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Skin Autofluorescence as a Risk Marker in People Receiving Dialysis. (AGED)

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ClinicalTrials.gov Identifier: NCT02878317
Recruitment Status : Active, not recruiting
First Posted : August 25, 2016
Last Update Posted : January 30, 2019
Sponsor:
Collaborator:
Derby Hospitals NHS Foundation Trust
Information provided by (Responsible Party):
University of Nottingham

Brief Summary:

The purpose of the present study is to investigate the association between the accumulation of advanced glycation end-products (AGE) and adverse outcomes (e.g. death) in people receiving haemodialysis and peritoneal dialysis based in Royal Derby Hospital, as well as the impact of a dietetic intervention on AGE accumulation. AGE will be measured non-invasively in the skin using a technique called skin autofluorescence (SAF).

The present study will be conducted in two parts:

Study 1: this will be a prospective study where participants will be followed-up for up to five years. The research team will measure the accumulation of AGE in the skin using a quick (less than five minutes) and painless technique called SAF. This involves placing the forearm on a piece of equipment that shines a light on the skin and measures the amount of light that is reflected back. Participants will be asked to complete nutritional and quality of life questionnaires, measurements of weight, height, arm circumference and skinfold thickness (i.e. anthropometry), simple eyesight tests and blood tests.

Study 2: observational non-randomized proof of principle study where malnourished dialysis participants will receive a dietitian supervised intensive nutritional support. Participants will be followed-up for 2 years and will receive precise oral and written instructions on how to comply with the intervention. Blood and eyesight tests, SAF measurements, anthropometry and nutritional and quality of life assessments will be conducted.

In Studies 1 and 2, approximately two teaspoons of blood will be collected to measure AGE levels and do some additional blood tests to help us investigate the effects of AGEs on the body. If the participants agree, the investigators will also store some of the blood for future research.


Condition or disease
Chronic Kidney Disease

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Detailed Description:

STUDY BACKGROUND

Chronic kidney disease (CKD) is a global public health problem and is associated with multiple adverse outcomes including reduced survival, especially in people requiring renal replacement therapy (peritoneal dialysis (PD), haemodialysis [HD] and transplantation). Multiple risk factors lead to the development and/or progression of CKD, such as obesity, hyperlipidaemia, glomerulonephritis, intercurrent infections, smoking, type 2 diabetes and hypertension, the two latter being considered as leading causes of CKD worldwide.

People on dialysis develop a variety of complications/abnormalities as a result of loss of endocrine or exocrine function of the kidneys, including anaemia, metabolic acidosis, bone and mineral disorders, fluid overload, hypertension, electrolyte disturbances and dyslipidaemia. In recent years, inflammation, oxidative stress and endothelial dysfunction (other common abnormalities in people on dialysis) have become areas of interest because of their strong relationship with higher rates of cardiovascular morbidity and mortality in people on dialysis.

Advanced glycation end-products (AGE) are uremic toxins that are markedly increased in people on dialysis. Formation of AGE starts with a non-enzymatic reaction between proteins and glucose molecules called the Maillard reaction; however, AGE are also formed more rapidly during oxidative stress with the subsequent formation of reactive carbonyl compounds like methyl glyoxal. At this point, AGE synthesis is irreversible and AGE will cross-link with tissue proteins; it seems that collagen in the skin and vascular basement membranes are especially susceptible to AGE accumulation and subsequent injury. AGE also interact with specific AGE receptors that will lead to the activation of systemic inflammation by increasing the release of cytokines and, consequently, exacerbate tissue damage. Importantly, AGE are also formed in food during cooking with dry heat at high temperatures such as in frying, grilling or roasting and about 10% of the ingested AGE is absorbed.

Skin autofluorescence (SAF) is a relatively new technique that measures the skin accumulation of AGE. It is a non-invasive, operator independent, quick (less than 5 minutes) and easy to perform technique that utilizes the fluorescent properties of AGE, like the extensively used collagen linked fluorescence (CLF) method, and has been validated with specific AGE measurements and CLF in skin biopsies. It has been reported that SAF is strongly correlated with overall and cardiovascular mortality in people with diabetes and undergoing HD. Several factors have been associated with higher SAF values in people on dialysis in cross-sectional studies, for instance, chronological age in both dialysis modalities, glucose exposure from peritoneal dialysis fluid and dialysis vintage only in people on PD and presence of diabetes in people on HD.

