Abstract
Breast cancer (BC) is a leading cause of cancer-related mortality among women globally. Inflammation and nutritional status play critical roles in BC development and progression. Albumin-related inflammatory biomarkers, which reflect both inflammatory and nutritional status, have been increasingly recognized as important indicators in cancer initiation and progression. However, their role in BC risk remains largely unexplored. This study aimed to investigate the association between albumin-related inflammatory biomarkers and BC risk. Female’ data within the age range of 20 to 80 from the National Health and Nutrition Examination Survey (1998–2018) were extracted. Weighted logistic regression and restricted cubic spline regression analyses were performed to explore the association between 4 albumin-related inflammatory biomarkers (neutrophil percentage-to-albumin ratio [NPAR], lymphocyte-to-albumin ratio [LYAR], leukocyte-to-albumin ratio, and platelet-to-albumin ratio) and BC risk. Subgroup analyses were conducted to assess the influence of covariates, and sensitivity analyses were performed to ensure the robustness of the findings. Among 14,211 female participants, 357 were diagnosed with BC. A linear positive relationship was observed between NPAR and BC (odds ratio = 1.08; 95% confidence interval: 1.02–1.14; P = .008), with participants in the highest quartile of NPAR exhibiting a significantly increased risk compared to those in the lowest quartile (odds ratio = 1.60; 95% confidence interval: 1.05–2.44; P for trend = 0.016). In contrast, LYAR demonstrated a nonlinear protective association with BC (P for overall < .001; P for nonlinear < .001), with higher levels linked to reduced risk. Leukocyte-to-albumin ratio and platelet-to-albumin ratio were not significantly associated with BC. Findings from subgroup and sensitivity analyses were consistent with the main findings. Our study highlighted the potential role of NPAR and LYAR in BC risk assessment. They may serve as available and cost-effective tools for identifying high-risk individuals and facilitating early intervention. Further studies are warranted to evaluate the potential of albumin-related inflammatory biomarkers in the early diagnosis of BC.
Keywords: albumin-related inflammatory biomarkers, breast cancer, LYAR, NHANES, NPAR
1. Introduction
Breast cancer (BC) is the most common malignancy among women and the second leading cause of cancer-related mortality worldwide.[1] In 2022, there were approximately 2.3 million new cases and 0.7 million new deaths from female BC globally, posing a significant threat to women’s health and contributing substantially to the global disease burden.[2,3] Therefore, there is an urgent need to identify novel biomarkers that can facilitate early detection and prevention of BC.
Inflammation, recognized as a hallmark of cancer, is linked to tumor initiation, progression, and metastasis.[4] Recent studies have highlighted the potential of various inflammatory indices, including the systemic immune-inflammatory index, platelet-to-lymphocyte ratio, and neutrophil-to-lymphocyte ratio in predicting BC risk.[5–8] However, these studies only focused on single inflammatory indicators, ignoring contributors that are associated with both cancer and inflammation.
Considerable evidence supports that nutritional status is another critical determinant in cancer risk and progression.[9] Several studies have shown that malnutrition was an independent risk factor for BC and associated with adverse clinical outcomes across multiple cancer types.[10–13] In addition, published studies reported that nutritional status significantly modulates systemic inflammatory responses through multiple biological pathways, while other studies indicated adverse inflammation levels may lead to malnutrition.[14,15] Nutritional status and inflammation appear to have a bidirectional relationship. Therefore, evaluating biomarkers that comprehensively reflect nutritional and inflammatory levels may provide a more accurate reflection of the multifactorial nature of BC and enable better risk stratification.[16,17]
Albumin is the fundamental of nutritional assessment and is inversely correlated with systemic inflammation, making it a valuable indicator of overall health and cancer risk.[14,18] Albumin-related inflammatory biomarkers combining inflammatory indicators with albumin were proven to be linked to various chronic diseases, including cancer. For instance, the neutrophil percentage-to-albumin ratio (NPAR) was associated with diabetes, heart disease, bladder cancer, and oral cancer.[19–24] In addition, lymphocyte-to-albumin ratio (LYAR), leukocyte-to-albumin ratio (LAR), and platelet-to-albumin ratio (PAR) are emerging as promising and easily measurable clinical indicators of various cancer risk and prognosis.[25–28] Studies demonstrated that elevated neutrophil and platelet levels can suppress lymphocyte activity, thereby promoting the formation of an inflammatory microenvironment.[29,30] During inflammation, cytokines such as interleukin-6 and tumor necrosis factor-alpha-α, produced by inflammatory cells, downregulate liver albumin production, resulting in low albumin levels.[31] Increased inflammation and decreased albumin may exacerbate BC risk by reducing anti-inflammatory capacity, weakening the antioxidant effect and increasing oxidative stress.[32,33] Nevertheless, the relationship between albumin-related inflammatory biomarkers and BC has received little attention to date.
