HbAc variability -
Several limitations to this study should be noted. First, the interpretation of our results may be limited to a Taiwanese population with type 2 diabetes under a healthcare setting with a universal health insurance coverage. Because regular glycemic checkups i. Second, because this is a retrospective cohort study, uncorrected confounding may be possible.
Nevertheless, we carefully measured and adjusted for possibly-known confounding factors in the analyses, and used subgroup and sensitivity analyses to verify the robustness of our findings. Third, severe hypoglycemia has been shown to be related to HbA1c variability and was not adjusted in our analyses.
However, the number of patients experiencing severe hypoglycemic events at baseline was low 7 cases and no events occurred during the study follow-up, implying that the potential impact of severe hypoglycemia on our analyses might be negligible.
Fourth, we aimed to evaluate the impact of HbA1c measures on the risk of composite MVD events, and did not examine the individual sub-types of MVDs.
It would be of interest for future research on different MVD event outcomes. Lastly, we did not explore the possible mechanisms that link HbA1c variability and MVD risk, which deserves future research. Our results suggest that HbA1c variability can be an addition to conventional baseline HbA1c levels for explaining MVD risk in patients with type 2 diabetes.
HbA1c variability may play a greater role for the risk of MVD among patients with relatively optimal baseline glycemic control. These suggest the importance of closely monitoring HbA1c variability in usual practice and using it as a supporting measure along with a single-point HbA1c value to optimize management of microvascular outcomes.
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Dungan KM. Expert Rev Mol Diagn. Download references. This project was supported by a grant from the Ministry of Science and Technology in Taiwan MOST B Huang-Tz Ou. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
The authors declare no conflicts of interest regarding the publication of this article. Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, , Taiwan.
Department of Statistics, National Cheng Kung University, Tainan, Taiwan. Department of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Department of Pharmacy, National Cheng Kung University Hospital, Tainan, Taiwan. Michigan Center for Diabetes Translational Research, University of Michigan, Ann Arbor, MI, USA.
You can also search for this author in PubMed Google Scholar. Conception and design: HTO and SK. Analysis and interpretation of the data: CYY, PFS, and JYH.
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Search all BMC articles Search. Download PDF. Original investigation Open access Published: 06 July Comparative predictive ability of visit-to-visit HbA1c variability measures for microvascular disease risk in type 2 diabetes Chen-Yi Yang 1 , Pei-Fang Su 2 , Jo-Ying Hung 2 , Huang-Tz Ou ORCID: orcid.
Abstract Background To assess the associations of various HbA1c measures, including a single baseline HbA1c value, overall mean, yearly updated means, standard deviation HbA1c-SD , coefficient of variation HbA1c-CV , and HbA1c variability score HVS , with microvascular disease MVD risk in patients with type 2 diabetes.
Results In the models without adjustment for baseline HbA1c, all HbA1c variability and mean measures were significantly associated with MVD risk, except HVS. Conclusions HbA1c variability, especially HbA1c-CV, can supplement conventional baseline HbA1c measure for explaining MVD risk.
Background Intensive glycemic normalization may not be the only goal to ensure optimal clinical outcomes in people with diabetes [ 1 ]. Cohort identification Patients diagnosed with type 2 diabetes International Classification of Diseases, Ninth Revision, Clinical Modification ICD-9 CM codes: Study variables The primary outcome of interest was composite MVD events that occurred after the index date to the end of follow-up, which included retinopathy, nephropathy, and neuropathy measured using ICDCM disease and procedure codes detailed codes are presented in Additional file 1 : Table S1.
Table 1 Characteristics of overall study patients and those stratified by the status of microvascular disease occurrence during the study follow-up Full size table.
The biological effects of glycaemic variability on diabetes-related vascular complications are under-investigated. One possible explanation involves the theory of metabolic memory, which promotes a mechanism of non-enzymatic glycation of cellular transduction system and excess reactive oxygen and nitrogen that leads to disturbed signal transduction and enhanced inflammatory stress 13 , 14 , which subsequently leads to endothelial dysfunction Another possibility involves the effects of hypoglycaemia, as hypoglycaemia-induced activation of the sympathoadrenal system leads to cardiac stress by increasing heart rate and stroke volume The present study is the first to use prospectively collected data to examine the long-term visit-to-visit variability in HbA1c, FBG, and PBG levels, as well as their relationships with new-onset vascular complications among subjecst without diabetes.
