Background Low–glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases. Methods Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18–45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)–defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level. Results There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance. Conclusions These exploratory metabolomics results support beneficial effects of a low–glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. This trial was registered at clinicaltrials.gov as NCT00622661.
To control the growth of Clostridium perfringens in cured meat products, the meat and poultry industries commonly follow stabilization parameters outlined in Appendix B, "Compliance Guidelines for Cooling Heat-Treated Meat and Poultry Products (Stabilization)" ( U.S. Department of Agriculture, Food Safety and Inspection Service [USDA-FSIS], 1999 ) to achieve cooling (54.4 to 4.4°C) within 15 h after cooking. In this study, extended cooling times and their impact on C. perfringens growth were examined. Phase 1 experiments consisted of cured ham with 200 mg/kg ingoing sodium nitrite and 547 mg/kg sodium erythorbate following five bilinear cooling profiles: a control (following Appendix B guidelines: stage A cooling [54.4 to 26.7°C] for 5 h, stage B cooling [26.7 to 4.4°C] for 10 h), extended stage A cooling for 7.5 or 10 h, and extended stage B cooling for 12.5 or 15 h. A positive growth control with 0 mg/kg nitrite added (uncured) was also included. No growth was observed in any treatment samples except the uncured control (4.31-log increase within 5 h; stage A). Phase 2 and 3 experiments were designed to investigate the effects of various nitrite and erythorbate concentrations and followed a 10-h stage A and 15-h stage B bilinear cooling profile. Phase 2 examined the effects of nitrite concentrations of 0, 50, 75, 100, 150, and 200 mg/kg at a constant concentration of erythorbate (547 mg/kg). Results revealed changes in C. perfringens populations for each treatment of 6.75, 3.59, 2.43, -0.38, -0.48, and -0.50 log CFU/g, respectively. Phase 3 examined the effects of various nitrite and erythorbate concentrations at 100 mg/kg nitrite with 0 mg/kg erythorbate, 100 with 250, 100 with 375, 100 with 547, 150 with 250, and 200 with 250, respectively. The changes in C. perfringens populations for each treatment were 4.99, 2.87, 2.50, 1.47, 0.89, and -0.60 log CFU/g, respectively. Variability in C. perfringens growth for the 100 mg/kg nitrite with 547 mg/kg erythorbate treatment was observed between phases 2 and 3 and may have been due to variations in treatment pH and NaCl concentrations. This study revealed the importance of nitrite and erythorbate for preventing growth of C. perfringens during a much longer (25 h) cooling period than currently specified in the USDA-FSIS Appendix B.
PurposeEstablish bedside biomarkers of myosteatosis for sarcopenia and cachexia. We compared ultrasound biomarkers against MRI-based percent fat, histology, and CT-based muscle density among healthy adults and adults undergoing treatment for lung cancer.MethodsWe compared ultrasound and MRI myosteatosis measures among young healthy, older healthy, and older adults with non-small cell lung cancer undergoing systemic treatment, all without significant medical concerns, in a cross-sectional pilot study. We assessed each participant's rectus femoris ultrasound-based echo intensity (EI), shear wave elastography-based shear wave speed, and MRI-based proton density fat-fraction (PDFF). We also assessed BMI, rectus femoris thickness and cross-sectional area. Rectus femoris biopsies were taken for all older adults (n = 20) and we analyzed chest CT scans for older adults undergoing treatment (n = 10). We determined associations between muscle assessments and BMI, and compared these assessments between groups.ResultsA total of 10 young healthy adults, 10 older healthy adults, and 10 older adults undergoing treatment were recruited. PDFF was lower in young adults than in older healthy adults and older adults undergoing treatment (0.3 vs. 2.8 vs. 2.9%, respectively, p = 0.01). Young adults had significantly lower EI than older healthy adults, but not older adults undergoing treatment (48.6 vs. 81.8 vs. 75.4, p = 0.02). When comparing associations between measures, PDFF was strongly associated with EI (ρ = 0.75, p < 0.01) and moderately negatively associated with shear wave speed (ρ = −0.49, p < 0.01) but not BMI, whole leg cross-sectional area, or rectus femoris cross-sectional area. Among participants with CT scans, paraspinal muscle density was significantly associated with PDFF (ρ = −0.70, p = 0.023). Histological markers of inflammation or degradation did not differ between older adult groups.ConclusionPDFF was sensitive to myosteatosis between young adults and both older adult groups. EI was less sensitive to myosteatosis between groups, yet EI was strongly associated with PDFF unlike BMI, which is typically used in cachexia diagnosis. Our results suggest that ultrasound measures may serve to determine myosteatosis at the bedside and are more useful diagnostically than traditional weight assessments like BMI. These results show promise of using EI, shear wave speed, and PDFF proxies of myosteatosis as diagnostic and therapeutic biomarkers of sarcopenia and cachexia.
