Key words
Pharmacoepidemiology - psychopharmacology - genetics - testing
Introduction
Pharmacogenetics (PGx) is the study of the genetic predisposition of individuals,
which can result in variability in drug metabolism, pharmacodynamics, or
immunogenicity linked to treatment (non-)response or adverse events [1]. PGx aims at optimising treatment outcomes
(i. e. increased efficacy or reduced risk of adverse events) by
personalising treatment for patients based on their genetic makeup. Actionable PGx
drugs are those for which drug or dosing recommendations have been made available
by
international PGx consortia in evidence-based PGx guidelines [2]
[3]
[4]. PGx guidelines have been
provided for a proportionally higher number of psychotropic drugs than other drug
classes [5]. Consequently, PGx testing could
be of specific benefit for individuals with mental disorders, who often experience
adverse events and delayed, insufficient, or non-response to psychopharmacological
treatment [6]
[7]. Nevertheless, the implementation of PGx testing in psychiatry is
lagging behind other specialities such as oncology [5]
[8]
[9]. The reasons for this have been discussed in
recent review articles with a common ground of identified numerous barriers
including perceived missing clinical evidence and utility, and knowledge and
consensus about PGx testing among psychiatrists [5]
[9].
Recently, several observational drug utilization studies of both somatic and
psychotropic drugs with actionable PGx implications (PGx drugs) in different
populations have contributed to a broader understanding of the potential impact of
PGx testing based on the frequency of PGx drug use in larger populations [10]
[11]
[12]
[13]
[14]
[15]. The findings of these
studies indicate, for example, that 50% of people using prescription drugs
received one or more actionable PGx drug(s) during a 4-year period, 23% of
first-time drug prescriptions included actionable drug-gene interactions (DGIs) and
in approximately 25% of prescriptions, dose adjustments were recommended
[11]
[12]
[13]
[14]. A preponderance of actionable psychotropic
PGx drug use and the involvement of cytochrome-P450 (CYP) enzymes 2C19 and 2D6 DGIs
were commonly observed [10]
[11]
[12]
[13]
[14]
[15].
Although people with mental disorders have not been the focus of these studies and
thus, the incidence and prevalence of PGx drug use in people with mental disorders
in comparison with an unselected population have not been investigated in detail
yet.
One of the reasons for the low implementation of PGx guided dosing in psychiatry is
a
lack of consensus and specific advice regarding when to test to obtain the optimum
and timely benefits. Several options regarding the timing of PGx testing are
available, e. g. i) the currently often used ˋreactive testingˊ when
a patient experiences drug toxicity or non-response, ii) ˋreactive
prospective testingˊ by executing a PGx test at the time of prescribing to know the
PGx status of the patient prior to the start of treatment, and iii)
ˋpre-emptive testingˊ by executing a panel-based PGx test at a certain time in a
personˊs life, to know his or her PGx status prior to all future PGx drug
prescriptions [11]. A panel-based PGx test
including multiple variants in different genes has advantages over single-gene
testing in that PGx guided dosing can be applied to PGx drugs with more than one
established associated gene (i. e. actionable DGI), or when multiple PGx
drugs are used subsequently with different DGIs or concomitantly resulting in
drug-drug-gene interactions (DDGI). In addition, panel-based testing offers
combinatorial PGx, in which multiple variants of different genes can be interpreted
simultaneously to provide a more accurate personal PGx based dosing advice [16]
[17].
While panel-based testing has been advocated more recently [5]
[9]
[13]
[18], guidance on whether to test pre-emptively
or at which age first PGx drug use can be expected is still missing, which could be
supported by observational studies investigating the exposure of actionable PGx
drugs since birth.
Aims of the study
To describe prescription drug use of current actionable somatic and psychotropic PGx
drugs according to international PGx consortia in people with and without hospital
diagnosed mental disorders in the Danish population. The specific aims were to
investigate (1) the (life-time) incidence and prevalence of PGx drug use, (2) age
at
first PGx drug prescription, (3) the mean number of different PGx drugs per
individual considering panel-based PGx testing (versus single-gene) and (4) the
frequency of PGx drug use related to different genes regarding combinatorial PGx
interpretation.
Materials & Methods
Study design
This was a population- and register-based cohort study of individuals born
between 1981 and 2005 investigating prescription drug use of PGx drugs in
Denmark between 1995 and 2016. The study used data of the Integrative
Psychiatric Research (iPSYCH) consortium, which has established a large, unique
Danish psychiatry-focused population-based case-cohort study sample
(iPSYCH2012), hereafter referred to as iPSYCH sample [19].
Details on the iPSYCH sample have been described previously [19]. In brief, the iPSYCH sample is nested
within the entire Danish population of singleton births born to known mothers
between 1981 and 2005 (study base: 1 472 762 individuals), who
were alive and resided in Denmark on their first birthday [19]. The iPSYCH sample contains five
cohorts of a combined total of 57 377 individuals with at least one
diagnosis of one of five selected mental disorders, further referred to as case
cohorts, i. e. affective/mood disorder(depression),
attention-deficit/hyperactivity disorder (ADHD), autism, bipolar
affective disorder (BD) and schizophrenia (SZ), and a representative, randomly
selected cohort of the general population of 30 000 individuals
corresponding to 2.04% of the study base. The members of the
population-based cohort are representative of the entire Danish population born
between 1981 and 2006, and are at risk of developing the disorder of interest
during follow-up.
Data sources
The iPSYCH sample is linked via the anonymized personal identification number,
since birth or immigration, to drug prescription data and clinical and
socio-demographic information from several Danish national registers, including
i) the Danish Civil Registration System (CPR) including information
since 1968 on e. g. birth registration, vital status and citizenship
[20]; ii) the Danish National
Prescription Registry including information on all prescription drugs dispensed
at pharmacies since 1994, including e. g. the anatomical therapeutic
chemical (ATC) classification code, date and quantity of dispensed drugs [21]; iii) the Danish National
Patient Registry including inpatient care information in Denmark since 1977 and
outpatient care information since 1995 [22]; iv) the Danish Psychiatric Central Research Register
including e. g. diagnosis at and dates of admission and discharge of
patients treated at psychiatric departments in Denmark since 1969 [23]; and v) the Danish Register of
Causes of Death including cause-specific mortality statistics, with computerized
individual records since 1970 [24].
Study population and study period
An overview of the study design and the included individuals with mental
disorders and the population sample is shown in [Fig. 1], panel a. The study period
was from January 1st, 1995 until December 31st, 2016. As
prescription drug information from birth was not available for individuals born
between 1981 and 1994, and to assess life-time exposure to prescription drugs,
we created two birth cohorts including birth cohort81 born in 1981–1994
and birth cohort95 born in 1995–2005 (with life-time prescription drug
exposure). The follow-up time of the individuals started in 1995 (or at birth
after 1995), and ended at the date of emigration, death, or December
31st, 2016, whatever came first ([Fig. 1], panel b). Individuals of
the population cohort developing mental disorders during follow-up were censored
from the population sample at the date of diagnosis. If these individuals had
received a diagnosis of one of the included mental disorders before December 31,
2012, they were, by study design, included in the case cohorts. These
individuals contributed observation time and drug use in both cohorts prior to
their diagnoses but accounted only for a small proportion of the population
cohort due to low incidence rates [37].
Thus, in this study, the population-based cohort represents the part of the
Danish population without the selected psychiatric disorders diagnosed at
psychiatric hospitals.
