To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The current study used data from an ethnically diverse population from South London to examine ethnic differences in physical and mental multimorbidity among working age (18–64 years) adults in the context of depression and anxiety.
The study included 44 506 patients who had previously attended Improving Access to Psychological Therapies services in the London Borough of Lambeth. Multinomial logistic regression examined cross-sectional associations between ethnicity with physical and mental multimorbidity. Patterns of multimorbidity were identified using hierarchical cluster analysis.
Within 44 056 working age adults with a history of depression or anxiety from South London there were notable ethnic differences in physical multimorbidity. Adults of Black Caribbean ethnicity were more likely to have physical multimorbidity [adjusted relative risk ratio (aRRR) = 1.25, 95% confidence interval (CI) 1.15–1.36] compared to adults of White ethnicity. Relative to adults of White ethnicity, adults of Asian ethnicity were more likely to have physical multimorbidity at higher thresholds only (e.g. 4 + conditions; aRRR = 1.53, 95% CI 1.17–2.00). Three physical (atopic, cardiometabolic, mixed) and three mental (alcohol/substance use, common/severe mental illnesses, personality disorder) multimorbidity clusters emerged. Ethnic minority groups with multimorbidity had a higher probability of belonging to the cardiometabolic cluster.
In an ethnically diverse population with a history of common mental health disorders, we found substantial between- and within-ethnicity variation in rates of physical, but not mental, multimorbidity. The findings emphasised the value of more granular definitions of ethnicity when examining the burden of physical and mental multimorbidity.
The creation of mathematical models for determining the risk of Alzheimer’s disease in patients with mild cognitive impairment based on immune markers of blood is important for differential diagnosis of AD, assessment of the clinical state of patients at different stages of the disease, and optimization of therapy.
To assess the risk of AD in patients with amnestic type of mild cognitive impairment (aMCI) with the use of such markers of inflammation as enzymatic activity of leukocyte elastase (LE) and the functional activity of α1-proteinase inhibitor (α1-PI).
The object of mathematical analysis on the basis of cluster analysis and logistic regression was the database, including the results of the above immunological parameters (enzyme activity of LE and functional activity of α1-PI.) in plasma of 78 patients with aMCI receiving outpatient treatment in the clinic of FSBSI “Mental Health Research Centre”. Among the examined patients there were 25 men and 53 women aged 44 to 89 years (69.1 ± 9.95).
Clustering by k-means and classification by logistic regression indicate the risk of developing AD in patients with aMCI depending on the activity of LE and α1-PI in blood plasma. The total coincidence of objects included in the clusters and in the AD risk group was 94%.
The high coincidence of the two different methods of grouping confirms the previously stated position on the possibility of identifying patients with a high risk of AD among patients with aMCI in the activity of LE and α1-PI in the blood of patients.
Schizophrenia and bipolar disorder are complex mental illnesses that are associated with cognitive deficits. There is considerable cognitive heterogeneity that exists within both disorders. Studies that cluster schizophrenia and bipolar patients into subgroups based on their cognitive profile increasingly demonstrate that, relative to healthy controls, there is a severely compromised subgroup and a relatively intact subgroup. There is emerging evidence that telomere shortening, a marker of cellular senescence, may be associated with cognitive impairments. The aim of this study was to explore the relationship between cognitive subgroups in bipolar-schizophrenia spectrum disorders and telomere length against a healthy control sample.
Participants included a transdiagnostic group diagnosed with bipolar, schizophrenia or schizoaffective disorder (n = 73) and healthy controls (n = 113). Cognitive clusters within the transdiagnostic patient group, were determined using K-means cluster analysis based on current cognitive functioning (MATRICS Consensus Cognitive Battery scores). Telomere length was determined using quantitative PCRs genomic DNA extracted from whole blood. Emergent clusters were then compared to the healthy control group on telomere length.
Two clusters emerged within the patient group that were deemed to reflect a relatively intact cognitive group and a cognitively impaired subgroup. Telomere length was significantly shorter in the severely impaired cognitive subgroup compared to the healthy control group.
