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Currently used risk scores for type 2 diabetes mellitus (T2DM) clearly underestimate cardiovascular risk in type 1 diabetes (T1DM). Hence, there is a need to develop novel and specific risk-estimation tools for this population. We aimed to assess the relationship between the Steno Type 1 Risk Engine (ST1RE) and arterial stiffness (AS), and to identify potential cut-off points of interest in clinical practice. A total of 179 patients with T1DM (50.8% men, mean age 41.2±13.1 years), without established cardiovascular disease, were evaluated for clinical and anthropometric data (including classical cardiovascular risk factors), and AS measured by aortic pulse-wave velocity (aPWV). The ST1RE was used to estimate 10-year cardiovascular risk and patients were classified into 3 groups: low- (<10%; n = 105), moderate- (10-20%; n = 53) and high-risk (≥20%; n = 21). When compared with the low- and moderate-risk groups, patients in the high-risk group were older, had higher prevalence of hypertension, dyslipidemia and insulin-resistance, and had higher body-mass index and HbA1c. aPWV increased in parallel with estimated cardiovascular risk (6.4±1.0, 8.4±1.3 and 10.3±2.6m/s; p<0.001). As an evaluation of model performance, the C-statistic of aPWV was 0.914 (95% confidence interval [CI]:0.873-0.950) for predicting moderate/high-risk and 0.879 (95%CI:0.809-0.948) for high-risk, according to the ST1RE. The best cut-off points of aPWV were 7.3m/s (sensitivity:86%, specificity:83%) and 8.7m/s (sensitivity:76%, specificity:86%) for moderate/high- and high-risk, respectively. AS is highly correlated with the scores obtained from the ST1RE. We have identified two cut-off points of AS that can clearly discriminate moderate/high- and high-risk T1DM patients, which could be of great value in clinical practice.

Obesity is an emerging disease worldwide. Changes in living habits, especially with increased consumption of high-calorie foods and decreased levels of physical activity, lead to an energy imbalance that brings weight gain. Overweight and obesity are major risk factors for several chronic diseases (including cardiovascular diseases, diabetes, and cancer), reduce quality of life, and are associated with higher mortality. For all these reasons, it is of the utmost importance that the trend be reversed and obese people enabled to lose weight. It is known that eating a healthy diet and exercising regularly can help prevent obesity, but data show that in many cases these steps are not enough. This is the reason why, over the last few decades, several antiobesity drugs have been developed. However, the disappointing results demonstrated for the vast majority of them have not discouraged the pharmaceutical industry from continuing to look for an effective drug or combination of drugs. The systematic review presented here focuses on naltrexone sustained-release/bupropion sustained-release combination (Contrave(®)). We conclude from the current published reports that its effectiveness in the treatment of obesity can be estimated as a placebo-subtracted weight loss of around 4.5%. This weight reduction is moderate but similar to other antiobesity drugs. The safety profile of this combination is acceptable, despite additional data regarding cardiovascular disease being needed.

The aim of the study was to develop a novel risk estimation model for predicting silent myocardial ischemia (SMI) in patients with type 1 diabetes (T1DM) and no clinical cardiovascular disease, evaluating the potential role of insulin resistance in such a model. Additionally, the accuracy of this model was compared with currently available models for predicting clinical coronary artery disease (CAD) in general and diabetic populations. Patients with T1DM (35-65years, >10-year duration) and no clinical cardiovascular disease were consecutively evaluated for: 1) clinical and anthropometric data (including classical cardiovascular risk factors), 2) insulin sensitivity (estimate of glucose disposal rate (eGDR)), and 3) SMI diagnosed by stress myocardial perfusion gated SPECTs. Eighty-four T1DM patients were evaluated [50.1±9.3 years, 50% men, 36.9% active smokers, T1DM duration: 19.0(15.9-27.5) years and eGDR 7.8(5.5-9.4)mg·kg-1·min-1]. Of these, ten were diagnosed with SMI (11.9%). Multivariate logistic regression models showed that only eGDR (OR = -0.593, p = 0.005) and active smoking (OR = 7.964, p = 0.018) were independently associated with SMI. The AUC of the ROC curve of this risk estimation model for predicting SMI was 0.833 (95%CI:0.692-0.974), higher than those obtained with the use of currently available models for predicting clinical CAD (Framingham Risk Equation: 0.833 vs. 0.688, p = 0.122; UKPDS Risk Engine (0.833 vs. 0.559; p = 0.001) and EDC equation: 0.833 vs. 0.558, p = 0.027). This study provides the first ever reported risk-estimation model for predicting SMI in T1DM. The model only includes insulin resistance and active smoking as main predictors of SMI.

