# Link Between Metabolic Syndrome, Blood Lipid Markers, Dietary Lipids, and Survival in Women with Early-Stage Breast Cancer

Christine Bobin-Dubigeon, Loic Campion, Clémence Bossard, Elsa Rossignol, Jean-Sébastien Frenel, Mario Campone, Jean-Marie Bard

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Christine Bobin-Dubigeon, Loic Campion, Clémence Bossard, Elsa Rossignol, Jean-Sébastien Frenel, et al. Link Between Metabolic Syndrome, Blood Lipid Markers, Dietary Lipids, and Survival in Women with Early-Stage Breast Cancer. Nutrients, 2024, 16 (21), pp.3579. 10.3390/nu16213579. hal-05088481

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Article

# Link Between Metabolic Syndrome, Blood Lipid Markers, Dietary Lipids, and Survival in Women with Early-Stage Breast Cancer

Christine Bobin-Dubigeon <fnref n="1" />,<fnref n="2" />,<fnref n="3" />,*, Loic Campion <fnref n="2" /> ID, Clémence Bossard <fnref n="1" />,<fnref n="3" />, Elsa Rossignol <fnref n="2" />, Jean-Sébastien Frenel <fnref n="2" /> ID, Mario Campone <fnref n="2" /> and Jean-Marie Bard <fnref n="3" /> ID

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**Citation:** Bobin-Dubigeon, C.; Campion, L.; Bossard, C.; Rossignol, E.; Frenel, J.-S.; Campone, M.; Bard, J.-M. Link Between Metabolic Syndrome, Blood Lipid Markers, Dietary Lipids, and Survival in Women with Early-Stage Breast Cancer. *Nutrients* **2024**, *16*, 3579. https://doi.org/10.3390/nu16213579

Academic Editor: Jianjun Deng

Received: 25 July 2024

Revised: 1 October 2024

Accepted: 11 October 2024

Published: 22 October 2024

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**Copyright:** © 2024 by the authors.
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**Abstract:** Background/Objectives: Nearly 10% of cancers could be prevented through dietary changes. In addition, breast cancer (BC) is the most common cancer in women worldwide. Inadequate diet may lead to several metabolic abnormalities, including metabolic syndrome (MS). The goal of our study is to evaluate the link between survival after BC and MS, as well as diet lipids and circulating lipids. Methods: This study was performed in an early-stage BC cohort (n = 73): MS, dietary lipids, and circulating biological parameters, including leucocyte expression in cholesterol carriers (ATP-binding cassette transporter ABCA1, ABCG1), were determined before any medication intervention. The data of each patient were analyzed using univariate logistic regression and are expressed by HR, 95%CI [5th–95th]. All these parameters were explored with survival parameters using Cox regression analyses. Results: Overall survival (OS) and invasive disease-free survival (iDFS) were significantly longer for the women without metabolic syndrome with HR 4.7 [1.11–19.92] and *p* = 0.036, and 3.58 [1.23–10.44] and *p* = 0.019, respectively. The expression of ABCG1 in peripheral leucocytes, an ATP-binding cassette transporter involved in cholesterol and phospholipid trafficking, is significantly associated with iDFS (1.38 [1.1–1.9], *p* = 0.0048). MS is associated with more pejorative survival parameters in early-stage breast cancer. Paraoxonase (or PON) activities differ according to PON gene polymorphism, but also diet. A link between PON activities and survival parameters was suggested and needs to be clarified. Conclusions: This study emphasizes the link between survival parameters of early-stage breast cancer, metabolic syndrome, and some parameters related to lipid metabolism.

**Keywords:** breast cancer; nutrition; lipids; paraoxonase; metabolic syndrome; dietary habits

## 1. Introduction

Breast cancer is the most common cancer in women worldwide with more than 2.26 millions new cases diagnosed in 2020 [1]. Diet, nutrition, and physical activity affect breast cancer risk, but also breast cancer survival and recurrence.

A diet rich in lipids may be a risk factor for many diseases, especially cardiovascular disease, but also for breast cancer [2]. However, the quality of the lipids must be taken into account. In fact, the Mediterranean diet, based on an abundant consumption of extra virgin olive oil and foods of plant origin in particular, has been suggested to have a protective effect against the occurrence of breast cancer [2], especially in menopausal women [3]. A poor lifestyle and an unbalanced and too rich a diet, associated with being overweight in postmenopausal women, are modifiable risk factors for BC, whereas being overweight seems to be associated with a lower risk in premenopausal women.

<footnote n="1">¹ Nantes Université CNRS, US2B, UMR 6286, 44000 Nantes, France; clemence.bossard@ico.unicancer.fr</footnote>

<footnote n="2">² ICO René Gauducheau, Bd Jacques Monod, 44805 Nantes Saint Herblain CEDEX, France; loic.campion@ico.unicancer.fr (L.C.); elsa.rossignol@ico.unicancer.fr (E.R.); jean-sebastien.frenel@ico.unicancer.fr (J.-S.F.); mario.campone@ico.unicancer.fr (M.C.)</footnote>

<footnote n="3">³ Centre de Recherche en Nutrition Humaine Ouest (CRNH), 44093 Nantes, France; jean-marie.bard@univ-nantes.fr</footnote>

<footnote>* Correspondence: christine.bobin-dubigeon@ico.unicancer.fr</footnote>
Metabolic syndrome (MS), which has been extensively studied in the cardiovascular context, is defined as a cluster of risk factors for cardiovascular disease (hyperglycemia, hypertriglyceridemia, low HDL cholesterol, visceral obesity, and high blood pressure), usually associated with hyperinsulinemia [4]. More recently, common mechanisms between cancer and metabolic syndrome, such as inflammation, insulin resistance, and excess adiposity, have prompted interest.

Dietary intake, especially lipid intake, may also play a role in various inflammatory processes [5], and the impact of food intake on inflammation could be determined by evaluating a dietary inflammation score through dietary questionnaire analyses [6].

Lipids play an important role in the development of tumors. Indeed, lipids are important for maintaining cellular homeostasis, but they are also used to synthesize cellular signaling molecules [7]. Among many lipid actors, we have focused for two decades on the expression of different genes such as LXRβ- and LXR-dependent proteins such as ABCA1 and ABCG1 involved in cholesterol efflux from cells [8,9]. In previous studies, we have identified another actor of lipid metabolism, paraoxonase (PON), as a potential marker of survival in patients with breast cancer recurrence. Its role and purpose are not completely discovered; yet, it is commonly recognized that it plays an antioxidant role [10,11].