AGE accumulation is postulated as the one of the modulating factors that drives visual disorders; the increased accumulation secondary to hyperglycaemia in diabetes is thought to cause vascular basement membrane thickening and destruction of pericytes from the retinal capillary bed in diabetic retinopathy. The accumulation of AGE has also been reported to be a mechanism in the deterioration in visual acuity associated with increasing age. Separately, the accumulation of AGE in other metabolic diseases (e.g. end stage kidney disease [ESKD]) has been found to cause nerve and eye dysfunction. Ocular abnormalities and therefore visual disturbances are reported in people with ESKD but the mechanism for this has not been fully explored or understood. In animal studies, AGE accumulation is seen in the lens, cornea and vitreous humour. In humans, systemic AGE levels and visual acuity scoring have not been previously investigated.

Because of the adverse outcomes strongly associated with higher levels of SAF, several options focused on reducing the accumulation of AGE have been proposed. One of these promising interventions is the reduction of dietary AGE; it has been suggested that cooking techniques that avoid very high temperatures such as poaching, steaming, stewing and boiling can significantly reduce the AGE content of food when compared to frying, broiling, grilling and roasting; nevertheless, most of the evidence regarding dietary modifications to reduce exogenous AGE is of low quality and therefore further studies are required.

Nevertheless, analysis of baseline data from Study 1 has identified strong associations between higher SAF and malnutrition whereas no correlations were observed between higher SAF and high dietary AGE intake. Correction of malnutrition may therefore represent a more important dietary intervention to reduce accumulation of AGE in people receiving dialysis. We further reasoned that placing people with malnutrition on a restrictive diet may worsen their malnutrition and we have therefore adapted our original research plan to include an observational study to assess the impact of correcting malnutrition on SAF by providing a dietitian supervised nutritional support intervention, which essentially involves the usual/standard dietetic care/advice provided by the NHS with some additional supervision, follow-up and approved dietary supplements (also provided by the NHS), rather than a randomised trial of dietary AGE restriction.

The results from the present project will benefit people on dialysis because they may demonstrate that the correction of malnutrition decreases the SAF levels in this population. Published observational studies suggest that reduction of SAF levels will in turn be associated with a reduction in the high morbidity and mortality rates associated with chronic dialysis and, consequently, healthcare costs. Improved survival and reduced comorbidity would also be expected to improve the quality of life of people on dialysis.

DURATION OF THE STUDY

Study 1:

Participant recruitment will begin on June 2016 with an anticipated recruitment period of 14 months. Therefore, all baseline data and measurements will be collected and performed between June 2016 and August 2017. Once recruited, all participants will be followed-up for up to five years; consequently, it is expected that the study will be completed by August 2022.

Study 2:

Participant recruitment will begin in December 2017 with an anticipated recruitment period of 6 months. Therefore, all baseline data and measurements will be collected and performed between December 2017 and June 2018. Once recruited, all participants will be followed-up for 24 months; consequently, it is expected that the study will be completed by June 2020.

RECRUITMENT

Potential participants on HD and PD will be recruited from the Renal Unit of the Department of Nephrology at the Royal Derby Hospital. The initial details of the study and participant information sheet will be provided by the usual care team (which may include the researcher). Participants will then be given at least 24 hours to consider whether they wish to participate, as well as ask any questions about the study, before being re-contacted by the investigators.

INFORMED CONSENT

The process for obtaining participant informed consent will be in accordance with the Research Ethics Committee (REC) guidance, Good Clinical Practice (GCP) and any other regulatory requirements that might be introduced.

All participants will provide written informed consent. The Informed Consent Form will be signed and dated by the participant before they enter the study. The Investigator will explain the details of the study and provide a Participant Information Sheet, ensuring that the participant has sufficient time to consider participating or not. The Investigator will answer any questions that the participant has concerning study participation.

STATISTICS

To compare baseline versus final evaluations, Wilcoxon test or paired t-test will be used in the case of dimensional variables, and McNemar test in the case of categorical variables. Intergroup comparisons will be performed using Mann Whitney test or Student t test for continuous variables and χ2 test or Fisher's exact test for categorical variables. To determine the significance and strength of associations, Pearson's correlation coefficient will be used for analyses of associations between continuous variables and Spearman rank for nonparametric variables. Linear regression analysis will be used to identify determinants of AGE accumulation. Cox proportional hazards models will be used to investigate the prognostic value of the accumulation of AGE for predicting mortality. A p-value less than or equal to 0.05 will be considered to have statistical significance.

Sample size calculation of Study 1 was performed by using the software nQuery Advisor v.6.0.

