Search

BackgroundHeterogeneity in the rate of β-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of complementary multi-omics data obtained after the diagnosis of T1D may provide mechanistic insight into the diverse rates of disease progression.MethodsWe collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in β-cell mass measured as fasting C-peptide.ResultsTwo molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signaling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signaling events that were inversely associated with rapid decline in β-cell function. The second signature was related to translation and viral infection were inversely associated with change in β-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid β-cell decline.ConclusionFeatures that differ between individuals with slow and rapid decline in β-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies, as well as offering biomarkers of therapeutic effect.FundingThis work is funded by the Innovative Medicine Initiative 2 Joint Undertaking (IMI2 JU) under grant agreement N° 115797 (INNODIA) and N° 945268 (INNODIA HARVEST). This Joint Undertaking receives support from the Union’s Horizon 2020 research and innovation program and ‘EFPIA’, ‘JDRF’ and ‘The Leona M. and Harry B. Helmsley Charitable Trust’.

We assessed whether blood lipid metabolites and their changes associate with various cardiometabolic, endocrine, bone- and energy-related comorbidities of Relative Energy Deficiency in Sport (RED-S) in female elite endurance athletes. Thirty-eight Scandinavian female elite athletes underwent a day-long exercise test. Five blood samples were obtained during the day - at fasting state and before and after two standardized exercise tests. Clinical biomarkers were assessed at fasting state, while untargeted lipidomics was undertaken using all blood samples. Linear and logistic regression was used to assess associations between lipidomic features and clinical biomarkers. Overrepresentations of findings with P PFDR P PFisher = 1.9×10−14). Mean lipid trajectories were created for 201 named features for the cohort and subsequently by stratifying participants by their energy availability and menstrual dysfunction status. This exploratory analysis of lipid trajectories indicates that participants with menstrual dysfunction might have decreased adaptive response to exercise interventions.

OBJECTIVE: Our aim was to apply state-of-the-art machine learning algorithms to predict the risk of future progression to diabetes complications, including diabetic kidney disease (≥30% decline in eGFR) and diabetic retinopathy (mild, moderate or severe). RESEARCH DESIGN AND METHODS: Using data in a cohort of 537 adults with type 1 diabetes we predicted diabetes complications emerging during a median follow-up of 5.4 years. Prediction models were computed first with clinical risk factors at baseline (17 measures) and then with clinical risk factors and blood-derived metabolomics and lipidomics data (965 molecular features) at baseline. Participants were first classified into two groups: type 1 diabetes stable (n=195) or type 1 diabetes with progression to diabetes complications (n=190). Furthermore, progression of diabetic kidney disease (≥30% decline in eGFR; n=79) and diabetic retinopathy (mild, moderate or severe; n=111) were predicted in two complication-specific models. Models were compared by 5-fold cross-validated area under the receiver operating characteristic (AUROC) curves. The Shapley additive explanations algorithm was used for feature selection and for interpreting the models. Accuracy, precision, recall, and F-score were used to evaluate clinical utility. RESULTS: During a median follow-up of 5.4 years, 79 (21 %) of the participants (mean+-SD: age 54.8 +- 13.7 years) progressed in diabetic kidney disease and 111 (29 %) of the participants progressed to diabetic retinopathy. The predictive models for diabetic kidney disease progression were highly accurate with clinical risk factors: the accuracy of 0.95 and AUROC of 0.92 (95% CI 0.857;0.995) was achieved, further improved to the accuracy of 0.98 and AUROC of 0.99 (95% CI 0.876;0.997) when omics-based predictors were included. The predictive panel composition was: albuminuria, retinopathy, estimated glomerular filtration rate, hemoglobin A1c, and six metabolites (five identified as ribitol, ribonic acid, myo-inositol, 2,4- and 3,4-dihydroxybutanoic acids). Models for diabetic retinopathy progression were less predictive with clinical risk predictors at, AUROC of 0.81 (95% CI 0.754;0.958) and with omics included at AUROC of 0.87 (95% CI 0.781;0.996) curve. The final retinopathy-panel included: hemoglobin A1c, albuminuria, mild degree of retinopathy, and seven metabolites, including one ceramide and the 3,4-dihydroxybutanoic acid). CONCLUSIONS: Here we demonstrate the application of machine learning to effectively predict five-year progression of complications, in particular diabetic kidney disease, using a panel of known clinical risk factors in combination with blood small molecules. Further replication of this machine learning tool in a real-world context or a clinical trial will facilitate its implementation in the clinic.

