Global diet quality score and quality of sleep in patients with breast cancer: a population-based cross-sectional study

Abstract

Background

Poor sleep quality is a common risk factor for a reduced quality of life in patients with breast cancer (BC). The Global Diet Quality Score (GDQS) is a validated measure of dietary quality; however, its association with sleep quality in BC patients remains unclear.

Objective

This study aimed to evaluate the association between dietary quality, as measured by the GDQS, and sleep quality, as assessed using the Pittsburgh Sleep Quality Index (PSQI), among patients with BC.

Methods

This cross-sectional study included 600 patients with BC registered in the Isfahan Breast Cancer Registry (2021–2023). Dietary intake was assessed using a validated food frequency questionnaire, and the GDQS was computed for 25 food groups. Sleep quality was evaluated using the Persian version of PSQI. Multiple linear regression models adjusted for confounders were used to assess the association between the GDQS and sleep quality.

Results

Each unit increase in the total and positive GDQS was associated with a decrease in the PSQI score and better sleep quality (β = -0.12, 95% confidence interval (CI): -0.19, -0.05; p = 0.001 and β = -0.14, 95% CI: -0.22, -0.06; p = 0.001, respectively). Patients in the highest total and positive GDQS quartiles had significantly better sleep than those in the lowest quartile (β = -1.20, 95%CI: -2.09, -0.32; p = 0.008 and β = -1.72, 95% CI: -2.88, -0.57; p = 0.004, respectively), with a clear inverse relationship for both total and positive GDQS (p < 0.05).

Conclusion

This study underscores the role of dietary quality in improving the sleep health of patients with BC. Nutritional counseling should be integrated into oncology care to enhance sleep and well-being. Further studies are required to confirm these results regarding temporality.

Trial registration

Not applicable.

Background

Breast cancer (BC) is a major global health concern, with an estimated 2.3 million new cases annually [1, 2]. BC accounts for approximately 11.7% of all new cancers and is the most common cancer among women worldwide [1, 2].

Along with well-documented genetic and hormonal risk factors, lifestyle factors, including diet and sleep, have gained attention for their roles in cancer prevention, progression, and patient quality of life (QOL) [3, 4]. Sleep quality is emerging as an essential component of the overall health and QOL of patients with BC. There is evidence linking poor sleep to increased inflammation, impaired immunity, diminished QOL, and a higher risk of BC recurrence and BC-specific mortality [5–9]. Moreover, a higher rate of sleep problems (67– 90%) has been documented in patients with BC than in those with other types of cancer [10].

Dietary patterns characterized by high-quality nutrient intake have been associated with a lower risk of BC and better outcomes [11]. Conversely, poor diet quality can lead to a higher prevalence of mental and psychological problems such as sleep disturbances and poor QOL [12]. With a comprehensive assessment of diet quality [13], the Global Diet Quality Score (GDQS) is considered a quick, easy, and cost-effective tool for monitoring the risks of non-communicable diseases (NCDs) and nutritional deficiencies [14]. Preliminary research has suggested a bidirectional association between diet and sleep quality [15–17]. GDQS, a novel global measure of overall dietary quality, has recently been validated in diverse populations [18]. However, its association with sleep quality, particularly in BC, remains unclear.

Understanding the possible link between the GDQS and sleep quality might be invaluable for optimizing both the clinical outcomes and long-term well-being of patients with BC. To our knowledge, this is the first large-scale study to explore the association between the GDQS and sleep quality in patients with BC. The findings of this study offer potential insights into integrative care strategies that encompass nutritional and sleep interventions.

Methods

This study analyzed the data of 600 BC cases from a large case-control study conducted between May 2021 and October 2023 in Isfahan, Iran. The subjects were all 18–75 years-old residing women in the 15 municipal areas of Isfahan with a confirmed diagnosis of BC who were registered in the Isfahan Breast Cancer Registry (18). All registered patients had a verified positive pathology. Of the 653 eligible patients approached, 53 declined to participate, resulting in a final sample size of 600 participants who provided informed consent and completed the required assessments. The study was approved by the ethics committee of Isfahan University of Medical Sciences (IR.MUI.REC.1399.010). Verbal and written informed consent was obtained from all participants.

