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 Table of Contents  
REVIEW ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 3  |  Page : 275-284

Understanding glycemic variability: Focus on diabetic kidney disease


1 Chellaram Diabetes Institute, Pune, Maharashtra, India
2 Department of Nephrology, D. Y. Patil Medical College, Sant Tukaram Nagar, Pimpri, Pune, Maharashtra, India
3 Department of Medicine, D. Y. Patil Medical College, Sant Tukaram Nagar, Pimpri, Pune, Maharashtra, India

Date of Submission05-Jan-2021
Date of Decision03-Mar-2021
Date of Acceptance04-Mar-2021
Date of Web Publication30-Sep-2021

Correspondence Address:
Dr. Ambika Gopalakrishnan Unnikrishnan
Lalani Quantum, Pune-Bangalore Highway, Bavdhan Budruk, Pune 411021, Maharashtra.
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/JOD.JOD_3_21

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  Abstract 

Purpose of Review: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease globally. Hyperglycemia, hypertension, and family history are the traditional risk factors for the initiation and progression of DKD. Glycemic variability (GV) has also been considered as a risk factor for diabetes complications. Studies have already shown that GV increases the risk of macrovascular complications. The purpose of this review is to study the association of GV with DKD. Journal articles related to this topic were reviewed to get the data for this article. Summary In the setting of DKD, a higher GV has been observed. However, evidence to suggest GV as a risk factor for the development and progression of DKD is not sufficient. The data seem to suggest that the link between GV and diabetic microvascular complications has remained a hypothetical one, yet to be proven or disproven by appropriate studies. Randomized controlled trials using continuous glucose monitoring data will clarify the impact of GV and its management in DKD.

Keywords: Continuous glucose monitoring, diabetic kidney disease, glycemic variability


How to cite this article:
Purandare VB, Bale C, Kakrani A, Unnikrishnan AG. Understanding glycemic variability: Focus on diabetic kidney disease. J Diabetol 2021;12:275-84

How to cite this URL:
Purandare VB, Bale C, Kakrani A, Unnikrishnan AG. Understanding glycemic variability: Focus on diabetic kidney disease. J Diabetol [serial online] 2021 [cited 2021 Dec 2];12:275-84. Available from: https://www.journalofdiabetology.org/text.asp?2021/12/3/275/327306




  Introduction Top


Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide.[1] Annual incidence rates of ESRD due to diabetic kidney disease (DKD) are rising and vary from as low as 10 per 1 million in Romania to as high as 67 per 1 million in Malaysia.[2]

DKD is a clinical syndrome characterized by gradually increasing urine albumin excretion, rising blood pressure, and decline in glomerular filtration rate (GFR) in the absence of other causes. Hyperglycemia, hypertension, family history of DKD, and smoking are the proven risk factors of DKD. In this article, we discuss the data linking GV and DKD.

Journal articles related to this topic were reviewed to extract the data for this review.

Glycemic variability (GV) is a fluctuation in blood glucose levels around mean glucose level. It is an extra dimension for assessing glucose control in addition to glycosylated hemoglobin and premeal and postmeal blood glucose levels.[3] GV is becoming increasingly important because of its link with diabetes complications. Glucometer readings or continuous glucose monitoring is often used to assess GV in routine clinical practice. In research studies, however, frequently sampled intravenous glucose tests have also been utilized to assess GV. Research studies have suggested that both hyperglycemia and hypoglycemia predispose to cellular toxicity, via oxidative stress and other pathways.[4] GV is looked upon as a risk factor for diabetes complications, independent of HbA1c. Patients with similar HbA1c may differ in terms of GV.[5] Hence while treating diabetes, clinicians need to focus on minimizing GV in addition to targeting HbA1c. There is evidence to suggest an association between GV and macro-vascular complications, mainly coronary artery disease, especially postprandial rises in glucose levels.[6] Postprandial hyperglycemia is linked with cardiovascular disease in type 2 diabetes. A meta-analysis authored by Hanefeld et al.[7] suggested that controlling postprandial hyperglycemia reduces the risk of myocardial infarction in type 2 diabetes patients.


