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Diabetes Care Volume 39, Supplement 2, August 2016
Valeriya Lyssenko,1,2 Cristina Bianchi,3 and
Stefano Del Prato3
Diabetes Care 2016;39(Suppl. 2):S127–S136 | DOI: 10.2337/dcS15-3002
Upon the publication of the results of the 20-year-long UK Prospective Diabetes
Study (UKPDS) (1), the general recommendation was to achieve, in as many as
possible of individuals with type 2 diabetes (T2DM), a target HbA1c value #7%,
i.e., the average value associated with significant reduction in the risk of micro- and
macrovascular complications. This “universal” glycemic target, however, has been
challenged by the results of subsequent intervention trials (2–4). Given the critical
analysis of the results of these trials, the American Diabetes Association (ADA) and
the American Heart Association suggested that an individualized HbA1c target should
be identified based on the duration of diabetes, life expectancy, presence and severity
of diabetes complications, and propensity for hypoglycemia (5). This suggestion has
been endorsed by several international guidelines. They also suggest a selection of
glucose-lowering strategies to be individualized in order to ensure the most appropriate risk-to-benefit ratio for all patients. According to the ADA/European Association for the Study of Diabetes position statement for T2DM management (6), this
can be achieved through balanced assessment of efficacy, risk of hypoglycemia,
effect on body weight, and costs of available glucose-lowering agents and their
combination.
Although the concept of treatment individualization is appealing, how to pursue it
in the clinical setting remains highly debated. To this purpose, careful phenotypic
description of the person with T2DM may be appropriate. A few years ago, the ABCD
approach was proposed to help physicians in individualizing glycemic target and
treatment selection (7). “ABCD” stands for four phenotypic traits that are easy to
collect and to factor in: age, body weight, complications and comorbidities, and
duration of diabetes.
Age
Age is the strongest risk factor for life expectancy and mortality. The number of
elderly people with diabetes is growing, and .70% of the elderly with T2DM have
some degree of impaired physical ability. Treatment of diabetes in these subjects
can be challenging, as it may expose to the risk of accelerating the development of a
geriatric syndrome. Treatment should aim at keeping the patient free of acute
metabolic complications, preventing development and/or progression of diabetes
complications, preserving cognitive function and physical ability, ensuring adequate
quality of life, and avoiding drug reactions. The selection process of glucose-lowering
agents should, therefore, take these recommendations into full consideration.
A critical appraisal of glucose-lowering therapy has recently been published (8).
Though age per se is not a contraindication to metformin, its use can be limited by
gastrointestinal side effects, which may be undesirable in older, frail patients. Metformin can lower vitamin B12 levels, which might accelerate cognitive dysfunction.
The main limitation for the use of the drug, however, remains impaired renal function and risk of lactic acidosis.
Pioglitazone is not contraindicated in people with reduced glomerular filtration
rate (GFR), but its use is limited by increased risk of bone fractures, fluid retention,
and heart failure.
The main concern with respect to sulfonylureas and glinides remains the increased risk of hypoglycemia. We have reported that 40% of patients accessing
the emergency room because of hypoglycemia were on antidiabetes agents with
sulfonylureas/glinides 6 insulin sensitizers being used in 92% of the cases and that
the average age was 79 6 11 years (9). Glibenclamide was the most frequently used
1
Department of Translational Pathophysiology,
Steno Diabetes Center A/S, Gentofte, Denmark
2
Diabetes and Endocrinology, Department of Clinical Sciences, Lund University, Malmö, Sweden
3
Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
Corresponding author: Stefano Del Prato, stefano
[email protected], or Valeriya Lyssenko,
[email protected] or [email protected].
This publication is based on the presentations
at the 5th World Congress on Controversies to
Consensus in Diabetes, Obesity and Hypertension (CODHy). The Congress and the publication
of this supplement were made possible in
part by unrestricted educational grants from
AstraZeneca.
© 2016 by the American Diabetes Association.
Readers may use this article as long as the work is
properly cited, the use is educational and not for
profit, and the work is not altered.
DIABETES TREATMENT OPTIONS
Personalized Therapy by
Phenotype and Genotype
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Personalized Therapy by Phenotype and Genotype
sulfonylurea (69%). Moreover, among
patients taking sulfonylureas, 47% had
an estimated GFR ,60 mL/min/1.73 m2
and 13.5% had ,30 mL/min/1.73 m2.
This observation supports the recommendation that the risk associated with insulin
secretagogues should be carefully assessed, particularly when prescribed in
vulnerable T2DM patients.