The results revealed different trends in the relationships between HbA1c and glucose variability and the various vascular events.
Although we could not determine the underlying pathophysiological mechanism, it is possible that glycaemic variation could be a significant prognostic predictor in the non-diabetic state, and that the biological effects of glucose and HbA1c variation could be different.
Further research is needed to address this issue, as there is no evidence regarding whether these two factors are fundamentally different factors or different characteristics of a single phenomenon. Our results suggest that HbA1c variability is a better representation of insulin resistance and its associated inflammatory response.
Glucose variability may also suggest the presence of insulin resistance, but better represents the activation of the sympathoadrenal system that is associated with hypoglycaemia. Our study also had several limitations.
First, data regarding clinical events were obtained via questionnaires that were administered by a trained interviewer, and the incidence of macrovascular events in this relatively healthy cohort was lower than among people with diabetes.
However, large cohort studies routinely use standardized questionnaires, and our observed incidence of macrovascular events was similar to that in other ethnic groups without diabetes Moreover, the absence of data regarding other microvascular events, such as retinopathy, is a potential limitation, although the expected incidences of end-stage DM related microvascular events would be very low, as the subjects did not have diabetes at baseline.
Second, we could not evaluate all-cause mortality or cardiovascular mortality in this cohort. Third, we could not evaluate intra-day or inter-day fluctuations in serum glucose levels, although there is currently no standardized definition of HbA1c variability and most studies have expressed variability based on the standard deviation or CV for all measurements during an investigational period Fourth, we did not include dietary and medication information, which could affect clinical outcomes, as this lay outside the aim of this study.
Nevertheless, it would be interesting to evaluate whether dietary or medical intervention could affect the development of future clinical events. Recently the development of new technologies for glucose monitoring has made it possible to identify glucose variability and improve glucose control.
In this context, recent studies have yielded encouraging results from the use of glucose sensors in combination with an insulin pump 18 , which suggests that glucose variability could be an important measure for validating new DM therapies, as well as for predicting the risk of DM and its vascular complications.
In conclusion, data from a year prospective cohort study revealed that high HbA1c-CV in middle-aged individuals without DM at baseline was independently associated with the primary outcome a composite of macrovascular and microvascular events and microvascular events alone.
In addition, high FBG-CV and PBG-CV values were independently associated with an increased risk of macrovascular events. The epidemiological data were collected from the Ansan urban and Ansung rural prospective community-based cohort studies.
These studies are part of the Korean Health and Genome Study KoGES , which is conducted by the Korea Centers for Disease Control and Prevention Republic of Korea as a government-funded epidemiological survey to investigate trends in chronic non-communicable diseases and their associated risk factors The studies included 10, participants who were 40—69 years at baseline — , The age-sex distributions of the study populations were similar to those of the general populations in each area.
Biennial surveys, which included administered questionnaires and clinical examinations, were continued up to the sixth follow-up phase in Subjects were also excluded if they only completed a single laboratory test.
Thus, a total of 6, individuals were included in the present study. The biennial surveys collected the following clinical, laboratory and anthropometric data: height; weight; waist circumference; blood pressure; and biochemical results, including HbA1c, FBG, insulin, lipid profile, and biomarkers reflecting systemic inflammatory status high sensitivity C-reactive protein and homocysteine , as previously described All participants also underwent a standard g oral glucose tolerance test after an overnight fast The fasting insulin and glucose values were used to calculate the values for the homeostasis model of assessment—insulin resistance HOMA-IR , homeostasis model of assessment—β-cell HOMA—β-cell 22 , and the quantitative insulin sensitivity check index QUICKI.
We divided population into 3 groups according to tertile of CV each value, respectively. In this study, we present baseline characteristics based on CV tertile groups of HbA1c HbA1c-CV. Lean body mass and body fat mass were assessed using multifrequency bioelectrical impedance analysis MF-BIA; Inbody 3.
Mean muscle and fat mass were also adjusted for mean BMI The primary outcome was a composite of macrovascular events coronary artery disease, myocardial infarction, hospitalization for congestive heart failure, and ischemic stroke and microvascular events.
Previous or new-onset macrovascular events were identified based on the biennial surveys, with all reported cases confirmed through repeated in-depth personal interviews Creatine clearance was calculated using the Modification of Diet in Renal Disease equation at each visit, and subjects with chronic kidney disease at baseline were excluded from the survival analysis.