Fig. 1 An overview of study sample selection, cohorts and
follow-up . Panel a shows the selection of the iPSYCH sample in
the study and panel b shows the overview of the study cohorts,
study period and follow-up. The timeline in panel B shows the study
cohort (1981-2005), comprising birth cohort81 (1981–1994) and
birth cohort95 (1995–2005). The study period was from 1995 to
2016. The cohort was divided into two birth cohorts because prescription
information was only available since 1995 and we did not have complete
prescription information for individuals born between 1981 and 1994. The
Danish Psychiatric Central Research Register contains registrations of
contact moments until December 31st, 2012, which is the
latest date of defining individuals as MD cases for the case
cohorts.
Pharmacogenetic drugs of interest
The international Clinical Pharmacogenetics Implementation Consortium (CPIC) and
the Dutch Pharmacogenetics Working Group (DPWG) execute literature reviews on
PGx and provide peer-reviewed, evidence-based, updatable and detailed PGx
guidelines [2]
[3]
[4]. National Danish PGx guidelines do not exist. CPIC and DPWG
recommendations partially overlap with labelling recommendations of drugs, such
as pimozide and warfarin regarding PGx testing, but PGx testing, in general, is
not routinely/widely integrated in Danish clinical practice. We combined
information from both CPIC and DPWG PGx guidelines and identified 69 drugs for
which actionable PGx recommendations were provided until February 2020
(i. e. actionable PGx drugs, Supplement Table 1) [25]
[26]. The list includes drugs from the following drug classes:
anaesthetics, antibiotics, analgesics, anti-cancer drugs, anticoagulants,
cardiovascular drugs, proton-pump inhibitors (PPIs) and psychotropic drugs. Of
the 69 PGx drugs, 20 drugs (29%) have more than one actionable DGI, and
thus have more than one actionable PGx guideline. Seven drugs have DGIs related
to both CYP2C19 and CYP2D6, three drugs to TPMT and
NUDT15, seven drugs to RYR1 and CACNA1S, one drug to
HLA-A and HLA-B, one drug to CYP2C9 and HLA-B
and one drug to VKORC1, CYP2C9 and CYP4F2 (Supplement
Table 1). We identified the use of actionable PGx using their ATC
classification codes from the Danish National Prescription Registry
(Supplement Table 1). The retrieved data thus describes drug
prescriptions redeemed by the patients at community pharmacies, but the terms
‘drug use’ and ‘drug users’ are applied as well
in this study.
Data accessibility
The iPSYCH study was approved by the Danish Scientific Ethics Committee (EC:
1-10-72-287-12), the Danish National Board of Health (Sundhedsdatastyrelsen,
SDS, FSEID 1999) and the Danish Data Protection Agency (Journal number
2015-57-0002, 62908, umbrella permission Aarhus University). All data is stored
at Statistics Denmark and was available in an anonymous form, by remote online
access, with special permission in compliance with the Danish Data Privacy
Act.
Statistical analyses
We have presented measures of drug use e. g. incidence rates and
prevalence, means, standard deviations, separately for two birth cohorts, males
and females, and mental disorders case cohorts and the population cohort. We
divided the number of users with at least one prescription of a respective PGx
drug by the number of total underlying person-years (PY) during follow-up
(incidence rates) and by the number of total underlying individuals at the
beginning of follow-up (prevalence). The mean age of first-time PGx drug use and
the mean number of prescribed different PGx drugs were tested with a t-test to
examine whether the mean differences between males and females were
statistically significant. A p-value <0.05 was considered statistically
significant. We used SAS %Lexis macro to calculate incidence rates [27]. Individuals with mental disorders
might have received a first actionable psychotropic PGx drug prescription prior
to their diagnosis of mental disorders, hence, we have reported descriptive
statistics for those individuals who used PGx drugs prior to their first
psychiatric hospital diagnosis within each cohort. The number of different DGIs
per individual was calculated as the sum of all unique DGIs during follow-up.
Concomitant drug use was considered if two or more different drugs had at least
one day of overlap of their drug prescriptions. To assess if panel-based testing
is more favourable than single-gene testing, we identified the number of
prescribed PGx drugs which have more than one actionable DGI, and the total
number of users for those drugs.
Due to a restriction from Statistics Denmark and the General Data Protection
Regulations, data can only be reported if the number of individuals contributing
to aggregated measures exceeds four, which can result in the grouping of data.
For sub-analyses to avoid too few counts in individual categories, we grouped
mental disorders into mental disorders (A) including predominantly childhood
onset disorders and (B) including predominantly adult-onset disorders. All data
were processed and analysed using SAS statistical software version 9.4 (SAS
Institute Inc, Cary, NC USA) and proportions were compared using MedCalc for
Windows, version 19.4 (MedCalc Software, Ostend, Belgium).
Results
[Table 1] shows the characteristics for both
the randomly selected population cohort (N=29 975) and the combined
mental disorders case cohorts (N=56 065) born between
1981–2005 in Denmark, further divided into the two birth cohorts. Males and
females were equally distributed in all cohorts, except in the mental disorders case
birth cohort95, which included more males due to a higher prevalence of the
young-onset of ADHD and autism in males. Individuals can have a diagnosis of more
than one mental disorder and can therefore contribute to several mental disorder
case cohorts. During the total follow up of 1 664 266 PY (average of
19.3 years), 115 individuals (0.13%) died and 3260 individuals
(3.8%) emigrated (0.19 and 6.1% in the population cohort, and 0.10
and 2.6% in the mental disorders cohorts, respectively).
Table 1 Characteristics of study sample.