Arterial stiffness (AS) integrates the cumulative burden of known and unknown cardiovascular risk factors on the elastic wall of large arteries along the lifespan of an individual. As a marker of vascular aging, AS is an independent predictor of cardiovascular events and improves cardiovascular risk prediction when added to the Framingham Risk Score. In addition, AS may affect the microvasculature and promote the development of microvascular complications. Its impact on both the macro- and microvasculature has led to the concept that the arterial wall itself should be considered as a target organ. Here, we review the biological and clinical consequences of AS on the macro- and microvasculature and the measurement of AS in routine clinical practice. We also discuss the pathophysiological mechanisms underpinning AS development using diabetes and, in particular, type 1 diabetes, as a disease model with a high risk of cardiovascular events and microvascular complications that are accelerated by AS.

Purpose. The prevalence of adrenal insufficiency (AI) in patients with decompensated liver cirrhosis is unknown. Because these patients have lower levels of cortisol-binding carrier proteins, their total serum cortisol (TSC) correlates poorly with free serum cortisol (FC). Salivary cortisol (SaC) correlates better with FC. We aimed to establish SaC thresholds for AI for the 250 μg intravenous ACTH test and to estimate the prevalence of AI in noncritically ill cirrhotic patients. Methods. We included 39 patients with decompensated cirrhosis, 39 patients with known AI, and 45 healthy volunteers. After subjects fasted ≥8 hours, serum and saliva samples were collected for determinations of TSC and SaC at baseline 0’(T0) and at 30-minute intervals after intravenous administration of 250 μg ACTH [30’(T30), 60’(T60), and 90’(T90)]. Results. Based on the findings in healthy subjects and patients with known AI, we defined AI in cirrhotic patients as SaC-T0< 0.08 μg/dL (2.2 nmol/L), SaC-T60 < 1.43 μg/dl (39.5 nmol/L), or ΔSaC<1 μg/dl (27.6 nmol/L). We compared AI determination in cirrhotic patients with the ACTH test using these SaC thresholds versus established TSC thresholds (TSC-T0< 9 μg/dl [248 nmol/L], TSC-T60 < 18 μg/dl [497 nmol/L], or ΔTSC<9 μg/dl [248 nmol/L]). SaC correlated well with TSC. The prevalence of AI in cirrhotic patients was higher when determined by TSC (48.7%) than by SaC (30.8%); however, this difference did not reach statistical significance. AI was associated with sex, cirrhosis etiology, and Child-Pugh classification. Conclusions. Measuring SaC was more accurate than TSC in the ACTH stimulation test. Measuring TSC overestimated the prevalence of AI in noncritically ill cirrhotic patients.

Background: We sought to assess the potential of insulin resistance (IR) for estimating cardiovascular disease (CVD) risk in adults with type 1 diabetes (T1DM) according to the scores of the Steno Type 1 Risk Engine (ST1RE). Methods: A total of 179 adults with T1DM (50.8% men, age 41.2 ± 13.1 years, duration of T1DM 16 (12–23) years) without established CVD were evaluated. IR was assessed by the estimation of insulin sensitivity (eIS) using two validated prediction equations: the estimated insulin sensitivity developed from the Pittsburgh Epidemiology of Diabetes Complications Study (eIS-EDC) and the estimated insulin sensitivity developed from Coronary Artery Calcification in T1DM Study (eIS-CACTI) ST1RE was used to estimate 10-year CVD risk and to classify subjects into three groups according to their risk: low (<10%; n = 105), moderate (10–20%; n = 53), and high (≥20%; n = 21). Results: Both eIS-EDC and eIS-CACTI correlated negatively with ST1RE scores (eIS-EDC: r = −0.636, p < 0.001; eIS-CACTI: r = −0.291, p < 0.001). The C-statistic for predicting moderate/high risk and high risk was 0.816 (95% confidence interval (CI): 0.754–0.878) and 0.843 (95% CI: 0.772–0.913), respectively, for the eIS-EDC equation, and was 0.686 (95% CI: 0.609–0.763) and 0.646 (95% CI: 0.513–0.778), respectively, for the eIS-CACTI equation. The eIS-EDC equation had a significantly higher C-statistic both for moderate-/high-risk (p = 0.001) and high-risk (p = 0.007) subjects. Two cut-off points of eIS-EDC were identified for detecting moderate/high risk (8.52 mg·kg−1·min−1; sensitivity 74% and specificity 76%) and high risk (8.08 mg·kg−1·min−1; sensitivity 65% and specificity 95%) with potential applicability in clinical practice. Conclusions: eIS negatively correlates with the score of CVD risk in the ST1RE. Two cut-off points of eIS are reported with potential utility in clinical practice for detecting adults with T1DM with the highest CVD risk.

