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A meta-analysis of polygenic risk scores for mood disorders, neuroticism, and schizophrenia in antidepressant response

01 Jun 2021-medRxiv (Cold Spring Harbor Laboratory Press)-

TL;DR: Although PRSs are still not able to predict non-response or non-remission, the results are in line with previous works; methodological improvements inPRSs calculation may improve their predictive performance and have a meaningful role in precision psychiatry.

AbstractAbout two-thirds of patients with major depressive disorder (MDD) fail to achieve symptom remission after the initial antidepressant treatment. Despite a role of genetic factors was proven, the specific underpinnings are not fully understood yet. Polygenic risk scores (PRSs), which summarise the additive effect of multiple risk variants across the genome, might provide insights into the underlying genetics. This study aims to investigate the possible association of PRSs for bipolar disorder, MDD, neuroticism, and schizophrenia (SCZ) with antidepressant non-response or non-remission in patients with MDD. PRSs were calculated at eight genome-wide P-thresholds based on publicly available summary statistics of the largest genome-wide association studies. Logistic regressions were performed between PRSs and non-response or non-remission in six European clinical samples, adjusting for age, sex, baseline symptom severity, recruitment sites, and population stratification. Results were meta-analysed across samples, including up to 3,637 individuals. Bonferroni correction was applied. In the meta-analysis, no result was significant after Bonferroni correction. The top result was found for MDD-PRS and non-remission (p=0.004), with patients in the highest vs. lowest PRS quintile being more likely not to achieve remission (OR=1.5, 95% CI=1.11-1.98, p=0.007). Nominal associations were also found between MDD-PRS and non-response (p=0.013), as well as between SCZ-PRS and non-remission (p=0.035). Although PRSs are still not able to predict non-response or non-remission, our results are in line with previous works; methodological improvements in PRSs calculation may improve their predictive performance and have a meaningful role in precision psychiatry.

Topics: Major depressive disorder (52%), Neuroticism (51%), Bipolar disorder (51%), Bonferroni correction (50%)

Summary (2 min read)

Introduction

  • About two-thirds of patients with major depressive disorder (MDD) fail to achieve symptom remission after the initial antidepressant treatment.
  • In conclusion, although PRSs are not able to significantly predict treatment response or remission in MDD yet, their study suggests an increased genetic susceptibility to MDD and SCZ in patients who do not achieve remission/response after the first antidepressant treatment, in line with the previous literature.

Statement of Ethics

  • The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
  • All subjects selected by clinicians were included in the screening phase after obtaining their written informed consent.
  • This research group certifies that data collected for the STAR*D were exclusively used for scientific investigation.
  • Before obtaining access to data, the objectives of their investigation were clearly described in the request form.

Contributors

  • Giuseppe Fanelli contributed to the conceptualisation of the study, performed the analyses, interpreted the results and wrote the first draft of the manuscript.
  • Alessandro Serretti and Chiara Fabbri conceptualised the study, helped with the interpretation of the results, reviewed the first draft of the manuscript and contributed to the funding acquisition.
  • Chiara Fabbri supervised the whole process leading to the final publication.
  • CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint this version posted June 1, 2021.

Conflicts of Interest

  • Advisory Board - Lundbeck, Janssen-Cilag; Consultant - National Health and Medical Research Council, Australia; Grant/Research Support - AstraZeneca, Fay Fuller Foundation, James & Diana Ramsay Foundation, National Health and Medical Research Council, Australia, German Research Council (DFG), Sanofi, Lundbeck; Honoraria - AstraZeneca, Bristol-Myers Squibb, Lundbeck, Pfizer, Servier Laboratories, Wyeth Pharmaceuticals, Takeda, Janssen, LivaNova PLC, also known as B.T. Baune.
  • K. Domschke is a member of the Steering Committee Neurosciences, Janssen Pharmaceuticals, Inc. P. Ferentinos received grants/research support, consulting fees and/or honoraria within the last three years from Angelini, Boehringer-Ingelheim, Janssen, Medochemie, Vianex, and Servier.
  • A. Serretti is or has been a consultant/speaker for Abbott, Abbvie, Angelini, AstraZeneca, Clinical Data, Boehringer, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, and Servier.
  • J. Zohar has received grant/research support from Lundbeck, Servier, and Pfizer; he has served as a consultant on the advisory boards for Servier, Pfizer, Solvay, and Actelion; and he has served on speakers’ bureaus for Lundbeck, GSK, Jazz, and Solvay.
  • The other authors declare no conflict of interest.