This study replicates previous findings of transdiagnostic cognitive subgroups and associates shorter telomere length with the severely impaired cognitive subgroup. These findings support emerging literature associating cognitive impairments in psychiatric disorders to accelerated cellular aging as indexed by telomere length.
The excessive intake of ultra-processed foods (UPF) is associated with an increase in cardiovascular risk. However, the effect of UPF intake on cardiovascular health in children and adolescents with congenital heart disease (CHD) is unknown. The aim of the present study was to describe UPF intake and evaluate associations with isolated cardiovascular risk factors and children and adolescents with CHD clustered by cardiovascular risk factors. A cross-sectional study was conducted involving 232 children and adolescents with CHD. Dietary intake was assessed using three 24-hour recalls. UPF were categorised using the NOVA classification. The cardiovascular risk factors evaluated were central adiposity, elevated high-sensitivity C-reactive protein (hs-CRP) and subclinical atherosclerosis. The clustering of cardiovascular risk factors (waist circumference, hs-CRP and carotid intima-media thickness) was performed, allocating the participants to two groups (high v. low cardiovascular risk). UPF contributed 40·69 % (sd 6·21) to total energy intake. The main UPF groups were ready-to-eat and take-away/fast foods (22·2 % energy from UPF). The multivariable logistic regression revealed that an absolute increase of 10 % in UPF intake (OR = 1·90; 95 % CI: 1·01;3·58) was associated with central adiposity. An absolute increase of 10 % in UPF intake (OR = 3·77; 95 % CI: 1·80, 7·87) was also associated with children and adolescents with CHD clustered by high cardiovascular risk after adjusting for confounding factors. Our findings demonstrate that UPF intake should be considered as a modifiable risk factor for obesity and its cardiovascular consequences in children and adolescents with CHD.
Assessing genetic diversity and identifying trait-specific germplasm within germplasm collections is necessary for a varietal development programme. Agronomic features were investigated in 318 diverse dry bean germplasm accessions, including check varieties. We observed a lot of genetic variability for the traits studied. A wide range of variations was noticed for days to 50% flowering, days to maturity, pod length, the number of seeds per pod and 100-seed weight (HSW). For eight of the agronomic features evaluated, the analysis of variance revealed substantial differences among the accessions. For all characters, phenotypic coefficient of variation estimations were more significant than genotypic coefficient of variation. Plant height, days to 50% flowering, seed yield (q/ha) and HSW had high heritability and genetic advance as a per cent of the mean. Association analysis revealed a significant positive relationship between HSW, plant height, pod length and seed yield (q/ha). According to a hierarchical clustering analysis based on agronomic features, the diversity of dry bean germplasm has no significant association with their geographical origin. The number of pods per plant, plant height, days to maturity, days to 50% flowering and seed yield had relatively long vectors based on principal components 1 and 2, indicating that genotypes differ significantly. Additionally, the trait-specific donors and bean common mosaic virus disease-resistant accessions, IC360831, ET4515, EC150250, IC340947, IC564797B, EC565693 and ET8409 could be of value for dry bean improvement.
To identify cognitive phenotypes in late-life depression (LLD) and describe relationships with sociodemographic and clinical characteristics.
Observational cohort study
Baseline data from participants recruited via clinical referrals and community advertisements who enrolled in two separate studies.
Non-demented adults with LLD (n = 120; mean age = 66.73 ± 5.35 years) and non-depressed elders (n = 56; mean age = 67.95 ± 6.34 years).
All completed a neuropsychological battery, and individual cognitive test scores were standardized across the entire sample without correcting for demographics. Five empirically derived cognitive domain composites were created, and cluster analytic approaches (hierarchical, k-means) were independently conducted to classify cognitive patterns in the depressed cohort only. Baseline sociodemographic and clinical characteristics were then compared across groups.
A three-cluster solution best reflected the data, including “High Normal” (n = 47), “Reduced Normal” (n = 35), and “Low Executive Function” (n = 37) groups. The “High Normal” group was younger, more educated, predominantly Caucasian, and had fewer vascular risk factors and higher Mini-Mental Status Examination compared to “Low Executive Function” group. No differences were observed on other sociodemographic or clinical characteristics. Exploration of the “High Normal” group found two subgroups that only differed in attention/working memory performance and length of the current depressive episode.
