Background: Worldwide, oral contraceptive (OC) use is a very common form of birth control, although it has been associated with symptoms of depression and insomnia. Insomnia is a risk factor for... Show moreBackground: Worldwide, oral contraceptive (OC) use is a very common form of birth control, although it has been associated with symptoms of depression and insomnia. Insomnia is a risk factor for major depressive disorder (MDD) but may also be a symptom of the disorder. Despite the large number of women who use OC, it is yet unknown whether women with previous or current diagnosis of depression are more likely to experience more severe depressive and insomnia symptoms during concurrent OC use than women without diagnosis of depression. Aim: This study examined associations between OC use and concurrent symptoms of depression (including atypical depression) and insomnia as well as between OC and prevalences of concurrent dysthymia and MDD. Participants were adult women with and without a history of MDD or dysthymia. We hypothesized that OC use is associated with concurrent increased severity of depressive symptoms and insomnia symptoms, as well as with an increased prevalence of concurrent diagnoses of dysthymia and MDD. We also hypothesized that a history of MDD or dysthymia moderates the relationship between OC use and depressive and insomnia symptoms. Methods: Measurements from premenopausal adult women from the Netherlands Study of Depression and Anxiety (NESDA) were grouped, based on whether participants were using OC or naturally cycling (NC). OC use, timing and regularity of the menstrual cycle were assessed with a structured interview, self-reported symptoms of depression (including atypical depression), insomnia with validated questionnaires, and MDD and dysthymia with structured diagnostic interviews. Results: We included a total of 1301 measurements in women who reported OC use and 1913 measurements in NC women (mean age 35.6, 49.8% and 28.9% of measurements in women with a previous depression or current depression, respectively). Linear mixed models showed that overall, OC use was neither associated with more severe depressive symptoms (including atypical depressive symptoms), nor with higher prevalence of diagnoses of MDD or dysthymia. However, by disentangling the amalgamated overall effect, within-person estimates indicated increased depressive symptoms and depressive disorder prevalence during OC use, whereas between-person estimated indicated lower depressive symptoms and prevalence of depressive disorders. OC use was consistently associated with more severe concurrent insomnia symptoms, in the overall estimates as well as in the within-person and between-person estimates. Presence of current or previous MDD or dysthymia did not mod-erate the associations between OC use and depressive or insomnia symptoms. Discussion: The study findings showed consistent associations between OC use and more severe insomnia symptoms, but no consistent associations between OC and depressive symptoms or diagnoses. Instead, post-hoc analyses showed that associations between OC and depression differed between within-and between person -estimates. This indicates that, although OC shows no associations on the overall level, some individuals might experience OC-associated mood symptoms. Our findings underscore the importance of accounting for individual differences in experiences during OC use. Furthermore, it raises new questions about mechanisms underlying associations between OC, depression and insomnia. Show less
Background: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression... Show moreBackground: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, and automated ML (Auto-sklearn).Methods: Data were derived from the Netherlands Study of Depression and Anxiety. We compared how well multinomial logistic regression, a na?ve Bayes classifier, and Auto-sklearn predicted depression and anxiety diagnoses at a 2-, 4-, 6-, and 9-year follow up, operationalized as binary or categorical variables. Predictor sets included demographic and self-report data, which can be easily collected in clinical practice at two initial time points (baseline and 1-year follow up).Results: At baseline, participants were 42.2 years old, 66.5% were women, and 53.6% had a current mood or anxiety disorder. The three methods were similarly successful in predicting (mental) health status, with correct predictions for up to 79% (95% CI 75?81%). However, Auto-sklearn was superior when assessing a more complex dataset with individual item scores.Conclusions: Automated ML methods added only limited value, compared to traditional data modelling when predicting the onset and course of depression and anxiety. However, they hold potential for automatization and may be better suited for complex datasets. Show less
Background: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression... Show moreBackground: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, and automated ML (Auto-sklearn).Methods: Data were derived from the Netherlands Study of Depression and Anxiety. We compared how well multinomial logistic regression, a naïve Bayes classifier, and Auto-sklearn predicted depression and anxiety diagnoses at a 2-, 4-, 6-, and 9-year follow up, operationalized as binary or categorical variables. Predictor sets included demographic and self-report data, which can be easily collected in clinical practice at two initial time points (baseline and 1-year follow up).Results: At baseline, participants were 42.2 years old, 66.5% were women, and 53.6% had a current mood or anxiety disorder. The three methods were similarly successful in predicting (mental) health status, with correct predictions for up to 79% (95% CI 75?81%). However, Auto-sklearn was superior when assessing a more complex dataset with individual item scores.Conclusions: Automated ML methods added only limited value, compared to traditional data modelling when predicting the onset and course of depression and anxiety. However, they hold potential for automatization and may be better suited for complex datasets. Show less
