Dietary inflammatory index and quality of life in patients with breast cancer: a cross-sectional study

Abstract

Background

Breast cancer survivorship is frequently accompanied by impaired quality of life (QoL). Diet-related systemic inflammation may influence physical, emotional, and psychological well-being, yet evidence linking the inflammatory potential of diet to QoL in women with breast cancer remains limited.

Methods

In this cross-sectional study, 600 women with confirmed breast cancer were recruited from the Isfahan Breast Cancer Registry between 2021 and 2023. Dietary intake and lifestyle characteristics were collected through structured telephone interviews. The Dietary Inflammatory Index (DII^®) and energy-adjusted DII (E-DII™) were calculated, and QoL was assessed using a five-point Likert scale. Associations of DII and E-DII with QoL were examined using multivariable linear regression with adjustment for potential confounders. Quartile-based analyses and tests for linear trend were also performed.

Results

Higher DII scores were associated with lower QoL in adjusted models (β = −0.12; 95% CI: −0.16 to − 0.08; p < 0.001), with a clear linear decline in QoL across increasing DII quartiles (p for trend < 0.001). E-DII was similarly inversely associated with QoL (β = −0.06; 95% CI: −0.11 to − 0.02; p = 0.007), and a consistent trend across E-DII quartiles was observed (p for trend = 0.01).

Discussion

Diets with greater pro-inflammatory potential were associated with poorer QoL among women with breast cancer. Although the cross-sectional design precludes causal inference, these findings support the hypothesis that anti-inflammatory dietary strategies may be a relevant target in survivorship care and justify longitudinal and interventional studies to clarify directionality and clinical impact.

Background

Breast cancer (BC) presents patients with a complex array of health challenges that encompasses multifaceted physical and emotional demands [1, 2]. Each year, approximately 2.3 million new cases of BC are identified globally, with the global mortality-to-incidence ratio (MIR) remaining high, approximately 19–29% [2–4]. BC accounts for one in every four of all cancers and one in six cancer-related deaths [2], highlighting its importance across both developed and developing nations [2].

In response to the rising incidence and prevalence of BC, significant progress in therapeutic management, including localized and systematic treatment approaches, has improved patients’ overall and disease-free survival [5, 6]. Owing to improvements in disease diagnosis and advanced treatments, overall quality of life (QoL) has become an essential measure of outcome in BC patients [7, 8]. QoL is a complex and multifactorial concept that subjectively assesses an individual’s life satisfaction encompassing several aspects of psychological, social, physical, and emotional functioning, making enhancement an important goal of BC patients and a central aim of the healthcare system [9, 10].

In patients with early-stage BC, chronic inflammation has been associated with fatigue, diminished physical functioning, and reduced survival, which can detrimentally impact QoL [11, 12]. Moreover, inflammatory status before treatment may be a prognostic indicator for developing prevalent musculoskeletal side effects, which could further diminish QoL and possibly affect adherence to treatment [13]. Research indicates that the inflammatory nature of one’s diet significantly influences the prognosis of BC and symptom burden, such that women with BC who consume pro-inflammatory diets often report higher rates of depressive symptoms, psychological distress, and fatigue​ [14–16].

A recently published study observed that healthy dietary patterns can be a modifiable and cost-effective target for interventions aimed at enhancing QoL associated with reduced inflammation [10]. Shivappa et al. created the Dietary Inflammatory Index (DII^®) score to assess the inflammatory potential of dietary elements following an extensive review of the literature [17]. Higher DII scores have been associated with an increase in inflammatory markers, which are, in turn, related to depressive symptoms in cancer patients [18, 19]. Research has previously demonstrated that an increased DII score correlates with a decline in QoL for both children and adults. However, no association was found in some studies, emphasizing inconsistent results within different populations [20, 21].

