Prediction premenstrual syndrome (PMS) with anxiety, and depression in female students

Abstract

Background

Premenstrual syndrome (PMS) is a prevalent and multifaceted disorder affecting women of childbearing age, characterized by emotional, physical, and behavioral symptoms. The psychological dimensions, particularly anxiety and depression, exacerbate the burden of PMS. This research aimed to find predictive factors of PMS using ordinal logistic regression models (OLR).

Materials and methods

624 female university students in Ilam, Iran, participated in this cross-sectional survey. The DASS-42 and the Premenstrual Symptoms Screening Tool (PSST) were used to measure the severity of PMS, anxiety, and depression. OLR models were used to predict PMS.

Results

Prevalence of low, moderate, and severe PMS according to DSM-5 was 33.4%, 52%, and 14.6%, respectively. Positive and substantial correlation coefficients were found between the overall PMS score and the total depression and anxiety scores (P < 0.001). Multivariate OLR showed that when depression levels rose from mild to moderate or moderate to severe, the risk of PMS increased by 41% (OR = 1.41, 95% CI [1.21, 1.65], P < 0.001), and the risk of anxiety increased by 51% (OR = 1.51, 95% CI [1.29, 1.76], P < 0.001). Higher sleeping hours were an independent risk factor for PMS severity (OR = 1.40, 95% CI [1.11, 1.77], P = 0.005).

Conclusion

Results advocate integrating mental health screening into PMS management protocols, particularly using tools like the DASS-42-5. OLR regression emerged as a statistically robust method for modeling ordinal PMS outcomes, offering clinical and research relevance for targeted interventions in reproductive-aged populations.

Background

Premenstrual syndrome is a common and complex illness that affects women of childbearing age. Only during the luteal phase of the menstrual cycle do the condition’s cyclical physical, mental, and behavioral symptoms manifest; these symptoms disappear as soon as menstruation starts [1]. The symptoms, which include disruptions in social, professional, and academic life, can vary from minor irritation to a severe incapacity to operate [2]. Breast pain, bloating in the abdomen, exhaustion, and headaches are common concerns; on the psychological side, mood swings, anxiety, and sadness are common [3]. Despite being a natural physiological process, PMS has a substantial impact on the mental health and quality of life of people who experience it, as evidenced by its frequent and incapacitating cycles. The prevalence of PMS varies greatly throughout the world as a result of differences in research methods, cultural perspectives, and diagnostic standards [4, 5].

According to reports, 20–90% of menstruating women experience some PMS symptoms, while clinically significant PMS (moderate-to-severe symptoms impairing daily life) affects 20–40% [6], highlighting the necessity of doing a study tailored to a particular location. The prevalence rates among university students are generally greater than those in the overall population, making them a particularly vulnerable demographic [7]. For instance, studies among university students in China, Ethiopia, India, and Egypt have reported prevalence rates of 18.4%, 37.9%, 87.1%, and 33.8%, respectively [8].

These findings show how academic stressors including rigorous schedules, sleep deprivation, and trouble adjusting to new situations might exacerbate PMS [8]. Furthermore, PMS’s negative impacts on these subjects extend beyond personal misery to include academic problems like low performance, missing classes, and severe cases of school dropout. Because they tend to impair mental health and functioning, psychological disorders particularly anxiety and depression have drawn the attention of numerous medical specialists among the many symptoms that have come to be associated with PMS [9, 10]. Despite being distinct clinical conditions, PMS and depressive disorders overlap numerous risk factors, including changes in hormones, genetic susceptibility, and environmental stressors [11].

According to epidemiological research, women with PMS are more likely to develop depressive disorders, including severe types like PMDD [12]. As an illustration, a study carried out in Switzerland revealed that 24.6% of women with severe PMS satisfied the criteria for PMDD [13]. In fact, a large number of additional research have repeatedly shown that there is a reciprocal association between PMS and depressed symptoms [14]. Anxiety is another common psychological symptom of PMS that contributes to the syndrome’s burden. Women who suffer from PMS often report experiencing restlessness, excessive anxiety, and difficulty concentrating, which often disrupts their daily lives [15]. When anxiety and depression co-occur with PMS, they both increase the burden of symptoms and make diagnosis and therapy more difficult because of their complex and mutually reinforcing interactions [16].

A number of studies confirm that PMS symptoms often overlap with anxiety and depression, and that the psychological disorders enhance the severity and impact of PMS. For instance, research has shown that women with PMS tend to report higher levels of depression and anxiety not only in the premenstrual but also in the menstrual cycle, suggesting a lasting susceptibility rather than a purely cyclical phenomenon [17, 18]. Moreover, mediation analyses show that depression and anxiety partially mediate the relationship between premenstrual symptoms and negative mental health outcomes such as psychotic-like experiences, confirming their central role in the psychological distress of PMS [19]. The symptomatology of more severe premenstrual disorders such as premenstrual dysphoric disorder (PMDD) also includes prominent mood symptoms, with depression and anxiety among the most common symptoms [20]. Additionally, the prevalence of past psychiatric illnesses, particularly mood and anxiety disorders, is higher in women with PMS and PMDD, lending credence to the fact that anxiety and depression must be employed as main predictive variables in studies of premenstrual symptomatology [21, 22]. This evidence justifies the need to address anxiety and depression when predicting and controlling PMS among female students.

