The association of preoperative serum free fatty acid with the survival in breast cancer patients

The association of preoperative serum free fatty acid with the survival in breast cancer patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association of preoperative serum free fatty acid with the survival in breast cancer patients Liuran Li, Liang Jin, Lili You, Qiang Liu, Li Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865368/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Several studies have analyzed the association between serum free fatty acid (FFA) and several types of cancer. However, the role of preoperative serum FFA and breast cancer (BC) prognosis remains largely unclear. This study aimed to elucidate the specific relationship between FFA and BC outcomes. Methods A retrospective review was conducted on 4133 breast cancer patients admitted to Sun Yat-sen Memorial Hospital from January 2015 to October 2021. Restricted cubic splines and multivariate Cox regression analyses were used to assess the relationship between preoperative serum FFA and overall survival (OS) in BC patients. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated. Results Restricted cubic spline analysis revealed a U-shaped relationship between preoperative serum FFA levels and OS after adjusting for other variables. According to the cutoff points of FFA, multivariate Cox regression analyses showed that patients with low FFA levels (≤ 250µmol/L) had higher rates of all-cause mortality and cancer-specific mortality compared to patients with high FFA levels (250–715µmol/L) in total population and those with a BMI of 18.5 to 24.0 kg/m2. Conclusion A nonlinear U-shaped association was identified between preoperative serum FFA levels and the survival in BC patients, with lower FFA levels associated with worse OS. Free fatty acids breast cancer overall survival U-shaped association Figures Figure 1 Figure 2 Figure 3 Introduction Breast cancer remains a worldwide public health dilemma and is the most common malignancy in women (Akram et al. 2017 ). According to the global cancer statistics 2020, female breast cancer has exceeded lung cancer as the most commonly diagnosed cancer and the fifth cause of cancer deaths in the world, with more than 2 million new cases and 685,000 deaths (Sung et al. 2021 ), and the new cases are expected to reach 4.4 million by 2070 (Soerjomataram et al. 2021). Among women, breast cancer accounted for approximately 24.5% of all newly diagnosed cancer cases and 15.5% of cancer deaths, with the highest incidence and mortality rates in most countries of the world in 2020 (Sung et al. 2021 ). The growth rate of breast cancer in China has exceeded the global average level. Coupled with the large population base, the number of cases and deaths of breast cancer in China ranks first in the world, accounting for approximately 18.4% of global breast cancer cases and 17.1% of all cancer deaths (Lei et al. 2021 ). Despite the increasing incidence of breast cancer in recent years, the survival rates have improved due to early diagnosis and the use of advanced therapeutic strategies based on prognostic factors. Thus, identifying prognostic factors is of great significance for establishing individual treatment plans and improving overall survival rate (Lee et al. 2013 ). An increasing number of studies have revealed that obesity is an established risk factor for breast cancer. Obesity has been associated with increased morbidity and mortality, more aggressive tumor phenotypes and worse prognosis (Sarkissyan et al. 2011 ; Stebbing et al. 2012 ). For a woman diagnosed with BC, being overweight or obese increases the risk of distant metastatic recurrence and reduces survival (Demark-Wahnefried et al. 2012 ). In addition, studies have demonstrated that BMI is an independent and important prognostic factor for postmenopausal breast cancer, and that the higher the BMI, the worse the prognosis for breast cancer (Imkampe et al. 2010). Overweight/obesity in both humans and rodents is characterized by elevated free fatty acids (FFA) levels (Boden 2008a ; Kinlaw et al. 2016 ). Besides, it has been shown that there was a significant positive correlation between FFA levels and BMI in non-cancer and breast cancer patients (Zhang et al. 2020a ). Increasing evidence points to FFA signaling playing an important role in tumorigenesis and the progression of breast cancer. Free fatty acids (FFAs), also named non-esterified fatty acids, are intermediate products of lipid mobilization that largely originate from the lipolysis of triglycerides stored in adipose tissue (Zhao et al. 2020 ). In addition to serving as critical energy sources, FFAs also act as signaling molecules that modulate the activation of gene transcription, the post-transcriptional modification of proteins and many other metabolic processes (Hara et al. 2013 ; Ichimura et al. 2012 ; Mao et al. 2023 ; Miyamoto et al. 2016 ). Accumulating evidence have clarified that FFA levels are associated with adverse cardiovascular events and play a role in the process of disease progression (Fatima et al. 2019 ). Over the past few years, several studies have shown that a high FFA level was an independent risk factor for several types of malignancy, such as lung cancer, gastric cancer, thyroid cancer, colon cancer, rectal cancer, ovarian cancer (Zhang et al. 2020b , 2022 ). Recently, a retrospective analysis of 1049 cases of breast cancer indicated that BMI was an independent prognostic factor for BC, and had a U-shaped relationship with OS and breast cancer-specific survival (BCSS) (Wei et al. 2023 ). Although the underlying mechanisms of BMI and BC outcomes have been well studied, the relationship between serum FFA levels and BC prognostic curves in different BMI subgroups remains unknown. In the present study, we retrospectively analyzed the data from 4133 breast cancer patients in our hospital. The aim of this study was to investigate the risk factors affecting the prognosis of BC and analyze the correlation between FFA levels and OS to provide a reliable basis for favorable prognosis of BC patients. Method Patient selection and clinical data collection This study was conducted in accordance with the principles of the Declaration of Helsinki II and approved by the Ethics Committee of Sun Yat-sen Memorial Hospital affiliated to Sun Yat-Sen University (No. SYSEC-KY-KS-2018-018). In this large-scale study, a total of 8633 breast cancer patients were included in Diseases Registry Center of Sun Yat-Sen Memorial Hospital from January 2015 to October 2021. Among these participants, 4500 subjects with missing records were excluded (ER, n = 108; PR, n = 190; HER2, n = 1346; survival state, n = 2044; survival time, n = 16; overall survival, n = 99; preoperative FFA, n = 835; BMI, n = 153). Finally, based on the screening flowchart depicted in Fig. 1 , a total of 4133 qualified patients were included in the final data analysis. Clinical characteristics, including follow-up for overall survival (OS), age, education information, marital status, menopausal status, tumor size, family history of BC, Tumor-Node-Metastasis (TNM) stage, hormonal receptor status, human epidermal growth factor receptor-2 (HER-2) status, Ki-67 labeling index, cancer-specific mortality and serum FFA levels, were obtained from medical records. Based on the IHC expression of ER, PR, HER-2 and cell proliferation antigen marker Ki-67, the breast cancer pathology can be categorized into four types. According to the guidelines of Chinese Society of Clinical Oncology (CSCO) for breast cancer, the molecular subtypes could be defined as follows: (1) Luminal A type: ER +/PR +, HER-2 and Ki-67 < 14%; (2) Luminal B type can be divided into two subtypes: 1) ER + and/or PR +, HER-2 + but Ki-67 is not required; 2) ER + and/or PR +, HER-2 and Ki-67 ≥ 14%); (3) HER-2 overexpression type: (ER -, PR -, HER-2 +, but Ki-67 is not required); (4) Triple negative breast cancer (TNBC): (ER -, PR -, HER-2 - but Ki-67 is not required). Besides, BMI was calculated by dividing body weight in kg by height in meters squared (kg/m2). Patients follow up All participants were followed up by telephone interviews and medical records review. Patients underwent physical examination, laboratory tests, and imaging studies including computed tomography (CT), X-ray mammography and ultrasonography every 3 months for the first two years. Subsequently, patients were examined every 6 months from the 3rd to 5th year, and annually after 5 years. Overall survival (OS) was measured in months from the date of surgery to the date of death or the last follow-up. The last date of follow-up was October 1, 2021. FFA Testing After an overnight fast for at least 10 h, peripheral blood samples were obtained from all patients for laboratory testing. The FFAs that we detected were non-esterified fatty acids. Measurement of serum FFA was performed by the standard method of enzymatic colorimetry assay using an automatic biochemical analyzer (AU5800, Beckman) in the Clinical Laboratory Department of Sun Yat-sen Memorial Hospital affiliated to Sun Yat-Sen