Management of glycemic variability- Role of DPP4i (1).pptx

Management of
glycemic variability:
Role of DPP4i
Dr Sachin Chittawar

LIMITATIONS WITH BLOOD GLUCOSE MEASUREMENT
MONITORING BLOOD GLUCOSE WITH FINGERSTICKS MAY MISS CERTAIN HIGH AND LOW BLOOD GLUCOSE
MEASUREMENTS
Ref:-
1. Sherwani SI. Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomark Insights. 2016; 11: 95–104.

Limitations of HbA1c
HbA1c displays only an approximate measure of
glucose levels & fails to address parameters such as
hypoglycemia & GV, which may influence clinical
outcome
HbA1c is limited in its ability to reflect short-term
glycemic changes, and it cannot reflect postprandial
hyperglycemia and fasting hyperglycemia
separately
HbA1c levels may not effectively reflect
hyperglycemic excursions that are compensated for
by hypoglycemia
Ref:-
1. Kovatchev B et al. Diabetes Care 2016 Apr; 39(4): 502-510
2. Chon S et al. Evaluation of Glycemic Variability in Well-Controlled Type 2 Diabetes Mellitus. Diabetes Technology & Therapeutics. 2013;15(6):455-460.
3. Ajjan R. How Can We Realize the Clinical Benefits of Continuous Glucose Monitoring? Diabetes Technol Ther. 2017 May 1; 19(Suppl 2): S-27–S-36.

GLYCEMIC VARIABILITY: THE FIFTH
COMPONENT OF THE GLYCEMIC PENTAD
Ref:-
1. Stratton IM, Adler AI, Neil HA, Mathews DR, Manley SE, Cull CA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabets (UKPDS 35):
Prospective observational study. BMJ. 2000;321:405–12. [PMCID: PMC27454] [PubMed: 10938048]
2. Raz I, Wilson PW, Strojek K, Kowalska I, Bozikov V, Gitt AK, et al. Effects of prandial versus fasting glycemia on cardiovascular outcomes in type 2 diabetes: The HEART 2D trial. Diabetes
Care. 2009;32:381–6.
3. Kota SK, Kota SK, Jammula S, Panda S, Modi KD. Effect of diabetes on alteration of metabolism in cardiac myocytes: therapeutic implications. Diabetes Technol Ther. 2011;13:1155–60.
4. Kota SK, Meher LK, Jammula S, Kota SK, Krishna SV, Modi KD. Aberrant angiogenesis: The gateway to diabetic complications. Indian J Endocrinol Metab. 2012;16:918–30.
5. Hari Kumar KV, Kota SK, Basile A, Modi KD. Profile of microvascular disease in type 2 diabetes in a tertiary health care hospital in India. Ann Med Health Sci Res. 2012;2:103–8.
6. Krishna SVSK et al. Glycemic Variability:Clinical implications. Indian Journal of Endrocrinology and Metabolism.2013;17(4):611-619.
Glycemic variability (GV) means
swings in blood glucose level
The broad definition of GV takes into
account the intraday glycemic
excursions including episodes of hyper
& hypoglycemia
Hyperglycemia & dysglycemia (peaks
and nadirs) lead to various
microvascular & macrovascular
complications in diabetes

HOW DOES GLYCEMIC VARIABILITY LEAD TO
COMPLICATIONS?
EXCESSIVE GLYCATION & GENERATION OF OXIDATIVE STRESS & GLUCOSE FLUCTUATIONS, COMBINE TO
PRODUCE COMPLICATIONS
MECHANISMS BY WHICH HIGH GV LEADS TO COMPLICATIONS
OXIDATIVE STRESS EXCESSIVE GLYCATION
Ref:-
1. Goh SY, Cooper ME. Clinical review: The role of advanced glycation end products in progression and complications of diabetes. J Clin Endocrinol Metab. 2008 Apr;93(4):1143-52.
2. Olivera MIA et al. RAGE receptor and its soluble isoforms in diabetes mellitus complications. J Bras Patol Med Lab. April 2013;49(2):97-108.
3. Yamagishi S, Matsui T. Advanced glycation end products, oxidative stress and diabetic nephropathy. Oxid Med Cell Longev. 2010 Mar-Apr;3(2):101-8.