Hypoglycemia is a major limiting factor in achieving recommended glycemic target for patients in insulin treatment and can indirectly lead to diabetic complications, morbidity and mortality. While counterregulatory hormonal responses have been studied extensively in patients with type 1 diabetes, a more comprehensive assessment of the metabolic responses has not been done previously. This post-hoc study explored potential responses of the metabolome to hypoglycemia. Twenty-one patients with type 1 diabetes were examined using a hypoglycemic clamp at day one, and 24 hours later, at day two. Blood samples were taken during normoglycemia (5.0-6.0 mmol/L) and hypoglycemia (2.0-2.5 mmol/L). Non-targeted plasma metabolomic analyses was conducted using two-dimensional gas chromatography/time-of-flight mass spectroscopy. Metabolites were analyzed by a linear mixed effect model, adjusting for age and sex. P-values were adjusted for multiple testing using the Benjamini-Hochberg method. A total of 79 metabolites were identified. At day one, two amino acids decreased in response to hypoglycemia, leucine (β±SE: -0.78±0.18, p = 0.004) and isoleucine (β±SE: -0.72±0.16, p = 0.002). At day two, five amino acids including all three branched-chained amino acids decreased: Leucine (β±SE: -0.62±0.13, p = 0.002), isoleucine (β±SE: -0.59±0.19, p = 0.026), valine (β±SE: -0.52±0.12, p=0.002), methionine (β±SE: -0.62±0.19, p = 0.026) and phenylalanine (β±SE: -0.65±0.20, p = 0.026). Two fatty acids were elevated, oleic acid (β±SE: 0.50±0.14, p = 0.016) and tetradecanoic acid (β±SE: 0.63±0.17, p = 0.013). No significant changes were observed between the two days. In conclusion, this study found that the metabolome alternates in response to insulin-induced hypoglycemia resulting in decrement of several amino acids and increment of two fatty acids, contributing to the knowledge of human physiology in individuals with type 1 diabetes. Disclosure R. She: None. N. Al-sari: None. A. Sejling: Employee; Self; Novo Nordisk A/S. I. Mattila: None. P. Henriksen: None. J. Pedersen: None. C. Legido-quigley: None. U. Pedersen-bjergaard: Advisory Panel; Self; Novo Nordisk A/S, Sanofi-Aventis, Consultant; Self; Abbott, Speaker’s Bureau; Self; Novo Nordisk A/S.

Background and aimsAdipose tissue plays a pivotal role in storing excess fat and its composition reflects the history of person’s lifestyle and metabolic health. Broad profiling of lipids with mass spectrometry has potential for uncovering new knowledge on the pathology of obesity, metabolic syndrome, diabetes and other related conditions. Here, we developed a lipidomic method for analyzing human subcutaneous adipose biopsies. We applied the method to four body areas to understand the differences in lipid composition between these areas.Materials and methodsAdipose tissue biopsies from 10 participants were analyzed using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The method development included the optimization of the lipid extraction, the sample amount and the sample dilution factor to detect lipids in an appropriate concentration range. Lipidomic analyses were performed for adipose tissue collected from the abdomen, breast, thigh and lower back. Differences in lipid levels between tissues were visualized with heatmaps.ResultsLipidomic analysis on human adipose biopsies lead to the identification of 187 lipids in 2 mg of sample. Technical variation of the lipid-class specific internal standards were below 5 %, thus indicating acceptable repeatability. Triacylglycerols were highly represented in the adipose tissue samples, and lipids from 13 lipid classes were identified. Long polyunsaturated triacylglycerols in higher levels in thigh (qConclusionThe method presented here is suitable for the analysis of lipid profiles in 2 mg of adipose tissue. The amount of fat across the body is important for health but we argue that also the distribution and the particular profile of the lipidome may be relevant for metabolic outcomes. We suggest that the method presented in this paper could be useful for detecting such aberrations.