Suicidal thoughts and behaviors are also serious consequences of untreated PMS, especially if it entails significant psychological symptoms [23, 24]. These links emphasize the significance of early detection and intervention to reduce the detrimental effects of PMS on mental health.

The western Iranian city of Ilam has a notably high rate of anxiety, depresion, and suicide [23–26]. The already concerning prevalence of psychological suffering was exacerbated by cultural, economic, and social stressors; young women are disproportionately impacted [26]. Women face numerous obstacles when addressing mental health issues in this particular sociocultural setting where conventional gender roles are ingrained and mental health options are limited [27]. In light of this, PMS becomes apparent as a crucial but mostly unexplored element that may be responsible for the psychological morbidity seen in the local populace [28].

Ilam Universities students are a particular demographic that is exposed to all the risk factors that increase their susceptibility to PMS and its psychological effects. The challenges that these young women face are now made worse by the rigors of education, the stigma associated with mental diseases, and a lack of awareness and access to medical care. Establishing a connection between PMS and its psychological impacts, such as anxiety and depression, is crucial. Understanding PMS as a contributing factor to these outcomes is crucial, especially in light of Ilam’s high rates of mental illness and suicide [23, 24]. The current study aims to explore the relationship between PMS and psychological effects among female university students in Ilam and, in turn, offer the required recommendations to improve the mental health of this susceptible group.

Comparing various classification schemes and their impact on PMS prevalence is one of our objectives. Given one or more independent variables, OLR often simply referred to as “ordinal logistic regression”—is used to predict an ordinal dependent variable. Both binomial logistic regression and multiple linear regression can be thought of as generalizations of it. Since the distinction between mild and moderate in ordinal variables, like the severity of PMS, differs from that between moderate and severe, the OLR approach has characteristics of other approaches when it comes to analyzing such variables with independent variables. Because PMS severity (low, moderate, and severe) is ordinal by nature, OLR is made especially for these kinds of results. OLR maintains the natural order of the outcome variable while taking into account differential spacing between severity levels, in contrast to binary logistic regression (which collapses ordinal data into dichotomous groups) and linear regression (which assumes equal intervals between categories) [29]. This study highlights how OLR ensures more accurate parameter interpretation by preventing information loss and biased estimates brought on by collapsing ordinal categories [29]. The proportionate odds assumption, which underpins OLR, examines whether predictor effects are constant over severity thresholds. Compared to multinomial models, this simplifies interpretation by enabling the simultaneous estimate of odds ratios (ORs) for all outcome categories [30].

To the best of our knowledge, no studies have been conducted to examine the relationship between PMS and anxiety and depression using OLR. The purpose of this study was to use multiple linear models, and OLR to predict PMS with anxiety, depression, and sleeping hours at night. In contrast to previous research in this area, this study offers a fresh viewpoint on the relationship between mental health and PMS classification by enabling the prediction of an individual’s PMS severity using the factors of anxiety, depression, and sleeping hours at night.

Materials and methods

Study design and context

This cross-sectional study looked at the relationship between psychological factors and premenstrual symptoms in female students at Ilam universities.

Setting and sample size

The study sample consisted of 624 students. Participants were recruited from all universities in Ilam City between April and September 2024. Data were collected online. Questionnaires were created on the Porsline website (https://porsline.ir/), and then we shared the link with university teachers and asked them to explain the research objectives and share the link in students’ social groups.

Participants

Inclusion criteria

Female students who were above 18, enrolled in a university program, currently students, and having regular menstrual cycles or the absence of gynecological diseases.

Exclusion criteria

The exclusion criteria included a history of major psychiatric disorders, hormonal therapies in use currently, or incomplete survey responses.

Data collection tools and ethical considerations

Ethical approval and consent to participate

As participants completed questions online, according to national regulations of "Research Ethics Committees of Ilam University of Medical Sciences,Ilam,Iran", informed consent form to participate was deemed unnecessary, However, we requested an ethical code conformation of "Research Ethics Committees of Ilam University of Medical Sciences,Ilam,Iran". Research Ethics Committees of Ilam University of Medical Sciences confirmed our study and gave this code to our study:

IR.MEDILAM.REC.1403.256: https://ethics.research.ac.ir/form/tph1hzssz1om5wny.pdf.

Participants were fully informed about the survey’s purpose, how their data will be used, and any potential risks involved. Clear consent forms were provided, allowing participants to voluntarily agree to participate without any coercion. We wrote an informed consent form according to the Helsinki Declaration (https://www.wma.net/policies-post/wma-declaration-of-helsinki/) form. Participants did not need to write their name, phone number, and personal data, if they requested about suicidal thought severity or PMS severity, we gave an option to participants, they could write their email, we sent suicidal thought and PMS severity by email, if they requested. Writing email was arbitrary. Participants without fear of repercussions completed the questionnaires. Secure platforms and encryption methods were used in online forms. Participants communicated openly about who was conducting the survey, its purpose, and how the data would be used. This transparency builds trust and encourages participation.