Three cognitive phenotypes in LLD were identified that slightly differed in sociodemographic and disease-specific variables, but not in the quality of specific symptoms reported. Future work on these cognitive phenotypes will examine relationships to treatment response, vulnerability to cognitive decline, and neuroimaging markers to help disentangle the heterogeneity seen in this patient population
Subthreshold hypomania during a major depressive episode challenges the bipolar-unipolar dichotomy. In our study we employed a cross-diagnostic cluster analysis - to identify distinct subgroups within a cohort of depressed patients.
A k-means cluster analysis— based on the domain scores of the Mood Spectrum Self-Report (MOODS-SR) questionnaire—was performed on a data set of 300 adults with either bipolar or unipolar depression. After identifying groups, between-clusters comparisons were conducted on MOODS-SR domains and factors and on a set of sociodemographic, clinical and psychometric variables.
Three clusters were identified: one with intermediate depressive and poor manic symptomatology (Mild), one with severe depressive and poor manic symptomatology (Moderate), and a third one with severe depressive and intermediate manic symptomatology (Mixed). Across the clusters, bipolar patients were significantly less represented in the Mild one, while the DSM-5 “Mixed features” specifier did not differentiate the groups. When compared to the other patients, those of Mixed cluster exhibited a stronger association with most of the illness-severity, quality of life, and outcomes measures considered. After performing pairwise comparisons significant differences between “Mixed” and “Moderate” clusters were restricted to: current and disease-onset age, psychotic ideation, suicidal attempts, hospitalization numbers, impulsivity levels and comorbidity for Cluster B personality disorder.
In the present study, a clustering approach based on a spectrum exploration of mood symptomatology led to the identification of three transdiagnostic groups of patients. Consistent with our hypothesis, the magnitude of subthreshold (hypo)manic symptoms was related to a greater clinical severity, regardless of the main categorical diagnosis.
Characterising a socio-technical system by its underlying structure is often achieved by cluster analyses and bears potentials for engineering design management. Yet, highly connected systems lack clarity when systematically searching for structures. At two stages in a clustering procedure (pre-processing and post-processing) modelled and external information were used to reduce ambiguity and uncertainty of clustering results. A holistic decision making on 1) which information, 2) when, and 3) how to use is discussed and considered inevitable to reliably cluster highly connected systems.
Patients with functional neurological disorders (FND) often present with multiple motor, sensory, psychological and cognitive symptoms. In order to explore the relationship between these common symptoms, we performed a detailed clinical assessment of motor, non-motor symptoms, health-related quality of life (HRQoL) and disability in a large cohort of patients with motor FND. To understand the clinical heterogeneity, cluster analysis was used to search for subgroups within the cohort.
One hundred fifty-two patients with a clinically established diagnosis of motor FND were assessed for motor symptom severity using the Simplified Functional Movement Disorder Rating Scale (S-FMDRS), the number of different motor phenotypes (i.e. tremor, dystonia, gait disorder, myoclonus, and weakness), gait severity and postural instability. All patients then evaluated each motor symptom type severity on a Likert scale and completed questionnaires for depression, anxiety, pain, fatigue, cognitive complaints and HRQoL.
Significant correlations were found among the self-reported and all objective motor symptoms severity measures. All self-reported measures including HRQoL correlated strongly with each other. S-FMDRS weakly correlated with HRQoL. Hierarchical cluster analysis supplemented with gap statistics revealed a homogenous patient sample which could not be separated into subgroups.
We interpret the lack of evidence of clusters along with a high degree of correlation between all self-reported and objective measures of motor or non-motor symptoms and HRQoL within current neurobiological models as evidence to support a unified pathophysiology of ‘functional’ symptoms. Our results support the unification of functional and somatic syndromes in classification schemes and for future mechanistic and therapeutic research.