|
Birth cohort81 (1981–1994)
†
Total=53,331
|
Birth cohort95 (1995–2005)
†
Total=32 709
|
|
Population (N=15 894)
|
MD case cohorts (N=37 437)
|
Population (N=14 081)
|
MD case cohorts (N=18 628)
|
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
|
Age in groups (y)
|
|
|
|
|
|
|
|
|
|
<18
|
-
|
-
|
-
|
-
|
8876
|
(63.0)
|
9705
|
(52.1)
|
|
18-23
|
2636
|
(16.6)
|
5605
|
(15.0)
|
5205
|
(37.0)
|
8923
|
(47.9)
|
|
24-29
|
7349
|
(46.2)
|
17 494
|
(46.7)
|
-
|
-
|
-
|
-
|
|
30-35
|
5909
|
(37.2)
|
14 338
|
(38.3)
|
-
|
-
|
-
|
-
|
|
Sex
|
|
|
|
|
|
|
|
|
|
Female
|
7817
|
(49.2)
|
19 619
|
(52.4)
|
6866
|
(48.8)
|
4971
|
(26.7)
|
|
Male
|
8077
|
(50.8)
|
17 818
|
(47.6)
|
7215
|
(51.2)
|
13 657
|
(73.3)
|
|
Ethnicity
‡
|
|
|
|
|
|
|
|
|
|
Africa
|
65
|
(0.4)*
|
73
|
(0.2)
|
218
|
(1.5)*
|
192
|
(1.0)
|
|
Asia
|
176
|
(1.1)*
|
146
|
(0.4)
|
206
|
(1.5)*
|
143
|
(0.8)
|
|
Australia/Greenland/N.&S.America/Unknown
|
16
|
(0.1)*
|
16
|
(0.0)
|
14
|
(0.1)
|
14
|
(0.1)
|
|
Denmark
|
13 861
|
(87.2)*
|
33 001
|
(88.2)
|
11 294
|
(80.2)*
|
15 638
|
(83.9)
|
|
Europe
|
308
|
(1.9)*
|
403
|
(1.1)
|
360
|
(2.6)*
|
271
|
(1.5)
|
|
Middle East
|
145
|
(0.9)*
|
161
|
(0.4)
|
339
|
(2.4)*
|
256
|
(1.4)
|
|
Mixed
|
1298
|
(8.2)*
|
3601
|
(9.6)
|
1609
|
(11.4)
|
2088
|
(11.2)
|
|
Scandinavia
|
25
|
(0.2)*
|
36
|
(0.1)
|
41
|
(0.3)*
|
26
|
(0.1)
|
|
Region in Denmark
|
|
|
|
|
|
|
|
|
|
Capital Region
|
4629
|
(29.1)
|
10 925
|
(29.2)
|
4609
|
(32.7)
|
7011
|
(37.6)
|
|
Middle Jutland
|
3580
|
(22.5)
|
8764
|
(23.4)
|
2991
|
(21.2)
|
3407
|
(18.3)
|
|
North Jutland
|
2178
|
(13.7)
|
4626
|
(12.4)
|
1863
|
(13.2)
|
2647
|
(14.2)
|
|
Southern Denmark
|
1804
|
(11.4)
|
3180
|
(8.5)
|
1453
|
(10.3)
|
1257
|
(6.7)
|
|
Zealand
|
3703
|
(23.3)
|
9942
|
(26.6)
|
3165
|
(22.5)
|
4306
|
(23.1)
|
|
Diagnosis with MD
§
|
|
|
|
|
|
|
|
|
|
ADHD
|
271
|
(1.7)
|
9402
|
(25.1)
|
292
|
(2.1)
|
10 303
|
(55.3)
|
|
Affective disorders
|
558
|
(3.5)
|
23 840
|
(63.7)
|
72
|
(0.5)
|
2228
|
(12.0)
|
|
Bipolar disorder
|
50
|
(0.3)
|
2014
|
(5.4)
|
<5
|
(0.0)
|
87
|
(0.5)
|
|
Depression
|
516
|
(3.2)
|
22 025
|
(58.8)
|
67
|
(0.5)
|
2085
|
(11.2)
|
|
Autism
|
134
|
(0.8)
|
6548
|
(17.5)
|
223
|
(1.6)
|
9564
|
(51.3)
|
|
Schizophrenia
|
140
|
(0.9)
|
4271
|
(11.4)
|
8
|
(0.1)
|
214
|
(1.1)
|
† Birth cohort81 includes individuals born in 1981-1994 and birth
cohort95 includes individuals born in 1995-2005; ‡
Ethnicity defined based on parental place of birth as described by Pedersen
et al.[18] Europe means countries
other than Denmark/Scandinavia and Scandinavia means only Norway,
Sweden, Finland, and Iceland. If one parent was born outside of Denmark,
that region was used. If both parents were born outside of Denmark, but in
different regions, mixed was used. § Summing the
percentages of individual disorders might add up to more than 100%,
as individuals can carry multiple diagnoses; *Significant difference
at a p-value= <0.05. Presented are the characteristics of
individuals in the population cohort and combined MD case cohort assessed in
2016. The data is presented in two separate birth cohorts. Data with a
number below 5 is presented as ‘<5’ for privacy
safety reasons. Abbreviations: ADHD: attention-deficit/hyperactivity
disorder, MD: mental disorders.
Incidence rates of pharmacogenetic drug use
Of the 69 drugs with actionable PGx recommendations, we identified 45 PGx drugs
for which prescriptions were redeemed at community pharmacies, 10 of which were
used by less than five users each, but were included in further analyses of
cumulative or combined use ([Table 2]).
In the population cohort, the highest incidence rates of PGx drug use per
10 000 PY were recorded for oestrogens (429 in females), the analgesics
codeine (27 in males and 55 in females) and tramadol (34 in males and 45 in
females), followed by PPIs (lansoprazole, omeprazole and pantoprazole) and the
antidepressant citalopram ([Table 2]). In
the mental disorders case cohorts, the incidence rates of any of the PGx drugs
use per 10 000 PY were higher compared with the population cohort, in
particular, psychotropic drugs matching their main indications, e. g.
atomoxetine (160 in males and 188 in females) in ADHD, citalopram (248 in males
and 306 in females) in depression, aripiprazole (229 in males and 316 in
females) in SZ, and lamotrigine (211 in males and 330 in females) in BD.
Individuals in the mental disorders case cohorts were prescribed at least one
psychotropic PGx drug before their first mental disorders diagnosis with
proportions of 50.4% (ADHD), 38.2% (autism), 86.9% (SZ),
86.1% (BD), and 83.3% (depression).
Table 2 Incidence rates of PGx prescription drug use per
10 000 PY of the iPSYCH sample, by population cohort and
individual MD case cohorts and sex.
|
Drug name
|
Population (N=29 975)
N/10 000 PY
|
ADHD (N=19 705) N/10 000
PY
|
Autism (N=16 112) N/10 000
PY
|
Bipolar disorder (N=2101) N/10 000
PY
|
Depression (N=24 110)
N/10 000 PY
|
Schizophrenia (N=4485) N/10 000
PY
|
PGx DGI
|
|
Females
|
Males
|
Females
|
Males
|
Females
|
Males
|
Females
|
Males
|
Females
|
Males
|
Females
|
Males
|
|
|
Allopurinol
|
|
-
|
-
|
-
|
-
|
-
|
-
|
1
|
1
|
1
|
1
|
2
|
1
|
HLA-B a,b
|
|
Amitriptyline
|
P
|
6
|
2
|
11
|
5
|
6
|
3
|
21
|
10
|
24