Data collection

Data were collected by five skilled female interviewers. The interviewer team was chosen following an extensive and detailed training protocol, based on their remarkable interviews and interpersonal abilities. At the beginning of the primary interview, the main study objectives and their possible benefits were clarified to increase the final study response rate. All the participants provided verbal informed consent. We strictly monitored the data collection process by randomly recording and reviewing interviews to detect any possible interviewer bias.

The interviewers utilized validated questionnaires to obtain the participants’ characteristics, including age, stress, marital status, family socioeconomic status (SES), energy intake, physical activity (metabolic equivalent of task (MET)), and calorie intake.

Dietary assessment

A validated qualitative food frequency questionnaire (FFQ) containing 168 items with serving sizes tailored to the Iranian population was used to evaluate dietary intake data [19]. The interviewers conducted phone interviews during which participants reported their consumption frequency and standard amount for each food item. Responses were provided in an open-ended format on a daily, weekly, or monthly basis. The average daily intake of different food items was calculated and portion sizes were quantified in grams using household scales. The total intake of each food item was determined by multiplying the daily intake frequency by the portion size in grams. We employed the Nutritionist IV software to calculate the daily consumption of energy, micronutrients, and macronutrients for all study participants.

The GDQS is a valid and robust food-based index for assessing nutrient adequacy and the risk of major diet-related NCDs across diverse global contexts [13]. In sum, the GDQS comprises 25 food groups, with 16 classified as healthy, seven classified as unhealthy, and two classified as unhealthy when consumed excessively. The healthy groups included citrus fruits, deep-orange fruits, other fruits, dark-green leafy vegetables, deep-orange vegetables, cruciferous vegetables, other vegetables, legumes, deep-orange tubers, nuts and seeds, whole grains, liquid oils, fish and shellfish, poultry, game meat, low-fat dairy products, and eggs. The unhealthy foods consist the following groups i.e. processed meat, refined grains and baked goods, sweets and ice creams, sugar-sweetened beverages, white roots and tubers, juice, and commercially prepared deep-fried foods. Moreover, the high-fat dairy and red meat groups were considered unhealthy when their intake was in excess.

The intake of each food group was classified into three or four ranges (g/day). Healthy components were evaluated by allocating higher points for increased intake (scores ranging from 0 to 4, based on the food group), whereas the scoring system for unhealthy components was inversely structured; larger points were assigned for reduced consumption (points ranging from 0 to 2). Finally, unhealthy components that could be unhealthy when consumed excessively were assigned points for optimal intake, whereas both low and excessive consumption earned zero points. The overall GDQS was calculated by summing the scores from all 25 food groups, yielding a total score between 0 and 49, with a higher score reflecting a healthier diet [13]. This study also calculated the positive GDQS submetric, which included only healthy food components with scores ranging from 0 to 32 points [13].

Quality of sleep

The Persian version of the Pittsburgh Sleep Quality Index (PSQI-P) was used to assess the sleep quality [20]. The PSQI-P evaluates the subjective sleep quality during the previous month. Nineteen distinct items produce seven “component” scores, encompassing: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleep medication use, and daytime dysfunction. Each component score ranges from 0 (optimal) to 3 (poor). The overall PSQI score was calculated by summing the seven component scores, resulting in a range of 0–21, with higher scores indicating poorer sleep quality.

Short-term stress

We obtained data on short-term stress asking; “Stress refers to a state in which a person feels tense, restless, nervous, or anxious, or is unable to sleep at night due to a constantly troubled mind. During the year, how often did you experience this type of stress? The response option was a 5-likert scale as not at all, a little, somewhat, much, and very much.

Family socioeconomic status (SES)

Respondents were asked to identify their families’ SES on a 10-step visual scale reflecting Isfahan’s social hierarchy [21]. Higher scores were associated with better education, job, and income levels.

Energy intake

Participants received detailed instructions from a qualified dietitian to report their food frequency and quantity consumed in the previous year. Subsequently, household measurements were employed to convert the portion sizes of food consumed into grams per day. In the final step, all food items were inputted into the Nutritionist IV software to calculate daily energy and nutrient intake.

