Figures
Abstract
SIRT1 polymorphisms have previously been associated with depressive and anxiety disorders. We aimed at confirming these earlier findings and extending the analyses to seasonal variations in mood and behavior. Three tag single-nucleotide polymorphisms (SNPs) were selected to capture the common variation in the SIRT1 gene. 5910 individuals (with blood sample, diagnostic interview, self-report of on seasonal changes in mood and behavior) were selected from a representative Finnish nationwide population-based sample. Logistic and linear regression models were used to analyze the associations between the SNPs and depressive and anxiety disorders, metabolic syndrome (EGIR criteria) and its components, and health examination measurements, Homeostasis Model Assessments, and diagnoses of type 2 and type 1 diabetes. SIRT1 rs2273773 showed evidence of association with seasonal variation in weight (C-allele, OR = 0.85, 95% CI = 0.76–0.95, p = 0.005). In addition, our study gave further support for the association of SIRT1 gene with depressive disorders (rs3758391) and diastolic blood pressure (rs2273773).
Citation: Kovanen L, Donner K, Partonen T (2015) SIRT1 Polymorphisms Associate with Seasonal Weight Variation, Depressive Disorders, and Diastolic Blood Pressure in the General Population. PLoS ONE 10(10): e0141001. https://doi.org/10.1371/journal.pone.0141001
Editor: Shengxu Li, Tulane School of Public Health and Tropical Medicine, UNITED STATES
Received: January 8, 2015; Accepted: October 2, 2015; Published: October 28, 2015
Copyright: © 2015 Kovanen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: Data cannot be made publicly available due to ethical and legal restrictions. Health2000 study samples and data are held at the THL Biobank, which grants access to samples and data to research projects that are of high scientific quality and impact and that correspond with the research areas of THL Biobank. More information on THL Biobank can be found at: https://www.thl.fi/en/web/thlfi-en/topics/information-packages/thl-biobank. Information related to applying for access to samples and data can be found at: https://www.thl.fi/en/web/thlfi-en/topics/information-packages/thlbiobank/researchers/sample-and-data-access.
Funding: The authors have no support or funding to report.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Many recent studies have focused on sirtuin 1 (SIRT1) in relation to metabolism, insulin resistance, cancer, and longevity [1–3]. SIRT1, which is a histone deacetylase, participates through its deacetylase activity for tens of substrates in the coordination of a range of cellular functions, such as cell-division cycle, response to DNA damage, apoptosis, and autophagy. SIRT1 is also a sensor of the cytosolic housekeeping redox reaction of nicotinamide adenine dinucleotide that is measured with the ratio of the oxidized and the reduced forms, and that is changed by glucose deprivation and the metabolic changes under caloric restriction or fasting. There is an earlier report on SIRT1 in metabolic syndrome, where there was no significant association [4].
So far, genetic variations in SIRT1 have been associated with depressive [5] and anxiety [6] disorders. In an elegant study, both common and rare variations in SIRT1 in humans were found to associate with the increased odds for anxiety disorders at large [6]. The study also demonstrated that in mice SIRT1 increases anxiety by deacetylating the brain-specific helix-loop-helix transcription factor, nescient helix loop helix 2 (NHLH2), which increases its activity on the monoamine oxidase A (MAOA) promoter. As the MAOA enzyme degrades serotonin and dopamine, the increased enzyme activity leads to reduced serotonin and dopamine levels in the brain, especially in those regions related to regulation of mood and emotions, and thereby to increased depression and anxiety [7–9].
Further, SIRT1 variants have been associated with depressive disorder [5], but not with bipolar disorder [10]. However, during a depressive episode due to major depressive disorder or bipolar disorder, the mRNA levels of sirtuin isoforms in peripheral white blood cells, are lowered whereas the levels of those mRNAs in a remissive state are equal to those in healthy controls [11]. Here, it is of note that 10–20% of patients with recurrent major depressive disorder and 15–22% of those with bipolar disorder have the seasonal pattern for mood disorder, or seasonal affective disorder [12].
It appears that not only mood and behavior, but also the components, or risk factors, of the metabolic syndrome of the individual do fluctuate over the year. The increase in metabolic syndrome prevalence is mainly due to the increases in blood pressure and glucose during the winter, and the seasonal variation in metabolic syndrome prevalence associates with insulin resistance being increased from the extent of mild to moderate [13,14].
