Showing papers by "Catharina Lavebratt published in 2021"
Niamh Mullins1, Andreas J. Forstner2, Andreas J. Forstner3, Andreas J. Forstner4 +396 more•Institutions (119)
TL;DR: The authors performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci, including genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics.
Abstract: Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
378 citations
University of Adelaide1, Mental Health Services2, United States Department of Health and Human Services3, Ludwig Maximilian University of Munich4, University of Göttingen5, University of Bonn6, National Institutes of Health7, Harvard University8, University of California, San Diego9, Charité10, Dokkyo Medical University11, University of Barcelona12, Geneva College13, Karolinska Institutet14, Karolinska University Hospital15, Paris Diderot University16, Medical University of Graz17, Mayo Clinic18, McGill University Health Centre19, National Taiwan University20, University Hospital of Basel21, Douglas Mental Health University Institute22, Poznan University of Medical Sciences23, University of Cagliari24, Johns Hopkins University25, French Institute of Health and Medical Research26, University of Basel27, University of New South Wales28, Neuroscience Research Australia29, Dalhousie University30, Osaka University31, University of Lorraine32, Goethe University Frankfurt33, Hokkaido University34, University of Gothenburg35, University of Paris36, Veterans Health Administration37, University of Perugia38, University of Cincinnati39, Carlos III Health Institute40, Max Planck Society41, University of Salerno42, Seconda Università degli Studi di Napoli43, Nagoya University44, Dresden University of Technology45, Montreal Neurological Institute and Hospital46, Heidelberg University47, Florey Institute of Neuroscience and Mental Health48, University of Melbourne49, University of Münster50
TL;DR: The findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
Abstract: Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18–2.01) and European sample: OR = 1.75 (95% CI: 1.30–2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61–4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
39 citations
Charité1, University of Würzburg2, Imperial College London3, University of Tartu4, Max Planck Society5, University of Gothenburg6, Karolinska University Hospital7, Karolinska Institutet8, University Hospital Bonn9, Harvard University10, University of Bonn11, University of Duisburg-Essen12, University of Marburg13, University of Basel14, Goethe University Frankfurt15, University of Mannheim16, University of Münster17, Humboldt University of Berlin18, University of Cologne19, University of Greifswald20, Dresden University of Technology21, Heidelberg University22, University of Freiburg23, University of Helsinki24, Stockholm County Council25, Public Health Research Institute26, VU University Amsterdam27, deCODE genetics28, University of Liverpool29, Mental Health Services30, University of Copenhagen31, Lundbeck32, Aarhus University33, Statens Serum Institut34, University of North Carolina at Chapel Hill35, Aarhus University Hospital36, Emory University37, Broad Institute38
TL;DR: The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD.
Abstract: Panic disorder (PD) has a lifetime prevalence of 2-4% and heritability estimates of 40%. The contributory genetic variants remain largely unknown, with few and inconsistent loci having been reported. The present report describes the largest genome-wide association study (GWAS) of PD to date comprising genome-wide genotype data of 2248 clinically well-characterized PD patients and 7992 ethnically matched controls. The samples originated from four European countries (Denmark, Estonia, Germany, and Sweden). Standard GWAS quality control procedures were conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project reference panel. A meta-analysis was then performed using the Ricopili pipeline. No genome-wide significant locus was identified. Leave-one-out analyses generated highly significant polygenic risk scores (PRS) (explained variance of up to 2.6%). Linkage disequilibrium (LD) score regression analysis of the GWAS data showed that the estimated heritability for PD was 28.0-34.2%. After correction for multiple testing, a significant genetic correlation was found between PD and major depressive disorder, depressive symptoms, and neuroticism. A total of 255 single-nucleotide polymorphisms (SNPs) with p < 1 × 10-4 were followed up in an independent sample of 2408 PD patients and 228,470 controls from Denmark, Iceland and the Netherlands. In the combined analysis, SNP rs144783209 showed the strongest association with PD (pcomb = 3.10 × 10-7). Sign tests revealed a significant enrichment of SNPs with a discovery p-value of <0.0001 in the combined follow up cohort (p = 0.048). The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD.
38 citations
TL;DR: It is shown that childhood adversity is associated with increased methylation levels of GRIN2B in adulthood, for three of the measured CpGs, which indicates that GRin2B methylation is susceptible to early life stress, and that methylation at this gene is persistent over time.
16 citations
TL;DR: In this article, a nationwide cohort study of all live births (n = 1 105 997) during 1996-2014 in Finland, excluding those with maternal diagnoses sharing signs and symptoms with PCOS, was included and followed up until 31 December 2018.
