• Revista Internacional de Investigación en Adicciones 2024 Vol. 10 (1)
• ISSN print: 2448-573X
• ISSN on-line: 2448-6396
• DOI: 10.28931/riiad.2024.1.05
• Received on: March 1st, 2024
  Accepted on: April 5th, 2024

Original article

Is Waist-Circumference Related to Anxiety Levels in Peri and Postmenopausal Women?

Resumen

Introducción: la depresión, la ansiedad, y el estado menopáusico parecen desempeñar un papel importante en la etiología de la obesidad, el sobrepeso y el aumento del perímetro de la cintura; variables que son importantes predictores del riesgo cardiometabólico. Dada esta asociación, nuestro objetivo es evaluar la relación entre el perímetro de cintura y los puntajes de ansiedad en un grupo de mujeres peri y posmenopáusicas.

Método: evaluamos a 329 participantes con una edad media de 52.4 años; 20.06% perimenopáusicas y 79.9% posmenopáusicas. Realizamos una evaluación tanto del estado reproductivo, con la clasificación del Taller de Etapas del Envejecimiento Reproductivo, como de la ansiedad, con el Inventario de Ansiedad Estado-Rasgo y medidas antropométricas. El análisis estadístico involucró análisis descriptivos usando chi-cuadrada y t-test. Las correlaciones bivariadas exploraron variables relacionadas con el perímetro de cintura, y finalmente un modelo logístico examinó la asociación entre el perímetro de la cintura y la ansiedad.

Resultados: observamos una correlación significativa entre el perímetro de cintura y la ansiedad de estado y ansiedad de rasgo. No observamos una correlación entre la ansiedad y el estado menopáusico, la terapia de reemplazo hormonal y la edad. Por cada punto de aumento en la puntuación de ansiedad de estado en la evaluación STAI, el riesgo de tener una cintura > 88 cm aumentó en un 4%.

Discusión y Conclusiones: los hallazgos sugieren que niveles altos de ansiedad durante la transición menopáusica están relacionados con un mayor perímetro de cintura. Estudios prospectivos con una muestra más grande podrían aclarar la naturaleza de esta asociación.

Palabras clave: ansiedad, menopausia, obesidad, perimenopausia.

Abstract

Introduction: depression, anxiety, and the menopausal status seem to play an important role on the etiology of obesity, overweight and increased waist circumference; variables which could be important predictors of cardiometabolic risk. Given this association, it is our aim to evaluate the relationship between waist circumference and anxiety scores in a group of peri and postmenopausal women.

Method: we evaluated 329 participants with a mean age of 52.4 years; 20.06% perimenopausal and 79.9% postmenopausal. We performed an evaluation of both reproductive status, using the classification of the Stages of Reproductive Aging Workshop, and anxiety, using the State and Trait Anxiety Inventory as well as anthropometric measures. Statistical analysis involved descriptive analysis using chi-square and t-test. Bivariate correlations explored variables related to waist circumference; and finally, a logistic model examined the association between waist circumference and anxiety.

Results: we observed a significant correlation between waist circumference and state anxiety and trait anxiety. We did not observe a correlation between anxiety and menopausal status, hormone replacement therapy and age. For every one-point increase in the anxiety-state score in the STAI evaluation, the risk of having a waist > 88 cm, increased by 4%.

Discussion and Conclusions: findings suggest that high levels of anxiety during menopausal transition are related to a greater circumference of the waist. Prospective studies with a larger sample could clarify the nature of this association.

Key words: anxiety, menopause, obesity, perimenopause.