In this chapter, we critically discuss contemporary approaches to infer identity statuses. We will focus on how identity statuses can be delineated through a person-centered approach (e.g., cluster analysis and latent class/profile analysis [LCA/LPA]). These methods can depict how multiple variables are configured within persons, capturing identity statuses as indicated by questionnaire data. We detail the theoretical rationale for deriving identity statuses using a person’s scores on identity processes. We focus on how these approaches integrate classic identity status research with more novel identity process research. We critically discuss the differences in the way that statuses are derived with structured interviews compared to questionnaires, debating what each of the approaches contributes. We also highlight how a person-centered approach for deriving identity status clusters can provide additional insights to identity status models. Next, we detail these procedures using concrete examples for cluster analysis and LCAs/LPAs. In this, we explain how identity status clusters were derived at the person-level, using participants’ scores on identity processes. For both techniques, we focus on a step-by-step description of how we depicted the identity statuses, also comparing the results of cluster analysis and LCA/LPA on the same dataset. Additionally, we present requirements, general concerns regarding person-centered approaches, and specific concerns for each technique. Last, we present limitations of this approach and detail directions for future research. We ground this discussion on the results of recent studies that depicted identity statuses through cluster-analytic procedures in different cultural in order to analyze differences and points of convergence.
A solid QCA does not end with the analytic moment. Researchers must make several analytic decisions at various stages in the analysis, some with more confidence than others. Researchers might also be confronted with data that are structured in analytically relevant ways. For example, cases might group into different geographic, substantive, or temporal clusters, or there might be relevant causal dependencies or sequences among conditions.
This chapter introduces the different robustness and diagnostic tools available in R to assess QCA results. It enables the reader to investigate to what extent their QCA results are robust against equally plausible analytic decisions regarding the selection of calibration anchors or consistency and frequency cut-offs. We present possibilities to assess robustness in R. Moreover, we introduce tools for cluster diagnostics and discuss strategies for dealing with timing and temporality, including ‘coincidence analysis’ (CNA).
- Basic understanding of different approaches to diagnosing and assessing QCA results.
- Familiarity with how the robustness of QCA results to different analytical decisions can be assessed.
- Familiarity with proposals on how to assess QCA results in the presence of clustered data.
- Familiarity with how to model sequences and causal chains in R.
Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.
Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.
Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.
MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.
Beachpea (Vigna marina) is a halophytic wild leguminous plant which occurs throughout tropical and subtropical beaches of world. As quantitative trait loci (QTLs) for salt tolerance in V. marina and its crossability with other Vigna species are known, the current study was undertaken to know the presence of these QTLs in the V. marina accessions along with check varieties of pulses. Accordingly, 20 Vigna genotypes (15 accessions of V. marina collected from sea-shore areas of Andaman and Nicobar Islands along with five check varieties of green gram and black gram) were subjected to molecular characterization using seven simple sequence repeat (SSR) markers associated with salt tolerance. Of the markers used, only four SSR markers amplified in the studied germplasm. Number of alleles detected per primer and size of alleles ranged from 1 to 3 and 100 to 325 bp, respectively. Polymorphism information content and heterozygosity values ranged from 0.305 to 0.537 and 0.375 to 0.612, respectively. Three major clusters, cluster I, II and III were obtained at Jaccard's similarity coefficient value of 0.48 through the un-weighted paired group method with arithmetic means method of cluster analysis. It grouped green gram and black gram genotypes in clusters I (04) and II (01), whereas all V. marina genotypes were grouped in cluster III (15). Principal co-ordinate analysis explained 85.9% of genetic variation among genotypes which was further confirmed by cluster analysis. This study indicated the effectiveness of SSR markers in separating cultivated Vigna species from wild V. marina. The findings will be useful for transferring trait of robust salt tolerance of V. marina in cultivated Vigna species using marker-assisted breeding.
One of the main capabilities of atom probe tomography (APT) is the ability to not only identify but also characterize early stages of precipitation at length scales that are not achievable by other techniques. One of the most popular methods to identify nanoscale clustering in APT data, based on the density-based spatial clustering of applications with noise (DBSCAN), is used extensively in many branches of research. However, it is common that not all of the steps leading to the selection of certain parameters used in the analysis are reported. Without knowing the rationale behind parameter selection, it may be difficult to compare cluster parameters obtained by different researchers. In this work, a simple open-source tool, PosgenPy, is used to justify cluster search parameter selection via providing a systematic sweep through parameter values with multiple randomizations to minimize a false-positive cluster ratio. The tool is applied to several different microstructures: a simulated material system and two experimental datasets from a low-alloy steel . The analyses show how values for the various parameters can be selected to ensure that the calculated cluster number density and cluster composition are accurate.