|
16
|
23
|
9
|
CYP2D6 a,b, CYP2C19 a
|
|
Aripiprazole
|
P
|
1
|
1
|
47
|
31
|
56
|
38
|
143
|
116
|
51
|
54
|
316
|
229
|
CYP2D6 b
|
|
Atomoxetine
|
P
|
2
|
4
|
188
|
160
|
49
|
51
|
32
|
57
|
23
|
40
|
27
|
38
|
CYP2D6 a,b
|
|
Atorvastatin
|
|
1
|
-
|
1
|
1
|
1
|
1
|
2
|
3
|
2
|
3
|
5
|
7
|
SLCO1B1 b
|
|
Azathioprine
|
|
2
|
2
|
3
|
2
|
2
|
2
|
4
|
3
|
4
|
4
|
4
|
3
|
TPMT a,b, NUDT15a,b
|
|
Carbamazepine
|
P
|
1
|
1
|
5
|
7
|
10
|
6
|
9
|
18
|
3
|
6
|
8
|
10
|
HLA-A a,b, HLA-B a,b
|
|
Citalopram
|
P
|
31
|
14
|
135
|
51
|
84
|
42
|
314
|
214
|
306
|
248
|
274
|
185
|
CYP2C19 a,b
|
|
Clomipramine
|
P
|
-
|
-
|
2
|
1
|
3
|
1
|
12
|
4
|
7
|
6
|
9
|
5
|
CYP2D6 a,b, CYP2C19 a,b
|
|
Clopidogrel
|
|
-
|
-
|
-
|
-
|
-
|
-
|
1
|
2
|
1
|
-
|
2
|
-
|
CYP2C19 a,b
|
|
Codeine
|
|
55
|
27
|
89
|
38
|
48
|
24
|
125
|
76
|
128
|
67
|
128
|
62
|
CYP2D6 a,b
|
|
Doxepin
|
P
|
-
|
-
|
-
|
-
|
-
|
-
|
-
|
1
|
-
|
1
|
-
|
-
|
CYP2D6 a,b, CYP2C19 a
|
|
Escitalopram
|
P
|
9
|
4
|
46
|
18
|
33
|
15
|
145
|
105
|
113
|
101
|
124
|
87
|
CYP2C19 a,b
|
|
Oestrogens
|
|
429
|
-
|
484
|
-
|
292
|
-
|
735
|
-
|
716
|
-
|
600
|
-
|
F5/FvL b
|
|
Flucloxacillin
|
|
10
|
10
|
22
|
16
|
17
|
14
|
14
|
20
|
20
|
15
|
28
|
17
|
HLA-B b HLA-B b
|
|
Fluvoxamine
|
P
|
-
|
-
|
6
|
-
|
-
|
-
|
1
|
1
|
1
|
-
|
1
|
-
|
CYP2D6 a
|
|
Haloperidol
|
P
|
-
|
-
|
2
|
2
|
4
|
1
|
7
|
12
|
2
|
2
|
20
|
12
|
CYP2D6 b
|
|
Imipramine
|
P
|
1
|
1
|
2
|
1
|
2
|
1
|
3
|
4
|
3
|
4
|
8
|
6
|
CYP2C19 a,b, CYP2D6 a,b
|
|
Lamotrigine
|
P
|
6
|
4
|
65
|
25
|
51
|
23
|
330
|
211
|
81
|
49
|
116
|
41
|
HLA-B b
|
|
Lansoprazole
|
|
35
|
20
|
84
|
36
|
39
|
21
|
107
|
60
|
99
|
64
|
129
|
74
|
CYP2C19 b
|
|
Metoprolol
|
|
7
|
2
|
9
|
5
|
9
|
3
|
15
|
10
|
17
|
9
|
22
|
11
|
CYP2D6 b
|
|
Nortriptyline
|
P
|
2
|
1
|
8
|
3
|
5
|
2
|
33
|
23
|
25
|
22
|
20
|
13
|
CYP2D6 a,b, CYP2C19 a
|
|
Omeprazole
|
|
38
|
20
|
70
|
31
|
50
|
23
|
86
|
41
|
86
|
53
|
107
|
55
|
CYP2C19 b
|
|
Ondansetron
|
|
4
|
1
|
7
|
1
|
3
|
2
|
13
|
2
|
10
|
2
|
10
|
2
|
CYP2D6 a
|
|
Oxcarbazepine
|
P
|
2
|
2
|
5
|
6
|
11
|
8
|
6
|
6
|
3
|
4
|
8
|
5
|
HLA-B a,b
|
|
Pantoprazole
|
|
35
|
21
|
82
|
39
|
45
|
24
|
108
|
69
|
98
|
68
|
137
|
85
|
CYP2C19 b
|
|
Paroxetine
|
P
|
3
|
2
|
16
|
6
|
11
|
6
|
26
|
19
|
27
|
23
|
32
|
24
|
CYP2D6 a,b
|
|
Pimozide
|
P
|
-
|
1
|
3
|
6
|
2
|
4
|
1
|
4
|
2
|
4
|
6
|
8
|
CYP2D6 b
|
|
Risperidone
|
P
|
2
|
3
|
60
|
63
|
73
|
69
|
111
|
110
|
53
|
70
|
237
|
226
|
CYP2D6 b
|
|
Sertraline
|
P
|
22
|
12
|
132
|
56
|
133
|
64
|
217
|
148
|
226
|
179
|
235
|
142
|
CYP2C19 a,b
|
|
Simvastatin
|
|
1
|
1
|
4
|
3
|
4
|
2
|
12
|
10
|
7
|
9
|
22
|
23
|
SLCO1B1 a,b
|
|
Tramadol
|
|
45
|
34
|
106
|
57
|
41
|
22
|
148
|
107
|
147
|
104
|
163
|
86
|
CYP2D6 b
|
|
Venlafaxine
|
P
|
10
|
4
|
66
|
24
|
36
|
15
|
158
|
104
|
152
|
128
|
146
|
88
|
CYP2D6 b
|
|
Warfarin
|
|
2
|
1
|
2
|
1
|
1
|
1
|
4
|
3
|
3
|
2
|
8
|
2
|
VKORC1 a,b, CYP2C9 a,b, CYP4F2
|
|
Zuclopenthixol
|
P
|
-
|
-
|
6
|
5
|
7
|
3
|
21
|
19
|
8
|
8
|
59
|
46
|
CYP2D6 b
|
Incidence rates of PGx drug prescriptions in the population cohort and
for the five individual MD case cohorts are shown as number of users per
10,000 PY of the total observed follow-up time. Ten drugs though
identified in the study population, but not included in this table are
flecainide, fluorouracil, mercaptopurine, phenprocoumon, phenytoin,
propafenone, tacrolimus, tamoxifen, trimipramine and voriconazole
because these drugs were used by less than 10 individuals in the total
population or MD case cohorts. a Actionable PGx guideline
from CPIC. b Actionable PGx guideline from DPWG.
Abbreviations: ADHD: attention-deficit/hyperactivity disorder,
ATC: Anatomical Therapeutic Chemical, CPIC: Clinical Pharmacogenetics
Implementation Consortium, DPWG: Dutch Pharmacogenetics Working Group,
DGI: drug-gene interaction, MD: mental disorders, PY: person-years, P:
psychotropic drug, PGx: pharmacogenetics.
Prevalence of pharmacogenetic drug use
In the population birth cohort81, besides oestrogens used by 80.7% of
females, tramadol (11.7%), codeine (11.3%) and lansoprazole
(7.7%) were the most frequently prescribed PGx drugs. In the younger
population birth cohort95, the most used PGx drugs besides oestrogens were
omeprazole (3.1%), codeine (2.7%) and pantoprazole
(2.4%) (Supplement Table 2). In general, the prevalence of drug
users was higher in both the mental disorders case birth cohorts compared with
the population cohorts. In the mental disorders case birth cohort81, the most
prevalent prescribed PGx drugs besides oestrogens were citalopram
(39.3%), sertraline (30.7%) and tramadol (23.5%). In the
mental disorders case birth cohort95, the most prevalent prescribed PGx drugs
were atomoxetine (17%), oestrogens (12.8%) and sertraline
(10%) (Supplement Table 3).
Age of first-time pharmacogenetic drug use
Age of first-time PGx drug use (life-time use) differed between sex in all
cohorts born since 1995 ([Table 3],
upper part). The mean age of starting a PGx drug for the first-time
ranged from 11.6–15.0 years for males and 13.1–15.2 years for
females. The youngest individuals starting a first-time PGx drug on average were
11.6 years old males with ADHD or autism, compared with nearly 13 years of age
in females with ADHD or autism.