Physical activity

The International Physical Activity Questionnaire (IPAQ) [22] was used to assess physical activity levels, excluding activities such as walking and sitting. Both vigorous and light physical activity levels were measured based on frequency (days per week) and duration (minutes per day). MET values were assigned for each activity level, with 8 for vigorous activities and 3 for light activities. The total MET minutes per week were categorized into ranges of: 0-1000, 1001–2000, and > 2000.

Statistical analysis

We used the mean (standard deviation, SD) and number (%) to describe continuous and categorical variables, respectively. Crude β and 95% confidence intervals (CIs) were estimated for the main exposure and potential confounders using simple linear regression models. Multivariable analysis using multiple linear regression models was performed to estimate the beta coefficients (β) and 95% CIs for the underlying association between GDQS and sleep quality. Statistical significance was defined as a p-value of < 0.05. All statistical analyses were performed using STATA version 12.

Results

The mean (SD) age of the 600 BC patients was 50.05 (9.69), and the majority of them, 480 (80.4%) were married. The mean (SD) and PSQI score were 7.63 (3.41). The mean (SD) total GDQS and positive GDQS were 26.16 (5.86) and 14.98 (6.26), respectively. The mean (SD) values for stress and SES were 3.85 (1.4) and 3.15 (0.91), respectively. As shown in Table 1, in the unadjusted analysis, the total GDQS was significantly associated with better sleep quality (β = -0.10, 95% CI: -0.15, − 0.046; p-value < 0.001). Similarly, there was a significant association between positive GDQS and sleep quality, with each unit increase in positive GDQS associated with a 0.09-unit increase in sleep quality (β = -0.09, 95% CI= -0.14, -0.04; p-value = 0.001) (Table 1).

Table 1.

Crude associations of baseline BC patient’s characteristics with sleep quality

Characteristics	N (%)	Crude β (95% CI)	P-value
Age, years; mean (SD)	50.05 (9.69)	0.039 (0.007, 0.071)	0.018
Marital status
Married	480 (80.4)	0 (Reference)
Single	23 (3.9)	-1.73 (-3.38, -0.08)	0.040
Widow	94 (15.8)	-0.09 (-0.96, 0.77)	0.832
Stress; mean (SD)	3.85 (1.4)	0.61 (0.39, 0.83)	< 0.001
SES; mean (SD)	3.15 (0.91)	-0.068 (-0.24, 0.10)	0.426
Energy Intake; mean (SD)	1649.83 (1055.6)	-0.0002 (-0.0005, 0.0002)	0.287
Physical activity (MET)
0-1000	59 (9.80)	0 (Reference)	-
1001–2000	61 (10.20)	0.85 (-0.60, 2.29)	0.252
> 2000	480 (80.00)	0.90 (-0.21, 2.01)	0.111
Total GDQS; mean (SD)	26.16 (5.86)	-0.10, (-0.15, − 0.046)	< 0.001
Categorized total GDQS
< 23.25 (Q1)	163 (27.17)	0 (Reference)	-
23.25–30.50 (IQR)	154 (25.67)	-0.62 (-1.48, 0.23)	0.153
> 30.50	283 (47.17)	-1.24 (-1.99, -0.49)	< 0.001
Positive GDQS; mean (SD)	14.98 (6.26)	-0.09 (-0.14, -0.04)	0.001
Categorized Positive GDQS
< 12.25 (Q1)	181 (30.17)	0 (Reference)
12.25–19.75 (IQR)	291 (48.50)	-1.41 (-2.13, -0.68)	< 0.001
> 19.75 (Q4)	128 (21.33)	-1.49 (-2.36, -0.61)	0.001

As noted in Table 2, after adjusting for the study confounders, there was a statistically significant association between the GDQS and quality of sleep in patients with BC (adjusted R² = ). Each unit increase in the total GDQS corresponded to a 0.12-unit decrease in the PSQI (β = -0.12, 95% CI= -0.19, -0.05; p = 0.001). In addition, each unit increase in positive GDQS was associated with − 0.14-unit decrease in PSQI (β = -0.14, 95% CI= -0.22, -0.06; p = 0.001).

Table 2.