University. Statistical analysis Continuous variables were presented as means and standard deviations (SD), while categorical variables were presented as numbers and percentages. Comparisons between different groups were performed using the χ 2 test for categorical variables and Student t test for continuous variables. Restricted cubic splines were performed to visualize the shape of the dose–response association among FFA and hazard ratio of breast cancer. Cox proportional hazard model was used to determine the relationship between FFA and OS in BC patients. Model 1 is unadjusted. Model 2 is adjusted for age, pathological T-stage, pathological N-stage and pathological M-stage. Model 3 is further adjusted for neoadjuvant therapy and post-menopause. The raw and adjusted hazard ratios (HRs) and the 95% confidence intervals (95% CIs) for OS were calculated based on the serum preoperative FFA levels. All statistical analyses were performed using RStudio version 4.2.2. A twotailed p < 0.05 was considered statistically significant. Results Clinical characteristics of the study population A total of 4133 patients diagnosed with breast cancer were enrolled in the analysis. The characteristics of these patients are shown in Table 1 . During a median of 2.83 (IQR: 1.80–3.94) years of follow-up period, 191 patients (4.62%) died. The mean age was 49.11 years (standard deviation: 10.86 years). According to FFA interquartile range, the correlation between the different levels of FFA and the other clinical features were showed in Table 1 . The statistical analysis presented that the FFA levels were significantly correlated with follow-up for OS (years) (P <0.001), age (P < 0.001), high education (P = 0.001), menopause (P = 0.010), family history of BC (P < 0.001), stage IV (P < 0.001). However, there were no significant links across quartiles of FFA level according to stage I-III, marriage, hormone status, HER2 status and BC molecular typing. Table 1 Descriptive characteristics of the breast cancer patients according to FFA interquartile range Variables Total cases(N = 4133) FFA interquartile group of breast cancer preoperative P difference P trend Q1 (N = 1042) Q2 (N = 1029) Q3 (N = 1033) Q4 (N = 1029) Distribution - [13, 291] (291, 421] (421, 585] (585,1599] - - Follow-up for OS (years) 2.99(1.43) 2.84(1.44) 2.96(1.43) 3.00(1.39) 3.16(1.44) < 0.001 < 0.001 Follow-up for OS (alive) 3942(95.38) 978(93.86) 992(96.40) 987(95.55)* 985(95.72)*# 0.041 0.102 Age, Mean (SD) 49.11(10.86) 47.70(10.37) 48.80(10.71) 50.16(11.22)*# 49.80(10.97)* < 0.001 < 0.001 Age, N (%) < 35 354(8.57) 112(10.75) 85(8.26) 77(7.46) 80(7.77) < 0.001 0.012 35- 1104(26.72) 306(29.37) 291(28.28)* 249(24.13) 258(25.07) 0.006 45- 1443(34.92) 355(34.07) 356(34.60) 370(35.85) 362(35.18) 0.490 55- 876(21.20) 216(20.73) 211(20.51) 223(21.61) 226(21.96) 0.401 65- 299(7.24) 45(4.32) 73(7.09)* 92(8.91)* 89(8.65)* < 0.001 ≥ 75 56(1.36) 8(0.77) 13(1.26) 21(2.03) 14(1.36) 0.0112 High education a, N (%) 1024(30.15) 282(32.12) 279(32.63) 237(28.28) 226(27.39) 0.037 0.001 Married, N (%) 3867(93.79) 980(94.23) 957(93.37) 969(93.99) 961(93.57) 0.846 0.729 Menopause, N (%) 1427(36.46) 323(32.66) 353(36.54) 384(39.30)* 367(37.37) 0.019 0.010 Family history of BC, N (%) 149(3.62) 29(2.80) 39(3.80) 40(3.89) 41(3.99) 0.434 < 0.001 TNM stage, N (%) 0 or Tis 235(6.23) 60(6.32) 59(6.38) 69(7.27) 47(4.95) 0.001 0.418 Stage Ⅰ 1136(30.11) 287(30.21) 291(31.46) 275(29.01) 283(29.79) 0.775 Stage Ⅱ 1548(41.03) 379(39.89) 379(40.97) 367(38.71) 423(44.53) 0.056 Stage Ⅲ 716(18.98) 171(18.00) 160(17.30) 205(21.62) 180(18.95) 0.152 Stage Ⅳ 138(3.66) 53(5.58) 36(3.89) 32(3.38) 17(1.79)*# < 0.001 ER positive, N (%) 2972(71.91) 761(73.03) 742(72.11) 742(71.83) 727(70.65) 0.687 0.235 PR positive, N (%) 2186(52.89) 546(52.40) 549(53.54) 540(52.27) 551(53.55) 0.914 0.733 HER2 positive, N (%) 1249(30.22) 326(31.29) 290(28.18) 312(30.20) 321(31.20) 0.384 0.790 BC Molecular typing, N (%) Luminal A 463(19.17) 119(20.41) 120(19.97) 129(20.12) 95(16.10) 0.224 0.193 Luminal B 807(33.42) 186(31.90) 199(33.11) 222(34.63) 200(22.90) 0.208 HER2 positive type 619(25.63) 161(27.62) 137(22.80) 155(24.18) 166(28.14) 0.456 TNBC 526(21.78) 117(20.07) 145(24.13) 135(21.06) 129(21.86) 0.525 *p < 0.05 versus first quartile of FFA levels.#p < 0.05 versus second quartile of FFA levels. $ p < 0.05 versus third quartile of FFA levels. The pairwise comparison of multiple groups of variables according to FFA interquartile range was based on the redefinition of group variables as whether or not; High education a represented associate college or above, belonging to the group of highly educated people. The curve correlation between preoperative serum FFA and OS in breast cancer In our study, restricted cubic splines were performed to investigate the association between preoperative serum FFA and OS in breast cancer. As presented in Fig. 2 , univariate analysis revealed that lower serum FFA levels (P = 0.0208) was significantly associated with shorter OS. After adjusting for by age, pathological T-stage, pathological N-stage, pathological M-stage, ER, PR, HER-2 and post-menopause, multivariate analysis showed that a lower serum FFA level (P = 0.0069) was a prognostic factor for shorter OS. The curve correlation between preoperative serum FFA and OS in breast cancer in different BMI subgroups In the present study, we grouped the subjects according to BMI and explored the nonlinear associations of preoperative serum FFA with OS in breast cancer using restricted cubic splines. The rms package in the RStudio with the lrm () function was used to adjust for age, pathological T-stage, pathological N-stage, pathological M-stage, ER, PR, HER-2 and post-menopause, in order to verify the nonlinear dose–response relationships. The preoperative serum FFA presented different shaped nonlinear associations with OS in breast cancer in the different populations. As shown in Fig. 3 , the results revealed that in the population with 18.5 ≤ BMI < 24 kg/m 2 , FFA has a U-shaped curve correlation with OS in breast cancer (P = 0.0034). However, no curvilinear correlation was found between FFA and OS in the subgroups with BMI < 18.5 kg/m 2 , 24 ≤ BMI < 28 kg/m 2 , and BMI ≥ 28 kg/m 2 (P = 0.1024, P = 0.3645, P = 0.8645, respectively). Association between preoperative serum FFA levels and cancer-specific and all-cause mortality with breast cancer As shown in Table 2 , in total population, patients with low FFA levels had higher rates of all-cause mortality. Furthermore, patients with low FFA level had higher rates of cancer-specific mortality in univariate and multivariate Cox regression analysis. After adjusting for age, pathological T-stage, pathological N-stage and pathological M-stage and further adjusting for neoadjuvant therapy and post-menopause (Model 3), we observed that low FFA level (≤ 250µmol/L) had higher rates of all-cause mortality and Cancer-specific mortality (HR, 1.86 [95% CI, (1.10–3.14)], P < 0.021; HR, 1.96 [95% CI, (1.10–3.50)]; P < 0.023, respectively). In BMI range in 18.5 ~ 24.0 kg/m 2 population, patients with low FFA levels had higher rates of all-cause mortality. The results in model 3 still presented that low FFA levels (≤ 250µmol/L) had higher rates of all-cause mortality and cancer-specific mortality (HR, 2.07 [95% CI, (1.01–4.24)], P < 0.046; HR, 2.27 [95% CI, (1.04–4.96)]; P < 0.040, respectively). Table 2 Association between preoperative serum FFA levels and cancer-specific and all-cause mortality with breast cancer Types Model 1 Model 2 Model 3 Patients Mortality HR (95%CI) P HR (95%CI) P HR (95%CI) P Total population All-cause mortality FFA range in 530 to 700 708 27(3.81) 1.00 - 1.00 - 1.00 - FFA range in ≤250 688 43(6.25) 1.80(1.12–2.91) 0.017 1.73(1.04–2.88) 0.035 1.86(1.10–3.14) 0.021 FFA range in ≥715 571 25(4.38) 1.09(0.63–2.88) 0.759 1.30(0.75–2.27) 0.351 1.29(0.72–2.30) 0.388 Cancer-specific mortality FFA range in 530 to 700 708 22(3.12) 1.00 - 1.00 - 1.00 - FFA range in ≤250 688 38(5.53) 1.94(1.15–3.28) 0.014 1.77(1.01–3.09) 0.046 1.96(1.10–3.50) 0.023 FFA range in ≥715 571 22(3.86) 1.18(0.65–2.13) 0.588 1.48(0.81–2.70) 0.206 1.47(0.78–2.77) 0.235 BMI range in 18.5 ~ 24.0 population All-cause mortality FFA range in 530 to 700 385 14(3.64) 1.00 - 1.00 - 1.00 - FFA range in ≤250 488 31(6.35) 1.99(1.06–3.75) 0.033 1.87(0.94–3.72) 0.075 2.07(1.01–4.24) 0.046 FFA range in ≥715 310 11(3.55) 0.95(0.43–2.09) 0.895 1.39(0.61–3.16) 0.438 1.39(0.59–3.30) 0.453 Cancer-specific mortality FFA range in 530 to 700 385 12(3.13) 1.00 - 1.00 - 1.00 - FFA range in ≤250 488 28(5.75) 2.09(1.06–4.12) 0.033 1.92(0.91–4.04) 0.087 2.27(1.04–4.96) 0.040 FFA range in ≥715 310 10(3.23) 1.01(0.43–2.33) 0.990 1.57(0.65–3.80) 0.318 1.61(0.63–4.11) 0.315 Model 1: Unadjusted model. Model 2: Adjusted by age, pathological T-stage, pathological N-stage and pathological M-stage. Model 3: Further adjusted by age, pathological T-stage, pathological N-stage, pathological M-stage, neoadjuvant therapy and post-menopause. Discussion In this study, we enrolled and analyzed the relationship between preoperative FFA levels and outcomes in 4133 breast