Nutrition最新文献

Background: The COntrolling NUTritional Status (CONUT) score and the Global Nutrition Risk Index (GNRI) are screening tools for assessing the risk of malnutrition based on widely available biochemical parameters. The primary objective of this study was to investigate the predictive value of CONUT and GNRI score on 36 months mortality and hospitalization risk in hospitalized older patients.

Methods: Data of 382 patients (196 women, mean age 80.9±6.8 years) were retrieved from the multicenter Italian Study conducted by the Gruppo Lavoro Italiano Sarcopenia-Trattamento e Nutrizione (GLISTEN) in 12 Acute Care Wards. Sarcopenia was defined as presence of low handgrip strength plus low skeletal mass index (EWGSOP2 criteria). CONUT score was calculated based on serum albumin, total cholesterol and total lymphocyte count, whilst the GNRI was calculated using serum albumin and present body weight/ideal body weight ratio.

Results: During the 36-month follow-up, 120 out of 382 participants died (31.4%). From the results of the survival analysis, and after adjustment for potential confounders, participants with CONUT-derived moderate to high risk of malnutrition had shorter survival (HR = 2.67, 95%CI 1.34-5.33 and HR = 3.98, 95% CI: 1.77-8.97, respectively), as well as shorter survival free of urgent hospitalization (HR = 1.91; 95% CI: 1.03-3.55 and HR = 1.98; 95% CI: 1.14-3.42, respectively). Conversely, only GNRI indicative of high risk of malnutrition was an independent predictor of mortality 1.96 (95% CI: 1.06-3.62), but not of hospitalization.

Conclusion: The CONUT score seems a valid tool to predict long-term mortality and hospitalization risk. Conversely, the GNRI is associated with long-term mortality, but not with hospital readmissions.

Objective: Although the effects of an unhealthy diet on the risks of diabetes and its renal complications are well understood, the effects of hygiene status have not been fully elucidated.

Research methods and procedures: We created four groups of mice according to the diet fed (standard [SD] or high-fat [HFD]) and their living environment (conventional [CV] or specific pathogen-free [SPF]), and characterized the extent of their kidney pathology, their gut microbiota, and their fecal short-chain fatty acid (SCFA) concentrations.

Results: The body masses and glycated hemoglobin levels of the HFD and CV groups were significantly higher than those of the SD and SPF groups, respectively. The renal mRNA expression of markers of inflammation and fibrosis and the protein level of CD31 were higher in the HFD and CV groups than in the SD and SPF groups, respectively. Although the alpha diversities and total SCFA concentrations of the HFD and CV groups were significantly lower than those of the SD and SPF groups, respectively, the mRNA expression of genes involved in inflammation, innate immunity, tight junctions, and glucose transporters in the gut was only affected by HFD.

Conclusions: Gut microbial dysbiosis, owing to the combined effects of inappropriate diet and excessive hygiene, accompanied by lower intestinal SCFA production, may contribute to the development and/or progression of diabetes and diabetic kidney disease through the induction of inflammation and fibrosis.

Objective: It is important to cover energy targets among patients with head and neck cancer (HNC) to minimize weight and skeletal muscles loss. This study aimed to assess the agreement between indirect calorimetry (IC) and predictive equations for determining resting energy expenditures (REE) in HNC patients receiving home enteral nutrition (HEN).

Research methods and procedures: Patients included in the study had to be diagnosed with HNC, be adults, have artificial access to the digestive tract, and participate in HEN. All measurements were conducted in the morning after prior patient preparation. Body weight and height were measured using a scale with an integrated height meter. A phase-sensitive, single-frequency bioimpedance analyzer was utilized to conduct bioelectrical impedance analysis. REE was measured using IC with a canopy hood and calculated using 27 different equations. Differences between variables were analyzed using appropriate t-tests and their nonparametric counterparts. The Bland-Altman test was used to assess the types of differences between measured REE (mREE) and predicted REE (pREE).

Results: The examined patients (n = 71, 73.2% male) had a mean age of 63.99 ± 11.42 years and a BMI of 22.84 ± 3.59 kg/m². The most common diagnosis was malignant tongue cancer. Most patients had stage III cancer. Treatment included surgery combined with radiotherapy or only chemoradiotherapy in most cases. The median duration of treatment and HEN was 206 days and 97 days, respectively. Men had a significantly higher REE than women. The Owen, Fredrix, Ireton-Jones, Korth, Weijs-Kruizenga, and Marra_w,h equations estimated REE without significant statistical differences from IC and showed the smallest percentage error between pREE and mREE. The Korth equation had the smallest average mean difference between pREE and mREE, reducing the REE value average by 7 ± 274 kcal/day. The highest percentage of individual accurate predictions for pREE was obtained with the Fredrix (48%), Weijs-Kruizenga (48%), Korth (45%), and Marra_PhA (45%) equations.

Conclusion(s): The predictive equations examined in this study cannot replace IC for determining REE in HNC patients at the individual level. When equations are used, special attention should be given to planning HEN to account for possible discrepancies between pREE and mREE.

Objective: Obesity is associated with liver depletion of ω-3 polyunsaturated fatty acids (ω-3 PUFAS) promoting steatosis and inflammation, whose levels are maintained by diet or biosynthesis involving Δ-5D, Δ-6D desaturases and elongases.

Method: We aimed to assess Δ-5D and Δ-6D activities in liver and brain from mice fed a control diet (CD) or high-fat diet (HFD) for four to sixteen weeks.

Results: HFD led to (1) an early (4 weeks) enhancement in liver Δ-5D, Δ-6D, and PPAR-α activities, without changes in oxidative stress, liver damage or fat accumulation; (2) a latter progressive loss in hepatic desaturation with insufficient compensatory increases in mRNA and protein expression, leading to ω-3 PUFA depletion, PPAR-α down-regulation reducing FA oxidation, and liver steatosis with enhancement in lipogenesis; and (3) brain ω-3 PUFA depletion after 12 to 16 weeks of HFD feeding.

Conclusion: In conclusion, the brain-liver axis is drastically affected by obesity in a time dependent fashion.