One aim of our current study was to confirm, as far as SIRT1 is concerned, the earlier findings that have demonstrated associations of sirtuins with depressive and anxiety disorders. Another aim of our current study was to extend the exploration of associations of SIRT1 to concern those with the seasonal variations in mood and behavior, metabolic disorder, and relevant health examination measurements. Here, we report associations to seasonal variation in weight, depressive disorders and diastolic blood pressure.
Materials and Methods
Subjects
The subjects were selected from the national Health 2000 survey [15] of Finnish population aged 30 years and older (n = 8028) living in mainland Finland that was approved by the ethics committees of the National Public Health Institute and the Helsinki and Uusimaa Hospital District. All participants provided a written informed consent. The selection (n = 5910) included individuals who had given blood samples, taken part to the Munich-Composite International Diagnostic Interview (M-CIDI) [15] and filled in the self-report on seasonal changes in mood and behavior adapted from the Seasonal Pattern Assessment Questionnaire (SPAQ) [16].
Phenotypes
Depressive disorders (major depressive disorder, dysthymia) and anxiety disorders (panic disorder w/o agoraphobia, generalized anxiety disorder, social phobia, agoraphobia) without hierarchy criteria were assessed using M-CIDI, a valid and reliable instrument for the assessment of depressive, anxiety and alcohol use disorders yielding diagnoses according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [16]. The controls did not have any diagnosis of mental disorders nor met any sub-threshold criteria as assessed with the M-CIDI.
The participants filled in a questionnaire of lifetime seasonal variations in mood and behavior adapted from SPAQ [17]. The six items of sleep length, social activity, mood, weight, appetite, and energy level were scored from 0 to 3 (none, slight, moderate or marked change) rather than from 0 to 4 (none, slight, moderate, marked or extremely marked change), with the sum or global seasonality score (GSS) then ranging from 0 to 18. The psychometric properties of this modified questionnaire have been reported to be good [18]. In this study, dichotomous variables (no variation, variation) were computed for the six items.
Routine fasting laboratory tests included the concentrations of blood glucose, serum insulin, serum total cholesterol, triglycerides, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol. The Homeostasis Model Assessment (HOMA) insulin resistance and beta-cell function indexes were computed. Blood pressure, height, weight, and waist circumference were measured. Body-mass index (BMI) was calculated (as kg per m2). Diagnosis of type 2 diabetes and that of type 1 diabetes were assessed on the basis of all available data collected for the health examination study (for details of the methods, see http://www.terveys2000.fi/indexe.html). Using these data, the metabolic syndrome was assessed with the criteria of European Group for the Study of Insulin Resistance (EGIR) modification of World Health Organization (WHO) criteria: diabetics and highest quartile of non-diabetics for fasting glucose were excluded. To fulfill the EGIR criteria for the metabolic syndrome, two of the following needed to be present: Fasting glucose of ≥6.1 mmol/l, elevated blood pressure (mean of systolic blood pressure measurements of ≥140 mmHg, or mean of diastolic blood pressure measurements of ≥90 mmHG, or medication for hypertension), triglycerides of ≥2.0 mmol/l or HDL of ≤1.0 mmol/l or lipid-lowering medication, waist circumference of ≥94 cm for men and that of ≥80 cm for women.
Gene and SNP selection
SIRT1 SNP selection was based on HapMap phase 3 data (http://www.hapmap.org/) and tagging was done using the Tagger program in the Haploview 4.2 software [19]. The linkage disequilibrium (LD) within the gene and 10 kb of their 5' and 3' flanking regions, i.e. 54 kb (chr10:69,304–69,358 kb, NCBI36/hg18 assembly), was used to select tag SNPs capturing most of the genetic variation. The aim was to capture all the SNPs having a minor allele frequency (MAF) of >5% in the European population (CEU and TSI) in the HapMap database. The pair-wise r2 was set to ≥0.9 in order to select a tag SNP among the SNPs that were in LD. Four out of 19 SIRT1 SNPs fulfilled the criterion, and three SNPs (rs3758391, rs2273773, rs17454621) were successfully included in the genotyping multiplex.