It has been well established that depressive disorders including perinatal depression are very heterogeneous, which partly explain the ineffectiveness of available treatments for many patients. Recent innovations in data science can help elucidate the nature of perinatal depression especially the heterogeneity in its presentation.
The present study aime to elucidate heterogeneous subtypes of PND and assess the effectiveness of a multicomponent cognitive behavioral therapy (CBT) across heterogenous subtypes of PND.
This study was conducted in 2005 in two rural areas of Rawalpindi, Pakistan. Out of a total of 3,898 women, 903 pregnant women were identifed with PND (using DSM-IV) and randomly assigned to intervention and control group. Baseline assessments included interviewer admininstered Hamilton Depression Scale (HDS) and social risk factors. Follow-up assessments were conducted at 6 months and 12 months post-intervention. Principle component analysis was run to reduce dimensionality of the HDS. Two step cluster analysis was then run to elucidate subtypes of PND using the dimensional scores. Thereafter, effectiveness of CBT was compared across these subtypes of PND using multilevel modelling.
Principle component analysis revealed a four component solution for the Hamilton depression rating scale. Using these dimensional scores, cluster analysis (average silhouette= 0.5) revealed a parsimonius four cluster soultion of participants with mild PND symptoms (n=326); predominant sleep problems (n=311) c) predominant atypical symptoms (n=80) and d) comorbid depressive and anxiety symptoms (n=186). CBT yielded moderate effect sizes across all these subtypes of PND (cohen’s d > 0.8).
Multicomponent CBT is effective across hetergeneous presentations of PND.
There is limited data on the dietary patterns of 5-year-old children in Asia. The study examined childhood dietary patterns and their maternal and child correlates in a multi-ethnic Asian cohort. Based on caregiver-reported 1-month quantitative FFQ of 777 children from the Growing Up in Singapore Towards healthy Outcomes cohort, cluster analysis identified two mutually exclusive clusters. Children in the ‘Unhealthy’ cluster (43·9 %) consumed more fries, processed meat, biscuits and ice cream, and less fish, fruits and vegetables compared with those in the ‘Healthy’ cluster (56·1 %). Children with mothers of lower educational attainment had twice the odds of being assigned to the ‘Unhealthy’ cluster (adjusted OR (95 % CI) = 2·19 (95 % CI 1·49–3·24)). Children of Malay and Indian ethnicities had higher odds of being assigned to the ‘Unhealthy’ cluster (adjusted OR = 25·46 (95 % CI 15·40, 42·10) and 4·03 (95 % CI 2·68–6·06), respectively), relative to Chinese ethnicity. In conclusion, this study identified two dietary patterns in children, labelled as the ‘Unhealthy’ and ‘Healthy’ clusters. Mothers’ educational attainment and ethnicity were two correlates that were associated with the children’s assignments to the clusters. These findings can assist in informing health promotion programmes targeted at Asian children.
Weedy rice (Oryza spp.) is one of the most troublesome weeds affecting rice (Oryza sativa L.) production in many countries. Weedy rice control is difficult in rice fields, because the weed and crop are phenotypically and morphologically similar. Weedy rice can be a source of genetic diversity for cultivated rice. Thus, this study aimed to characterize the morphological diversity of weedy rice in southern Brazil. Qualitative and quantitative traits of 249 accessions from eight rice-growing mesoregions in Rio Grande do Sul (RS) and Santa Catarina (SC) states were analyzed. For each accession, 24 morphological descriptors (14 qualitative and 10 quantitative) were evaluated. All 249 accessions from RS and SC are of indica lineage. Considering all the phenotypic traits evaluated, the accessions separated into 14 distinct groups. One of the largest groups consisted of plants that were predominantly tall with green leaves, intermediate shattering, and variable flowering time. Distinct subgroups exist within larger clusters, showing discernible phenotypic diversity within the main clusters. The variability in flowering time was high (77 to 110 d after emergence), indicating high potential for flowering synchrony with rice cultivars and, consequently, gene flow. This indicates the need to remove escapes when planting herbicide-resistant rice. Thus, weedy rice populations in southern Brazil are highly diverse, and this diversity could result in variable response to weed management.