INTRODUCTION

Obesity is a public health problem, with its prevalence on the rise in recent years (Mohn, 2015). According to the World Health Organization (WHO), in 2022 more than 2.5 billion adults were classified as overweight, and among them, over 890 million were identified as obese (WHO, 2024). Apart from the health implications and heightened mortality associated with obesity (Flegal et al., 2013), waist circumference has emerged as a predictor of cardiometabolic outcomes independently of body mass index (BMI; Browning et al., 2010; Liu et al., 2014). To determine metabolic syndromes in postmenopausal women in Latin America, the optimal waist circumference cut-off is identified as 88 cm (Blümel et al., 2012). Also, the risk for cardiometabolic outcomes in women increases during postmenopausal age (Marlatt et al., 2022), hence the assessment of anthropometric measures becomes crucial in this group. Some of the factors that have been linked to obesity in postmenopausal women are sedentary lifestyle, psychiatric symptoms (depression and anxiety), medical conditions (diabetes and arterial hypertension), socio-demographic aspects (such as having a stable partner, or many individuals residing in a household), reproductive variables (including contraceptive use) and vasomotor symptoms (Blümel et al., 2015), as well hormonal changes during the transition to menopause, all of these contribute significantly to weight gain (Greendale et al., 2019).

Regarding psychiatric disorders, there is an observed connection between obesity and a lifetime diagnosis of anxiety and depression (Avila et al., 2015). Nevertheless, evidence suggests that the association between anxiety disorders and obesity is stronger than that between mood disorders and obesity (Scott et al., 2008). This correlation may be influenced by unhealthy behaviors such as smoking, physical inactivity, binge drinking and heavy drinking (Strine et al., 2008).

A meta-analysis that included 16 studies, most of them with a cross-sectional design, provides evidence for the link between obesity and anxiety disorders in both women and men (Gariepy et al., 2010), however, the results are insufficient to establish a clear causal relationship between these two variables and discrepancies arise when different anthropometric measure assessments are conducted. For instance, in a group of patients diagnosed with metabolic syndrome, no correlation was found between waist circumference and anxiety and depressive disorders, but a correlation was observed with the body fat percentage (Guedes et al., 2013).

In patients with type 2 diabetes, depression has been associated with abdominal obesity and cardiovascular disease, but anxiety has not shown any similar associations (Labad et al., 2010). Sex differences have also been noted, with waist-hip ratio demonstrating an independent association with anxiety among males (Rivenes et al., 2009), while depression was associated with both males and females.

In summary, the etiology of obesity involves the intricate interplay of multiple factors, with anxiety and reproductive variables, such as menopausal status, playing a significant role in its occurrence. Moreover, beyond overall body weight, waist circumference alone could serve as a substantial risk factor to cardiovascular disease. In light of these considerations, our objective is to evaluate the relationship between waist circumference and anxiety scores in a group of peri and postmenopausal women. Additionally, we are interested in exploring the relevance of waist-hip ratio in the assessment of this group of women.

METHOD

Participants

Patients attending a women’s specialized hospital from 2016-2018 were invited to participate in the present study. All of them were between 40 and 65 years old, either in the perimenopause or postmenopause stage according to the classification of the Stages of Reproductive Aging Workshop (STRAW) criteria (stages -2 to +2). All of them had both ovaries, but not all had an intact uterus. Exclusion criteria included patients already enrolled in a structured weight-loss program, patients with chronic or uncontrolled medical illness, substance abuse according to the DSM-IV criteria, consumption of drugs that reduce or increase weight, anxiolytics or antidepressants use as well as other drugs with sedative effects. All participants signed an informed consent approved by the institutional ethics committee.

The participants' assessment included a complete clinical evaluation, socio-demographic variables registration, anthropometric measurements and anxiety rating.

Sociodemographic Variables

A form was created to register the sociodemographic characteristics of the patients. Marital status was recorded as follows: single, married or living with a partner, separated or divorced, and widowed. Educational level was recorded in study years and after that grouped in three categories that correspond to the educational system, that is elementary level (zero to six study years), middle and high school (seven to 12 study years), and graduate or postgraduate (equal or more than 13 study years). Socio-economic level was measured with a special questionnaire developed by the Mexican Association of Market Research and Public Opinion (AMAI, 2016). This questionnaire contains 10 items, rating seven socioeconomic levels: very low, low, middle-low, middle-middle, middle-high, high, and very high. We simplified the categories grouping the middle level together resulting in: very low, low, medium, and high.