CHRONIC HYPERGLYCEMIA VS FREQUENT ACUTE
GLYCEMIC VARIABILITY: WHICH IS MORE HARMFUL?
Multiple fluctuations of glycemia in the same individual could be more harmful than a simple episode of
acute hyperglycemia or chronic stable hyperglycemia1
Glucose fluctuations during postprandial periods and during glucose swings triggers oxidative stress to a
greater extent than chronic sustained hyperglycemia2
Ref:-
1. Cereiello A et al. Glycemic Variability: Both Sides of the Story. Diabetes Care. 2013 Aug; 36(Suppl 2): S272–S275.
2. Monierre L et al. Activation of Oxidative Stress by Acute Glucose Fluctuations Compared With Sustained Chronic Hyperglycemia in Patients With Type 2 Diabetes. JAMA. 2006;295(14):1681-1687.

LARGE BODY OF EVIDENCE SUPPORTING THE IMPACT
GLYCEMIC VARIABILTY AND ITS IMPLICATIONS
PRE- AND
POSTPRANDIAL BLOOD
GLUCOSE, MBG WERE
SIGNIFICANTLY
RELATED TO CVD RISK
THE VERONA
DIABETES
STUDY
GV HAS A GREATER
EFFECT THAN DEGREE
OF METABOLIC
CONTROL ON SURVIVAL
IN ELDERLY T2DM
PATIENTS
RISK OF
HYPOGLYCEMIA IS AS
RELATED TO GLUCOSE
VARIABILITY AS IT IS TO
THE MEAN GLUCOSE
VALUE
DIABETES
OUTCOMES IN
VETERANS STUDY
(DOVES)
THE DIABETES
CONTROLAND
COMPLICATIONS
TRIAL (DCCT)
Ref:-
1. Nathan DM. The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study at 30 Years: Overview. Diabetes Care 2014 Jan; 37(1): 9-16.
2. Muggeo M et al. The Verona diabetes study: a population-based survey on known diabetes mellitus prevalence and 5-year all-cause mortality. Diabetologia. 1995 Mar;38(3):318-25.
3. Murata GH, Duckworth WC, Shah JH, Wendel CS, Hoffman RM. Sources of glucose variability in insulin-treated type 2 diabetes: the Diabetes Outcomes in Veterans Study (DOVES). Clin Endocrinol (Oxf). 2004 Apr;60(4):451-6.

RELATIONSHIPS BETWEEN LONG TERM VS SHORT TERM GLYCEMIC
VARIABILITY AND CV COMPLICATIONS: EVIDENCE LEVELS
No compelling evidence that elevated short-term GV is an
independent risk factor of microvascular complications
of diabetes
Numerous studies supporting the association of
long-term GV with an enhanced risk of cardiovascular
events (supportive evidence level C)
Exaggerated glucose fluctuations are associated with an
enhanced risk of adverse CV outcomes primarily due to
hypoglycemia (supportive evidence level C)
Chronic exposure to glucose is associated with
cardiovascular complications
(clear evidence level A)
Ref:-
1. Monnier L, Colette C, Owens DR. The application of simple metrics in the assessment of glycaemic variability. Diabetes Metab. 2018 Sep;44(4):313-319.

Need for metrics beyond A1c
1.
A I c
A1c does not reflect
acute glycaemic
excursions of hypo- and
hyperglycaemia
2. A1c fails to identify
the magnitude and
frequency of intra- and
interday glucose
variation
3.Conditions such as anemia,
hemoglobinopathies, iron deficiency and
pregnancy can confound A1c
measurements and can fail to accurately
reflect mean glucose
• In people with Type 1 and Type 2 diabetes , A1c is recognized as the key indicator for long-term
diabetes complications .
• While A1c reflects the average glucose over the last 2-3 months, it has limitations on the short-
term changes in blood glucose