Abstract: Study question Are children of mothers with polycystic ovary syndrome (PCOS) or anovulatory infertility at increased risks of obesity or diabetes? Summary answer Maternal PCOS/anovulatory infertility is associated with an increased risk of offspring obesity from early age and diabetes in female offspring from late adolescence. What is known already Women with PCOS often have comorbid metabolic disorders such as obesity and diabetes, and children of mothers with PCOS have an increased risk of subtle signs of cardiometabolic alterations. Study design, size, duration This was a nationwide cohort study of all live births (n = 1 105 997) during 1996-2014 in Finland, excluding those with maternal diagnoses sharing signs and symptoms with PCOS (n = 8244). A total of 1 097 753 births were included and followed up until 31 December 2018. Participants/materials, setting, methods National registries were linked to identify births with maternal PCOS or anovulatory infertility (n = 24 682). The primary outcomes were diagnoses of obesity (ICD-10: E65, E66) and diabetes (ICD-10: E10-E14) in offspring recorded in the Finnish Care Register for Health Care. Cox proportional hazards regression was modeled to analyze the risk of offspring obesity and diabetes in relation to prenatal exposure to maternal PCOS/anovulatory infertility. Differently adjusted models and stratified analyses were used to assess whether the risk was modified by maternal obesity or diabetes diagnoses, pre-pregnancy BMI, fertility treatment or perinatal problems. Main results and the role of chance Exposure to maternal PCOS/anovulatory infertility was associated with a higher cumulative incidence of obesity in the children (exposed: 1.83%; 95% CI 1.66-2.00% vs unexposed: 1.24%; 95% CI 1.22-1.26%). Accounting for birth factors and maternal characteristics such as obesity and diabetes diagnoses, the hazard ratio (HR) for obesity was increased in offspring below 9 years of age (HR 1.58; 95% CI 1.30-1.81), and in those 10-16 years of age (HR 1.37; 95% CI 1.19-1.57), but not in those aged 17-22 years (HR 1.24; 95% CI 0.73-2.11). Sex-stratified analyses revealed similar risk estimates for boys (HR 1.48; 95% CI 1.31-1.68) and girls (HR 1.45; 95% CI 1.26-1.68). Notably, the joint effect of PCOS/anovulatory infertility and BMI-based pre-pregnancy obesity on offspring obesity (HR 8.89; 95% CI 7.06-11.20) was larger than that of either PCOS/anovulatory infertility or obesity alone. Furthermore, PCOS/anovulatory infertility was associated with offspring obesity in children without perinatal problems (HR 1.27; 95% CI 1.17-1.39), with larger effect size for maternal PCOS/anovulatory infertility and joint perinatal problems (HR 1.61; 95% CI 1.35-1.91). However, the risk estimates were comparable between maternal PCOS/anovulatory infertility with (HR 1.54; 95% CI 1.17-2.03) and without fertility treatment (HR 1.46; 95% CI 1.32-1.61). For offspring diabetes, the HR was increased only between 17 and 22 years of age (HR 2.06; 95% CI 1.23-3.46), and specifically for Type 1 diabetes in females (HR 3.23; 95% CI 1.41-7.40). Limitations, reasons for caution The prevalence of PCOS/anovulatory infertility in this study was 2.2%, lower than that reported in previous studies. In addition, the incidence of obesity in offspring was lower than that reported in studies based on measured or self-reported weight and height and may include mainly moderate and severe obesity cases who needed and/or actively sought medical care. Moreover, mothers with PCOS/anovulatory infertility were identified based on ICD codes, with no information on PCOS phenotypes. Furthermore, maternal pre-pregnancy BMI was available only from 2004. The PCOS/anovulatory infertility association with female offspring diabetes was based on only a few cases. Mothers' weight gain during pregnancy, use of fertility treatment other than fresh or frozen IVF/ICSI, offspring lifestyle, as well as fathers' age, medical disorders or medication prescriptions were not available for this study. Wider implications of the findings These findings support that prenatal PCOS/anovulatory infertility exposure influences metabolic health in the offspring from early age. Study funding/competing interest(s) This study was supported by Shandong Provincial Natural Science Foundation, China [ZR2020MH064 to X.C.], Shandong Province Medical and Health Technology Development Plan [2018WS338 to X.C.], the joint research funding of Shandong University and Karolinska Institute [SDU-KI-2019-08 to X.C. and C.L.], the Finnish Institute for Health and Welfare: Drug and Pregnancy Project [M.G.], the Swedish Research Council [2014-10171 to C.L.], the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institute Stockholm County Council [SLL20170292 and SLL20190589 to C.L.], the Swedish Brain Foundation [FO2018-0141 and FO2019-0201 to C.L.]. X.C. received grants from the China Scholarship Council at the beginning of the study. The authors have no competing interests to disclose. Trial registration number N/A.
