39 research outputs found

    Exploration of chronic kidney disease prevalence estimates using new measures of kidney function in the health survey for England

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    Background: chronic kidney disease (CKD) diagnosis relies on glomerular filtration rate (eGFR) estimation, traditionally using the creatinine-based Modification of Diet in Renal Disease (MDRD) equation. The Chronic Kidney Disease Epidemiology Collaboration (CKDEPI) equation performs better in estimating eGFR and predicting mortality and CKD progression risk. Cystatin C is an alternative glomerular filtration marker less influenced by muscle mass. CKD risk stratification is improved by combining creatinine eGFR with cystatin C and urinary albumin to creatinine ratio (uACR). We aimed to identify the impact of introducing CKDEPI and cystatin C on the estimated prevalence and risk stratification of CKD in England and to describe prevalence and associations of cystatin C.Methods and findings: cross sectional study of 5799 people in the nationally representative 2009 and 2010 Health Surveys for England. Primary outcome measures: prevalence of MDRD, CKDEPI and cystatin C-defined eGFR&lt;60ml/min/1.73m2; prevalence of CKD biomarker combinations (creatinine, cystatin C, uACR). Using CKDEPI instead of MDRD reduced the prevalence of eGFR&lt;60ml/min/1.73m2 from 6.0% (95% CI 5.4–6.6%) to 5.2% (4.7–5.8%) equivalent to around 340,000 fewer individuals in England. Those reclassified as not having CKD evidenced a lower risk profile. Prevalence of cystatin C eGFR&lt;60ml/min/1.73m2 was 7.7% and independently associated with age, lack of qualifications, being an ex-smoker, BMI, hypertension, and albuminuria. Measuring cystatin C in the 3.9% people with CKDEPI-defined eGFR&lt;60ml/min/1.73m2 without albuminuria (CKD Category G3a A1) reclassified about a third into a lower risk group with one of three biomarkers and two thirds into a group with two of three. Measuring cystatin C in the 6.7% people with CKDEPI eGFR &gt;60ml/min/1.73m2 with albuminuria (CKD Category G1-2) reclassified almost a tenth into a higher risk group.Limitations: cross sectional study, single eGFR measure, no measured (‘true’) GFR.Conclusions: introducing the CKDEPI equation and targeted cystatin C measurement reduces estimated CKD prevalence and improves risk stratification<br/

    Prevalence of chronic kidney disease in adults in England: comparison of nationally representative cross-sectional surveys from 2003 to 2016

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    Objectives: To identify recent trends in chronic kidney disease (CKD) prevalence in England and explore their association with changes in sociodemographic, behavioural and clinical factors. Design: Pooled cross-sectional analysis.Setting: Health Survey for England 2003, 2009/2010 combined, and 2016.Participants: 17,663 individuals (aged 16+) living in private households.Primary and secondary outcome measures: Prevalence of estimated glomerular filtration rate (eGFR

    Utility of Phase Angle to Identify Cachexia and Assess Mortality in End-Stage Renal Disease

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    © 2020 American Society for Nutrition. Published by Elsevier Inc. This is an Open access article under the CC-BY-NC-ND license. https://creativecommons.org/licenses/by-nc-nd/4.0/Objectives This cross-sectional analysis sought to identify cachexia and assess survival using phase angle (PA) in patients with end-stage renal disease (ESRD) receiving haemodialysis. Methods Patients receiving haemodialysis (n = 87, mean age 65.9 +/– 13.0) completed a Phase Angle (PA; 50 khz) measurement using bioelectrical impedance analysis. Cachexia variables were recorded according to Evans et al. definition (2008) including nutritional and functional measures (weight, Body Mass Index (BMI), Hand Grip Strength (HGS), Lean Tissue Mass (LTM), C-Reative Protein (CRP), serum albumin, haemoglobin, appetite (Functional Assessment of Anorexia/Cachexia Treatment (FAACT)) and fatigue (Functional Assessment of Chronic Illness Therapy (FACIT)). Survival was assessed at 12 months. Mann Whitney-U and Spearman correlation coefficient were conducted. Results The majority of patients completed follow up (n = 76). Eleven patients had died. Mean PA was not statistically different between those identified as cachectic and non-cachectic according to Evans et al. (2008) definition or between those patients that survived and died. However, patients that survived had better mean scores of weight, BMI, HGS, CRP, serum albumin and fatigue (FACIT). In addition, LTM scores were significantly better in patients that survived (P < .01). Appetite scores were also significantly better in patients that survived (P < .01) and those without cachexia (P = .01). Conclusions This study was part of a larger effort to clarity a phenotype of cachexia in ESRD. Unlike previous research, this study did not find PA useful in identifying patients at a higher risk of cachexia or death. However overall these patients had a very low mean PA. FAACT did discriminate between groups indicating self-reporting measurement tools of nutritional status were useful in identifying patients at a higher risk of cachexia and death. A larger sample and longer follow up is required to balance the limitations of this small study. Timing the administration of PA also requires consideration in future studies. Funding Sources Public Health Agency; Northern Ireland Kidney Research Fund.Peer reviewe

