|The PsyCoLaus study: methodology and characteristics of the sample of a population-based survey on psychiatric disorders and their association with genetic and cardiovascular risk factors.|
|Jump to Full Text|
|PMID: 19292899 Owner: NLM Status: MEDLINE|
|BACKGROUND: The Psychiatric arm of the population-based CoLaus study (PsyCoLaus) is designed to: 1) establish the prevalence of threshold and subthreshold psychiatric syndromes in the 35 to 66 year-old population of the city of Lausanne (Switzerland); 2) test the validity of postulated definitions for subthreshold mood and anxiety syndromes; 3) determine the associations between psychiatric disorders, personality traits and cardiovascular diseases (CVD), 4) identify genetic variants that can modify the risk for psychiatric disorders and determine whether genetic risk factors are shared between psychiatric disorders and CVD. This paper presents the method as well as sociodemographic and somatic characteristics of the sample. METHODS: All 35 to 66 year-old persons previously selected for the population-based CoLaus survey on risk factors for CVD were asked to participate in a substudy assessing psychiatric conditions. This investigation included the Diagnostic Interview for Genetic Studies to elicit diagnostic criteria for threshold disorders according to DSM-IV and algorithmically defined subthreshold syndromes. Complementary information was collected on potential risk and protective factors for psychiatric disorders, migraine and on the morbidity of first-degree relatives, whereas the collection of DNA and plasma samples was already part of the original CoLaus survey. RESULTS: A total of 3,691 individuals completed the psychiatric evaluation (67% participation). The gender distribution of the sample did not differ significantly from that of the general population in the same age range. Although the youngest 5-year band of the cohort was underrepresented and the oldest 5-year band overrepresented, participants of PsyCoLaus and individuals who refused to participate revealed comparable scores on the General Health Questionnaire, a self-rating instrument completed at the somatic exam. CONCLUSION: Despite limitations resulting from the relatively low participation in the context of a comprehensive and time-consuming investigation, the PsyCoLaus study should significantly contribute to the current understanding of psychiatric disorders and comorbid somatic conditions by: 1) establishing the clinical relevance of specific psychiatric syndromes below the DSM-IV threshold; 2) determining comorbidity between risk factors for CVD and psychiatric disorders; 3) assessing genetic variants associated with common psychiatric disorders and 4) identifying DNA markers shared between CVD and psychiatric disorders.|
|Martin Preisig; Gérard Waeber; Peter Vollenweider; Pascal Bovet; Stéphane Rothen; Caroline Vandeleur; Patrice Guex; Lefkos Middleton; Dawn Waterworth; Vincent Mooser; Federica Tozzi; Pierandrea Muglia|
Related Documents :
|11768009 - Socio-demographic and clinical profile of patients attending a private psychiatric hosp...
24494099 - Identity disturbance and substance-dependence in patients with borderline personality d...
20584389 - Looking at comorbidity through the glasses of neuroscientific memory research: a brain-...
12325049 - Older criminals: a descriptive study of psychiatrically examined offenders in sweden.
2724199 - The recognition of psychiatric morbidity on a medical oncology ward.
6733369 - Psychiatric disorder in pregnancy and the first postnatal year.
23632099 - Non-classic congenital adrenal hyperplasia.
22186769 - Influence of comorbid mental disorders on time to seeking treatment for major depressiv...
22175049 - Activity of selected aromatic amino acids in biological systems.
|Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't Date: 2009-03-17|
|Title: BMC psychiatry Volume: 9 ISSN: 1471-244X ISO Abbreviation: BMC Psychiatry Publication Date: 2009|
|Created Date: 2009-04-10 Completed Date: 2009-06-10 Revised Date: 2009-11-18|
Medline Journal Info:
|Nlm Unique ID: 100968559 Medline TA: BMC Psychiatry Country: England|
|Languages: eng Pagination: 9 Citation Subset: IM|
|Department of Psychiatry, CHUV, Lausanne, Switzerland. firstname.lastname@example.org|
|APA/MLA Format Download EndNote Download BibTex|
Biological Markers / analysis
Cardiovascular Diseases / epidemiology*
Mental Disorders / diagnosis, epidemiology*, genetics
Population Surveillance / methods*
Switzerland / epidemiology
Journal ID (nlm-ta): BMC Psychiatry
Publisher: BioMed Central
Copyright ? 2009 Preisig et al; licensee BioMed Central Ltd.
open-access: 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 work is properly cited.
Received Day: 12 Month: 8 Year: 2008
Accepted Day: 17 Month: 3 Year: 2009
collection publication date: Year: 2009
Electronic publication date: Day: 17 Month: 3 Year: 2009
Volume: 9First Page: 9 Last Page: 9
Publisher Id: 1471-244X-9-9
PubMed Id: 19292899
|The PsyCoLaus study: methodology and characteristics of the sample of a population-based survey on psychiatric disorders and their association with genetic and cardiovascular risk factors|
|Martin Preisig1||Email: email@example.com|
|G?rard Waeber2||Email: Gerard.Waeber@chuv.ch|
|Peter Vollenweider2||Email: Peter.Vollenweider@chuv.ch|
|Pascal Bovet2||Email: Pascal.Bovet@chuv.ch|
|St?phane Rothen1||Email: Stephane.Rothen@chuv.ch|
|Caroline Vandeleur3||Email: Caroline.Vandeleur@hcuge.ch|
|Patrice Guex1||Email: Patrice.Guex@chuv.ch|
|Lefkos Middleton4||Email: firstname.lastname@example.org|
|Dawn Waterworth5||Email: email@example.com|
|Vincent Mooser5||Email: firstname.lastname@example.org|
|Federica Tozzi6||Email: email@example.com|
|Pierandrea Muglia6||Email: firstname.lastname@example.org|
1Department of Psychiatry, CHUV, Lausanne, Switzerland
2Department of Medicine, Internal Medicine, CHUV, Lausanne, Switzerland
3Department of Psychiatry, University Hospital of Geneva, Geneva, Switzerland
4Division of Neurosciences and Mental Health, Imperial College, London, UK
5Medical Genetics, GlaxoSmithKline, Philadelphia, Pennsylvania, USA
6Genetics Division, Drug Discovery, GlaxoSmithKline R&D, Verona, Italy
Both cardiovascular disease (CVD) and psychiatric disorders are major public health issues which lead to increased mortality and disability. Epidemiological studies based on structured diagnostic interviews have consistently documented high lifetime prevalence of psychiatric disorders [1-7] with even higher rates in more recent surveys. Several of these studies [8-10] as well as research in primary care settings  have also revealed a substantial proportion of individuals that have mood or anxiety symptoms not meeting diagnostic criteria for corresponding disorders. Although, clinical and a small number of epidemiological studies have supported the clinical significance of these syndromes [10,12-15] there is still an ongoing debate on whether or not these syndromes require treatment .
The bulk of research focusing on associations between depressive symptoms or disorders and CVD has documented increased prevalence of depression (ranging from 16% to 23%) among patients with various manifestations of coronary heart diseases (CHD), including myocardial infarction (MI), unstable angina, stable coronary artery disease, congestive heart failure and coronary catheterization or angioplasty. The presence of depression in patients with established CHD was found to be a predictor of poor course with increased mortality (reviews: [17-19]). Conversely, population-based prospective studies on individuals with depression or depressive symptoms have documented increased cardiovascular morbidity and mortality in these individuals, thereby implicating depression as an independent risk factor in the pathophysiologic progression of CVD, rather than merely a secondary emotional response to the illness [18,20-23]. Other symptoms and disorders investigated for their association with CVD were anxiety (review: ), heavy drinking (review: ) and personality traits [26-33].