Moreover, its role in some diseases such as cardiovascular disease, inflammatory disease, and cancer has been shown in previous studies. Indeed, in the main cancer localizations, plasmatic PON activities are drastically lower in patients compared to controls [10]. Diet may influence PON, as high fat intake has been shown to be associated with lower activity of the enzyme [12], and it has also been shown that some genetic polymorphism (L55M and Q192R) can impact PON1 activity [13,14].

The goals of our study are to evaluate the link between metabolic syndrome, blood lipid markers, dietary lipids, and survival parameters in a cohort of patients suffering from an early-stage breast cancer positive for hormone receptors.

## 2. Materials and Methods

### 2.1. *Patients*

#### Study Design

Eligible patients of this multicenter open-label phase 2 trial, called TAM, were women aged 18 years or older, newly diagnosed for early-stage breast cancer (tumor size > 15 mm) with estrogen-receptor-positive, HER2-negative operable primary cancer, without evidence of metastatic spread. This study was carried out in accordance with the guidelines for Good Clinical Practice and the Declaration of Helsinki and was approved by the CPP Ouest IV (EUDRACT 2008-007652-10). Written informed consent and specific genetic consent were obtained from each participant. The current study protocol is available online (https://clinicaltrials.gov/ct2/show/NCT01220076, accessed on 6 July 2024). Tamoxifen 20 mg per day was prescribed for 5 weeks after enrollment, prior to breast surgery.

One of the secondary objectives was to examine the interaction of diet and tamoxifen treatment. Nutritional parameters such as dietary habits and circulating nutritional biomarkers were evaluated at baseline, immediately after inclusion and before any treatment.

The flowchart of this study is summarized in Figure 1.

### 2.2. *Metabolic Syndrome*

Metabolic syndrome (MS) was defined for women according to [15] which requires the presence of three of the five following criteria: (1) hypertriglyceridemia (~1.50 g/litre or 1.7 mmol/litre); (2) high arterial blood pressure (~130/85 mm Hg); (3) low HDL cholesterol (<0.5 g/L or 1.1 mmol/L); (4) hyperglycemia (~1.0 g/L or 5.6 mmol/L); and (5) waist measurement equal to or higher than 88 cm. Treatment for hypertension, hyperglycemia, or dyslipidemia was considered a positive criterion.
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Flowchart of this study.</figcaption></figure>

For each patient, the clinico-biological data (age, including menopausal status, weight, height, breast cancer subtype, tumor overexpression) were collected at baseline, prior to any treatment, to assess the prevalence of MS. Performance status (PS, a standard criterion for measuring the impact of the disease on a patient's daily living abilities) was collected for each patient. Overall survival (OS), invasive disease-free survival (iDFS), and relapse-free survival (RFS) were explored in relation to MS.

## 2.3. *Dietary Lipids and Lipidic Biomarkers*

### 2.3.1. Dietary Intake

Dietary intake was assessed at baseline using 3-day self-report questionnaires (2 weeks and 1 weekend day) to evaluate the relationship between diet, metabolic syndrome, and lipid biomarkers as described [16]. This 72 h food recall provides detailed information on the quality and quantity of the meals consumed. For each patient, dietary intakes were converted into energy and macro- and micronutrients according to the Ciqual French food composition table 2020 [17] by using Nutrilog software (version 3.20). Validation of dietary intake was performed according to the Golberg cut-off for energy intake (>1.35) as described in [18].

A dietary inflammatory score (DII score) was calculated for each validated questionnaire according to Shivappa's adapted method [6,19–21]. The DII score was calculated by taking into account the intake of 27 nutrients (total energy, alcohol, vitamins B6 and B12, beta carotene, carbohydrates, cholesterol, fat, fiber, folic acid, iron, magnesium, monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), niacin, protein, omega 3 and omega 6 fatty acids, riboflavin, saturated fatty acids, selenium, thiamin, vitamins A, C, D, and E, and zinc). The DII score was explored in relation to the survival parameters and the biological parameters related to paraoxonase activities.

### 2.3.2. Circulating Lipids and Apolipoproteins

Total cholesterol, plasma triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) concentrations were measured at baseline using enzymatic kits from Diasys®, according to the manufacturer's instructions (Grabels, France). Apolipoproteins were quantified by LC MS/MS as previously described [22].
### 2.3.3. LXR-Regulated Genes of Cholesterol Trafficking

In order to explore the LXR-regulated genes of cholesterol trafficking and lipidic patterns, nucleic acids were extracted from Tempus blood RNA tubes (Thermo Fisher, Nantes, France) at baseline and at week 5, as previously described [23]. Relative quantification was performed using the ΔCT method. The genes involved in cholesterol trafficking and studied in this work were ABCA1, ABCG1, PON2, and LXRβ. The genes and Taqman® probes (Life Technologies, Villebon sur Yvette, France) were as follows (gene name, assay ID): ATP-binding cassette, sub-family A, member 1, Hs00194045_m1 for ABCA1; ATP-binding cassette, subfamily G (WHITE), member 1 (ABCG1), Hs01555189_m1 for ABCG1; nuclear receptor subfamily 1, group H, member 2 (NR1H2), Hs01027208_m1 for LXRβ; and paraoxonase, Hs00165563_m1 for PON2, as described in [23]. The values were normalized against three housekeeping genes as described previously.

The expression of these genes was explored in relation to the three survival parameters and to the intake of dietary fatty acids.

### 2.3.4. Paraoxonase

At baseline, serum paraoxonase (PON1) activities were quantified on three different substrates, paraoxon (PON), lactonase (LAC), and phenylacetate (ARE), as adapted from a previous description [24]. Hydrolysis rate was followed in duplicate using a continuously recording spectrophotometer (TECAN, Männedorf, Switzerland, Infinite M200).

In addition, the genotypic frequencies of PON1 L55M and Q192R were determined by an allelic discrimination assay using real-time PCR using TaqMan fluorogenic probes as described in [25], on saliva samples (FTA cards). Biological parameters related to paraoxonase activities were also calculated such as individual enzyme activities/HDL ratio and enzyme activities/apolipoprotein A1 ratio as previously described [22,26].

## 2.4. Statistical Considerations

Qualitative factors are described by means of the frequency of their respective modalities and compared using Pearson's Chi-square test (or Fisher's test). Continuous factors are described by means of their median (IQR) and compared using the Mann-Whitney test (or Kruskal-Wallis test if more than 2 groups).

Invasive disease-free survival (iDFS) was defined as the time from the date of inclusion in the study to the date the event occurred first (as defined in the DATECAN guidelines) or censored at the last event-free date; iDFS was calculated and plotted using Kaplan-Meier curves. Survival curves were compared using the log-rank test. Overall survival (OS) was defined as the time from the date of inclusion to the date of death.