The QoL data in clinical research are increasingly recognized for improving health services for cancer patients and as a metric for comparing cancer treatments [11]. However, in BC patients, this association has not been thoroughly examined. This research seeks to explore the relationship between DII and energy-adjusted DII (E-DII) scores and the QoL in women with BC to provide insight into whether targeted dietary strategies could potentially mitigate inflammation and enhance patient outcomes.

Methods

Study design

This study involved a cross-sectional analysis in which information was gathered from 600 women aged 18 to 75 years., all of whom had a histologically confirmed diagnosis of BC. Data were collected as part of a large case-control study conducted between May 2021 and October 2023 in Isfahan, Iran [22]. BC patients were recruited from the Isfahan Breast Cancer Registry, which covers 15 municipal areas in Isfahan, Iran. Of the 653 new cases identified during the study period, 600 (92%) met the eligibility criteria and participated in the study. The research adhered to the Declaration of Helsinki and was approved by the Ethics Committee of Isfahan University of Medical Sciences (IR.MUI.REC.1399.010). All participants provided their verbal informed consent.

Data collection

A team of five trained female interviewers selected for their interview and communication skills performed the data collection step after case identification via telephone interviews. The interviewers completed a rigorous training program to ensure standardized data collection and minimize interviewer bias. To encourage participation, individuals were briefed on the study’s aims, measures in place to ensure confidentiality, and potential advantages of the study. To maintain data quality, a subset of the interviews was randomly recorded and reviewed for consistency and to detect interviewer bias. Information was collected on demographic, clinical, and lifestyle aspects, such as age, marital status, socioeconomic status (SES), stress levels, dietary factors, energy consumption, body mass index (BMI), and physical activity.

Dietary assessment

Dietary intake was assessed through a Persian-adapted version of a validated Food Frequency Questionnaire (FFQ) that includes 168 items [23]. Telephone interviews were used to administer questionnaires. Detailed instructions were provided by a qualified dietitian to help participants report their food intake frequency and quantities over the previous year. Participants were directed to specify the frequency with which they consumed each food item mentioned in the questionnaire as well as the amount they consumed. Household measurements were then used to convert portion sizes into grams per day. All food items were subsequently entered into Nutritionist IV^® software, which calculates the average energy and nutrient intake per day. The daily consumption of each food item was determined by multiplying the frequency of daily intake by gram portion size.

Calculating DII and E-DII

The Dietary Inflammatory Index (DII^®) measures the inflammatory potential of diets, categorizing them from highly anti-inflammatory (most negative scores) to highly pro-inflammatory (most positive scores). Detailed information on DII creation can be found in other studies [24]. A comprehensive review of 1,943 peer-reviewed articles identified 45 dietary components, including macronutrients, such as specific classes of fatty acids, carbohydrates, and proteins, as well as micronutrients like vitamins and minerals, flavonoids, and certain whole foods, herbs, and spices. These components were consistently linked to six circulating inflammatory biomarkers: interleukin (IL)-1β, -4, -6, -10, tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP).

In this study, the self-reported intake of 35 out of the 45 components gathered through the FFQ was initially standardized against a global reference database from 11 countries by converting each measurement into a z-score. These z-scores were subsequently adjusted to centered proportions (doubling the 0–1 proportion and subtracting 1 to produce a distribution ranging from − 1 to + 1). To determine the participant-specific DII score, each centered proportion was multiplied by its corresponding inflammatory weight, as derived from the literature, and the resulting products were then summed.

An energy-adjusted version (E-DII™) was computed by repeating the same procedure with nutrient densities (per 1,000 kcal) and utilizing an energy-standardized reference database [25, 26]. The indices range from approximately − 9, indicating maximum anti-inflammatory properties, to + 8, indicating maximum pro-inflammatory properties, making them directly comparable across different studies [25]. The choice between using DII or E-DII in a specific model depends on which index provides a better fit and greater explanatory power.

Short-term stress scale

Data on short-term stress were collected by asking participants: “Stress was defined as a feeling of tension, restlessness, nervousness, or anxiety, or an inability to sleep at night due to a constantly troubled mind. How often did you experience this type of stress in the past year?” Responses were captured using a 5-point Likert scale ranging from “not at all” to “very much [27].