Acknowledgments

  • The European College of Neuropsychopharmacology (ECNP) Pharmacogenomics & Transcriptomics Thematic Working Group commissioned this manuscript and contributed by sharing individual genotyped data, as well as providing comments and critical review to the manuscript.
  • The copyright holder for this preprint this version posted June 1, 2021.
  • Antidepressant Response in Major Depressive Disorder: A Genome-wide Association Study. ; https://doi.org/10.1101/2021.05.28.21257812doi: medRxiv preprint Table 1. Target samples used for the computation of polygenic risk scores and subsequent analyses, after quality control.

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A meta-analysis of polygenic risk scores for mood disorders, neuroticism, and schizophrenia in
antidepressant response
Giuseppe Fanelli
1,2
, Katharina Domschke
3
, Alessandra Minelli
4,5
, Massimo Gennarelli
4,5
, Paolo
Martini
4
, Marco Bortolomasi
6
, Eduard Maron
7,8,9
, Alessio Squassina
10
, Siegfried Kasper
11
, Joseph
Zohar
12
, Daniel Souery
13
, Stuart Montgomery
14
, Diego Albani
15
, Gianluigi Forloni
15
, Panagiotis
Ferentinos
16
, Dan Rujescu
11
, Julien Mendlewicz
17
, Diana De Ronchi
1
, European College of
Neuropsychopharmacology (ECNP) Pharmacogenomics & Transcriptomics Thematic Working
Group, Bernhard T Baune
18,19,20
, Alessandro Serretti
1
*, Chiara Fabbri
1,21
1
Department of Biomedical and Neuromot or Sciences, University of Bologna, Bol o gna, Italy
2
Department of Human Genetics, Radboud University Medical Center, Donder s Inst itute for Br ain, Cognition and
Behaviour, Nijmegen, The Netherlands
3
Department of Psy chiatry and Psychothera py, Medical Center University of Freiburg, Faculty of Medicine, Un iversity
of Freiburg, Freiburg, Germany
4
Department of Molecular and Translational Medicine, Univers ity of Br es cia, Bresci a, Ital y
5
Genetics Unit, IR C CS Istit uto Centro San Giovanni di Dio Fate benefrate lli, Brescia, Ital y
6
Ps ychiatr ic Hos pital "Villa Santa Chiara" , Verona, Ital y
7
Department of Psych iatry, University of Tartu, Tartu, Estonia
8
Psychiatric Clinic, Wes t Tallinn Central Hospital, Ta llinn, Estonia
9
Centre for N europs ychopharmacology, Division of Brain Scie nces, Imperial College London, London, UK
10
Department of Biomedical Sciences, University of Cagliari, Cagliari, Ital y
11
Department of Psychiatry and Psychotherapy, Medical University Vienna, Vienna, Austri a
12
Department of Psychia try, Sheba Medical Center, Tel Hashomer, a nd Sac kler School of Med icine, Tel Aviv University,
Tel Hashomer, Isra el
13
Laboratoire de Ps ycho logie M édicale, U niversit é Libre de Br uxelles and Ps y Pluriel, C en tre Européen de Psychol ogie
Médicale, Brussels, Belgium
14
Imperial College School of Medicine, London, UK
15
Laboratory of Biology of Neurodegen erative Disorders, Department of Neuroscience, Istituto di Ricerche
Farmacologic he Mario Negri IR C CS, Milan, Italy
16
Department of Psych iatry, Athens University Medical School, Athens, Greece
17
Unive rsité Libre de Bruxelles, Brussels, Belgium
18
Department of Psychiatry and Psychot herapy, Universit y of Münster, Müns ter, Germany
19
Department of Ps y chiatry, Melbour ne Medical School, University of Melbourn e, Parkville, VIC, Aus tralia
20
The Florey Institute of Neuroscience and M ental Health, The University of Melbo urne, Parkville, VIC, Australia
21
Social, Genetic & Development al Ps ychiatry Centre, Institute of Psychiatry, Psycho logy & Neuroscience, King’s
College London, London, UK
*
Corresponding author:
Alessandro Serretti, MD, PhD
Departm ent of Biom edical and Neuromotor Sciences
Universit y of Bologna
Viale Carlo Pepol i 5, 40123 Bologna, Ital y
Tel +39 051 6584233
Fax +39 051 521030
Mobile +39 320 4269332 +39 347 3024020
E-mail alessandro.serretti@ unibo.it
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.05.28.21257812doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Abstract
About two-thirds of patients with major depressive disorder (MDD) fail to achieve symptom
remission after the initial antidepressant treatment. Despite a role of genetic factors was
proven, the specific underpinnings are not fully understood yet. Polygenic risk scores (PRSs),
which summarise the additive effect of multiple risk variants across the genome, might
provide insights into the underlying genetics. This study aims to investigate the poss ible
association of PRSs for bipolar disorder, MDD, neuroticism, and schizophrenia (SCZ) with
antidepressant non-response or non-remission in patients with MDD. PRSs were calculated
at eight genome-wide P-thresholds based on publicly available summary statistics of the
largest genome-wide association studies. Logistic regressions were performed between
PRSs and non-response or non-remission in six European clinical samples, adjusting for age,
sex, baseline symptom severity, recruitment sites, and population stratification. Res ults
were meta-analysed across samples, including up to 3,637 individuals. Bonferroni correction
was applied. In the meta-analysis, no result was significant after Bonferroni correction. The
top result was found for MDD-PRS and non-remission (p=0.004), with patients in the highest
vs. lowest PRS quintile being more likely not to achieve remission (OR=1.5, 95% CI=1.11-
1.98, p=0.007). Nominal associations were also found between MDD-PRS and non-response
(p=0.013), as well as between SCZ-PRS and non-remission (p=0.035). Although PRSs are still
not able to predict non-response or non-remission, our results are in line with previous
works; methodological improvements in PRSs calculation may improve their predictive
performance and have a meaningful role in precision psychiatry.
Keywords
: Polygenic risk scores; Major depressive disorder; Antidepressants;
Pharmacogenomics; Remission; Treatment response.
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.05.28.21257812doi: medRxiv preprint