The median of follow-up was calculated using the inverse Kaplan-Meier method.

Analyses were performed using StataSE 17.0 software (StataCorp, College Station, TX, USA). The tests were performed in two-tailed formulation and the significance limit was set at 5%. Multivariate analyses were performed by including MS status, age, and Dietary Inflammatory Index with survival parameters.

# 3. Results

## 3.1. *Description of the Study Cohort and Metabolic Syndrome*

The study cohort included n = 73 patients, and the clinico-biological parameters are synthesized in Table 1 (one missing datum). The prevalence of MS in the whole study population was 22/72 (30.5%) according to the NCEP definition.

As expected, SM+ patients were significantly older, mainly with menopausal status with a lower performance status. In the same way, the subtype of breast cancer differed according to the SM status.

We explored the interaction between metabolic syndrome and survival parameters. As shown in Figure 2, overall survival (OS) was significantly longer in women without metabolic syndrome (MS−) with HR 4.7 [1.11–19.92], *p* = 0.036.
<table><caption>**Table 1.** Clinico-biological parameters of the studied population associated with validated dietary questionnaires.</caption><thead><tr><th></th><th>SM+ (n = 22)</th><th>SM- (n = 50)</th><th>p-Value</th></tr></thead><tbody><tr><td>Age (years) *</td><td>65 (54–75)</td><td>52 (44–63)</td><td>0.006</td></tr><tr><td>BMI (kg m<sup>-2</sup>) *</td><td>28.5 (25.7–30.9)</td><td>22.5 (20.6–24.9)</td><td>&lt;0.001</td></tr><tr><td>Menopausal status **</td><td>18 (81.8%)</td><td>23 (46%)</td><td>0.005</td></tr><tr><td>Performance status = 0</td><td>17 (77.3%)</td><td>50 (100%)</td><td>0.002</td></tr><tr><td>Type of cancer **</td><td></td><td></td><td></td></tr><tr><td>Invasive carcinoma of no special type (ductal)</td><td>13 (59.1%)</td><td>41 (82.0%)</td><td rowspan="3">0.03</td></tr><tr><td>Invasive lobular carcinoma</td><td>9 (40.9%)</td><td>7 (14.0%)</td></tr><tr><td>Other</td><td>0 (0%)</td><td>2 (4.0%)</td></tr><tr><td>Overexpression of estrogen receptor</td><td>22 (100%)</td><td>50 (100%)</td><td></td></tr><tr><td>HER2+</td><td>0%</td><td>0%</td><td></td></tr><tr><td>Circulating lipids (mmol/L)</td><td></td><td></td><td></td></tr><tr><td>Plasma cholesterol</td><td>5.42 (4.74–5.94)</td><td>5.68 (4.94–6.67)</td><td>0.144</td></tr><tr><td>Plasma triglycerides</td><td>1.07 (0.74–1.59)</td><td>0.94 (0.72–1.30)</td><td>0.309</td></tr><tr><td>HDL cholesterol</td><td>1.26 (1.15–1.51)</td><td>1.81 (1.58–2.03)</td><td>&lt;0.001</td></tr><tr><td>LDL cholesterol</td><td>3.26 (2.73–3.86)</td><td>3.42 (2.91–4.21)</td><td>0.303</td></tr></tbody></table>

## Overall survival

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Overall survival with and without the presence of metabolic syndrome (MS+ vs. MS−); n = 68.</figcaption></figure>

In the same way (Figure 3), iDFS was significantly longer in women with MS− with HR 3.58 [1.23–10.44], *p* = 0.019, although the metabolic syndrome status did not appear to have influence on the relapse-free survival (HR = 2.14 [0.57–8.08], *p* = 0.262).

However, on multivariate analysis including age, MS still remained significantly associated with iDFS but not with OS.

## 3.2. *Dietary Habits and Dietary Inflammatory Index*

In patients with validated dietary questionnaires (n = 73), the median [25th–75th] daily energy intake and macronutrients carbohydrate, protein, and lipids were 2042 kcal/d [1550–2910], 220 g/d [188–254] (43.4% of energy intake or EI), 85.9 g/d [76.9–100] (16.7% EI), and 82.9 g/d [72.3–92] (36.7% EI), respectively. More specifically for lipids, the daily intakes of total FA, saturated FAs, MUFAs, and PUFAs ω3 and w6 daily amounts were

<footnote>* Median (25th–75th); ** frequency expressed as n (percentage); Mann–Whitney test; n = 72; n = 1 missing datum.</footnote>
61.4 g/d [51.1–76.0], 30.3 g/d [20.8–37.4], 22.7 g/d [17.5–27.4], 8.8 g/d [6.6–13.1] g/d, 1.1 g/d [0.9–1.9], and 7.2 g/d [5.2–9.9], respectively.

<figure><img src="image_7.png" /><figcaption>**Figure 3.** iDFS with and without the presence of metabolic syndrome (MS+ vs. MS−); n = 68.</figcaption></figure>

For each validated questionnaire, the Dietary Inflammatory Index or DII was calculated by including 27 items as described above. The median value of the DII was −0.43 [25th–75th: −1.61, 0.26]. The negative value of the DII score suggests that the dietary habits in our cohort tend to be anti-inflammatory. None of the survival parameters, such as overall survival, relapse-free survival, and invasive disease-free survival, were associated with DII score with HR [95% CI]: 1.43 [0.92–2.23] (p = 0.11), 0.996 [0.69–1.43] (p = 0.98), and 1.11 [0.82–1.50] (p = 0.5), respectively. Moreover, the DII score was not associated with any other biological parameters related to paraoxonase activities.

### 3.3. LXR-Regulated Genes of Cholesterol Trafficking, Lipidic Dietary Habits, and Survival Parameters

The expression of different LXR-regulated genes of cholesterol trafficking in peripheral leukocytes was evaluated at baseline and at week 5 (Table 2).

<table><caption>**Table 2.** LXR-regulated genes of cholesterol trafficking expression of peripheral leucocytes at baseline and week 5 (arbitrary units = AU).</caption><thead><tr><th rowspan="2">Genes</th><th colspan="3">AU (n = 50)</th></tr><tr><th>Baseline</th><th>Week 5</th><th>p-Value</th></tr></thead><tbody><tr><td>ABCG1</td><td>1.47 [1.06–1.85]</td><td>1.23 [0.94–1.70]</td><td>0.72</td></tr><tr><td>ABCA1</td><td>1.40 [1.01–2.10]</td><td>1.40 [1.01–2.10]</td><td>0.61</td></tr><tr><td>PON2</td><td>0.58 [0.50–0.73]</td><td>0.65 [0.52–0.70]</td><td>0.47</td></tr><tr><td>LXRb</td><td>1.46 [0.95–2.34]</td><td>1.49 [1.01–2.30]</td><td>0.98</td></tr></tbody></table>

Results expressed as median [25th–75th]; missing data n = 23 insufficient RNA.