Family socioeconomic status (SES)

A 10-step graphic scale that depicted the social hierarchy of Isfahan was used to ask participants to rate the SES of their families’ SES. The validity and reliability of the scale have been previously established [28]. Higher scores corresponded to better education, occupation, and income levels.

Physical activity

The International Physical Activity Questionnaire (IPAQ) was used to evaluate the degree of physical activity [29]. We excluded activities such as walking and sitting and instead focused on both vigorous and light physical activities, which were evaluated based on frequency (number of days per week) and duration (minutes per day). The corresponding Metabolic Equivalent of Task (MET) values were assigned to each activity level, with vigorous activities receiving a value of eight and light activities receiving three. The total MET minutes per week were categorized into three ranges: 0-1000, 1001–2000, and greater than 2000. BMI was calculated by dividing a person’s weight in kilograms by the square of their height in meters [30].

Loneliness

A direct single-item frequency measure with 5-Likert response options (1 = never, 2 = rarely, 3 = sometimes, 4 = often, and 5 = always) was employed to measure loneliness during the last few years. The reliability and convergent validity of this measure have been demonstrated previously [31]. Loneliness before the diagnosis date was measured by asking “During the last years, how often did you feel lonely?” [31].

Quality of Life (QoL)

Participants gauged their current QoL through a single-item, five-point Likert scale adapted from the WHOQOL-BREF instrument: ‘How do you rate your quality of life?’ [32]. The scale offered responses from “very poor” to “very good,” with higher scores signifying a more favorable perception of QoL. This single-item global measure has been validated and demonstrates acceptable reliability and construct validity as a brief indicator of overall QoL [33].

Statistical analysis

Descriptive statistics were used to outline participant characteristics, employing means and standard deviations (SD) for continuous variables and frequencies and percentages for categorical variables. Model assumptions were assessed using residual vs. fitted plots for linearity, Jarque-Bera test for normality, and the Breusch-Pagan test for homoscedasticity. Variance inflation factors (VIFs) were used to evaluate multicollinearity, with values of < 5 indicating acceptable collinearity. Crude associations between DII/E-DII and QoL were examined using simple linear regression, reporting crude beta coefficients (β) and 95% confidence intervals (CIs). Multiple linear regression models were employed to estimate adjusted associations, controlling for age, marital status, stress, loneliness, SES, energy consumption, BMI, and physical activity. DII and E-DII were examined as continuous variables and divided into quartiles to evaluate linear trends. The trend tests used median quartile values as continuous variables. Statistical significance was set at P < 0.05. E-values determined the strength of the observed relationships between the exposure variables (E-DII/DII) and QoL against unmeasured confounding factors. E-values were computed for point estimates and 95% CI lower bounds [34, 35]. The regression coefficient and standard error were standardized using the residual standard deviation from the linear regression model. This method calculates the minimum level of association that an unmeasured confounder must have for both the exposure and the outcome, given the measured covariates, to account for the observed relationship. Analyses were performed using STATA version 15 (StataCorp, College Station, TX, USA).

Results

Table 1 shows the baseline characteristics of BC patients with BC examined for QoL associations. The average age of the patients was 50.05 years (SD: 9.69). The mean stress score was 3.85 (SD: 1.4), which was significantly linked to lower QoL, with a crude β of -0.25 (95% CI: -0.31, -0.19; p < 0.001). Loneliness during the past year, with a mean score of 2.92 (SD: 1.46), also showed a significant inverse relationship with QoL (β = -0.25, 95% CI: -0.31, -0.19; p < 0.001). Physical activity was categorized into three groups based on MET scores: 0-1000 MET (n = 59; 9.80%), 1001–2000 MET (n = 61; 10.20%), and > 2000 MET (n = 480; 80%). No significant association was found between physical activity levels and QoL. The mean BMI of participants was 27.10 kg/m² (SD: 4.8), and no significant association was observed between BMI and QoL (β = -0.006, 95% CI: -0.01, 0.02; p = 0.54). SES had a mean of 3.15 (SD: 0.91), but it did not have a significant impact on QoL (β = -0.002, 95% CI: -0.05, 0.04; p = 0.93).

