Previous research has established a relationship between smell identification deficits (SID) and particular aspects of cognitive function among patients with schizophrenia.

To expand the extant literature, we examined the relationship between SID and the Trail Making Test to determine if processing speed is related to SID.

Our sample included 60 inpatients from the New York State Psychiatric Institute's Schizophrenia Research Unit. We considered age, deficit syndrome, verbai intelligence quotient, and education in our analyses due to their documented relationship to smell identification ability.

Trails A errors and Trails A seconds accounted for a significant amount of the variance in University of Pennsylvania Smell Identification Test scores in a regression analysis (R2=.10, P=.008 and R2=.05, P=.04).

Linking neurocognition to smell identification deficits may prove to be an essential marker for schizophrenia research.

Research suggests an association between metabolic disorders, such as type 2 diabetes mellitus (T2DM), and schizophrenia. However, the risk of metabolic disorders in the unaffected siblings of patients with schizophrenia remains unclear.

Using the Taiwan National Health Insurance Research Database, 3135 unaffected siblings of schizophrenia probands and 12,540 age-/sex-matched control subjects were included and followed up to the end of 2011. Individuals who developed metabolic disorders during the follow-up period were identified.

The unaffected siblings of schizophrenia probands had a higher prevalence of T2DM (3.4% vs. 2.6%, p = 0.010) than the controls. Logistic regression analyses with the adjustment of demographic data revealed that the unaffected siblings of patients with schizophrenia were more likely to develop T2DM (odds ratio [OR]: 1.39, 95% confidence interval [CI]: 1.10–1.75) later in life compared with the control group. Moreover, only female siblings of schizophrenia probands had an increased risk of hypertension (OR: 1.47, 95% CI: 1.07–2.01) during the follow-up compared with the controls.

The unaffected siblings, especially sisters, of schizophrenia probands had a higher prevalence of T2DM and hypertension compared with the controls. Our study revealed a familial link between schizophrenia and T2DM in a large sample. Additional studies are required to investigate the shared pathophysiology of schizophrenia and T2DM.

To (1) confirm whether the Habit, Reward, and Fear Scale is able to generate a 3-factor solution in a population of obsessive-compulsive disorder and alcohol use disorder (AUD) patients; (2) compare these clinical groups in their habit, reward, and fear motivations; and (3) investigate whether homogenous subgroups can be identified to resolve heterogeneity within and across disorders based on the motivations driving ritualistic and drinking behaviors.

One hundred and thirty-four obsessive-compulsive disorder (n = 76) or AUD (n = 58) patients were assessed with a battery of scales including the Habit, Reward, and Fear Scale, the Yale-Brown Obsessive-Compulsive Scale, the Alcohol Dependence Scale, the Behavioral Inhibition/Activation System Scale, and the Urgency, (lack of ) Premeditation, (lack of ) Perseverance, Sensation Seeking, and Positive Urgency Impulsive Behavior Scale.

A 3-factor solution reflecting habit, reward, and fear subscores explained 56.6% of the total variance of the Habit, Reward, and Fear Scale. Although the habit and fear subscores were significantly higher in obsessive-compulsive disorder (OCD) and the reward subscores were significantly greater in AUD patients, a cluster analysis identified that the 3 clusters were each characterized by differing proportions of OCD and AUD patients.

While affective (reward- and fear-driven) and nonaffective (habitual) motivations for repetitive behaviors seem dissociable from each other, it is possible to identify subgroups in a transdiagnostic manner based on motivations that do not match perfectly motivations that usually described in OCD and AUD patients.

The Diagnostic Criteria for Psychosomatic Research (DCPR) are those of psychosomatic syndromes that did not find room in the classical taxonomy. More recently, the DCPR were updated, called DCPR-revised (DCPR-R). The present study was conducted to test the criterion-related validity of the DCPR-R.