The expression of LXR-regulated genes of cholesterol trafficking in peripheral leukocytes remained stable after tamoxifen treatment, as no difference was observed between baseline and week 5.

The link between the expressions of these genes at baseline and dietary habits, more specifically lipids, was explored, in addition to the relation with the survival parameters. The expression of ABCG1 at baseline, an ATP-binding cassette transporter involved
in cholesterol and phospholipid trafficking, was significantly associated with invasive disease-free survival (1.38-[1.1–1.9], *p* = 0.048) and tended to be significant with overall survival (1.44-[1.0–2.1], *p* = 0.055). In addition, ABCA1, PON2, and LXRb expression in leukocytes were associated with overall survival with HR [95%CI]: 1.38 [0.56–3.39], *p* = 0.48; 0.53 [0.02–19.06], *p* = 0.73; and 1.28 [0.65–2.53], *p* = 0.47, respectively. Moreover, no link was observed with invasive disease-free survival.

As described in Figure 4A, high ABCG1 expression in peripheral leukocytes is correlated with a higher ratio of dietary PUFA ω6/ω3 (Spearman r correlation = 0.412, *p* = 0.01) and % of ω3 expressed as % total dietary lipids (B) (Spearman r correlation = −0.391, *p* = 0.024).

<figure><img src="image_8.png" /><figcaption>**Figure 4.** Relationship between ABCG1 leucocyte expression and the ratio of dietary ω6/ω3 (A) and omega 3, expressed as % of total dietary lipids (B).</figcaption></figure>

### 3.4. Paraoxonase

#### 3.4.1. Link Between Enzyme Activities and Paraoxonase Genotypes

The three enzymatic activities of paraoxonase ARE, LAC, and PON quantified at baseline on the cohort studied were 21.6 mmol/L/min [17.1–31.9], 0.174 µmol/L/min [0.155–0.192], and 114 µmol/L/min [64–201], respectively. As shown in Table 3, the genetic polymorphism L155M did not seem to impact any of the three enzyme activities PON, ARE, or LAC (n = 69).

<table><caption>**Table 3.** Paraoxonase activities according to the PON L155M gene polymorphism.</caption><thead><tr><th>Activities</th><th>L/L (n = 30)</th><th>M/L (n = 34)</th><th>M/M (n = 5)</th><th>P Global</th><th>LL vs. ML</th><th>P ML vs. MM</th><th>LL vs. MM</th></tr></thead><tbody><tr><td>ARE<br>mmol/L/min</td><td>20.5<br>[17.4–25.9]</td><td>23.9<br>[15.9–38.2]</td><td>50.9<br>[18.2–59.0]</td><td>0.18</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>LAC<br>µmol/L/min</td><td>0.177<br>[0.16–0.187]</td><td>0.174<br>[0.15–0.20]</td><td>0.171<br>[0.13–0.177]</td><td>0.71</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>PON<br>µmol/L/min</td><td>109<br>[64–165]</td><td>123.5<br>[71–209]</td><td>81<br>[51–112]</td><td>0.34</td><td>ns</td><td>ns</td><td>ns</td></tr></tbody></table>

Results expressed as median [25th–75th], n = 69 patients; missing data n = 4.

Conversely, ARE, PON, and LAC activities differed according to the Q192R polymorphism (Table 4). Indeed, the presence of the allele R seemed to decrease the ARE activity, whereas increased LAC and PON activities were observed (n = 68).

#### 3.4.2. Link Between Paraoxonase Activities and Lipid Dietary Habits

The ARE activity is positively correlated with the daily dietary PUFA ratio ω6/ω3 with R = 0.265 (*p* = 0.027). The daily amount of dietary PUFA ω6 is also positively associated with LAC activity and the ARE activity/apolipoprotein A1 ratio with R = 0.24 (*p* = 0.04) and
R = 0.278 (p = 0.036), respectively. No other correlation was observed between paraoxonase activities expressed as HDL or the apolipoprotein ratio.

<table><caption>**Table 4.** Paraoxonase activities according to PON Q192R gene polymorphism.</caption><thead><tr><th rowspan="2">Activities</th><th>Q/Q</th><th>R/Q</th><th>R/R</th><th colspan="4">P</th></tr><tr><th>(n = 34)</th><th>(n = 25)</th><th>(n = 9)</th><th>P Global</th><th>QQ vs. RQ</th><th>RQ vs. RR</th><th>QQ vs. RR</th></tr></thead><tbody><tr><td>ARE<br>mmol/L/min</td><td>27.9<br>[18.5–38.0]</td><td>19.3<br>[14.8–23.9]</td><td>18.2<br>[14.8–31]</td><td>0.036</td><td>0.016</td><td>1</td><td>0.11</td></tr><tr><td>LAC<br>µmol/L/min</td><td>0.172<br>[0.143–0.18]</td><td>0.174<br>[0.16–0.20]</td><td>0.187<br>[0.18–0.21]</td><td>0.032</td><td>0.27</td><td>0.081</td><td>0.01</td></tr><tr><td>PON<br>µmol/L/min</td><td>69<br>[52.2–99.5]</td><td>168<br>[120–218]</td><td>242<br>[69–285]</td><td>&lt;0.0001</td><td>&lt;0.0001</td><td>0.61</td><td>0.04</td></tr></tbody></table>

Results expressed as median [25th–75th], n = 68 patients; missing data n = 5.

### 3.4.3. Link Between Paraoxonase Activities and Survival Parameters

Paraoxonase activities, whatever their expression—global activities, the ratio on HDL, or the ratio on apolipoprotein A1—were not associated with overall survival in breast cancer patients. However, our results suggest that a higher LAC/HDL ratio was associated with an increased risk of recurrence (HR = 2.54 [1.25–5.19], *p* = 0.01). In addition, the PON/apoA1 ratio was inversely associated with iDFS (HR = 0.34 [0.12–0.98], *p* = 0.04).

## 4. Discussion

The aim was to explore the link between survival metabolic syndrome, lipid biomarkers, and dietary habits with survival parameters in a cohort of women with early-stage hormone-dependent breast cancer (BC).

The molecular subtype of breast cancer is luminal A, which is the main type of early-stage BC. The clinical and anthropometric characteristics are in agreement with the usually described data.