Multiple linear regression model demonstrating the adjusted associations between different types of GDQS and quality of sleep (PSQI) in breast cancer patients

	β (95% CI)*	P-value
Total GDQS	-0.12 (-0.19, -0.05)	0.001
Categorized total GDQS
< 23.25 (Q1)	1 (Reference)	-
23.25–30.50 (IQR)	-0.68 (-1.60, 0.23)	0.139
> 30.50 (Q4)	-1.20 (-2.09, -0.32)	0.008
Test for Trend	-0.59 (-1.03, -0.15)	0.008
Positive GDQS	-0.14 (-0.22, -0.06)	0.001
Categorized Positive GDQS
< 12.25 (Q1)	1 (Reference)	-
12.25–19.75 (IQR)	-1.43 (-2.25, -0.62)	0.001
> 19.75 (Q4)	-1.72 (-2.88, -0.57)	0.004
Test for Trend	-0.91 (-1.48, -0.33)	0.002

* Adjusted for age, stress, marital status, family socioeconomic status, energy intake, and physical activity

The association exhibited greater strength when GDQS was categorized; patients in the highest GDQS quartile demonstrated significantly superior sleep quality compared to those in the lowest quartile in both total GDQS (β = -1.20 95% CI= -2.09, -0.32; p = 0.008) and positive GDQS (β = -1.72, 95% CI= -2.88, -0.57; p = 0.004) (adjusted R² = ). Importantly, a clear inverse trend was found for total GDQS (β = -0.59; 95% CI= -1.03, -0.15; p = 0.008) and positive GDQS (β = -0.91; 95% CI= -1.48, -0.38; p = 0.002).

Discussion

This study investigated the association between GDQS and sleep quality in Iranian patients with BC. We demonstrated a significant inverse association, wherein higher total GDQS and positive GDQS scores were associated with lower sleep quality. Importantly, we also identified a clear dose-response association for the total (p = 0.008) and positive GDQS (p = 0.002) with improved sleep quality.

To the best of our knowledge, the association between the GDQS and sleep quality in patients with BC has not been examined. Sleep difficulties frequently accompanied by fatigue and depression, which can trigger inflammatory pathways, alter cortisol rhythms, and increased mortality rates [23–25].

Our findings align with those of previous studies in the general population, where higher dietary quality has been linked to improved sleep quality [26–28]. A systematic review of 29 studies found that a high intake of plant-derived foods and seafood as the main sources of fat (i.e., in the Mediterranean diet) and low consumption of processed and free sugar-rich foods increased sleep quality in the general population [26]. Similarly, our findings confirm the results of a pilot clinical trial by Zick et al., in which women randomized to a high-quality dietary intervention experienced a significant improvement in sleep quality [29]. Another study reported that higher dietary antioxidant quality, reflecting a greater intake of nutrient-dense, antioxidant-rich foods, was significantly associated with healthier sleep patterns among cancer survivors [30]. However, conflicting results also exist, as Conway et al. found no significant association between dietary components, such as fiber, fruit, and vegetable intake or added sugar, and sleep quality among cancer survivors [31].

Although it is well acknowledged that diet can modify the risk of cancer [32–35], it directly or indirectly plays a key role in regulating sleep wellness [36, 37]. For example, foods containing melatonin can directly affect sleep because melatonin influences sleep induction and circadian rhythms through its receptors [37]. Moreover, long-term nutritional aspects may influence inflammatory status, which is closely associated with insomnia [36]. A considerable body of research supports the association between sleep disturbances and altered levels of circulating inflammatory cytokines, including interleukin-6 and C-reactive protein [38–40].

As an index of nutritional adequacy and diet quality [18, 41], it has been demonstrated that low GDQS significantly increases the odds of depression in Vietnamese women [42], which has been proven to be associated with sleep quality [43, 44]. Sleep quality is also a significant and independent predictor of both physical and mental health-related QoL among cancer survivors, with better sleep being linked to improved vitality, emotional well-being, and general health status [45]. In this regard, previous studies have highlighted the role of healthy dietary patterns in improving QoL outcomes in patients with chronic conditions. For example, Barchitta et al. reported that BC survivors adhering to a healthier diet, particularly with lower consumption of red meat and sugar-sweetened beverages, experienced better QoL across multiple subscales, including emotional and functional well-being [46]. Similarly, Arslan et al. demonstrated that higher adherence to the Mediterranean diet was associated with reduced fatigue and greater independence in activities of daily living among elderly patients with chronic obstructive pulmonary disease (COPD) patients [47].