Genotyping
Genomic DNA was isolated from whole blood according to standard procedures. The SNPs were genotyped at the Institute for Molecular Medicine Finland, Technology Centre, University of Helsinki using the MassARRAY iPLEX method (Sequenom, San Diego, CA, USA) [20], with excellent success (>95%) and accuracy (100%) rates [21]. For quality control purposes, positive (CEPH) and negative water controls were included in each 384-plate. Genotyping was performed blind to phenotypic information.
440 of 5910 individuals were removed due to a high missing genotype rate (i.e. >0.1). The total genotyping rate in the remaining individuals was 0.996. Finally, there were 5470 individuals and three SIRT1 SNPs for the statistical analyses.
Statistical analyses
Statistical analyses were performed using logistic or linear regression and additive genetic model. Unadjusted, age and sex adjusted, and age, sex and BMI adjusted models were calculated using PLINK software v1.07 [22]. The values presented in the text are from the age and sex adjusted models. Haplotype blocks were defined using Haploview software [19] and the confidence interval algorithm. For the continuous phenotypes (GSS, BMI, waist circumference, diastolic and systolic blood pressure, blood glucose, insulin resistance index, beta-cell index, LDL, total cholesterol, HDL, insulin, triglycerides) 10,000 permutations were used to produce empirical p-values in order to relax the assumption of normality. The p-values were corrected for multiple testing with the Bonferroni method by taking into account the number of SNPs and independent phenotypes. After the Bonferroni correction, p-values of <0.0056 are significant for seasonality, p<0.0071 for metabolic syndrome and p<0.0029 for health examination measurements, HOMAs, and diagnoses of type 2 and type 1 diabetes. For replication of the previous findings reported in the literature, i.e. depressive and anxiety disorders, p-values of <0.05 were considered significant. Population stratification was not addressed.
Results
The participants’ general characteristics are reported in Table 1. The study population of 5910 subjects was 55.4% women and had a mean age of 53.1 years (SD = 15.0), BMI of 27.0 (SD = 4.7), GSS of 5.0 (SD = 3.0), blood pressure of 81.7/134.9 (SD = 11.3/21.3). 8.2% had depressive disorder, 5.3% had anxiety disorder, 23.2% had metabolic syndrome (EGIR). Most participants presented seasonal variations in sleep length, social activity, mood and energy level.
MDD; major depressive disorder. HDL; High-density lipoprotein cholesterol. BMI; body mass index. LDL; Low-density lipoprotein cholesterol. GSS; global seasonality score.
Genotype and allele frequencies and the Hardy-Weinberg equilibrium estimates are shown in Table 2. No haplotype blocks were formed for SIRT1 (Fig 1). All the SNP association results are shown in S1 Table. SIRT1 rs3758391 T allele showed nominally significant associations with depressive disorders (OR = 1.19, 95% CI of 1.01 to 1.40, p = 0.040, see Table 3), metabolic syndrome (OR = 0.88, 95% CI of 0.80 to 0.97, p = 0.01, see Table 3), insulin resistance index (beta = -0.26, 95% CI of -0.48 to -0.04, p = 0.019, empirical p = 0.02, see Table 3) and blood glucose (beta = -0.05, CI of -0.09 to -0.002, p = 0.04, empirical p = 0.04, Table 3). The associations with metabolic syndrome, insulin resistance index and blood glucose did not remain significant after correcting for multiple testing.
The confidence interval algorithm implemented in the Haploview program was used to construct the haplotype blocks.
BP; Base pair position. A1; Minor allele. A2; Major allele. MAF; Minor allele frequency. A1A1, A1A2, A2A2; Genotype counts and frequencies (%). P; Hardy-Weinberg p-value
A1; Tested allele (minor allele). N; Number of genotypes for the phenotype. L95, U95; Lower and upper bounds of 95% confidence interval for odds ratio. P/EMP: p-value / empirical p-value
The association of SIRT1 rs2273773 with the seasonal variation in weight (OR = 0.85, 95% CI of 0.76 to 0.95, p = 0.005) remained significant after the Bonferroni correction, the C-allele being associated with the decreased odds for the seasonal variation in weight (Table 3). SIRT1 rs2273773 C allele associated with both high diastolic (beta = 1.06, 95% CI of 0.43 to 1.68, p = 0.001, empirical p-value = 0.001) and systolic blood pressure (beta = 1.23, 95% CI of 0.19 to 2.28, p = 0.02, empirical p-value = 0.02), of which the association with diastolic blood pressure remained significant after the Bonferroni correction, the C-allele having the odds for higher diastolic blood pressure.