Demographic information questionnaire

Demographic factors included age, marital status, school year, education, height, weight, and sleep patterns.

PMS

The Premenstrual Symptoms Screening Questionnaire (PSST)

Premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) intensity are frequently assessed with the PSST. It is a standardized test with 19 items broken down into two halves. Five items in the second portion measure how these symptoms affect day-to-day functioning, while fourteen symptom-based questions in the first section evaluate emotional, physical, and behavioral problems. Every question receives a score of 0, 1, 2, or 3. Questions do not have a reverse score. Mild, moderate, and severe PMS were classified in most studies based on a total score that ranged from 0 to 19, 20 to 28, and more than 28, respectively (scoring 2) [31, 32]. In some studies Mild, moderate, and severe PMS were classified on a total score that ranged from 0 to 19, 20 to 38, and more than 38 resoectively (scoring 1) [33, 34]. The DSM-5 states that a diagnosis of moderate-to-severe PMS requires the presentation of the following criteria:

(1) According to PSST inventory [35], at least one of questions 1, 2, 3, and 4 is moderate to severe; (2) at least four of questions 1 through 14 are also moderate to severe; and (3) at least one of questions 15 through 19 (A, B, C, D, and E) is moderate to severe.

To classify participants as having mild, moderate, or severe PMS, we create a program in STATA and call it PMS_class.

The PSST has proved valid and reliable in various populations. In the study, Steiner et al. [31] reported Cronbach’s alpha to be 0.91. The reliability of the Persian version of this questionnaire was estimated 0.93 [36] and 0.91 [35]. The questionnaire’s Content Validity Ratio and Content Validity Index were estimated as 0.7 and 0.8, respectively [36]. Cronbach’s Alpha was calculated for our investigation at 0.914, which demonstrated excellent reliability.

Depression and anxiety

The Depression, Anxiety, and Stress Scale (DASS-42):

It has 42 items total, broken down into three subscales: stress, anxiety, and depression. Each subscale has 14 items. It was created especially to gauge how severe these three aspects of emotional anguish are. The validity and reliability of the DASS-42 have been established globally. In the original study by Lovibond [37], it showed a Cronbach’s alpha of 0.94 for depression, 0.87 for anxiety, and 0.91 for stress. In Iran, a study by Sahebi et al. [38] found a Cronbach’s alpha of 0.87 for an overall scale, showing it is also reliable for this group of people. Cronbach’s Alpha was calculated for our investigation at 0.92, which demonstrated excellent reliability. Every instrument was evaluated and standardized in Persian for the intended audience. To maintain anonymity and lessen response bias, questionnaires were sent out electronically and filled out anonymously.

Statistical analysis

To guarantee adequate representation of the universities and academic levels included, cluster random sampling was used. Assuming a 10% non-response rate, the sample size of 624 was pre-calculated with error (d = 2.4%), P = 0.15 with the sample size calculation formula for prevalence [39].

The severity of premenstrual symptoms, which were divided into three groups—none/mild, moderate, and severe—was the primary outcome variable. Potential confounding factors included independent variables such as anxiety, depression, age, marital status, height, weight, and the average number of hours spent sleeping at night. Chi-square tests were used to find an association between PMS severity and categorical data. After adjusting for demographic confounders, the ORs and 95% CIs for the psychological components and premenstrual symptoms were determined using a multivariate ordinal logistic regression model. We employed proportional odds in cumulative odds and ordinal logistic regression. Four presumptions for ordinal logistic regression are examined, including:

1. The dependent variable was measured at the ordinal scale (PMS measured as mild, moderate, and severe).

2. continuous, ordinal, or categorical independent variables

3. the independent variables did not exhibit multicollinearity

4. For ordinal regression models, proportional odds were satisfied (chi² [4] = 5.71, P = 0.2215). ROC (Receiver Operating Characteristic) Curve and AUC (Area Under the Curve) were used to evaluate the performance of classification models, particularly in binary or ordinal outcomes [40, 41]. AUC is a scalar value (between 0 and 1) quantifying the overall ability of a model to distinguish between classes.

Interpretation:

AUC = 0.5: No discrimination (random model).

AUC = 0.7–0.8: Acceptable discrimination.

AUC = 0.8–0.9: Excellent discrimination.

AUC > 0.9: Outstanding discrimination.

AUC is a widely used measure for evaluating prediction models, such as PMS models, since it calculates the model’s ability to distinguish between controls (non-PMS) and cases (students with PMS) at all possible thresholds, instead of a single cut-off point([42]. This threshold-independence is specially useful in relation to measures such as accuracy, precision, or F1-score, which quote only performance at a single threshold and can be misleading, especially in imbalanced datasets where one class (PMS cases, in this study) is by far less frequent. AUC is also statistically more consistent and discriminative than accuracy, i.e., it distinguishes between better and worse models more often even when accuracy is equal [40, 41].