16 citations
University of Adelaide1, Mental Health Services2, University of New South Wales3, Heidelberg University4, Charité5, United States Department of Health and Human Services6, Dokkyo Medical University7, University of Barcelona8, Geneva College9, Karolinska University Hospital10, University of California, San Diego11, Paris Diderot University12, Medical University of Graz13, Mayo Clinic14, McGill University Health Centre15, National Taiwan University16, University Hospital of Basel17, University of Bonn18, Douglas Mental Health University Institute19, Poznan University of Medical Sciences20, University of Cagliari21, Johns Hopkins University22, Ludwig Maximilian University of Munich23, University of Basel24, Neuroscience Research Australia25, Dalhousie University26, Osaka University27, Harvard University28, Paris 12 Val de Marne University29, University of Lorraine30, Juntendo University31, Goethe University Frankfurt32, Hokkaido University33, University of Gothenburg34, Karolinska Institutet35, University of Paris36, Veterans Health Administration37, Seconda Università degli Studi di Napoli38, University of Cincinnati39, Carlos III Health Institute40, University of Salerno41, National Institutes of Health42, Nagoya University43, Dresden University of Technology44, Montreal Neurological Institute and Hospital45, University of Perugia46, Florey Institute of Neuroscience and Mental Health47, University of Melbourne48, University of Münster49
TL;DR: In this paper, the authors used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD).
Abstract: Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.
15 citations
Dalhousie University1, Dokkyo Medical University2, National Institutes of Health3, University of Geneva4, Karolinska Institutet5, Karolinska University Hospital6, Technical University of Berlin7, French Institute of Health and Medical Research8, Paris Diderot University9, McGill University10, National Taiwan University11, Max Planck Society12, University Hospital Bonn13, University of Cagliari14, University of Marburg15, Mayo Clinic16, University of New South Wales17, Osaka University18, University of Barcelona19, University of California, San Diego20, University of Würzburg21, Hokkaido University22, University of Bonn23, Nagoya University24, Heidelberg University25, Montreal Neurological Institute and Hospital26, Poznan University of Medical Sciences27, Ludwig Maximilian University of Munich28
TL;DR: In this paper, the authors evaluated the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data using the largest existing genomic dataset (n = 2210 across 14 international sites; 29% responders).
Abstract: Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Wurzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures.
7 citations
Conservatoire national des arts et métiers1, University of Paris2, Florey Institute of Neuroscience and Mental Health3, University of Melbourne4, University of Münster5, University of Adelaide6, Mental Health Services7, United States Department of Health and Human Services8, Ludwig Maximilian University of Munich9, University Hospital Bonn10, National Institutes of Health11, Harvard University12, University of California, San Diego13, Charité14, Dokkyo Medical University15, University of Barcelona16, Geneva College17, Karolinska Institutet18, Karolinska University Hospital19, Paris Diderot University20, Medical University of Graz21, Mayo Clinic22, McGill University Health Centre23, National Taiwan University24, Forschungszentrum Jülich25, University Hospital of Basel26, Douglas Mental Health University Institute27, Poznan University of Medical Sciences28, University of Cagliari29, Johns Hopkins University30, Paris 12 Val de Marne University31, University of Marburg32, Neuroscience Research Australia33, University of New South Wales34, Dalhousie University35, Osaka University36, University of Lorraine37, Juntendo University38, Goethe University Frankfurt39, Hokkaido University40, University of Gothenburg41, Veterans Health Administration42, University of Perugia43, University of Cincinnati44, Carlos III Health Institute45, University of Salerno46, Seconda Università degli Studi di Napoli47, Nagoya University48, Dresden University of Technology49, Montreal Neurological Institute and Hospital50, Sigmund Freud University Vienna51, Heidelberg University52
TL;DR: In this article, a GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region.
Abstract: Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10-3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.
6 citations
TL;DR: Morning cortisol concentration, an indicator of hypothalamic-pituitary-adrenal axis activation, is prospectively associated with tobacco use in adolescents, and whether this activation indicates the cumulative effect of stressors during the life course remains to be elucidated.
5 citations
TL;DR: For example, the authors found that DNA methylation at the exon 1'F locus of the NR3C1 gene can predict substance use in middle adolescents, and the strongest association was found for cigarette use in males.