Table 1.

Crude associations of baseline BC patient’s characteristics with Quality of Life

Characteristics	Mean (SD)	Crude β (95% CI)	P-value
Age, years	50.05 (9.69)	-0.005 (-0.014, 0.004)	0.28
Stress	3.85 (1.4)	-0.25 (-0.31, -0.19)	< 0.001
SES	3.15 (0.91)	-0.002 (-0.05, 0.04)	0.93
Physical activity (MET) (N %)
0-1000	59 (9.80)	0	-
1001–2000	61 (10.20)	-0.09 (-0.47, 0.30)	0.66
> 2000	480 (80.00)	-0.02 (-0.32, 0.27)	0.88
BMI; mean (SD)	27.10 (4.8)	-0.006 (-0.01, 0.02)	0.54
Loneliness during last year; mean (SD)	2.92 (1.46)	-0.25 (-0.31, -0.19)	< 0.001

SD, Standard Deviation; CI, Confidence Interval; SES, Socioeconomic Status; BMI, Body Mass Index; MET, Metabolic Equivalent of Task

Table 2 presents the relationships between DII, E-DII, and QoL in BC patients using multiple linear regression analysis. There was an inverse relationship between DII scores and QoL in BC patients. The initial analysis revealed significant negative associations (β = -0.15, 95% CI: -0.19, -0.11; p < 0.001), which remained significant after accounting for confounding variables (adjusted β = -0.12, 95% CI: -0.16, -0.08; p < 0.001). Quartile analysis further highlighted this negative trend, with the highest DII quartile exhibiting notably lower QoL in both the unadjusted (β = -0.90, 95% CI: -1.13, -0.67; p < 0.001) and adjusted models (β = -0.70, 95% CI: -0.94, -0.46; p < 0.001). The trend test confirmed these findings (adjusted β = -0.23, 95% CI: -0.30, -0.15; p < 0.001), suggesting a linear relationship between increased dietary inflammation and decreased QoL.

Table 2.

Multiple linear regression model demonstrating the adjusted associations between DII, E-DII and quality of life in breast cancer patients, Isfahan Iran, 2021–2023

	Crude β (95% CI), P-value	Adjusted β (95% CI) *, P-value
DII (continuous)	-0.15 (-0.19, -0.11), < 0.001	-0.12 (-0.16, -0.08), < 0.001
DII (quartiles)
-2.56, 0.91 (Q1)	1	1
0.92, 2.95 (Q2)	-0.17 (-0.43, 1.31), 0.16	-0.18 (-0.40, 0.04), 0.12
2.96, 4.72 (Q3)	-0.42 (-0.65, -0.19), < 0.001	-0.36 (-0.59, -0.12), 0.003
> 4.72 (Q4)	-0.90 (-1.13, -0.67), < 0.001	-0.70 (-0.94, -0.46), < 0.001
Test for Trend	-0.29 (-0.37, -0.22), < 0.001	-0.23 (-0.30, -0.15), < 0.001
E-DII (continuous)	-0.09 (-0.13, -0.04), < 0.001	-0.06 (-0.11, -0.02), 0.007
E-DII; (quartiles)
-2.19, 0.24 (Q1)	1	1
0.24,1.46 (Q2)	-0.07 (-0.31, 0.18), 0.59	-0.15 (-0.37, 0.08), 0.21
1.47, 2.97 (Q3)	-0.21 (-0.45, 0.04), 0.10	-0.22 (-0.46, 0.01), 0.06
> 2.97 (Q4)	-0.41 (-0.65, -0.17), 0.001	-0.31 (-0.55, -0.07), 0.01
Test for Trend	-0.14 (-0.22, -0.07), < 0.001	-0.10 (-0.18, -0.02), 0.01