Among the different objectives of this work, we aimed to determine the frequency of metabolic syndrome (MS) in the studied population and explore its link with survival parameters. A cluster of metabolic risk factors including abdominal obesity, hypertension, dyslipidemia, insulin resistance, and low HDL-C plasma concentration defines metabolic syndrome [15]. For many decades, this syndrome has been associated with an increased cardiovascular risk [27]. This is mainly due to the poor quality of nutritional habits with high saturated fatty acids and carbohydrates [28,29]. The quality of dietary fatty acids must be taken into account as a Mediterranean diet regimen rich in olive oil intake clearly improved MS criteria [29]. The prevalence of MS reaches epidemic proportions in Western countries and varies according to sociodemographic status, age, and gender. The prevalence of MS in our cohort (30.5%) is consistent with that described in epidemiological studies such as MONICA related to the age of patients (26% for 55–65-year-olds [30]) and in women with breast cancer [31], and will probably increase in the next decade in parallel with obesity. The link between MS and cancer has been explored with a central role in insulin resistance and insulin-like GF1, but also secreted adipokines and free fatty acids [32].

Therefore, MS is strongly suggested to increase the risk of several cancer localizations such as breast cancer as described in a meta-analysis [33] in postmenopausal women (1.56, *p* = 0.017).

Our results show a link between metabolic syndrome and the decrease in overall survival, and also iDFS. These data are in complete agreement with those previously published [34–37], for example, in the large epidemiologic study WHI, which concluded that postmenopausal women with 3–4 MS components had a higher risk of death from breast cancer [36] (compared to none).
Recent work in early ER+ cancer [38] even suggests that the relative risk of endocrine-
resistant tumor was 1.4-fold greater for patients with MS (*p* = 0.0197).

The dietary habits of the study population were explored by self-assessment ques-
tionnaires. Different strategies could be used to evaluate dietary patterns of a population,
such as a food frequency questionnaire, a food diary or food record with data collected for
at least 2 days, or two or more 24 h recalls. Nevertheless, a standardized approach could
be helpful [39].

The macronutrients and lipid consumption of our cohort are in the same range as
those described in a large French nutritional epidemiological study INCA3 [40] for the
main parameters (energy, lipids, carbohydrates, total protein, and % energy amount). It
seems that our patients’ dietary habits are closer to the French dietary recommendations
with, for example, a daily energy intake around 2072 kcal/d vs. 1787 kcal/d for women
age-matched to INCA3 for a recommendation of 1800–2200 kcal/d. Our patients also have
the same dietary intakes of total FA and PUFAs ω6 and ω3 compared to INCA3. However,
our approach to evaluate dietary intakes in our cohort could be improved by using a more
complete nutrient database than Ciqual 2020, such as EPIC nutrient database [41].

The impact of food intake on inflammation could be determined by the dietary inflam-
matory score [8]. The dietary inflammatory score is described as a new tool that enables
researchers to describe the pattern of people's diet to determine whether their food intake
is more anti- or pro-inflammatory. The interest in this score has risen for two decades and
originally included 45 nutrients to characterize the inflammatory profile of the diet [6].

The link between breast cancer risk and DII has been largely suggested in Swedish,
Chinese, and Iranian studies and meta-analyses [20,42–44]. Few studies like ours have
investigated the link between DII score and breast cancer survival parameters, and like
Zuchetto et al. (2017) [45], our findings did not suggest an association between the inflam-
matory potential of the diet, measured by the DII, and breast cancer survival parameters, in
contrast to [46] where a higher risk of death from BC was associated with the consumption
of a more pro-inflammatory diet at baseline (HRQ<sub>5vsQ1</sub>, 1.33; 95% CI, 1.01–1.76; P<sub>trend</sub> = 0.03).
Our results could probably be explained by the small number of studied questionnaires but
also by the inability to include different components (like in the original DII calculation),
such as ginger, turmeric, garlic, oregano, pepper, rosemary, eugenol, saffron, flavan-3-ol,
flavones, flavonols, flavonones, and anthocyanidins. Experiments have been undertaken to
validate our DII as described in [47].

To explore the impact of the cell lipidic metabolic actors on breast cancer survival
parameters, LXR-regulated genes of cholesterol trafficking expressions in peripheral leuko-
cytes were evaluated, at baseline.

The link between the peripheral expression of these genes and survival parameters has
not been extensively studied. In the present study, ABCG1 (involved in cholesterol efflux
from the cell) expression at baseline was significantly associated with iDFS and tended to
be associated with overall survival.

On the other hand, in a previous work, we had shown that tumor ABCG1 was related
to the disease severity with a lower expression in the highest SBR-grade tumor (pejorative
situation) [23]. The tumor expression levels of LXR-regulated genes of cholesterol trafficking
also differ according to the subtype of metastatic breast cancer as previously suggested [48].

The expression of liver X receptor (LXR)-regulated genes of cholesterol trafficking
such as ABCG1 expression in cholesterol influx and efflux mediators is also influenced by
dietary intake. Our data suggest that ABCG1 expression in PBMC is associated with a high
dietary PUFA ratio ω6/ω3, which is not really in agreement with the literature. According
to the literature, the expressions of ABCG1 and ABCA1 on PBMC are increased with high
intakes of SFA rather than PUFA [48].

However, the significance of our results is limited by the small number of patients and
therefore needs to be confirmed by a larger-cohort approach.

Previously extensively studied from a cardiovascular point of view, paraoxonase 1 is
carried in the plasma by high-density lipoproteins (HDLs) and protects low-density lipopro-
teins from oxidation (LDL). It is well accepted that PON activity is lower in cancer patients than in controls and, in a previous work, we identified paraoxonase as a potential marker of survival in patients with breast cancer recurrence [49]. The enzymatic activities quantified in our cohort are consistent with those previously described by our team. However, it seems difficult to compare our results with other published data as many different methods of enzymatic measurement have been described [50,51]. As it has been described, PON1 levels depend on genetic polymorphisms [52]. In our cohort, the frequencies of the polymorphisms PON1₁₉₂ and PON1₅₅ are in agreement with those described in the Caucasian population (recent review in [53]).

PON1 activities are modified by dietary intake. The consumption of polyphenols-enriched diets may increase PON1 activity. The Mediterranean diet has also been shown to have a beneficial effect on PON1 activity. Extra virgin olive oil (ω9) seems to increase PON1 activity through the oleic acid enrichment of phospholipids present in HDL. This oil favored PON1 activity and increased hepatic PON1 mRNA and protein expression. These effects could be attributed to minor components present in this oil (terpenes, phytosterols). The high consumption of fruits and vegetables in the Mediterranean diet could also contribute to the modulation of paraoxonase activities [54].