However, the existing studies on potential associations between psychiatric disorders and CVD have suffered from serious methodological limitations, which are also likely to account for the large body of conflicting findings. These methodological limitations include: 1) the use of clinical rather than epidemiological samples (risk of treatment-seeking bias); 2) the lack of a comparison group; 3) the application of psychiatric scales for a single psychiatric syndrome rather than structured diagnostic interviews; 4) the assessment of the incidence of CVD (or risk factors for CVD) by interview techniques rather than by physical examinations, biological measures and the use of medical records; 5) the lack of assessing both CVD and risk factors for CVD, which did not allow studies to examine whether a specific psychiatric disorder was directly associated with CVD or through associations with already well-established risk factors for CVD.
Association studies represent a very powerful approach for investigating the biological basis of human diseases, comparing genotype frequencies in well-defined clinical groups to appropriate controls. However, this approach presents limits: population stratification, genetic heterogeneity and phenotype complexity affect the case-control design of genetic association studies . Moreover, the real effect of a susceptibility gene and the impact of its discovery in the clinics can only be established using unselected and representative population samples, which allow for estimating prevalence of gene variants and relative genotypic disease risks. In recent years, genetics has greatly advanced and large scale genome-wide association studies (GWAS) have already delivered numerous new susceptibility genes for a variety of common conditions including type 1 and type 2 diabetes, prostate and breast cancer [35-39]. Other areas are in rapid expansion with novel loci implicated in the predisposition to complex traits, such as coronary heart disease, asthma and obesity [40-42]. The CoLaus study has already contributed to GWAS successes for several somatic traits including height, LDL, obesity [42-44] and heavy smoking .
The clinical practice in psychiatry suffers from the lack of objective measures. The search of peripheral markers reflecting psychiatric disease state and trait, and objective read-outs of response to treatment has been under constant scrutiny for several decades and numerous candidates have been tested based on several disease pathogenetic hypotheses. Whilst biomarkers are recognized as a great need across all disease areas, the need is possibly even more important for psychiatric disorders where disease aetiology is unknown and there is a lack of objective diagnostic criteria. However, the relevance of periphery biomarkers for common psychiatric disorders remains to be demonstrated. Recent developments in proteomic and genomic approaches are expanding the number of testable hypothesis by some orders of magnitude and allowing the exploration of patterns or signatures rather than single markers (see  for a review). Expectations are that, with the decreasing costs of genomic and proteomic applications, the investigation of large population-based data sets will provide the opportunity to identify more homogeneous disease subtypes and investigate biomarkers, so that biomarker studies may substantiate disease sub-groups. PsyCoLaus represents therefore a great opportunity to also investigate the presence of periphery biomarkers related to behavioral traits .
The PsyCoLaus study is based on the large epidemiological sample of the CoLaus survey, which assessed CVD risk factors and the genetic variants associated with these conditions in the general population of the City of Lausanne . The specific aims of the PsyCoLaus investigation were to 1) establish the lifetime and 12-month prevalence of threshold (DSM-IV) and subthreshold psychiatric syndromes and migraine in 35 to 66 year-old residents of the city of Lausanne; 2) test the validity of postulated definitions for subthreshold psychiatric disorders, and especially mood and anxiety syndromes as well as the concept of atypical depression using comorbidity patterns, risk of suicidal attempts, health service use, social functioning (Global Assessment of Function scores, GAF) and family history as validator variables; 3) determine the association between the lifetime history of major depressive disorder (MDD), and other psychiatric disorders and risk factors for CVD; and 4) identify genetic variants and biomarkers that can modify the risk for various psychiatric disorders and for comorbid CVD and psychiatric disorders.
PsyCoLaus is a psychiatric study conducted in a population-based cohort assessed for cardiovascular risk factors (CoLaus) (see  for detailed description). In brief, the CoLaus study, which was based on a sample of 6,738 individuals randomly selected from the list of residents of the city of Lausanne (Switzerland), assessed CVD risk factors and collected DNA and plasma samples for the study of genetic variants and biomarkers. Lausanne is the 5th largest city of Switzerland, localized in the French speaking part of the country. Foreigners mostly from other central European countries represent about a third of the population of Lausanne. This proportion is comparable to that of other Swiss cities, but higher than the average of approximately 20% in the whole country. Compared to other European countries, the Swiss population is relatively stable favoring the completion of prospective follow-up studies, such as the Zurich cohort study, over decades .
The present study (PsyCoLaus), based on the CoLaus sample, included a semi-structured diagnostic interview and a number of self-rating instruments that evaluated personality traits, attitudes, functioning and sleep patterns.
The Institutional Ethic's Committee of the University of Lausanne approved the CoLaus and subsequently the PsyCoLaus study. All participants signed a written informed consent after having received a detailed description of the goal and funding of the study.
The recruitment and medical assessment of the CoLaus sample, which was completed between 2003 and 2006, has been described in detail . The random sampling procedure was based on a complete list of the Lausanne inhabitants aged 35?75 years (n = 56,694 in 2003), provided by the population register of the city. Of the initial 19,830 subjects sampled, 54 subjects were considered as non-eligible before contact and 15,109 (76%) responses were obtained. Among responders, 6,189 (41%) subjects refused to participate and 799 (5%) were considered as non-eligible (moved away, out of the age range or deceased). The sample of 8,121 subjects who agreed to participate represented 41% of the initially sampled population and 57% of all eligible responders. Among these subjects, 6,738 completed the examination (6,188 Caucasians and 549 Non-Caucasians), whereas 1,383 could not be included into the study despite their will to participate because the number of subjects who agreed to participate was higher than the number of subjects initially planned for the CoLaus study (one additional subject withdrew after consent).
All 35 to 66-year old subjects of the CoLaus sample (n = 5,535), were invited by letters to also participate in the psychiatric evaluation. Those who did not respond to the letter were contacted by phone. All subjects who were sufficiently fluent in French or English and agreed to participate were included into the PsyCoLaus sub-study and underwent the psychiatric assessment between 2004 and 2008.
Assessment within the CoLaus study  included the collection of socio-demographic, personal and treatment history data as well as family history information of CVDs (myocardial infarction, stroke and coronary artery disease) and their risk factors. In women, further data regarding reproductive and obstetrical history, oral contraception and hormonal replacement therapy was collected. The somatic exam encompassed measurements of body weight, height, blood pressure (triplicate measure three times on the left arm after at least a 10-minute rest in the seated position), heart rate (triplicate measure), waist and hip circumferences, fat and fat-free mass assessed by electrical bioimpedance . Moreover, venous blood samples were drawn from each participant after an overnight fast, in order to measure the levels of glucose, LDL-cholesterol, HDL cholesterol, and triglycerides. A random subgroup also performed an oral glucose tolerance test. A urine sample was collected for the assessment of creatinine and albumin. Finally, participants completed the 12-item General Health Questionnaire (GHQ-12; ; French translation: ). This self-rating instrument was specifically developed to detect the presence of minor psychiatric symptoms. In a study including 25,916 patients in 15 countries, the GHQ was found to work as well as the longer 28-item version of the instrument . According to the Likert scoring method, a threshold score of 12 revealed a sensitivity of 78.9% and a specificity of 67.4% to detect psychopathology.