The ROC curve is generated by plotting the True Positive Rate (TPR) (y-axis) against the False Positive Rate (FPR) (x-axis) at varying classification thresholds (e.g., probability cutoffs for predicting a class). A curve closer to the top-left corner indicates better model performance. A diagonal line (AUC = 0.5) represents a model with no discriminative power (equivalent to random guessing) [40, 41].

Sensitivity analyses were also performed to evaluate how reliable the results were. The study’s significance level was set at P < 0.05. STATA version 17 and SPSS version 27 were used for statistical analysis. Using a STATA program, PMS total scores were divided into three categories: mild, moderate, and severe this commends in STATA:

STATA program for classification PMS.

Generate PMS_category =.

* category based on PMS definition in DSM-5.

replace PMS_category = 0 if (Q1 < 2 & Q2 < 2 & Q3 < 2 & Q4 < 3)//No/Mild PMS.

replace PMS_category = 1 if (Q1 > = 2| Q2 > = 2| Q3 > = 2| Q4 > = 3) &///.

(Q5 + Q6 + Q7 + Q8 + Q9 + Q10 +///.

Q11 + Q12 + Q13 + Q14 > = 3) &///.

(Q15 > = 2| Q16 > = 2| Q17 > = 2|///.

Q18 > = 2| Q19 > = 2)//Moderate PMS.

replace PMS_category = 2 if (Q1 = = 3| Q2 = = 3| Q3 = = 3| Q4 = = 3) &///.

(Q5 + Q6 + Q7 + Q8 + Q9 + Q10 +///.

Q11 + Q12 + Q13 + Q14 > = 5) &///.

(Q15 = = 4| Q16 = = 4| Q17 = = 4|///.

Q18 = = 4| Q19 = = 4)//Severe PMS/PMDD.

label define pms_labels 0 “No/Mild PMS” 1 “Moderate PMS” 2 “Severe PMS/PMDD”.

label values PMS_category pms_labels.

Results

Baseline characteristics and participant profiles

Among the 624 female university students in Ilam, 71% were unmarried, and 56.7% were aged between 20 and 25 (Table 1). The considerable mental health burden associated with PMS in this population was emphasized through a detailed analysis of the prevalence and severity of PMS, depression, and anxiety, along with their interactions. Table 1 presents the primary demographic and lifestyle characteristics of the participants, while Table 2 categorizes their psychological and physiological states based on PMS severity.

Table 1.

Demographic and Lifestyle Characteristics of Participants

Variables				N (%)
Age (Years)		Less than 20 years		59 (9.5)
		Between 20 and 25 years		354 (56.7)
		Between 25 and 30 years		89 (14.3)
		Above 30 years		122 (19.6)
Weight (kg)		Less than 50 kg		59 (9.5)
		Between 50 and 55 kg		140 (22)
		Between 55 and 60 kg		93 (14.9)
		Between 60 and 65 kg		113 (18.1)
		Between 65 and 70 kg		105 (16.8)
		Above 70 kg		114 (18.3)
Height (cm)		Less than 160 cm		149 (23.9)
		Between 160 and 165 cm		221 (35.4)
		Between 165 and 170 cm		170 (27.2)
		Above 170 cm		84 (13.5)
Length of day and night sleep pattern		Less than 5 h		22 (3.5)
		Between 5 and 7 h		164 (26.3)
		Between 7 and 9 h		343 (55.0)
		More than 9 h		95 (15.2)
Educational Level		Bachelor’s Degree		461 (73.9)
		Master’s Degree		64 (10.3)
		Professional or Doctoral Degree		99 (15.9)
Marital Status		Single		443 (71.0)
		Married		171 (27.4)
		Divorced		10 (1.6)

Table 2.

Frequency Depression, Anxiety, and Premenstrual Syndrome (PMS) Severity with three types of classification.

	Severity		N (%)
Depression	Normal		231 (37.0)
	Mild		114 (18.3)
	Moderate		138 (22.1)
	Severe		79 (12.7)
	Extremely Severe		41 (6.6)
Anxiety	Normal		257 (41.2)
	Mild		67 (10.7)
	Moderate		147 (23.6)
	Severe		93 (14.9)
	Extremely Severe		58 (9.3)
Category PMS (DSM-5)	No/Mild		188 (33.4)
	Moderate		293 (52)
	Severe		82 (14.6)
Category of PMS (Scoring 1)	Score 0 to 19 (No significant PMS)		223 (35.7)
	Score 20 to 39 (Mild)		309 (49.5)
	Score 40 to 57 (Severe)		88 (14.1)
Category of PMS (Scoring 2)	Score 0 to 19 (No significant PMS)		223 (35.7)
	Score 20 to 28 (Mild)		151 (24.4)
	Score 29 to 57 (Severe)		246 (39.7)

Association of PMS severity with independent variables

There was a significant association between PMS severity and age (P < 0.001), weight (P < 0.001), average hours of sleep per night (P = 0.011), marital status (P = 0.006), anxiety (P < 0.001), and depression (P < 0.001). Table 3. Severe PMS was more common in participants with higher levels of anxiety and depression. Severe PMS was more common among participants in the 25–30 age range, weighing between 50 and 55 kg. Education levels and the severity of PMS did not significantly correlate (Table 3). The mean scores for anxiety and depression for mild, moderate, and severe PMS were displayed in Table 2, which again indicated a favorable correlation between PMS and anxiety and depression. Figure 1 demonstrated that the PMS total score was predicted by both the anxiety and depression total scores. The slope of regression lines in Fig. 1 showed that with an increasing anxiety score, the PMS score increased by 1.05 units, and it was 0.69 for depression.

