Validation in type 2 diabetes of a metabolomic signature of all-cause mortality.

CONTEXT: Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed. OBJECTIVE: To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models. METHODS: This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics. RESULTS: In type 2 diabetes, the 14-MS showed a significant (p < 0.0001) association with mortality, which was lower (p < 0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p = 0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p < 0.0001 for all measures). CONCLUSIONS: The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

Association of osteocalcin, osteoprotegerin, and osteopontin with cardiovascular disease and retinopathy in type 2 diabetes.

BACKGROUND: Novel biomarkers of vascular disease in diabetes could help identify new mechanistic pathways. Osteocalcin, osteoprotegerin, and osteopontin are key molecules involved in bone and vascular calcification processes, both of which are compromised in diabetes. We aimed to evaluate possible associations of osteocalcin, osteoprotegerin, and osteopontin with cardiovascular disease (CVD) and diabetic retinopathy (DR) among people with type 2 diabetes (T2D). MATERIALS AND METHODS: Osteocalcin, osteoprotegerin, and osteopontin concentrations were measured at enrolment in 848 participants with T2D from the Sapienza University Mortality and Morbidity Event Rate (SUMMER) Study (ClinicalTrials.gov: NCT02311244). Logistic regression models and propensity score matching were used to assess possible associations of osteocalcin, osteoprotegerin, and osteopontin with a history of CVD and with evidence of any grade of DR adjusting for confounders. RESULTS: Previous CVD was reported in 139 (16.4%) participants, while 144 (17.0%) had DR. After adjusting for possible confounders, osteocalcin but not osteoprotegerin or osteopontin concentrations were associated with a history of CVD (Odds Ratio [OR] and 95% CI for one standard deviation (SD) increase in osteocalcin concentrations (natural log): 1.35 (1.06-1.72), p = 0.014). Associations with prevalent DR were seen for osteoprotegerin (OR for one SD increase in osteoprotegerin concentrations (natural log): 1.25 (1.01-1.55), p = 0.047) and osteopontin (OR for one SD increase in osteopontin concentrations (natural log): 1.25 (1.02-1.53), p = 0.022), but not osteocalcin. CONCLUSIONS: In T2D, higher serum osteocalcin concentrations are associated with macrovascular complications and higher osteoprotegerin and osteopontin concentrations with microvascular complications, suggesting that these osteokines might be involved in pathways directly related to vascular disease.

Circulating metabolites improve the prediction of renal impairment in patients with type 2 diabetes.

INTRODUCTION: Low glomerular filtration rate (GFR) is a leading cause of reduced lifespan in type 2 diabetes. Unravelling biomarkers capable to identify high-risk patients can help tackle this burden. We investigated the association between 188 serum metabolites and kidney function in type 2 diabetes and then whether the associated metabolites improve two established clinical models for predicting GFR decline in these patients. RESEARCH DESIGN AND METHODS: Two cohorts comprising 849 individuals with type 2 diabetes (discovery and validation samples) and a follow-up study of 575 patients with estimated GFR (eGFR) decline were analyzed. RESULTS: Ten metabolites were independently associated with low eGFR in the discovery sample, with nine of them being confirmed also in the validation sample (ORs range 1.3-2.4 per 1SD, p values range 1.9×10-2-2.5×10-9). Of these, five metabolites were also associated with eGFR decline (ie, tiglylcarnitine, decadienylcarnitine, total dimethylarginine, decenoylcarnitine and kynurenine) (ß range -0.11 to -0.19, p values range 4.8×10-2 to 3.0×10-3). Indeed, tiglylcarnitine and kynurenine, which captured all the information of the other three markers, improved discrimination and reclassification (all p<0.01) of two clinical prediction models of GFR decline in people with diabetes. CONCLUSIONS: Further studies are needed to validate our findings in larger cohorts of different clinical, environmental and genetic background.