Discussion
Our current results from the population-based health examination study suggested the minor C-allele of synonymous (Leu→Leu) SIRT1 rs2273773 polymorphism to contribute to higher diastolic blood pressure, and to protect from seasonal variation in body weight. However, the SNP showed no evidence of association with BMI or the metabolic syndrome or its components, as assessed with the EGIR modification of WHO criteria. In agreement, CC carriers have previously been reported to have high systolic and diastolic blood pressures in men [23], and no association with metabolic syndrome in morbidly obese subjects has been found [24]. However, the C-allele (or CC genotype or C carriers) has been reported to be protective against cardiovascular diseases [25] and contribute to higher energy expenditure [26], a lower BMI [27], and lower fasting glucose concentrations and body fat ratios in men [23]. Moreover, the T-allele of SIRT1 rs2273773 was seen, as part of two haplotypes of SIRT1, to be associated with schizophrenia but not with bipolar disorder [10]. We were not able to test this association, since these disorders were not assessed with the method used for diagnostic interview in our study. In addition, our study provides further support of the association between SIRT1 (rs3758391) and depressive disorders (major depressive disorder and dysthymia).
Our study does not come without limitations. The assessment of the seasonal variations in mood and behavior was based on the self-report only and only limited variables were controlled for in the statistical analysis. On the other hand, there are strengths in our study such as the number of participants enrolled from a nation-wide and representative sample of the adult general population aged 30 years and older, the health examination protocol for the assessment of the metabolic syndrome, the diagnostic interview for the assessment of depressive and anxiety disorders, and the coverage of SIRT1 for the assessment of genetic association.
In conclusion, we found that SIRT1 (rs2273773) accounts for the seasonal variation in body weight. In addition, our study gave further support for the role of SIRT1 in depressive disorders (rs3758391) and diastolic blood pressure (rs2273773). Thus, SIRT1 appears to contribute to seasonal, mood and cardiovascular physiology in humans.
Supporting Information
S1 Table. All results of the SIRT1 SNP associations.
https://doi.org/10.1371/journal.pone.0141001.s001
(XLSX)
Author Contributions
Conceived and designed the experiments: TP KD LK. Performed the experiments: KD. Analyzed the data: KD LK. Contributed reagents/materials/analysis tools: KD LK. Wrote the paper: TP KD LK.
References
- 1. Liang F, Kume S, Koya D. SIRT1 and insulin resistance. Nat Rev Endocrinol. 2009;5: 367–373. pmid:19455179
- 2. Sahar S, Sassone-Corsi P. Metabolism and cancer: the circadian clock connection. Nat Rev Cancer. 2009;9: 886–896. pmid:19935677
- 3. Baur JA, Ungvari Z, Minor RK, Le Couteur DG, de Cabo R. Are sirtuins viable targets for improving healthspan and lifespan? Nat Rev Drug Discov. 2012;11: 443–461. pmid:22653216
- 4. Cruz M, Valladares-Salgado A, Garcia-Mena J, Ross K, Edwards M, Angeles-Martinez J, et al. Candidate gene association study conditioning on individual ancestry in patients with type 2 diabetes and metabolic syndrome from Mexico City. Diabetes Metab Res Rev. 2010;26: 261–270. pmid:20503258
- 5. Kishi T, Yoshimura R, Kitajima T, Okochi T, Okumura T, Tsunoka T, et al. SIRT1 gene is associated with major depressive disorder in the Japanese population. J Affect Disord. 2010;126: 167–173. pmid:20451257
- 6. Libert S, Pointer K, Bell EL, Das A, Cohen DE, Asara JM, et al. SIRT1 activates MAO-A in the brain to mediate anxiety and exploratory drive. Cell. 2011;147: 1459–1472. pmid:22169038
