Rob Wanders

Group-Level Symptom Networks in Depression

To the Editor About 8 years ago, network models entered the field of psychiatry.1 In this approach, psychiatric disorders are conceptualized as a dynamic interplay between symptoms over time, contrasting the traditional disease model of an underlying common cause. With the intention to describe how symptoms give rise to each other, the network approach would deal with processes taking place at the level of the individual.1 Nevertheless, almost all studies on networks have investigated this phenomenon at the group level.

What does the beck depression inventory measure in myocardial infarction patients? a psychometric approach using item response theory and person-fit

Observed associations between depression following myocardial infarction (MI) and adverse cardiac outcomes could be overestimated due to patients’ tendency to over report somatic depressive symptoms. This study was aimed to investigate this issue with modern psychometrics, using item response theory (IRT) and person‐fit statistics to investigate if the Beck Depression Inventory (BDI) measures depression or something else among MI‐patients.

Differential reporting of depressive symptoms across distinct clinical subpopulations: What DIFference does it make?

OBJECTIVE To investigate the impact of differences in depressive symptom reporting across clinical groups (healthcare setting, chronic illness, depression diagnosis and anxiety diagnosis) on clinical interpretability and comparability of depression scores. METHODS Participants from the Netherlands Study of Depression and Anxiety (n=2981) completed the self-report Inventory of Depressive Symptomatology (IDS-SR). Differences in depressive symptom reporting between distinct clinical subpopulations were assessed using a Differential Item Functioning (DIF) analysis. The effects of DIF on symptom level were evaluated by examining whether DIF-adjustment had clinically relevant effects. RESULTS Significant DIF was detected across all tested clinical subpopulation groupings. Clinically relevant DIF was found on the symptom level for 13 IDS-SR items. However, impact of DIF on the aggregate level ranged from small to negligible: adjustment for DIF only led to salient changes in aggregate scores for 0.2-12.7% of individuals across tested sources of DIF. CONCLUSION Differences in endorsement patterns of depressive symptoms were observed across clinical populations, challenging the assumptions regarding the measurement properties of self-reported depression. However, effects of DIF on the aggregate level of IDS-SR total scores were found to be minimal and not clinically important. The IDS-SR thus seems robust against DIF across clinical populations.

Problems with latent class analysis to detect data-driven subtypes of depression

Depressed patients differ considerably with respect to symptom profiles, course of illness and treatment response. These differences likely contribute to the on average low efficacy of treatment, and drive the search for more homogenous subtypes of depression in order to facilitate treatment decisions in clinical practice. Latent class analysis (LCA) presents a common statistical method in current depression research that aims to identify depressed patients with similar symptom profiles. LCA recovers hidden groups in multivariate data of heterogenous populations such that subjects within classes are similar to each other but different form subjects in other classes. It does so by dividing subjects into groups for which the observed variables are unrelated within each class, so-called ‘conditional independence’. Given the heterogeneity and multifactorial nature of depression, LCA and other multivariate subtyping strategies may yield subtypes with a more homogenous etiology, course of illness or treatment response, than subtyping depressed patients purely on one characteristic, such as with or without anxiety or psychotic features. In a recent report in Molecular Psychiatry, Milaneschi et al. used LCA and identified three classes described as ‘severe typical’ (T), ‘severe atypical’ (A) and ‘moderate’. The two depressed classes T and A differed predominantly with regard to appetite and weight symptoms: most T subjects reported appetite and weight decrease, but almost none reported appetite or weight gain; for A subjects, it was the other way around. Importantly, T and A subtypes did not differ substantially with respect to other depressive symptoms, illustrated by the fact that increased appetite/weight perfectly predicted membership in A (area under the receiver operating characteristic curve (area under curve) = 0.99, sensitivity 98.4%, specificity 99.5%), and decreased appetite/ weight predicted membership in T very well (area under curve = 0.81, sensitivity 87.8%, specificity 72.8%). Both T and A classes are consistent with results from prior LCA-based depression studies. 2,3,8 We commend the authors for their insightful study with important findings concerning the genetic background of depression, in particular that severe depression—especially when it involves appetite and weight loss (T class)—shares genetic risk factors with schizophrenia. Milaneschi et al. also demonstrated that results from multivariate classification procedures such as LCA can be used to derive more parsimonious subtypes that could serve as an alternative in case complete symptom data are unavailable (for example, in case of missing data in combined genome-wide association study data sets), which would complicate the application of classical LCA, as well as other multivariate subtyping techniques that we advocate below. However, we see several difficulties with the LCA-results and their interpretation that are common in the literature and not limited to the report by Milaneschi et al. First, the symptom profiles of T and A were remarkably similar and mainly differed regarding appetite/weight loss or gain. This implies that substantive variability is likely to remain among patients within these two classes with regard to other symptoms, etiology, course and prognosis, raising concerns about the value of the identified classes as means to effectively decrease the heterogeneity of depression. Validation studies are needed to test whether the T and A subtypes, despite their relatively similar symptom profiles, are differentially associated with clinically relevant external variables such as course of illness, family history or treatment outcome. The second point pertains to the validity of these classes. Like prior reports, LCA classes were primarily based on weight/ appetite differences that possibly reflect methodological artifacts based on violations of conditional independence. In LCA, associations between symptoms are assumed to be explained exclusively by their relation with the underlying depression subtype: symptoms within classes are statistically independent, conditional on class membership. However, appetite/weight gain excludes appetite/weight loss in most patients (and vice versa), making these symptom-variants inherently dependent. High levels of dependence might exist as well for other opposite depressive symptoms, such as insomnia versus hypersomnia and psychomotor agitation versus psychomotor retardation. In such cases, local independence can always be achieved by increasing the number of LCA classes to account for this dependence, for instance with appetite/weight gainers allocated to a different class than appetite/weight losers. The strong dependence between weight and appetite symptoms can therefore dominate the model and lead to biased parameters and posterior classifications as well as artificial classes. Several solutions exist to account for this problem of local dependence, such as local dependence models or using Bayesian priors in so-called ‘flexible LCA’. A recent study applied both LCA and flexible LCA to depression data; regular LCA identified weight/appetite-based classes, whereas these classes disappeared in flexible LCA, which found classes differing primarily on anxiety. The results emphasize the possible methodological artificiality of appetite/weight based LCA classes. Controlling for violations of conditional independence and analyzing common symptoms beyond the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria for depression (like anxiety) may provide important venues for future research. Third, the authors labeled the class with increased appetite/ weight as ‘atypical’, which is custom in studies with similar results. However, the symptom profile of this LCA-class differs considerably from the atypical specifier in the DSM, which includes additional criteria such as hypersomnia, mood reactivity, leaden paralysis and interpersonal rejection sensitivity. Using the term ‘atypical’ for a class mainly characterized by increased appetite and weight might lead to further confusion in the already conflicting and contentious literature on subtypes of major depression, in which labels such as ‘atypical’ are used in different contexts for different combinations of criteria. To prevent confusion, we suggest to use different labels for latent classes if there is no substantial overlap with specifiers used in the DSM. Lastly, LCA assumes that classes differ only qualitatively, contrasting evidence that depression may be dimensional for some people. 11 Hybrid factor mixture models combine aspects of both LCA and factor models, allowing for the identification of Molecular Psychiatry (2016) 00, 1–2