Clinical Evaluation

A complete medical history was performed stressing the information about their menstrual cycles in order to classify them into perimenopause or postmenopause groups according to the STRAW criteria. Perimenopause was considered when the patient had persistent menstrual irregularities with variable cycle length (more than seven days different from normal) or more than 2 skipped cycles and/or an interval of amenorrhea ≥ 60 days within the past 12 months. Postmenopause was considered when no menstrual period or menstrual bleeding was present within the last 12 months and this was not due to a condition such as being pregnant, undergoing chemotherapy or some other treatment that could affect menstruation. Family and past medical history was also investigated. Information regarding the use of medication, specifically hormonal and psychiatric drugs, was registered. Hormone replacement therapy (HRT) was recorded by the gynecologist including dosage and formulation. Information about physical activity was collected through self-reports.

Anthropometric Measures

Body weight was assessed in all patients with a scale TANITA BWB 800 ®, with minimal clothing and barefoot, at a time between 8 and 11 a.m. Body size was evaluated with a stadiometer, barefoot and with heels, hip and shoulder blade attached to the vertical surface. The waist measurement was performed with level type applying it to the narrowest area of the abdomen and hip measurements were performed on the higher perimeter of the buttocks. Both measurements were made on a horizontal plane.

The BMI was determined using the formula: weight/height2, and according to this measurement we classified the women into the following categories: underweight, normal weight, overweight, obese I, obese II, and obese III, according to WHO criteria. The waist-hip ratio was calculated by dividing the waist measurement by the hip measurement, and was used to classify patients according to the distribution of fat into gynecoid (< .79) and Android (≥ .8).

Anxiety Evaluation

All participants completed the State and Trait Anxiety Inventory (STAI; Spielberger et al., 1970). These are two distinct self-report instruments that assess the temporary condition of state anxiety (A-State) and the longstanding quality of trait anxiety (A-Trait) respectively. The State questionnaire comprises 20 items with response options based on a 4-point Likert scale, evaluating how individuals have felt in the past two weeks; it serves as an indicator of a transient state, encompassing feelings of tension, nervousness, unexplained fear, irritability or restlessness, along with sensations of racing or pounding heart. The Trait questionnaire also consists of 20 items on a Likert scale and inquires about how individuals typically feel. Trait anxiety reflects a relatively stable characteristic associated with a general increase in negative emotions. The Spanish version of the STAI was validated for its use in the Spanish-speaking population (Arias-Galicia, 1990).

Statistical Analysis

Statistical analysis was performed using the program R version 3.2.2. Descriptive analysis were performed, using chi-square and t test. Waist circumference was compared according to menopausal status (perimenopausal vs postmenopausal) and the use of HRT (yes or no) using the t test. Bivariate correlations were assessed in order to explore variables related to waist circumference.

A dichotomous variable was constructed called “largewaist” where “0” corresponds to women with a waist circumference equal or lower than 88 centimeters, and “1” to women with a waist circumference greater than 88 centimeters. We adjusted a logistic model:

Where:

p = is the Bernoulli distribution parameter of the “largewaist” variable.

A-State = total score of state anxiety subscale of the STAI.

We used the stepwise method to build the model in both directions: backward and forward. The Akaike information criterion (AIC) was used to the model selection (Akaike, 1998). The Hosmer Lemenshow test was used to evaluate the goodness of fit of the model. Significance level was set at 0.01.

RESULTS

A total of 433 patients were invited to participate in the study. 61 refused to participate, therefore 372 patients were evaluated in a first interview; 15 were not included because they did not meet the inclusion criteria. Of the remaining patients, only 329 completed all the evaluations and were included in the final analysis. Mean age of the sample was 52.44 (standard deviation = 7.13) years; 20.06% of the participants were perimenopausal and 79.9% postmenopausal. The clinical and sociodemographic characteristics of the participants are shown in Table 1. It is worth noting that more than 90% of the participants reported physical activity for only one hour per week. Distribution of the sample according to HRT was similar between women using HRT and women who did not. Anthropometric measures and anxiety scores by menopausal status are shown in Table 2. Differences in anthropometric measures between perimenopausal and postmenopausal women were observed in weight (70.29 vs 66.79 kg; T = 2.12, fd = 325, p = .035) and waist-hip (0.81 vs 0.83; t = -2.06, fd = 325, p = .04). No differences were observed regarding waist circumference, body mass index and hip measures (Tables 1 y and 2).