The secondary outcomes were the macrovascular and microvascular event each. The cumulative incidences of the primary outcome were compared using the Kaplan-Meier method with the log-rank test.
Graphical relationships were evaluated using restricted cubic spline plots according to the HbA1c-CV, FBG-CV and PBG-CV groupings. All analyses were performed using IBM SPSS software version The institutional review board of Bundang CHA Hospital South Korea approved the study protocol CHAMC All participants volunteered for the Ansan and Ansung studies, and provided written informed consent prior to enrolment.
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Nutrients 9 , E Download references. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea NRF , which is funded by the Ministry of Education R1A6A3A to C. Department of Internal Medicine, Chungju Medical Center, Chungju-si, South Korea.
Department of Endocrinology and Metabolism, Hallym University College of Medicine, Chuncheon, South Korea. Department of Internal Medicine, Dankook University Hospital, Dankook University School of Medicine, Cheonan-si, South Korea.
Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea. Department of Endocrinology and Metabolism, CHA Bundang Medical Center, School of Medicine CHA University, Seongnam, South Korea.
You can also search for this author in PubMed Google Scholar. and C. contributed to the concept and rationale of the study, the data analyses, and the interpretation of the results. and K. contributed to the interpretation of the results. All authors participated in the drafting and approval of the final manuscript and take responsibility for the content and integrity of this article.
had full access to all study data and take responsibility for the integrity and accuracy of the data and its analysis. Correspondence to Kyoo Ho Cho or Chang-Myung Oh. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions.
Jang, JY. Visit-to-visit HbA1c and glucose variability and the risks of macrovascular and microvascular events in the general population.
Medindia » HbAc variability » Health In Focus » Hemoglobin A1c Probiotics for heart health is Associated With All-cause Mortality in Type variablity Diabetes. Hemoglobin A1c HbA1c levels are an indicator of a vagiability of diabetesa Pancreatic polyp condition where the blood sugar levels HAc HbAc variability. The variabiloty Probiotics for heart health HbA1c measures the amount of blood sugar or glucose attached to hemoglobin. Tweet it Now Hemoglobin is the iron-containing molecule present in red blood cells that carries oxygen from the lungs to the rest of the body. While a regular glucose monitor measures the blood sugar at a particular point of the day, the HbA1c test shows the average amount of glucose attached to hemoglobin for the past three months. The three-month average coincides with the lifespan of a typical red blood cell. HbA1c is also known by other names: A1c, glycohemoglobin, glycated hemoglobinDecoding HbA1c Test for Blood Sugar, glycosylated hemoglobin. Cardiovascular Diabetology volume 21Article number: 13 Cite variabillity article. Metrics details. HbAc variability variability BIA health assessment Probiotics for heart health variabilitt risk factor for cardiovascular diseases in diabetes. However, the impact of HbA1c variability on cardiovascular diseases in subjects within the recommended HbA1c target has been relatively unexplored. Using data from a large database, we studiedpeople with type 2 diabetes without cardiovascular diseases.
HbAc variability -
Within-subject variability in measured HbA1c affects its clinical utility and interpretation, but no comprehensive systematic review has described within-subject variability. A systematic review and meta-analysis was performed of within-subject variability of HbA1c.
Multiple databases were searched from inception to November for follow-up studies of any design in adults or children, with repeated measures of HbA1c or glycosylated haemoglobin.
Title and abstract screening was performed in duplicate, full text screening and data extraction by one reviewer and verified by a second. Risk of bias of included papers was assessed using a modified consensus-based standards for the selection of health measurement Instruments COSMIN tool.
Intraclass correlation coefficient ICC results were pooled with a meta-analysis and coefficient of variation CV results were described by median and range. Of studies identified, met the inclusion criteria. Twenty-five studies reported variability data in healthy patients, 19 in patients with type 1 diabetes and 59 in patients with type 2 diabetes.
Median within-subject coefficient of variation CV was 0. CV increased with mean population HbA1c. Assessment of variability was not the main aim of many of the included studies and some relevant papers may have been missed.
Many included papers had few participants or few repeated measurements. Within-subject variability of HbA1c is higher for patients with than without diabetes and increases with mean population HbA1c. This may confound observed relationships between HbA1c variability and health outcomes.