Trials preformed in elderly patients
with T2DM (rev. in 10) have confirmed
efficacy, body weight neutrality, and low
risk of hypoglycemia of dipeptidyl peptidase (DPP)-4 inhibitors. In patients
with moderate-to-severe renal impairment, appropriate dosage adjustment,
with the exception of linagliptin, which
has no renal excretion, provides a good
benefit-to-risk ratio with no further loss
in renal function or increased rate of
hypoglycemia.
Data on glucagon-like peptide 1 (GLP-1)
analogs in older patients with T2DM are
scantier. These agents have a favorable
benefit-to-risk ratio, but their use in the
elderly may be limited by concomitant
impaired kidney function. The GLP-1
agonists, though with some difference
within the class, can cause nausea and
vomiting, which may contraindicate
their use in elderly patients with lower
body weight, weight loss, or erratic
feeding.
Efficacy of sodium–glucose cotransporter (SGLT) 2 inhibitors is likely to be
self-limited in patients with reduced estimated GFR, as this will reduce tubular
load and, therefore, glucose excretion.
Though these compounds are generally
considered to have a satisfactory efficacy-to-safety ratio, the risk for dehydration and hypotension is higher in
the elderly in those using a loop diuretic and in those with impaired kidney function.
Finally, insulin remains an effective
form of treatment, although focused trials on insulin therapy in the elderly are
needed. The risk of hypoglycemia is the
highest with insulin, and it may become
particularly challenging in the frail, unsupported elderly patients.
Diabetes Care Volume 39, Supplement 2, August 2016
.30 kg/m2, and 13% .30 kg/m2. Moreover, 69% had a waist circumference
.100 cm. In the overall population, it
was quite apparent that the greater
the BMI, the higher the HbA1c, triglyceride levels, and systolic and diastolic
blood pressure and the lower the HDL
cholesterol concentration. Therefore, in
individualizing treatment, body weight
may provide some guidance (Fig. 1). In
class II and III (i.e., BMI 35 to ,40 and
$40 kg/m2) obese patients, reduction
of body weight is a major target along
with restoration of glycemic control.
Hence, treatment associated with
weight loss, i.e., GLP-1 receptor agonists
and SGLT2 inhibitors, particularly if used
in combination with metformin, may
be a preferred choice. On the opposite
side of BMI distribution, the few leaner
patients with T2DM, in whom a major
defect of b-cell function can be hypothesized, may be better candidates for earlier insulin therapy. Finally, subjects with
diabetes with intermediate obesity (BMI
30 to ,35 kg/m2), in whom the risks
associated with obesity are still a matter
of debate (11), a main goal should be
preventing weight gain or inducing moderate weight loss. This suggests avoiding
drugs that may favor weight gain (e.g.,
sulfonylureas, pioglitazone, and insulin)
while favoring weight-neutral agents
such as DPP-4 inhibitors or agents that
may cause a moderate though sustained
weight loss such as SGLT2 inhibitors
(12).
Complications
The existence of diabetes complications
requires careful assessment of pharmacologic glucose-lowering therapy. A typical example is the presence of diabetic
nephropathy and loss of kidney function, as a number of agents have to be
either reduced in dose or not be used.
Out of many therapeutic options, only
insulin, pioglitazone, and DPP-4 inhibitors can be used through all stages of
renal function. The risk of hypoglycemia
is high with insulin, and careful dose adjustment is required. Pioglitazone is not
eliminated through the kidney, and
therefore it can be used with no limitation except for the risk of bone fracture
and fluid retention, conditions for which
people with low GFR are already at increased risk (8). DPP-4 inhibitors, with
appropriate dosage adjustment with
the exception of linagliptin, retain
efficacy comparable with that of sulfonylureas with almost no risk of
hypoglycemia in patients with moderateto-severe renal failure (13). Similar results have been reported in patients
with end-stage renal disease receiving
dialysis (14).
Along with safety considerations, the
physician may also consider added
values a pharmacologic agent may
have with respect to prevention of
complications. For instance, in a number of trials, the use of DPP-4 inhibitors
was found to be associated with reduction of albuminuria (rev. in 15). These
Body Weight
Body weight and adipose tissue distribution are common phenotypic traits and
potential markers of insulin resistance
and metabolic syndrome. Of 1,598 patients with T2DM referring to our clinic,
50% had a BMI .27 kg/m2, 27% a BMI
Figure 1—Schematic recommendation on how body weight may guide individualization of
glucose-lowering therapy. RAs, receptor agonists; SUs, sulfonylureas.
care.diabetesjournals.org
observations find ground on the identification of DPP-4 in the kidney where it
may be involved in neuropeptide Y and
peptide YY metabolism, cell proliferation, and collagen production by preglomerular and mesangial cells (16). GLP-1
receptors also are expressed in the kidney where they may exert natriuretic
and diuretic properties (16).