  Clinical Importance of GV and HbA1c Variability: Data from the Diabetes Control and Complications Trial (DCCT) Top


Short-term glucose fluctuations are usually referred to GV, whereas very long-term fluctuations of glycosylated hemoglobin (HbA1c) are referred to as HbA1c variability. HbA1c variability is not synonymous with GV but is a closely related topic. The DCCT and Epidemiology of Diabetes Interventions and Complications (EDIC) study group analyzed data of 1441 type 1 diabetes subjects treated in two arms: conventional and intensive arm. The study group made a remarkable observation regarding diabetic retinopathy. Intensively treated patients who reached the same HbA1c levels as conventionally treated subjects still had a lower risk of progression of retinopathy. In other words, despite attaining similar levels of HbA1c, conventional and intensive treatment groups may have differing outcomes as far as retinopathy was concerned.[8] This finding led to a hypothesis that there could be another risk factor independent of the HbA1c and mean glucose level, which could contribute to retinopathy progression in conventionally treated subjects. This additional factor was hypothesized to be glucose variability. This analysis was later retracted by the DCCT Study Group, which still concluded that GV could explain a small part of the differences in risk between intensive and conventional treatment groups.[8]

An initial DCCT data analysis by Kilpatrick et al.[9] suggested that there is no significant relationship between diabetic retinopathy and GV. In 2008, the same group analyzed the follow-up data and found a positive correlation of HbA1c variability with microvascular complication risk. The analysis suggested that long-term fluctuations in glycemia in the form of A1C variability seem to contribute to the development of nephropathy and retinopathy.[10] In 2009, the follow-up study of the DCCT suggested that though A1c variability predicts development of retinopathy or nephropathy, GV did not.[11] In 2017, Lachin et al.[12] concluded that within-day GV, as determined from quarterly glucose profiles, does not play an apparent role in the development of microvascular complications beyond the influence of the mean glucose. Overall, the data seem to suggest that the link between GV and diabetic microvascular complications has remained hypothetical, yet to be established by appropriate studies.


  Pathophysiological Role of GV on Diabetic Vascular Complications Top


It has been suggested that the pathophysiology of vascular complications of diabetes could be explained by a unifying hypothesis which includes GV.[13] Human cells which cannot downregulate glucose uptake are damaged due to high glucose levels in the blood. Hyperglycemia causes cellular damage by various ways including production of advanced glycation end-products, sorbitol pathway, and increase in the mitochondrial reactive oxygen species.[13] Experimental animal studies done in vitro and in vivo have suggested that intermittent hyperglycemic exposure is more hazardous than continuous hyperglycemia, leading to increased oxidative stress and subsequent endothelial dysfunction.[14] There are studies which suggest that hypoglycemia causes similar cell damage as hyperglycemia does. Hypoglycemia leads to increase in adrenaline levels, inflammatory cytokines, and platelet activation and aggregation, all of which contribute to an increased risk for cardiovascular diseases.[15] These studies suggest that fluctuating glucose levels play an important role in causation and progression of vascular complications in diabetes.

In a trial published by Esposito et al.,[16] people with type 2 diabetes were randomized to receive glibenclamide and repaglinide. HbA1c reduction was similar in both the groups. In the repaglinide arm, subjects had regression of carotid intimal thickness, a sign of improvement of macrovascular disease. It is noteworthy that repaglinide reduced GV by improving postprandial hyperglycemia with less risk of hypoglycemia and was found to be vasculoprotective when compared with glibenclamide, an agent causing significant hypoglycemia. Taken together. all these studies seem to suggest a link between GV and diabetic macrovascular complications.


  Measuring GV Top


Prior to the discussion on links between GV and diabetic complications, a critical appraisal of various measures of GV is essential. Currently, given the widespread utilization of continuous glucose monitoring (CGM), it is also important to view GV through the perspective that CGM offers. The following section summarizes the various measures of GV that could be studied for an association with DKD from the CGM perspective.