Eating habits of lactating women can influence the nutrient composition of human milk, which in turn influences nutrient intake of breastfed infants. The aim of the present study was to identify food patterns and nutritional adequacy among lactating women in Europe. Data from a multicentre European longitudinal cohort (ATLAS study) were analysed to identify dietary patterns using cluster analysis. Dietary information from 180 lactating women was obtained using 3-d food diaries over the first 4 months of lactation. Four dietary patterns were identified: ‘vege-oils’, ‘fish-poultry’, ‘confectionery-salads’ and ‘mixed dishes’. Nutrition adequacy was not significantly different between clusters, but the ‘vege-oils’ cluster tended to yield the highest nutrition adequacy measured by Mean Adequacy Ratio. Compared with European dietary reference values (DRVs) for lactating women, women in all clusters had inadequate intakes of energy, pantothenic acid, folate, vitamin C, vitamin A, vitamin D, zinc, iodine, potassium and linoleic acid. Adequate intake for fibre and α-linolenic acid was only achieved in the ‘vege-oils’ cluster. Overall, fat intake was above DRVs. The present study showed that various dietary patterns do not adequately supply all nutrients, indicating a need to promote overall healthy dietary habits for European lactating women.
The present study aims were (1) to identify the proportion of terminally ill cancer patients with desire for hastened death (DHD) receiving specialized palliative care, (2) to identify the reasons for DHD, and (3) to classify patients with DHD into some interpretable subgroups.
Advanced cancer patients admitted to 23 inpatients hospices/palliative care units in 2017 were enrolled. Data were prospectively obtained by the primarily responsible physicians. The presence/absence of DHD and reasons for DHD were recorded. A cluster analysis was performed to identify patterns of subgroups in patients with DHD.
Data from 971 patients, whose Richmond Agitation–Sedation Scale score at admission was zero and who died in palliative care units, were analyzed. The average age was 72 years, common primary cancer sites were the gastrointestinal tract (31%) and the liver/biliary ducts/pancreas (19%). A total of 174 patients (18%: 95% confidence interval, 16–20) expressed DHD. Common reasons for DHD were dependency (45%), burden to others (28%), meaninglessness (24%), and inability to engage in pleasant activities (24%). We identified five clusters of patients with DHD: cluster 1 (35%, 61/173): “physical distress,” cluster 2 (21%, 37/173): “dependent and burdensome,” cluster 3 (19%, 33/173): “hopelessness,” cluster 4 (17%, 30/173): “profound fatigue,” and cluster 5 (7%, 12/173): “extensive existential suffering.”
A considerable number of patients expressed DHD and could be categorized into five subgroups. These findings may contribute to the development of therapeutic strategies.
Differences in psychiatric background and dose–response to asenapine in patients with schizophrenia were examined based on efficacy and safety, using data obtained in a double-blind, placebo-controlled trial.
Patients with schizophrenia were classified into three clusters by a cluster analysis based on the Positive and Negative Symptom Scale (PANSS) subscores at baseline, using the data from a 6-week, double-blind, placebo-controlled trial. PANSS Marder factor scores were calculated for each cluster. The efficacy of 10 or 20 mg/day of asenapine on PANSS score was used as the primary endpoint, with the incidence of adverse events evaluated as the secondary endpoint.
A total of 529 asenapine-treated patients were classified into 3 clusters: Cluster-P with the higher scores in positive symptoms, disorganized thoughts, and hostility/excitement, Cluster-N with higher scores in negative symptoms, and Cluster-L with overall lower scores. In Cluster-N and Cluster-L, both 10 and 20 mg/day groups showed significant improvement in PANSS scores, while only the 20 mg/day group showed a significant difference in Cluster-P. Cluster-N and Cluster-L had differences in the incidence of adverse events, but this was not seen in Cluster-P.
The efficacy and safety of asenapine 10 and 20 mg/day differed between the 3 clusters of patients. This suggests that background information regarding baseline psychiatric symptoms may affect the therapeutic response in patients with schizophrenia.