Chronic kidney disease (CKD) is a global health burden with a high economic cost to health systems and is an independent risk factor for cardiovascular disease (CVD). All stages of CKD are associated with increased risks of cardiovascular morbidity, premature mortality, and/or decreased quality of life. CKD is usually asymptomatic until later stages and accurate prevalence data are lacking. Thus we sought to determine the prevalence of CKD globally, by stage, geographical location, gender and age. A systematic review and meta-analysis of observational studies estimating CKD prevalence in general populations was conducted through literature searches in 8 databases. We assessed pooled data using a random effects model. Of 5,842 potential articles, 100 studies of diverse quality were included, comprising 6,908,440 patients. Global mean(95%CI) CKD prevalence of 5 stages 13·4%(11·7–15·1%), and stages 3–5 was 10·6%(9·2–12·2%). Weighting by study quality did not affect prevalence estimates. CKD prevalence by stage was Stage-1 (eGFR>90+ACR>30): 3·5% (2·8–4·2%); Stage-2 (eGFR 60–89+ACR>30): 3·9% (2·7–5·3%); Stage-3 (eGFR 30–59): 7·6% (6·4–8·9%); Stage-4 = (eGFR 29–15): 0·4% (0·3–0·5%); and Stage-5 (eGFR<15): 0·1% (0·1–0·1%). CKD has a high global prevalence with a consistent estimated global CKD prevalence of between 11 to 13% with the majority stage 3. Future research should evaluate intervention strategies deliverable at scale to delay the progression of CKD and improve CVD outcomes.

Background: Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability. Methods: Seventy-eight subjects without diabetes having a fasting plasma glucose of < 120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS Ò Gold device. Glycemic variability was calculated using EasyGV ª software (available free for noncommercial use at www.easygv.co.uk), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG). Results: Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean -2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, LBGI, HBGI, GRADE, MODD, ADDR, and MAG,. Conclusions: We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.

Multicomponent interventions are effective in preventing incident delirium among elderly inpatients. Effects seemed to be stable among different settings. Due to the limited amount of data, potential benefits in survival need to be confirmed in further studies. Future research should be aimed at contrasting different multicomponent programmes to select the most useful interventions.

The GRADE score of a glucose profile summarises the degree of risk associated with a glucose profile. Values < 5 correspond to euglycaemia. The GRADE score is simple to generate from any blood glucose profile and can be used as an adjunct to HbA1c to report the degree of risk associated with glycaemic variability.

BackgroundAtrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of the undiagnosed may be supported by risk-prediction models relating patient factors to AF risk. However, there exists a need for an implementable risk model that is contemporaneous and informed by routinely collected patient data, reflecting the real-world pathology of AF.MethodsThis study sought to develop and evaluate novel and conventional statistical and machine learning models for risk-predication of AF. This was a retrospective, cohort study of adults (aged ≥30 years) without a history of AF, listed on the Clinical Practice Research Datalink, from January 2006 to December 2016. Models evaluated included published risk models (Framingham, ARIC, CHARGE-AF), machine learning models, which evaluated baseline and time-updated information (neural network, LASSO, random forests, support vector machines), and Cox regression.ResultsAnalysis of 2,994,837 individuals (3.2% AF) identified time-varying neural networks as the optimal model achieving an AUROC of 0.827 vs. 0.725, with number needed to screen of 9 vs. 13 patients at 75% sensitivity, when compared with the best existing model CHARGE-AF. The optimal model confirmed known baseline risk factors (age, previous cardiovascular disease, antihypertensive medication usage) and identified additional time-varying predictors (proximity of cardiovascular events, body mass index (both levels and changes), pulse pressure, and the frequency of blood pressure measurements).ConclusionThe optimal time-varying machine learning model exhibited greater predictive performance than existing AF risk models and reflected known and new patient risk factors for AF.

OBJECTIVETo describe and make available an interactive, 24-variable homeostasis model assessment (iHOMA2) that extends the HOMA2 model, enabling the modeling of physiology and treatment effects, to present equations of the HOMA2 and iHOMA2 models, and to exemplify iHOMA2 in two widely differing scenarios: changes in insulin sensitivity with thiazolidinediones and changes in renal threshold with sodium glucose transporter 2 (SGLT2) inhibition.RESEARCH DESIGN AND METHODSiHOMA2 enables a user of the available software to examine and modify the mathematical functions describing the organs and tissues involved in the glucose and hormonal compartments. We exemplify this with SGLT2 inhibition modeling (by changing the renal threshold parameters) using published data of renal effect, showing that the modeled effect is concordant with the effects on fasting glucose from independent data.RESULTSiHOMA2 modeling of thiazolidinediones effect suggested that changes in insulin sensitivity in the fasting state are predominantly hepatic. SGLT2 inhibition modeled by iHOMA2 resulted in a decrease in mean glucose of 1.1 mmol/L. Observed data showed a decrease in glucose of 0.9 mmol/L. There was no significant difference between the model and the independent data. Manipulation of iHOMA2's renal excretion threshold variable suggested that a decrease of 17% was required to obtain a 0.9 mmol/L decrease in mean glucose.CONCLUSIONSiHOMA2 is an extended mathematical model for the assessment of insulin resistance and β-cell function. The model can be used to evaluate therapeutic agents and predict effects on fasting glucose and insulin and on β-cell function and insulin sensitivity.

OBJECTIVETo validate continuous glucose monitoring (CGM) in children and adolescents with cystic fibrosis.RESEARCH DESIGN AND METHODSPaired oral glucose tolerance tests (OGTTs) and CGM monitoring was undertaken in 102 children and adolescents with cystic fibrosis (age 9.5–19.0 years) at baseline (CGM1) and after 12 months (CGM2). CGM validity was assessed by reliability, reproducibility, and repeatability.RESULTSCGM was reliable with a Bland-Altman agreement between CGM and OGTT of 0.81 mmol/l (95% CI for bias ± 2.90 mmol/l) and good correlation between the two (r = 0.74–0.9; P < 0.01). CGM was reproducible with no significant differences in the coefficient of variation of the CGM assessment between visits and repeatable with a mean difference between CGM1 and CGM2 of 0.09 mmol/l (95% CI for difference ± 0.46 mmol/l) and a discriminant ratio of 13.0 and 15.1, respectively.CONCLUSIONSIn this cohort of children and adolescents with cystic fibrosis, CGM performed on two occasions over a 12-month period was reliable, reproducible, and repeatable.

This study was undertaken to determine whether the production of melatonin, a hormone regulating sleep in relation to the light/dark cycle, is altered in Huntington's disease. We analyzed the circadian rhythm of melatonin in a 24‐hour study of cohorts of control, premanifest, and stage II/III Huntington's disease subjects. The mean and acrophase melatonin concentrations were significantly reduced in stage II/III Huntington's disease subjects compared with controls. We also observed a nonsignificant trend toward reduced mean and acrophase melatonin in premanifest Huntington's disease subjects. Onset of melatonin rise was significantly more temporally spread in both premanifest and stage II/III Huntington's disease subjects compared with controls. A nonsignificant trend also was seen for reduced pulsatile secretion of melatonin. Melatonin concentrations are reduced in Huntington's disease. Altered melatonin patterns may provide an explanation for disrupted sleep and circadian behavior in Huntington's disease, and represent a biomarker for disease state. Melatonin therapy may help the sleep disorders seen in Huntington's disease. © 2014 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.