In our previous study, paraoxonase 1 was identified as a marker of short-term death with cancer recurrence [23]. This new study confirms the interest of the early quantification of paraoxonase activity as we have shown the significant inverse association between PON/apoA1 and iDFS, but also a positive association between LAC/HDL and RFS.

These unexpected data need to be confirmed in a large breast cancer cohort.

**5. Conclusions**

MS is associated with reduced survival parameters in early-stage breast cancer. The peripheral leukocytes expression of ABCG1 involved in cholesterol trafficking is related to survival parameters. Despite the smaller sample size of the present study, our results are in agreement with previously published data on the impact of genetic and non-genetic factors (diet) to modulate paraoxonase activities. The impact of PON1 on iDFS in women with early-stage breast cancer is described for the first time. A mechanistic approach and larger clinical studies are needed to confirm these data.

This study highlights the relationship between early-stage breast cancer survival parameters, metabolic syndrome, and some parameters related to lipid metabolism.

**Author Contributions:** Methodology, C.B.-D., L.C. and J.-M.B.; Formal analysis, L.C. and C.B.; Investigation, C.B.-D., J.-S.F. and M.C.; Resources, C.B.-D., C.B., E.R. and J.-M.B.; Writing—original draft, C.B.-D. and C.B.; Writing—review & editing, C.B.-D. and J.-M.B.; Visualization, C.B.-D.; Supervision, C.B.-D. and J.-M.B.; Project administration, J.-M.B.; Funding acquisition, C.B.-D. and M.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** PHRC 2008, Ministère de la Santé et des Solidarités France.

**Institutional Review Board Statement:** The trial was conducted following the Declaration of Helsinki, Good Clinical Practice Guides (CPMP/ICH/135/95) and the current French legislation regarding clinical trials (EUDRACT 2008-007652-10). The protocol was approved by CPP Centre Ouest, Tours, France (Approval date: 6 April 2009).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
## References

1. Breast Cancer Statistics | World Cancer Research Fund International. WCRF International. Available online: https://www.wcrf.org/cancer-trends/breast-cancer-statistics/ (accessed on 24 July 2024).

2. Toledo, E.; Salas-Salvadó, J.; Donat-Vargas, C.; Buil-Cosiales, P.; Estruch, R.; Ros, E.; Corella, D.; Fitó, M.; Hu, F.B.; Arós, F.; et al. Mediterranean Diet and Invasive Breast Cancer Risk Among Women at High Cardiovascular Risk in the PREDIMED Trial: A Randomized Clinical Trial. *JAMA Intern. Med.* **2015**, *175*, 1752–1760. [CrossRef] [PubMed]

3. González-Palacios Torres, C.; Barrios-Rodríguez, R.; Muñoz-Bravo, C.; Toledo, E.; Dierssen, T.; Jiménez-Moleón, J.J. Mediterranean Diet and Risk of Breast Cancer: An Umbrella Review. *Clin. Nutr.* **2023**, *42*, 600–608. [CrossRef]

4. Huang, P.L. A Comprehensive Definition for Metabolic Syndrome. *Dis. Models Mech.* **2009**, *2*, 231–237. [CrossRef] [PubMed]

5. D'Angelo, S.; Motti, M.L.; Meccariello, R. ω-3 and ω-6 Polyunsaturated Fatty Acids, Obesity and Cancer. *Nutrients* **2020**, *12*, 2751. [CrossRef]

6. Shivappa, N.; Steck, S.E.; Hurley, T.G.; Hussey, J.R.; Hébert, J.R. Designing and Developing a Literature-Derived, Population-Based Dietary Inflammatory Index. *Public Health Nutr.* **2014**, *17*, 1689–1696. [CrossRef]

7. Guo, R.; Chen, Y.; Borgard, H.; Jijiwa, M.; Nasu, M.; He, M.; Deng, Y. The Function and Mechanism of Lipid Molecules and Their Roles in The Diagnosis and Prognosis of Breast Cancer. *Molecules* **2020**, *25*, 4864. [CrossRef] [PubMed]

8. Lund, E.G.; Peterson, L.B.; Adams, A.D.; Lam, M.-H.N.; Burton, C.A.; Chin, J.; Guo, Q.; Huang, S.; Latham, M.; Lopez, J.C.; et al. Different Roles of Liver X Receptor α and β in Lipid Metabolism: Effects of an α-Selective and a Dual Agonist in Mice Deficient in Each Subtype. *Biochem. Pharmacol.* **2006**, *71*, 453–463. [CrossRef]

9. Nazih, H.; Bard, J.M. Cholesterol, Oxysterols and LXRs in Breast Cancer Pathophysiology. *Int. J. Mol. Sci.* **2020**, *21*, 1356. [CrossRef]

10. Bacchetti, T.; Ferretti, G.; Sahebkar, A. The Role of Paraoxonase in Cancer. *Semin. Cancer Biol.* **2019**, *56*, 72–86. [CrossRef]

11. Mackness, M.; Mackness, B. Human Paraoxonase-1 (PON1): Gene Structure and Expression, Promiscuous Activities and Multiple Physiological Roles. *Gene* **2015**, *567*, 12–21. [CrossRef]

12. Ferretti, G.; Bacchetti, T. Effect of Dietary Lipids on Paraoxonase-1 Activity and Gene Expression. *Nutr. Metab. Cardiovasc. Dis.* **2012**, *22*, 88–94. [CrossRef] [PubMed]

13. Hussein, Y.M.; Gharib, A.F.; Etewa, R.L.; ElSawy, W.H. Association of L55M and Q192R Polymorphisms in Paraoxonase 1 (PON1) Gene with Breast Cancer Risk and Their Clinical Significance. *Mol. Cell Biochem.* **2011**, *351*, 117–123. [CrossRef] [PubMed]

14. Farmohammadi, A.; Momeni, A.; Bahmani, B.; Ghorbani, H.; Ramzanpour, R. Association of PON1-L55M Genetic Variation and Breast Cancer Risk: A Case-Control Trial. *Asian Pac. J. Cancer Prev.* **2020**, *21*, 255–258. [CrossRef]

15. Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z. The Metabolic Syndrome. *Lancet* **2005**, *365*, 1415–1428. [CrossRef] [PubMed]

16. Bard, J.; Drouet, L.; Lairon, D.; Cazaubiel, M.; Marmonier, C.; Ninio, E.; Bal Dit Sollier, C.; Martin, J.; Boyer, C.; Bobin-Dubigeon, C. Effect of Milk Fat on LDL Cholesterol and Other Cardiovascular Risk Markers in Healthy Humans: The INNOVALAIT Project. *Eur. J. Clin. Nutr.* **2020**, *74*, 285–296. [CrossRef] [PubMed]

17. Ciqual Table de Composition Nutritionnelle Des Aliments. Available online: https://ciqual.anses.fr/ (accessed on 27 March 2023).