Table 3.

Associations between participant characteristics and PMS severity

		Severity of PMS(DSM-5)			Df	P-Value*
		No/MildN (%)	ModerateN (%)	SevereN (%)
Depression	Mild	154 (48.9)	129 (41.0)	32 (10.2)	4	<0.001
	Moderate	21 (17.8)	70 (59.3)	27 (22.9)
	Severe	9 (8.1)	81 (73.0)	21 (18.9)
Anxiety	Mild	142 (49.3)	122 (42.4)	24 (8.3)	4	<0.001
	Moderate	33 (24.8)	74 (55.6)	26 (8.3)
	Severe	13 (9.3)	95 (67.9)	32 (22.9)
Age (Years)	Less than 20 years	24 (42.1)	30 (52.6)	3 (5.3)	6	<0.001
	Between 20 and 25 years	85 (27.2)	179 (57.2)	49 (15.7)
	Between 25 and 30 years	18 (22.8)	37 (46.8)	24 (30.4)
	Above 30 years	61 (53.5)	47 (41.2)	6 (5.3)
Educational Level	Bachelor's Degree	144 (34.2)	210 (49.9)	67 (15.9)	4	0.221
	Master's Degree	21 (36.2)	30 (51.7)	7 (12.1)
	Professional or Doctoral Degree	23 (27.4)	53 (63.1)	8 (9.5)
Marital Status	Single	124 (31.3)	223 (56.3)	49 (12.4)	3	0.006
	Married	60 (38.2)	64 (40.8)	33 (21.0)
	Divorced	4 (40.0)	6 (60.0)	0 (0.0)
Height (cm)	Less than 160 cm	51 (36.4)	72 (51.4)	17 (12.1)	6	0.068
	Between 160 and 165 cm	58 (30.4)	99 (51.8)	34 (17.8)
	Between 165 and 170 cm	44 (28.0)	88 (56.1)	25 (15.9)
	Above 170 cm	35 (46.7)	34 (45.3)	6 (8.0)
Average hours of sleep per night	Less than 5 hours	10 (45.5)	6 (27.3)	6 (27.3)	6	0.011
	Between 5 and 7 hours	64 (42.4)	69 (45.7)	18 (11.9)
	Between 7 and 9 hours	93 (30.3)	172 (56.0)	42 (13.7)
	More than 9 hours	21 (25.3)	46 (55.4)	16 (19.3)
Weight (kg)	Less than 50 kg	7 (12.7)	40 (72.7)	8 (14.5)	10	<0.001
	Between 50 and 55 kg	39 (29.5)	68 (51.5)	25 (18.9)
	Between 55 and 60 kg	35 (42.2)	37 (44.6)	11 (13.3)
	Between 60 and 65 kg	25 (24.3)	68 (66.0)	10 (9.7)
	Between 65 and 70 kg	37 (41.1)	37 (41.1)	16 (17.8)
	Above 70 kg	45 (45.0)	43 (43.0)	12 (12.0)

*= P-value computed using Person Chi-Square test

Fig. 1.

ROC curve prediction PMS severity using anxiety and depression score

Table 4.

Comparison of mean scores for depression and anxiety across severity levels

Variables		N	Mean ± SD	95% CI*		F	P-Value**
				Lower	Upper
Depression	Mild	212	7.68 ± 6.25	6.84	8.53	96.65	< 0.001
	Moderate	147	13.02 ± 7.28	11.83	14.21
	Severe	242	17.52 ± 8.60	16.43	18.61
Anxiety	Mild	223	6.00 ± 3.69	5.51	6.48	137.95	< 0.001
	Moderate	151	9.59 ± 5.32	8.73	10.45
	Severe	246	14.37 ± 6.76	13.52	15.22

*= Confidence Interval, **= P-value computed using analysis of variance (ANOVA)

Predictors of PMS severity using ordinal logistic regression

In model 1, anxiety and depression were classified into three categories: mild, moderate, and severe. It showed that with an increase in depression severity (mild to moderate or moderate to severe), the odds of moving to a higher category of PMS severity (mild to moderate or moderate to severe) increase 1.74 times. The odds ratio for anxiety severity was 1.76 (95% CI [1.54, 2.01]). Consequently, a higher chance of experiencing severe PMS was linked to higher levels of depression and anxiety. Model 2 showed an association between the total score of depression and anxiety on the severity of PMS; it showed that with an increase of one unit to the depression score, the risk of PMS increases by 9% (from mild to moderate or from moderate to severe). It was 11% for anxiety. All predictor variables with P < 0.2 were entered into the multivariate model; finally, three variables—depression, anxiety, and average hours of sleep per night—remained in the final model (model 3). Model 3 demonstrated anxiety, depression, and average hours of sleep per night were independent predictors of PMS severity. More than 9 h of sleep per night increases the risk of severe PMS 1.4 times. Model 3 was the best model for predicting the severity of PMS (log-likelihood=−488.16) (Table 3).

