Real-World Evidence of Automated Insulin Delivery System Use.

Objective: Pivotal trials of automated insulin delivery (AID) closed-loop systems have demonstrated a consistent picture of glycemic benefit, supporting approval of multiple systems by the Food and Drug Administration or Conformité Européenne mark receipt. To assess how pivotal trial findings translate to commercial AID use, a systematic review of retrospective real-world studies was conducted. Methods: PubMed and EMBASE were searched for articles published after 2018 with more than five nonpregnant individuals with type 1 diabetes (T1D). Data were screened/extracted in duplicate for sample size, AID system, glycemic outcomes, and time in automation. Results: Of 80 studies identified, 20 met inclusion criteria representing 171,209 individuals. Time in target range 70-180 mg/dL (3.9-10.0 mmol/L) was the primary outcome in 65% of studies, with the majority of reports (71%) demonstrating a >10% change with AID use. Change in hemoglobin A1c (HbA1c) was reported in nine studies (range 0.1%-0.9%), whereas four reported changes in glucose management indicator (GMI) with a 0.1%-0.4% reduction noted. A decrease in HbA1c or GMI of >0.2% was achieved in two-thirds of the studies describing change in HbA1c and 80% of articles where GMI was described. Time below range <70 mg/dL (<3.9 mmol/L) was reported in 16 studies, with all but 1 study showing stable or reduced levels. Most systems had >90% time in automation. Conclusion: With larger and more diverse populations, and follow-up periods of longer duration (∼9 months vs. 3-6 months for pivotal trials), real-world retrospective analyses confirm pivotal trial findings. Given the glycemic benefits demonstrated, AID is rapidly becoming the standard of care for all people living with T1D. Individuals should be informed of these systems and differences between them, have access to and coverage for these technologies, and receive support as they integrate this mode of insulin delivery into their lives.

Endurance Exercise Performance Is Reduced after 6-h Dives at 1.35 ATA When Breathing 100% Oxygen Compared with Air.

INTRODUCTION: Long-duration dives on consecutive days reduces muscular performance, potentially affecting military personnel. However, a paucity of data exists on how breathing gases affect endurance performance. This study examined the influence of long-duration diving with different breathing gases on aerobic endurance and handgrip performance. METHODS: Twenty-three military divers completed a single 6-h dive (single dive [SD]) and five 6-h dives over consecutive days (dive week [DW]) with 30-min cycling intervals using air (AIR, n = 13) or 100% oxygen (OXY, n = 10). Before and after SD and DW, subjects completed a maximum handgrip strength test, a handgrip endurance test at 40% maximal strength, and a time to exhaustion run. RESULTS: Handgrip endurance decreased after DW in OXY (SD, 1.9 ± 0.0 vs 1.4 ± 0.3 min) compared with AIR (1.8 ± 0.0 vs 1.8 ± 0.2 min) ( P < 0.001). Run time decreased after SD (Pre, 20.7 ± 10.4 min; Post, 16.6 ± 7.6 min; P = 0.039) and DW (Pre, 21.6 ± 9.0 min; Post, 11.2 ± 4.0 min; P < 0.001) in OXY and after overall diving in AIR (Pre, 26.5 ± 10.2 min; Post, 22.3 ± 7.5 min; P = 0.025). V̇O 2 decreased after diving only in AIR (Pre, 42.6 ± 3.4 mL·kg -1 ⋅min -1 ; Post, 40.4 ± 3.7 mL·kg -1 ⋅min -1 ; P = 0.010). There were no other significant effects. CONCLUSIONS: Breathing 100% oxygen during long-duration dives on consecutive days may exacerbate decreases in aerobic endurance and impairs handgrip endurance compared with air. Additional research is needed to elucidate mechanisms of action and possible mitigation strategies.

Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review.

BACKGROUND: The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care. METHODS: Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years). RESULTS: We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact. CONCLUSIONS: Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes.

In the last decade, there have been significant advances in how technology is used in the treatment of people living with type 1 diabetes. These technologies primarily aim to help manage blood sugar levels. Here, we reviewed research published over the last decade to evaluate the impact of such technologies on type 1 diabetes treatment. We find that various types of novel technologies, such as devices to monitor blood sugar levels continuously or deliver insulin, improve important diabetes-related measures and can reduce the risk of having low blood sugar levels. Importantly, several studies showed a positive impact of technologies on quality of life in people living with diabetes. Our findings highlight the benefits of novel technologies in the treatment of type 1 diabetes and identify areas for further research to optimize and personalize diabetes care.

Loss of renal olfactory receptor 1393 leads to improved glucose homeostasis in a type 1 diabetic mouse model.

Renal olfactory receptor 1393 (Olfr1393) is an understudied sensory receptor that contributes to glucose handling in the proximal tubule. Our previous studies have indicated that this receptor may serve as a regulator of the sodium glucose co-transporters (SGLTs) and contributes to the development of glucose intolerance and hyperfiltration in the setting of diet-induced obesity. We hypothesized that Olfr1393 may have a similar function in Type 1 Diabetes. Using Olfr1393 wildtype (WT) and knockout (KO) mice along with streptozotocin (STZ) to induce pancreatic ß-cell depletion, we tracked the development and progression of diabetes over 12 weeks. Here we report that diabetic male Olfr1393 KO mice have a significant improvement in hyperglycemia and glucose tolerance, despite remaining susceptible to STZ. We also confirm that Olfr1393 localizes to the renal proximal tubule, and have uncovered additional expression within the glomerulus. Collectively, these data indicate that loss of renal Olfr1393 affords protection from STZ-induced type 1 diabetes and may be a general regulator of glucose handling in both health and disease.

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective.

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel.

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks' gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks' gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks' GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of "Bile Acids", "Prostaglandins", "Vitamin D and derivatives" and "Fatty Acids and Conjugates".

A Tool to Encourage Minimum Reporting Guideline Uptake for Data Analysis in Metabolomics.

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis' sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

Between-group analysis of microarray data.

MOTIVATION: Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data. RESULTS: We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data.