Hormonal use differences were observed only in body weight, heavier women without hormone therapy (68.82 vs 66.08 kg; t = 2.08, fd = 325, p = .03). There were no differences in waist circumference measurements between women using and not using HRT (t = 0.05, fd = 325, p = .95). No significant correlation of waist circumference was observed with menopausal status, HRT, and age (Table 3).

Variables that correlated significantly with waist circumference were state anxiety (Pearson correlation = .195, p < .001) and trait anxiety (Pearson correlation = .184, p = .001).

Regarding the logistic regression analysis, we performed different models including all of the variables that could influence the waist circumference. The initial proposed model included the weight, height, state-anxiety, trait-anxiety, hormone therapy and menopausal status variables. We did not include physical activity because the proportion of women who exercised two or more hours was too small. We used the stepwise method in both directions for the construction of the model. We observed that four variables were significant for waist circumference: weight, height, state-anxiety and hormone therapy. Due to the diverse hormone therapy formulations used by our patients, we decided to eliminate this variable from the final model presented in Table 4. When we compared the model including the hormonal therapy variable and the model without it, we observed no significant differences, therefore, state-anxiety explained the waist circumference in either presence or absence of hormonal therapy. Additionally, hormonal therapy seems not to impact the waist circumference, but it is related to anxiety (Table 4).

We used the Akaike Information Criterion in order to choose the most appropriate model, since it considers both how well a model fits the data and the number of parameters used It provides a balance between goodness of fit and simplicity. The best fitted model included the variables A-State, weight and height; in this model, the three coefficients of the variables were statistically significant. We observed that for every one-point increase in the A-state score, the risk of having a waist > 88 cm increased by 4% (Table 5).

DISCUSSION AND CONCLUSIONS

While obesity is a multifactorial public health problem, most of the involved factors, such as diet, physical activity and psychiatric symptoms, are modifiable. The menopause transition has been linked to weight gain and body fat distribution changes. It has been observed that in women moving toward menopause, a sedentary lifestyle increases the severity of menopausal, depressive and anxiety symptoms, as well as insomnia (Santoro, 2021). In the present study we observed a great proportion of sedentary women, only 1.8% of the evaluated women had physical activities for 5 or more hours per week, and almost all reported to do physical exercise between 0 to 1 hours per week. However, the proportion of sedentary women among perimenopausal and postmenopausal women was similar in the evaluated participants, indicating that differences in anthropometric measures between groups were not derived from sedentary life-style.

When we compared the anthropometric measures according to menopausal status, perimenopausal women had a significantly greater body weight, but a smaller waist-hip ratio compared to postmenopausal women. According to this, we may assume that menopausal status may modify the body composition, probably mediated by an estrogen deficiency that leads to increased abdominal fat accumulation. (Clegg, 2012; Ko & Kim, 2020; Shea et al., 2015). This conjecture, however, continues to be controversial and must be clarified with new clinical studies (Van Pelt et al., 2015).

Moreover, it seems that the waist circumference increase in women is not correlated with BMI as it is observed in men--that is to say, there is an independent increase in waist circumference (Freedman & Ford, 2015). The observation above becomes important when we consider the relationship observed between waist circumference, cardiovascular risk (Kannel et al., 1991) and metabolic syndrome (Nicklas et al., 2003). Additionally, trait anxiety has been associated with biomarkers for hypertension, and thus linked with an increase in cardiovascular risk (Lemche et al., 2016). It is important to mention that trait anxiety reflects a characteristic that an individual carries throughout their life, which may have different implications compared to state anxiety, which only reflects what occurs at the moment of assessment.