Background: Dyslipidemia has been associated with vascular complications of type 1 diabetes mellitus (T1DM). We examined the proton nuclear magnetic resonance (NMR)-assessed lipoprotein subclass profiles in subjects with T1DM compared with those of healthy subjects and assessed the potential relationship of these profiles with arterial stiffness. Methods: Eighty-four participants with T1DM of at least 10 years duration and no clinical cardiovascular disease (age: 35–65 years; 50% men) and 42 healthy participants were evaluated for: (1) clinical and anthropometric data (including classical cardiovascular risk factors), (2) insulin sensitivity by estimated glucose disposal rate, (3) microvascular complications, (4) NMR-assessed lipoprotein subclass profile, and (5) arterial stiffness (aortic pulse wave velocity). Results: Participants with T1DM had an apparently better conventional lipid profile than healthy participants, but with significant differences in NMR-assessed lipoprotein profiles such as higher triglyceride content of low-density lipoprotein (LDL) and high-density lipoprotein (HDL). In healthy participants, arterial stiffness was associated with NMR-based LDL subclasses. By contrast, in T1DM participants, arterial stiffness was independently associated mainly with NMR-based very-low-density lipoprotein (VLDL) subclasses: positively with total VLDL particles (and subclasses) and VLDL triglyceride content, and negatively with LDL and HDL particle sizes. These results were maintained after adjustments for classical cardiovascular risk factors. Conclusions: Subjects with T1DM, while having an apparently better conventional lipid profile than healthy controls, presented significant alterations in their NMR-assessed lipoprotein profile. The association between arterial stiffness and NMR-assessed lipoprotein profiles also differed in both groups. These results support a potential role of the identified differences in the residual cardiovascular risk in T1DM.

3 Eliminate the guesswork. The ability to market to contexts is new. As a result, the majority of marketers don't have experience with it and many of the tools on the market today aren't equipped to use it. What is required is a technology solution that uses machine learning to automatically determine which contexts matter for which customers. Without it, marketers are left having to guess about contexts, which can be a long, difficult road.

Saliency-based models of visual attention postulate that, when a scene is freely viewed, attention is predominantly allocated to those elements that stand out in terms of their physical properties. However, eye-tracking studies have shown that saliency models fail to predict gaze behavior accurately when social information is included in an image. Notably, gaze pattern analyses revealed that depictions of human beings are heavily prioritized independent of their low-level physical saliency. What remains unknown, however, is whether the prioritization of such social features is a reflexive or a voluntary process. To investigate the early stages of social attention in more detail, participants viewed photographs of naturalistic scenes with and without social features (i.e., human heads or bodies) for 200 ms while their eye movements were being recorded. We observed significantly more first eye movements to regions containing social features than would be expected from a chance level distribution of saccades. Additionally, a generalized linear mixed model analysis revealed that the social content of a region better predicted first saccade direction than its saliency suggesting that social features partially override the impact of low-level physical saliency on gaze patterns. Given the brief image presentation time that precluded visual exploration, our results provide compelling evidence for a reflexive component in social attention. Moreover, the present study emphasizes the importance of considering social influences for a more coherent understanding of human attentional selection.

The Pacific region is of major importance for addressing questions regarding human dispersals, interactions with archaic hominins and natural selection processes 1 . However, the demographic and adaptive history of Oceanian populations remains largely uncharacterized. Here we report high-coverage genomes of 317 individuals from 20 populations from the Pacific region. We find that the ancestors of Papuan-related (‘Near Oceanian’) groups underwent a strong bottleneck before the settlement of the region, and separated around 20,000–40,000 years ago. We infer that the East Asian ancestors of Pacific populations may have diverged from Taiwanese Indigenous peoples before the Neolithic expansion, which is thought to have started from Taiwan around 5,000 years ago 2 – 4 . Additionally, this dispersal was not followed by an immediate, single admixture event with Near Oceanian populations, but involved recurrent episodes of genetic interactions. Our analyses reveal marked differences in the proportion and nature of Denisovan heritage among Pacific groups, suggesting that independent interbreeding with highly structured archaic populations occurred. Furthermore, whereas introgression of Neanderthal genetic information facilitated the adaptation of modern humans related to multiple phenotypes (for example, metabolism, pigmentation and neuronal development), Denisovan introgression was primarily beneficial for immune-related functions. Finally, we report evidence of selective sweeps and polygenic adaptation associated with pathogen exposure and lipid metabolism in the Pacific region, increasing our understanding of the mechanisms of biological adaptation to island environments. Genomic analyses of human populations in the Pacific provide insights into the peopling history of the region and reveal episodes of biological adaptation relating to the immune system and lipid metabolism through introgression from archaic hominins and polygenic adaptation.