How to measure Glycemic Variability?
• Postprandial hyperglycemia
• 2 h, 1 h, 90 min after meal,
meal, however, often
undefined
• In trials mainly 2h after an
oral glucose load (75 g)
• Target glycemic variability:
• 40 mg/dl can be proposed
as the level of glucose
variability that should be
targeted
• Glycemic variability
• Average glucose + SD
• Hyperglycemic index (self-
monitoring of BG)
• MAGE (CGMS glucose
excursions)
• CONGA (CGMS intraday
variability)
• ADRR (log transformation)
• Time-in-Range through
Ambulatory Glucose Profile
(AGP)
Eberhard S et al. Diabetes Care 34(Suppl. 2):S120–S127, 2011

CGM based Clinical Targets: Recommendations
from the International Consensus on Time In Range
Background: In February 2019, the ATTD Congress convened an international
panel of physicians, researchers, and individuals with diabetes to supplement the
currently agreed-upon metrics
• The goal is to establish clear targets for CGM metrics that diabetes care teams
and people with diabetes can work together to achieve
• The panel identified cut points for time below, within and above glycemic ranges
for various populations (T1D, T2D, older/high risk, pregnancy)
• The group recommends a standardised CGM report incorporating key TIR
metrics and targets along with a 14-day ambulatory glucose profile (AGP) to be
an integral component of clinical decision making
Battelino T, Danne T, Bergenstal R, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.2019 (Published online June 8, 2019. at: https://doi.org/10.2337/dci19-0028).
ATTD = Advanced Technologies and Treatments for Diabetes

International Consensus on Time In Range is published in June
2019
14 of 23
“We conclude that, in clinical practice,
time in ranges (within target range,
below range, above range) are both
appropriate and useful as clinical
targets and outcome measurements
that complement A1C for a wide
range of people with diabetes and
that the target values specified in this
article should be considered an
integral component of CGM data
analysis and day-to-day treatment
decision making.”
This international consensus report has been endorsed by the
American Diabetes Association, American Association of Clinical
Endocrinologists, American Association of Diabetes Educators,
European Association for the Study of Diabetes, Foundation of
European Nurses in Diabetes, International Society for Pediatric
and Adolescent Diabetes, JDRF and Paediatric Endocrine
Society.
https://care.diabetesjournals.org/content/diacare/early/2019/06/07/dci19-0028.full.pdf.

Standardisation of CGM metrics - Time In Ranges
15 of 23
To streamline data interpretation, “time in ranges” was identified as a metric of glycaemic
control that provides more actionable information than A1c alone
The “time in ranges” includes three key measurements:
1. Percentage of readings and time per day within target glucose range
2. Time below target glucose range
3. Time above target glucose range
The primary goal for effective and
safe glucose control is to increase
the TIR while reducing the TBR
Increase
TIR
Time within range
(TIR)
Time above range
(TAR)
Time below range
(TBR) Decrease TBR
Battelino T, Danne T, Bergenstal R, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.2019 (Published
online June 8, 2019. at: https://doi.org/10.2337/dci19-0028).

What is Time In Range (TIR)?
16 of 23
Time within range
Time in Ranges refers to the percentage of time that a person with diabetes
spends within their Target Glucose Range, or above or below that target
Time above
range
Time below range
For illustration purposes only. Not actual patient data.

A direct relationship between TIR and A1C
17 of 23
• Time in range >70% is what T1D and T2D patients should aim for
• Every 10% increase in TIR = ~0.5% (5.5mmol/mol) A1c reduction in T1D
=~0.8% (8.7mmol/mol) A1c reduction in T1D and T2D

TIR targets for T1D or T2D, non-pregnant
18 of 23
• For T1D/T2D, non-pregnant: >70% of time/day in target range (70-180mg/dL). For age <25
years, if A1c is 7.5%, TIR target is 60%
• For older/high risk: >50% of time/day in target range (70-180mg/dL)
• Older: age not defined, but noted to have higher risk for severe hypoglycaemia
• High risk: higher risk for complications, comorbid conditions (e.g. renal disease), require assisted care
• Each incremental 5% in TIR is associated with clinically significant benefits