Table 3 Age at first-time PGx drug use and mean number of
different PGx drugs of the iPSYCH sample, by birth and population
and MD cohorts and sex.
|
Age at first-time PGx drug use
|
|
Cohort
|
Birth cohort81
(1981–1994)
†
|
Birth cohort95
(1995–2005)
†
|
|
Females (N=25 726)
|
Males (N=16 005)
|
Females (N=6299)
|
Males (N=5949)
|
|
Mean
|
±SD
|
Mean
|
±SD
|
Mean
|
±SD
|
Mean
|
±SD
|
|
Population
|
17.8
|
3.5
|
21.6
|
5.7
|
15.2
|
3.3
|
12.9
|
5.1
|
|
ADHD
|
16.1
|
3.0
|
19.0
|
4.9
|
13.2
|
3.6
|
11.6
|
4.1
|
|
Autism
|
16.2
|
4.0
|
17.4
|
5.4
|
13.1
|
4.3
|
11.6
|
4.4
|
|
Bipolar disorder¥
|
17.0
|
2.8
|
20.0
|
4.2
|
15.0
|
1.8
|
14.1
|
3.9
|
|
Depression
|
16.8
|
2.8
|
19.9
|
4.2
|
14.7
|
2.6
|
13.8
|
3.5
|
|
Schizophrenia¥
|
16.9
|
2.9
|
19.8
|
4.1
|
14.3
|
2.9
|
15.0
|
2.8
|
|
Mean number of different PGx drugs prescribed
|
|
Cohort
|
Birth cohort81
(1981–1994)
†
|
Birth cohort95
(1995–2005)
†
|
|
Females (N=25 726)
|
Males (N=16 005)
|
Females (N=6299)
|
Males (N=5949)
|
|
Mean
|
±SD
|
Mean
|
±SD
|
Mean
|
±SD
|
Mean
|
±SD
|
|
Population
|
2.0
|
1.4
|
1.6
|
1.1
|
1.4
|
0.8
|
1.2
|
0.6
|
|
ADHD
|
4.4
|
2.5
|
2.8
|
2.0
|
2.1
|
1.3
|
1.6
|
1.0
|
|
Autism
|
3.5
|
2.3
|
2.4
|
1.6
|
2.0
|
1.2
|
1.7
|
1.0
|
|
Bipolar disorder
|
5.3
|
2.6
|
3.6
|
2.2
|
3.4
|
1.8
|
2.3
|
1.3
|
|
Depression
|
4.4
|
2.4
|
3.1
|
2.0
|
2.6
|
1.5
|
2.0
|
1.2
|
|
Schizophrenia
|
5.6
|
2.9
|
3.6
|
2.2
|
3.5
|
1.6
|
2.2
|
1.1
|
† Birth cohort81 includes individuals born between 1981 and 1994,
birth cohort95 includes individuals born between 1995 and 2005.
¥ The differences between males and females in all cohorts are
statistically significant, except for the birth cohort95 with bipolar
disorder or schizophrenia. Mean age of drug users at their first-time
PGx drug prescription and the mean of the number of PGx drugs prescribed
in the population and for five individual MD diagnoses are presented.
The table is split based on birth cohorts, as we do not have life-time
drug use available for individuals in birth cohort81. Individuals who
were not prescribed any PGx drug prior to death or data capping are not
included in this table. Abbreviations: MD: mental disorders; ADHD:
attention-deficit/hyperactivity disorder; iPSYCH: Integrative
Psychiatric Research (iPSYCH) consortium, SD: standard deviation.
Multiple (different) pharmacogenetic drugs per user
Individuals used on average more than one PGx drug over a maximum follow-up time
of 22 years ([Table 3], lower
part) with sex and birth cohort differences in both the population and
mental disorders case cohorts. Overall, higher means of different PGx drugs were
seen in the mental disorders case cohorts compared with the population cohorts,
with maximum means of more than four different PGx drugs in females of the
mental disorders birth cohort81.
The prevalence of individuals using different PGx drugs (0 –
> 9) is mentioned in [Table
4]. Within the population cohort, 23.1% of males and
65.4% of females used one or more PGx drugs during the follow-up time.
Among individuals of the mental disorders case birth cohorts combined,
56.2% of males and 84.9% of females with ADHD, 43.8% and
72.3% with autism, 87% and 96% with BD, 86.5%
and 96.9% with depression, and 87.3% and 97.2% with SZ,
respectively, used one or more PGx drugs during the follow-up time. The highest
prevalence of individuals using more than three different PGx drugs exceeded
80% in females with BD or SZ. The highest prevalence of individuals
using six or more PGx drugs was approximately 40% in females with BD or
SZ.
Table 4 Prevalence of individuals of the iPSYCH sample
with increasing number of different PGx drugs, by population and MD
cohorts and sex.
|
No. of different PGx drugs
|
Population
|
ADHD
|
Autism
|
Bipolar disorder
|
Depression
|
Schizophrenia
|
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
|
(N=14 683)
|
(N=15 292)
|
(N=5289)
|
(N=14 416)
|
(N=3525)
|
(N=12 587)
|
(N=1292)
|
(N=809)
|
(N=16 476)
|
(N=7634)
|
(N=2083)
|
(N=2402)
|
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
|
0
|
5081
|
(34.6)
|
11 766
|
(76.9)
|
799
|
(15.1)
|
6317
|
(43.8)
|
976
|
(27.7)
|
7079
|
(56.2)
|
52
|
(4.0)
|
105
|
(13.0)
|
518
|
(3.1)
|
1028
|
(13.5)
|
58
|
(2.8)
|
305
|
(12.7)
|
|
1
|
5409
|
(36.8)
|
2374
|
(15.5)
|
1029
|
(19.5)
|
3662
|
(25.4)
|
854
|
(24.2)
|
2641
|
(21.0)
|
76
|
(5.9)
|
125
|
(15.5)
|
1430
|
(8.7)
|
1600
|
(21.0)
|
87
|
(4.2)
|
377
|
(15.7)
|
|
2
|
2263
|
(15.4)
|
732
|
(4.8)
|
822
|
(15.5)
|
1878
|
(13.0)
|
588
|
(16.7)
|
1383
|
(11.0)
|
125
|
(9.7)
|
154
|
(19.0)
|
2534
|
(15.4)
|
1565
|
(20.5)
|
201
|
(9.6)
|
398
|
(16.6)
|
|
3
|
1002
|
(6.8)
|
239
|
(1.6)
|
712
|
(13.5)
|
1097
|
(7.6)
|
397
|
(11.3)
|
732
|
(5.8)
|
144
|
(11.1)
|
122
|
(15.1)
|
2949
|
(17.9)
|
1222
|
(16.0)
|
262
|
(12.6)
|
426
|
(17.7)
|
|
4
|
489
|
(3.3)
|
114
|
(0.7)
|
590
|
(11.2)
|
593
|
(4.1)
|
289
|
(8.2)
|
366
|
(2.9)
|
210
|
(16.3)
|
99
|
(12.2)
|
2684
|
(16.3)
|
887
|
(11.6)
|
289
|
(13.9)
|
320
|
(13.3)
|
|
5
|
228
|
(1.6)
|
37
|
(0.2)
|
448
|
(8.5)
|
358
|
(2.5)
|
182
|
(5.2)
|
192
|
(1.5)
|
189
|
(14.6)
|
85
|
(10.5)
|
2116
|
(12.8)
|
558
|
(7.3)
|
293
|
(14.1)
|
204
|
(8.5)
|
|
6
|
125
|
(0.9)
|
19
|
(0.1)
|
355
|
(6.7)
|
241
|
(1.7)
|
102
|
(2.9)
|
107
|
(0.9)
|
141
|
(10.9)
|
49
|
(6.1)
|
1600
|
(9.7)
|
341
|
(4.5)
|
247
|
(11.9)
|
152
|
(6.3)
|
|
7
|
45
|
(0.3)
|
11
†
|
(0.0)
|
177
|
(3.3)
|
118
|
(0.8)
|
54
|
(1.5)
|
43
|
(0.3)
|
116
|
(9.0)
|
35
|
(4.3)
|
1028
|
(6.2)
|
203
|
(2.7)
|
186
|
(8.9)
|
92
|
(3.8)
|
|
8
|
17
|
(0.1)
|
|
|
158
|
(3.0)
|
76
|
(0.5)
|
34
|
(1.0)
|
26
|
(0.2)
|
93
|
(7.2)
|
13
|
(1.6)
|
722
|
(4.4)
|
123
|
(1.6)
|
150
|
(7.2)
|
62
|
(2.6)
|
|
9
|
16
|
(0.1)
|
|
|
77
|
(1.5)
|
42
|
(0.3)
|
20
|
(0.6)
|
12
|
(0.1)
|
65
|
(5.0)
|
9
|
(1.1)
|
387
|
(2.3)
|
65
|
(0.9)
|
110
|
(5.3)
|
37
|
(1.5)
|
|
>9
|
8
|
(0.1)
|
|
|
122
|
(2.3)
|
34
|
(0.2)
|
29
|
(0.8)
|
6
|
(0.0)
|
81
|
(6.3)
|
13
|
(1.6)
|
508
|
(3.1)
|
42
|
(0.6)
|
200
|
(9.6)
|
29
|
(1.2)
|
†
This number is grouped with data from
>7 different PGx drugs, due to data otherwise being <5.