Person-fit feedback on inconsistent symptom reports in clinical depression care

BACKGROUND Depressive patients can present with complex and different symptom patterns in clinical care. Of these, some may report patterns that are inconsistent with typical patterns of depressive symptoms. This study aimed to evaluate the validity of person-fit statistics to identify inconsistent symptom reports and to assess the clinical usefulness of providing clinicians with person-fit score feedback during depression assessment. METHODS Inconsistent symptom reports on the Inventory of Depressive Symptomatology Self-Report (IDS-SR) were investigated quantitatively with person-fit statistics for both intake and follow-up measurements in the Groningen University Center of Psychiatry (n = 2036). Subsequently, to investigate the causes and clinical usefulness of on-the-fly person-fit alerts, qualitative follow-up assessments were conducted with three psychiatrists about 20 of their patients that were randomly selected. RESULTS Inconsistent symptom reports at intake (12.3%) were predominantly characterized by reporting of severe symptoms (e.g. psychomotor slowing) without mild symptoms (e.g. irritability). Person-fit scores at intake and follow-up were positively correlated (r = 0.45). Qualitative interviews with psychiatrists resulted in an explanation for the inconsistent response behavior (e.g. complex comorbidity, somatic complaints, and neurological abnormalities) for 19 of 20 patients. Psychiatrists indicated that if provided directly after the assessment, a person-fit alert would have led to new insights in 60%, and be reason for discussion with the patient in 75% of the cases. CONCLUSIONS Providing clinicians with automated feedback when inconsistent symptom reports occur is informative and can be used to support clinical decision-making.

Common psychiatric and metabolic comorbidity of adult attention-deficit/hyperactivity disorder: A population-based cross-sectional study

Attention-deficit/hyperactivity disorder (ADHD) is often comorbid with other psychiatric conditions in adults. Yet, less is known about its relationship with common metabolic disorders and how sex and ageing affect the overall comorbidity patterns of adult ADHD. We aimed to examine associations of adult ADHD with several common psychiatric and metabolic conditions. Through the linkage of multiple Swedish national registers, 5,551,807 adults aged 18 to 64 years and living in Sweden on December 31, 2013 were identified and assessed for clinical diagnoses of adult ADHD, substance use disorder (SUD), depression, bipolar disorder, anxiety, type 2 diabetes mellitus (T2DM), and hypertension. Logistic regression models and regression standardization method were employed to obtain estimates of prevalence, prevalence difference (PD), and prevalence ratio (PR). All comorbid conditions of interest were more prevalent in adults with ADHD (3.90% to 44.65%) than in those without (0.72% to 4.89%), with the estimated PRs being over nine for psychiatric conditions (p < 0.001) and around two for metabolic conditions (p < 0.001). Sex differences in the prevalence of comorbidities were observed among adults with ADHD. Effect modification by sex was detected on the additive scale and/or multiplicative scale for the associations of adult ADHD with all comorbidities. ADHD remained associated with all comorbidities in older adults aged 50 to 64 when all conditions were assessed from age 50 onwards. The comorbidity patterns of adult ADHD underscore the severity and clinical complexity of the disorder. Clinicians should remain vigilant for a wide range of psychiatric and metabolic problems in ADHD affected adults of all ages and both sexes.