Efficacy and safety of endoscopic sleeve gastroplasty and laparoscopic sleeve gastrectomy with 12+ months of adjuvant multidisciplinary support.

BACKGROUND: The laparoscopic sleeve gastrectomy (LSG) and the incisionless endoscopic sleeve gastroplasty (ESG) weight loss procedures require further investigation of their efficacy, safety and patient-centered outcomes in the Australian setting. METHODS: The aim was to examine the 6- and 12-month weight loss efficacy, safety, and weight-related quality of life (QoL) of adults with obesity who received the ESG or LSG bariatric procedure with 12+ months of adjuvant multidisciplinary pre- and postprocedural support. Data were from a two-arm prospective cohort study that followed patients from baseline to 12-months postprocedure from a medical center in Queensland. Percent excess weight loss (%EWL) was the primary outcome. Secondary outcomes were body composition (fat mass, fat-free mass, android:gynoid ratio, bone mineral content) via dual energy X-ray absorptiometry, weight-related QoL, lipid, glycemic, and hepatic biochemistry, and adverse events. RESULTS: 16 ESG (19% attrition; 81.2% female; aged:41.4 (SD: 10.4) years; BMI: 35.5 (SD: 5.2) kg/m2) and 45 LSG (9% attrition; 84.4% female; aged:40.4 (SD: 9.0) years; BMI: 40.7 (SD: 5.6) kg/m2) participants were recruited. At 12-months postprocedure, ESG %EWL was 57% (SD: 32%; p < 0.01) and LSG %EWL was 79% (SD: 24%; p < 0.001). ESG and LSG cohorts improved QoL (19.8% in ESG [p > 0.05]; 48.1% in LSG [p < 0.05]), liver function (AST: - 4.4 U/L in ESG [p < 0.05]; - 2.7 U/L in LSG [p < 0.05]), HbA1c (- 0.5% in ESG [p < 0.05]; - 0.1% in LSG [p < 0.05]) and triglycerides (- 0.6 mmol/L in ESG [p > 0.05]; - 0.4 mmol/L in LSG [P < 0.05]) at 12-months. Both cohorts reduced fat mass (p < 0.05). The ESG maintained but LSG decreased fat-free mass at 6-months (p < 0.05); and both cohorts lost fat-free mass at 12-months (p < 0.05). There were no adverse events directly related to the procedure. The ESG reported 25% mild-moderate adverse events possibly related to the procedure, and the LSG reported 27% mild-severe adverse events possibly related to the procedure. CONCLUSIONS: In this setting, the ESG and LSG were safe and effective weight loss treatments for obese adults alongside multidisciplinary support. Patients who elected the ESG maintained fat-free mass at 6-months but both cohorts lost fat-free mass at 12-months postprocedure. Patients who elected the LSG had large and significant improvements to weight-related quality of life. Further well-powered studies are required to confirm these findings. TRIAL REGISTRATION: This study was registered prospectively at the Australia New Zealand Clinical Trials Registry on 06/03/2018, Registration Number ACTRN12618000337279 .

Nutrient Dense, Low-Cost Foods Can Improve the Affordability and Quality of the New Zealand Diet-A Substitution Modeling Study.

The high prevalence of non-communicable disease in New Zealand (NZ) is driven in part by unhealthy diet selections, with food costs contributing to an increased risk for vulnerable population groups. This study aimed to: (i) identify the nutrient density-to-cost ratio of NZ foods; (ii) model the impact of substituting foods with a lower nutrient density-to-cost ratio with those with a higher nutrient density-to-cost ratio on diet quality and affordability in representative NZ population samples for low and medium socioeconomic status (SES) households by ethnicity; and (iii) evaluate food processing level. Foods were categorized, coded for processing level and discretionary status, analyzed for nutrient density and cost, and ranked by nutrient density-to-cost ratio. The top quartile of nutrient dense, low-cost foods were 56% unprocessed (vegetables, fruit, porridge, pasta, rice, nuts/seeds), 31% ultra-processed (vegetable dishes, fortified bread, breakfast cereals unfortified <15 g sugars/100 g and fortified 15-30 g sugars/100 g), 6% processed (fruit juice), and 6% culinary processed (oils). Using substitution modeling, diet quality improved by 59% and 71% for adults and children, respectively, and affordability increased by 20-24%, depending on ethnicity and SES. The NZ diet can be made healthier and more affordable when nutritious, low-cost foods are selected. Processing levels in the healthier, modeled diet suggest that some non-discretionary ultra-processed foods may provide a valuable source of low-cost nutrition for food insecure populations.

Diet and Economic Modelling to Improve the Quality and Affordability of the Australian Diet for Low and Medium Socioeconomic Households.