Physiologically, blood glucose levels increase after the food intake and decrease when there is no calorie intake. But when the amplitude of this “roller coaster” of glucose fluctuations is high, it worsens GV. Blood glucose fluctuation occurring within a day is referred as “intraday GV,” and day-to-day fluctuations are referred as “interday GV.” To measure GV, multiple glucose readings are required. Self-monitoring of blood glucose (SMBG) on a glucometer and CGM are the two available modalities which help us quantify GV in routine clinical practice.

Thus, GV indices are obtained by using the following types of data

  • SMBG data. During SMBG, the person with diabetes keeps a log of fasting, premeal, postmeal, and random capillary glucometer readings, which may then be used to assess GV.


  • CGM. The CGM is a better tool to assess GV. A CGM sensor detects and records glucose level in the interstitial fluid at a fixed interval. The sensor has a wired enzyme glucose-sensing technology. CGM provides information on daily glucose fluctuations and shows how the values are affected by everyday activities and is a useful tool to assess GV. When compared with SMBG, CGM provides more information related to glucose fluctuations and asymptomatic and nocturnal hypoglycemia. The ambulatory glucose profile (AGP) is a report based on data obtained from a CGM device which gives an immediate visual interpretation. There are various types of CGM devices available. Some are for personal use; hence, patients can track glucose readings on a reader and some are for professional use. While using a professional CGM device, patients have to visit the clinic to download the data.


  • Indices of GV

    There are numerous indices to assess GV. There is no gold standard test to assess GV. There are various published articles which explain in detail various GV indices and how to calculate these indices.[17] In this article, we will discuss about GV indices in brief. Standard deviation (SD), coefficient of variation (CV), and mean amplitude of glucose excursions are widely used to quantify GV. GV may be reported as coefficient of variation (primary) and standard deviation (secondary). As shown in [Table 1], CV is a metric relative to the mean; hence, it is more descriptive of glycemic excursions than the SD alone. Stable glucose levels are defined when CV < 36%, and unstable glucose levels are defined when CV ≥36%.[18] A higher SD and CV indicates higher GV. Some of the commonly used GV indices are mentioned in [Table 1].[17],[19] These GV indices can be obtained by using online apps such as GlyCulator 2.0 (https://apps.konsta.com.pl/app/glyculator/) and EasyGV (https://www.phc.ox.ac.uk/research/technology-outputs/easygv).
    Table 1: Indices of GV

    Click here to view


    GV has three dimensions: amplitude, time, and frequency of glucose fluctuations.[20] They are mentioned in [Table 2]. Some GV indices are related to amplitude of glucose excursion, e.g., MAGE, SD, CV, LBGI, HBGI, and some are related to time, e.g., CONGA 1, CONGA 2, etc.
    Table 2: Dimensions of GV

    Click here to view



      AGP Obtained by CGM in DKD Top


    The AGP obtained from a CGM sensor provides parameters related to GV such as:

    • (1) Time in range (TIR): the time spent in target glucose range (70–180 mg/dL).


    • (2) Time above target: the time when the glucose levels are above 180 mg/dL. It is divided into Level 1 (L1) when glucose levels are in the range of 181–250 mg/dL and Level 2 (L2) when glucose levels are > 250 mg/dL.


    • (3) Time below target: the time when the glucose levels are below 70 mg/dL. It is also divided into Level 1(L1) when glucose levels are in the range of 69–53 mg/dL and Level 2 (L2) when glucose levels are < 53 mg/dL.[21] Targets of time in range and time above and below target for type 1 diabetes mellitus (DM) and type 2 DM are mentioned in [Table 3].
    Table 3: Targets of time in range and time above and below target for type 1 DM and type 2 DM