Table 5.

Association PMS severity with depression, anxiety, and average hours sleep per night using ordinal logistic regression

		OR	95%CI	Log-likelihood	Z	P-value
Model 1(univariate)	Severity of depression*	1.74	1.51–1.99	−504.4	7.95	< 0.001
	Severity of anxiety*	1.76	1.54–2.01	−516.2	8.36	< 0.001
Model 2(univariate)	Total score Depression**	1.09	1.07–1.11	−493.5	9.06	< 0.001
	Total score Anxiety**	1.11	1.08–1.14	−514.6	8.51	< 0.001
Model 3 (multivariate)	Severity of Depression*	1.41	1.21–1.65	−488.16	4.28	< 0.001
	Severity of Anxiety*	1.51	1.29–1.76		5.20	< 0.001
	average hours of sleep per night	1.40	1.11–1.77		2.80	0.005
* Category variable (mild, moderate, severe) ** Continuous variable (total score in DASS42 inventory)

In model 2, the area under the curve (AUC) was 0.76 for depression and 0.81 for anxiety, indicating that these variables were highly accurate in predicting the severity of PMS (Fig. 2). Anxiety scores have excellent discriminatory power to predict PMS severity. Depression scores show acceptable discriminatory power. The AUC displayed cutoff points of 11 for the depression score and 9 for the anxiety score, which were the best cutoff points for predicting PMS severity(Fig. 2).

Fig. 2.

ROC curve prediction PMS severity using anxiety and depression score

Discussion

Using ordinal logistic regression to find predictive factors, this study examined the association between premenstrual syndrome (PMS) and psychological symptoms, specifically anxiety, depression, and average sleep duration per night, in female university students in Ilam, Iran. With 52% and 14.6% of participants reporting moderate and severe symptoms, respectively, the results show a significant burden of PMS. The prevalence of PMS aligns with global estimates that 20–40% of reproductive-aged women report clinically significant PMS [6]. This study’s high PMS prevalence is probably due to a confluence of methodological decisions, cultural norms, psychological stressors, and biological vulnerabilities. The impact of PMS may be lessened by addressing these factors with focused therapies (such as stress management, dietary changes, and lifestyle adjustments). These hypotheses should be further investigated in future studies, especially in a variety of socioeconomic and cultural circumstances. The way that people in Ilam, Iran, experience and report PMS and mental health is greatly influenced by sociocultural factors. Women’s behaviors and expectations are frequently dictated by cultural conventions and traditional gender roles in this area, which can make menstruation more stressful and emotionally upsetting. Research indicates that Iranian women face specific societal pressures related to traditional gender roles, family expectations, and social restrictions, which can negatively impact their mental health. These sociocultural challenges often require women to balance personal aspirations with societal demands, leading to increased psychological stress [43, 44]. For example, social demands around marriage, fertility, and household duties may exacerbate psychological strains and exacerbate PMS. Furthermore, underreporting or poor symptom management may result from the stigma associated with menstruation and mental health conditions. The situation of women’s mental health in Ilam is made more difficult by the combination of these sociocultural elements with restricted access to medical and educational resources. Culturally sensitive interventions that empower women, lessen stigma, and encourage candid conversations about menstruation and mental health are necessary to address these factors [45]. The findings indicate that severe PMS is significantly associated with higher levels of anxiety (r = 0.58) and depression (r = 0.51), highlighting the adverse impact of anxiety and depression on PMS. Three different PMS classifications were assessed, along with their effects on PMS prevalence and related factors. Additionally, three OLRs were analyzed for predicting PMS. According to ROC analysis, anxiety and depression effectively predict PMS severity with adequate sensitivity and specificity. The analysis revealed a sensitivity of 75% and specificity of 64%, with cutoff scores of 11 for depression and 9 for anxiety. The strong positive correlations between PMS severity and anxiety (r = 0.58) and depression (r = 0.51), consistent with prior work linking affective disorders to exacerbated PMS symptoms [36]. These results reinforce the hypothesis that dysregulated emotional states may amplify somatic and cognitive PMS manifestations, possibly through shared neuroendocrine pathways involving serotonin and GABAergic systems [46]. The results show that the intensity of PMS was substantially correlated with both continuous and categorical measures of anxiety and depression. Even though there are established links between PMS and symptoms like anxiety and depression, a large portion of the literature highlights the psychological toll that PMS takes [47, 48].

The OLR (model 3) highlighted anxiety and depression as robust predictors of PMS severity. AUC values were 0.81 for anxiety and 0.76 for depression, further validating their utility in PMS risk stratification and supporting the integration of mental health screening into PMS management protocols [49].