Sample size Study 1:

The primary outcome for sample size determination is one-year survival in relation to increased SAF levels in people on HD and PD. With a power of 80%, a two-sided alpha of 5% and an expected hazard ratio of 3.5 and 2.0 in people on PD and HD, respectively, 100 HD and 40 PD participants will be needed.

Sample size Study 2 Since this is a proof of principle study, it would be reasonable to include 40 dialysis participants (either HD or PD).

ETHICS COMMITTEE AND REGULATORY APPROVALS

The study will not be initiated before the protocol, informed consent forms and participant information sheets have received approval / favourable opinion from the REC, and the respective NHS Research & Development (R&D) department.

PROCEDURES FOR MISSING DATA AND ADVERSE EVENTS

All SAF measurements, biochemistry, nutritional and quality of life assessments for Study 1 and 2 will be used in the statistical analysis, including data from participants who did not complete the entire study protocol.

The occurrence of an adverse event as a result of participation within this study is not expected and as such no adverse event data will be collected.

QUALITY ASSURANCE & AUDIT

Study conduct may be subject to systems audit of the Trial Master File for inclusion of essential documents; permissions to conduct the study; Trial Delegation Log; CVs of study staff and training received; local document control procedures; consent procedures and recruitment logs; adherence to procedures defined in the protocol (e.g. inclusion / exclusion criteria, correct randomisation, timeliness of visits); AE recording and reporting; accountability of study materials and equipment calibration logs.

Monitoring of study data shall include confirmation of informed consent; source data verification; data storage and data transfer procedures; local quality control checks and procedures, back-up and disaster recovery of any local databases and validation of data manipulation.

Entries on Case Report Forms (CRFs) will be verified by inspection against the source data. A sample of CRFs (10% or as per the study risk assessment) will be checked on a regular basis for verification of all entries made. In addition the subsequent capture of the data on the study database will be checked. Where corrections are required these will carry a full audit trail and justification.

Study data and evidence of monitoring and systems audits will be made available for inspection by REC as required.


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Study Type : Observational
Actual Enrollment : 40 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Association of Advanced Glycation End-product Accumulation and Adverse Outcomes in Peritoneal Dialysis and Haemodialysis Patients and the Impact of a Dietetic Intervention on Skin Autofluorescence
Actual Study Start Date : September 21, 2017
Estimated Primary Completion Date : August 2022
Estimated Study Completion Date : August 2022

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Dialysis

Group/Cohort
Malnourished participants

Presence of malnutrition will be assessed by using the Subjective Global Assessment.

Once the identified malnourished participants have given their informed consent, they will receive intensive dietitian supervised nutritional support with the aim of improving their malnutrition. In addition, participants will receive standard dietary advice for people on dialysis based on the Nutritional Guidelines in CKD published by the Renal Association in March 2010 in the UK (Wright and Jones, 2010) and will include the following: energy (35 kcal/kg/day) and protein intake (1.2 g/kg/day), as well as potassium, phosphate and sodium restriction, according with biochemical blood parameters.




Primary Outcome Measures :
  1. All cause mortality [ Time Frame: One year ]
    Association of increased skin autofluorescence (SAF) levels with mortality/one year survival

  2. SAF levels [ Time Frame: Study 1: 0, 3, 6, 9 and 12 months; Study 2: 0, 3 and 6 months ]

    Study 1: Mean change in SAF levels from baseline to 12 months of follow-up

    Study 2: Mean change in SAF levels after 6 months of a dietetic intervention (i.e. intensive dietetic supervised nutritional support)



Secondary Outcome Measures :
  1. Association between SAF levels with serum levels of haemoglobin, albumin and total proteins [ Time Frame: 0-12 months ]
    Pearson correlation between SAF levels with serum levels of haemoglobin, albumin and total proteins (unit of measure: g/L) at baseline and every month, until 12 months of follow-up.

  2. Association between SAF levels with haemoglobin A1C [ Time Frame: 0-12 months ]
    Pearson correlation between SAF levels with percentage of haemoglobin A1C at baseline and every month, until 12 months of follow-up.

  3. Association between SAF levels with serum levels of glucose, urea, potassium, phosphate, calcium, sodium, cholesterol and triglycerides. [ Time Frame: 0-12 months ]
    Pearson correlation between SAF levels with serum levels of glucose, urea, potassium, phosphate, calcium, sodium, cholesterol and triglycerides (unit of measure: mmol/L) at baseline and every month, until 12 months of follow-up.