    Click here to view



      Interpretation of TIR and GV Indices Top


    We discuss here laboratory and AGP data of two patients (patient A and patient B) mentioned in [Table 4]. These patients are from outpatient department of Chellaram Diabetes Institute, Pune. AGP graphs are displayed in [Figure 1] and [Figure 2]. AGP data in [Figure 1] are of patient A. She is a 61-year-old lady with type 2 DM for 10 years and stage 2 DKD. [Figure 2] is AGP of patient B, a 72-year-old lady with type 2 DM for 20 years with stage 5 DKD (not on dialysis). [Figure 1] clearly shows that most of the readings are in the target range, whereas [Figure 2] shows fluctuations in the glucose levels. In [Figure 2], 40% of the readings are above target (shown in yellow color) and 8% are below target range (shown in red color). AGP data are used to calculate GV indices, i.e., SD and CV. Higher SD and CV values in [Table 4] indicate higher GV in patient B when compared with subject A. TIR of 52% (patient B) when compared with TIR of 96% (patient A) also suggests higher GV in patient B. Note that these data are only a clinical comparison of two cases, and our explanation should not be construed to imply that GV causes DKD. As mentioned earlier, the association between GV and DKD is unknown. Nevertheless, given that both hyperglycemia and hypoglycemia are associated with significant mortality and morbidity, it is important to correct glycemic fluctuations in these subjects, where GV occurs.
    Table 4: Patient characteristics, laboratory data, and AGP data of patients A and B (their AGP graphs are displayed in [Figure 1] and [Figure 2], respectively)

    Click here to view
    Figure 1: Daily glucose summary of patient A with stage 2 DKD

    Click here to view
    Figure 2: Daily glucose summary of patient B with end-stage DKD

    Click here to view



      GV and DKD Top


    Long duration of hyperglycemia in diabetes leads to mesangial expansion and glomerular sclerosis, which are manifested as DKD. Glomerular sclerosis and glomerular hypertension lead to albuminuria and decline in GFR. While it is well known that hyperglycemia predisposes to DKD, there are insufficient data to suggest that there is significant association between GV and DKD. There is a small observational study looking at association of GV with DKD. Jin et al.[22] from Republic of Korea published a study titled association between the extent of urinary albumin excretion and GV indices measured by CGM. CGM (3 days) was done in 173 patients with type 2 diabetes with large fluctuations in blood glucose values. The study did not demonstrate an independent association between GV and albuminuria.

    Post hoc analysis of Diabetes Control and Complications Trial (DCCT) data showed link between TIR and microalbuminuria. The analyses used data collected during the years 1983–1993 of 1440 DCCT participants with type 1 DM. Seven-point finger-stick blood samples (premeal and 90 min postmeal and bedtime) were collected once in 3 months for blood glucose estimation at a central laboratory. Beck et al.[23] demonstrated that TIR (70–180 mg/dL) has a significant association with the development of microalbuminuria. This retrospective analysis indicated that patients who developed microalbuminuria and retinopathy had lower TIR percent [Table 5]. Lower TIR indicates higher GV and high TIR indicates lower GV. This suggested that patients with higher GV showed increased frequency of microalbuminuria. But to ascertain a significant relation between GV and DKD, robust evidence in the form of randomized controlled trials is needed.
    Table 5: Frequency of development of microalbuminuria according to the level of TIR

    Click here to view



      HbA1c Variability and DKD Top


    Though HbA1c variability is not synonymous with GV, it is indeed a form of GV. There is evidence to suggest that HbA1c variability in type 1 and type 2 diabetes is associated with renal disease. HbA1c variability is measured by using CV and SD of serial HbA1c values. There are studies which suggest association of HbA1c variability with diabetic retinopathy, neuropathy, and kidney disease in type 1 diabetes.[10] In a systematic review and meta-analysis by Gorst et al.,[24] seven studies evaluated HbA1c variability in type 1 diabetes and showed an association of HbA1c variability with renal disease. Marcovecchio et al.[25] published a study in which A1c measurements of 1232 subjects with type 1 diabetes were analyzed. A1c-SD values were higher in subjects with microalbuminuria (n = 227) than in those with normoalbuminuria concluding A1c variability as an independent risk factor for microalbuminuria in type 1 diabetes. In 2009, the Finnish Diabetic Nephropathy Study Group showed that A1c variability predicts microalbuminuria and overt diabetic nephropathy in patients with type 1 diabetes.[26] A retrospective analysis using electronic health records of 1195 type 1 diabetes subjects from a pediatric diabetes clinic from 1993 to 2009 showed association of A1c variability and the risk of microalbuminuria among children with type 1 DM.[27]