Even more interest exists with
respect to cardiovascular (CV) risk,
with metformin still believed to exert
some CV protection. The PROspective
pioglitAzone Clinical Trial In macroVascular Events (PROactive) provides
the strongest evidence supporting
beneficial effects of pioglitazone on
CV outcomes (17). Insulin has been
deemed free of risk as far as it is used
in high-risk individuals with early-stage
T2DM (18), though better focused trials
may still be needed in light of recent
analysis (19).
Great expectation was raised by the
CV outcomes trials based on DPP-4 inhibitors and GLP-1 receptor agonists because of their efficacy and concomitant
effects on the CV system (20). Preclinical
studies with GLP-1 and its analogs have
shown reduced infarct area upon ischemia (20). DPP-4 inhibition could provide
CV protection not only by inhibiting
GLP-1 degradation but also by blocking
the degradation of chemokines and
stromal cell–derived factor-1a with
increased endothelial progenitor cells
(15). The results of SAVOR-TIMI (Saxagliptin
Assessment of Vascular Outcomes
Recorded in Patients with Diabetes
Mellitus–Thrombolysis in Myocardial
Infarction) (21), Examination of Cardiovascular Outcomes with Alogliptin versus
Standard of Care (EXAMINE) (22), and
TECOS (Trial Evaluating Cardiovascular
Outcomes With Sitagliptin) (23), assessing,
respectively, the effect of saxagliptin,
alogliptin, and sitagliptin, and those of
ELIXA (24), where lixisenatide was
tested, have provided evidence for their
safe use in T2DM patients with high CV
risk. No reduction in CV events or mortality was detected, though it must be
kept in mind that these were noninferiority studies, as required by regulatory
agencies for all new diabetes drugs.
The CV outcome trials so far completed support the safe use of the incretinbased therapies in T2DM patients with
high CV risk but leave unanswered the
question of whether their use earlier
Lyssenko, Bianchi, and Del Prato
in the course of the disease may also
offer protection. Finally, the increased
risk of hospitalization for heart failure
observed in SAVOR-TIMI (21), but not
in EXAMINE (22) and TECOS (23), will
require further evaluation to establish
whether this is a drug-specific effect or
if it may be accounted for by differences
in trial design or conduct.
SGLT2 inhibitors have the potential
of reducing CV risk given their glucoselowering efficacy associated with sustained
moderate weight loss and blood pressure reduction and lowering of uric acid
levels (11). In support of potential CV
protection, the results of the first CV
outcomes trial comparing empagliflozin
with placebo on top of standard of care
have shown a strong risk reduction of CV
mortality and mortality for all causes in
people with high-risk T2DM (25). In patients with T2DM with prior CV events,
treatment with empagliflozin added on
top of standard-of-care therapy was associated with a 16% risk reduction (95%
CI 0.79–0.99, P , 0.04) for the composite end point of CV mortality, nonfatal
myocardial infarction, and nonfatal
stroke. Moreover, a significant risk
reduction was documented for death
from CV causes (238%), hospitalization
for heart failure (235%), and death
from any cause (232%). Whether these
results may apply to the general population with diabetes (i.e., individuals at
lower CV risk) remains to be ascertained, while they should be kept in
mind in dealing with patients with
T2DM who already have experienced a
CV event.
To conclude, in the individualization
process, the item “complications” is
complex, as it requires ascertainment
of presence and severity of complications and relative safety of pharmacologic treatments and appreciation of
their potential contribution to prevention
of complications beyond their glucoselowering effect.
Duration of Diabetes
The longer the duration of diabetes, the
greater the chance of complications and
the lower the life expectancy. From this
point of view, the implications of longstanding diabetes have already been
discussed. What remains for comment
is the case of short duration and, by
default, all cases of newly diagnosed
T2DM. The results obtained in such
patients in the UKPDS (1) support the
recommendation of achieving strict
and persistent glycemic control. However, to date, little information on demographic, clinical, and other factors
that may influence response to medications is available, making it difficult to
decide which drug to select. Metformin
is universally accepted as first-line treatment. More uncertain is how to proceed
upon metformin failure. Some answers
will come from GRADE (Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study) comparing
a sulfonylurea, DPP-4 inhibitor, GLP-1
receptor agonist, and basal insulin in
patients with recently diagnosed
T2DM treated with metformin to examine their effectiveness in maintaining the glycemic goal (HbA 1c ,7%)
over the time (26). Other outcomes
will include relative effects on selected
microvascular complications and CV
risk factors, patient-centered outcomes,
and cost-effectiveness. GRADE will also
evaluate the phenotypic characteristics
associated with success, failure, and adverse events of different drug combinations (26).