1.
Introduction
Major depressive disorder (MDD) is a common psychiatric condition that is among the
leading causes of disability worldwide, accounting for a 61.1% increase in the number of
disability-adjusted life years (DALYs) over the past two decades (Diseases and Injuries,
2020). Up to 60% of patients with MDD do not respond adequately to the first prescribed
antidepressant, requiring either a dose increase, switching to another antidepressant, or
augmentation with a different pharmacological agent (De Carlo et al., 2016). Remission is
achieved by only 37.5% of patients after six weeks of treatment with first-line
antidepressants, and non-responding patients undergoing several consecutive treatment
steps achieve lower remission and higher relapse rates (Rush et al., 2006). Therefore, early
identification of each patient’s most appropriate treatment might help to reduce the burden
of the disease and the related cost to society.
Several socio-demographic and clinical predictors of non-response or non-remission have
been identified, including older age, longer duration of the depressive episode, greater
severity at baseline, and the presence of anxiety symptoms (Kautzky et al., 2018). Genetic
variability may also contribute, as indicated by a single-nucleotide polymorphism (SNP)-
based heritability of 13.2% for remission, though the specific loci involved were not
identified (Pain et al., 2020).
Polygenic risk scores (PRSs) summarise the additive effect of common genetic risk variants
across the genome, and they have shown promising clinical utility in other fields of medicine
(Natarajan et al., 2017). Interestingly, people with a PRS for coronary artery disease above
the 80
th
percentile were shown to benefit most from statin treatment in terms of
preventing acute cardiac events, with a relative risk reduction of 44% compared to 26%
observed in patients with a lower PRS (Natarajan et al., 2017). Using the same approach, we
found that higher PRSs for schizophrenia (SCZ) in patients with MDD may be associated with
worse response to the first antidepressant treatment, and that individuals having a lower
SCZ-PRS showed higher chances of response when antidepressants were not augmented
with antipsychotics (Fanelli et al., 2021). A positive genetic correlation was also identified
between non-response to antidepressants and neuroticism (NEU), schizotypy, and mood
disorders, suggesting the existence of underlying shared genetics (Wigmore et al., 2020).
In light of these findings, we aimed to extend our previous results to other samples (Fanelli
et al., 2021), through a large meta-analysis of relevant PRSs (MDD, bipolar disorder (BP),
SCZ, NEU) and antidepressant non-response and non-remission in MDD. PRSs could indeed
help to better stratify patients with respect to their chances of response/remission and lead
to the early implementation of second-line treatment strategies.
2.
Methods
2.1.
Target samples
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.05.28.21257812doi: medRxiv preprint