Abstract: Early life stress has been linked to increased methylation of the Nuclear Receptor Subfamily 3 Group C Member 1 (NR3C1) gene, which codes for the glucocorticoid receptor. Moreover, early life stress has been associated with substance use initiation at a younger age, a risk factor for developing substance use disorders. However, no studies to date have investigated whether NR3C1 methylation can predict substance use in young individuals. This study included adolescents 13–14 years of age that reported no history of substance use at baseline, (N = 1041; males = 46%). Participants contributed saliva DNA samples and were followed in middle adolescence as part of KUPOL, a prospective cohort study of 7th-grade students in Sweden. Outcome variables were self-reports of (i) recent use, (ii) lifetime use, and (iii) use duration of (a) alcohol, (b) tobacco products, (c) cannabis, or (d) any substance. Outcomes were measured annually for three consecutive years. The predictor variable was DNA methylation at the exon 1 F locus of NR3C1. Risk and rate ratios were calculated as measures of association, with or without adjustment for internalizing symptoms and parental psychiatric disorders. For a subset of individuals (N = 320), there were also morning and afternoon salivary cortisol measurements available that were analyzed in relation to NR3C1 methylation levels. Baseline NR3C1 hypermethylation associated with future self-reports of recent use and use duration of any substance, before and after adjustment for potential confounders. The overall estimates were attenuated when considering lifetime use. Sex-stratified analyses revealed the strongest association for cigarette use in males. Cortisol analyses revealed associations between NR3C1 methylation and morning cortisol levels. Findings from this study suggest that saliva NR3C1 hypermethylation can predict substance use in middle adolescence. Additional longitudinal studies are warranted to confirm these findings.
4 citations
TL;DR: The hypothesis that dysregulation of the hypothalamic-pituitary-adrenal axis might be involved in the association between smoking behavior and depressive symptoms during adolescence was not supported and cortisol levels were not associated with depressive symptoms.
Abstract: Several studies have shown that smoking increases the risk of depressive symptoms, and suggested a possible role of the hypothalamic-pituitary-adrenal axis in the smoking-depression pathway. This study aimed to assess if smokers have higher cortisol levels than non-smokers, and if higher cortisol levels are associated with depressive symptoms. Saliva samples were collected from a subgroup of 409 participants at enrolment (13-14 years old) and two years later (15-16 years old). First, we examined the association between smoking phenotypes and cortisol concentration. Second, we evaluated whether these associations differed between adolescents with and without depressive symptoms. The mean difference between smokers and non-smokers in cortisol concentrations was close to zero at both time points. For instance, the adjusted mean difference for morning cortisol concentration between current and non-current smokers was 0.000 µg/dl [95% CI -0.055, 0.056]. In addition, there were no differences in cortisol concentration at the second time-point between those who had smoked and those who did not during the two previous years. Moreover, cortisol levels were not associated with depressive symptoms. The hypothesis that dysregulation of the hypothalamic-pituitary-adrenal axis might be involved in the association between smoking behavior and depressive symptoms during adolescence was not supported by this data.
TL;DR: The present study collected blood samples from FitForLife participants based on the hypothesis that circulating levels of inflammatory analytes, such as C-reactive protein (CRP), serum amyloid-alpha (SAA), soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecules-2 (sVCAM-2) may serve as biomarkers for monitoring cardiovascular health.
TL;DR: In this paper, SIRT1, Sirt2 and Npy mRNA were determined using qRT-PCR in prefrontal cortex (PFC) from young and old FSL and FRL, and in hippocampi from young FSL, FRL and control FRL rats.
Abstract: Objective Since the NAD+-dependent histone deacetylases sirtuin-1 (SIRT1) and sirtuin-2 (SIRT2) are critically involved in epigenetics, endocrinology and immunology and affect the longevity in model organisms, we investigated their expression in brains of 3 month old and 14-15 month old rat model of depression Flinders Sensitive Line (FSL) and control Flinders Resistant Line (FRL) rats. In view of the dysregulated NPY system in depression we also studied NPY in young and old FSL to explore the temporal trajectory of depressive-like-ageing interaction. Methods Sirt1, Sirt2 and Npy mRNA were determined using qRT-PCR in prefrontal cortex (PFC) from young and old FSL and FRL, and in hippocampi from young FSL and FRL. Results PFC. Sirt1 expression was decreased in FSL (p=0.001). An interaction between age and genotype was found (p=0.032); young FSL had lower Sirt1 with respect to both age (p=0.026) and genotype (p=0.001). Sirt2 was lower in FSL (p=0.003). Npy mRNA was downregulated in FSL (p=0.001) but did not differ between the young and old rat groups. Hippocampus.Sirt1 was reduced in young FSL compared to young FRL (p=0.005). There was no difference in Sirt2 between FSL and FRL. Npy levels were decreased in hippocampus of young FSL compared to young FRL (p=0.003). Effects of ageing could not be investigated due to loss of samples. Conclusions i. This is the first demonstration that SIRT1 and SIRT2 are changed in brain of FSL, a rat model of depression ; ii. The changes are age dependent; iii. Sirtuins are potential targets for treatment of age-related neurodegenerative diseases.