In summary, the ABCD approach can
identify common phenotypes to be used
in selecting pharmacologic treatment.
Nonetheless, they may not be sufficient
unless the next letter of the alphabet is
added. In this case, E stands for “etiology,” i.e., the identification of the main
pathophysiologic mechanisms responsible for the progression of the disease
in a given individual.
Etiology of the Disease
A better understanding of the pathophysiologic mechanisms may help in defining the best strategy for managing
T2DM. In the most recent years, along
with insulin resistance and impaired
b-cell function, a number of other mechanisms have been identified (27) accounting for an unexpected complexity
in the etiology of the disease. Most of
these mechanisms are now targeted for
pharmacologic intervention with more
to come as illustrated in Fig. 2. It is because of such complexity that the use
of a single drug is unlikely to provide a
sustained effect, while addressing more
than one mechanism at diagnosis may be
expected to result in a more sustained
efficacy, particularly if rational combination is pursued.
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Diabetes Care Volume 39, Supplement 2, August 2016
Figure 2—Organs and mechanisms involved in the pathogenesis of T2DM and mechanism-targeted treatments. In gray: drugs currently in development. Reprinted with permission from DeFronzo et al. (27). GK, glucokinase; GPRs, G-protein–coupled receptors; HGP, hepatic glucose production; IL, interleukin; -I, inhibitors; PPARs, peroxisome proliferator–activated receptors; RAs, receptor agonists.
Metformin is an insulin sensitizer
with a prevalent effect in restraining hepatic glucose production. Metformin
also increases GLP-1 gene expression in
L cells and enhances b-cell response to
GLP-1 (28). Therefore, metformin is a
“natural” companion of DPP-4 inhibitors, which prevent degradation of
endogenous GLP-1, improve b-cell sensitivity, and increase intraislet GLP-1
availability. A number of trials have
proven efficacy and low rate of side effects of this combination in drug-naı̈ve
patients, though whether this translates
into a durable effect remains to be verified. The VERIFY (Vildagliptin Efficacy in
combination with metfoRmIn For earlY
treatment of type 2 diabetes mellitus)
trial is a 5-year multinational, doubleblind, parallel-group study, investigating
the long-term effects of initial combination of metformin plus vildagliptin in
recently diagnosed patients with mild
hyperglycemia, designed to assess durability of glycemic control (HbA 1c ),
changes in b-cell function and insulin
sensitivity, and time to insulin initiation (29).
Glitazones have better durability than
sulfonylureas and metformin (30), and
preclinical studies have suggested a potential protection of b-cell function. Initial combination therapy with DPP-4
inhibitors and pioglitazone has led to
improvement in several markers of
b-cell function, suggesting that, if sustained, this combination may be beneficial for preservation of b-cell function in
T2DM.
Early use of SGLT2 inhibitors can relieve glucose toxicity and, in turn, improve
b-cell function and insulin sensitivity.
However, their use is associated with
concomitant increase in glucagon levels
and hepatic glucose production (31,32),
which may restrain some therapeutic
effect. Concomitant use of metformin
can prevent the increase in hepatic glucose production (33), while a DPP-4
inhibitor can prevent the increase in
glucagon levels (34). The combination
of the two inhibitors has shown a
greater, though not additive, effect
compared with the use of the single
components in reducing HbA1c, with almost twice as many patients reaching
the HbA1c target ,7% and no increase
in side effects (35).
In spite of potential rational combination of different glucose-lowering
agents, the choice of the most appropriate combination remains challenging.
Some information will come from
GRADE (26), though the study will not
include recent drugs such as SGLT2 inhibitors. DeFronzo and colleagues (36)
have proposed a different approach. Since
development and progression of hyperglycemia require the concomitance of
care.diabetesjournals.org
hepatic and peripheral insulin resistance
and impaired insulin secretion, a triple
therapy with metformin, pioglitazone,
and exenatide was initiated in subjects
newly diagnosed with T2DM. Over the
2-year follow-up, the combination therapy, compared with classic sequential
therapy with metformin, sulfonylurea,
and insulin, was more effective (HbA1c
5.95 vs. 6.50%; P , 0.001) and had a
7.5-fold lower rate of hypoglycemia
and a mean weight loss of 1.2 kg vs. a
mean weight gain of 4.1 kg (P , 0.01)
with sequential add-on therapy (36).
