Combination therapy as a potential risk factor for the development of type 2 diabetes in patients with schizophrenia: the GOMAP study.

BACKGROUND: Schizophrenia (SCZ) is associated with increased risk of type 2 diabetes (T2D). The potential diabetogenic effect of concomitant application of psychotropic treatment classes in patients with SCZ has not yet been evaluated. The overarching goal of the Genetic Overlap between Metabolic and Psychiatric disease (GOMAP) study is to assess the effect of pharmacological, anthropometric, lifestyle and clinical measurements, helping elucidate the mechanisms underlying the aetiology of T2D. METHODS: The GOMAP case-control study (Genetic Overlap between Metabolic and Psychiatric disease) includes hospitalized patients with SCZ, some of whom have T2D. We enrolled 1653 patients with SCZ; 611 with T2D and 1042 patients without T2D. This is the first study of SCZ and T2D comorbidity at this scale in the Greek population. We retrieved detailed information on first- and second-generation antipsychotics (FGA, SGA), antidepressants and mood stabilizers, applied as monotherapy, 2-drug combination, or as 3- or more drug combination. We assessed the effects of psychotropic medication, body mass index, duration of schizophrenia, number of hospitalizations and physical activity on risk of T2D. Using logistic regression, we calculated crude and adjusted odds ratios (OR) to identify associations between demographic factors and the psychiatric medications. RESULTS: Patients with SCZ on a combination of at least three different classes of psychiatric drugs had a higher risk of T2D [OR 1.81 (95% CI 1.22-2.69); p = 0.003] compared to FGA alone therapy, after adjustment for age, BMI, sex, duration of SCZ and number of hospitalizations. We did not find evidence for an association of SGA use or the combination of drugs belonging to two different classes of psychiatric medications with increased risk of T2D [1.27 (0.84-1.93), p = 0.259 and 0.98 (0.71-1.35), p = 0.885, respectively] compared to FGA use. CONCLUSIONS: We find an increased risk of T2D in patients with SCZ who take a combination of at least three different psychotropic medication classes compared to patients whose medication consists only of one or two classes of drugs.

Patient-reported needs predict perceived psychosocial disability and quality of life beyond symptom severity in schizophrenia.

PURPOSE: We examined whether patient-rated or clinician-rated needs are more strongly associated with perceived psychosocial disability (PPD) and subjective quality of life (SQOL) of schizophrenia patients, beyond symptom severity. METHODS: Hierarchical regression analyses were computed to test patient and clinician-rated unmet and met needs (estimated by eighty-two patient-clinician pairs) as predictors of PPD and SQOL above and beyond demographics and psychopathology. Needs, symptomatology, PPD and SQOL were estimated using Camberwell Assessment of Need (CAN), PANSS, WHODAS 2.0 and WHOQOL-BREF respectively. RESULTS: Needs were significantly associated with all WHODAS 2.0 and WHOQOL-BREF domains above and beyond demographics and PANSS variables. Clinician-rated needs were better predictors of only one WHODAS 2.0 domain, while patient-rated needs were better predictors of all other WHODAS 2.0 and WHOQOL-BREF domains. Patient-rated unmet needs were more strongly than met needs associated with the most WHODAS 2.0 and WHOQOL-BREF subscores. CONCLUSION: This study offers the first evidence that patient-rated needs, especially unmet needs, are strongly associated, above and beyond symptomatology, with global and domain-specific PPD of schizophrenia patients. Accordingly, strong relations of patient-rated needs with SQOL emerged. Identifying and addressing patient-reported needs could facilitate PPD and SQOL improvement more effectively than interventions confined solely to symptom remission.IMPLICATIONS FOR REHABILITATIONSchizophrenia is associated with poor rehabilitation and recovery outcomes, i.e., perceived psychosocial disability (PPD) and subjective quality of life (SQOL).Assessment of patients' needs constitutes the basis of determining treatment goals and planning tailor-made interventions to achieve crucial rehabilitative outcomes.Higher levels of patient-reported unmet needs are associated with poorer SQOL and higher global and domain-specific PPD of schizophrenia patients, above and beyond symptom severity.Addressing patient-reported needs through personalized interventions can facilitate more effectively PPD and SQOL improvement, than treatment confined to symptomatic alleviation.