Mood swings, impatience, anxiety, and despair are some of the psychological symptoms that are frequently associated with PMS, which is well acknowledged to be a contributing cause of emotional distress. Research from all over the world has consistently demonstrated a clear link between higher levels of psychological symptoms and the severity of PMS. As an illustration, a meta-analysis study carried out by Zaks et al. discovered a strong cross-population correlation between PMS and anxiety and depression, supporting the results of the current investigation [1]. Accordingly, they found in their systematic analysis that women who have severe PMS symptoms are more likely to self-report clinically significant levels of depression and anxiety. Findings by Li et al. showed a strong correlation between heightened vulnerability to mood disorders and the intensity of PMS symptoms [4]. In line with the current study, which identified a significant increase in the intensity of depression among individuals with severe PMS symptoms, this multi-center investigation demonstrated that women with severe PMS were more likely to develop PMDD globally. This may be partly explained by the hormonal changes in the luteal phase of a menstrual cycle, which are influencing neurotransmitter systems related to mood regulation [4, 16].

The socio-cultural context of Ilam, to which this study is applicable, and its potential influence on the previously known link between PMS and mental health constitutes one of the study’s main contributions. Prior cross-cultural research has indicated that cultural influences have a substantial role in how PMS symptoms are experienced and reported. For instance, Sarokhani et al. [26] showed that PMS and its psychological aftermath were aggravated in settings with poor mental health facilities and a high level of stigma related to mental problems. In Ilam, where mental health problems are stigmatized and there are few resources for psychiatric assistance, this is especially important. Women internalize psychological suffering more in such environments, which may lead to low symptom reporting or a refusal to seek treatment. The findings of this present were consistent with those of Daneshvar et al. [25] who investigated the impact of cultural and socio-economic factors on PMS severity in Iran. They also found that women from areas with higher levels of social and economic stress, like Ilam, reported more severe symptoms of PMS. This might be the result of exacerbated stressors like academic demands, ignorance of PMS, and societal stigmas associated with getting mental health treatment. These results are further supported by the current study, which shows that severe PMS symptoms are particularly linked to higher levels of anxiety and depression in this population. It also emphasizes the urgent need for culturally appropriate interventions that target the psychological and physical aspects of PMS. The intensity of PMS varied significantly by age in univariate analysis. While older participants (over 30 years) showed lower incidences of severe PMS, participants with 25 to 30 years showed higher incidences of severe PMS. Higher age was associated with a protective effect (OR = 0.74) for PMS severity. The reduction in PMS severity with age is a multifactorial process involving hormonal stabilization, improved coping strategies, and lifestyle adaptations [46, 49]. These findings underscore the importance of longitudinal studies to track symptom trajectories and develop age-specific interventions. Clinically, educating younger women about symptom management may help mitigate PMS burden as they age. The largest prevalence of moderate PMS was found in participants with lower body weights, especially those < 50 kg. These results are in line with other studies that linked hormonal changes and a lower body mass index, which supported Hou’s findings that younger and lighter people may be more vulnerable to PMS-related misery [2]. They found that younger women, especially those between 20 and 30 years of age, tend to have severe PMS symptoms because of hormonal changes and that this age group is more prone to psychological consequences of PMS, such as anxiety and depression. Furthermore, research by Liu et al. [50] has shown that body weight may be related to the severity of PMS, suggesting in some cases that symptoms may be worse in women with a lower BMI. This confirms the results of the current study, in that those with the lowest body weights (less than 50 kg) were found to have a higher prevalence of severe symptoms of PMS. These findings highlight the importance of individual health interventions, considering demographic factors like age and body weight in addressing PMS and its psychological aftermath.

In contrast, no significant association was observed between PMS severity and variables such as education level (bachelor, master, professional, or doctoral degree) and height. Severe PMS was higher in married students; it was in contrast to other studies [49, 51]. Average hours of sleep per night was an independent risk factor for PMS severity (P < 0.01), with participants sleeping less than five hours or more than nine hours reporting higher levels of PMS symptoms. These findings, while not definitive, warrant further investigation into lifestyle factors and their role in PMS severity. Using a sample of young females, Mitsuhashi et al. [9] examined the association between sleep deprivation and PMS severity and psychological distress. Likewise, Hashim et al. [10] found that sleep quality and short-duration sleep were associated with increasing levels of anxiety and depression in women with PMS. Though the present study finds a relationship between sleep and PMS severity, further research is necessary to explain the role of sleep patterns in influencing the psychological outcomes of PMS, particularly among university students, who are very vulnerable to disturbed sleep schedules due to academic pressure.

Both anxiety and depression are not only typical presentations but they also mediate severity and impact of PMS, and in women with PMS or its more severe counterpart, premenstrual dysphoric disorder (PMDD), clinically significant luteal phase mood disturbance with irritability, depressed mood, and anxiety is often reported by women which resolves with menstruation [52, 53]. ariations between studies in the rates and prevalence of comorbidity described in PMS, depression, and anxiety can be accounted for by variations in sample type and diagnostic criteria, and assessment methods; for example, population-based evidence indicates that major depression is significantly more common in women with moderate to severe PMS compared to those without it, and co-occurrence of PMS and depression is more impairing than depression or PMS alone. Furthermore, evidence suggests that while PMS and major depression are two different conditions, they do not infrequently occur together, and measures such as high psychological distress, poor self-rated health, and psychotropic medication use might account for the observed heterogeneity between studies [22, 54].