cancer patients. We found that lower preoperative serum FFA level was significantly associated with worse OS in breast cancer. FFA exhibited a nonlinear U-shaped curve correlation with the survival in BC patients. Compared to patients with high FFA levels (250–715µmol/L), patients with low FFA levels (≤ 250µmol/L) had significantly higher rates of all-cause mortality and cancer-specific mortality in total population and those with a normal BMI. Breast cancer is the most common malignancies in Chinese women and seriously impairs patients' physical and mental health (Fan et al. 2014 ; Li et al. 2020 ). In addition to well-characterized genetic influences, several environmental risk factors such as dietary habits and obesity have a significant influence on development and progression of breast cancer (Blucher et al. 2017; Karmokar et al. 2022). FFA, an energy-generating nutrient, serves as signaling molecules in various cellular process (Al et al. 2022 ; Lymperopoulos et al. 2022 ). Furthermore, as a metabolite substrate, FFA is involved in epigenetic regulation of tumor occurrence and progression through histone acetylation, malondialdehyde, butylation and palmitoylation (Currie et al. 2013 ; Jiang et al. 2022 ; Martin-Perez et al. 2022 ; Sabari et al. 2017 ; Zhang et al. 2021 ). A retrospective study of 2206 patients has indicated that abnormal serum FFA levels were associated with an increased risk of cancer, especially lung cancer, gastric cancer, thyroid cancer, rectal cancer, colon cancer, and ovarian cancer, but not breast cancer (Zhang et al. 2020b ). Several epidemiological and experimental studies have reported that FFAs are important factors in breast cancer risk (MacLennan et al. 2010; Madak-Erdogan et al. 2019a ). The curve relationship between FFA and the prognosis of BC remain largely unclear. Therefore, we analyzed the clinical data of breast cancer patients, finding that the serum preoperative FFA had a U-shaped curve correlation with OS in BC patients and lower preoperative serum FFA levels were associated with worse OS in BC. Previous studies have shown that elevated plasma free fatty acid levels are associated with an increased risk of breast cancer in obese patients (Madak-Erdogan et al. 2019b ). Obesity is closely related to the level of free fatty acids (Boden 2008b ). Therefore, we aimed to explore the relationship between preoperative serum FFA levels and breast cancer OS in different BMI subgroups. The results showed that in the population with normal BMI (18.5–24.9 kg/m2), patients with low levels of free fatty acids had worse OS in breast cancer. But no similar effect was observed in the other BMI subgroups. The probable explanation was that compared with other subgroups, patients with normal BMI accounted for the majority of the population in our research. Thus, the association between FFA and OS in BC could not be better analyzed due to insufficient data for other BMI subgroups. What’s more, in the population with normal BMI (18.5–24.9 kg/m2), we found that the association between serum FFA levels and the OS presented a nonlinear U-shaped curve. Patients with lower levels of free fatty acids (≤ 250µmol/L)had worse OS in breast cancer. Although those with high free fatty acids (≥ 715µmol/L) also had worse outcomes, no statistical significance was observed. One possible reason for this that there are relatively few patients with high levels of free fatty acids in normal BMI group. The pathogenetic mechanisms underlying the association between low preoperative serum FFA levels and adverse outcomes remain unknown. A recent study by Ying Pan et al. demonstrated that low FFA levels were associated with a higher risk of mortality in coronary artery disease patients with type 2 diabetes mellitus (T2DM) (Pan et al. 2023 ). Furthermore, Kathryn Moore et al. indicated that myocardial infarction (MI) accelerated breast cancer outgrowth and cancer-specific mortality both in mice and humans (Koelwyn et al. 2020 ). Thus, it is still unclear whether the physiological deficiency of FFA affects insufficient energy supply to the heart itself, which exacerbates the occurrence of long-term adverse cardiovascular events and in turn may lead to unfavorable prognosis in breast cancer patients. FFAs, as critical energy sources, mainly play a pivotal role in lipid metabolism and can be metabolized through β-oxidation in the mitochondrial matrix (Adeva-Andany et al. 2019 ). Due to chronic restriction of dietary energy intake suppressing FFA levels (Henderson 2021 ) and affecting the proliferation of hematopoietic stem cells (HSCs) (Mistry et al. 2021 ) and the function of CD8 + T cell (Ringel et al. 2020 ), another explanation may lead to a nonlinear U-shaped curve with increased mortality at lower FFA levels. Nevertheless, the current study has certain limitations. First, the analysis was performed in the Southern China population and the results of this study did not necessarily apply to Northern China population or other ethnic groups. In addition, a multicenter large sample cohort study is needed to verify the reliability of the conclusion. Second, we only collected the preoperative FFA level, and we did not analyze the dynamics of total FFA and FFA subclasses over the duration of the study. Our failed to assess all metabolic factors and parameters in patients with breast cancer, including insulin resistance, diet, inflammation and other confounding factors, which may help reveal the possible mechanism of action between FFA levels and OS in BC patients. Finally, it is important to note that the exact cutoff values used to define FFA may vary depending on the study population and the FFA measurement method. Conclusion In conclusion, we identified a U-shaped relationship between preoperative serum FFA levels and the survival in BC patients. These data indicate that lower preoperative serum FFA levels was significantly associated with worse OS in BC. In the future, more well-designed prospective cohort studies are needed to clarify the correlation between preoperative serum FFA levels and prognosis. Declarations Data availability statement The original data in this article will be made available by the authors without reservation. Ethics statement The clinical studies were reviewed and approved by Institutional Review Committee of Sun Yat-sen Memorial Hospital affiliated to Sun Yat-Sen University. Conflict of interest The authors declare that they have no conflict of interest. Funding This work was supported by National Natural Science Foundation of China (U20A20352), Guang Dong Clinical Research Center for Metabolic Diseases (2020B1111170009), National Natural Science Foundation of China (82230057, 82272859), and the National Key Research and Development Program of China (2022YFC2505101). Author Contribution All authors listed have made substantial, direct, and intellectual contributions to this research. LY and QL conceived and designed the project; LL and LJ performed material preparation, data collection and analysis. 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Cancer Res 79(10):2494-2510. https://doi.org/10.1158/0008-5472.CAN-18-2849 Madak-Erdogan Z, Band S, Zhao YC, Smith BP, Kulkoyluoglu-Cotul E, Zuo Q (2019b) Free Fatty Acids Rewire Cancer Metabolism in Obesity-Associated Breast Cancer via Estrogen Receptor and mTOR Signaling. Cancer Res 79(10):2494-2510. https://doi.org/10.1158/0008-5472.CAN-18-2849 Mao C, Xiao P, Tao XN, Qin J, He QT, Zhang C (2023) Unsaturated bond recognition leads to biased signal in a fatty acid receptor. Science 380(6640):eadd6220. https://doi.org/10.1126/science.add6220 Martin-Perez M, Urdiroz-Urricelqui U, Bigas C, Benitah SA (2022) The role of lipids in cancer progression and metastasis. Cell Metab 34(11):1675-1699. https://doi.org/10.1016/j.cmet.2022.09.023 Mistry JJ, Hellmich C, Moore JA, Jibril A, Macaulay I, Moreno-Gonzalez M (2021) Free fatty-acid transport via CD36 drives beta-oxidation-mediated hematopoietic stem cell response to infection. Nat Commun 12(1):7130. https://doi.org/10.1038/s41467-021-27460-9 Miyamoto J, Hasegawa S, Kasubuchi M, Ichimura A, Nakajima A, Kimura I (2016) Nutritional Signaling via Free Fatty Acid Receptors. Int J Mol Sci 17(4):450. https://doi.org/10.3390/ijms17040450 Pan Y, Wu TT, Mao XF, Hou XG, Yang Y, Deng CJ (2023) Decreased free fatty acid levels associated with adverse clinical outcomes in coronary artery disease patients with type 2 diabetes: findings from the PRACTICE study. Eur J Prev Cardiol 30(8):730-739. https://doi.org/10.1093/eurjpc/zwad073 Ringel AE, Drijvers JM, Baker GJ, Catozzi A, Garcia-Canaveras JC, Gassaway BM (2020) Obesity Shapes Metabolism in the Tumor Microenvironment to Suppress Anti-Tumor Immunity. Cell 183(7):1848-1866. https://doi.org/10.1016/j.cell.2020.11.009 Sabari BR, Zhang D, Allis CD, Zhao Y (2017) Metabolic regulation of gene expression through histone acylations. Nat Rev Mol Cell Biol 18(2):90-101. https://doi.org/10.1038/nrm.2016.140 Sarkissyan M, Wu Y, Vadgama JV (2011) Obesity is associated with breast cancer in African-American women but not Hispanic women in South Los Angeles. Cancer 