Employing a large sample of incident BC patients, we highlight the potential of dietary interventions based on the GDQS to enhance sleep and consequently support better prognostic outcomes for this population. Our findings have several important clinical implications. As sleep disturbances are highly prevalent among patients with BC and adversely affect their QOL and treatment outcomes, addressing modifiable factors, such as diet quality, could be useful in developing practical and noninvasive interventions. Moreover, nutritional counseling should be integrated into routine oncology care, promoting dietary patterns that are high in anti-inflammatory and nutrient-dense foods, to optimize sleep well-being.

The results of our study have some limitations that should be considered. First, the temporality issue, as an inherent limitation of cross-sectional designs, allows only a statistical relationship between diet quality and sleep outcomes. Moreover, the possibility of reverse causality should be considered: poor sleep quality may potentially influence dietary behaviors and choices, rather than vice versa. Additionally, recall bias in self-reported dietary data may have introduced some degree of information bias. Although the employed FFQ was validated, the risk of misreporting or recall bias remains a concern in dietary assessments. Third, the potential influence of unmeasured confounders may have affected the established associations. Furthermore, while the visual ladder scale for socioeconomic status (SES) is a valid and intuitive tool, it may not fully capture the differences in income or educational attainment. While this study adds to the growing body of literature on the underlying interplay between diet and sleep, it also underscores the need for further research to address these limitations. Future longitudinal studies are essential to replicate these findings and to confirm the causality of the observed associations. Future longitudinal studies are essential to replicate these findings and to confirm the causality of the observed associations.

Conclusions

This study underscores a significant inverse association between total GDQS and positive GDQS as indicators of diet quality and quality of sleep as measured by the PSQI among patients with BC, demonstrating a clear inverse relationship. These results highlight the potential role of dietary quality as a modifiable factor for improving sleep health in this population, suggesting that better dietary quality is linked to improved sleep quality. By integrating nutritional counseling into cancer care protocols, patient outcomes can be enhanced by addressing sleep disturbances and promoting overall wellbeing. Moreover, it is recommended to explore the underlying mechanisms linking diet and sleep quality in patients with BC and to validate these findings.

Abbreviations

BC

Breast Cancer

GDQS

Global Diet Quality Score

PSQI

Pittsburgh Sleep Quality Index

QOL

Quality of Life

NCDs

Non-communicable Diseases

FFQ

Food Frequency Questionnaire

MET

Metabolic Equivalent of Task

SES

Socioeconomic Status

CI

Confidence Interval

SD

Standard Deviation

IPAQ

International Physical Activity Questionnaire

Author contributions

B. D.: Data curation, Formal analysis, Resources, Writing– original draft, Writing– review and editing. R. A. B: Formal analysis, Visualization, Writing– original draft, Writing– review and editing. I. A.: Conceptualization, Project administration, data curation, Methodology, Formal analysis, Supervision, Writing– Original draft, Writing– Review and Editing. M. K.: Conceptualization, Formal analysis, and Writing– Review and Editing. M. Y.: Software, Data curation, Formal analysis, Writing - Review and Editing. S. H. J.: Data curation and Investigation. K. Z.: Data curation, Writing - Review and editing. All authors have read and approved the final manuscript.

Funding

This work was supported by Isfahan University of Medical Sciences (grant number: 199061) and the Shams Charity Organization and Cancer Institute of Iran, Tehran University of Medical Sciences (grant number: 1400-2-244-53323).

Data availability

The data will be available whenever requested. As the corresponding author, Dr. Ibrahim Abdollahpour had full access to all the data in the study and took responsibility for the integrity of the data and accuracy of the data analysis. Dr. Ibrahim Abdollahpour conducted and is responsible for the data analysis.

Declarations

Ethics approval and consent to participate

This study was performed in accordance with the principles of the Declaration of Helsinki. Verbal and written informed consent was obtained from all participants. The ethics committee of the Isfahan University of Medical Sciences approved this study (IR.MUI.REC.1399.010).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.