Food costs are a barrier to healthier diet selections, particularly for low socioeconomic households who regularly choose processed foods containing refined grains, added sugars, and added fats. In this study, the objectives were to: (i) identify the nutrient density-to-cost ratio of Australian foods; (ii) model the impact of substituting foods with lower nutrient density-to-cost ratio with those with the highest nutrient density-to-cost ratio for diet quality and affordability in low and medium socioeconomic households; and (iii) evaluate food processing levels. Foods were categorized, coded for processing level, analysed for nutrient density and cost, and ranked by nutrient density-to-cost ratio. The top quartile of nutrient dense, low-cost foods included 54% unprocessed (vegetables and reduced fat dairy), 33% ultra-processed (fortified wholegrain bread and breakfast cereals <20 g sugars/100 g), and 13% processed (fruit juice and canned legumes). Using substitution modelling, diet quality improved by 52% for adults and 71% for children across all households, while diet affordability improved by 25% and 27% for low and medium socioeconomic households, respectively. The results indicate that the quality and affordability of the Australian diet can be improved when nutritious, low-cost foods are selected. Processing levels in the healthier modelled diets suggest that some ultra-processed foods may provide a beneficial source of nutrition when consumed within national food group recommendations.

Quantifying catastrophic and climate impacted hazards based on local expert opinions.

Quantifying the potential costs of catastrophic and climate impacted hazards is a challenging but important exercise as the occurrence of such events is usually associated with high damage and uncertainty. At the local level, there is often a lack of information on rare extreme events, which means that the available data is not sufficient to fit a distribution and derive parameter values for frequency and severity distributions. This paper discusses the use of local assessments of extreme events and utilises expert elicitation in order to obtain values for distribution parameters that will feed into management decisions with regards to quantifying catastrophic risks. We illustrate a simple approach, where a local expert is required to only specify two percentiles of the loss distribution in order to provide an estimate for the severity distribution of climate impacted hazards. In our approach we use heavy-tailed distributions to capture the severity of events. Our method allows local government decision makers to focus on extreme losses and the tail of the distribution. An illustration of the method is provided utilising an example that quantifies property losses from bushfires for a local area in northern Sydney. We further illustrate how key variables, such as discount rates, assumptions about climatic change and adaptation measures, will impact the estimates of losses.

Coverage and consistency: bioinformatics aspects of the analysis of multirun iTRAQ experiments with wheat leaves.

The hexaploid genome of bread wheat (Triticum aestivum) is large (17 Gb) and repetitive, and this has delayed full sequencing and annotation of the genome, which is a prerequisite for effective quantitative proteomics analysis. Aware of these constraints we investigated the most effective approaches for shotgun proteomic analyses of bread wheat that would support large-scale quantitative comparisons using iTRAQ reagents. We used a data set that was generated by two-dimensional LC-MS of iTRAQ labeled peptides from wheat leaves. The main items considered in this study were the choice of sequence database for matching LC-MS data, the consistency of identification when multiple LC-MS runs were acquired, and the options for downstream functional analysis to generate useful insight. For peptide identification we examined the extensive NCBInr plant database, a smaller composite cereals database, the Brachypodium distachyon model plant genome, the EST-based SuperWheat database, as well as the genome sequence from the recently sequenced D-genome progenitor Aegilops tauschii. While the most spectra were assigned by using the SuperWheat database, this extremely large database could not be readily manipulated for the robust protein grouping that is required for large-scale, multirun quantitative experiments. We demonstrated a pragmatic alternative of using the composite cereals database for peptide spectra matching. The stochastic aspect of protein grouping across LC-MS runs was investigated using the smaller composite cereals database where we found that attaching the Brachypodium best BLAST hit reduced this problem. Further, assigning quantitation to the best Brachypodium locus yielded promising results enabling integration with existing downstream data mining and functional analysis tools. Our study demonstrated viable approaches for quantitative proteomics analysis of bread wheat samples and shows how these approaches could be similarly adopted for analysis of other organisms with unsequenced or incompletely sequenced genomes.

Label-free quantitative shotgun proteomics using normalized spectral abundance factors.

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.

Proteomic analysis indicates massive changes in metabolism prior to the inhibition of growth and photosynthesis of grapevine (Vitis vinifera L.) in response to water deficit.