117(16):3814-3823. https://doi.org/10.1002/cncr.25956 Soerjomataram I, Bray F (2021) Planning for tomorrow: global cancer incidence and the role of prevention 2020-2070. Nat Rev Clin Oncol 18(10):663-672. https://doi.org/10.1038/s41571-021-00514-z Stebbing J, Sharma A, North B, Athersuch TJ, Zebrowski A, Pchejetski D (2012) A metabolic phenotyping approach to understanding relationships between metabolic syndrome and breast tumour responses to chemotherapy. Ann Oncol 23(4):860-866. https://doi.org/10.1093/annonc/mdr347 Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209-249. https://doi.org/10.3322/caac.21660 Wei W, Wei S, Huang Z, Zhang Q, Liu F, Xie Y (2023) The relationship between women's body mass index and breast cancer outcomes was U-shaped. Front Oncol 13:1191093. https://doi.org/10.3389/fonc.2023.1191093 Zhang J, Yang S, Wang J, Xu Y, Zhao H, Lei J (2021) Integrated LC-MS metabolomics with dual derivatization for quantification of FFAs in fecal samples of hepatocellular carcinoma patients. J Lipid Res 62:100143. https://doi.org/10.1016/j.jlr.2021.100143 Zhang L, Han L, He J, Lv J, Pan R, Lv T (2020a) A high serum-free fatty acid level is associated with cancer. J Cancer Res Clin Oncol 146(3):705-710. https://doi.org/10.1007/s00432-019-03095-8 Zhang L, Han L, He J, Lv J, Pan R, Lv T (2020b) A high serum-free fatty acid level is associated with cancer. J Cancer Res Clin Oncol 146(3):705-710. https://doi.org/10.1007/s00432-019-03095-8 Zhang L, Zhao X, Chu H, Zhao H, Lai X, Li J (2022) Serum Free Fatty Acids and G-Coupled Protein Receptors Are Associated With the Prognosis of Epithelial Ovarian Cancer. Front Oncol 12:777367. https://doi.org/10.3389/fonc.2022.777367 Zhao L, Hao F, Huang J, Liu X, Ma X, Wang C (2020) Sex- and Age-Related Metabolic Characteristics of Serum Free Fatty Acids in Healthy Chinese Adults. J Proteome Res 19(4):1383-1391. https://doi.org/10.1021/acs.jproteome.9b00502 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3865368","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267508087,"identity":"cdf403f4-dc8f-4fd3-a727-d301506ddac0","order_by":0,"name":"Liuran Li","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liuran","middleName":"","lastName":"Li","suffix":""},{"id":267508088,"identity":"7c900d62-f821-4745-9584-c32974087043","order_by":1,"name":"Liang Jin","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Jin","suffix":""},{"id":267508089,"identity":"55b1a92c-f245-4cc4-b493-4a70b5752058","order_by":2,"name":"Lili You","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"You","suffix":""},{"id":267508090,"identity":"fd739063-ec4b-4aa3-a424-15085abe3ed5","order_by":3,"name":"Qiang Liu","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Liu","suffix":""},{"id":267508091,"identity":"15cffb20-0919-4641-b76b-5ff2b7577067","order_by":4,"name":"Li Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYHACxgMJFQwMbOxwgQTCeg4knAFqYSZJC2MbkCRai8HxswcOPJy3TZ6PmYH5M8+fwwz87DkGDD934NFyJi/hQOK224ZtzAxs0rxthxkke94YMPaewaPlQI4BSAsjSAszb8NhBoMbOQbMYKfi0nL+DVDLnNv2bTCH2RPUcgNkS8PtRKAWBmkeNqAtEgS0SN4A2pJw7HZyG1CZ5Ny2dB6JM88KDvbi0cJ3Psfw4Y+a27bz25sPf3jzx1qOvz1544OfeLQoHIAzGRuYeBgYeEDMAzhUg4F8AxKH8Qc+paNgFIyCUTBiAQBIUVMpB6tAGwAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-01-15 04:14:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3865368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3865368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49826422,"identity":"0540c782-f01f-42ff-a283-9acbc45ee67c","added_by":"auto","created_at":"2024-01-18 15:50:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":364268,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participants in the study\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3865368/v1/247206443b04fc2d0d3aef01.png"},{"id":49825085,"identity":"60047274-8589-4411-b0b7-46ed70eee204","added_by":"auto","created_at":"2024-01-18 15:42:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218204,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between preoperative serum FFA and OS in breast cancer. Restricted cubic splines were performed. (A) Association between preoperative serum FFA and OS in breast cancer with univariate analysis. (B) Association between preoperative serum FFA and OS in breast cancer with multivariate analysis adjusted by age, pathological T-stage, pathological N-stage, pathological M-stage, ER, PR, HER-2 and post-menopause.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3865368/v1/d560f450b9a252d23fdf4a82.png"},{"id":49825086,"identity":"fb9ba0af-72c6-4074-905c-ca6d89159005","added_by":"auto","created_at":"2024-01-18 15:42:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":399539,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between preoperative serum FFA and OS in breast cancer in different BMI subgroup. Restricted cubic splines were performed. (A) Association between preoperative serum FFA and OS in breast cancer in the subgroup with BMI \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e. (B) Association between preoperative serum FFA and OS in breast cancer in the subgroup with 18.5 ≤BMI \u0026lt;24 kg/m\u003csup\u003e2\u003c/sup\u003e. (C) Association between preoperative serum FFA and OS in breast cancer in the subgroup with 24 ≤BMI \u0026lt;28 kg/m\u003csup\u003e2\u003c/sup\u003e. (D) Association between preoperative serum FFA and OS in breast cancer in the subgroup with BMI ≥28 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3865368/v1/d0325744c420746ec1fb0a84.png"},{"id":50395969,"identity":"5f1d45b2-4fd3-43ae-8916-2b85cf5d0721","added_by":"auto","created_at":"2024-01-30 22:37:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3865368/v1/421b9f29-f5f7-4f45-aa6b-f54360e085d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association of preoperative serum free fatty acid with the survival in breast cancer patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer remains a worldwide public health dilemma and is the most common malignancy in women (Akram et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to the global cancer statistics 2020, female breast cancer has exceeded lung cancer as the most commonly diagnosed cancer and the fifth cause of cancer deaths in the world, with more than 2\u0026nbsp;million new cases and 685,000 deaths (Sung et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the new cases are expected to reach 4.4\u0026nbsp;million by 2070 (Soerjomataram et al. 2021). Among women, breast cancer accounted for approximately 24.5% of all newly diagnosed cancer cases and 15.5% of cancer deaths, with the highest incidence and mortality rates in most countries of the world in 2020 (Sung et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The growth rate of breast cancer in China has exceeded the global average level. Coupled with the large population base, the number of cases and deaths of breast cancer in China ranks first in the world, accounting for approximately 18.4% of global breast cancer cases and 17.1% of all cancer deaths (Lei et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite the increasing incidence of breast cancer in recent years, the survival rates have improved due to early diagnosis and the use of advanced therapeutic strategies based on prognostic factors. Thus, identifying prognostic factors is of great significance for establishing individual treatment plans and improving overall survival rate (Lee et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn increasing number of studies have revealed that obesity is an established risk factor for breast cancer. Obesity has been associated with increased morbidity and mortality, more aggressive tumor phenotypes and worse prognosis (Sarkissyan et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Stebbing et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For a woman diagnosed with BC, being overweight or obese increases the risk of distant metastatic recurrence and reduces survival (Demark-Wahnefried et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition, studies have demonstrated that BMI is an independent and important prognostic factor for postmenopausal breast cancer, and that the higher the BMI, the worse the prognosis for breast cancer (Imkampe et al. 2010). Overweight/obesity in both humans and rodents is characterized by elevated free fatty acids (FFA) levels (Boden \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e; Kinlaw et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Besides, it has been shown that there was a significant positive correlation between FFA levels and BMI in non-cancer and breast cancer patients (Zhang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Increasing evidence points to FFA signaling playing an important role in tumorigenesis and the progression of breast cancer.