Within the PsyCoLaus sub-study, diagnostic information was collected using the Diagnostic Interview for Genetic Studies (DIGS, ). The DIGS was developed by the NIMH Molecular Genetics Initiative in order to obtain a more precise assessment of phenotypes through 1) a semi-structured design corresponding to a wide spectrum of DSM-IV Axis I criteria and suicidal behavior, and 2) the collection of extensive information on the course and chronology of comorbid conditions. An updated version of the DIGS  includes DSM-IV criteria. The French translation of the DIGS  resulted from a collaborative effort between the Department of Psychiatry of Lausanne and the INSERM in Paris. Several modifications were incorporated into the French version: 1) a screening question was added to the mania section to lower the threshold for entering the chapter by asking whether friends or family members had observed episodes where the subject's mood was more elated than normal; 2) additional questions were added to the depression section in order to elicit criteria for atypical depression features (leaden paralysis, long-standing patterns of interpersonal rejection sensitivity, mood reactivity) and recurrent brief depression (maximal number of episodes within a 12-month period); 3) a section on generalized anxiety disorder (GAD) was added using the questions from the Schedule for Affective Disorders and Schizophrenia ? Lifetime Version (SADS-L, ); 4) the brief phobia chapter of the DIGS was replaced by the corresponding more extensive chapters from the SADS-L; and 5) the original DIGS section on nicotine consumption was largely extended to elicit DSM-IV abuse and dependence criteria. As long as a subject was treated in a psychiatric setting in Switzerland, personal history information was completed by the collection of medical records in order to obtain supplementary data on symptoms, impairment, duration, timing of illness and treatment. The applied semi-structured interview allowed for the establishment of lifetime and 12-month prevalence of a large array of specific DSM-IV axis-I (threshold) disorders as well as algorithmically-defined subthreshold mood and anxiety syndromes according to [8,9]. The French version of the DIGS revealed excellent inter-rater reliability in terms of kappa and Yule's Y coefficients for major mood and psychotic disorders  and substance use disorders , whereas the 6-week test-retest reliability was slightly lower [56,57].
Additional data collection based on interview techniques included headache symptoms ('Diagnostic Interview for Headache Syndromes' DIHS), life-events (short interview of F. Amiel-Lebigre; ) and family history information. Family history information was gathered using the modified version of the Family History-Research Diagnostic Criteria (FH-RDC; , as initially used in the Yale Family Study . This version (adaptation to DSM-III-R and DSM-IV) was translated into French by our group, who undertook extensive validation efforts of this tool by establishing the agreement and prevalence estimates between this instrument and semi-structured interviews for a series of specific psychiatric diagnoses [61,62]. Generally, the family history method revealed high specificity but low sensitivity.
Complementary information on personality and temperamental features, familial functioning, coping and sleep were obtained using a self-report battery including the following instruments: the State-Trait Anxiety Inventory (STAI; [63,64]), the Retrospective Self Report Childhood Inhibition (RSRCI; ), the Dimensions of Temperament Survey Revised (DOTS-R; ), the Eysenck Personality Questionnaire (EPQ; [67,68]), the Type A behavior [69,70], the Sensitivity to Reward (STR), the Parental Bonding Instrument (PBI; [71-73]), the Family Adaptability and Cohesion Scale III (FACES III; [74,75]), the Dyadic Adjustment Scale (DAS; [76,77]), the Family Attitude Scale (FAS-30; ), the Euronet: Problem Resolution Strategy  and the MOS-Sleep Module .
During the CoLaus evaluation, participants donated blood after a 12-hr fasting period for clinical chemistry and genetic analyses. Most of the assays were performed by the Clinical Chemistry Laboratory within the Lausanne University Hospital. Plasma, serum and RNA are available for biomarkers studies. Nuclear DNA was extracted from whole blood for whole genome scan analysis and genome-wide genotyping was performed on all the 6,188 participants to the CoLaus, using the Affymetrix 500 K SNP chip. Participants were removed from the analysis on the basis of the following sample quality control criteria: any participant whose sex was inconsistent with genetic data from X-linked SNPs; the proportion of genotypes called was less than 90%; having inconsistent genotypes when compared with duplicate samples. In total, 5636 participants remained after sample quality control exclusions. We then applied SNP exclusions with the following criteria: SNPs that were monomorphic among all samples; SNPs with genotypes on less than 95% participants; SNPs that were out of Hardy-Weinberg equilibrium (p < 1?0 ? 10-7). After these quality control procedures, 370 697 SNPs remained for analysis .
The inflation factor (?), which was estimated from the mean of the ?2 tests generated on all SNPs that were tested, was calculated to be 1,010. This lambda value, which is very close to 1, indicates the absence of major population structure, i.e. that the sample is quite homogeneous genetically .
Interviewers were required to be psychologists or psychiatrists, who were trained over a two-months period. Their training included rating tapes and supervised co-ratings. In order to provide ongoing supervision throughout the study, each interview and diagnostic assignment was reviewed by an experienced senior psychologist.
Phenotypic data were entered into a secured, internet-based database. The database was designed to confirm the validity of the identification codes, establish the completeness of the information keyed in and to perform basic data checks. All discrepancies were recorded in a case report form kept in a locked room. All modifications of the data were automatically recorded, including the identity of the investigator who made each modification, the date, the old and the new values.
Within the whole sample, disorders such as schizophrenia or bipolar-I disorder with an expected lifetime prevalence of 1% can be estimated with 95% confidence within a range of +/- 0.31% (i.e. the lower and upper bounds of a 95% confidence interval for a disorder with a 1% prevalence would be 0.69% and 1.31%). For more common disorders, such as MDD, with an expected lifetime prevalence of 15%, the prevalence can be estimated within a range of about +/- 1.1% in the whole sample and within a range of about +/- 4% in a 5-year age-sex stratum.
The power for the analysis of associations between disorders and dichotomous variables is provided on Table 1 according to the formula for dichotomous variables  and assuming a two-tailed p-value of 0.05. Even for relatively rare disorders or syndromes with a prevalence of 1% (bipolar disorder or schizophrenia) an association with correlates present in 25% of the sample (e.g. 25 highest percentile regarding triglycerides or cholesterol levels) could be detected with a probability of 63% if the relative risk is 2 and already 88% if the relative risk is 2.5. However, typical psychiatric disorders such as MDD documented to be associated with risk factors for CVD have prevalence rates of 10% or more. For such conditions, a 2 times increased risk with respect to a correlate present in 5% of the sample (e.g. diabetes) could be detected with a probability of 97%, whereas a 1.5 times increased risk could be detected with a probability of 81% for correlates present in 10% of the sample (e.g. high blood pressure).
For genetic analyses, power calculations were done using the program Genetic Power Calculator (; ).
We have estimated that for a dichotomous trait such as recurrent MDD ? that has a prevalence of around 15% in the PsyCoLaus cohort ? the study has approximately 85% power to detect an allele with 50% allele frequency that has a genotypic relative risk = 3 under a dominant model (type 1 error rate of 10-7 taking into account 500'000 genetics markers). For a continuous behavioral trait such as Neuroticism that has been measured in our cohort we would have power of approximately 99% to detect additive QTL effect that explains 2% of the variance (sample size = 3000; type 1 error rate of 10-7 taking into account 500'000 genetics markers); power drops rapidly for smaller effect (i.e. < 2%). However, this large genotypic relative risk of 3 is unlikely to exist for common traits, including psychiatric disorders. Therefore, the sample size in our study does not provide enough statistical power to detect SNPs with small effects. In order to increase the power to detect such SNPs, the sample needs to be combined with those of similar studies .