TIR targets during pregnancy
19 of 23
• For T1D pregnancy: >70% of time/day in target range (63-140mg/dL)
• Gestational DM or T2D in pregnancy: % for TIR targets have not been included
as there are no clinical studies to guide recommendations
• Each incremental 5-7% in TIR is associated with clinically significant benefits for
pregnancy in women with T1D

Summary of TIR targets for different diabetes population
20 of 23

Report with TIR proposed by International Consensus
21 of 23
% of Time in Ranges by levels:
• “Very high”(>250mg/dL)
• ‘High” (181-250mg/dL)
• Target range (70-180mg/dL)
• “Low”(54-69mg/dL)
• “Very low” (<54mg/dL)
Ambulatory Glucose Profile (AGP)
• AGP is a summary of glucose values from the
report period, with median (50%) and other
percentiles shown as if occurring in a single day
• Visualization of glucose patterns to facilitate
treatment decisions
Daily Glucose Profiles
• Each daily profile represents a midnight-to-
midnight period
• Individual daily traces for patient coaching

23
Unnikrishnan et al. JAPI 2019; 67 (76).

Clinical Indications for AGP in T2DM patients on
OADs
24
 Disparity between FBS/PPBS levels and HbA1c
o HbA1c > 7.5%, with FBS/PPBS levels on target
o HbA1c on target, with FBS/PPBS levels not on target
 At risk/with hypoglycemia episodes
 Need for Patient education
o Not adherent to Life style modification
o Noncompliance to treatment
Unnikrishnan et al. JAPI 2019; 67 (76).

Role of DPP4i in Management of GV
25 of 21

Gliptins
DPP4 Inhibition
Hyperglycemia
Insulin biosynthesis
Plasmatic Incretin levels
Endothelial
function
Adiponectin
Influence on
GV
Inflammatory markers
Ischemia / reperfusion
injury
Oxidative stress
Or = Blood pressure
Or = Lipidemia
Or = Body weight
DPP4i: Pleiotropic Benefits

Effect of DPP-4 inhibitors on glycemic control
• Many studies have shown that DPP-4 inhibitors enhance GLP-1
level and prolong its effects for glycemic control
• They have been proved to enhance glucose-induced insulin
secretion, decrease glucagon secretion, and reduce postprandial
glycemic excursions
• By mechanism of action, the potency of stabilizing DPP-4
inhibition offer the better regulation of daily glucose fluctuations

Fonseca, V., et al. Diabetes Obes Metab. 2011 ; DeFronzo RA. Ann Intern Med. 1999:281–303;UKPDS. Lancet. 1998:837–853; Aschner P, et al. Diabetes Care.2006:2632-7;ADA and
EASD Consensus statement. Diabetes Care. 2009:193–203; Nesto RW, et al. Circulation 2003:2941–2948; Matthaei S Endocrine Reviews. 2000;21:585–618; Raptis SA & Dimitriadis GD.
J Exp Clin Endocrinol. 2001:S265–S287
Glycemic Variability
Effect
on IR
Impact on
β cell
function
Effect on
PPG
Effect on
weight
Risk of
Hypoglycemia
Met ↓ ↔  ↔ ↔ ↔
SU ↔ - ↓ ↑ ↑
Glitazones ↓ ↔ ↔ ↑ ↔
AGIs ↔ ↔ ↓ ↔ ↔
Glinides ↔ - ↓ ↑ ↑
Gliptins ↔ + ↓↓ ↔ ↔