Premorbid adjustment predictors of cognitive dysfunction in schizophrenia.

Premorbid adjustment (PA) in academic and social domain is a key-predictor of cognitive performance in schizophrenia. Prior studies provided inconsistent findings regarding the differential relationships of PA domains with post-illness cognition. Multivariate associations of academic and social PA in each developmental stage (childhood, early and late adolescence) with post-onset cognitive variables were explored. Furthermore, possible differential relationships of PA domain deterioration courses with post-onset cognitive dysfunction were investigated. Seventy-five schizophrenia patients were evaluated with Premorbid Adjustment Scale (PAS). General cognitive ability, verbal IQ, verbal memory and learning, processing speed, working memory, executive function and premorbid IQ were assessed. Canonical Correlation Analyses revealed that poorer academic PA across childhood and early adolescence was related to worse post-onset verbal IQ, working memory, verbal learning and executive function, while academic PA deterioration between early and late adolescence was associated with poorer verbal learning and executive function and, as further analysis indicated, predicts IQ decline. Academic PA was exclusively associated with post-onset cognitive impairment. New evidence emerged for the specificity of each developmental period in constructing academic PA in its relation to post-illness cognition. Early premorbid academic maladjustment possibly constitutes the onset of a cognitive dysmaturational process which results to post-diagnosis impaired cognition.

Use of color texture in determining the nature of melanocytic skin lesions--a qualitative and quantitative approach.

Melanocytic nevi are recognized as precursors of melanoma. Aiding in early recognition of melanoma, we estimated color texture parameters, fractal dimension and lacunarity of melanoma and other melanocytic nevi. Digital images of the lesions were processed. Graphic three-dimensional pseudoelevation images of the lesions and surrounding skin were produced to identify irregularities in color texture within the lesions. Estimation of lacunarity and fractal dimension followed in order to produce a numerical estimate of the coarseness of color texture. Clinicians readily perceive the resulting "geographical" images. Irregularity in the anaglyph, which might veil malignancy, is effortlessly identified through these images, and therefore an early excision of a suspect lesion is indicated.

A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit: a preliminary report.

BACKGROUND: For early melanoma diagnosis, experienced dermatologists have an accuracy of 64-80% using clinical diagnostic criteria, usually the ABCD rule, while automated melanoma diagnosis systems are still considered to be experimental and serve as adjuncts to the naked-eye expert prediction. In an attempt to aid in early melanoma diagnosis, we developed an image processing program with the aim to discriminate melanoma from melanocytic nevi, establishing a mathematical model to come up with a melanoma probability. METHODS: Digital images of 132 melanocytic skin lesions (23 melanomas and 109 melanocytic nevi) were studied in features of geometry, color, and color texture. A total of 43 variables were studied for all lesions, e.g., geometry, color texture, sharpness of border, and color variables. Univariate logistic regression analysis followed by "-2 log likelihood" test and Spearman's rank correlation coefficient were used to eliminate inappropriate variables, as the presence of multi-collinearity among variables could cause severe problems in any stepwise variable selection method. Initially, "-2 log likelihood" and nonparametric Spearman's rho picked five variables to be included in a multivariate model of prediction. The five-variable model was then reduced to three variables and the performance of each model was tested. The "jackknife" method was performed in order to validate the model with the three variables and its accuracy was weighed vs. the five-variable model by receiver-operating characteristics (ROC) curve plotting. It was concluded that the reduced model did not compromise discriminatory power. RESULTS: Not all variables contributed much to the model, therefore they were progressively eliminated and the model was finally reduced to three covariates of significance. A predictive equation was calculated, incorporating parameters of geometry, color, and color texture as independent covariates for the prediction of melanoma. The proposed model provides melanoma probability with a 60.9% sensitivity and 95.4% specificity of prediction, an overall accuracy of 89.4% (probability level 0.5), and 8% false-negative results. CONCLUSIONS: Through a digital image processing system and the development of a mathematical model of prediction, discrimination between melanomas and melanocytic nevi seems feasible with a high rate of accuracy using multivariate logistic regression analysis. The proposed model is an alternative method to aid in early melanoma diagnosis. Expensive and sophisticated equipment is not required and it can be easily implemented in a reasonably priced portable programmable computer, in order to predict previously undiagnosed skin melanoma before histopathology results confirm diagnosis.