\u003c/p\u003e \u003cp\u003eFree fatty acids (FFAs), also named non-esterified fatty acids, are intermediate products of lipid mobilization that largely originate from the lipolysis of triglycerides stored in adipose tissue (Zhao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to serving as critical energy sources, FFAs also act as signaling molecules that modulate the activation of gene transcription, the post-transcriptional modification of proteins and many other metabolic processes (Hara et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ichimura et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mao et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miyamoto et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accumulating evidence have clarified that FFA levels are associated with adverse cardiovascular events and play a role in the process of disease progression (Fatima et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Over the past few years, several studies have shown that a high FFA level was an independent risk factor for several types of malignancy, such as lung cancer, gastric cancer, thyroid cancer, colon cancer, rectal cancer, ovarian cancer (Zhang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recently, a retrospective analysis of 1049 cases of breast cancer indicated that BMI was an independent prognostic factor for BC, and had a U-shaped relationship with OS and breast cancer-specific survival (BCSS) (Wei et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although the underlying mechanisms of BMI and BC outcomes have been well studied, the relationship between serum FFA levels and BC prognostic curves in different BMI subgroups remains unknown.\u003c/p\u003e \u003cp\u003eIn the present study, we retrospectively analyzed the data from 4133 breast cancer patients in our hospital. The aim of this study was to investigate the risk factors affecting the prognosis of BC and analyze the correlation between FFA levels and OS to provide a reliable basis for favorable prognosis of BC patients.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection and clinical data collection\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the principles of the Declaration of Helsinki II and approved by the Ethics Committee of Sun Yat-sen Memorial Hospital affiliated to Sun Yat-Sen University (No. SYSEC-KY-KS-2018-018).\u003c/p\u003e \u003cp\u003eIn this large-scale study, a total of 8633 breast cancer patients were included in Diseases Registry Center of Sun Yat-Sen Memorial Hospital from January 2015 to October 2021. Among these participants, 4500 subjects with missing records were excluded (ER, n\u0026thinsp;=\u0026thinsp;108; PR, n\u0026thinsp;=\u0026thinsp;190; HER2, n\u0026thinsp;=\u0026thinsp;1346; survival state, n\u0026thinsp;=\u0026thinsp;2044; survival time, n\u0026thinsp;=\u0026thinsp;16; overall survival, n\u0026thinsp;=\u0026thinsp;99; preoperative FFA, n\u0026thinsp;=\u0026thinsp;835; BMI, n\u0026thinsp;=\u0026thinsp;153). Finally, based on the screening flowchart depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 4133 qualified patients were included in the final data analysis.\u003c/p\u003e \u003cp\u003eClinical characteristics, including follow-up for overall survival (OS), age, education information, marital status, menopausal status, tumor size, family history of BC, Tumor-Node-Metastasis (TNM) stage, hormonal receptor status, human epidermal growth factor receptor-2 (HER-2) status, Ki-67 labeling index, cancer-specific mortality and serum FFA levels, were obtained from medical records. Based on the IHC expression of ER, PR, HER-2 and cell proliferation antigen marker Ki-67, the breast cancer pathology can be categorized into four types. According to the guidelines of Chinese Society of Clinical Oncology (CSCO) for breast cancer, the molecular subtypes could be defined as follows: (1) Luminal A type: ER +/PR +, HER-2 and Ki-67\u0026thinsp;\u0026lt;\u0026thinsp;14%; (2) Luminal B type can be divided into two subtypes: 1) ER\u0026thinsp;+\u0026thinsp;and/or PR +, HER-2\u0026thinsp;+\u0026thinsp;but Ki-67 is not required; 2) ER\u0026thinsp;+\u0026thinsp;and/or PR +, HER-2 and Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;14%); (3) HER-2 overexpression type: (ER -, PR -, HER-2 +, but Ki-67 is not required); (4) Triple negative breast cancer (TNBC): (ER -, PR -, HER-2 - but Ki-67 is not required). Besides, BMI was calculated by dividing body weight in kg by height in meters squared (kg/m2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients follow up\u003c/h2\u003e \u003cp\u003eAll participants were followed up by telephone interviews and medical records review. Patients underwent physical examination, laboratory tests, and imaging studies including computed tomography (CT), X-ray mammography and ultrasonography every 3 months for the first two years. Subsequently, patients were examined every 6 months from the 3rd to 5th year, and annually after 5 years. Overall survival (OS) was measured in months from the date of surgery to the date of death or the last follow-up. The last date of follow-up was October 1, 2021.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFFA Testing\u003c/h2\u003e \u003cp\u003eAfter an overnight fast for at least 10 h, peripheral blood samples were obtained from all patients for laboratory testing. The FFAs that we detected were non-esterified fatty acids. Measurement of serum FFA was performed by the standard method of enzymatic colorimetry assay using an automatic biochemical analyzer (AU5800, Beckman) in the Clinical Laboratory Department of Sun Yat-sen Memorial Hospital affiliated to Sun Yat-Sen University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as means and standard deviations (SD), while categorical variables were presented as numbers and percentages. Comparisons between different groups were performed using the χ\u003csup\u003e2\u003c/sup\u003e test for categorical variables and Student t test for continuous variables. Restricted cubic splines were performed to visualize the shape of the dose\u0026ndash;response association among FFA and hazard ratio of breast cancer. Cox proportional hazard model was used to determine the relationship between FFA and OS in BC patients. Model 1 is unadjusted. Model 2 is adjusted for age, pathological T-stage, pathological N-stage and pathological M-stage. Model 3 is further adjusted for neoadjuvant therapy and post-menopause. The raw and adjusted hazard ratios (HRs) and the 95% confidence intervals (95% CIs) for OS were calculated based on the serum preoperative FFA levels. All statistical analyses were performed using RStudio version 4.2.2. A twotailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 4133 patients diagnosed with breast cancer were enrolled in the analysis. The characteristics of these patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. During a median of 2.83 (IQR: 1.80\u0026ndash;3.94) years of follow-up period, 191 patients (4.62%) died. The mean age was 49.11 years (standard deviation: 10.86 years). According to FFA interquartile range, the correlation between the different levels of FFA and the other clinical features were showed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The statistical analysis presented that the FFA levels were significantly correlated with follow-up for OS (years) (P \u0026lt;0.001), age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), high education (P\u0026thinsp;=\u0026thinsp;0.001), menopause (P\u0026thinsp;=\u0026thinsp;0.010), family history of BC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), stage IV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there were no significant links across quartiles of FFA level according to stage I-III, marriage, hormone status, HER2 status and BC molecular typing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026nbsp;Descriptive\u0026nbsp;characteristics\u0026nbsp;of\u0026nbsp;the\u0026nbsp;breast\u0026nbsp;cancer\u0026nbsp;patients\u0026nbsp;according\u0026nbsp;to\u0026nbsp;FFA\u0026nbsp;interquartile\u0026nbsp;range\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u0026nbsp;cases(N\u0026thinsp;=\u0026thinsp;4133)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eFFA\u0026nbsp;interquartile\u0026nbsp;group\u0026nbsp;of\u0026nbsp;breast\u0026nbsp;cancer\u0026nbsp;preoperative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026nbsp;difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u0026nbsp;trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u0026nbsp;(N\u0026thinsp;=\u0026thinsp;1042)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u0026nbsp;(N\u0026thinsp;=\u0026thinsp;1029)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u0026nbsp;(N\u0026thinsp;=\u0026thinsp;1033)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u0026nbsp;(N\u0026thinsp;=\u0026thinsp;1029)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[13,\u0026nbsp;291]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(291,\u0026nbsp;421]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(421,\u0026nbsp;585]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(585,1599]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up\u0026nbsp;for\u0026nbsp;OS\u0026nbsp;(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.99(1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.84(1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.96(1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00(1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.16(1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up\u0026nbsp;for\u0026nbsp;OS\u0026nbsp;(alive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3942(95.