- 7. Hotamisligil GS, Breakefield XO. Human monoamine oxidase A gene determines levels of enzyme activity. Am J Hum Genet. 1991;49: 383–392. pmid:1678250
- 8. Hampp G, Ripperger JA, Houben T, Schmutz I, Blex C, Perreau-Lenz S, et al. Regulation of monoamine oxidase A by circadian-clock components implies clock influence on mood. Curr Biol. 2008;18: 678–683. pmid:18439826
- 9. Reif A, Richter J, Straube B, Hofler M, Lueken U, Gloster AT, et al. MAOA and mechanisms of panic disorder revisited: from bench to molecular psychotherapy. Mol Psychiatry. 2014;19: 122–128. pmid:23319006
- 10. Kishi T, Fukuo Y, Kitajima T, Okochi T, Yamanouchi Y, Kinoshita Y, et al. SIRT1 gene, schizophrenia and bipolar disorder in the Japanese population: an association study. Genes Brain Behav. 2011;10: 257–263. pmid:20977650
- 11. Abe N, Uchida S, Otsuki K, Hobara T, Yamagata H, Higuchi F, et al. Altered sirtuin deacetylase gene expression in patients with a mood disorder. J Psychiatr Res. 2011;45: 1106–1112. pmid:21349544
- 12. Partonen T, Lonnqvist J. Seasonal affective disorder. Lancet. 1998;352: 1369–1374. pmid:9802288
- 13. Kamezaki F, Sonoda S, Tomotsune Y, Yunaka H, Otsuji Y. Seasonal variation in metabolic syndrome prevalence. Hypertens Res. 2010;33: 568–572. pmid:20300109
- 14. Kamezaki F, Sonoda S, Nakata S, Muraoka Y, Okazaki M, Tamura M, et al. Association of seasonal variation in the prevalence of metabolic syndrome with insulin resistance. Hypertens Res. 2013;36: 398–402. pmid:23235713
- 15.
Aromaa A, Koskinen S, editors. Health and functional capacity in Finland. Baseline Results of the Health 2000 Health Examination Survey. Helsinki: Publications of the National Public Health Institute; 2004.
- 16.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-IV. Washington DC: American Psychiatric Association; 1994.
- 17.
Rosenthal NE, Bradt GH, Wehr TA. Seasonal Pattern Assessment Questionnaire. Bethesda, Maryland: National Institute of Mental Health, 1984.
- 18. Rintamaki R, Grimaldi S, Englund A, Haukka J, Partonen T, Reunanen A, et al. Seasonal changes in mood and behavior are linked to metabolic syndrome. PLoS One. 2008;3: e1482. pmid:18213390
- 19. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21: 263–265. pmid:15297300
- 20. Jurinke C, van den Boom D, Cantor CR, Koster H. Automated genotyping using the DNA MassArray technology. Methods Mol Biol. 2002;187: 179–192. pmid:12013745
- 21. Lahermo P, Liljedahl U, Alnaes G, Axelsson T, Brookes AJ, Ellonen P, et al. A quality assessment survey of SNP genotyping laboratories. Hum Mutat. 2006;27: 711–714. pmid:16786507
- 22. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81: 559–575. pmid:17701901
- 23. Shimoyama Y, Suzuki K, Hamajima N, Niwa T. Sirtuin 1 gene polymorphisms are associated with body fat and blood pressure in Japanese. Transl Res. 2011;157: 339–347. pmid:21575918
- 24. Liguori R, Labruna G, Alfieri A, Martone D, Farinaro E, Contaldo F, et al. The FTO gene polymorphism (rs9939609) is associated with metabolic syndrome in morbidly obese subjects from southern Italy. Mol Cell Probes. 2014;28: 195–199. pmid:24675148
- 25. Kilic U, Gok O, Bacaksiz A, Izmirli M, Elibol-Can B, Uysal O. SIRT1 gene polymorphisms affect the protein expression in cardiovascular diseases. PLoS One. 2014;9: e90428. pmid:24587358
- 26. Lagouge M, Argmann C, Gerhart-Hines Z, Meziane H, Lerin C, Daussin F, et al. Resveratrol improves mitochondrial function and protects against metabolic disease by activating SIRT1 and PGC-1alpha. Cell. 2006;127: 1109–1122. pmid:17112576
- 27. van den Berg SW, Dolle ME, Imholz S, van der ADL, van 't Slot R, Wijmenga C, et al. Genetic variations in regulatory pathways of fatty acid and glucose metabolism are associated with obesity phenotypes: a population-based cohort study. Int J Obes (Lond). 2009;33: 1143–1152.