    Thirteen studies evaluated HbA1c variability in patients with type 2 DM. Higher HbA1c variability was associated with higher risk of renal disease. A prospective cohort study which included 8439 type 2 DM subjects concluded that HbA1c variability predicted development of renal disease in type 2 diabetes.[28] A study with 821 normoalbuminuric type 2 diabetes individuals when followed for 7 years showed that HbA1c variability was independently associated with the development of microalbuminuria.[29] The Renal Insufficiency and Cardiovascular Events (RIACE) Study, an Italian multicenter study, also concluded HbA1c variability as an independent correlate of diabetic nephropathy in patients with type 2 diabetes.[30] Overall, data indicate an association between HbA1c variability and DKD.


      Impact of DKD on GV Top


    There are various factors in DKD which increase the risk of hypoglycemia as well as hyperglycemia.[31] These factors contribute to GV [Figure 3] and [Figure 4]. Uremia and dialysis together adversely affect glycemic control by altering insulin secretion, clearance, and insulin sensitivity. Secondary hyperparathyroidism and metabolic acidosis in patients with kidney disease increase insulin resistance which contributes to hyperglycemia.[31] These factors lead to unpredictable insulin action and hence fluctuating blood glucose levels. About one-third of insulin clearance takes place in the kidneys and hence reduced kidney function is associated with a prolonged half-life of insulin which increases the risk of hypoglycemia. It has been shown that in ESRD there is impaired renal gluconeogenesis which can also predispose to hypoglycemia.[31] Patients with diabetes and ESRD often have gastroparesis which leads to prolonged food retention in the stomach, resulting in erratic glucose absorption and alternating hypoglycemia and hyperglycemia.
    Figure 3: Factors which increase the risk of hypoglycemia in DKD

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    Figure 4: Factors which increase the risk of hyperglycemia in DKD

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      Case Study and Daily Glucose Summary of Type 1 DM with DKD Stage 5 Not on Dialysis Top


    To discuss further the impact of DKD on GV, we will now discuss the daily glucose summary of patients with type 1 DM with end-stage DKD not on dialysis [see [Figure 5]A and B]. This is a daily glucose summary of a 28-year-old lady, known case of type 1 DM since 20 years on basal bolus insulin therapy. She takes insulin aspart before meals as per the carbohydrate counting around 8–10 U and insulin lantus 25 U at bedtime. For the past 2 years, she was irregular in the glucose monitoring and also would skip insulin dose sometimes. She was taking antidepressants since last 2 years. Here eating pattern was not regular. She has proliferative retinopathy in both the eyes.
    Figure 5: (A) Part 1 of daily glucose summary of patient with type 1 DM with end-stage DKD not on dialysis. (B) Part 2 of daily glucose summary of patient with type 1 DM with end-stage DKD not on dialysis

    Click here to view


    Her recent investigations in the laboratory are as follows: HbA1c—8.3%, FBSL—146 mg/dL, PPBSL—298 mg/dL, serum creatinine—5.8 mg/dL, eGFR (as per CKD EPI equation)—9.1 mL/min/m2. Her AGP was done from November 16 to November 29, 2019. It suggested higher GV as SD was 110.2 and CV was 62.5 (see the details in [Table 6]).
    Table 6: Information of a patient with type 1 DM with ESRD not on hemodialysis

    Click here to view



      GV in Patients on Hemodialysis Top


    There are a few pilot studies showing high GV in DKD subjects on hemodialysis. In these studies, CGM sensors of duration 2 days to 2 weeks were used. The retrospective professional AGP Libre Pro was used in a pilot study conducted in Pune city from India, which included 10 ESRD patients on hemodialysis. The AGP record showed asymptomatic hypoglycemia in these patients during hemodialysis. Glucose levels showed a fall during hemodialysis and then rised to higher levels after dialysis. AGP data demonstrated GV in these subjects on dialysis days and on the days without dialysis.[32] Antidiabetic treatment in subjects with DKD and ESRD undergoing hemodialysis should be planned in such a way that the severity of glycemic fluctuations is less preventing severe hypoglycemia. Hence on the day of hemodialysis, patients are advised not take insulin or antidiabetic tablet before dialysis and eat some snack prior to dialysis or during dialysis.

