Paternal age related schizophrenia (PARS): Latent subgroups detected by k-means clustering analysis

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Abstract

Background

Paternal age related schizophrenia (PARS) has been proposed as a subgroup of schizophrenia with distinct etiology, pathophysiology and symptoms. This study uses a k-means clustering analysis approach to generate hypotheses about differences between PARS and other cases of schizophrenia.

Methods

We studied PARS (operationally defined as not having any family history of schizophrenia among first and second-degree relatives and fathers' age at birth 35 years) in a series of schizophrenia cases recruited from a research unit. Data were available on demographic variables, symptoms (Positive and Negative Syndrome Scale; PANSS), cognitive tests (Wechsler Adult Intelligence Scale—Revised; WAIS-R) and olfaction (University of Pennsylvania Smell Identification Test; UPSIT). We conducted a series of k-means clustering analyses to identify clusters of cases containing high concentrations of PARS.

Results

Two analyses generated clusters with high concentrations of PARS cases. The first analysis (N = 136; PARS = 34) revealed a cluster containing 83% PARS cases, in which the patients showed a significant discrepancy between verbal and performance intelligence. The mean paternal and maternal ages were 41 and 33, respectively. The second analysis (N = 123; PARS = 30) revealed a cluster containing 71% PARS cases, of which 93% were females; the mean age of onset of psychosis, at 17.2, was significantly early.

Conclusions

These results strengthen the evidence that PARS cases differ from other patients with schizophrenia. Hypothesis-generating findings suggest that features of PARS may include a discrepancy between verbal and performance intelligence, and in females, an early age of onset. These findings provide a rationale for separating these phenotypes from others in future clinical, genetic and pathophysiologic studies of schizophrenia and in considering responses to treatment.

Introduction

The schizophrenias are characterized by significant heterogeneity in symptoms, course of illness, and clinical profiles (Tsuang et al., 1990). This heterogeneity complicates the interpretation of research findings and inhibits the discovery of novel treatments for the disorder. Some of the variability in symptoms and illness features among schizophrenia patients may be explained by the presence of latent subgroups that differ in etiology and key neurobiological underpinnings. Identifying these subgroups is important to set the stage for targeted person-specific pharmacological and/or psychological treatments (Jindal et al., 2005).

Advanced paternal age has been associated with the risk for schizophrenia in cohort studies in Israel (Malaspina et al., 2001, Brown et al., 2002), Denmark, (Byrne et al., 2003), Sweden (Zammit et al., 2003, Sipos et al., 2004), Japan (Tsuchiya et al., 2005), and the United States (Torrey et al., 2009). In the Israeli study, a quarter of the risk for schizophrenia was attributable to paternal age and the risk in offspring of fathers aged 50+ at birth was three-fold that of children whose fathers were younger than 25 at birth (Malaspina et al., 2001). Clinical studies have suggested that paternal age related schizophrenia (PARS) may be a specific variant of the disease, as symptom and cognitive profiles, regional cerebral metabolism, sex effects, and heart rate variability have been shown to differ from those of other cases (Malaspina, 2001, Malaspina et al., 2001, Malaspina et al., 2002a, Malaspina et al., 2005, Rosenfield et al., 2010, Antonius et al., 2010). If these studies are confirmed, then PARS may account for a substantial portion of the disease in clinical treatment. The confirmation of PARS as a separate group could lead to better understanding of its etiology and pathogenesis, and allow targeted modes of treatment.

Currently, however, it is not clear whether PARS explains any of the heterogeneity of schizophrenia. To explore this, we have chosen to use an approach based on clustering analysis in order to generate new hypotheses related to PARS. Among various clustering techniques, k-means clustering (MacQueen, 1967) has been favored over others (e.g. Yeung et al., 2001, Gibbons and Roth, 2002), and has been used in schizophrenia research (Richards et al., 2008). k-Means clustering is a partitioning method often used in data mining and machine learning (Huang, 1998, Wagstaff et al., 2001). It aims to partition, or minimize, the average squared distance between n observations and a cluster centroid, such that each observation is assigned to the cluster with the nearest mean (Hand and Heard, 2005).

In schizophrenia research, k-means clustering has previously been used to examine the heterogeneity of psychosis symptoms (Mohr et al., 2004), antipsychotic responses (Garver et al., 2000), prognostic features (Jonsson and Jonsson, 1992), and cognitive symptoms (Silver and Shmoish, 2008, Bell et al., 2010). This clustering approach, however, has not, to our knowledge, been used to examine the distinctiveness of the PARS subgroup.

Section snippets

Participants

This study relies on cases with schizophrenia or schizoaffective disorder recruited at the New York State Psychiatric Institute (NYSPI) Schizophrenia Research Unit (SRU) in 1992–2007. The study was approved by the Institutional Review Board at NYSPI and all patients provided written informed consent.

For our analyses we were interested in a set of core factors consisting of demographic, clinical and cognitive variables. Thus, we included in our analyses only cases on whom we had the following

Results

Two of our k-means clustering analyses produced clusters with high PARS concentration. Each of the two clustering analyses generated seven clusters (k = 7) and yielded some prominent features related to the PARS subjects. The first analysis included the 11 WAIS-R subtests in addition to the four core demographic variables (age of onset of psychosis, sex, paternal age, and family history of schizophrenia). The second analysis included the VIQ-PIQ variable and the PANSS factors from the standard

Discussion

This study employed k-means clustering analyses to examine if specific illness features of schizophrenia are associated with later paternal age. We identified PARS cases that clustered in groups with particular characteristics. One group was characterized by a greater differential between verbal and performance intelligence, and the other group showed a high concentration of female cases and significant early onset of psychosis.

Our first clustering analysis, which considered demographic

Role of funding source

DM (RC1MH088843-02 and 2K24MH00169). The funding sources had no role in study design; in the collection, analysis and interpretation of data; in writing the report; and in the decision to submit the paper for publication.

Contributors

Drs. Malaspina and Goetz were involved in the design and writing of the study protocol. Ms. Lee and Drs. Ahn, Harlap, Goetz and Antonius managed literature reviews and statistical analyses pertaining to the study. Ms. Lee and Drs. Malaspina, Ahn, Perrin, Opler, Kleinhaus, Harlap, Goetz and Antonius were involved in the writing of various drafts and the final manuscript. All authors contributed to and have approved the final manuscript.

Conflict of interest

All authors declare that they have no conflicts of interest.

Acknowledgment

The authors thank Benjamin Barasch for assistance in editing this paper.

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