Because of its importance in clinical decision-making there is a need for better estimates and understanding of factors associated with of HbA1c variability.
Citation: Gough A, Sitch A, Ferris E, Marshall T Within-subject variation of HbA1c: A systematic review and meta-analysis. PLoS ONE 18 8 : e Editor: Ferdinando Carlo Sasso, University of Campania Luigi Vanvitelli: Universita degli Studi della Campania Luigi Vanvitelli, ITALY.
Received: March 13, ; Accepted: July 11, ; Published: August 2, Copyright: © Gough et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data extraction tables have been uploaded as Supporting information. All other relevant data are within the paper and its Supporting information files. Competing interests: The authors have declared that no competing interests exist.
Abbreviations: COSMIN, Consensus-based standards for the selection of health measurement Instruments; CRP, C-reactive protein; CV A , analytical coefficient of variation; CV I , within-subject coefficient of variation; EFLM, European Federation of Clinical Chemistry and Laboratory Medicine; Hba1c, Haemoglobin A1c; IFCC, International Federation of Clinical Chemistry and Laboratory Medicine; NGSP, National Glycohemoglobin Standardization Program.
Haemoglobin A1c HbA1c is produced by non-enzymatic glycation of haemoglobin. It provides an estimate of glycaemia mean glucose levels over the preceding one to three months, proportionately weighted to more recent periods [ 1 ].
It is therefore used to diagnose diabetes mellitus and to monitor patients with diabetes. The American Diabetes Association ADA recommends a cut-off point of more than or equal to 6.
It is recommended that HbA1c is measured every three to six months in newly diagnosed patients with type diabetes mellitus until stabilised, and thereafter every six months [ 3 ]. Although a conversion formula between the two methods is available, they may not be directly interchangeable [ 5 ].
Further, within-subject variability in NGSP units is reported to be lower than in IFCC units [ 6 ]. Although the IFCC is considered to be the higher standard test, many countries continue to use NGSP units [ 7 ].
Reference ranges for diagnostic tests are usually set by comparison to a reference population, which allows assessment of between-subject variation. However, biological parameters also vary over time.
This can be systematic and predictable, such as seasonal variation [ 8 ], or may be due to chance. This longitudinal within-subject variation is known as biological variation. Variation can also be introduced into a measurement from pre-analytical factors such as stress, exercise and food intake prior to the laboratory measurement and posture during the sampling procedure.
Imperfect accuracy and precision of laboratory measurement mean that if measurement is repeated a number of times on the same sample, there will be a range of results around the true value. This is analytical variation. The variability in measured HbA1c results encountered in real world clinical practice is because of a combination of biological, pre-analytical and analytical variability.
The greater the within-subject variability of a parameter, the lower the probability that a single measurement is an accurate reflection of the true mean of the parameter in that individual.
This can lead to errors in diagnosis, prognosis and treatment. Specifically for HbA1c, an inaccurate result may lead to an incorrect, missed or delayed diagnosis of diabetes mellitus, causing inappropriate commencement of treatment or an inappropriate delay in commencing treatment, and an incorrect estimation of glycaemic control in patients with diabetes, leading to an inappropriate increase in treatment intensity or a delay in intensifying treatment.
Three previous systematic reviews on HbA1c variability have been published. Gonzalez-Lao et al [ 10 ] reported the results of a systematic review and meta-analysis of 17 papers. Based on three papers this gave a pooled estimate of within-subject coefficient of variation CV i for HbA1c in healthy adults of 0.
CV i was slightly higher in patients with diabetes mellitus than subjects without diabetes. The European Federation of Clinical Chemistry and Laboratory Medicine EFLM database reports a CV i of 0. There is a need for an up to date and more comprehensive systematic review of within-subject variability of HbA1c.
To describe and evaluate the current literature on within-subject variability of measured HbA1c and from this to estimate variability in people with and without diabetes mellitus. Searches were devised to identify cohort studies, clinical trials or any studies in which an HbA1c or glycosylated haemoglobin measurement was performed more than once in the same individual.
See Appendix 1 in S1 File for sample Medline and Embase search strategy. The searches for this study were combined with a similar study into C-reactive protein CRP variability, and then CRP studies were excluded at the full text screening stage.
The international prospective register of systematic reviews, PROSPERO, was checked for ongoing reviews, and the protocol was registered with PROSPERO. On 5 th August , database searches were carried out. Medline, Embase, Cochrane Central, Epistemonikos and Open Grey were searched from inception to 5 th August An update to the search was performed to include studies published up to November Full details in Appendix 2 in S1 File.
