Association of Premenstrual Dysphoric Disorder with Depression and Anxiety in Women of Reproductive Age Group: A Cross-sectional Study

Introduction: Premenstrual Dysphoric Disorder (PMDD) is a mood disorder affecting women during the luteal phase of the menstrual cycle. Its association with depression and anxiety has been documented. Despite of its significant impact on women’s quality of life, physical and mental health, PMDD remains under evaluated and under researched in the Indian population. Aim: To evaluate the association of PMDD with depression and anxiety. Materials and Methods: The present cross-sectional study was conducted from August 2024 to January 2025 among 100 women aged 15-49 years presenting to Outpatient Department of Gynaecology in GS Medical College, Hapur, Uttar Pradesh, India with PMDD like symptoms. The Premenstrual Symptom Screening Tool (PSST) was applied to all these women and PSST score was calculated some other variables like age, education, occupation, length of menstrual cycle, duration and amount of menstrual flow was also recorded. All 100 patients diagnosed with PMDD and those who were having mild premenstrual symptoms were further screened and scored for anxiety and depression by using Beck’s Anxiety Inventory (BAI) and Beck’s Depression Inventory (BDI) scale. Association of PMDD with severity of anxiety and depression was analysed using Chi-square test. The difference in PSST score among various categorical groups like education, occupation, marital status and amount of menstrual flow was analysed using F-test of Analysis of Variance (ANOVA). Regression analysis was run to identify the significant predictors of PMDD. Results: Out of 100, 65 women were diagnosed to have PMDD using PSST and 35 women had mild Premenstrual Syndrome (PMS). Significant differences in PSST Score were observed among groups categorised by education (p=0.01) and menstrual flow (p=0.006) implying that these factors influence PSST scores. Duration of menstrual flow found to be a significant predictor of PSST Score (p=0.04). The results showed a significant association between PSST score and both depression (χ2=84.22, p<0.001) and anxiety (χ2=64.84, p<0.001). Most women with PMDD experienced severe depression and moderate-to-high anxiety levels. Conclusion: PMDD was significantly associated with increased severity of depression and anxiety. Low education levels and heavier or prolonged menstrual bleeding were the contributing factors.

Authors

Publication Details

Journal
JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH
Published
2025-08-30
DOI
https://doi.org/10.7860/jcdr/2025/80492.21470
Primary Topic
Menstrual Health and Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association of Premenstrual Dysphoric Disorder with Depression and Anxiety in Women of Reproductive Age Group: A Cross-sectional Study

Meenu Saini, Manisha Upadhyay, Priya Kumari, Saurav Kumar
JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH
Menstrual Health and Disorders
article

Association of Premenstrual Dysphoric Disorder with Depression and Anxiety in Women of Reproductive Age Group: A Cross-sectional Study

Meenu Saini, Manisha Upadhyay, Priya Kumari, Saurav Kumar
article en

Abstract

Introduction: Premenstrual Dysphoric Disorder (PMDD) is a mood disorder affecting women during the luteal phase of the menstrual cycle. Its association with depression and anxiety has been documented. Despite of its significant impact on women’s quality of life, physical and mental health, PMDD remains under evaluated and under researched in the Indian population. Aim: To evaluate the association of PMDD with depression and anxiety. Materials and Methods: The present cross-sectional study was conducted from August 2024 to January 2025 among 100 women aged 15-49 years presenting to Outpatient Department of Gynaecology in GS Medical College, Hapur, Uttar Pradesh, India with PMDD like symptoms. The Premenstrual Symptom Screening Tool (PSST) was applied to all these women and PSST score was calculated some other variables like age, education, occupation, length of menstrual cycle, duration and amount of menstrual flow was also recorded. All 100 patients diagnosed with PMDD and those who were having mild premenstrual symptoms were further screened and scored for anxiety and depression by using Beck’s Anxiety Inventory (BAI) and Beck’s Depression Inventory (BDI) scale. Association of PMDD with severity of anxiety and depression was analysed using Chi-square test. The difference in PSST score among various categorical groups like education, occupation, marital status and amount of menstrual flow was analysed using F-test of Analysis of Variance (ANOVA). Regression analysis was run to identify the significant predictors of PMDD. Results: Out of 100, 65 women were diagnosed to have PMDD using PSST and 35 women had mild Premenstrual Syndrome (PMS). Significant differences in PSST Score were observed among groups categorised by education (p=0.01) and menstrual flow (p=0.006) implying that these factors influence PSST scores. Duration of menstrual flow found to be a significant predictor of PSST Score (p=0.04). The results showed a significant association between PSST score and both depression (χ2=84.22, p<0.001) and anxiety (χ2=64.84, p<0.001). Most women with PMDD experienced severe depression and moderate-to-high anxiety levels. Conclusion: PMDD was significantly associated with increased severity of depression and anxiety. Low education levels and heavier or prolonged menstrual bleeding were the contributing factors.

JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH
Openalex Percentile: Top 69%
Menstrual Health and Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.