This approach, however, assumes that
all patients share the same pathogenetic
profile. Though reasonable, this may not
be universally acceptable, suggesting
the need for individual pathogenetic
profiling. Direct assessment of pathogenetic mechanism is theoretically possible but unrealistic in the clinical setting.
Recently, biomarkers have been identified that are associated with insulin
sensitivity and risk of developing diabetes (37) while there is still an unmet
need with respect to biomarkers for
b-cell function and mass and other
pathogenetic components.
In summary, the patient’s phenotype
can be complex, and though some phenotype traits can be easily accessible,
their interpretation may still rely on
the physician’s personal knowledge
and experience. Development of dedicated algorithm might help (38), but validation may require extensive work. We
have recently proposed an algorithm
(39) based on the result of a survey performed presenting a number of case vignettes to 244 top diabetologists from
all world regions. The algorithm was
then used to define individualized glycemic targets and validated by presenting
three new cases to 57 leading diabetologists who suggested glycemic targets
that were similar to those calculated
by the algorithm. We concluded that
the suggested algorithm could be an additional decision-making tool offered to
the clinician to supplement clinical decision making in considering a glycemic
target for the individual patient with diabetes. Available data on individual responses to drugs remain insufficient to
be built into an efficient algorithm, and
the phenotype description may remain
too broad to provide sufficient information on the underlying pathogenic factors. Moreover, other factors such as
Lyssenko, Bianchi, and Del Prato
early response to glucose-lowering
treatment may be needed to considered
as well, as recently suggested by Fu et al.
(40). More recently, the development of
more accessible genotyping techniques
and the discovery of a number of genetic
variants associated with the risk of
T2DM have raised the expectation
for a more precise personalized treatment. Whether combining phenotyping
and genotyping may offer better opportunities, to the best our knowledge, has
not been fully investigated. In the few
studies where this was attempted (41–
43) there was only a small improvement
in the ability to predict future development of T2DM.
Personalized Therapy by Genotype
T2DM recognizes a complex pathogenesis, and each mechanism does
recognize a genetic background. The
great expectation is that, through genotyping, precision medicine could be generated. Pharmacogenetics is still young,
with very few studies, most of which are
of small sample size and different design.
Nevertheless, despite current limitations,
there are examples for a guided treatment choice for diabetes (Table 1).
Lessons From Monogenic Forms of
Diabetes
MODY (maturity-onset diabetes of the
young) is a monogenic form of diabetes
with autosomal dominant inheritance.
MODY is characterized by defects in insulin secretion, and there are at least six,
and most likely more, different MODY
types. MODY offers a good example for
pharmacogenetic recommendations.
MODY 3, the most prevalent MODY
type (;61%), is due to mutations in
the HNF1A (hepatic nuclear factor 1-a)
gene and is often misdiagnosed as
type 1 diabetes requiring insulin treatment. However, these patients are very
sensitive to sulfonylureas, requiring
one-quarter or less of the dose usually
prescribed in patients with T2DM (44).
This hypersensitivity is due to decreased
HNF1A expression causing reduced liver
uptake of sulfonylurea and elevation in
its circulating levels. MODY 2 (;15%) is
caused by mutations in the GCK (glucokinase) gene and is characterized by
mild hyperglycemia usually not requiring treatment. The mutation leads to reduced activity of pancreatic glucokinase
resulting in increased glucose sensing
threshold (between 6 and 8 mmol/L)
for stimulation of insulin secretion. Diagnosis of MODY 2 has important implication in the treatment of women with
gestational diabetes mellitus. If the fetus carries the same mutation, treatment of the mother with insulin can
lower glucose to a level not sufficient to
sustain fetal insulin secretion resulting in
intrauterine growth retardation (45).
Another example of genetics providing guidance for treatment is neonatal
diabetes (NDM) caused by activating
mutations in the KCNJ11 and ABCC8
genes. These genes encode for two subunits of the KATP channels of sulfonylurea receptor in the b-cells causing
inability to close the KATP channels and
defective insulin secretion. Patients carrying the KCNJ11/ABCC8 mutations are
poorly regulated by insulin but respond
very well to high doses of sulfonylureas
(46). Children with NDM often present
with DEND syndrome (developmental
delay, epilepsy, and neonatal diabetes).
Since the KCNJ11 gene is also expressed
in the brain, switching the children to
high doses of sulfonylureas can also positively affect developmental defects
(46).