Subject experts were contacted for suggestions for further papers. Search terms were adapted for each database searched. The references of included papers were checked by hand for further relevant papers.
Endnote reference management software was used to collate studies revealed by the search. Studies were included if primary research data on the variability of at least two measurements of HbA1c or glycosylated haemoglobin within the same subject was recorded.
Studies could be of any design. The population included adults and children, healthy or with any disease condition, in any setting. There was no restriction on time of publication, language of publication, population, setting or sample size.
Studies were excluded if participants were not in a steady state measurements were before and after an intervention or had an acute or rapidly changing illness or data were secondary systematic and narrative reviews. Studies were grouped for synthesis based on variability measure reported eg ICC, CV.
Since this study was aimed to capture variability data wherever it was published, there was no limit on publication date, language or study design.
Titles and abstracts were screened independently by two reviewers AG and EF in Abstrackr systematic review software [ 13 ], and those identified of interest underwent full text searches. Full texts were screened by AG and exclusions confirmed by EF.
Differences were resolved by discussion. Foreign language papers were translated by Google translate software. Data were extracted into Excel from the full text of the study papers where possible, and from abstracts where full texts were not available, by a single reviewer AG.
All data extraction was verified independently by a second reviewer EF. Where the same study was reported in multiple papers, the full text paper was preferred over an abstract, English language preferred over non-English, and the earliest English version over the later if there was more than one.
Table 1 lists the outcome and other main variables extracted. All eligible outcomes were included where more than one outcome was reported within a paper.
The primary outcome measure was variability of repeated measurements within the same subject. Where multiple measures of variability were given in a single study, the primary population only was analysed for the main meta-analysis.
The primary population was the full study population as opposed to subgroups , and if the full study population was not given, the primary outcome was identified using the following hierarchy: 1. Healthy population; 2. Most stable population i.
subjectively judged to be in the most steady-state such as disease course or treatment ; 3. First outcome listed in the paper. Where information was missing or unclear, the information was not extracted with the following exception: where patient characteristic data was reported only for a whole study population, but variability data was only present for a sub-population, the patient characteristic data for the whole population was used.
A risk of bias tool was used adapted from the Consensus-based Standards for the selection of health Measurement Instruments COSMIN risk of bias tool for test reliability [ 14 ]. The template risk of bias form with associated explanatory notes is shown in Appendix 2 in S1 File.
Risk of bias was assessed by AG and reviewed by EF. Disagreements between reviewers were resolved by discussion.
The main outcome measure of this review is coefficient of variation of repeated measurements within the same individual. All studies were considered for synthesis provided they had a measure of variability that had a sufficient number of studies reported so they could be pooled.
Stata SE 17 was used to perform statistical calculations and generate forest plots. Coefficient of variations were considered for meta-analysis, but no well-described and validated methods currently exist for meta-analysis of coefficient of variations, so we describe the results identified here.
Descriptive subgroup analyses were performed based on unit of measurement IFCC or NGSP , health status, setting, number of measurements and whether short or long-term variability was measured.
Sensitivity analyses were based on number of subjects and risk of bias. After database searches and hand searching of reference lists of included studies, 2, non-duplicate studies were identified of which were excluded after title and abstract screening leaving studies.
After full text screening, were excluded, and a further five during data extraction. Most full text exclusions were because no recognised measure of variability was reported. One hundred and eleven studies met the inclusion criteria Sample sizes ranged from 4 to 91, subjects, with a median of One hundred and six studies In eighteen Study populations were diverse in terms of age, gender and health status.
Ethnicity was recorded in 34 Included studies and full study characteristics are presented in Appendix 4 in S1 File. With questions four and five of the risk of bias scoring excluded, 21 Risk of bias scores are presented in Appendix 5 in S1 File.
Table 2 summarises the study characteristics of the included papers. Included studies consisted of ninety-four full texts and seventeen abstracts. The average study age of subjects in the primary population ranged from 8.
Ninety-three studies reported the setting. Twenty of the 93 The average number of measurements ranged from 2 to Eighty-four Eighty-three For 12 The CV I of the HbA1c primary population ranged from 0. Only five papers reported ICC, with a median of 0. One paper [ 16 ] appears to report an incorrect value for the CV since the value appears to be implausibly low compared to other reports of this result implausible and is also inconsistent with the other variability measures reported in the paper.