The number of individuals who were prescribed different numbers of PGx
drugs in the population cohort and for five individual MD case cohorts
over a mean follow-up time of 19-21 years is shown. Mean (±SD)
follow-up times were 19 years in the male population (±4.1),
male patients with ADHD (±3.7) and patients with autism
(±3.8), 20 years in the female population (±4) and
female patients with ADHD (±3.5) and 21 years in patients with
BD (F ±3.6, M ±3.4), depression (F ±3.1, M
±3) and SZ (F ±3, M ±3.1). Abbreviations: MD:
mental disorders; ADHD: attention-deficit/hyperactivity
disorder; iPSYCH: Integrative Psychiatric Research, PGx:
pharmacogenetics, SD: standard deviation, F: females, M: males.
Pharmacogenetic drug use relevant to panel-based testing and combinatorial
PGx
Of the 39 PGx drugs, nine drugs (23%) with more than one actionable DGI,
including genes coding for CYP2D6, CYP2C19, HLA-A, HLA-B, CYP2C9, CYP4F2
or VKORC1 were used by 3.9% of the PGx drug users of the population and
9.7% of the PGx drug users of the combined mental disorders case
cohorts.
The prevalence of individuals using different PGx drugs of different DGIs at any
time during the follow-up was 43% in females and 25.3% in males
in the population cohort ([Table 5]). In
individuals with mental disorders, these numbers ranged between 39.6% in
males with autism and 94.1% in females with SZ. The involved DGIs are
listed in the legend of [Table 5].
Table 5 Number of individuals using PGx drugs and the
relation with different genes of the iPSYCH sample, by populations
and MD cohorts and sex.
|
No. of different types of genes
|
Population
|
ADHD
|
Autism
|
Bipolar disorder
|
Depression
|
Schizophrenia
|
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
Female
|
Male
|
|
(N=9602)
|
(N=3526)
|
(N=4490)
|
(N=8099)
|
(N=2549)
|
(N=5508)
|
(N=1240)
|
(N=704)
|
(N=15 958)
|
(N=6606)
|
(N=2025)
|
(N=2097)
|
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
N
|
(%)
|
|
1
|
5473
|
(57.0)
|
2635
|
(74.7)
|
1113
|
(24.8)
|
4720
|
(58.3)
|
960
|
(37.7)
|
3328
|
(60.4)
|
83
|
(6.7)
|
182
|
(25.9)
|
1695
|
(10.6)
|
2382
|
(36.1)
|
120
|
(5.9)
|
600
|
(28.6)
|
|
2
|
2703
|
(28.2)
|
765
|
(21.7)
|
1237
|
(27.6)
|
2682
|
(33.1)
|
821
|
(32.2)
|
1768
|
(32.1)
|
207
|
(16.7)
|
269
|
(38. 2)
|
4575
|
(28.7)
|
3258
|
(49.3)
|
458
|
(22.6)
|
1127
|
(53.7)
|
|
3
|
1237
|
(12.9)
|
98
|
(2.8)
|
1538
|
(34.3)
|
536
|
(6.6)
|
544
|
(21.3)
|
334
|
(6.1)
|
404
|
(32.6)
|
217
|
(30.8)
|
6991
|
(43.8)
|
793
|
(12.0)
|
942
|
(46.5)
|
291
|
(13.9)
|
|
4
|
142
|
(1.5)
|
21
|
(0.6)
|
532
|
(12.1)
|
145
|
(1.8)
|
185
|
(7.3)
|
78
†
|
(1.4)
|
493
|
(39.8)
|
28
|
(4.0)
|
2345
|
(14.7)
|
144
|
(2.2)
|
399
|
(19.7)
|
68
|
(3.2)
|
|
5
|
47
†
|
(0.5)
|
7
|
(0.2)
|
58
|
(1.3)
|
16
†
|
(0.2)
|
39
†
|
(1.5)
|
-
|
-
|
39
|
(3.2)
|
8
†
|
(1.1)
|
291
|
(1.8)
|
29
†
|
(0.4)
|
84
|
(4.2)
|
11
|
(0.5)
|
|
≥6
|
-
|
-
|
-
|
-
|
12
†
|
(0.3)
|
-
|
-
|
-
|
-
|
-
|
-
|
14
†
|
(1.1)
|
-
|
-
|
61
†
|
(0.4)
|
-
|
-
|
22
†
|
(1.1)
|
-
|
-
|
†
This number is grouped with data from the
immediate cell below, due to data otherwise being <5. The
numbers of individuals are shown for different numbers of genes involved
in the prescribed PGx drugs in the population and for five individual MD
diagnoses over a mean follow-up time of 20-22 years. Mean (±SD)
follow-up times were 20 years in the population (F ±3, M
±3.1), patients with ADHD (F ±2.7, M ±3) and
autism (F ±2.7, M ±3), 21 years in patients with BD (F
±2.5, M ±2) and female patients with depression
(±2.2), and 22 years in male patients with depression
(±1.7) and patients with SZ (F ±2, M ±1.7).
Percentages are calculated only among PGx drugs users. Genes linked to
drug use per cohort: Population (CYP2C19, CYP2D6, CYP4F2,
F5/FvL, HLA-A, HLA-B, NUDT15, SLCO1B1, TPMT, VKORC1, CYP3A5);
ADHD (CYP2C19, CYP2D6, CYP4F2, F5/FvL, HLA-A, HLA-B, NUDT15,
SLCO1B1, TPMT, VKORC1); Autism (CYP2C19, CYP2D6, CYP4F2, F5/FvL,
HLA-A, HLA-B, NUDT15, SLCO1B1, TPMT, VKORC1, CYP3A5*,
DPYD* (* only in males); BD (CYP2C19, CYP2D6, CYP4F2,
F5/FvL*, HLA-A, HLA-B, NUDT15, SLCO1B1, TPMT, VKORC1
(* only in females); MDD (CYP2C19, CYP2D6, CYP4F2,
F5/FvL, HLA-A, HLA-B, NUDT15, SLCO1B1, TPMT, VKORC1); SZ
(CYP2C19, CYP2D6, CYP4F2, F5/FvL, HLA-A, HLA-B, NUDT15, SLCO1B1,
TPMT, VKORC1). Abbreviations: MD: mental disorders; ADHD:
attention-deficit/hyperactivity disorder; iPSYCH: Integrative
Psychiatric Research, PGx: pharmacogenetics, SD: standard deviation; F:
females; M: males.