Common T2DM
The advent of genotyping and sequencing technologies has contributed to
discovery of many genetic variants contributing to T2DM pathogenetic complexity. These advances open attractive
opportunity to determine the utility of
genomic regions to predict treatment
response. However, interpretation of
pharmacogenetic findings is not always
clear. Whereas most studies use improvement in HbA1c levels as the main
outcome, others define responders in
terms of percentage of HbA1c decline
or reduction of fasting and postprandial
glucose and, in some cases, changes in
insulin secretion and sensitivity. These
diversities might contribute to inconsistency and heterogeneity between
findings. In the following section, pharmacogenomics studies with different
glucose-lowering agents will be briefly
examined.
Metformin
A number of genetic variants have been
associated with different responses
to metformin. The drug is an organic
cation, which is not metabolized in the
body and requires transporters for absorption, distribution, metabolism, and
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Sulfonylureas
Biguanides
T2DM
Linagliptin
Nateglinide
Repaglinide
Changes in HbA1c
Glucose levels up to 8 h
Glucose and insulin sensitivity
GLP-1 stimulated insulin secretion
Targeted reduction to HbA1c ,7%
Targeted reduction to HbA1c ,7%
Decrease in glucose and HbA1c values
Decrease in fasting glucose or
HbA1c values
Decrease in HbA1c and edema
Development of edema
Decrease in glucose levels
187 patients with T2DM, RCT
268 patients with T2DM
209 patients with newly diagnosed T2DM,
RCT, 48 weeks
232 persons without diabetes undergoing
hyperinsulinemic clamp
961 patients with T2DM, RCT, 24 weeks
35 healthy Chinese males
209 patients with T2DM, RCT, 48 weeks
357 patients with T2DM, 1,988 healthy
subjects
1,073 patients with T2DM
901 patients with T2DM, 3–12 months
198 patients with T2DM, RCT, 12 weeks
131 patients with T2DM, RCT, 26 weeks
1,531 patients with T2DM, 18 months
116 patients with T2DM, 30 days
2,896 patients with T2DM, 18 months
525 patients with T2DM
Targeted reduction in HbA1c ,7%
Reduction in HbA1c values
Targeted reduction to HbA1c ,7%
Risk for secondary failure to sulfonylurea,
in vitro glibenclamide-stimulated
insulin release
Severe hypoglycemia
TCF7L2 (30%) (61)
SLCO1B1 and CYP2C9 (39%) (62)
KCNQ1 (2 SNPs, 19 and 41%) (63)
CTRB1 (9–11%) (59)
CYP2C8 (12%) (57)
AQP2 (11%), SLC12A1 (48%) (58)
KCNQ1 (28%) (63)
CYP2C9 (13.4 and 7.3%) (53)
TCF7L2 (35%) (54)
PPARG (4%) (55)
PPARG (9.9%) (56)
CYP2C9 (9–13%) (52)
OCT1 (2 SNPs, 6.7 and 19.8%) (48)
MATE1, MATE2 (43%) (49)
ATM (44%) (50)
KCNJ11/ABCC8 (64.2%) (51)
HNF1A (;73% of all MODY) (44)
KCNJ11/ABCC8 (30–60% of all NDM) (46)
43 patients, 12 weeks
Gene(s) (RAF) (reference no.)
Clinical cases
Study population, type, duration
Complete discontinuation of insulin
treatment and switch to sulfonylureas;
decrease in HbA1c values
Change to sulfonylureas
Treatment response
DPP-4I, DPP-4 inhibitors; RAF, response allele frequency; RCT, randomized controlled trial.
Metiglinides
d
Rosiglitazone
Rosiglitazone
Rosiglitazone
Glimepiride,
glibenclamide
Any sulfonylureas
Any sulfonylureas
Rosiglitazone
Pioglitazone
Metformin
Metformin
Metformin
Any sulfonylureas
Glyburide
Glibenclamide
Drug
Personalized Therapy by Phenotype and Genotype
Incretins (DPP-4I,
GLP-1 agonists)
TZDs
Sulfonylureas
Sulfonylureas
NDM
Class
MODY 3
Diabetes type
Table 1—Pharmacogenetic effects of drugs in patients with diabetes carrying reported genetic variants
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elimination. Variants in the organic cation transporter, OCT1 (SLC22A1), have
been associated in some (47), but not
all (48), studies with lower metformin
efficacy and increased renal clearance.
Similarly, better efficacy and reduced
risk of developing diabetes have been
associated with genetic variants of
MATE1 and MATE2 (multidrug extrusion
proteins 1 and 2) (49). Finally, by genomewide association, variants in the ATM
(ataxia teleangiectasia mutation) have
been associated with significant reduction in HbA 1c levels after metformin
treatment (50).