Sixty-seven percent of the participants of the CoLaus study in the age range between 35 and 66 years accepted the psychiatric evaluation, which resulted in a sample of 3,691 individuals who underwent both the medical and psychiatric exam. Ninety-two percent of them were Caucasians. The gender distribution of the PsyCoLaus sample (46.9% males) did not differ significantly from that of the general population in the same age range, but the youngest 5-year band of the cohort was underrepresented and the oldest 5-year band overrepresented (Table 2). Table 3 provides socio-demographic characteristics of the sample. The mean age of the participants was 49.6 years (s.d. 8.8 years) at the CoLaus and 50.9 (s.d. 8.8 years) years at the psychiatric exam. With a representation of 70.7%, Swiss citizens were over-sampled as their proportion is only 67.8% in the whole population of the city of Lausanne within the same age range. Two thirds of the males but only about half of the females were living with a partner, whereas more than a quarter of females and a sixth of males were separated or divorced. The educational level was higher in males than in females. Similarly, the proportion of professionally active people was larger in males than females. The main difference resulted from the fact that 33.2% of females were housewives, whereas only 2.4% of males undertook the role of housekeepers. Among the professionally active persons, the mean degree of professional activity was 95.4% in males and 74.4% in females.
The prevalence of somatic cardiovascular risk factors such as obesity (BMI ? 30 kg/m2), hypertension (systolic BP ? 140 or diastolic BP ? 90 mmHg or current treatment for hypertension), diabetes (fasting blood glucose ? 7 mmol/l or current treatment with oral hypoglycemic agents or insulin) and dyslipidemia (HDL-cholesterol <1.0 mmol/l or triglycerides ? 2,2, mmol/l or LDL cholesterol ? 4.1 mmol) was 13.5%, 28.7%, 5.5% and 32.0%, respectively. Except for diabetes, these rates were slightly lower than those in the CoLaus sample (14.8%, 31.3%, 5.5%, 33.8%, respectively), indicating that individuals exhibiting obesity, hypertension and dyslipidemia were slightly less prone to participate at the psychiatric evaluation than those without these cardiovascular risk factors.
Among the 5,230 participants of CoLaus aged between 35 and 66 years 5,020 responded to all questions of the GHQ-12 (96.0%), which allowed for an estimation of potential selection bias due to non-participation in PsyCoLaus. However, GHQ-12 scores between participants and non-participants of the psychiatric exam did not differ significantly (z = 1.92; p = n.s.). The mean scores according to the Likert method were 11.11 (s.d = 4.63) and 10.87 (s.d. = 4.60), respectively. Moreover, after adjustment for multiple testing according to Bonferroni, GHQ-12 scores did not differ according to the presence or absence of the somatic cardiovascular risk factors obesity, hypertension, diabetes or dyslipidemia. This lack of association was observed in both the participants and non-participants of PsyCoLaus.
A total of 23,238 family history reports could be collected from 3,310 subjects (mean: 7 records per subject). These reports included 6,558 reports on parents, 7,501 on siblings, 517 on half-siblings, 4,984 on children and 3,462 on spouses. Moreover, according to the DIGS interview 892 respondents (24.2%) reported that they suffered from headache. Consequently, these symptoms were investigated in detail using the DIHS. Finally, 71.6% of the participants completed the self report battery.
The herein presented PsyCoLaus study combines an investigation of cardiovascular risk factors with a comprehensive psychiatric assessment and the collection of DNA in individuals recruited from the general population. The large majority of previous research on associations between psychiatric disorders and CVD included depression rating scales rather than diagnostic interviews. Only four studies (two of them based on the same sample) elicited criteria for depression using structured diagnostic interviews. However, in these studies most data on somatic risk factors for CVD were collected using reports from study subjects (see additional file 1: Table 4), which entailed the risk of inaccurate information and affected the ability to accurately adjust for them in the analyses. Moreover, all these studies focused on depression only and did not determine the effects of potential comorbid conditions such as anxiety disorders. In addition, none of these studies included genotype assessment.
The CoLaus/PsyCoLaus design has attempted to overcome a series of limitations of previous research that focused on associations between psychiatric disorders and risk factors for CVD. Besides the use of a sample recruited in the general population, which should prevent the risk of Berkson's bias and minimize the problem of inappropriate comparison groups, the application of a semi-structured psychiatric interview and a thorough somatic investigation including also blood chemistry measures ensures the collection of valid data on both psychiatric disorders and risk factors for CVD. The simultaneous assessment of a large array of DSM-IV axis-I disorders also allows for the identification of specific psychiatric disorders which are most strongly associated with risk factors for CVD. In contrast to the bulk of previous population-based research in psychiatry, the applied semi-structured interview also enables the assessment of algorithmically-defined mood and anxiety syndromes below the level of DSM-IV and of their clinical impact.
There is general consensus among geneticists and genetic epidemiologists [85-89] on the value of conducting genetic studies in large population-based association studies. Susceptibility genes for common diseases are by and large identified in clinical samples and very often in narrowly defined categories of disorders in order to increase power since most severe conditions are associated with higher genetic loading (e.g. recurrent MDD). Genetic studies in the community are essential to determine the genetic risk attributable to susceptible gene variants at a population level for both narrowly and broadly defined disorders. They also provide the opportunity to estimate a population based genotype relative risk. The PsyCoLaus study offers such a unique opportunity and, in addition, it provides the chance to identify genetic variants that may be shared risk factors for psychiatric disorders and CVD.
Limitations mainly result from constraints regarding the sample and the cross-sectional design. Indeed, the comprehensive physical exam, blood chemistry tests including fasting glucose and DNA collection is easier to organize in an urban area with a central hospital, where the clinical and laboratory research team is localized. However, urban populations are generally not representative of the whole country, as they typically include an increased proportion of diseased subjects. This could be reflected by the mean GHQ score of the sample, which was higher than in a population-based Australian study . Indeed, as GHQ scores did not differ between participants of CoLaus who accepted and those who refused the psychiatric exam, either the general population of the city of Lausanne reveals a relatively high level of psychopathology or those with increased levels of psychopathology were already more likely to participate in the original CoLaus study. The over-representation of diseased or disabled subjects in an urban region would entail the establishment of increased prevalence estimates as compared to the general population of a country, whereas the assessment of associations between psychiatric disorders and risk factors for CVD as well as genetic analyses should be at least less affected or not affected by the choice of the population. Indeed, there was no evidence for differential associations between somatic risk factors for CVD and GHQ-scores according to participation status, although individuals exhibiting these risk factors were slightly less likely to participate at the psychiatric evaluation.
The comprehensive assessment including several distinct components has certainly contributed to the relatively low participation rate. Nonetheless, the response rate of 67% for the psychiatric part was very similar to that of the EPIC-Norfolk United Kingdom Prospective Cohort Study (response rate = 72%; Surtees et al. 2008), which, however, was based on a self-assessment approach and did not include a diagnostic interview. Given the time-consuming psychiatric evaluation, it is not surprising that 35 to 39-year-old individuals with typically high levels of professional activity and familial constraints revealed the lowest and the 60 to 66 year-old mostly retired individuals the highest response rate. As specific data on the work/disability status for the general population of the city of Lausanne could not be obtained, it was not possible to test the presence of selection bias with respect to work/disability status.
The requirement to be fluent in French or English to complete the psychiatric interview has only slightly reduced the participation of foreigners. Nevertheless, morbidity estimates for specific groups of migrants could be biased in the case of an association between the level of social integration and morbidity.
