Correspondence and offprint requests to: Maria Ayako Kamimura: E-mail: m.kamimura@uol.com.br A B S T R AC TBackground. In chronic kidney disease (CKD), multiple metabolic and nutritional abnormalities contribute to the impairment of skeletal muscle mass and function thus predisposing patients to the condition of sarcopenia. Herein, we investigated the prevalence and mortality predictive power of sarcopenia, defined by three different methods, in non-dialysis-dependent (NDD) CKD patients. Methods. We evaluated 287 NDD-CKD patients in stages 3-5 [59.9 ± 10.5 years; 62% men; 49% diabetics; glomerular filtration rate (GFR) 25.0 ± 15.8 mL/min/1.73 m 2 ]. Sarcopenia was defined as reduced muscle function assessed by handgrip strength (HGS <30th percentile of a population-based reference adjusted for sex and age) plus diminished muscle mass assessed by three different methods: (i) midarm muscle circumference (MAMC) <90% of reference value (A), (ii) muscle wasting by subjective global assessment (B) and (iii) reduced skeletal muscle mass index (<10.76 kg/m² men; <6.76 kg/m² women) estimated by bioelectrical impedance analysis (BIA) (C). Patients were followed for up to 40 months for all-cause mortality, and there was no loss of follow-up.

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Abdominal fat deposition in haemodialysis patients is linked to both inflammation and PEW, resulting in an increased mortality risk. Our results support the idea that regional differences in adiposity accumulation may have diverse implications on patient outcome.

MIS appears to be a useful tool to assess PEW in nondialyzed CKD patients. In addition, MIS identified patients at increased mortality risk.

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We still face many unknowns when understanding the putative pleiotrophic effects that adipokines exert in the uremic milieu. Mechanistic and interventional studies are needed to move forward in this area. Conflicting results in patients with ESRD, in whom both beneficial and detrimental effects in uremia outcome are found, are perhaps the consequence of different timing or context-sensitive effects. Specifically, the presence of protein energy wasting and the changing pattern of disease risk may hinder or even reverse the natural action of these molecules.

Aims/hypothesisMultiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes.MethodsWe combined data from six prospective epidemiological studies of 30–77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay. Multivariable-adjusted Cox regression was used in a discovery/replication design to identify biomarkers for incident MACE. We used gradient-boosted machine learning and lasso regularised Cox regression in a random 75% training subsample to assess whether adding proteins to risk factors included in the Swedish National Diabetes Register risk model would improve the prediction of MACE in the separate 25% test subsample.ResultsOf 1211 adults with type 2 diabetes (32% women), 211 experienced a MACE over a mean (±SD) of 6.4 ± 2.3 years. We replicated associations (<5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)-12, IL-27 subunit α (IL-27a), kidney injury molecule (KIM)-1, fibroblast growth factor (FGF)-23, protein S100-A12, TNF receptor (TNFR)-1, TNFR-2 and TNF-related apoptosis-inducing ligand receptor (TRAIL-R)2. Addition of the 80-protein assay to established risk factors improved discrimination in the separate test sample from 0.686 (95% CI 0.682, 0.689) to 0.748 (95% CI 0.746, 0.751). A sparse model of 20 added proteins achieved a C statistic of 0.747 (95% CI 0.653, 0.842) in the test sample.Conclusions/interpretationWe identified eight protein biomarkers, four of which are novel, for risk of MACE in community residents with type 2 diabetes, and found improved risk prediction by combining multiplex proteomics with an established risk model. Multiprotein arrays could be useful in identifying individuals with type 2 diabetes who are at highest risk of a cardiovascular event.Electronic supplementary materialThe online version of this article (10.1007/s00125-018-4641-z) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

Background It has been hypothesized that epicardial adipose tissue (EAT) exerts pathogenic effects on cardiac structures. We analysed the associations between EAT and both cardiovascular (CV) disease risk factors and CV events in patients with chronic kidney disease (CKD). Patients and methods We included 277 nondialysed patients [median age 61, interquartile range (IQR) 53–68 years; 63% men] with stages 3–5 CKD in this cross‐sectional evaluation. EAT and abdominal visceral adipose tissue (VAT) were assessed by computed tomography. Patients were followed for median 32 (IQR 20–39) months, and the composite of fatal and nonfatal CV events was recorded. Results With increasing EAT quartiles, patients were older, had higher glomerular filtration rate, body mass index, waist, VAT and coronary calcification, higher levels of haemoglobin, triglycerides, albumin, C‐reactive protein and leptin and higher prevalence of left ventricular hypertrophy and myocardial ischaemia; total and high‐density lipoprotein cholesterol, 25‐hydroxy‐vitamin D and 1, 25‐dihydroxy‐vitamin D progressively decreased. Associations between EAT and cardiac alterations were not independent of VAT. During follow‐up, 58 CV events occurred. A 1‐SD higher EAT volume was associated with an increased risk of CV events in crude [hazard ratio (HR) 1.41, 95% confidence interval (CI) (1.12–1.78) and adjusted (HR 1.55, 95% CI 1.21–1.99) Cox models. However, adding EAT to a standard CV disease risk prediction model did not result in a clinically relevant improvement in prediction. Conclusion Epicardial adipose tissue accumulation in patients with CKD increases the risk of CV events independent of general adiposity. This is consistent with the notion of a local pathogenic effect of EAT on the heart or heart vessels, or both. However, EAT adds negligible explanatory power to standard CV disease risk factors.