Vildagliptin Different : Compared to other gliptins?
SUSTAINED
OVERNIGHT GLP-1
AND GIP LEVELS
94-97% DPP4
INHIBITION OVER 24
HOURS (BD DOSE)
LESSER GLYCEMIC
FLUCTUATIONS
OVERNIGHT
PROTECTION AGAINST
HYPOGLYCEMIA (BD
DOSE)
HIGHER INSULIN
DOSE REDUCTION
BETTER FPG
REDUCTION
Balas B, et al. J Clin Endocrinol Metab 2007;92:1249–55. Christensen M, et al. Diabetes 2011;60:3103–9. Ahrén B, et al. Diabetes Obesity Metab 2011;13:775–83. Li et al. Diabetology &
Metabolic Syndrome 2014, 6:69. Tang et al. Diabetol Metab Syndr (2015) 7:91. Guerci, B . (2012). Continuous glucose profiles with vildagliptin versus sitagliptin in add-on to metformin: Results
from the randomized Optima study. Diabetes and Metabolism, 38(4), 359–366. http://doi.org/10.1016/j.diabet.2012.06.001

• This study evaluated the CGM profiles on Vildagliptin (n=14) and Sitagliptin (n=16) in
addition to Metformin, inadequately T2DM patients (HbA1c= 6.5% t0 8%)
• CGM data acquired over 3 days – firstly on metformin alone and then 8 weeks after
addition of either Vildagliptin or Sitagliptin was analyzed
30 of 21
Guerci B et al. Continuous glucose profiles with vildagliptin versus sitagliptin in add-on to metformin: results from the randomized Optima study. Diabetes & metabolism. 2012 Oct 1;38

Key Results:
Mean glycaemic profiles in the Per Protocol PP population at baseline on metformin alone (grey line)
and after 8 weeks of the addition of gliptin treatment (black line). (A) vildagliptin (B) sitagliptin.

Values at baseline (BL) and mean changes from baseline in overall (AUCtotal), postprandial (AUCpp)
and basal hyperglycemia (AUCb) after 8 weeks (W8) of treatment with vildagliptin and with sitagliptin.
*: indicate a P value < 0.05 in change from baseline.
Key Results:

Results: Time-in-Range Improvement with Vilda and Sita
• Vildagliptin significantly increased time-in-range (70-140mg/dL) by 24.2% (p=0.02)
vs Sitagliptin’s non-significant increase of 9.8% (p=0.33)
33 of 21
Change in TIR Vildagliptin Sitagliptin
Base line 917 min 872 min
After 8 weeks of
Rx
1139 min 958 min
Change in TIR 24.2% 9.8%
P value 0.02 0.33

Results: Addressing GV
34
Vildagliptin Sitagliptin p value
Mean+/-SD P value vs BL Mean+/-SD P value vs BL
Mean Amplitude of Glycemic Excursion (MAGE) (mg/dL)
 Baseline 67.0+/-21.3 67.9+/-17.3
 Week 8 52.6+/-16.4 0.03 51.4+/-16.2 0.02 0.83
Mean 24 hr Blood Glucose reading (mg/dL) – Circadian Glycemic Control
 Baseline 130.6+/-12.0 131.0+/-14.7
 Week 8 118.5+/-12.5 0.01 129.4+/-18.2 0.62 0.07
Glycemic control at baseline and after 8 weeks of adjunctive vildagliptin and sitagliptin treatment
Both Vilda & Sita improved glycemic variability but Vildagliptin induced better
circadian glycemic control than sitagliptin.

Rauch T et al. Diabetes Therapy 2012
Linagliptin
Lowered 24-h Weighted Mean Glucose versus placebo
Linagliptin n = 40
0
20
40
60
80
100
190.05
19.91
At baseline Day 28
WMG
(mg/dl)
(P<0.0001)

Rauch T et al. Diabetes Therapy 2012
Linagliptin was associated with lower mean plasma
glucose concentrations

Conclusions:
• GV needs to be assessed in T2DM patients for prompt management
in order to prevent/delay further complications.
• Time-in-Range is an internationally recommended metric for
assessing GV and can be assessed using AGP.
• Several clinical studies have shown that the addition of DPP4i
significantly reduced GV – with not much difference between
different DPP4i molecules.
• However, Vildagltin has induced better circadian glycemic control
than sitagliptin with a significant decrease in overall hyperglycemia,
mainly driven by reduction in basal hyperglycemia.

Management of glycemic variability- Role of DPP4i (1).pptx

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