Another limitation is the cross-sectional study design, which does inherently not allow us to easily distinguish between cause and consequence (temporal ambiguity) given the risk of inaccurate recall of the onset of diseases. For this reason, potential associations between psychiatric disorders and risk factors for CVD will be difficult to interpret regarding the direction of causality.
Finally, the sample size is still too small to detect SNPs with small effects as expected for psychiatric disorders. Therefore, in order to increase the power to detect such SNPs, the sample needs to be combined with those of similar studies.
As the sample of individuals suffering from threshold and subthreshold mood and anxiety syndromes constitutes an ideal proband group for an epidemiological family study, we will also investigate all available first-degree relatives of the PsyCoLaus sample within the next two years using the same psychiatric assessments. In the present study, probands were asked whether they would allow us to contact their first-degree family members and spouses. Such a population-based family study will allow for the testing of the generalizability of findings from existing family studies, which were based on clinical probands, and will extend the scope from DSM-IV disorders to subthreshold syndromes.
Moreover, a longitudinal follow-up of all participants of the PsyCoLaus study is planned. More than 95% of the sample has consented to be contacted for follow-up.
Despite limitations, the presented study should significantly contribute to the current scientific knowledge and subsequent clinical benefice by: 1) establishing the potential clinical relevance of specific psychiatric syndromes below the threshold of DSM-IV, which will be crucial to decide whether and which of the postulated subthreshold syndromes are a public health issue requiring treatment; 2) completing a genome wide association analysis to identify DNA markers for risk factors for various psychiatric disorders; and 3) assessing the associations between risk factors for CVD and a large array of psychiatric disorders and personality traits, which should allow for the identification of specific psychiatric disorders or personality traits and genetic variants that are most strongly associated with risk factors for CVD. The better understanding of the interplay between specific psychiatric disorders, personality traits and risk factors for CVD should ultimately lead to the development of more specific and effective behavioral interventions in individuals suffering from psychiatric conditions and to a more successful prevention of CVD.
Pierandrea Muglia, Federica Tozzi, Dawn Waterworth, Vincent Mooser and Lefkos Middleton were or are full-time employees of GlaxoSmithkline.
All authors participated in the study design and conception of the project. MP and SR analyzed the epidemiological data, FT, PM the genetic data. MP, FT and PM drafted the article, which was revised by PV, PB, SR, CV, PG, VM, DW, FT, and PM. All authors read and approved the final manuscript.
The pre-publication history for this paper can be accessed here:
The PsyCoLaus study was supported by grants from the Swiss National Science Foundation (#3200B0-105993) and from GlaxoSmithKline (Psychiatry Center of Excellence for Drug Discovery and Genetics Division, Drug Discovery ? Verona, R&D).
The authors would like to express their gratitude to the Lausanne inhabitants who volunteered to participate in the PsyCoLaus study. We would also like to thank all the investigators of the CoLaus study, which made the psychiatric study possible, as well as many GSK employees who contributed to the execution of this study including Alun McCarthy, Paul Matthews and Emiliangelo Ratti for supporting this work within GSK Drug Discovery.
|Angst J,Merikangas K. The depressive spectrum: diagnostic classification and courseJournal of Affective Disorders 1997;45:31–39. [pmid: 9268773] [doi: 10.1016/S0165-0327(97)00057-8]|
|Bijl RV,Ravelli A,van Zessen G. Prevalence of psychiatric disorder in the general population: results of The Netherlands Mental Health Survey and Incidence Study (NEMESIS)Social Psychiatry & Psychiatric Epidemiology 1998;33:587–595. [pmid: 9857791] [doi: 10.1007/s001270050098]|
|Kessler RC,McGonagle KA,Zhao S,Nelson CB,Hughes M,Eshleman S,Wittchen HU,Kendler KS. Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity SurveyArch Gen Psychiatry 1994;51:8–19. [pmid: 8279933]|
|L?pine JP. Comorbidity of anxiety and depression: epidemiologic perspectivesEncephale 1994;20:683–692. [pmid: 7895636]|
|Szadoczky E,Papp Z,Vitrai J,Rihmer Z,Furedi J. The prevalence of major depressive and bipolar disorders in Hungary. Results from a national epidemiologic surveyJournal of Affective Disorders 1998;50:153–162. [pmid: 9858075] [doi: 10.1016/S0165-0327(98)00056-1]|
|Wacker HR,M?llejans R,Klein KH,Battegay R. Identification of cases of anxiety disorders and affective disorders in the community according to the ICD-10 and DSM-III-R by using the Composit International Diagnostic Interview CIDIInt J Meth Psychiatr Res 1992;2:91–100.|
|Wittchen HU,Essau CA,von Zerssen D,Krieg JC,Zaudig M. Lifetime and six-month prevalence of mental disorders in the Munich Follow-Up StudyEuropean Archives of Psychiatry & Clinical Neuroscience 1992;241:247–258. [pmid: 1576182] [doi: 10.1007/BF02190261]|
|Angst J,Merikangas KR,Preisig M. Subthreshold syndromes of depression and anxiety in the communityJournal of Clinical Psychiatry 1997;58:6–10. [pmid: 9236729]|
|Angst J,Gamma A,Benazzi F,Ajdacic V,Eich D,Rossler W. Toward a re-definition of subthreshold bipolarity: epidemiology and proposed criteria for bipolar-II, minor bipolar disorders and hypomaniaJournal of Affective Disorders 2003;73:133–146. [pmid: 12507746] [doi: 10.1016/S0165-0327(02)00322-1]|
|Kessler RC,Zhao S,Blazer DG,Swartz M. Prevalence, correlates, and course of minor depression and major depression in the National Comorbidity SurveyJournal of Affective Disorders 1997;45:19–30. [pmid: 9268772] [doi: 10.1016/S0165-0327(97)00056-6]|
|Sartorius N,Ustun TB,Lecrubier Y,Wittchen HU. Depression comorbid with anxiety: results from the WHO study on psychological disorders in primary health careBr J Psychiatry Suppl 1996:38–43. [pmid: 8864147]|
|Altamura AC,Carta MG,Carpiniello B,Piras A,Maccio MV,Marcia L. Lifetime prevalence of brief recurrent depression (results from a community survey)European Neuropsychopharmacology 1995;5:99–102. [pmid: 8775767] [doi: 10.1016/0924-977X(95)00037-P]|