    Daily glucose summary of patients with ESRD undergoing dialysis

    In the DIALYDIAB pilot study, 15 patients on hemodialysis were recruited. Five-day CGM was done at baseline and after 2 weeks This CGM helped for better glucose control, without increased risk of hypoglycemia.[33] Twelve patients of DKD on hemodialysis on insulin were studied by a glucose sensor for 2 days, including the dialysis day (HD) and consecutive dialysis-free day (FD). The mean 24-h glucose, the mean amplitude of glucose excursions, and the SD of mean glucose were significantly higher in the HD than in the FD. During HD, they showed higher GV when compared with the dialysis-free day.[34]

    These studies suggest that GV is present in DKD during hemodialysis, and CGM is useful in this setting.


      Case Study and Daily Glucose Summary of Type 2 DM with ESRD on Hemodialysis Top


    To discuss further topic of GV in patients on hemodialysis, we discuss the daily glucose summary of patients with type 2 DM with ESRD initiated on hemodialysis [see [Figure 6]A and B]. This is a daily glucose summary of a 68-year-old gentleman, known case of type 2 DM since 25 years on basal bolus insulin therapy. He takes premixed regular/Neutral Protamine Hagedorn (NPH) insulin (30:70) 10 U before breakfast and 8 U before dinner. He has diabetic neuropathy and severe non-proliferative retinopathy in both the eyes.
    Figure 6: (A) Part 1 of daily glucose summary of patient with type 2 DM with end-stage DKD on hemodialysis. (B) Part 2 of daily glucose summary of patient with type 2 DM with end-stage DKD on hemodialysis

    Click here to view


    His recent investigations in the laboratory are as follows: HbA1c—10.2%, FBSL—206 mg/dL, PPBSL—348 mg/dL, serum creatinine—7.2 mg/dL, eGFR (as per CKD EPI equation)—7.1 mL/min/m2. His AGP was done from December 14–27, 2017.

    His hemodialysis was done on December 21, 2017, Thursday, from 4 pm to 8 pm and also on December 23, 2017, Saturday, from 4 pm to 8 pm. In the AGP graph, we can see that there was hypoglycemia during the hemodialysis followed by hyperglycemia. AGP suggested higher GV as SD was 85.3 and CV was 43.1. TIR is 33%, TAT is 60%, and TBT is 7% with 4% of the values ≤ 53 mg/dL (see the details in [Table 7]).
    Table 7: Information of a patient with type 2 DM with ESRD on hemodialysis

    Click here to view



      Conclusion Top


    Patients with DKD are at increased risk of glycemic excursions. It is not clear whether GV may cause DKD or if it is the result of DKD. However, given the clinical importance of hyperglycemia and hypoglycemia, such variability is to be avoided. GV is inversely related to quality of life and is associated with increased risk of cardiovascular disease, which is an important cause of mortality in people with DKD. There is evidence to support the concept of GV in various ethnic groups.[22],[30],[32] Hence, while treating DKD minimizing GV should be an ideal goal. One study demonstrated an association between TIR and microalbuminuria, but there needs to be more evidence in the form of randomized controlled trials to see if GV increases the risk of DKD. Studies using CGM may help in studying this topic further. These studies could assess whether improving GV along with TIR could have a beneficial impact on the complications, as well as the mortality and morbidity attributable to diabetes.

    Financial support and sponsorship

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    Conflicts of interest

    There are no conflicts of interest.



     
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        Figures

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        Tables

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    Abstract
    Introduction
    Clinical Importa...
    Pathophysiologic...
    Measuring GV
    AGP Obtained by ...
    Interpretation o...
    GV and DKD
    HbA1c Variabilit...
    Impact of DKD on GV
    Case Study and D...
    GV in Patients o...
    Case Study and D...
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