Concerning combinatorial PGx, the prevalence of individuals who used
concomitantly different PGx drugs affected by different DGIs ranged between
24.4% of the individuals without mental disorders, 41.3% of
individuals with autism or ADHD to 69.2% of individuals with BD, SZ or
depression ([Table 6]). In over
80% of these users, the PGx drugs were linked to two different DGIs, in
8.1% to three, and in 1.1% of these users, to four DGIs.
Table 6 Number of individuals using concomitant PGx drugs
and affected by different genes.
|
Cohorts†
|
Number of individuals with concomitant drugs N
(%)‡,§
|
One gene N (%)¶
|
Two genes N (%)¶
|
Three genes N (%)¶
|
Four genes N (%)¶,ͳ
|
|
Total
|
Female
|
Male
|
Total
|
Female
|
Male
|
Total
|
Female
|
Male
|
Total
|
Female
|
Male
|
Total
|
Female
|
Male
|
|
Population (N=13 128)
(F=9602/M=3526)
|
3199 (24.4)
|
2727 (28.4)
|
472 (13.4)
|
728 (22.8)
|
501 (18.4)
|
227 (48.1)
|
2887 (90.2)
|
2589 (95.0)
|
298 (63.1)
|
198 (6.2)
|
162 (5.9)
|
36 (7.6)
|
36 (1.1)
|
29 (1.1)
|
7 (1.5)
|
|
MD-A
ψ (N=18 531)
(F=6570/M=11 961)
|
7653 (41.3)
|
3818 (58.1)
|
3835 (32.1)
|
3800 (49.7)
|
1588 (41.6)
|
2212 (57.7)
|
6419 (83.9)
|
3587 (93.9)
|
2832 (73.8)
|
441 (5.8)
|
250 (6.5)
|
191 (5.0)
|
48 (0.6)
|
22 (0.6)
|
26 (0.7)
|
|
MD-B
χ (N=25 355)
(F=17 255/M=8100)
|
17 546 (69.2)
|
13 349 (77.4)
|
4197 (51.8)
|
8499 (48.4)
|
6083 (45.6)
|
2416 (57.6)
|
16 137 (92.0)
|
12 769 (95.7)
|
3368 (80.2)
|
1418 (8.1)
|
1205 (9.0)
|
213 (5.1)
|
148 (0.8)
|
116 (0.9)
|
32 (0.8)
|
† Only those individuals who had a history of using
PGx drugs were included; ‡ The percentages are
estimated out of the total number of individuals in each cohort;
§ Concomitant drug use implies at least two
different drugs overlapping for at least one day; ¶
The percentages are estimated out of the number of individuals using
concomitant PGx drugs; ͳ Only a few individuals used
concomitant drugs metabolized by five genes (all less than five
observations); ψ MD-A includes ADHD and autism;
χ MD-B includes schizophrenia, bipolar disorder
and depression. The number of individuals using PGx drugs concomitantly
is shown, followed by the number of involved genes related to the PGx
drugs. The mean (±SD) days of overlap were 676 (±2,300);
2,584 (7,510) and 3,045 (7,732) for the population cohort, MD-A MD-B
case cohorts, respectively. Abbreviations: MD: mental disorders; ADHD:
attention-deficit/hyperactivity disorder; SD: standard
deviation; F: female; M: male.
Discussion
This is the first population-based PGx drug utilization study in 86 040 young
people with and without mental disorders in Denmark describing (life-time) incident
use of 39 of the 69 actionable PGx drugs according to international guidelines. We
found that, by the age of 35 years, at least one actionable PGx had been used by up
to 97% of individuals with mental disorders, i. e. with SZ, and by
65% of females and 23% of males of the population cohort (without
mental disorders). In individuals with mental disorders, the most frequent
actionable PGx drugs corresponded to their psychiatric indications, i. e.
atomoxetine in ADHD, citalopram in depression, aripiprazole in SZ, and lamotrigine
in BD, related to DGIs involving CYP2D6, CYP2C19, and HLA-B.
Moreover, the high use of oestrogens in oral contraceptives related to Factor V
Leiden (FvL), the weak opioid analgesics codeine and tramadol, the PPIs
lansoprazole, omeprazole, and pantoprazole, and citalopram in both the mental
disorders and population cohorts also related to the CYP2D6, CYP2C19, and HLA-B
indicate the broad applicability of PGx testing in the general population. The
first-time users of PGx drugs were as young as (mean age of) 11 years in males with
ADHD. Panel-based testing including at least the most commonly identified DGIs could
be applicable for 95% of females with SZ down to approximately 25%
of males in the general population. Combinatorial PGx testing, considering several
different drugs and different DGIs at the same time, could be relevant for up to
70% of individuals with mental disorders and 24% of the general
population without mental disorders.
Life-time incidence and prevalence of pharmacogenetic drug use
Recently several studies in different settings have investigated the incidence
and prevalence of actionable PGx drug use, but they neither addressed life-time
use or age at first PGx drug prescription nor were they conducted in unselected
case cohorts with mental disorders or population-based, thus comparisons are
hampered [7]
[11]
[12]
[13]
[14]
[15]
[28]
[29]
[30]. Still, the pattern of prevalence of PGx drug use in our study,
e. g., the most frequent use of oestrogens, followed by codeine,
tramadol, PPIs and citalopram, is similar to the patterns reported by a previous
study in Denmark based on publically available prescription sales data of the
general population by age 44 years in 2017 [14]. In comparison with studies in other populations, our
observations are in line with findings from the US, UK and the Netherlands
applying similar actionable PGx drug criteria. In the US, Samwald et al. found
in individuals below 40 years that weak opioids, PPIs, SSRIs, atomoxetine and
selected antipsychotics were among the top 8 of incident PGx drugs [11]. In UK, Youssef et al. investigated
sales data of overall prescriptions dispensed in 2018, where patients by age 39
years most commonly dispensed prescriptions for antidepressants, oral
contraceptives, anti-infectives, and PPIs [10]. Of note, we found generally frequent use of oestrogens,
i. e. oral contraceptives related to FvL, which is similar to
frequencies of prescriptions reported from the UK and the Netherlands [10]
[13]. Although oestrogen-containing oral contraceptives are included
in the uPGx panel of PGx drugs, which we applied in this study, it should be
noted that oral contraceptives containing oestrogens are currently only
considered actionable in females with a previous personal or family history of
thrombosis or additional risk factors for thrombosis such as smoking, diabetes,
and obesity according to DWPG [4]. This
may lead to the impression that the number of women where PGx actions should be
considered is inflated in our study, with 8647 female users of oestrogens in the
population cohort, and 18908 female users with psychiatric disorders, and among
those, an estimated 6.6% were heterozygous and 0.1% were
homozygous carriers of FvL [31]
[32]. We did not assess additional risk
factors, thus the actual number of females where PGx would be applicable is
unknown. However, among females with psychiatric disorders considering
oestrogen-containing oral contraceptives, PGx guidance could be considered
applicable to a larger extent due to common (comorbid) conditions, including
diabetes, obesity and smoking [33]
[34]
[35]. Moreover, it has been previously studied that establishing FvL
testing in all women before initiating oral contraceptives is unfeasible due to
costs and a low predictive value of FvL testing [32]. Now, considering the increasing utility of PGx in general and
panel-based testing decreasing costs, the cost-benefit of FvL testing should be
revisited for inclusion in core panels of actionable PGx tests and
multifactorial treatment decisions.