BACKGROUND: Cabernet Sauvignon grapevines were exposed to a progressive, increasing water defict over 16 days. Shoot elongation and photosynthesis were measured for physiological responses to water deficit. The effect of water deficit over time on the abundance of individual proteins in growing shoot tips (including four immature leaves) was analyzed using nanoflow liquid chromatography - tandem mass spectrometry (nanoLC-MS/MS). RESULTS: Water deficit progressively decreased shoot elongation, stomatal conductance and photosynthesis after Day 4; 2277 proteins were identified by shotgun proteomics with an average CV of 9% for the protein abundance of all proteins. There were 472 out of 942 (50%) proteins found in all samples that were significantly affected by water deficit. The 472 proteins were clustered into four groups: increased and decreased abundance of early- and late-responding protein profiles. Vines sensed the water deficit early, appearing to acclimate to stress, because the abundance of many proteins changed before decreases in shoot elongation, stomatal conductance and photosynthesis. Predominant functional categories of the early-responding proteins included photosynthesis, glycolysis, translation, antioxidant defense and growth-related categories (steroid metabolism and water transport), whereas additional proteins for late-responding proteins were largely involved with transport, photorespiration, antioxidants, amino acid and carbohydrate metabolism. CONCLUSIONS: Proteomic responses to water deficit were dynamic with early, significant changes in abundance of proteins involved in translation, energy, antioxidant defense and steroid metabolism. The abundance of these proteins changed prior to any detectable decreases in shoot elongation, stomatal conductance or photosynthesis. Many of these early-responding proteins are known to be regulated by post-transcriptional modifications such as phosphorylation. The proteomics analysis indicates massive and substantial changes in plant metabolism that appear to funnel carbon and energy into antioxidant defenses in the very early stages of plant response to water deficit before any significant injury.

PloGO: plotting gene ontology annotation and abundance in multi-condition proteomics experiments.

We describe the PloGO R package, a simple open-source tool for plotting gene ontology (GO) annotation and abundance information, which was developed to aid with the bioinformatics analysis of multi-condition label-free proteomics experiments using quantitation based on spectral counting. PloGO can incorporate abundance (raw spectral counts) or normalized spectral abundance factors (NSAF) data in addition to the GO annotation, as well as handle multiple files and allow for a targeted collection of GO categories of interest. Our main aims were to help identify interesting subsets of proteins for further analysis such as those arising from a protein data set partition based on the presence and absence or multiple pair-wise comparisons, as well as provide GO summaries that can be easily used in subsequent analyses. Though developed with label-free proteomics experiments in mind it is not specific to that approach and can be used for any multi-condition experiment for which GO information has been generated.

Shotgun proteomic analysis of long-distance drought signaling in rice roots.

Rice (Oryza sativa L. cv. IR64) was grown in split-root systems to analyze long-distance drought signaling within root systems. This in turn underpins how root systems in heterogeneous soils adapt to drought. The approach was to compare four root tissues: (1) fully watered; (2) fully droughted and split-root systems where (3) one-half was watered and (4) the other half was droughted. This was specifically aimed at identifying how droughted root tissues altered the proteome of adjacent wet roots by hormone signals and how wet roots reciprocally affected dry roots hydraulically. Quantitative label-free shotgun proteomic analysis of four different root tissues resulted in identification of 1487 nonredundant proteins, with nearly 900 proteins present in triplicate in each treatment. Drought caused surprising changes in expression, most notably in partially droughted roots where 38% of proteins were altered in level compared to adjacent watered roots. Specific functional groups changed consistently in drought. Pathogenesis-related proteins were generally up-regulated in response to drought and heat-shock proteins were totally absent in roots of fully watered plants. Proteins involved in transport and oxidation-reduction reactions were also highly dependent upon drought signals, with the former largely absent in roots receiving a drought signal while oxidation-reduction proteins were strongly present during drought. Finally, two functionally contrasting protein families were compared to validate our approach, showing that nine tubulins were strongly reduced in droughted roots while six chitinases were up-regulated, even when the signal arrived remotely from adjacent droughted roots.

Remote access methods for exploratory data analysis and statistical modelling: Privacy-Preserving Analytics.

This paper is concerned with the challenge of enabling the use of confidential or private data for research and policy analysis, while protecting confidentiality and privacy by reducing the risk of disclosure of sensitive information. Traditional solutions to the problem of reducing disclosure risk include releasing de-identified data and modifying data before release. In this paper we discuss the alternative approach of using a remote analysis server which does not enable any data release, but instead is designed to deliver useful results of user-specified statistical analyses with a low risk of disclosure. The techniques described in this paper enable a user to conduct a wide range of methods in exploratory data analysis, regression and survival analysis, while at the same time reducing the risk that the user can read or infer any individual record attribute value. We illustrate our methods with examples from biostatistics using publicly available data. We have implemented our techniques into a software demonstrator called Privacy-Preserving Analytics (PPA), via a web-based interface to the R software. We believe that PPA may provide an effective balance between the competing goals of providing useful information and reducing disclosure risk in some situations.