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e978(93.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e992(96.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e987(95.55)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e985(95.72)*#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, \u003cem\u003eMean\u0026nbsp;(SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.11(10.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.70(10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.80(10.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.16(11.22)*#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.80(10.97)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e354(8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112(10.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85(8.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77(7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80(7.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1104(26.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306(29.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291(28.28)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249(24.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e258(25.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1443(34.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355(34.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356(34.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e370(35.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e362(35.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e876(21.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216(20.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211(20.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e223(21.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226(21.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e299(7.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(7.09)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92(8.91)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89(8.65)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14(1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u0026nbsp;education\u0026nbsp;a,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1024(30.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282(32.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e279(32.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237(28.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226(27.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3867(93.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e980(94.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e957(93.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e969(93.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e961(93.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1427(36.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323(32.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353(36.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e384(39.30)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e367(37.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily\u0026nbsp;history\u0026nbsp;of\u0026nbsp;BC,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149(3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41(3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM\u0026nbsp;stage,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026nbsp;or\u0026nbsp;Tis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235(6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(6.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69(7.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47(4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u0026nbsp;Ⅰ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1136(30.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287(30.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291(31.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275(29.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e283(29.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u0026nbsp;Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1548(41.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379(39.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e379(40.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e367(38.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e423(44.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u0026nbsp;Ⅲ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e716(18.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160(17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205(21.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180(18.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u0026nbsp;Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138(3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17(1.79)*#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u0026nbsp;positive,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2972(71.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e761(73.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e742(72.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e742(71.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e727(70.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u0026nbsp;positive,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2186(52.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e546(52.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549(53.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e540(52.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e551(53.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026nbsp;positive,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1249(30.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326(31.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e290(28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e312(30.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e321(31.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC\u0026nbsp;Molecular\u0026nbsp;typing,\u0026nbsp;\u003cem\u003eN\u0026nbsp;(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal\u0026nbsp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463(19.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119(20.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120(19.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129(20.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95(16.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal\u0026nbsp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e807(33.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186(31.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199(33.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e222(34.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e200(22.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026nbsp;positive\u0026nbsp;type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e619(25.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161(27.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137(22.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155(24.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e166(28.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e526(21.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117(20.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145(24.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135(21.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129(21.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 versus first quartile of FFA levels.#p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 versus second quartile of FFA levels.\u003cspan\u003e$\u003c/span\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 versus third quartile of FFA levels. The pairwise comparison of multiple groups of variables according to FFA interquartile range was based on the redefinition of group variables as whether or not; High education\u003csup\u003ea\u003c/sup\u003e represented associate college or above, belonging to the group of highly educated people.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe curve correlation between preoperative serum FFA and OS in breast cancer\u003c/h2\u003e \u003cp\u003eIn our study, restricted cubic splines were performed to investigate the association between preoperative serum FFA and OS in breast cancer. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, univariate analysis revealed that lower serum FFA levels (P\u0026thinsp;=\u0026thinsp;0.0208) was significantly associated with shorter OS. After adjusting for by age, pathological T-stage, pathological N-stage, pathological M-stage, ER, PR, HER-2 and post-menopause, multivariate analysis showed that a lower serum FFA level (P\u0026thinsp;=\u0026thinsp;0.0069) was a prognostic factor for shorter OS.