While overall patterns of actionable PGx drugs were similar across the different
cohorts, the prevalence of any of the investigated actionable PGx drugs was
higher among people with mental disorders than in the population without these
conditions. This is mainly due to the preponderance of psychotropic drugs among
actionable PGx drugs matching the psychiatric disorders, but the more frequent
use of PGx drugs such as analgesics and PPIs with indications for somatic
conditions also indicates a higher burden of somatic disorders in younger people
with mental disorders compared with their peers.
Age of first pharmacogenetic drug use and timing of PGx testing
We found that the mean age of the earliest PGx drug use in the cohort born
between 1995–2006 was 11 years in males and 13 years in females with
ADHD or autism, and of 13 years in males and 15 years in females without mental
disorders, suggesting earliest reactive prospective testing around these ages.
We are not aware of any other study assessing life-time incident use of PGx
drugs.
Considering pre-emptive PGx testing, which is unlike reactive prospective testing
unrelated to a prescription of a PGx drug in the first place, our PGx drug
prevalence findings indicate that pre-emptive testing could support
pharmacological treatment decisions in 23% of males and 65% of
females of the general population (without mental disorders) by the age of 35
years. In individuals with mental disorders, pre-emptive PGx could be applicable
in, e. g. up to 87% of males and 97% of females. We
further estimate that pre-emptive testing and test results could be applied for
a mean of 3.1 PGx drugs in young individuals with mental disorders and a mean of
1.6 PGx drugs in young individuals without mental disorders by age of 35 years.
Several other studies have investigated the potential of pre-emptive testing,
but not based on life-time use, in different patient populations across
disorders, age ranges, settings and study set-ups (e. g. follow-up
times) leading to a wider range of 11.2% to 97% of individuals
exposed to one or more PGx drugs over a 2–20 year follow-up period, thus
are not directly comparable with our findings [11]
[28]
[36]
[37]
[38].
Panel-based and combinatorial pharmacogenetic testing
Our results indicate, as discussed earlier, the utility of panel-based testing
due to the frequent use of several PGx drugs related to different DGIs.
Panel-based testing in particular, of a core panel including variants of
CYP2D6 and CYP2C19 has been endorsed by many PGx societies and
implementation initiatives [14]
[15]
[30]
[39]. These efforts are now
being further supported by a recent meta-analysis finding that plasma levels of
various antidepressant and antipsychotic drugs are associated with
CYP2D6/CYP2C19 genotype-predicted metabolizer status supporting
genotype-based dosing recommendations and ultimately PGx testing in people with
mental disorders initiating psychotropic treatment [40].
An additional benefit of panel-based PGx testing is the opportunity to execute
combinatorial PGx, which considers the effect of multiple variants in different
genes for PGx-based dose adjustments. This is of importance when a single drug
has multiple actionable DGIs or when multiple PGx drugs are used concomitantly.
The substantial number (a third) of individuals affected by multiple DGIs and
24.4% (population) to 69.2% (BD, SZ, depression cohorts) using
multiple PGx drugs concomitantly in our study indicates the potential benefit
from combinatorial PGx. Multiple PGx drugs acting on the same enzyme affected by
genetic variation leading to DDGIs and related PGx guided drug dosage
recommendations are yet not provided in international public guidelines and
rarely transparently in commercial combinatorial PGx tests but are under
development [41]
[42]
[43]. In addition, the difference between the number of individuals
using a drug and the number of individuals who require necessary action vary
from<1% to 50% with divergent geno-phenotypes [14], which is largely dependent on the
combination of actionable PGx guidelines for geno- or phenotypes and frequencies
of these geno- and phenotypes.
Strengths and limitations
The strengths of the current study are that it presents data of a large
population-based case-cohort of individuals with mental disorders and a
population cohort representative for the entire Danish population of young age.
The study has no bias in the selection of individuals, very little missing data
and little loss to follow-up due to the registry-based study set-up using the
Danish Civil Registration System. All these strengths allow a valid estimation
of first-time as well as life-time PGx drug use in Denmark, and these estimates
are likely to extrapolate to countries with similar drug utilization patterns
and health care systems. This study has some limitations. First, a considerable
number of individuals have no drug data registered from birth until 1995 solely
affecting birth cohort81. By dividing the cohort into two birth cohorts, we were
able to present PGx drug use from birth up to the end of the study for the
remaining individuals born since 1995 (ages 11–21). This affects the
interpretation of the age of first PGx drug use and the mean number of PGx drug
use. We lack data of the youngest years of individuals in birth cohort81, with a
maximum of the first 11 years of an individual born in 1984. This might result
in a higher mean age for birth cohorts81 due to the left-truncation. We have
data until a maximum age of 11 years for individuals born in 2005, thus
individuals starting a first PGx drug at age 12 or older are not included in the
calculations. This might result in a lower mean age for birth cohorts95 due to
right-truncation. Yet, since we know the average age of onset of disease, we
expect that the real answer, e. g., ADHD and autism lies close to the
mean age identified in birth cohorts95, compared to the other disorders with a
real age of first PGx drug use in between the results for birth cohorts 1 and 2
[39]
[40]
[44]
[45]. Another limitation is that
hospital-based data is missing from the drug registries, excluding some
actionable PGx drugs such as anti-infectives and drugs used in anaesthesia.
Moreover, we present a drug utilization study of current actionable PGx drugs.
The results presented in this study might be affected in the future by changing
trends in drug use or updated PGx guidelines, for example including new PGx
drugs. Lastly, the iPSYCH population is rather homogeneous with 88% of
Danish or European ancestry, 10% mixed ancestry, and only up to
2% of Non-European ancestry, partially due to the design of the study
including individuals born in Denmark since 1981. Among the commonly used PGx
drugs identified in the current study, this may affect estimates for the even
greater utility of PGx testing of drugs affected by CYP2C19 variations or HLA-B
variants, both of which are more frequent in individuals with Asian ancestry; or
CYP2C9 variations in individuals with African ancestry and should be considered
in PGx adjusted dosing recommendations of relevant drugs [46]
[47].
In conclusion, PGx drugs are commonly used by young individuals, with more
frequent PGx drug use among young individuals with mental disorders and females.
PGx testing could be beneficial already at a very young age (adolescent).
Panel-based PGx testing would be preferable over single-gene testing, based on
the number of individuals using PGx drugs subsequently or concomitantly and the
number of different drug-gene interactions involved.
Data Availability Statement
Data Availability Statement
The data that support the findings of this study are available from Statistics
Denmark. Restrictions apply to the availability of these data, which were used under
license for this study. Data are available in an anonymous form, by remote online
access with the permission of Statistics Denmark, the National Centre for
Register-based Research (NCRR) and the Centre for Integrated Register-Based Research
at Aarhus University (CIRRAU).
FundingThe study was funded by unrestricted grants received by C. Gasse of the Alfred
Benzon Foundation, Denmark, and NovoNordisk Foundation, Denmark (NNF17OC0029488) and
by C. Lunenburg of the Lundbeck Foundation, Denmark (R322-2019-2404). The funders
had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
