Environmental implications of the use of agro‐industrial residues for biorefineries: application of a deterministic model for indirect land‐use changes

Biorefining agro‐industrial biomass residues for bioenergy production represents an opportunity for both sustainable energy supply and greenhouse gas (GHG) emissions mitigation. Yet, is bioenergy the most sustainable use for these residues? To assess the importance of the alternative use of these residues, a consequential life cycle assessment (LCA) of 32 energy‐focused biorefinery scenarios was performed based on eight selected agro‐industrial residues and four conversion pathways (two involving bioethanol and two biogas). To specifically address indirect land‐use changes (iLUC) induced by the competing feed/food sector, a deterministic iLUC model, addressing global impacts, was developed. A dedicated biochemical model was developed to establish detailed mass, energy, and substance balances for each biomass conversion pathway, as input to the LCA. The results demonstrated that, even for residual biomass, environmental savings from fossil fuel displacement can be completely outbalanced by iLUC, depending on the feed value of the biomass residue. This was the case of industrial residues (e.g. whey and beet molasses) in most of the scenarios assessed. Overall, the GHGs from iLUC impacts were quantified to 4.1 t CO₂‐eq.ha^?1_demanded yr^?1 corresponding to 1.2–1.4 t CO₂‐eq. t^?1 dry biomass diverted from feed to energy market. Only, bioenergy from straw and wild grass was shown to perform better than the alternative use, as no competition with the feed sector was involved. Biogas for heat and power production was the best performing pathway, in a short‐term context. Focusing on transport fuels, bioethanol was generally preferable to biomethane considering conventional biogas upgrading technologies. Based on the results, agro‐industrial residues cannot be considered burden‐free simply because they are a residual biomass and careful accounting of alternative utilization is a prerequisite to assess the sustainability of a given use. In this endeavor, the iLUC factors and biochemical model proposed herein can be used as templates and directly applied to any bioenergy consequential study involving demand for arable land.     

Network Analysis of Metabolite GWAS Hits: Implication of CPS1 and the Urea Cycle in Weight Maintenance

Background and Scope

Weight loss success is dependent on the ability to refrain from regaining the lost weight in time. This feature was shown to be largely variable among individuals, and these differences, with their underlying molecular processes, are diverse and not completely elucidated. Altered plasma metabolites concentration could partly explain weight loss maintenance mechanisms. In the present work, a systems biology approach has been applied to investigate the potential mechanisms involved in weight loss maintenance within the Diogenes weight-loss intervention study.

Methods and Results

A genome wide association study identified SNPs associated with plasma glycine levels within the CPS1 (Carbamoyl-Phosphate Synthase 1) gene (rs10206976, p-value = 4.709e-11 and rs12613336, p-value = 1.368e-08). Furthermore, gene expression in the adipose tissue showed that CPS1 expression levels were associated with successful weight maintenance and with several SNPs within CPS1 (cis-eQTL). In order to contextualize these results, a gene-metabolite interaction network of CPS1 and glycine has been built and analyzed, showing functional enrichment in genes involved in lipid metabolism and one carbon pool by folate pathways.

Conclusions

CPS1 is the rate-limiting enzyme for the urea cycle, catalyzing carbamoyl phosphate from ammonia and bicarbonate in the mitochondria. Glycine and CPS1 are connected through the one-carbon pool by the folate pathway and the urea cycle. Furthermore, glycine could be linked to metabolic health and insulin sensitivity through the betaine osmolyte. These considerations, and the results from the present study, highlight a possible role of CPS1 and related pathways in weight loss maintenance, suggesting that it might be partly genetically determined in humans.     

Quality Assessment and Circularity Potential of Recovery Systems for Household Plastic Waste

Plastic recycling is promoted in the transition toward a circular economy and a closed plastic loop, typically using mass‐based recycling targets. Plastic from household waste (HHW) is contaminated and heterogeneous, and recycled plastic from HHW often has a limited application range, due to reduced quality. To correctly assess the ability to close plastic loops via recycling, both plastic quantities and qualities need to be evaluated. This study defines a circularity potential representing the ability of a recovery system to close material loops assuming steady‐state market conditions. Based on an average plastic waste composition including impurities, 84 recovery scenarios representing a wide range of sorting schemes, source‐separation efficiencies, and material recovery facility (MRF) configurations and performances were assessed. The qualities of the recovered fractions were assessed based on contamination and the circularity potential calculated for each scenario in a European context. Across all scenarios, 17% to 100% of the generated plastic mass could be recovered, with higher source‐separation and MRF efficiencies leading to higher recovery. Including quality, however, at best 55% of the generated plastic was suitable for recycling due to contamination. Source‐separation, a high number of target fractions, and efficient MRF recovery were found to be critical. The circularity potential illustrated that less than 42% of the plastic loop can be closed with current technology and raw material demands. Hence, Europe is still far from able to close the plastic loop. When transitioning toward a circular economy, the focus should be on limiting impurities and losses through product design, technology improvement, and more targeted plastic waste management.     

Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index

Background

Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance.

Objective

To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.

Design

Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.

Results

Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.

Conclusions

A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.

Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00390637","term_id":"NCT00390637"}}NCT00390637     

Effects of 2 wk of GH administration on 24-h indirect calorimetry in young, healthy, lean men

The present study was designed as a randomized, double-blind placebo (Plc)-controlled study to determine the effect of 2 wk of growth hormone administration (GH-adm.) on energy expenditure (EE) and substrate oxidation in healthy humans. Sixteen young healthy men were divided into two groups. The study consisted of two 24-h measurements (indirect calorimetry), separated by 2 wk of either Plc or GH injections (6 IU/day). At baseline, no significant differences were observed between the two groups in any of the measured anthropometric, hormonal, or metabolic parameters, neither did the parameters change over time in the Plc group. GH-adm. resulted in a 4.4% increase in 24-h EE (P < 0.05) and an increase in fat oxidation by 29% (P < 0.05). However, a decrease in the respiratory quotient was only observed in the postabsorptive phase after an overnight fast (0.84 +/- 0.1 to 0.79 +/- 0.1, P < 0.05). Furthermore, lean body mass (LBM) was increased by GH-adm. only [62.8 +/- 2.5 kg (baseline) vs. 64.7 +/- 2.4 kg (after), P < 0.001]. In conclusion, GH-adm. increases 24-h EE, which may be partly explained by increased LBM. Furthermore, GH-adm. stimulates fat combustion, especially in the postabsorptive state.     

Assessment of dietary exposure related to dietary GI and fibre intake in a nutritional metabolomic study of human urine

There is a need for a tool to assess dietary intake related to the habitual dietary glycaemic index (GI) and fibre in groups with large numbers of individuals. Novel metabolite-profiling techniques may be a useful approach when applied to human urine. In a long-term, controlled dietary intervention study, metabolomics were applied to assess dietary patterns. A targeted approach was used to evaluate the effects on urinary C-peptide excretion caused by the dietary treatments. Seventy-seven overweight subjects followed an 8-week low-calorie diet (LCD) and were then randomly assigned to a high-GI or low-GI diet for 6 month during which they completed 24-h urine collections at baseline (prior to the 8-week LCD) and after randomisation to the dietary intervention, at month 1, 3 and 6, respectively. Metabolite profiling in 24-h urine was performed by ¹H NMR and chemometrics. Partial least squares (PLS) analysis indicated that urinary formate could discriminate between high-GI and low-GI diets (correlation coefficient r = 0.82), and this finding was confirmed statistically (P = 0.01). PLS analysis also indicated that urinary hippurate could be associated with fibre intake, but this finding was not confirmed statistically. No associations between GI and urinary C-peptide were found. Our results emphasise that application of metabolomics is useful in the assessment of dietary exposure related to dietary GI and fibre seen at group level in a nutritional metabolomic study of human urine. As our design allowed for large variations in individually selected food items, biomarkers identified at group level may be interpreted as more general and robust markers, largely not confounded with markers from single dietary factors.     

Fat Mass Measured by DXA Varies with Scan Velocity

Objective: To study the influence of scan velocities of DXA on the measured size of fat mass, lean body mass, bone mineral content and density, and total body weight. Research Methods and Procedures: The subjects were 71 healthy white adults, 38 women and 33 men. The mean age was 41.7 ± 13.5 years and body mass index was 28.6 ± 5.6 kg/m². The subjects were scanned consecutively in slow, medium, and fast scan mode by a Lunar DPX-IQ DXA scanner. Results: Throughout the body mass index and sagittal height ranges, scanned lean body mass significantly decreased with higher scan velocity and lean body mass was 2.7% lower in fast than in medium mode (p < 0.0001). In contrast, fat mass, percentage of body fat, and bone mineral contents were higher with increasing scan velocity. Areas not analyzed by the scanner, so called blue spots, increased with scan velocity and sagittal height, and their presence significantly enhanced the error. Body weight estimated by DXA in slow mode was −0.8% lower than scale weight in the women (p < 0.001) and −0.2% in men (not significant), and the difference was greater with increasing scan velocity. Discussion: Scan velocity significantly influences the measured fat mass size, lean body mass, bone mineral content, and body weight. To obtain the most accurate results, slow mode is preferable and fast scans should be avoided. Future studies should report and take scan velocity into consideration.