|Judd LL,Akiskal HS,Paulus MP. The role and clinical significance of subsyndromal depressive symptoms (SSD) in unipolar major depressive disorderJournal of Affective Disorders 1997;45:5–17. [pmid: 9268771] [doi: 10.1016/S0165-0327(97)00055-4]|
|Maier W,Gansicke M,Weiffenbach O. The relationship between major and subthreshold variants of unipolar depressionJournal of Affective Disorders 1997;45:41–51. [pmid: 9268774] [doi: 10.1016/S0165-0327(97)00058-X]|
|Pezawas L,Wittchen HU,Pfister H,Angst J,Lieb R,Kasper S. Recurrent brief depressive disorder reinvestigated: a community sample of adolescents and young adultsPsychological Medicine 2003;33:407–418. [pmid: 12701662] [doi: 10.1017/S0033291702006967]|
|Narrow WE,Rae DS,Robins LN,Regier DA. Revised prevalence estimates of mental disorders in the United States: using a clinical significance criterion to reconcile 2 surveys' estimatesArchives of General Psychiatry 2002;59:115–123. [pmid: 11825131] [doi: 10.1001/archpsyc.59.2.115]|
|Pignay-Demaria V,Lesperance F,Demaria RG,Frasure-Smith N,Perrault LP. Depression and anxiety and outcomes of coronary artery bypass surgeryAnnals of Thoracic Surgery 2003;75:314–321. [pmid: 12537248] [doi: 10.1016/S0003-4975(02)04391-6]|
|Musselman DL,Evans DL,Nemeroff CB. The relationship of depression to cardiovascular disease: epidemiology, biology, and treatmentArchives of General Psychiatry 1998;55:580–592. [pmid: 9672048] [doi: 10.1001/archpsyc.55.7.580]|
|Fenton WS,Stover ES. Mood disorders: cardiovascular and diabetes comorbidityCurrent Opinion in Psychiatry 2006;19:421–427. [pmid: 16721175] [doi: 10.1097/01.yco.0000228765.33356.9f]|
|Glassman AH,Shapiro PA. Depression and the course of coronary artery diseaseAmerican Journal of Psychiatry 1998;155:4–11. [pmid: 9433332]|
|Nicholson A,Kuper H,Hemingway H. Depression as an aetiologic and prognostic factor in coronary heart disease: a meta-analysis of 6362 events among 146 538 participants in 54 observational studiesEuropean Heart Journal 2006;27:2763–2774. [pmid: 17082208] [doi: 10.1093/eurheartj/ehl338]|
|Rudisch B,Nemeroff CB. Epidemiology of comorbid coronary artery disease and depressionBiological Psychiatry 2003;54:227–240. [pmid: 12893099] [doi: 10.1016/S0006-3223(03)00587-0]|
|Kooy, K Van der;van Hout, H.;Marwijk, H.;Marten, H.;Stehouwer, C.;Beekman, A.Depression and the risk for cardiovascular diseases: systematic review and meta analysisInternational Journal of Geriatric Psychiatry 2007;22:613–626. [pmid: 17236251] [doi: 10.1002/gps.1723]|
|H?rter MC,Conway KP,Merikangas KR. Associations between anxiety disorders and physical illnessEuropean Archives of Psychiatry & Clinical Neuroscience 2003;253:313–320. [pmid: 14714121] [doi: 10.1007/s00406-003-0449-y]|
|Sher L. Effects of heavy alcohol consumption on the cardiovascular system may be mediated in part by the influence of alcohol-induced depression on the immune systemMedical Hypotheses 2003;60:702–706. [pmid: 12710906] [doi: 10.1016/S0306-9877(03)00031-8]|
|Case RB,Heller SS,Case NB,Moss AJ. Type A behavior and survival after acute myocardial infarctionNew England Journal of Medicine 1985;312:737–741. [pmid: 3974650]|
|Dimsdale JE,Hackett TP,Hutter AM Jr,Block PC,Catanzano DM,White PJ. Type A behavior and angiographic findingsJournal of Psychosomatic Research 1979;23:273–276. [pmid: 529200] [doi: 10.1016/0022-3999(79)90030-8]|
|Friedman M,Rosenman RH. Association of specific overt behavior pattern with blood and cardiovascular findings; blood cholesterol level, blood clotting time, incidence of arcus senilis, and clinical coronary artery diseaseJ Am Med Assoc 1959;169:1286–1296. [pmid: 13630753]|
|Ragland DR,Brand RJ. Type A behavior and mortality from coronary heart diseaseNew England Journal of Medicine 1988;318:65–69. [pmid: 3336396]|
|Rosenman RH,Brand RJ,Jenkins D,Friedman M,Straus R,Wurm M. Coronary heart disease in Western Collaborative Group Study. Final follow-up experience of 8 1/2 yearsJAMA 1975;233:872–877. [pmid: 1173896] [doi: 10.1001/jama.233.8.872]|
|Rozanski A,Blumenthal JA,Kaplan J. Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapyCirculation 1999;99:2192–2217. [pmid: 10217662]|
|Shekelle RB,Gale M,Norusis M. Type A score (Jenkins Activity Survey) and risk of recurrent coronary heart disease in the aspirin myocardial infarction studyAmerican Journal of Cardiology 1985;56:221–225. [pmid: 3895879] [doi: 10.1016/0002-9149(85)90838-0]|
|Shekelle RB,Hulley SB,Neaton JD,Billings JH,Borhani NO,Gerace TA,Jacobs DR,Lasser NL,Mittlemark MB,Stamler J. The MRFIT behavior pattern study. II. Type A behavior and incidence of coronary heart diseaseAmerican Journal of Epidemiology 1985;122:559–570. [pmid: 4025299]|
|McCarthy MI,Abecasis GR,Cardon LR,Goldstein DB,Little J,Ioannidis JP,Hirschhorn JN. Genome-wide association studies for complex traits: consensus, uncertainty and challengesNature Reviews Genetics 2008;9:356–369. [pmid: 18398418] [doi: 10.1038/nrg2344]|
|Zeggini E,Scott LJ,Saxena R,Voight BF,Marchini JL,Hu T,de Bakker PI,Abecasis GR,Almgren P,Andersen G,et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetesNature Genetics 2008;40:638–645. [pmid: 18372903] [doi: 10.1038/ng.120]|
|Todd JA,Walker NM,Cooper JD,Smyth DJ,Downes K,Plagnol V,Bailey R,Nejentsev S,Field SF,Payne F,et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetesNature Genetics 2007;39:857–864. [pmid: 17554260] [doi: 10.1038/ng2068]|
|Thomas G,Jacobs KB,Yeager M,Kraft P,Wacholder S,Orr N,Yu K,Chatterjee N,Welch R,Hutchinson A,et al. Multiple loci identified in a genome-wide association study of prostate cancerNature Genetics 2008;40:310–315. [pmid: 18264096] [doi: 10.1038/ng.91]|
|Easton DF,Pooley KA,Dunning AM,Pharoah PD,Thompson D,Ballinger DG,Struewing JP,Morrison J,Field H,Luben R,et al. Genome-wide association study identifies novel breast cancer susceptibility lociNature 2007;447:1087–1093. [pmid: 17529967] [doi: 10.1038/nature05887]|
|Diabetes Genetics Initiative of Broad Institute of Harvard and MITand Lund University and Novartis Institutes of BioMedical ResearchSaxena R,Voight BF,Lyssenko V,Burtt NP,de Bakker PI,Chen H,Roix JJ,Kathiresan S,Hirschhorn JN,et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levelsScience 2007;316:1331–1336. [pmid: 17463246] [doi: 10.1126/science.1142358]|
|Scuteri A,Sanna S,Chen WM,Uda M,Albai G,Strait J,Najjar S,Nagaraja R,Orru M,Usala G,et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traitsPLoS Genet 2007;3:e115. [pmid: 17658951] [doi: 10.1371/journal.pgen.0030115]|