  4. Association between SAF levels with serum levels of creatinine. [ Time Frame: 0-12 months ]
    Pearson correlation between SAF levels with serum levels of creatinine (unit of measure: µmol/L) at baseline and every month, until 12 months of follow-up.

  5. Association between SAF levels with serum levels of intact parathyroid hormone. [ Time Frame: 0-12 months ]
    Pearson correlation between SAF levels with serum levels of intact parathyroid hormone (unit of measure: pmol/L) at baseline and every month, until 12 months of follow-up.

  6. Association between SAF levels with serum levels of carboxymethyl lysine [ Time Frame: 0, 3, 6, 9, 12 months ]
    Pearson correlation between SAF levels with serum levels of carboxymethyl lysine (unit of measure: ng/mL) at baseline, 3rd, 6th, 9th and 12th month of follow-up.

  7. Association between SAF levels with serum levels of C reactive protein [ Time Frame: 0, 3, 6, 9, 12 months ]
    Pearson correlation between SAF levels with serum levels of C reactive protein (unit of measure: mg/L) at baseline, 3rd, 6th, 9th and 12th month of follow-up.

  8. Association between SAF levels with serum levels of tumour necrosis factor-α and interleukin-6 [ Time Frame: 0, 3, 6, 9, 12 months ]
    Pearson correlation between SAF levels with serum levels of tumour necrosis factor-α and interleukin-6 (unit of measure: pg/mL) at baseline, 3rd, 6th, 9th and 12th month of follow-up.

  9. Association between SAF levels with energy intake [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with energy intake (unit of measure: kcal/day) estimated from a 24-h dietary recall, at baseline, 6th and 12th month of follow-up.

  10. Association between SAF levels with protein intake [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with protein intake (unit of measure: g/day) estimated from a 24-h dietary recall, at baseline, 6th and 12th month of follow-up.

  11. Association between SAF levels with dietary AGE intake [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with dietary AGE intake (unit of measure: Equivalents/day), estimated from a database of ~560 foods which listed AGE values, at baseline, 6th and 12th month of follow-up.

  12. Association between SAF levels with nutritional status [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with number of patients classified as well-nourished, at risk of malnutrition/moderate malnutrition or malnourished according with the Subjective Global Assessment score, at baseline, 6th and 12th month of follow-up.

  13. Association between SAF levels with weight and handgrip strength. [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with dry weight and handgrip strength (unit of measure: kg), at baseline, 6th and 12th month of follow-up.

  14. Association between SAF levels with body mass index [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with body mass index (unit of measure: kg/m2), at baseline, 6th and 12th month of follow-up.

  15. Association between SAF levels with mid-arm circumference [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with mid-arm circumference (unit of measure: cm), at baseline, 6th and 12th month of follow-up.

  16. Association between SAF levels with skinfold thickness. [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with triceps and subscapular skinfold thickness (unit of measure: mm), at baseline, 6th and 12th month of follow-up.

  17. Association between SAF levels with mid-arm muscle and fat areas. [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with mid-arm muscle and fat areas (unit of measure: cm2), at baseline, 6th and 12th month of follow-up.

  18. Association between SAF levels with quality of life [ Time Frame: 0, 6, 12 months ]
    Pearson correlation between SAF levels with quality of life score obtained from the EQ-5D and SF-36 questionnaires, at baseline, 6th and 12th month of follow-up.

  19. Change in serum levels of haemoglobin, albumin and total proteins [ Time Frame: 0-6 months ]
    Monthly changes in serum levels of haemoglobin, albumin and total proteins (unit of measure: g/L) after the dietetic intervention.

  20. Change in haemoglobin A1C [ Time Frame: 0-6 months ]
    Monthly change in percentage of haemoglobin A1C after the dietetic intervention.

  21. Change in serum levels of glucose, urea, potassium, phosphate, calcium, sodium, cholesterol and triglycerides. [ Time Frame: 0-6 months ]
    Monthly changes in serum levels of glucose, urea, potassium, phosphate, calcium, sodium, cholesterol and triglycerides (unit of measure: mmol/L) after the dietetic intervention.

  22. Change in serum levels of creatinine [ Time Frame: 0-6 months ]
    Monthly change in serum levels of creatinine (unit of measure: µmol/L) after the dietetic intervention.

  23. Change in serum levels of intact parathyroid hormone [ Time Frame: 0-6 months ]
    Monthly change in serum levels of intact parathyroid hormone (unit of measure: pmol/L) after the dietetic intervention.