Furthermore, psychiatric disorders, particularly anxiety and depression, could influence the body weight and waist circumference in women. Depressive symptoms have been related to increased waist circumference and BMI in women between 18 and 75 years (Moreira et al., 2007); similar results have been observed in postmenopausal women (Katz et al., 2000). Not all authors agree with this observation: in a large sample of 10,026 women, BMI and waist circumference were not related to depression in neither men nor women ("Turley et al., 2006). Another study in the aging Chinese population also failed to demonstrate an association between depressive scores and anthropometric measures (Ho et al., 2008). The severity of the anxiety or depressive symptoms, more than the disorder diagnosis itself, have also been associated with abdominal obesity, as observed in some studies (Fulton et al., 2022; Hiles et al., 2016).

We observed in our sample that anxiety was related to waist circumference, therefore increasing the risk for a waist circumference higher than 88 centimeters, which is the suggested waist measurement for cardiovascular risk. Anxiety was not correlated to body weight or any other anthropometric variable. A plausible explanation of this finding includes the metabolic changes that have been related to depression, such as hypercortisolemia, insulin and leptin resistance, and metabolic inflammatory signals (Milaneschi et al., 2019); it was also observed that people suffering from stress are more prone to consume palatable foods (Dallman, 2010; Epel et al., 2004). Anxiety by itself explains the perimenopausal distress experienced by women, since the anxiety sensitivity is more important than the trait anxiety in women older than 40 years (Muslić & Jokić-Begić, 2016).

Hormone therapy has been related to both increased weight and cardiovascular risk. We found an association between waist circumference and hormonal use; although this effect appears to be dependent on the chemical compound used in the hormonal therapy, our sample, however, included diverse formulations for patients. In general, different studies agree with the fact that hormonal replacement therapy did not increase the body weight in perimenopausal and postmenopausal women (Junge et al., 2009; Khoo et al., 1998; Opoku et al., 2023). Our results suggest that the evaluation of anthropometric measures is essential in women transitioning to menopause who will use hormonal therapy, considering their associated risks. Oral estrogen administration has been associated with an increased risk of venous thromboembolism as well as norpregnane derivatives (Lobo, 2011). Additionally, estrogen plus progestin increase the risk of an ischemic stroke (Wassertheil-Smoller et al., 2003). Even though it is still controversial, some kinds of cancer have also been related to specific formulations of hormonal compounds, such as some types of breast cancer (Li et al., 2008), ovarian cancer (Liu et al., 2019) and endometrial cancer (Sjögren et al., 2016).

Limitations of the present study include the lack of assessment of vasomotor symptoms. Some authors have proposed that the BMI is associated with the presence of vasomotor symptoms, particularly hot flashes (Freeman et al., 2005; Gold et al., 2006). Additionally, vasomotor symptoms seem to be a factor that increases the risk of suffering anxiety and depressive symptoms during the menopausal transition (Cohen et al., 2006). Furthermore, we did not differentiate between women in the early postmenopause and late postmenopause; waist circumference and as anxiety levels may differ between these groups. Lastly, the hormone therapy formulations in our sample were diverse and we only grouped in using or not using hormone compounds. In sum, prospective studies with a large sample to evaluate the relationship between waist-circumference and anxiety are needed in order to make this relationship clearer; it could be important to be alert about cardiovascular risk in women at this stage of life.

In conclusion, our findings suggest that high levels of anxiety during menopausal transition are related to a greater waist circumference. Prospective studies with a larger sample could clarify the nature this association, which in turn becomes important due to its implications on cardiovascular risk.

ETHICAL CONSIDERATIONS

The study was approved by the Ethics Committee of the National Institute of Perinatology.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

AUTHOR CONTRIBUTIONS

Mónica Flores-Ramos and Roberto Silvestri-Tomassoni designed the study, the main conceptual ideas and proof outline. Rodrigo Guiza-Zayas and Martín Armando Burrola-Suárez performed the measurements and field work. Leticia Gracia-Medrano performed the calculations, statistical and analytic calculations. Mónica Flores-Ramos and Roberto Silvestri-Tomassoni contributed to the interpretation of the results. All the authors read, analyzed and approved the final manuscript.

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