The OLR model showed increasing depression and anxiety severity increased the severity of PMS by 41% and 51%, respectively. This aligns with longitudinal studies suggesting bidirectional relationships between PMS and mood disorders, where pre-existing anxiety or depression may lower stress tolerance during the luteal phase. In a study done by Śliwerski [55], they attempted to verify how much depression changes the results of a PMS diagnosis. Specificity and sensitivity values for PSST in depressed females were 21% and 79%, respectively; it was lower than our estimate. In our study, AUC showed a cutoff point of 11 for the depression score and 9 for the anxiety score, with sensitivity 75% and specificity 64% [55].

The mechanistic link between premenstrual syndrome (PMS) and mood disorders like anxiety and depression lies in hormonal fluctuations and neurotransmitter imbalance. The hormonal fluctuations in the luteal phase of progesterone and estrogen affect serotonin and GABA neurotransmission that are vital for mood stability [15, 56]. Women with PMS have reduced serotonin transporter density and distorted serotonin activity, which exacerbate symptoms like irritability and depressed mood [56]. In addition, chronic exposure to PMS symptoms represents an allostatic load, in which repeated exposure to physical and emotional symptoms of stress dysregulates physiological responses to stressors, making one vulnerable to depression [15, 19]. Prospective research demonstrates that premenstrual symptom recurrence predicts future depressive episodes, with hormonal sensitivity and stress accumulation effects mediating this effect [15]. Anxiety and depression also facilitate this process by heightening emotional sensitivity to premenstrual change, creating a bidirectional relationship that enhances both PMS severity and mood disorder risk [19, 25].

Conclusion

This study revealed a strong association between the severity of PMS and psychological issues such as anxiety, depression, and average hours of sleep per night among female university students in Ilam. These three variables were independent predictor variables for PMS severity in OLR models. In univariate analysis, PMS severity was linked with age, weight, and marital status, but in multivariate analysis, they were not independent predictor variables. OLR effectively illustrates the link between PMS severity and independent variables. The AUC determined cutoff points of 11 for depression scores and 9 for anxiety scores in the DASS-42 inventory, providing optimal sensitivity and specificity for predicting PMS severity. This supports integrating mental health screening (e.g., DASS-42) into PMS management protocols.

Strengths and Limitations

The strengths of this study include a large sample size (624 participants) and the use of validated tools such as the PSST and DASS-42 questionnaires that ensure the reliability and validity of the results. Based on our best knowledge, this is the first study to compare several classification types of PMS and find new cut-off points for depression and anxiety to predict PMS severity. There are, however, several limitations that need to be identified. Firstly, this is a cross-sectional study; thus, no causal relationship between PMS and psychological symptoms could be established. Longitudinal studies are required to detect the directionality and long-term impact of PMS on mental health. Second, though the sample size is huge, it is confined to a college female student, so we must beware of generalizing to other society groups. In this research we used a self report questioner so maybe we have self reprt bias.

Suggestions for Future Research

Future research should be done to elucidate the causal relationship between PMS and psychological symptoms in larger and more diverse populations. Longitudinal studies would be extremely useful in determining how PMS influences mental health over a while and whether the psychological symptoms remain constant or change with every menstrual cycle. Moreover, examining whether cultural factors moderate PMS severity and its psychological consequences might foster further insight into how social norms and gender roles influence the experience of PMS. There is a need for interventional studies that can test the effectiveness of specific programs on mental health, which would be targeted at women with PMS. Such programs may target stress, anxiety, and depression associated with PMS through cognitive behavior therapy (CBT), mindfulness practices, and hormonal management techniques. Moreover, lifestyle factors like research on diet, exercise, and sleep in managing PMS can also contribute valuable information to holistic approaches to treatment. Future longitudinal research should focus on evaluating personalized treatment approaches for PMS symptoms and identifying causal relationships.

Abbreviations

PMS

Premenstrual syndrome

PMDD

Premenstrual dysphoric disorder

PSST

The premenstrual symptom screening tool

DASS-42

Depression anxiety and stress scale

Authors’ contributions

Conceptualization: Sayehmiri K, Sarokhani M, Li M.; Methodology: Li M, Sarokhani M. Valizade R, software and statistical analysis; Sayehmiri K, Li M, writing original draft, preparation; Li M, Sahami Gilan MH, Sarokhani M, Vallizadeh R, Sayehmiri K; data collection, Sarokhani M, Valizadeh R. All authors approve the final draft.

Funding

No Funding.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study has an ethical code from Research Ethics Committees of Ilam University of Medical Sciences Ilam,Iran. The ethical code was IR.MEDILAM.REC.1403.256

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

https://ethics.research.ac.ir/form/tph1hzssz1om5wny.pdf ).

Clinical trial number

not applicable.