Age at onset in Canadian Schizophrenia patients: Admixture analysis

Panariello, Fabio; O'Driscoll, Lauren; Souza, Renan P.; Tiwari, Arun K.; Manchia, Mirko; Kennedy, James L.; Luca, Vincenzo De

doi:10.1016/j.schres.2009.10.024

Cited by 21 publications

(19 citation statements)

References 3 publications

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“…In order to determine the most likely number of origin populations, we estimated log likelihood and c 2 goodness-of-fit for 1, 2, 3, and 4 populations of origin. Based on previous studies, we compared between log likelihoods for each of these models using forward stepwise estimation and maximum likelihood ratio tests (Bellivier, Golmard, Henry, Leboyer, & Schürhoff, 2001;Bellivier et al, 2003;Delorme et al, 2005;Panariello et al, 2010;Slama et al, 2009). …”

Section: Analytic Strategymentioning

confidence: 99%

Admixture analysis of the diagnostic subtypes of social anxiety disorder: Implications for the DSM-V

Aderka

Nickerson

Hofmann

2012

Journal of Behavior Therapy and Experimental Psychiatry

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Section: Analytic Strategymentioning

confidence: 99%

Admixture analysis of the diagnostic subtypes of social anxiety disorder: Implications for the DSM-V

Aderka

Nickerson

Hofmann

2012

Journal of Behavior Therapy and Experimental Psychiatry

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“…In other neuropsychiatric disorders, such as obsessivecompulsive disorder [8], bipolar disorder [16] and schizophrenia [23], the AAO cut-off has been established using validated statistical procedures. The AAO could be an important criterion for better understanding the neurobiology and the genetic heterogeneity of AD.…”

Section: Aao In Alzheimer's Diseasementioning

confidence: 99%

“…The best studied genetic risk factor for AD is the ε4 allele of the apolipoprotein E gene (APOE) [23,30]. Between 40 and 80 % of patients with AD possess at least one APOE ε4 allele [23].…”

Section: Genetics Of Alzheimer's Diseasementioning

confidence: 99%

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Inverse effect of the APOE epsilon4 allele in late- and early-onset Alzheimer’s disease

Luca

Orfei

Gaudenzi

et al. 2015

Eur Arch Psychiatry Clin Neurosci

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In Alzheimer's disease patients (AD), the age at onset (AAO) ranges from 40 to 90. Usually, AD patients who develop symptoms before the age of 65 are classified as early onset (EO). The best known genetic risk factor for AD is the ε4 allele of the apolipoprotein E (APOE). In this study, 474 subjects with AD were consecutively recruited in the memory clinic of the Santa Lucia Foundation in Rome. The best fitting model for the discrimination between EO and late onset (LO) was chosen based on lowest value of the Bayesian Information Criterion, which suggests the theoretical model with minimal deviation from the empirical distribution function of AAO in our sample. The FMM was used to compare EO and LO groups with respect to the following demographic and clinical variables: gender, age, education, MMSE and NPI. Furthermore a quantitative assessment of ADL and IADL was performed. Finally, the frequency of the APOE ε4 allele was compared in EO and LO groups. Using the admixture analysis, we established that the AAO discriminating EO from LO-AD was 63-64. Higher education was associated with earlier onset in the EO but not in LO, and duration of illness was associated with earlier onset only in LO. The ε4 allele was associated with later onset in EO but earlier onset in LO. Finally, increased impairment in ADL, IADL and NPI was associated with later onset only in the LO subgroup. Thus, the ε4 allele of the APOE gene was significantly associated with both EO and LO distributions but with opposite effect, suggesting genetic heterogeneity. Additional studies are needed to further clarify the genetic mechanisms differentiating EO- and LO-AD.

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“…Various studies have employed AAO cutoffs that ranged from 18 to 25 years of age [11–14]. The admixture analysis of AAO was previously used in bipolar spectrum disorder and schizophrenia populations to identify cutoff age for subgroups with distinct clinical and demographic features [15–17]. Hitherto, a similar analysis has not been endeavored in individuals with MDD.…”

Section: Introductionmentioning

confidence: 99%

Admixture analysis of age at onset in major depressive disorder

Zhu¹,

Luca²,

Gallaugher³

et al. 2012

General Hospital Psychiatry

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Objectives This study aimed to determine the distributions of the age at onset (AAO) in patients with major depressive disorder (MDD) using admixture analysis and to determine the clinical differences between subgroups with different AAO. Methods Participants were administered the Mini-International Neuropsychiatric Interview and the Montgomery–Asberg Depression Rating Scale to obtain clinical data. Admixture analysis was performed using the STATA module DENORMIX to identify subgroups characterized by differences in AAO. Results The best fit model was the three-component model with the following means, standard deviations and proportions: 14.60 (3.75) years (49.1%), 29.15 (6.75) years (34.1%) and 46.96 (6.06) years (16.8%) (χ2=3.64, 2 df, P=.162). The three subgroups were divided by AAO of 22 and 40. After controlling for duration of illness, there were no significant differences between the three subgroups in terms of gender and family history. However, the early-onset subgroup was significantly more likely to report being single compared to the intermediate- and late-onset groups. The proportion of individuals meeting criteria for lifetime comorbid panic disorders and obsessive–compulsive disorder did not differ significantly between the AAO groups. However, there was a trend for higher incidence of generalized anxiety disorder in the early- and intermediate-onset compared to the late-onset subgroup (43.4% and 41.9% vs. 30.0%, P=.095). Furthermore, the early-onset group reported a higher incidence of attention-deficit/hyperactivity disorder (5.1% vs. 1.7% and 1.2%, P=.085) diagnosis, although this was also not statistically significant. In addition, there was a trend for higher rate of lifetime substance use in the early-onset group compared to the intermediate- and late-onset groups (18.9% compared to 12.4% and 10.0%, respectively; P=.061). Conclusions Our study identified clinically different AAO subgroups in individuals suffering from MDD. The subgroups may reflect different underlying neurobiological mechanisms involved.

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Age at onset in Canadian Schizophrenia patients: Admixture analysis

Cited by 21 publications

References 3 publications

Admixture analysis of the diagnostic subtypes of social anxiety disorder: Implications for the DSM-V

Admixture analysis of the diagnostic subtypes of social anxiety disorder: Implications for the DSM-V

Inverse effect of the APOE epsilon4 allele in late- and early-onset Alzheimer’s disease

Admixture analysis of age at onset in major depressive disorder

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