    Phenotypic Responses to a Lifestyle Intervention Do Not Account for Inter-Individual Variability in Glucose Tolerance for Individuals at High Risk of Type 2 Diabetes

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    Background: Lifestyle interventions have been shown to delay or prevent the onset of type 2 diabetes among high risk adults. A better understanding of the variability in physiological responses would support the matching of individuals with the best type of intervention in future prevention programmes, in order to optimize risk reduction. The purpose of this study was to determine if phenotypic characteristics at baseline or following a 12 weeks lifestyle intervention could explain the inter-individual variability in change in glucose tolerance in individuals with high risk for type 2 diabetes.Methods: In total, 285 subjects with normal glucose tolerance (NGT, FINDRISC score &gt; 12), impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) were recruited for a 12 weeks lifestyle intervention. Glucose tolerance, insulin sensitivity, anthropometric characteristics and aerobic fitness were measured. Variability of responses was examined by grouping participants by baseline glycemic status, by cluster analysis based on the change in glucose tolerance and by Principal Component Analysis (PCA).Results: In agreement with other studies, the mean response to the 12 weeks intervention was positive for the majority of parameters. Overall, 89% improved BMI, 80% waist circumference, and 81% body fat while only 64% improved fasting plasma glucose and 60% 2 h glucose. The impact of the intervention by glycaemic group did not show any phenotypic differences in response between NGT, IFG, and IGT. A hierarchical cluster analysis of change in glucose tolerance identified four sub-groups of “responders” (high and moderate) and “non-responders” (no response or deteriorated) but there were few differences in baseline clincal and physiological parameters or in response to the intervention to explain the overall variance. A further PCA analysis of 19 clinical and physiological univariables could explain less than half (48%) of total variability.Conclusion: We found that phenotypic characteristics from standard clinical and physiological parameters were not sufficient to account for the inter-individual variability in glucose tolerance following a 12 weeks lifestyle intervention in inidivuals at high risk for type 2 diabetes. Further work is required to identify biomarkers that complement phenotypic traits and better predict the response to glucose tolerance

    A systematic review of correlates of sedentary behaviour in adults aged 18–65 years: a socio-ecological approach

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    Background: Recent research shows that sedentary behaviour is associated with adverse cardio-metabolic consequences even among those considered sufficiently physically active. In order to successfully develop interventions to address this unhealthy behaviour, factors that influence sedentariness need to be identified and fully understood. The aim of this review is to identify individual, social, environmental, and policy-related determinants or correlates of sedentary behaviours among adults aged 18-65 years. Methods: PubMed, Embase, CINAHL, PsycINFO and Web of Science were searched for articles published between January 2000 and September 2015. The search strategy was based on four key elements and their synonyms: (a) sedentary behaviour (b) correlates (c) types of sedentary behaviours (d) types of correlates. Articles were included if information relating to sedentary behaviour in adults (18-65 years) was reported. Studies on samples selected by disease were excluded. The full protocol is available from PROSPERO (PROSPERO 2014:CRD42014009823). Results: 74 original studies were identified out of 4041: 71 observational, two qualitative and one experimental study. Sedentary behaviour was primarily measured as self-reported screen leisure time and total sitting time. In 15 studies, objectively measured total sedentary time was reported: accelerometry (n = 14) and heart rate (n = 1). Individual level factors such as age, physical activity levels, body mass index, socio-economic status and mood were all significantly correlated with sedentariness. A trend towards increased amounts of leisure screen time was identified in those married or cohabiting while having children resulted in less total sitting time. Several environmental correlates were identified including proximity of green space, neighbourhood walkability and safety and weather. Conclusions: Results provide further evidence relating to several already recognised individual level factors and preliminary evidence relating to social and environmental factors that should be further investigated. Most studies relied upon cross-sectional design limiting causal inference and the heterogeneity of the sedentary measures prevented direct comparison of findings. Future research necessitates longitudinal study designs, exploration of policy-related factors, further exploration of environmental factors, analysis of inter-relationships between identified factors and better classification of sedentary behaviour domains
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