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe curve correlation between preoperative serum FFA and OS in breast cancer in different BMI subgroups\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the present study, we grouped the subjects according to BMI and explored the nonlinear associations of preoperative serum FFA with OS in breast cancer using restricted cubic splines. The rms package in the RStudio with the lrm () function was used to adjust for age, pathological T-stage, pathological N-stage, pathological M-stage, ER, PR, HER-2 and post-menopause, in order to verify the nonlinear dose\u0026ndash;response relationships. The preoperative serum FFA presented different shaped nonlinear associations with OS in breast cancer in the different populations. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the results revealed that in the population with 18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e, FFA has a U-shaped curve correlation with OS in breast cancer (P\u0026thinsp;=\u0026thinsp;0.0034). However, no curvilinear correlation was found between FFA and OS in the subgroups with BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, 24\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e, and BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e (P\u0026thinsp;=\u0026thinsp;0.1024, P\u0026thinsp;=\u0026thinsp;0.3645, P\u0026thinsp;=\u0026thinsp;0.8645, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between preoperative serum FFA levels and cancer-specific and all-cause mortality with breast cancer\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in total population, patients with low FFA levels had higher rates of all-cause mortality. Furthermore, patients with low FFA level had higher rates of cancer-specific mortality in univariate and multivariate Cox regression analysis. After adjusting for age, pathological T-stage, pathological N-stage and pathological M-stage and further adjusting for neoadjuvant therapy and post-menopause (Model 3), we observed that low FFA level (\u0026le;\u0026thinsp;250\u0026micro;mol/L) had higher rates of all-cause mortality and Cancer-specific mortality (HR, 1.86 [95% CI, (1.10\u0026ndash;3.14)], P\u0026thinsp;\u0026lt;\u0026thinsp;0.021; HR, 1.96 [95% CI, (1.10\u0026ndash;3.50)]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.023, respectively).\u003c/p\u003e \u003cp\u003eIn BMI range in 18.5\u0026thinsp;~\u0026thinsp;24.0 kg/m\u003csup\u003e2\u003c/sup\u003e population, patients with low FFA levels had higher rates of all-cause mortality. The results in model 3 still presented that low FFA levels (\u0026le;\u0026thinsp;250\u0026micro;mol/L) had higher rates of all-cause mortality and cancer-specific mortality (HR, 2.07 [95% CI, (1.01\u0026ndash;4.24)], P\u0026thinsp;\u0026lt;\u0026thinsp;0.046; HR, 2.27 [95% CI, (1.04\u0026ndash;4.96)]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.040, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026nbsp;Association\u0026nbsp;between\u0026nbsp;preoperative serum FFA\u0026nbsp;levels\u0026nbsp;and\u0026nbsp;cancer-specific\u0026nbsp;and\u0026nbsp;all-cause\u0026nbsp;mortality\u0026nbsp;with\u0026nbsp;breast\u0026nbsp;cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel\u0026nbsp;1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel\u0026nbsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel\u0026nbsp;3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u0026nbsp;(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u0026nbsp;(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u0026nbsp;(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eTotal\u0026nbsp;population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause\u0026nbsp;mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;530\u0026nbsp;to\u0026nbsp;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026le;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80(1.12\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73(1.04\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.86(1.10\u0026ndash;3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026ge;715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09(0.63\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30(0.75\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29(0.72\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer-specific\u0026nbsp;mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;530\u0026nbsp;to\u0026nbsp;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026le;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94(1.15\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77(1.01\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.96(1.10\u0026ndash;3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026ge;715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(3.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18(0.65\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48(0.81\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47(0.78\u0026ndash;2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eBMI\u0026nbsp;range\u0026nbsp;in\u0026nbsp;18.5\u0026thinsp;~\u0026thinsp;24.0\u0026nbsp;population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause\u0026nbsp;mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;530\u0026nbsp;to\u0026nbsp;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026le;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99(1.06\u0026ndash;3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87(0.94\u0026ndash;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.07(1.01\u0026ndash;4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026ge;715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.43\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39(0.61\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39(0.59\u0026ndash;3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer-specific\u0026nbsp;mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;530\u0026nbsp;to\u0026nbsp;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026le;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09(1.06\u0026ndash;4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.92(0.91\u0026ndash;4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.27(1.04\u0026ndash;4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFA\u0026nbsp;range\u0026nbsp;in\u0026nbsp;\u0026ge;715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01(0.43\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57(0.65\u0026ndash;3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.61(0.63\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: Unadjusted model.\u003c/p\u003e \u003cp\u003eModel 2: Adjusted by age, pathological T-stage, pathological N-stage and pathological M-stage.\u003c/p\u003e \u003cp\u003eModel 3: Further adjusted by age, pathological T-stage, pathological N-stage, pathological M-stage, neoadjuvant therapy and post-menopause.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we enrolled and analyzed the relationship between preoperative FFA levels and outcomes in 4133 breast cancer patients. We found that lower preoperative serum FFA level was significantly associated with worse OS in breast cancer. FFA exhibited a nonlinear U-shaped curve correlation with the survival in BC patients. Compared to patients with high FFA levels (250\u0026ndash;715\u0026micro;mol/L), patients with low FFA levels (\u0026le;\u0026thinsp;250\u0026micro;mol/L) had significantly higher rates of all-cause mortality and cancer-specific mortality in total population and those with a normal BMI.\u003c/p\u003e \u003cp\u003eBreast cancer is the most common malignancies in Chinese women and seriously impairs patients' physical and mental health (Fan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to well-characterized genetic influences, several environmental risk factors such as dietary habits and obesity have a significant influence on development and progression of breast cancer (Blucher et al. 2017; Karmokar et al. 2022). FFA, an energy-generating nutrient, serves as signaling molecules in various cellular process (Al et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lymperopoulos et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, as a metabolite substrate, FFA is involved in epigenetic regulation of tumor occurrence and progression through histone acetylation, malondialdehyde, butylation and palmitoylation (Currie et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Martin-Perez et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sabari et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A retrospective study of 2206 patients has indicated that abnormal serum FFA levels were associated with an increased risk of cancer, especially lung cancer, gastric cancer, thyroid cancer, rectal cancer, colon cancer, and ovarian cancer, but not breast cancer (Zhang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Several epidemiological and experimental studies have reported that FFAs are important factors in breast cancer risk (MacLennan et al. 2010; Madak-Erdogan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). The curve relationship between FFA and the prognosis of BC remain largely unclear. Therefore, we analyzed the clinical data of breast cancer patients, finding that the serum preoperative FFA had a U-shaped curve correlation with OS in BC patients and lower preoperative serum FFA levels were associated with worse OS in BC.