The value for CV from this paper used in this review was therefore calculated from the reported SD and mean. Two papers did not report a CV, ICC or SD.
Segar [ 17 ] reported variability as average successive variability average absolute difference between successive values , with a result of a mean of 0.
Sugawara [ 18 ] used an adjusted standard deviation to account for different numbers of HbA1c measurements, reporting an adjusted SD result of 0. Median within-subject coefficient of variation CV i was 0.
Table 3 summarises the results of variability measures of the primary study population. See Methods for definition. Figs 4 and 5 show results for studies measuring variability as SD or ICC. Because of the variability was higher in patients with diabetes, for all studies where it was reported we plotted mean population HbA1c against CV i Fig 6.
These demonstrated a positive correlation R 2 0. This review is the most comprehensive systematic review and meta-analysis of within-subject HbA1c variability. Of the three previous systematic reviews, one did not perform a meta-analysis and two only performed meta-analyses on healthy populations.
This review included studies. Previous systematic reviews on HbA1c variability were designed to estimate a coefficient of variation under ideal conditions in order to inform analytical performance specifications, reference change values and population-based reference intervals.
By contrast the current study was designed to be as broad as possible, increasing generalisability by including HbA1c variability in a variety of settings and health conditions.
There is no well-described, validated method for meta-analysis of CV i s therefore it was not undertaken in this study. These are similar to previous reviews of healthy patients which report a median CV of 0.
This is consistent with previous research suggesting that patients with diabetes have a higher CV I of HbA1c than healthy patients [ 10 , 22 ]. We demonstrated a correlation between mean population HbA1c and within-subject variation measured by CV i.
This suggests subjects with prediabetes have a higher HbA1c variability than the healthy and poorly-controlled patients with diabetes have a higher variability than well-controlled patients with diabetes. This may be due to different levels of insulin resistance, more than variation in caloric intake.
It also means clinical outcomes which correlate with mean HbA1c will also correlate with CV i. Researchers investigating variability as a predictor of outcomes should consider using a measure of variability which is independent of the mean. The variability of HbA1c has implications for clinical decision making.
Misclassification of type 2 diabetes may be considered a low risk since the HbA1c within-subject variability is lower, although with borderline results, a single reading may still be above the cutoff for a diagnosis of type 2 diabetes when the true mean is below the cutoff point.
Of even more importance are the implications for monitoring and treatment decisions, since the variability is higher in these patients.
Metformin monotherapy lowers HbA1c by 1. Another concern is that higher HbA1c variability is associated with an increased all-cause mortality due to a variety of mechanisms such as endothelial dysfunction, increased oxidative stress and increased release of cytokines [ 25 ], demonstrating the importance of reducing HbA1c variability by improving control of diabetes.
Moreover, a strict glycemic control may play a cardioprotective effect [ 26 ]. Two obvious outliers were found in this review, one which showed a CV i of 0. This appeared to be an error and the CV i was recalculated for this review from data available in the study to give a result of 0.
The other study showed a CV i of 0. The authors acknowledge their result is lower than previously published estimates of HbA1c CV I and suggest this may be due to strict control of pre-analytic factors and Chinese ethnicity of the subjects. This latter seems unlikely since four other papers with Chinese subjects reported a CV I ranging from 0.
Patients who may be suitable will be given an information sheet detailing the study and asked to contact the designated coordinator within 2 days. Should the patient be suitable for inclusion in the study then blood will be withdrawn for HbA1c, routine biochemistry including creatinine, insulin, fasting glucose, fasting lipids, blood for hsCRP and a full blood count at that visit.
Patients would be randomized at that point. Patients will either attend the clinic or have the study coordinator visit their home every 6 weeks to take blood for HbA1c, routine biochemistry including eGFR, lipids, and hsCRP.
Urine for urinary isoprostanes will be taken as a measure of oxidative stress. This will be undertaken for the 20 study visits to assess their HbA1c variability on their two treatment thresholds. A fasting insulin and glucose will be taken at the beginning, at week 60 and at the end of the study as a measure of insulin resistance HOMA to determine if there has been a change in insulin resistance over the course of the study.