Background Computed tomography (CT)‐derived measures of tissue quality can add to frailty assessment and improve selection of candidates for heart transplant. We investigated the prognostic value of CT measures of tissue density for predicting hospital length of stay (LOS) and mortality post‐transplant. Methods All patients at a quaternary care hospital between 1999 to 2018 with preheart transplant CT scans and available data on transplant outcomes were eligible (n = 189), including a subset within the total cohort with scans 6‐month pretransplant (n = 103). Axial chest CT scans were analysed for liver and muscle density at the 12th thoracic vertebrae and aortic arch landmarks, respectively. Cox and linear regression models examined the risk of death and LOS, respectively, according to median (above or below) pectoral muscle density. Low‐density muscle (LDM) area and liver density were analysed as continuous variables. Results Out of 157 patients with readable CT scans (median age 55 years, interquartile range [50–60] 10% women), 31 died on 1‐year follow up. Patients with higher than and at median pectoral muscle density (39.5 Hounsfield Unit [HU]) had better 1‐year survival in the overall cohort (hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.673, 0.989; p = 0.039), with the 6‐month cohort showing a trend (HR 0.79, 95% CI 0.603, 1.023; p = 0.074) towards improved survival. Conversely, every 5‐cm2 increase in pectoral LDM area was associated with 2.4‐day lower LOS (p = 0.045) in the overall cohort, and a 2.6‐day lower LOS in the 6‐month cohort (p = 0.05). Patients with higher ratio of normal‐density muscle to LDM had higher LOS (p < 0.01). Every 5‐HU increase in liver density at a region of interest was associated with 0.24‐day higher post‐transplant LOS in the overall cohort, and a 0.41 higher LOS in the 6‐month cohort (p ≤ 0.05). Conclusions Patients with higher preheart transplant pectoral muscle density had greater 1‐year survival. Higher pectoral LDM area was associated with decreased LOS post‐transplant and higher liver density was associated with increased LOS. These findings raise possibilities that measures of muscle density as they reflect to quality of muscle may have prognostic implications. Future studies with prospective design are needed to confirm these findings.
Background Lung cancer patients have low survival rates resulting in over 131,000 deaths in the US in 2021. Muscle health decline from sarcopenia and cachexia increases the risk of death among patients with lung cancer. Though muscle mass is often used for sarcopenia diagnosis and non‐tissue specific weight loss is typically used for cachexia diagnosis, both conditions have no consensus definitions. Therefore, the need for quantitative biomarkers that reflect muscle health changes due to aging and weight loss is critical to mitigate sarcopenia and cachexia. We propose using highly accurate MRI‐based proton density fat fraction (PDFF) as a reference measure of muscle health to develop bedside ultrasound measures as biomarkers of muscle health. We hypothesized that PDFF is associated with ultrasound‐based echointensity (EI) and shear wave elastography (SWE), and that all three modalities are sensitive to muscle health differences between healthy young, healthy older, and older adults undergoing treatment for lung cancer. Methods We compared muscle health operationalized as myosteatosis in the mid‐thigh of the rectus femoris using ultrasound‐based brightness‐mode for EI, ultrasound‐based SWE for tissue elasticity, and MRI‐based PDFF for percent fat, which was the reference measure. We compared the 3 measures among young healthy (n=10), older healthy (n=10), and older adults with non‐small cell lung cancer (n=10) in a cross‐sectional pilot study. We also compared non‐myosteatosis measures including subcutaneous adipose tissue thickness and rectus femoris thickness via ultrasound, whole‐leg cross‐sectional area via MRI, and BMI between groups and between myosteatosis measures. We used ANOVAs with Tukey post‐hoc comparisons to assess muscle health between groups and Pearson correlations to determine muscle health measure associations between imaging modalities. Results We found young healthy adults had significantly lower PDFF than older healthy adults and older adults with lung cancer (0.33% vs 2.83% vs 2.93%, respectively; p=0.001). Young adults also had significantly lower EI than older healthy adults, but not older adults with cancer (48.58 vs 81.81 vs 75.35; p=0.008). When comparing between measures, PDFF was highly associated with EI (r=0.62, p<0.001) and moderately inversely associated with SWE (r=‐0.41, p=0.025) but not BMI, rectus femoris thickness, or rectus femoris cross‐sectional area. Conclusion PDFF was sensitive to myosteatosis differences between young and both older adult groups. EI was less sensitive to myosteatosis differences between groups, yet EI was highly associated with PDFF unlike BMI. Our results suggest that the ultrasound measures could serve to determine muscle health at the bedside and are more sensitive to muscle health differences than BMI, which could improve interventions for cachexia and patient outcomes.
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