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Clinical Subtypes of Premenstrual Dysphoric Disorder Among Chinese College Students: A Latent Class Analysis.

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Title: Clinical Subtypes of Premenstrual Dysphoric Disorder Among Chinese College Students: A Latent Class Analysis.
Authors: Sun, Yu (AUTHOR), Huang, Shanyi (AUTHOR), Chen, Ying (AUTHOR), Zhang, Qiao (AUTHOR), Zhang, Yuqun (AUTHOR), Bhardwaj, Parth (AUTHOR)
Source: Perspectives in Psychiatric Care. 5/15/2026, Vol. 2026, p1-9. 9p.
Subjects: Cross-sectional method, Pearson correlation (Statistics), Scale analysis (Psychology), Research funding, Cronbach's alpha, T-test (Statistics), Data analysis, Premenstrual syndrome, Undergraduates, Questionnaires, Fatigue (Physiology), Psychology of women, Symptoms, Descriptive statistics, Chi-squared test, Structural equation modeling, Affective disorders, Anxiety, Disease prevalence, Surveys, Statistical reliability, One-way analysis of variance, Statistics, Abdominal bloating, Pain, Psychology of college students, Data analysis software, Dysmenorrhea
Abstract: Objective: Premenstrual dysphoric disorder (PMDD) is highly prevalent among women of reproductive age. However, the heterogeneity and complexity of PMDD symptoms present considerable challenges for accurate diagnosis. This study aims to investigate the heterogeneity of symptoms among Chinese female college students to identify clinical subtypes based on PMDD symptomatology. Methods: A total of 669 Chinese female college students participated in the study and were assessed using a standardized set of questionnaires, including the Chinese version of CTDP‐DSM‐5 (CTDP‐C), the Patient Health Questionnaire‐9 (PHQ‐9), and the General Anxiety Disorder‐7 (GAD‐7). A total of 218 participants were identified as PMDD‐positive based on the Chinese version of the CTDP‐DSM‐5. Latent class analysis (LCA) was conducted on the CTDP‐C scores of the 218 participants diagnosed with PMDD to empirically evaluate the validity of proposed PMDD subtype classifications. Results: LCA identified two distinct phenotypic subtypes of PMDD. Class 1 (severe subtype; mean CTDP‐C score = 8.78) was characterized by a range of comorbid psychological symptoms (e.g., emotional lability, irritability, and anxiety) as well as physical manifestations (e.g., fatigue, bloating, and pain). In contrast, Class 2 (mild subtype; mean CTDP‐C score = 1.52) exhibited attenuated and atypical symptoms. Notably, significant differences were observed between the two classes in 90% of the CTDP‐C items (p < 0.001). Physical comorbidities, especially dysmenorrhea (p < 0.001), were identified as key contributing factors, suggesting potential shared pathophysiological mechanisms. Conclusions: Two distinct subtypes of PMDD were identified: one characterized by severe symptoms and the other by milder manifestations, with either psychological or physical symptoms predominating, or a combination of both. Further investigation into the heterogeneity of PMDD is crucial to delineate more distinct phenotypes, which will facilitate advancements in future biological and genetic research. [ABSTRACT FROM AUTHOR]
Copyright of Perspectives in Psychiatric Care is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Clinical Subtypes of Premenstrual Dysphoric Disorder Among Chinese College Students: A Latent Class Analysis.
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  Data: Objective: Premenstrual dysphoric disorder (PMDD) is highly prevalent among women of reproductive age. However, the heterogeneity and complexity of PMDD symptoms present considerable challenges for accurate diagnosis. This study aims to investigate the heterogeneity of symptoms among Chinese female college students to identify clinical subtypes based on PMDD symptomatology. Methods: A total of 669 Chinese female college students participated in the study and were assessed using a standardized set of questionnaires, including the Chinese version of CTDP‐DSM‐5 (CTDP‐C), the Patient Health Questionnaire‐9 (PHQ‐9), and the General Anxiety Disorder‐7 (GAD‐7). A total of 218 participants were identified as PMDD‐positive based on the Chinese version of the CTDP‐DSM‐5. Latent class analysis (LCA) was conducted on the CTDP‐C scores of the 218 participants diagnosed with PMDD to empirically evaluate the validity of proposed PMDD subtype classifications. Results: LCA identified two distinct phenotypic subtypes of PMDD. Class 1 (severe subtype; mean CTDP‐C score = 8.78) was characterized by a range of comorbid psychological symptoms (e.g., emotional lability, irritability, and anxiety) as well as physical manifestations (e.g., fatigue, bloating, and pain). In contrast, Class 2 (mild subtype; mean CTDP‐C score = 1.52) exhibited attenuated and atypical symptoms. Notably, significant differences were observed between the two classes in 90% of the CTDP‐C items (p &lt; 0.001). Physical comorbidities, especially dysmenorrhea (p &lt; 0.001), were identified as key contributing factors, suggesting potential shared pathophysiological mechanisms. Conclusions: Two distinct subtypes of PMDD were identified: one characterized by severe symptoms and the other by milder manifestations, with either psychological or physical symptoms predominating, or a combination of both. Further investigation into the heterogeneity of PMDD is crucial to delineate more distinct phenotypes, which will facilitate advancements in future biological and genetic research. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Perspectives in Psychiatric Care is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1155/ppc/5276137
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      – Code: eng
        Text: English
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        PageCount: 9
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      – SubjectFull: Cross-sectional method
        Type: general
      – SubjectFull: Pearson correlation (Statistics)
        Type: general
      – SubjectFull: Scale analysis (Psychology)
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Cronbach's alpha
        Type: general
      – SubjectFull: T-test (Statistics)
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Premenstrual syndrome
        Type: general
      – SubjectFull: Undergraduates
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Fatigue (Physiology)
        Type: general
      – SubjectFull: Psychology of women
        Type: general
      – SubjectFull: Symptoms
        Type: general
      – SubjectFull: Descriptive statistics
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      – SubjectFull: Chi-squared test
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      – SubjectFull: Structural equation modeling
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      – SubjectFull: Affective disorders
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      – SubjectFull: Disease prevalence
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      – SubjectFull: Surveys
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      – SubjectFull: Statistical reliability
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      – SubjectFull: One-way analysis of variance
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      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Abdominal bloating
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      – SubjectFull: Pain
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      – SubjectFull: Psychology of college students
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      – SubjectFull: Data analysis software
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      – SubjectFull: Dysmenorrhea
        Type: general
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      – TitleFull: Clinical Subtypes of Premenstrual Dysphoric Disorder Among Chinese College Students: A Latent Class Analysis.
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              Text: 5/15/2026
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