¹ Adjusted for age, stress, marital status, current family socioeconomic status, Current quality of life, Loneliness during the last year, energy intake, body mass index and Physical activity

DII, Dietary Inflammatory Index; E-DII, Energy-adjusted Dietary Inflammatory Index; CI, Confidence Interval; β, Beta Coefficient

Similarly, the unadjusted analysis indicated a significant inverse relationship between E-DII and QoL (β = -0.09, 95% CI: -0.13, -0.04; p < 0.001). This association remained significant after adjusting for potential confounders (adjusted β = -0.06, 95% CI: -0.11, -0.02; p = 0.007). A clear trend was observed across E-DII quartiles, with those in the highest quartile showing significantly poorer QoL compared to those in the lowest quartile in both unadjusted (β = -0.41, 95% CI: -0.65, -0.17; p = 0.001) and adjusted analyses (β = -0.31, 95% CI: -0.55, -0.07; p < 0.001). The significant trend test further supported the observed linear association (adjusted β = -0.10, 95% CI: -0.18, -0.02; p = 0.01). Due to the stronger and more consistent associations observed with DII compared to E-DII, DII was chosen as the primary variable for interpreting the results of this study.

To evaluate the strength of the links between DII, E-DII, and QoL in the face of potential unmeasured confounding factors, E-values were determined. For the DII, the E-value for the point estimate was 1.49, while the lower limit of the 95% CI was 1.38. In the case of E-DII, the E-value for the point estimate was 1.32, with the lower bound of the CI at 1.15. These findings suggest that an unmeasured confounder, which is associated with both the exposure and the outcome by risk ratios of at least 1.38 (for DII) and 1.15 (for E-DII), beyond the measured covariates, would be necessary to completely account for the observed associations.

Discussion

This research investigated the association between dietary inflammatory potential, measured by DII and E-DII, and QoL in women diagnosed with BC. It was found that higher scores on these indices were significantly associated with a reduced QoL, even after adjusting for other influencing factors. Women in the top quartiles of DII and E-DII experienced notably worse QoL compared to those in the lowest quartiles. A distinct linear trend further supported these results (DII trend: adjusted β = -0.23, E-DII trend: adjusted β = -0.10), highlighting the detrimental effect of pro-inflammatory dietary habits on QoL in BC patients.

Our findings align with those of previous studies that show a negative link between dietary inflammation and QoL across various groups; however, there are limited data regarding the specific patient population of BC. A study from Korea derived an inflammatory dietary pattern marked by high red meat, white rice, and noodle consumption, and found that BC survivors with higher inflammatory scores had significantly lower physical QoL, particularly in the role-physical and bodily pain domains [36]. An analysis of four longitudinal studies showed that lower DII scores were associated with decreased depression risk (relative risk (RR): 0.76, 95% CI: 0.63–0.92) [37]. Golmohammadi et al. reviewed the link between DII and QoL across populations [10]. Seven of the eight studies revealed a significant negative correlation between higher DII scores and QoL [10]. This relationship was found in individuals with asthma, osteoarthritis, multiple sclerosis, hemodialysis, obese women, and healthy individuals. However, a study on postmenopausal women found no such association [20].

A 12-week educational program focused on an anti-inflammatory diet was shown in a randomized controlled trial (RCT) to significantly enhance depressive symptoms and QoL in BC patients undergoing adjuvant chemotherapy [38]. The intervention also reduced DII scores and lowered TNF-α and CRP levels, highlighting dietary education as a promising and cost-effective strategy to ease depression and inflammation in this population [38]. A meta-analysis involving more than 100,000 women with BC revealed that diets high in pro-inflammatory foods were associated with a 40% increased risk of depression compared to diets rich in anti-inflammatory foods.(OR = 1.40; 95% CI: 1.21–1.62) [39].

