Context: Some studies have surprisingly indicated that serum adiponectin level is positively related to mortality rate, thus casting doubts on its role as a therapeutic target for cardiovascular disease. Objective: To summarize evidence about direction, strength, and modulators of this controversial association. Methods: MEDLINE, Web of Science, CINHAL, Cochrane Library, and Scopus databases were searched from their inception dates through June 2018 for English-language prospective studies reporting the association between adiponectin and all-cause or cardiovascular mortality. Two investigators independently extracted data and assessed study quality using standard criteria following the Preferred Reporting Items for Systematic Reviews and Meta-analyses and The Newcastle-Ottawa Scale. Pooled hazard ratios (HRs) and 95% CIs were derived using fixed- or random-effects models when appropriate, and results were expressed to a 1-SD increment of adiponectin. Results: We identified 55 studies (n = 61,676 subjects) with all-cause mortality data and 28 (n = 43,979 subjects) studies with cardiovascular mortality data. Pooled HRs were 1.24 (1.17-1.31) and 1.28 (1.19-1.37) for all-cause and cardiovascular mortality, respectively. Similar results were obtained for high-molecular-weight adiponectin. When meta-analyses were restricted to studies reporting data on natriuretic peptides, reductions of 43% and 28% on a log scale of these respective associations were observed after adjusting for natriuretic peptides. Conclusions: Our results point strongly to a paradoxical association between high adiponectin levels and increased mortality rate, which is partly modulated by natriuretic peptides.

OBJECTIVE: Type 2 diabetes is characterized by increased death rate. In order to tackle this dramatic event, it becomes essential to discover novel biomarkers capable of identifying high-risk patients to be exposed to more aggressive preventive and treatment strategies. hs-CRP and serum amyloid P component (SAP) are two acute-phase inflammation proteins, which interact physically and share structural and functional features. We investigated their combined role in associating with and improving prediction of mortality in type 2 diabetes. RESEARCH DESIGN AND METHODS: Four cohorts comprising 2,499 patients with diabetes (643 all-cause deaths) were analyzed. The improvement of mortality prediction was addressed using two well-established prediction models, namely, EstimatioN oF mORtality risk in type 2 diabetiC patiEnts (ENFORCE) and Risk Equations for Complications of Type 2 Diabetes (RECODe). RESULTS: Both hs-CRP and SAP were independently associated with all-cause mortality (hazard ratios [HRs] [95% CIs]: 1.46 [1.34–1.58] [P < 0.001] and 0.82 [0.76–0.89] [P < 0.001], respectively). Patients with SAP ≤33 mg/L were at increased risk of death versus those with SAP >33 mg/L only if hs-CRP was relatively high (>2 mg/L) (HR 1.96 [95% CI 1.52–2.54] [P < 0.001] and 1.20 [0.91–1.57] [P = 0.20] in hs-CRP >2 and ≤2 mg/L subgroups, respectively; hs-CRP-by-SAP strata interaction P < 0.001). The addition of hs-CRP and SAP significantly (all P < 0.05) improved several discrimination and reclassification measures of both ENFORCE and RECODe all-cause mortality prediction models. CONCLUSIONS: In type 2 diabetes, hs-CRP and SAP show opposite and synergic associations with all-cause mortality. The use of both markers, possibly in combination with others yet to be unraveled, might improve the ability to predict the risk of death in the real-life setting.