Lagou, V.; Magi, R.; Hottenga, J.J.; Grallert, H.; Perry, J.R.B.; Bouatia-Naji, N.; ... ; Meta-Analyses of Glucose and 2021
Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and... Show moreDifferences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes. Show less
Hijne, K.; Penninx, B.W.; Hemert, A.M. van; Spinhoven, P. 2020
Background: Repetitive negative thinking (RNT) is a common feature of different mental disorders in the affective spectrum. Most measures of RNT are disorder-specific and measure e.g. rumination in... Show moreBackground: Repetitive negative thinking (RNT) is a common feature of different mental disorders in the affective spectrum. Most measures of RNT are disorder-specific and measure e.g. rumination in depression or worry in anxiety.Methods: In the Netherlands Study of Depression and Anxiety (NESDA), 1820 adults completed the Perseverative Thinking Questionnaire to assess content-independent RNT over a 3-year follow-up period. We investigated the relative stability of content-independent RNT (Perseverative Thinking Questionnaire), over time as well as the association between changes in RNT and changes in affective disorder status (Composite International Diagnostic Interview) and depressive and anxiety severity (Inventory of Depressive Symptomatology, Beck Anxiety Inventory, Fear Questionnaire).Results: In the total group, baseline RNT was strongly related to RNT three years later, while the difference between the scores at baseline and three years later was negligible. Increases and decreases in RNT were associated with the occurrence and recovery of affective disorders, respectively. Furthermore, changes in RNT between baseline and three years later were associated with corresponding changes in depression, anxiety, and avoidance symptom severity. These associations were small or negligible.Limitations: Our findings may not be representative of all affective disorders as individuals with an obsessive-compulsive disorder or bipolar disorder were excluded from our sample.Conclusions: The findings suggest that RNT is not primarily an index of disorder status or epiphenomenon of symptom severity and may constitute a relatively stable transdiagnostic person characteristic. Show less
Hijne, K.; Penninx, B.W.; Hemert, A.M. van; Spinhoven, P. 2020
Background and objectives: Comorbidity among anxiety and depression disorders and their symptoms is high. Rumination and worry have been found to mediate prospective cross-disorder relations... Show moreBackground and objectives: Comorbidity among anxiety and depression disorders and their symptoms is high. Rumination and worry have been found to mediate prospective cross-disorder relations between anxiety and depression disorders and their symptoms in adolescents and adults. We examined whether generic repetitive negative thinking (RNT), that is content- and disorder-independent, also mediates prospective cross-disorder associations between anxiety and depressions disorders and their symptoms.Methods: This was studied using a 5-year prospective cohort study. In a mixed sample of 1859 adults (persons with a prior history of or a current affective disorder and healthy individuals), we assessed DSM-IV affective disorders (Composite Interview Diagnostic Instrument), anxiety (Beck Anxiety Inventory) and depression symptoms (Inventory of Depressive Symptomatology) and RNT (Perseverative Thinking Questionnaire).Results: We found that baseline depression disorders and symptom severity have predictive value for anxiety disorders and symptom severity five years later (and vice versa) and that these associations were significantly mediated by level of RNT as assessed two years after baseline. The significant and rather large mediation effects seemed mainly due to the mental capacity captured by RNT, especially in the prospective relation of anxiety with future depression.Limitations: The mediation effects were greatly attenuated or even nullified after rigorously controlling for concomitant psychopathology at two years after baseline.Conclusions: From these results it can be concluded that repetitive negative thinking could be an important transdiagnostic factor, that may constitute a suitable target for treatment. Show less
Background: The large between-person differences in symptomatology suggest that major depressive disorder (MDD) is a heterogeneous psychiatric disorder. However, symptom-specific prospective... Show moreBackground: The large between-person differences in symptomatology suggest that major depressive disorder (MDD) is a heterogeneous psychiatric disorder. However, symptom-specific prospective studies are scarce. We hypothesized that chronicity (i.e., being depressed for 24 months during a patient's preceding 48 months at baseline) and neuroticism at baseline would predict adverse course trajectories over 9 years of follow up with differential magnitudes for individual depressive symptoms.Methods: In total, 560 patients with a current MDD were included from the Netherlands Study of Depression and Anxiety (NESDA-cohort). We used a multivariate linear mixed model with repeated measures, with a history of chronicity and neuroticism separately as main independent variables and with Inventory of Depressive Symptomatology self-report (IDS-SR) item scores as outcome variables. For each individual symptom, the model was adjusted for age, gender, and baseline depression severity.Results: Patients were on average 42.7 (SD = 12.1) years old and 64.7% were women. Patients with chronic depression or high levels of neuroticism showed similar absolute rates of decline over time compared to their counterparts. However, because symptoms had higher starting points for mood, cognitive, and somatic/vegetative symptoms (in that order), symptom severity remained higher over time. Chronicity and neuroticism were especially linked to persistent low self-esteem and high interpersonal sensitivity.Limitations: Neuroticism is partly state dependent and likely affected by depression severity.Conclusions: Chronicity and neuroticism predict long-term persistence of diverse psychiatric symptoms, in particular low self-esteem and high interpersonal sensitivity. Show less