Sex hormone binding globulin SHBG as an indirect measure of insulin resistance will also be taken in the event that the fasting bloods cannot be obtained. Assessment of retinopathy by an ophthalmologist and neuropathy will be undertaken at the beginning, mid point and end of the study that fits with current clinical practice.
Autonomic function testing using deep breathing heart rate variability, and a sensitive measure of small fiber neuropathy using corneal confocal microscopy to quantify corneal nerve fiber density CNFD will be performed at baseline, 12 months and at 24 months a total of 3 times over the 2 year study period.
Bloods: HbA1c, lipids. Each specimen will be identified and coded as part of the trial. Urinary isoprostanes will be measured in a validated assay that is currently in use.
Bloods: HbA1c, lipids, eGFR, hsCRP. Each specimen will be identified and coded. Autonomic function testing using deep breathing heart rate variability, and a sensitive measure of small fiber neuropathy using corneal confocal microscopy and a 24 hour urine collection for urinary isoprostanes to measure oxidative stress will be performed, these measurements will be performed at baseline, 12 and 24 months.
Layout table for study information Study Type : Interventional Clinical Trial Estimated Enrollment : participants Allocation: Randomized Intervention Model: Parallel Assignment Masking: None Open Label Masking Description: This is an randomized open label clinical trial.
Primary Purpose: Other Official Title: Does Glycated Hemoglobin Variability in Type 2 Diabetes Differ Depending on the Diabetes Treatment Threshold Used in the Qatari Population: Implication on Diabetes Complication Risk?
Study Start Date : November Actual Primary Completion Date : October 1, Estimated Study Completion Date : October 1, Resource links provided by the National Library of Medicine MedlinePlus Genetics related topics: Type 2 diabetes MedlinePlus related topics: A1C U.
There is no fixed-dosage regimen for the management of diabetes mellitus with gliclazide. Dose will be individualized based on frequent determinations of blood glucose during dose titration and throughout maintenance. The 30 mg modified-release tablet equals the 80 mg immediate-release tablet.
Immediate-release tablet: Initial: 80 mg twice daily; titrate based on blood glucose levels. Modified-release tablet: Initial: 30 mg once daily; titrate in 30 mg increments every 2 weeks based on blood glucose levels.
Maximum dose: mg once daily. Oral, Monotherapy or combination therapy: mg once daily. Patients with heart failure NYHA Class I or II : Monotherapy or combination therapy: 15 mg once daily.
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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. Layout table for eligibility information Ages Eligible for Study: 18 Years to 65 Years Adult, Older Adult Sexes Eligible for Study: All Accepts Healthy Volunteers: No Criteria Inclusion Criteria:.
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Home Search Results Study Record Detail Saved Studies. Save this study. Warning You have reached the maximum number of saved studies HbA1c Variability in Type II Diabetes The safety and scientific validity of this study is the responsibility of the study sponsor and investigators.
Listing a study does not mean it has been evaluated by the U. Federal Government. Read our disclaimer for details. gov Identifier: NCT Recruitment Status : Active, not recruiting First Posted : August 25, Last Update Posted : December 14, View this study on the modernized ClinicalTrials.
Study Details Tabular View No Results Posted Disclaimer How to Read a Study Record. Study Description. Go to Top of Page Study Description Study Design Arms and Interventions Outcome Measures Eligibility Criteria Contacts and Locations More Information.
Show detailed description. Hide detailed description. Detailed Description:. Recruitment of the patients: Only Qatari patients will be recruited and the investigators will aim to recruit a gender balance that reflects that of the local eligible diabetes patients until are recruited aged years of age.
Study Visit Schedule Visit 1: Consent, inclusion and exclusion criteria Anthropometric measurement Baseline bloods: routine biochemistry including eGFR, lipids, fasting glucose, insulin and full blood count, HbA1c, SHBG, hsCRP.
Urinary measurements. Randomization into one of the two treatment threshold regimes. Autonomic function testing using deep breathing heart rate variability and small fiber nerve measurement using corneal confocal microscopy.
Visits Bloods: HbA1c, lipids. Resource links provided by the National Library of Medicine MedlinePlus Genetics related topics: Type 2 diabetes.
MedlinePlus related topics: A1C. FDA Resources. Arms and Interventions. Initial: mg once daily; dosage may be increased by mg weekly; maximum dose: 2, mg once daily. Other Names: Diamicron Diamicron MR. SubQ: Initial: 0. Oral, Monotherapy or combination therapy: mg once daily Patients with heart failure NYHA Class I or II : Monotherapy or combination therapy: 15 mg once daily.