Multigenerational diabetes of adulthood is a mostly overlooked entity, simplistically lumped into the large pool of type 2 diabetes. The general aim of our research in the past few years is to unravel the genetic causes of this form of diabetes. Identifying among families with multigenerational diabetes those who carry mutations in known monogenic diabetes genes is the first step to then allow us to concentrate on remaining pedigrees in which to unravel new diabetes genes. Targeted next-generation sequencing of 27 monogenic diabetes genes was carried out in 55 family probands and identified mutations verified among their relatives by Sanger sequencing. Nine variants (in eight probands) survived our filtering/prioritization strategy. After likelihood of causality assessment by established guidelines, six variants were classified as “pathogenetic/likely pathogenetic” and two as “of uncertain significance.” Combining present results with our previous data on the six genes causing the most common forms of maturity-onset diabetes of the young allows us to infer that 23.6% of families with multigenerational diabetes of adulthood carry mutations in known monogenic diabetes genes. Our findings indicate that the genetic background of hyperglycemia is unrecognized in the vast majority of families with multigenerational diabetes of adulthood. These families now become the object of further research aimed at unraveling new diabetes genes.

Death rate is increased in type 2 diabetes. Unraveling biomarkers of novel pathogenic pathways capable to identify high-risk patients is instrumental to tackle this burden. We investigated the association between serum metabolites and all-cause mortality in type 2 diabetes and then whether the associated metabolites mediate the effect of inflammation on mortality risk and improve ENFORCE (EstimatioN oF mORtality risk in type2 diabetic patiEnts) and RECODe (Risk Equation for Complications Of type 2 Diabetes), two well-established all-cause mortality prediction models in diabetes. Two cohorts comprising 856 individuals (279 all-cause deaths) were analyzed. Serum metabolites (n = 188) and pro- and anti-inflammatory cytokines (n = 7) were measured. In the pooled analysis, hexanoylcarnitine, kynurenine, and tryptophan were significantly and independently associated with mortality (hazard ratio [HR] 1.60 [95% CI 1.43–1.80]; 1.53 [1.37–1.71]; and 0.71 [0.62–0.80] per 1 SD). The kynurenine-to-tryptophan ratio (KTR), a proxy of indoleamine-2,3-dioxygenase, which degrades tryptophan to kynurenine and contributes to a proinflammatory status, mediated 42% of the significant association between the antiatherogenic interleukin (IL) 13 and mortality. Adding the three metabolites improved discrimination and reclassification (all P < 0.01) of both mortality prediction models. In type 2 diabetes, hexanoylcarnitine, tryptophan, and kynurenine are associated to and improve the prediction of all-cause mortality. Further studies are needed to investigate whether interventions aimed at reducing KTR also reduce the risk of death, especially in patients with low IL-13.

Context We previously developed and validated an inexpensive and parsimonious prediction model of 2-year all-cause mortality in real-life patients with type 2 diabetes. Objective This model, now named ENFORCE (EstimatioN oF mORtality risk in type 2 diabetiC patiEnts), was investigated in terms of (i) prediction performance at 6 years, a more clinically useful time-horizon; (ii) further validation in an independent sample; and (iii) performance comparison in a real-life vs a clinical trial setting. Design Observational prospective randomized clinical trial. Setting White patients with type 2 diabetes. Patients Gargano Mortality Study (GMS; n = 1019), Foggia Mortality Study (FMS; n = 1045), and Pisa Mortality Study (PMS; n = 972) as real-life samples and the standard glycemic arm of the ACCORD (Action to Control Cardiovascular Risk in Diabetes) clinical trial (n = 3150). Main Outcome Measure The endpoint was all-cause mortality. Prediction accuracy and calibration were estimated to assess the model's performances. Results ENFORCE yielded 6-year mortality C-statistics of 0.79, 0.78, and 0.75 in GMS, FMS, and PMS, respectively (P heterogeneity = 0.71). Pooling the three cohorts showed a 6-year mortality C-statistic of 0.80. In the ACCORD trial, ENFORCE achieved a C-statistic of 0.68, a value significantly lower than that obtained in the pooled real-life samples (P < 0.0001). This difference resembles that observed with other models comparing real-life vs clinical trial settings, thus suggesting it is a true, replicable phenomenon. Conclusions The time horizon of ENFORCE has been extended to 6 years and validated in three independent samples. ENFORCE is a free and user-friendly risk calculator of all-cause mortality in white patients with type 2 diabetes from a real-life setting.