2.1.1.
Brescia
This sample included a total of 501 subjects with MDD (Diagnostic and Statistical Manual of
Mental Disorders-IV (DSM-IV) criteria) who had been referred to the Villa Santa Chiara”
Psychiatric Hospital in Verona, Italy. Diagnosis of unipolar depression was confirmed using
the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I). Patients were excluded
if they had met criteria for another primary neuropsychiatric disorder or comorbid eating
disorder, substance/alcohol abuse or dependency. Treatment non-response was defined as
the failure to respond to at least one adequate trial of antidepressants. Genotyping was
performed using the Infinium PsychArray-24 BeadChip or the Infinium Multi-Ethnic
Genotyping Array (N=215 and 286, respectively). Additional information is a vailable
elsewhere (Minelli et al., 2015).
2.1.2.
European Group for the Study of Resistant Depression (GSRD
)
The sample included 1,346 genotyped patients (Infinium PsychArray-24 BeadChip) w ith
MDD recruited from the European Group for the Study of Resistant Depression (GSRD) as
part of a multicentric study. MDD was diagnosed using the Mini International
Neuropsychiatric Interview (MINI). Patients were excluded if they had met criteria for
another primary psychiatric disorder in the six months prior to enrolment. Treatment
response/remission were determined using the Montgomery-Åsberg Depression Rating
Scale (MADRS) (50% improvement and MADRS ≤10, respectively). Further details are
available elsewhere (Souery et al., 2007). This sample was previously included in a similar
PRS study focused on non-response to the last antidepressant and treatment-resistant
depression (TRD) (Fanelli et al., 2021).
2.1.3.
Münster
It is a naturalistic study of 621 participants aged 18 85 years with MDD, as assessed by the
SCID-I. Participants were recruited at the Department of Psychiatry, University of Münster,
Germany (Baune et al., 2010). Patients with SCZ spectrum disorders, BD, current alcohol or
drug dependence, neurological or neurodegenerative illnesses were excluded. Response
and remission at week six were measured using the 21-item Hamilton Depression Rating
Scale (HAMD
21
) (50% improvement and HAMD
21
7, respectively). Genotyping was
performed using the Infinium PsychArray-24 BeadChip.
2.1.4.
Sequenced Treatment Alternatives to Relieve Depression (STAR*D)
The STAR*D study was conducted to compare tolerability and efficacy of antidepressants
throughout four sequential treatment levels in patients with MDD of at least moderate
severity. Symptom severity was assessed using the Quick Inventory of Depressive
Symptomatology Clinician-rated scale (QIDS-C
16
) every two weeks; level 1 exit data were
considered for this study. Response/remission were defined as a 50% decrease in symptom
severity and QIDS-C
16
5 at week 12, respectively. A total of 1,948 participants were
. CC-BY-NC-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.05.28.21257812doi: medRxiv preprint