Two hundred consecutive subjects were enrolled at the Headache Center of Careggi University Hospital (Italy): 100 subjects had a diagnosis of chronic migraine (CM) and 100 had a diagnosis of episodic migraine (EM). Participants received a clinical assessment, which included the DCPR-revised Semi-Structured Interview (DCPR-R SSI), the Structured Clinical Interview for DSM-5 (SCID-5), and the psychosocial index (PSI).

Forty-seven subjects (23.5%) had at least one DSM-5 diagnosis: major depressive disorder (8.5%; n = 17) and agoraphobia (7.5%; n = 15) were the most frequent. One hundred and ten subjects (55%) reported a DCPR-R diagnosis: allostatic overload (29%; n = 58) and type A behavior (10.5%; n = 21) were the most frequent. When the incremental validity of the DCPR system over the DSM system was tested using PSI subscales as the criterion variable, the DCPR-R increased up to 0.11–0.24 the amount of explained variance. Subjects with at least one DCPR-R diagnosis showed lower PSI well-being scores (p = .001), higher PSI stress scores (p < .001), and higher PSI psychological distress scores (p = .008) than subjects without a DCPR-R diagnosis.

The DCPR-R showed a good criterion-related validity in migraine outpatients. Thus, they might be implemented, together with the DSM-5, in the assessment of migraine subjects.

A number of recent investigations have focused on the neurobiology of obsessive–compulsive personality disorder (OCPD). However, there have been few reviews of this literature with no detailed model proposed. We therefore undertook a systematic review of these investigations, aiming to map the available evidence and investigate whether it is possible to formulate a detailed model of the neurobiology of OCPD.

OCPD can be considered from both categorical and dimensional perspectives. An electronic search was therefore conducted using terms that would address not only OCPD as a category, but also related constructs, such as perfectionism, that would capture research on neuropsychology, neuroimaging, neurochemistry, and neurogenetics.

A total of 1059 articles were retrieved, with 87 ultimately selected for abstract screening, resulting in a final selection of 49 articles focusing on neurobiological investigations relevant to OCPD. Impaired executive function and cognitive inflexibility are common neuropsychological traits in this condition, and suggest that obsessive–compulsive disorder (OCD) and OCPD may lie on a continuum. However, neuroimaging studies in OCPD indicate the involvement of specific neurocircuitry, including the precuneus and amygdala, and so suggest that OCD and OCPD may have important differences. Although OCPD has a heritable component, we found no well-powered genetic studies of OCPD.

Although knowledge in this area has advanced, there are insufficient data on which to base a comprehensive model of the neurobiology of OCPD. Given the clinical importance of OCPD, further work to understand the mechanisms that underpin this condition is warranted.

Altered immunity and metabolic profiles have been compared between bipolar depression (BD) and major depressive disorder (MDD). This study aimed at developing a composite predictor of appetite hormones and proinflammatory cytokines to differentiate BD from MDD.

This cross-sectional study enrolled patients with BD and those with MDD aged 20 to 59 years and displaying depressive episodes. Clinical characteristics (age, sex, body mass index, and depression severity), cytokines (C-reactive protein, interleukin [IL]-2, IL-6, tumor necrosis factor [TNF]-α, P-selectin, and monocyte chemoattractant protein), and appetite hormones (leptin, adiponectin, ghrelin, and insulin) were assessed as potential predictors using a classification and regression tree (CRT) model for differentiating BD from MDD.

The predicted probability of a composite predictor of ghrelin and TNF-α was significantly greater (for BD: area under curve = 0.877; for MDD: area under curve = 0.914) than that of any one marker (all P > .05) to distinguish BD from MDD. The most powerful predictors for diagnosing BD were high ghrelin and TNF-α levels, whereas those for MDD were low ghrelin and TNF-α levels.

A composite predictor of ghrelin and TNF-α driven by CRT could assist in the differential diagnosis of BD from MDD with high specificity. Further clinical studies are warranted to validate our results and to explore underlying mechanisms.