  24. Change in serum levels of carboxymethyl lysine [ Time Frame: 0, 3, 6 months ]
    Change in serum levels of carboxymethyl lysine (unit of measure: ng/mL) after the dietetic intervention, from baseline to 3rd and 6th months.

  25. Change in serum levels of C reactive protein [ Time Frame: 0, 3, 6 months ]
    Change in serum levels of C reactive protein (unit of measure: mg/L) after the dietetic intervention, from baseline to 3rd and 6th months.

  26. Change in serum levels of tumour necrosis factor-α and interleukin-6 [ Time Frame: 0, 3, 6 months ]
    Change in serum levels of tumour necrosis factor-α and interleukin-6 (unit of measure: pg/mL) after the dietetic intervention, from baseline to 3rd and 6th months.

  27. Change in energy intake [ Time Frame: 0 and 6 months ]
    Change in energy intake (unit of measure: kcal/day) estimated from a 24-h dietary recall after 6 months of the dietetic intervention

  28. Change in protein intake [ Time Frame: 0 and 6 months ]
    Change in protein intake (unit of measure: g/day) estimated from a 24-h dietary recall after 6 months of the dietetic intervention

  29. Change in dietary AGE intake [ Time Frame: 0 and 6 months ]
    Change in dietary AGE intake (unit of measure: Equivalents/day), estimated from a database of ~560 foods which listed AGE values after 6 months of the dietetic intervention.

  30. Change in nutritional status [ Time Frame: 0 and 6 months ]
    Change in number of patients classified as well-nourished, at risk of malnutrition/moderate malnutrition or malnourished according with the Subjective Global Assessment score after 6 months of the dietetic intervention.

  31. Change in weight and handgrip strength [ Time Frame: 0 and 6 months ]
    Change in dry weight and handgrip strength (unit of measure: kg) after 6 months of the dietetic intervention.

  32. Change in body mass index [ Time Frame: 0 and 6 months ]
    Change in body mass index (unit of measure: kg/m2) after 6 months of the dietetic intervention.

  33. Change in mid-arm circumference [ Time Frame: 0 and 6 months ]
    Change in mid-arm circumference (unit of measure: cm) after 6 months of the dietetic intervention.

  34. Change in skinfold thickness [ Time Frame: 0 and 6 months ]
    Change in triceps and subscapular skinfold thickness (unit of measure: mm) after 6 months of the dietetic intervention.

  35. Change in mid-arm muscle and fat areas. [ Time Frame: 0 and 6 months ]
    Change in mid-arm muscle and fat areas (unit of measure: cm2) after 6 months of the dietetic intervention.

  36. Change in quality of life [ Time Frame: 0 and 6 months ]
    Change in quality of life score obtained from the EQ-5D and SF-36 questionnaires after 6 months of the dietetic intervention.



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Participants undergoing HD and PD treatment.
Criteria

Inclusion Criteria:

Haemodialysis cohort:

  • Three dialysis sessions per week for 4 hours.
  • Dialysis with biocompatible membranes.
  • Able to give informed consent.

Peritoneal Dialysis cohort:

  • Dialysis with lactate/bicarbonate-buffered solutions with different glucose concentrations as prescribed for routine clinical care.
  • Able to give informed consent.

Exclusion Criteria:

  • Does not wish to participate.
  • Renal transplant.
  • Pregnancy or breast feeding or intending pregnancy.
  • Expected survival less than one year.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02878317


Locations
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United Kingdom
Derby Hospitals NHS Foundation Trust
Derby, Derbyshire, United Kingdom, DE22 3NE
Sponsors and Collaborators
University of Nottingham
Derby Hospitals NHS Foundation Trust
Investigators
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Study Director: Maarten Taal, Doctor Derby Hospitals NHS Foundation Trust

Publications:
Darlene, A., Dartt Reza, D. and D'Amore, P. (2011) Immunology, inflammation and diseases of the eye. Elsevier Press: pp. 287-288.
Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group (2013). KDIGO 2012 Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int 4(Suppl. 3): pp. 1-150.

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Responsible Party: University of Nottingham
ClinicalTrials.gov Identifier: NCT02878317     History of Changes
Other Study ID Numbers: 16050
First Posted: August 25, 2016    Key Record Dates
Last Update Posted: January 30, 2019
Last Verified: May 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University of Nottingham:
Glycosylation End Products, Advanced
Skin autofluorescence
Renal Dialysis
Additional relevant MeSH terms:
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Kidney Diseases
Renal Insufficiency, Chronic
Urologic Diseases
Renal Insufficiency