genotyped (Affymetrix GeneChip Human Mapping 500K Array Set or Affymetrix Genome-
Wide Human SNP Array 5.0). The study is described in depth elsewhere (Howland, 2008).
2.1.5.
Tartu
This sample included 83 outpatients with MDD recruited at the Psychiatric Clinic of the
University Hospital of Tartu, Estonia. The diagnosis was made using the MINI 5.0.0,
psychiatric history, and medical records. Patients with other primary neurological or
psychiatric disorders were excluded from the study. Treatment response/remission were
measured using the MADRS, in line with the previous samples. Further information is
available elsewhere (Aluoja et al., 2018). The samples sequenced were genotyped using the
Illumina 370CNV array. Further information is available elsewhere (Tammiste et al., 2013).
2.2.
Quality control of the target datasets
Quality control (QC) and population principal component analysis (PCA) were performed
through the Ricopili pipeline in each of the six target samples separately (Lam et al., 2020).
Single-nucleotide polymorphisms (SNPs) were retained if they had a call rate 0.95,
differences in call rates between cases and controls (missing difference) ≤0.02, minor allele
frequency (MAF) ≥0.01, and Hardy-Weinberg equilibrium p-value 1e-6. Individuals were
retained if they had an autosomal heterozygosity deviation within ±0.2, call rate 0.98, and
no genetic/pedigree sex mismatch.
To assess between-subjects relatedness and population stratification, all pairs of individuals
with identity-by-descent proportion >0.2 were identified using linkage disequilibrium-
pruned data (r
2
<0.2), and one individual from each pair was removed. PCA was used to
determine population stratification (Eigenstrat); population outliers were removed
according to the mean ±6 standard deviations of the first 20 principal components.
Genotype imputation was carried out on the Michigan Imputation Server (Das et al., 2016)
using Minimac4 and the Haplotype Reference Consortium (HRC) r1.1 2016 (GRCh37/hg19).
Post-imputation QC was performed by filtering out variants having a poor imputation
quality score (i.e., R
2
<0.3) and MAF <0.05.
2.3.
Statistical analyses
Polygenic risk scores for BP, MDD, NEU, and SCZ were calculated in each target sample after
hard-calling with a genotype probability threshold of 0.9, using PRSice v2.3.3
(https://prsice.info). Summary statistics of the largest genome-wide association studies
(GWASs) on BP, MDD, NEU, and SCZ available at the time of conducting our analyses were
used as base datasets (Table S1). Clumping was performed to remove SNPs in linkage
disequilibrium (r
2
> 0.1, 250 kb window). Eight
a priori
GWAS P-value thresholds (P
T
) (1e-4,
0.001, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5) were used to select SNPs to be included in each PRS (Choi
et al., 2020).
Logistic regressions between each scaled PRS and the two clinical outcomes (non-response
and non-remission) were conducted in R v4.0.2, adjusting for age, sex, baseline symptom
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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 1, 2021. ; https://doi.org/10.1101/2021.05.28.21257812doi: medRxiv preprint

References
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TL;DR: The acute and longer-term treatment outcomes associated with each of four successive steps in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial are described and compared.
Abstract: Objective: This report describes the participants and compares the acute and longer-term treatment outcomes associated with each of four successive steps in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. Method: A broadly representative adult outpatient sample with nonpsychotic major depressive disorder received one (N=3,671) to four (N=123) successive acute treatment steps. Those not achieving remission with or unable to tolerate a treatment step were encouraged to move to the next step. Those with an acceptable benefit, preferably symptom remission, from any particular step could enter a 12-month naturalistic follow-up phase. A score of ≤5 on the Quick Inventory of Depressive Symptomatology–Self-Report (QIDS-SR 16 ) (equivalent to ≤7 on the 17-item Hamilton Rating Scale for Depression [HRSD 17 ]) defined remission; a QIDS-SR 16 total score of ≥11 (HRSD 17 ≥14) defined relapse. Results: The QIDS-SR 16 remission rates were 36.8%, 30.6%, 13.7%, and 13.0% for the first, second, t...

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Abstract: Christian Fuchsberger, Goncalo Abecasis and colleagues describe a new web-based imputation service that enables rapid imputation of large numbers of samples and allows convenient access to large reference panels of sequenced individuals. Their state space reduction provides a computationally efficient solution for genotype imputation with no loss in imputation accuracy.

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"A meta-analysis of polygenic risk s..." refers methods in this paper

  • ...Genotype imputation was carried out on the Michigan Imputation Server (Das et al., 2016) using Minimac4 and the Haplotype Reference Consortium (HRC) r1....

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Journal ArticleDOI
Theo Vos1, Theo Vos2, Theo Vos3, Stephen S Lim  +2416 moreInstitutions (246)
TL;DR: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates, and there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries.
Abstract: Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation.