In contrast, Long Parma et al.‘s RCT reported mixed findings [40]. Their anti-inflammatory dietary intervention, which included food preparation workshops and motivational interviewing, significantly reduced perceived stress at 6 months in BC survivors; however, this benefit was not sustained at 12 months [40]. These inconsistencies, which are consistent with findings from other studies that show a decay of intervention effects over time [41] may be due to differences in intervention duration, baseline dietary habits, and participant characteristics, suggesting that longer or more personalized approaches may be necessary to sustain the benefits.

Adopting healthy eating habits can enhance well-being by minimizing inflammation and inflammatory reactions while also offering antioxidant benefits [10]. Unhealthy diets can provoke inflammatory responses [42, 43], and the resulting cytokine activity has been linked to poor QoL, including increased physical disability, psychosocial challenges, pain, mood disturbances, and sexual dysfunction [10, 44–46]. For example, research has shown that stronger adherence to the Mediterranean diet is associated with improved QoL in BC survivors, particularly improvements in physical functioning and overall well-being, alongside reduced insomnia symptoms and pain [47].

DII highlights that certain components such as fiber, vitamins A, C, D, E, omega-3s, and flavonoids help reduce inflammation, while calorie-dense components such as saturated fat, refined carbohydrates, and trans fats increase inflammation [17]. In a study involving Chinese women diagnosed with early stage BC, a diet rich in fruits and vegetables, known for their anti-inflammatory properties, was linked to enhanced overall health [48]. These women showed improvements in physical, emotional, and cognitive functions and reported fewer issues, such as difficulty breathing, sleep problems, reduced appetite, and gastrointestinal discomfort [48].

Emerging evidence supports the prognostic value of the DII in BC outcomes. Among a group of 511 Korean women who underwent BC surgery, elevated DII scores after diagnosis were strongly associated with a higher likelihood of recurrence (HR = 2.35) and increased overall mortality (HR = 3.05), regardless of established clinical predictors [49]. In the Women’s Health Initiative cohort, a more anti-inflammatory diet after BC diagnosis was not associated with BC-specific or overall mortality [50]. Nevertheless, it was associated with a notably reduced risk of death due to cardiovascular issues [50], whereas a different outcome was observed in an Italian study, where DII scores measured before diagnosis did not show a significant link with mortality from any cause or specifically from BC [51]. This null result may be due to the overall anti-inflammatory nature of the cohort’s diet, given their high adherence to Mediterranean dietary habits and limited variability in DII scores [51].

In addition to the role of cytokines in systemic inflammation, there is evidence indicating that dietary patterns with low DII scores might also influence inflammation by affecting the gut microbiota [52]. An anti-inflammatory diet enhances microbial diversity by fostering the growth of beneficial bacteria, such as Bacteroides, Bifidobacteria, Faecalibacterium, and Roseburia [53]. These microorganisms are essential for producing short-chain fatty acids (SCFAs), especially butyrate, which engages G-protein-coupled receptors found on epithelial and immune cells [52, 54]. In contrast, the typical dietary habits of Western societies, which are high in saturated fats and processed foods, have been associated with imbalances in the gut microbiota, known as dysbiosis [53]. It compromises intestinal barrier function and elevates levels of metabolic endotoxemia [53]. Such gut-derived immune responses have been linked to impaired physical and mental health domains of QoL, suggesting that microbiota modulation may represent a key biological pathway that links diet to patient-reported outcomes [55–57].

Finally, one of the most clinically relevant aspects of the DII is its modifiability. Unlike fixed genetic or disease-related factors, the inflammatory potential of diet can be adjusted through intentional lifestyle changes. This positions the DII as a practical and scalable target for survivorship interventions. Given the strong inverse trend observed in this and other studies, increasing the consumption of fruits and vegetables or decreasing the intake of processed foods can lower DII scores and potentially improve QoL in patients with BC.

