnutrition was identified using a validated Mini Nutrition Assessment-Short Form (MNA-SF). Variables on sociodemographic, health status, and dietary practices were also obtained. The complex sampling analysis was used to determine the prevalence and associated factors of at-risk or malnutrition among the elderly.

Result: A total of 3,977 elderly completed the MNA-SF. The prevalence of malnutrition and at-risk of malnutrition was 7.3% and 23.5%, respectively. Complex sample multiple logistic regression found that the elderly who lived in a rural area, with no formal or primary level of education, had depression, Instrumental Activity of Daily Living (IADL) dependency, and low quality of life (QoL), were underweight, and had food insecurity and inadequate plain water intake were at a significant risk of malnutrition (malnutrition and at-risk), while Chinese, Bumiputra Sarawak, and BMI more than 25 kgm-2 were found to be protective.

METHODS: Over half a million participants from 10 European countries were followed up for over 11 years, after which 865 newly diagnosed exocrine pancreatic cancer cases were identified. Adherence to the MD was estimated through an adapted score without the alcohol component (arMED) to discount alcohol-related harmful effects. Cox proportional hazards regression models, stratified by age, sex and centre, and adjusted for energy intake, body mass index, smoking status, alcohol intake and diabetes status at recruitment, were used to estimate hazard ratios (HRs) associated with pancreatic cancer and their corresponding 95% confidence intervals (CIs).

RESULTS: Adherence to the arMED score was not associated with risk of pancreatic cancer (HR high vs low adherence=0.99; 95% CI: 0.77-1.26, and HR per increments of two units in adherence to arMED=1.00; 95% CI: 0.94-1.06). There was no convincing evidence for heterogeneity by smoking status, body mass index, diabetes or European region. There was also no evidence of significant associations in analyses involving microscopically confirmed cases, plausible reporters of energy intake or other definitions of the MD pattern.

METHODS: To gain a more comprehensive picture on how these markers can modulate BC risk, alone or in conjunction, we performed simultaneous measurements of LTL and mtDNA copy number in up to 570 BC cases and 538 controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. As a first step, we measured LTL and mtDNA copy number in 96 individuals for which a blood sample had been collected twice with an interval of 15 years.

RESULTS: According to the intraclass correlation (ICC), we found very good stability over the time period for both measurements, with ICCs of 0.63 for LTL and 0.60 for mtDNA copy number. In the analysis of the entire study sample, we observed that longer LTL was strongly associated with increased risk of BC (OR 2.71, 95% CI 1.58-4.65, p = 3.07 × 10- 4 for highest vs. lowest quartile; OR 3.20, 95% CI 1.57-6.55, p = 1.41 × 10- 3 as a continuous variable). We did not find any association between mtDNA copy number and BC risk; however, when considering only the functional copies, we observed an increased risk of developing estrogen receptor-positive BC (OR 2.47, 95% CI 1.05-5.80, p = 0.04 for highest vs. lowest quartile).

BACKGROUND: Continuous effort has been made to identify patients at high risk of malnutrition, but monitoring and documentation of nutritional intake are relative less emphasized upon.

METHODS: A needs assessment through a cross-sectional study design was carried out at six hospitals in Yogyakarta, Indonesia. A self-administered semi-structured questionnaire was filled out by 111 respondents recruited from three different professions (nurses, dietitians and serving assistants) in the wards.

RESULTS: Seventy per cent of the respondents perceived that the current dietary assessment tool used to record patients' food intake was simple; however, the disadvantage of this tool was its tedious process of computing nutritional values of food consumed. Furthermore, more than half respondents encountered problems in conducting food intake record of patients, primarily due to limited number of human resources, followed by time constraints and perception that such dietary assessment as not part of their job scope.

DISCUSSION: This study has revealed important information in developing a simple, valid and reliable dietary assessment tool for monitoring food intake of hospitalized patients to be applied by interdisciplinary hospital professionals.

CONCLUSIONS: Awareness of the important on monitoring nutrient intake of patients should be emphasized among healthcare professionals. The current dietary assessment tool requires modification due to lengthy time taken to complete the task and poor accuracy in intake estimation.

METHODS: Using data from 272,098 women participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we assessed dietary intake of 92 foods and nutrients estimated by dietary questionnaires. Cox regression was used to quantify the association between each food/nutrient and risk of breast cancer. A false discovery rate (FDR) of 0.05 was used to select the set of foods and nutrients to be replicated in the independent Netherlands Cohort Study (NLCS).

RESULTS: Six foods and nutrients were identified as associated with risk of breast cancer in the EPIC study (10,979 cases). Higher intake of alcohol overall was associated with a higher risk of breast cancer (hazard ratio (HR) for a 1 SD increment in intake = 1.05, 95% CI 1.03-1.07), as was beer/cider intake and wine intake (HRs per 1 SD increment = 1.05, 95% CI 1.03-1.06 and 1.04, 95% CI 1.02-1.06, respectively), whereas higher intakes of fibre, apple/pear, and carbohydrates were associated with a lower risk of breast cancer (HRs per 1 SD increment = 0.96, 95% CI 0.94-0.98; 0.96, 95% CI 0.94-0.99; and 0.96, 95% CI 0.95-0.98, respectively). When evaluated in the NLCS (2368 cases), estimates for each of these foods and nutrients were similar in magnitude and direction, with the exception of beer/cider intake, which was not associated with risk in the NLCS.