976 citations


Journal ArticleDOI
TL;DR: The findings provide a set of 11 relevant clinical variables associated with treatment resistance in major depressive disorder that can be explored at the clinical level and show that comorbid anxiety disorder is the most powerful clinical factor associated with TRD.
Abstract: Objectives Very few studies have investigated clinical features associated with treatment-resistant depression (TRD) defined as failure of at least 2 consecutive antidepressant trials. The primary objective of this multicenter study was to identify specific clinical and demographic factors associated with TRD in a large sample of patients with major depressive episodes that failed to reach response or remission after at least 2 consecutive adequate antidepressant treatments. Method A total of 702 patients with DSM-IV major depressive disorder, recruited from January 2000 to February 2004, were included in the analysis. Among them, 346 patients were considered as nonresistant. The remaining 356 patients were considered as resistant, with a 17-item Hamilton Rating Scale for Depression score remaining greater than or equal to 17 after 2 consecutive adequate antidepressant trials. Cox regression models were used to examine the association between individual clinical variables and TRD. Results Among the clinical features investigated, 11 variables were found to be associated with TRD. We found anxiety comorbidity (p 1 (p = .003, OR = 1.6), recurrent episodes (p = .009, OR = 1.5), early age at onset (p = .009, OR = 2.0), and nonresponse to the first antidepressant received lifetime (p = .019, OR = 1.6) to be the factors associated with TRD. Conclusions Our findings provide a set of 11 relevant clinical variables associated with treatment resistance in major depressive disorder that can be explored at the clinical level. The statistical model used in this analysis allowed for a hierarchy of these variables (based on the OR) showing that comorbid anxiety disorder is the most powerful clinical factor associated with TRD.

393 citations


Additional excerpts

  • ...Further details are available elsewhere (Souery et al., 2007)....

    [...]


Journal ArticleDOI
TL;DR: Those at high genetic risk have a greater burden of subclinical atherosclerosis and derive greater relative and absolute benefit from statin therapy to prevent a first coronary heart disease event.
Abstract: Background —Relative risk reduction with statin therapy has been consistent across nearly all subgroups studied to date. However, in analyses of two randomized controlled primary prevention trials (ASCOT and JUPITER), statin therapy led to a greater relative risk reduction among a subgroup at high genetic risk. Here, we sought to confirm this observation in a third primary prevention randomized controlled trial. Additionally, we assessed if those at high genetic risk had a greater burden of subclinical coronary atherosclerosis. Methods —We studied participants from a randomized controlled trial of primary prevention with statin therapy (WOSCOPS, n=4,910) and two observational cohort studies (CARDIA and BioImage, n=1,154 and 4,392). For each participant, we calculated a polygenic risk score (PRS) derived from up to 57 common DNA sequence variants previously associated with coronary heart disease (CHD). We compared the relative efficacy of statin therapy in those at high genetic risk (top quintile of PRS) versus all others (WOSCOP)S as well as the association between the PRS and coronary artery calcification (CARDIA) and carotid artery plaque burden (BioImage). Results —Among WOSCOPS trial participants at high genetic risk, statin therapy was associated with a relative risk reduction of 44% (95% CI, 22%-60%; P < 0.001) whereas in all others, relative risk reduction was 24% (95% CI 8%-37%; P = 0.004) despite similar LDL cholesterol lowering. In a study-level meta-analysis across the WOSCOPS, ASCOT, and JUPITER primary prevention, relative risk reduction in those at high genetic risk was 46% versus 26% in all others ( P for heterogeneity = 0.05). Across all three studies, the absolute risk reduction with statin therapy was 3.6% (95% CI, 2.0%-5.1%) among those in the high genetic risk group and was 1.3% (95% CI, 0.6%-1.9%) in all others. Each standard deviation increase in the polygenic risk score was associated with 1.32-fold (95% CI, 1.04-1.68) greater likelihood of having coronary artery calcification and 9.7% higher (95% CI, 2.2-17.8%) burden of carotid plaque. Conclusions —Those at high genetic risk have a greater burden of subclinical atherosclerosis and derive greater relative and absolute benefit from statin therapy to prevent a first CHD event. Clinical Trial Registration —BioImage: [NCT00738725][1] www.clinicaltrials.gov, CARDIA: [NCT00005130][2] clinicaltrials.gov [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00738725&atom=%2Fcirculationaha%2Fearly%2F2017%2F02%2F20%2FCIRCULATIONAHA.116.024436.atom [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00005130&atom=%2Fcirculationaha%2Fearly%2F2017%2F02%2F20%2FCIRCULATIONAHA.116.024436.atom

259 citations


"A meta-analysis of polygenic risk s..." refers background in this paper

  • ...Polygenic risk scores (PRSs) summarise the additive effect of common genetic risk variants across the genome, and they have shown promising clinical utility in other fields of medicine (Natarajan et al., 2017)....

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  • ...Interestingly, people with a PRS for coronary artery disease above the 80th percentile were shown to benefit most from statin treatment in terms of preventing acute cardiac events, with a relative risk reduction of 44% compared to 26% observed in patients with a lower PRS (Natarajan et al., 2017)....

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