Sulfonylureas
Pharmacogenetic studies of sulfonylureas have been focused on genetic variants affecting pancreatic b-cell KATP
channels and drug-metabolizing enzyme
CYP2C9. Variants in the KCNJ11 and
ABCC8 genes coding for KATP channel
subunits have been found to be associated with T2DM risk and reduced
response to sulfonylurea (51). Additionally, two variants in the CYP2C9 gene
(CYP2C9*2 and CYP2C9*3) were found
to be associated with increased drug
levels and increased risk of hypoglycemia
after treatment with sulfonylurea (52),
fitting with a better HbA1c response in
the Genetics of Diabetes Audit Research in Tayside Scotland (GoDARTS)
study (53). Finally, in GoDARTS, individuals carrying the TCF7L2 variant have
been reported to have a lesser response
to sulfonylurea compared with that in
noncarriers (54).
Thiazolidinediones
Thiazolidinediones (TZDs) are attractive
candidates for pharmacogenetic studies, as variants in the drug target PPARG
gene are associated with T2DM risk and
insulin sensitivity in populations without
diabetes. Yet, pharmacogenetic studies
in subjects with prediabetes and subjects with diabetes yielded inconsistent
results as far response to TZD therapy
is concerned (55,56). Genetic variants
of the CYP2C8*3 enzyme have been
associated with reduced TZD activity
(57). Finally, genetic variants in the aquaporin 2 (AQP2) and sodium/potassium/
chloride transporter (SLC12A1) genes
have been associated with increased
risk of edema in rosiglitazone-treated
patients (58), offering opportunity for
pharmacogenomics not only in terms
of therapeutic response but also in
Lyssenko, Bianchi, and Del Prato
identifying underlying mechanisms in
subjects at risk for side effects.
Incretins
Variants in the chymotrypsinogen gene
(CTRB1/2) have been linked to impaired
GLP-1–stimulated insulin secretion in 232
individuals undergoing hyperinsulinemic
clamp. The variants also were associated
with increased chymotrypsinogen activity and gene expression in human pancreatic islets (59). These findings found
support in a GoDARTS study reporting
greater HbA1c reduction in carriers of
the CTRB1/2 variant treated with gliptins
(54). Many of the genetic variants (TCF7L2,
WFS1, and KCNQ1) increasing susceptibility to T2DM are associated with impaired
incretin action, whereas variants in the
glucose-dependent insulinotropic peptide
(GIP) receptor gene (GIPR) were associated
with reduced incretin secretion (60). Recently, some differences in response
to linagliptin have been reported in patients with diabetes with and without
the TCF7L2 gene variants (61).
Metiglinides
Metiglinides (nateglinide and repaglinide)
close the KATP channels to stimulate insulin secretion through interaction with
subunits other than those activated by
sulfonylureas. Effects of genetic variants
in transporters (SLCO1B1) and metabolizing enzymes (CYP2C9) have been associated with decreased rate of glinide
metabolism, though no effect was observed in treatment response (62).
Interestingly, a variant in the imprinted
KCNQ1 gene was associated with better
glucose and insulin sensitivity responses
after 48-week repaglinide therapy but
not when these effects were corrected
for age, sex, and BMI (63). KCNQ1 has
previously been implicated in T2DM
pathogenesis, displaying parent-oforigin effects, suggesting the importance of taking into account whether
alleles are inherited from the mother
or from the father.
on underlying biology of the protective effect and hopefully facilitate development of drugs based on these
mechanisms.
In addition to biologic factors, response to drug therapy also involves
gene-environment interactions including dietary patterns and physical activity
as well as psychosocial factors such as
motivation, education, family support,
and treatment adherence. The decline
in cognitive function in T2DM patients
may play a role in treatment adherence.
Genomic regions associated with cognitive function have been shown to have
positive evolutionary selection, but this
phenomenon was not observed for the
regions associated with risk factors
for late-onset metabolic diseases (65).
Therefore, it will be interesting to examine to what extent response to therapy
and poor compliance are determined by
the patient’s cognitive function. Furthermore, individual genetic susceptibility could be modified by epigenetic
mechanisms involved in different tissues and organs, including several brain
areas. Finally, multiethnic studies are
warranted to study generalization
of treatment responses in different populations. In summary, there is accumulating evidence that genetics might
become an important tool for diabetes
diagnosis, classification, and treatment,
as it is the case for monogenic forms of
diabetes. Similarly, pharmacogenetics
has been successfully used to guide
choice of therapy in monogenetic forms
of diabetes, while so far having limited
clinical utility in the polygenic T2DM
phenotypes. However, genetic effects
can be modified by different environmental factors with subsequent epigenetic modifications. Therefore, more
and larger studies are needed to provide
better classification of heterogeneous
T2DM into different subgroups to appreciate the potential of pharmacogenomics for personalized treatment.