Onderwater, G.L.J.; Ligthart, L.; Bot, M.; Demirkan, A.; Fu, J.Y.; Kallen, C.J.H. van der; ... ; BBMRI Metabolomics Consortium 2019
ObjectiveTo identify a plasma metabolomic biomarker signature for migraine.MethodsPlasma samples from 8 Dutch cohorts (n = 10,153: 2,800 migraine patients and 7,353 controls) were profiled on a H-1... Show moreObjectiveTo identify a plasma metabolomic biomarker signature for migraine.MethodsPlasma samples from 8 Dutch cohorts (n = 10,153: 2,800 migraine patients and 7,353 controls) were profiled on a H-1-NMR-based metabolomics platform, to quantify 146 individual metabolites (e.g., lipids, fatty acids, and lipoproteins) and 79 metabolite ratios. Metabolite measures associated with migraine were obtained after single-metabolite logistic regression combined with a random-effects meta-analysis performed in a nonstratified and sex-stratified manner. Next, a global test analysis was performed to identify sets of related metabolites associated with migraine. The Holm procedure was applied to control the family-wise error rate at 5% in single-metabolite and global test analyses.ResultsDecreases in the level of apolipoprotein A1 (beta -0.10; 95% confidence interval [CI] -0.16, -0.05; adjusted p = 0.029) and free cholesterol to total lipid ratio present in small high-density lipoprotein subspecies (HDL) (beta -0.10; 95% CI -0.15, -0.05; adjusted p = 0.029) were associated with migraine status. In addition, only in male participants, a decreased level of omega-3 fatty acids (beta -0.24; 95% CI -0.36, -0.12; adjusted p = 0.033) was associated with migraine. Global test analysis further supported that HDL traits (but not other lipoproteins) were associated with migraine status.ConclusionsMetabolic profiling of plasma yielded alterations in HDL metabolism in migraine patients and decreased omega-3 fatty acids only in male migraineurs. Show less
Onderwater, G.L.J.; Ligthart, L.; Bot, M.; Demirkan, A.; Fu, J.Y.; Kallen, C.J.H. van der; ... ; BBMRI Metabolomics Consortium 2019
BackgroundDepression shows a large heterogeneity of symptoms between and within persons over time. However, most outcome studies have assessed depression as a single underlying latent construct,... Show moreBackgroundDepression shows a large heterogeneity of symptoms between and within persons over time. However, most outcome studies have assessed depression as a single underlying latent construct, using the sum score on psychometric scales as an indicator for severity. This study assesses longitudinal symptom-specific trajectories and within-person variability of major depressive disorder over a 9-year period.MethodsData were derived from the Netherlands Study of Depression and Anxiety (NESDA). This study included 783 participants with a current major depressive disorder at baseline. The Inventory Depressive Symptomatology-Self-Report (IDS-SR) was used to analyze 28 depressive symptoms at up to six time points during the 9-year follow-up.ResultsThe highest baseline severity scores were found for the items regarding energy and mood states. The core symptoms depressed mood and anhedonia had the most favorable course, whereas sleeping problems and (psycho-)somatic symptoms were more persistent over 9-year follow-up. Within-person variability was highest for symptoms related to energy and lowest for suicidal ideation.ConclusionsThe severity, course, and within-person variability differed markedly between depressive symptoms. Our findings strengthen the idea that employing a symptom-focused approach in both clinical care and research is of value. Show less
Abramovitch, A.; Anholt, G.E.; Cooperman, A.; Balkom, A.J.L.M. van; Giltay, E.J.; Penninx, B.W.; Oppen, P. van 2019
Background: : Psychiatric disorders are associated with overweight/obesity. Obsessive-compulsive disorder(OCD) may be an exception, as anecdotal evidence suggests lower BMI in OCD. Additionally,... Show moreBackground: : Psychiatric disorders are associated with overweight/obesity. Obsessive-compulsive disorder(OCD) may be an exception, as anecdotal evidence suggests lower BMI in OCD. Additionally, depression isassociated with elevated BMI, but effects of comorbid secondary depression are unknown. The aim of the presentstudy was to assess BMI and risk for overweight/obesity in OCD and to assess the effect of comorbid depressionon BMI.Methods: : BMI, demographics, and clinical status were assessed in large samples of individuals with OCD,anxiety disorders, depressive disorders, comorbid anxiety/depressive disorders, and non-clinical controls (NCC).Results: : Although no initial differences were found between the samples on BMI, the non-depressed OCDsubsample had significantly lower BMI and risk for overweight/obesity compared to all other clinical samples.NCC were nearly twice as likely to be overweight compared to non-depressed OCD.Limitations: : Eating disorders were excluded in the OCD sample, but BMI < 17 was used as an exclusion cri-terion in the clinical control groups in lieu of screening for Anorexia. Group differences on demographics werecontrolled for. Recruitment methodology differed between samples.Conclusions: : OCD is associated with significantly lower rates of obesity and overweight, but this relationshipwas not found when comorbid depression was present. This suggests that the purer the phenotype of OCD, themore substantial protective factor against overweight/obesity emerges compared to other clinical samples andNCC. An OCD-specific reward/anhedonia model, previously offered to elucidate lower smoking rates in OCD,may account for lower BMI in OCD. These results warrant careful clinical attention to the negative impact ofcomorbid depression on OCD that spans from increasing risk for obesity and cigarette smoking, to hinderingtreatment response. Show less