18. Black, A.E. The Sensitivity and Specificity of the Goldberg Cut-off for EI:BMR for Identifying Diet Reports of Poor Validity. *Eur. J. Clin. Nutr.* **2000**, *54*, 395–404. [CrossRef]

19. Phillips, C.M.; Shivappa, N.; Hébert, J.R.; Perry, I.J. Dietary Inflammatory Index and Biomarkers of Lipoprotein Metabolism, Inflammation and Glucose Homeostasis in Adults. *Nutrients* **2018**, *10*, 1033. [CrossRef]

20. Phillips, C.M.; Chen, L.-W.; Heude, B.; Bernard, J.Y.; Harvey, N.C.; Duijts, L.; Mensink-Bout, S.M.; Polanska, K.; Mancano, G.; Suderman, M.; et al. Dietary Inflammatory Index and Non-Communicable Disease Risk: A Narrative Review. *Nutrients* **2019**, *11*, 1873. [CrossRef]

21. Kranz, S.; Hasan, F.; Kennedy, E.; Zoellner, J.; Guertin, K.A.; Shivappa, N.; Hébert, J.R.; Anderson, R.; Cohn, W. Diet Quality and Dietary Inflammatory Index Score among Women's Cancer Survivors. *Int. J. Environ. Res. Public Health* **2022**, *19*, 1916. [CrossRef]

22. Bobin-Dubigeon, C.; Nazih, H.; Croyal, M.; Bard, J.-M. Link between Omega 3 Fatty Acids Carried by Lipoproteins and Breast Cancer Severity. *Nutrients* **2022**, *14*, 2461. [CrossRef]

23. Bobin-Dubigeon, C.; Chauvin, A.; Brillaud-Meflah, V.; Boiffard, F.; Joalland, M.; Bard, J. Liver X Receptor (LXR)-Regulated Genes of Cholesterol Trafficking and Breast Cancer Severity. *Anticancer Res.* **2017**, *37*, 5495–5498. [CrossRef] [PubMed]

24. Bobin-Dubigeon, C.; Lefrançois, A.; Classe, J.; Joalland, M.; Bard, J. Paired Measurement of Serum Amyloid A (SAA) and Paraoxonase 1 (PON1) as Useful Markers in Breast Cancer Recurrence. *Clin. Biochem.* **2015**, *48*, 1181–1183. [CrossRef] [PubMed]

25. Alegría-Torres, J.A.; García-Domínguez, M.L.; Cruz, M.; Aradillas-García, C. Q192R Polymorphism of Paraoxonase 1 Gene Associated with Insulin Resistance in Mexican Children. *Arch. Med. Res.* **2015**, *46*, 78–83. [CrossRef]

26. Bobin-Dubigeon, C.; Nazih, H.; Blanchard, V.; Croyal, M.; Bard, J.-M. Circulating HDL and Non-HDL Associated Apolipoproteins and Breast Cancer Severity. *J. Clin. Med.* **2022**, *11*, 1345. [CrossRef]

27. Samson, S.L.; Garber, A.J. Metabolic Syndrome. *Endocrinol. Metab. Clin. N. Am.* **2014**, *43*, 1–23. [CrossRef]

28. Ambroselli, D.; Masciulli, F.; Romano, E.; Catanzaro, G.; Besharat, Z.M.; Massari, M.C.; Ferretti, E.; Migliaccio, S.; Izzo, L.; Ritieni, A.; et al. New Advances in Metabolic Syndrome, from Prevention to Treatment: The Role of Diet and Food. *Nutrients* **2023**, *15*, 640. [CrossRef]
29. Castro-Barquero, S.; Ruiz-León, A.M.; Sierra-Pérez, M.; Estruch, R.; Casas, R. Dietary Strategies for Metabolic Syndrome: A Comprehensive Review. *Nutrients* **2020**, *12*, 2983. [CrossRef]

30. Saklayen, M.G. The Global Epidemic of the Metabolic Syndrome. *Curr. Hypertens. Rep.* **2018**, *20*, 12. [CrossRef] [PubMed]

31. Zhou, Z.; Zhang, Y.; Li, Y.; Jiang, C.; Wu, Y.; Shang, L.; Huang, Y.; Cheng, S. Metabolic Syndrome Is a Risk Factor for Breast Cancer Patients Receiving Neoadjuvant Chemotherapy: A Case-Control Study. *Front. Oncol.* **2023**, *12*, 1080054. [CrossRef]

32. Uzunlulu, M.; Caklili, O.T.; Oguz, A. Association between Metabolic Syndrome and Cancer. *ANM* **2016**, *68*, 173–179. [CrossRef]

33. Esposito, K.; Chiodini, P.; Colao, A.; Lenzi, A.; Giugliano, D. Metabolic Syndrome and Risk of Cancer: A Systematic Review and Meta-Analysis. *Diabetes Care* **2012**, *35*, 2402–2411. [CrossRef] [PubMed]

34. Bjørge, T.; Lukanova, A.; Jonsson, H.; Tretli, S.; Ulmer, H.; Manjer, J.; Stocks, T.; Selmer, R.; Nagel, G.; Almquist, M.; et al. Metabolic Syndrome and Breast Cancer in the Me-Can (Metabolic Syndrome and Cancer) Project. *Cancer Epidemiol. Biomark. Prev.* **2010**, *19*, 1737–1745. [CrossRef] [PubMed]

35. Gathirua-Mwangi, W.G.; Song, Y.; Monahan, P.; Champion, V.L.; Zollinger, T. Associations of Metabolic Syndrome and C-Reactive Protein with Mortality from Total Cancer, Obesity-Linked Cancers and Breast Cancer among Women in NHANES III. *Int. J. Cancer* **2018**, *143*, 535–542. [CrossRef]

36. Pan, K.; Aragaki, A.K.; Neuhouser, M.L.; Simon, M.S.; Luo, J.; Caan, B.; Snetselaar, L.; Mortimer, J.E.; Manson, J.E.; Kroenke, C.; et al. Low-Fat Dietary Pattern and Breast Cancer Mortality by Metabolic Syndrome Components: A Secondary Analysis of the Women's Health Initiative (WHI) Randomised Trial. *Br. J. Cancer* **2021**, *125*, 372–379. [CrossRef]