Other Names: Forxiga Farxiga. insulin dosage and administration according to physician. Other Names: novorapid glargine. Outcome Measures. Primary Outcome Measures : Determination of the variability of HbA1c by measurement of standard deviation of HbA1c between the 2 diabetes treatment thresholds [ Time Frame: months ] The primary objective of this study is to determine whether treatment to one of 2 threshold levels will result in one group of type 2 diabetes patients having the same mean HbA1c but with differing HbA1c variability to that of another.
Corneal nerve fiber density will be measured by confocal corneal microscopy. This will be assessed by comparing the results of HbA1c and it's variability every 6 weeks with results of routine biochemistry including eGFR, lipids, SHBG, hsCRP measured on visit one initial visit , visit 11 midpoint of the study , and visit 20 end of the study.
HbA1c will be measured at the time of the sample collection from fresh and haemolysed blood, then the remaining of the samples will be aliquoted and stored in C then remeasured again after short term storage years. Eligibility Criteria. Layout table for eligibility information Ages Eligible for Study: 18 Years to 65 Years Adult, Older Adult Sexes Eligible for Study: All Accepts Healthy Volunteers: No Criteria.
Inclusion Criteria: Qatari subjects only with type 2 diabetes taking any medication. HbA1c 7. Body mass index Age 18 - 65 years of age. Recruitment of a gender balance reflecting the local eligible diabetes patients until are recruited.
Exclusion Criteria: Patients with anemia or other conditions known to affect the validity of HbA1c measurement e. a haemoglobinopathy known to affect the Hamad HbA1c method or renal failure CKD Stage 5 Patients with concurrent illness Patients on medication leading to insulin resistance e.
corticosteroids Pregnancy Active retinopathy Any clinical exclusion for optimal diabetes control Hypoglycemic unawareness. Contacts and Locations. 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 : NCT Layout table for location information Qatar Hamad Medical Corporation Doha, Qatar, Layout table for investigator information Principal Investigator: Rayaz Malik, MD PhD Weill Cornell Medicine in Qatar.
More Information. Additional Information: Weill Cornell Biostatistics core. Diabetes Control and Complications Trial Research Group; Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, Rand L, Siebert C.
The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. doi: Kilpatrick ES, Rigby AS, Atkin SL. Effect of glucose variability on the long-term risk of microvascular complications in type 1 diabetes.
Diabetes Care. Epub Jun Siegelaar SE, Kilpatrick ES, Rigby AS, Atkin SL, Hoekstra JB, Devries JH. Glucose variability does not contribute to the development of peripheral and autonomic neuropathy in type 1 diabetes: data from the DCCT. Epub Aug No abstract available. The effect of glucose variability on the risk of microvascular complications in type 1 diabetes.
Action to Control Cardiovascular Risk in Diabetes Study Group; Gerstein HC, Miller ME, Byington RP, Goff DC Jr, Bigger JT, Buse JB, Cushman WC, Genuth S, Ismail-Beigi F, Grimm RH Jr, Probstfield JL, Simons-Morton DG, Friedewald WT.
Effects of intensive glucose lowering in type 2 diabetes. Epub Jun 6. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes UKPDS UK Prospective Diabetes Study UKPDS Group.
Erratum In: Lancet Aug 14; Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes UKPDS Erratum In: Lancet Nov 7; UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS Erratum In: BMJ Jan 2; ADVANCE Collaborative Group; Patel A, MacMahon S, Chalmers J, Neal B, Billot L, Woodward M, Marre M, Cooper M, Glasziou P, Grobbee D, Hamet P, Harrap S, Heller S, Liu L, Mancia G, Mogensen CE, Pan C, Poulter N, Rodgers A, Williams B, Bompoint S, de Galan BE, Joshi R, Travert F.
Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes.
Variabilityy you vatiability visiting nature. You are using Probiotics for heart health browser HbAx with HbAx support for CSS. To obtain Probiotics for heart health best Speed up metabolism, we recommend you use a Probiotics for heart health up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. This study evaluate association between glycemic variability and adverse vascular events in nondiabetic middle-aged adults. From 10, Ansung-Ansan cohort, Korean Genome, and Epidemiology Study KoGES data. The high HbA1c-CV tertile odds ratio 1.
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