|Moffatt MF,Kabesch M,Liang L,Dixon AL,Strachan D,Heath S,Depner M,von Berg A,Bufe A,Rietschel E,et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthmaNature 2007;448:470–473. [pmid: 17611496] [doi: 10.1038/nature06014]|
|Loos RJ,Lindgren CM,Li S,Wheeler E,Zhao JH,Prokopenko I,Inouye M,Freathy RM,Attwood AP,Beckmann JS,et al. Common variants near MC4R are associated with fat mass, weight and risk of obesityNature Genetics 2008;40:768–775. [pmid: 18454148] [doi: 10.1038/ng.140]|
|Sandhu MS,Waterworth DM,Debenham SL,Wheeler E,Papadakis K,Zhao JH,Song K,Yuan X,Johnson T,Ashford S,et al. LDL-cholesterol concentrations: a genome-wide association studyLancet 2008;371:483–491. [pmid: 18262040] [doi: 10.1016/S0140-6736(08)60208-1]|
|Weedon MN,Lango H,Lindgren CM,Wallace C,Evans DM,Mangino M,Freathy RM,Perry JR,Stevens S,Hall AS,et al. Genome-wide association analysis identifies 20 loci that influence adult heightNature Genetics 2008;40:575–583. [pmid: 18391952] [doi: 10.1038/ng.121]|
|Berrettini W,Yuan X,Tozzi F,Song K,Francks C,Chilcoat H,Waterworth D,Muglia P,Mooser V. Alpha-5/alpha-3 nicotinic receptor subunit alleles increase risk for heavy smokingMol Psychiatry 2008;13:368–373. [pmid: 18227835] [doi: 10.1038/sj.mp.4002154]|
|Domenici E,Muglia P. The search for peripheral disease markers in psychiatry by genomic and proteomic approachesExpert Opinion on Medical Diagnostics 2007;1:235–251. [doi: 10.1517/17530059.1.2.235]|
|Firmann M,Mayor V,Vidal PM,Bochud M,Pecoud A,Hayoz D,Paccaud F,Preisig M,Song KS,Yuan X,et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndromeBMC Cardiovascular Disorders 2008;8:6. [pmid: 18366642] [doi: 10.1186/1471-2261-8-6]|
|Eich D,Ajdacic-Gross V,Condrau M,Huber H,Gamma A,Angst J,Rossler W. The Zurich Study: participation patterns and Symptom Checklist 90-R scores in six interviews, 1979?99Acta Psychiatr Scand Suppl 2003:11–14. [pmid: 12956807] [doi: 10.1034/j.1600-0447.108.s418.3.x]|
|Goldberg DP. The detection of psychiatric illness by questionnaire. 1972Oxford: Oxford University Press;|
|Bettschart W,Bolognini M. Guelfi JQuestionnaire de sant? GHQ-12L'?valuation clinique standardis?e en psychiatrie Tome I 1996Boulogne: M?dicales Pierre Fabre; :157.|
|Goldberg DP,Gater R,Sartorius N,Ustun TB,Piccinelli M,Gureje O,Rutter C. The validity of two versions of the GHQ in the WHO study of mental illness in general health carePsychological Medicine 1997;27:191–197. [pmid: 9122299] [doi: 10.1017/S0033291796004242]|
|Nurnberger JI Jr,Blehar MC,Kaufmann CA,York-Cooler C,Simpson SG,Harkavy-Friedman J,Severe JB,Malaspina D,Reich T. Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics InitiativeArchives of General Psychiatry 1994;51:849–859. [pmid: 7944874]|
|NIMH Molecular Genetics InitiativeDIGS (updated version). 1995Bethesda: NIMH;|
|Leboyer M,Barbe B,Gorwood P,Teherani M,Allilaire JF,Preisig M,Matthey ML,Poyetton V,Ferrero F. Interview Diagnostique pour les Etudes G?n?tiques. 1995Paris: INSERM;|
|Endicott J,Spitzer RL. A diagnostic interview: the schedule for affective disorders and schizophreniaArchives of General Psychiatry 1978;35:837–844. [pmid: 678037]|
|Preisig M,Fenton BT,Matthey ML,Berney A,Ferrero F. Diagnostic interview for genetic studies (DIGS): inter-rater and test-retest reliability of the French versionEuropean Archives of Psychiatry & Clinical Neuroscience 1999;249:174–179. [pmid: 10449592] [doi: 10.1007/s004060050084]|
|Berney A,Preisig M,Matthey ML,Ferrero F,Fenton BT. Diagnostic interview for genetic studies (DIGS): inter-rater and test-retest reliability of alcohol and drug diagnosesDrug & Alcohol Dependence 2002;65:149–158. [pmid: 11772476] [doi: 10.1016/S0376-8716(01)00156-9]|
|Ferreri M. Guelfi JQuestionnaire d'?v?nements de vie de F. Amiel-LebigreL'?valuation clinique standardis?e en psychiatrie Tome II 1996Boulogne: M?dicales Pierre Fabre;; :627–632.|
|Andreasen NC,Endicott J,Spitzer RL,Winokur G. The family history method using diagnostic criteria. Reliability and validityArch Gen Psychiatry 1977;34:1229–1235. [pmid: 911222]|
|Merikangas KR,Stevens DE,Fenton B,Stolar M,O'Malley S,Woods SW,Risch N. Co-morbidity and familial aggregation of alcoholism and anxiety disordersPsychological Medicine 1998;28:773–788. [pmid: 9723135] [doi: 10.1017/S0033291798006941]|
|Rougemont-Buecking A,Rothen S,Jeanpretre N,Lustenberger Y,Vandeleur CL,Ferrero F,Preisig M. Inter-informant agreement on diagnoses and prevalence estimates of anxiety disorders: direct interview versus family history methodPsychiatry Research 2008;157:211–223. [pmid: 17881063] [doi: 10.1016/j.psychres.2006.04.022]|
|Vandeleur CL,Rothen S,Jeanpretre N,Lustenberger Y,Gamma F,Ayer E,Ferrero F,Fleischmann A,Besson J,Sisbane F,et al. Inter-informant agreement and prevalence estimates for substance use disorders: direct interview versus family history methodDrug & Alcohol Dependence 2008;92:9–19. [pmid: 17643870] [doi: 10.1016/j.drugalcdep.2007.05.023]|
|Spielberger CD,Gorsuch RL,Lushene RE. Manual for the State-Trait Anxiety Inventory (Self Evaluation Questionnaire). 1970Palo Alto CA: Consulting Psychologists Press;|
|Spielberger CD. Inventaire d'Anxiete Etat-Trait. 1993Paris: Les Editions du Centre de Psychologie Appliqu?e;|
|Reznick JS,Hegeman IM,Kaufman ER,Woods SW,Jacobs M. Retrospective and Concurrent Self-report of Behavioral Inhibition and their relation to adult mental healthDevelopment and Psychopathology 1992;4:301–321. [doi: 10.1017/S095457940000016X]|
|Windle M,Lerner RM. Reassessing the Dimensions of Temperamental Individuality Across the Life Span:The Revised Dimensions of Temperament Survey (DOTS-R)Journal of Adolescent Research 1986;1:213–229. [doi: 10.1177/074355488612007]|
|Eysenck HJ,Eysenck SBG. Manual of the Eysenck Personality Questionnaire. 1975London: Hodder & Stoughton Educational;|
|Eysenck HJ,Eysenck SBG,Gauquelin M,Gauqelin F,Pascal C,Pascal D. The structure of the personality among French compared to that of an English: cross-cultural comparisonLa Personalit? 1980;1?2:7–29.|
|Pichot P,De Bonis M,Somogyl M,Degr?-Coustry C,Kittel-Bossuyt F,Rustin-Vandenhende R-M,Dramaix M,Berney A. Etude m?trologique d'une batterie de tests destin?e ? l'?tude des facteurs psychologiques en ?pid?miologie cardio-vasculaireApplied Psychology 1977;26:11–19. [doi: 10.1111/j.1464-0597.1977.tb01049.x]|
|Bortner RW. A short rating scale as a potential measure of pattern A behaviorJournal of Chronic Diseases 1969;22:87–91. [pmid: 5795891] [doi: 10.1016/0021-9681(69)90061-7]|