37. Chlebowski, R.T.; Aragaki, A.K.; Pan, K.; Simon, M.S.; Neuhouser, M.L.; Haque, R.; Rohan, T.E.; Wactawski-Wende, J.; Orchard, T.S.; Mortimer, J.E.; et al. Breast Cancer Incidence and Mortality by Metabolic Syndrome and Obesity: The Women's Health Initiative. *Cancer* **2024**, *130*, 3147–3156. [CrossRef]

38. Bergman, R.; Berko, Y.A.; Sanchez, V.; Sanders, M.E.; Gonzalez-Ericsson, P.I.; Arteaga, C.L.; Rexer, B.N. Obesity and Metabolic Syndrome Are Associated with Short-Term Endocrine Therapy Resistance in Early ER + Breast Cancer. *Breast Cancer Res. Treat.* **2023**, *197*, 307–317. [CrossRef] [PubMed]

39. Wingrove, K.; Lawrence, M.A.; McNaughton, S.A. A Systematic Review of the Methods Used to Assess and Report Dietary Patterns. *Front. Nutr.* **2022**, *9*, 892351. [CrossRef]

40. Rapport d'Expertise Collective. Avis de l'ANSES. Etude Individuelle des Consommations Alimentaires 3 (INCA3) Juin 2017. Available online: https://www.anses.fr/fr/content/avis-et-rapport-de-lanses-sur-lactualisation-de-la-base-de-donnees-des-consommations (accessed on 24 July 2024).

41. Iguacel, I.; Perez-Cornago, A.; Schmidt, J.A.; Van Puyvelde, H.; Travis, R.; Casagrande, C.; Nicolas, G.; Riboli, E.; Weiderpass, E.; Ardanaz, E.; et al. Evaluation of Protein and Amino Acid Intake Estimates from the EPIC Dietary Questionnaires and 24-h Dietary Recalls Using Different Food Composition Databases. *Nutr. Metab. Cardiovasc. Dis.* **2022**, *32*, 80–89. [CrossRef]

42. Shivappa, N.; Sandin, S.; Löf, M.; Hébert, J.R.; Adami, H.-O.; Weiderpass, E. Prospective Study of Dietary Inflammatory Index and Risk of Breast Cancer in Swedish Women. *Br. J. Cancer* **2015**, *113*, 1099–1103. [CrossRef]

43. Huang, W.-Q.; Mo, X.-F.; Ye, Y.-B.; Shivappa, N.; Lin, F.-Y.; Huang, J.; Hébert, J.R.; Yan, B.; Zhang, C.-X. A Higher Dietary Inflammatory Index Score Is Associated with a Higher Risk of Breast Cancer among Chinese Women: A Case-Control Study. *Br. J. Nutr.* **2017**, *117*, 1358–1367. [CrossRef]

44. Fowler, M.E.; Akinyemiju, T.F. Meta-Analysis of the Association between Dietary Inflammatory Index (DII) and Cancer Outcomes. *Int. J. Cancer* **2017**, *141*, 2215–2227. [CrossRef] [PubMed]

45. Zucchetto, A.; Serraino, D.; Shivappa, N.; Hébert, J.R.; Stocco, C.; Puppo, A.; Falcini, F.; Panato, C.; Dal Maso, L.; Polesel, J. Dietary Inflammatory Index before Diagnosis and Survival in an Italian Cohort of Women with Breast Cancer. *Br. J. Nutr.* **2017**, *117*, 1456–1462. [CrossRef]

46. Tabung, F.K.; Steck, S.E.; Liese, A.D.; Zhang, J.; Ma, Y.; Caan, B.; Chlebowski, R.T.; Freudenheim, J.L.; Hou, L.; Mossavar-Rahmani, Y.; et al. Association between Dietary Inflammatory Potential and Breast Cancer Incidence and Death: Results from the Women's Health Initiative. *Br. J. Cancer* **2016**, *114*, 1277–1285. [CrossRef]

47. Tabung, F.K.; Steck, S.E.; Zhang, J.; Ma, Y.; Liese, A.D.; Agalliu, I.; Hingle, M.; Hou, L.; Hurley, T.G.; Jiao, L.; et al. Construct Validation of the Dietary Inflammatory Index among Postmenopausal Women. *Ann. Epidemiol.* **2015**, *25*, 398–405. [CrossRef]

48. Pan, H.; Zheng, Y.; Pan, Q.; Chen, H.; Chen, F.; Wu, J.; Di, D. Expression of LXR-β, ABCA1 and ABCG1 in Human Triple-Negative Breast Cancer Tissues. *Oncol. Rep.* **2019**, *42*, 1869–1877. [CrossRef] [PubMed]

49. Bobin-Dubigeon, C.; Jaffré, I.; Joalland, M.; Classe, J.; Campone, M.; Hervé, M.; Bard, J. Paraoxonase 1 (PON1) as a Marker of Short Term Death in Breast Cancer Recurrence. *Clin. Biochem.* **2012**, *45*, 1503–1505. [CrossRef]

50. Balci, H.; Genc, H.; Papila, C.; Can, G.; Papila, B.; Yanardag, H.; Uzun, H. Serum Lipid Hydroperoxide Levels and Paraoxonase Activity in Patients With Lung, Breast, and Colorectal Cancer. *J. Clin. Lab. Anal.* **2012**, *26*, 155–160. [CrossRef]

51. Camps, J.; Marsillach, J.; Joven, J. Measurement of Serum Paraoxonase-1 Activity in the Evaluation of Liver Function. *World J. Gastroenterol.* **2009**, *15*, 1929–1933. [CrossRef] [PubMed]

52. Mazzuferi, G.; Bacchetti, T.; Islam, M.O.; Ferretti, G. High Density Lipoproteins and Oxidative Stress in Breast Cancer. *Lipids Health Dis.* **2021**, *20*, 143. [CrossRef]
53. Pan, X.; Huang, L.; Li, M.; Mo, D.; Liang, Y.; Liu, Z.; Huang, Z.; Huang, L.; Liu, J.; Zhu, B. The Association between PON1 (Q192R and L55M) Gene Polymorphisms and Risk of Cancer: A Meta-Analysis Based on 43 Studies. *BioMed Res. Int.* **2019**, *2019*, 5897505. [CrossRef]

54. Lou-Bonafonte, J.M.; Gabás-Rivera, C.; Navarro, M.A.; Osada, J. PON1 and Mediterranean Diet. *Nutrients* **2015**, *7*, 4068–4092. [CrossRef] [PubMed]

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