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that elevated plasma free fatty acid levels are associated with an increased risk of breast cancer in obese patients (Madak-Erdogan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Obesity is closely related to the level of free fatty acids (Boden \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e). Therefore, we aimed to explore the relationship between preoperative serum FFA levels and breast cancer OS in different BMI subgroups. The results showed that in the population with normal BMI (18.5\u0026ndash;24.9 kg/m2), patients with low levels of free fatty acids had worse OS in breast cancer. But no similar effect was observed in the other BMI subgroups. The probable explanation was that compared with other subgroups, patients with normal BMI accounted for the majority of the population in our research. Thus, the association between FFA and OS in BC could not be better analyzed due to insufficient data for other BMI subgroups. What\u0026rsquo;s more, in the population with normal BMI (18.5\u0026ndash;24.9 kg/m2), we found that the association between serum FFA levels and the OS presented a nonlinear U-shaped curve. Patients with lower levels of free fatty acids (\u0026le;\u0026thinsp;250\u0026micro;mol/L)had worse OS in breast cancer. Although those with high free fatty acids (\u0026ge;\u0026thinsp;715\u0026micro;mol/L) also had worse outcomes, no statistical significance was observed. One possible reason for this that there are relatively few patients with high levels of free fatty acids in normal BMI group.\u003c/p\u003e \u003cp\u003eThe pathogenetic mechanisms underlying the association between low preoperative serum FFA levels and adverse outcomes remain unknown. A recent study by Ying Pan et al. demonstrated that low FFA levels were associated with a higher risk of mortality in coronary artery disease patients with type 2 diabetes mellitus (T2DM) (Pan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Kathryn Moore et al. indicated that myocardial infarction (MI) accelerated breast cancer outgrowth and cancer-specific mortality both in mice and humans (Koelwyn et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, it is still unclear whether the physiological deficiency of FFA affects insufficient energy supply to the heart itself, which exacerbates the occurrence of long-term adverse cardiovascular events and in turn may lead to unfavorable prognosis in breast cancer patients.\u003c/p\u003e \u003cp\u003eFFAs, as critical energy sources, mainly play a pivotal role in lipid metabolism and can be metabolized through β-oxidation in the mitochondrial matrix (Adeva-Andany et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Due to chronic restriction of dietary energy intake suppressing FFA levels (Henderson \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and affecting the proliferation of hematopoietic stem cells (HSCs) (Mistry et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the function of CD8\u003csup\u003e+\u003c/sup\u003eT cell (Ringel et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), another explanation may lead to a nonlinear U-shaped curve with increased mortality at lower FFA levels.\u003c/p\u003e \u003cp\u003eNevertheless, the current study has certain limitations. First, the analysis was performed in the Southern China population and the results of this study did not necessarily apply to Northern China population or other ethnic groups. In addition, a multicenter large sample cohort study is needed to verify the reliability of the conclusion. Second, we only collected the preoperative FFA level, and we did not analyze the dynamics of total FFA and FFA subclasses over the duration of the study. Our failed to assess all metabolic factors and parameters in patients with breast cancer, including insulin resistance, diet, inflammation and other confounding factors, which may help reveal the possible mechanism of action between FFA levels and OS in BC patients. Finally, it is important to note that the exact cutoff values used to define FFA may vary depending on the study population and the FFA measurement method.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we identified a U-shaped relationship between preoperative serum FFA levels and the survival in BC patients. These data indicate that lower preoperative serum FFA levels was significantly associated with worse OS in BC. In the future, more well-designed prospective cohort studies are needed to clarify the correlation between preoperative serum FFA levels and prognosis.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe original data in this article will be made available by the authors without reservation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The clinical studies were reviewed and approved by Institutional Review Committee of Sun Yat-sen Memorial Hospital affiliated to Sun Yat-Sen University.\u003c/p\u003e \u003c/div\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by National Natural Science Foundation of China (U20A20352), Guang Dong Clinical Research Center for Metabolic Diseases (2020B1111170009), National Natural Science Foundation of China (82230057, 82272859), and the National Key Research and Development Program of China (2022YFC2505101).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors listed have made substantial, direct, and intellectual contributions to this research. LY and QL conceived and designed the project; LL and LJ performed material preparation, data collection and analysis. LY participated in data statistical analysis and interpretation. LL wrote the initial draft and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe are grateful to all participants in this study for their continuous support, as well as our colleagues for their valuable assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdeva-Andany MM, Carneiro-Freire N, Seco-Filgueira M, Fernandez-Fernandez C, Mourino-Bayolo D (2019) Mitochondrial beta-oxidation of saturated fatty acids in humans. Mitochondrion 46:73-90. https://doi.org/10.1016/j.mito.2018.02.009\u003c/li\u003e\n\u003cli\u003eAkram M, Iqbal M, Daniyal M, Khan AU (2017) Awareness and current knowledge of breast cancer. 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J Proteome Res 19(4):1383-1391. https://doi.org/10.1021/acs.jproteome.9b00502\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Free fatty acids, breast cancer, overall survival, U-shaped association","lastPublishedDoi":"10.21203/rs.3.rs-3865368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3865368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSeveral studies have analyzed the association between serum free fatty acid (FFA) and several types of cancer. However, the role of preoperative serum FFA and breast cancer (BC) prognosis remains largely unclear. This study aimed to elucidate the specific relationship between FFA and BC outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A retrospective review was conducted on 4133 breast cancer patients admitted to Sun Yat-sen Memorial Hospital from January 2015 to October 2021. Restricted cubic splines and multivariate Cox regression analyses were used to assess the relationship between preoperative serum FFA and overall survival (OS) in BC patients. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRestricted cubic spline analysis revealed a U-shaped relationship between preoperative serum FFA levels and OS after adjusting for other variables. According to the cutoff points of FFA, multivariate Cox regression analyses showed that patients with low FFA levels (\u0026le;\u0026thinsp;250\u0026micro;mol/L) had higher rates of all-cause mortality and cancer-specific mortality compared to patients with high FFA levels (250\u0026ndash;715\u0026micro;mol/L) in total population and those with a BMI of 18.5 to 24.0 kg/m2.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA nonlinear U-shaped association was identified between preoperative serum FFA levels and the survival in BC patients, with lower FFA levels associated with worse OS.\u003c/p\u003e","manuscriptTitle":"The association of preoperative serum free fatty acid with the survival in breast cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 15:42:49","doi":"10.21203/rs.3.rs-3865368/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbbb3faf-3724-4856-9558-e2d69b22d1e8","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-05T07:38:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 15:42:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3865368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3865368","identity":"rs-3865368","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}