Future Directions
Pharmacogenetics possesses the potential for the discovery of novel drug targets. For instance, a recent study from
the Botnia region in Finland comparing
individuals with early-onset T2DM and
subjects remaining diabetes free despite
clustering of T2DM risk factors has led
to the identification of a rare protective
variant in the SLC30A8 gene (R138X)
(64). Follow-up studies will shed light
Conclusions
Precision medicine is not just an academic discussion, as it already belongs
to the political agenda of the most industrialized countries. President Barack
Obama, in his State of the Union Address
in January 2015, announced the launch
of a “Precision Medicine Initiative to
bring us closer to curing diseases like
cancer and diabetes.” This scenario
S133
S134
Diabetes Care Volume 39, Supplement 2, August 2016
Personalized Therapy by Phenotype and Genotype
has been clearly described in its challenges, expectations, research, and scientific approaches in a clear editorial by
Collins and Varmus (66) where cancer
was identified as the “near-term focus,”
whereas a “whole range of diseases”
will be targeted in a longer term. For
people with diabetes, the promise of
personalized therapy has the main
objectives of providing effective, sustained, and safe treatment. On the basis
of the patients’ individualdphenotypic
and geneticdprofile, precision medicine aims at predicting which patient is
more likely to benefit and which one is
more likely to experience side effects in
response to therapeutic modalities.
Though a number of phenotypic traits
and genetic variants have been identified, their interpretation and translation into clinical guidance remain
uncertain and insufficiently investigated
and thus, of limited, if any, clinical use.
More data and more clinical trials are
needed. To this purpose, it sounds desirable that data generated in clinical trials
for new drug classes could be pooled in
order to gain sufficient power for identifying the main predictors of therapeutic
success and risk of adverse events. Such
an approach may require the industry to
move out of the expectation of the
“blockbuster drug,” that is, a drug that
can be used in the vast majority of individuals with T2DM. On the contrary, it
would be of great value to understand
which drug(s) better suits a given individual in a given phase of disease. Collection
of this information may not be limited to
randomized clinical trials. Rather, postmarketing large-scale surveillance, electronic health registries, and even mobile
health technology could be used to gain
stronger information over much longer
follow-up in larger and more diversified
populations with diabetes. Currently, up
to 13 classes of drugs are available for
the treatment of T2DM targeting different pathogenetic mechanisms. The challenge is how to use the right drug (and
the right combination of drugs) in the
right patient. While we wait for a more
scientific and less pragmatic guidance, a
few things should be kept in mind. We
must appreciate that T2DM is a complex
disease with complex pathogenesis and,
therefore, is not easy to treat, particularly in the pursuit of long-term glycemic
control. From this point of view, the
multiplicity of available pharmacologic
agents should be welcome, though appropriate use requires expert knowledge
of advantages and disadvantages of each
available drug and the potential for rational combination. This complexity may
sound at odds with the need for effective treatment in a growing number of
individuals with T2DM worldwide and
the need to develop treatment strategies that can be implemented in primary health care. Yet, inappropriate
use of drugs may not provide the necessary response and, particularly in
the case of novel agents, may result
in unjustified expense excess. It is,
therefore, the duty of the diabetes
community to search for a new strategic model of interaction between the
specialist and the other health care
providers ensuring appropriate management of personalized treatment
for the person with diabetes.
Funding. This work was in part supported
by Progetti di ricerca di Rilevante Interesse
Nazionale (PRIN) grant 2010 YK7Z5K_006.
Duality of Interest. V.L. is employed by Steno
Diabetes Center A/S, a research hospital working
in the Danish National Health Service and owned
by Novo Nordisk A/S. C.B. has received honoraria
for advisory work from Eli Lilly. S.D.P. has received honoraria for advisory work and lectures
from AstraZeneca, Boehringer Ingelheim,
Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline,
Hanmi Pharmaceuticals, Intarcia, Janssen, Merck
Sharp & Dohme, Novartis, Novo Nordisk, Sanofi,
and Takeda as well as research support from
Merck Sharp & Dohme, Novartis, and Novo
Nordisk. No other potential conflicts of interest
relevant to this article were reported.
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