Despite the strengths of this study, including an excellent response rate and comprehensive adjustment for potential confounders, several limitations must be acknowledged. First, its cross-sectional design presents a challenge in establishing temporal relationships and limiting causal inferences. However, longitudinal or interventional studies are required. Second, the subjective nature of the main study outcome, QoL, could have led to some degree of measurement error. However, the reliability and validity indices of QoL decrease the possibility of major information bias. Third, potential measurement errors inherent in dietary self-reporting scales may have led to the misclassification of DII scores. This may be exacerbated by the existence of a disease in which there is an alleged connection to diet. Moreover, cultural dietary differences and the varying availability of DII components across populations may affect valid comparisons. In addition, biological mechanisms such as gut microbiota interactions and the influence of treatment-related factors remain underexplored. Future studies should incorporate microbiome assessments, inflammatory biomarkers, and treatment data to elucidate the underlying pathways. Although we adjusted for a broad set of covariates, it was not possible to eliminate the possibility of residual confounding. However, the E-value for the lower limit of the CI (1.38 for DII) indicates that a confounder of considerable strength is necessary to account for the observed relationship. This enhances the inferential strength of the observed relationship between pro-inflammatory dietary exposure and a diminished quality of life in this population.

In conclusion, this study’s results indicate that dietary patterns with pro-inflammatory characteristics, as assessed by DII and E-DII, are closely linked to poorer QoL among women with BC. The consistent pattern suggests that dietary inflammation could be a modifiable element affecting the physical, emotional, and functional health of BC survivors. These findings advocate for integrating anti-inflammatory dietary strategies into oncology care to enhance patient outcomes. Future research should prioritize longitudinal studies to establish causality and explore underlying mechanisms, such as inflammatory biomarkers and gut microbiota interactions. Randomized controlled trials testing personalized nutrition interventions are also essential to determine their efficacy in reducing dietary inflammation and improving QoL for BC survivors.

Author contributions

[B.D.]: Data curation; Investigation; Writing, original draft; Writing, review and editing. [P.T.]: Formal analysis; Methodology; Writing, original draft; Writing, review and editing. [S.P.]: Formal analysis; Methodology; Writing, original draft; Writing, review and editing. [R.A.B.]: Investigation; Data curation; Visualization; Writing, review and editing. [I.A.]: Conceptualization; Study design; Methodology; Supervision; Formal analysis; Project administration; Writing, original draft; Writing, review and editing. [M.Y.]: Conceptualization; Writing, review and editing. [S.H.J.] Data curation; Writing, original draft. [K.Z.]: Validation; Writing, review and editing. [J.R.H.]: Validation; Writing, review and editing. All authors read and approved the final manuscript.

Funding

This work received financial support from Isfahan University of Medical Sciences (Grant No. 199061), the Shams Charity Organization, and the Cancer Institute of Iran, affiliated with Tehran University of Medical Sciences (Grant No. 1400-2-244-53323). Additional support was provided to PT, SP, and JRH by the REMEDY Study (U01CA272977-01) under the MeDOC Consortium. JRH also received support from the National Institute for General Medical Sciences (NIGMS) under Grant P20 GM155896.

Data availability

The datasets generated and analyzed during this study are available upon reasonable request. The corresponding author, Dr. Ibrahim Abdollahpour, had full access to the data and is responsible for the integrity and accuracy of the data analysis.

Declarations

Ethics approval

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Isfahan University of Medical Sciences (IR.MUI.REC.1399.010).

Consent to participate

Verbal informed consent to participate was obtained from all individual participants prior to enrollment.

Consent to publish

Not applicable.

Disclosure

Dr. James R. Hébert is the majority owner of Connecting Health Innovations LLC (CHI), which holds the exclusive license for the Dietary Inflammatory Index^® (DII^®) from the University of South Carolina. CHI develops technology-based applications for dietary assessment and counseling. However, this manuscript is independent of any CHI-related activities, and CHI did not influence the design, analysis, or reporting of the current study.

Competing interests

The authors declare no competing interests.