|Mohr S,Preisig M,Fenton BT,Ferrero F. Validation of the French version of the parental bonding instrument in adultsPersonality and Individual Differences 1999;26:1065–1074. [doi: 10.1016/S0191-8869(98)00210-4]|
|Parker G,Tupling H,Brown LB. A parental bonding instrumentBritish Journal of Medical Psychology 1979;52:1–10.|
|Tousignant M,Hamel S,Bastien MF. Structure familiale, relations parents-enfants et conduite suicidaire ? l'?cole secondaireSant? Medicale au Qu?bec 1988;13:79–93.|
|Olson DH,Portner J,Lavee Y. FACES III. 1985Minnesota: St. Paul, University of Minnesota;|
|Vandeleur CL,Preisig M,Fenton BT,Ferrero F. Construct validity and internal reliability of a French version of FACES III in adolescents and adultsSwiss Journal of Psychology 1999;58:161–169. [doi: 10.1024//1421-018.104.22.168]|
|Spanier GB. Measuring Dyadic Adjustment: New Scales for Assessing the Quality of Marriage and Similar DyadsJournal of Marriage and the Family 1976;38:15–28. [doi: 10.2307/350547]|
|Vandeleur CL,Fenton BT,Ferrero F,Preisig M. Construct validity of the French version of the Dyadic Adjustment ScaleSwiss Journal of Psychology 2003;62:167–175. [doi: 10.1024//1421-022.214.171.124]|
|Kavanagh DJ,O'Halloran P,Manicavasagar V,Clark D,Piatkowska O,Tennant C,Rosen A. The Family Attitude Scale: reliability and validity of a new scale for measuring the emotional climate of familiesPsychiatry Research 1997;70:185–195. [pmid: 9211580] [doi: 10.1016/S0165-1781(97)00033-4]|
|Grob A,Bodmer N,Flammer A. Living Conditions and the Development of Adolescents in Europe: The Case of Switzerland. Research Report (Nr. 5). 1993Institute of Psychology. University of Berne. Switzerland;|
|Hayes RD,Stewart AL. Steward AL, Ware JEJSleep measuresMeasuring functioning and well-being. 1992Durham, NC: Duke University Press;|
|Sandhu MS,Waterworth DM,Debenham SL,Wheeler E,Papadakis K,Zhao JH,Song K,Yuan X,Johnson T,Ashford S,et al. LDL-cholesterol concentrations: a genome-wide association studyLancet 2008;371:483–491. [pmid: 18262040] [doi: 10.1016/S0140-6736(08)60208-1]|
|Freeman DJ. Bracken MBSample size determination in comparative studiesPerinatal Epidemiology. 1984New York: Oxford University Press;|
|Purcell S,Cherny SS,Sham PC. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traitsBioinformatics 2003;19:149–150. [pmid: 12499305] [doi: 10.1093/bioinformatics/19.1.149]|
|Muglia P,Tozzi F,Galwey NW,Francks C,Upmanyu R,Kong XQ,Antoniades A,Domenici E,Perry J,Rothen S,et al. Genome-wide association study of recurrent major depressive disorder in two European case-control cohortsMol Psychiatry. 2008|
|Ellsworth DL,Manolio TA. The Emerging Importance of Genetics in Epidemiologic Research III. Bioinformatics and statistical genetic methodsAnnals of Epidemiology 1999;9:207–224. [pmid: 10332927] [doi: 10.1016/S1047-2797(99)00007-1]|
|Khoury MJ,Yang Q. The future of genetic studies of complex human diseases: an epidemiologic perspectiveEpidemiology 1998;9:350–354. [pmid: 9583430] [doi: 10.1097/00001648-199805000-00023]|
|Merikangas KR,Chakravarti A,Moldin SO,Araj H,Blangero JC,Burmeister M,Crabbe J Jr,DePaulo JR Jr,Foulks E,Freimer NB,et al. Future of genetics of mood disorders researchBiological Psychiatry 2002;52:457–477. [pmid: 12361664] [doi: 10.1016/S0006-3223(02)01471-3]|
|Peltonen L,McKusick VA. Genomics and medicine. Dissecting human disease in the postgenomic eraScience 2001;291:1224–1229. [pmid: 11233446] [doi: 10.1126/science.291.5507.1224]|
|Risch NJ. Searching for genetic determinants in the new millenniumNature 2000;405:847–856. [pmid: 10866211] [doi: 10.1038/35015718]|
|Donath S. The validity of the 12-item General Health Questionnaire in Australia: a comparison between three scoring methodsAustralian & New Zealand Journal of Psychiatry 2001;35:231–235. [pmid: 11284906] [doi: 10.1046/j.1440-1614.2001.00869.x]|
Power for the analysis of the associations between two dichotomous variables (%)
|Prevalence of the index disorder||Relative risk for the presence of the dichotomous correlate in individuals with the index disorder||Frequency of dichotomous correlates|
Age and sex distributions of the sample
|Age||Recruited sample (n = 3691)||Difference recruited vs. intended sample according to the distribution in the general population (%)|
Sex: ?2 = 1.0; df = 1; p = n.s.
Age: ?2 = 25.4; df = 5; p = < 0.0001
Socio-demographic characteristics of the PsyCoLaus sample
|Overall (n = 3691)||Males (n = 1730)||Females (n = 1961)|
|Age (mean, s.d.)|
|?Somatic exam||49.6 (8.8)||49.2 (8.8)||50.0 (8.8)|
|?Psychiatric exam||50.9 (8.8)||50.5 (8.8)||51.3 (8.8)|
|Marital status (%)|
|Work status (%)|
Studies of associations between psychiatric disorders and cardiovascular diseases including diagnostic interviews for psychiatric disorders
|Sample||Assessed cardiovascular risk factors|
|Study authors||Target population and Age at baseline (years)||N (males/
|Diagnostic interview for psychiatric disorders||Psychiatric disorders analyzed||Outcome measure||Socio- demographic variables||Measured medical variables||Self-reported variables||Genetic testing|
|Aromaa et al. (1994)||Finnish adults
|3811 (1825/1986)||Present State Examination (PSE)||Depression||Fatal cardio-vascular disease||Age||No|
|Pratt et al. (1996)||US adults (ECA study, Baltimore)
|1551* (583/968)||Diagnostic Interview Schedule (DIS)||Depression Dysphoria||Non-fatal myocardial infarction||Age Sex Marital status||Hyper-tension||No|
|Larson et al. (2001)||US adults (ECA study, Baltimore)
|1703* (632/1071)||Diagnostic Interview Schedule (DIS)||Depression Dysthymia||Stroke (fatal and non fatal measures combined)||Age Sex Education||Diabetes Blood-pressure Heart-problems Smoking||No|
|Penninx et al. (2001)||Dutch older adults (LASA study)
|2847 (1367/1480)||Diagnostic Interview Schedule (DIS)||Major depression Minor depression||Fatal cardio-vascular disease||Age Sex Education||Hyper-tension BMI||Diabetes Stroke Lung-disease Cancer Smoking Alcohol||No|
|Current study||Swiss urban adults (CoLaus/PsyCoLaus)
|3691 (1730/1961)||Diagnostic Interview for Genetic Studies (DIGS)||Depression Anxiety disorders Substance use disorders||Coronary heart- disease, stroke||Age Sex Education Marital status||Hyper-tension Diabetes Dyslipi-demia BMI||Smoking Alcohol Physical- activity||Yes|
* = CVD free population at entry
Previous Document: Chromato-panning: an efficient new mode of identifying suitable ligands from phage display libraries...
Next Document: Enhanced antitumor efficacy of cisplatin in combination with HemoHIM in tumor-bearing mice.