Therefore, this study aimed to investigate the association between 4 albumin-related inflammatory biomarkers (NPAR, LYAR, LAR, and PAR) and BC using the National Health and Nutrition Examination Survey (NHANES) dataset, providing valuable insights for the prevention and early management of BC.
2. Methods
2.1. Study population
This study is a cross-sectional and population-based analysis of data extracted from the 1998 to 2018 NHANES database (accessed on October 15, 2024; https://www.cdc.gov/nchs/nhanes/). NHANES is a program of surveys designed to create a representative sample of the civilian, noninstitutionalized US population using a complex, multistage, probability sampling. Since 1999, the survey has been conducted continuously and examines about 5000 persons each year. Data were collected by standardized interviews, physical examinations, and laboratory tests. Detailed information on the study design and data collection procedures for NHANES has been provided elsewhere.[34] The data are publicly available and de-identified, and the authors did not have access to information that could identify individual participants. The NHANES protocol received approval from the US National Center for Health Statistics Ethics Review Board, and all participants provided informed consent.
Out of the 101,316 individuals who participated in NHANES 1998 to 2018, the current analysis included 28,608 female participants who were aged 20 and older. Pregnant individuals, as well as those with incomplete data on age, race, education level, marital status, body mass index (BMI), neutrophil percentage, lymphocyte count, platelet count and leukocyte count, serum albumin, diabetes, hypertension, depression, or cancer, were excluded. Overall, 14,211 participants defined the final analytical sample (Fig. 1).
Figure 1.
Flowchart of inclusion and exclusion of study participants. LAR = leukocyte-to-albumin ratio, LYAR = lymphocyte-to-albumin ratio, NHANES = National Health and Nutrition Examination Survey, NPAR = neutrophil-to-albumin ratio, PAR = platelet-to-albumin ratio.
2.2. Definition of albumin-related inflammatory biomarkers
Lymphocyte count, platelet count and leukocyte count (expressed in ×1000 cells/μL), neutrophil percentage in total white blood cell count, and albumin concentration (g/dL) from “Laboratory Data” were extracted to calculate the following albumin-related inflammatory biomarkers: NPAR, calculated as neutrophil percentage in total white blood cell divided by the serum albumin concentration; LYAR, calculated as lymphocyte count divided by the serum albumin concentration; LAR, calculated as leukocyte count divided by the serum albumin concentration; and PAR, calculated as platelet count divided by the serum albumin concentration.[25,26,35,36]
2.3. Assessment of BC
The Medical Conditions Questionnaire was used to collect self-reported BC diagnoses. Based on previous studies, BC patients were defined as those who reported solely BC.[16] In the sensitivity analysis, individuals with a history of BC along with other primary cancers were classified as BC cases.
2.4. Covariates
Based on previous literature, we selected covariates associated with albumin-related inflammatory biomarkers and BC, including age, race, education level, marital status, smoking status, alcohol consumption, BMI, diabetes, hypertension, and depression.[16,17] Demographic and clinical information including age (20–39 years, 40–59 years, and ≥60 years), race (Mexican American, Other Hispanic, non-Hispanic White, non-Hispanic Black, and Other), education level (less than high school, high school, and college or above), marital status (married, alone, and never married or living with partner), smoking status (current, ever, and never), and alcohol consumption (above moderate, moderate, ever, and never) were collected. BMI was calculated using a standardized method based on weight and height measurements, and participants were classified into 3 categories: normal (<24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (≥30 kg/m2). The presence of previous diseases such as diabetes, hypertension, and depression, were determined based on physician-diagnosed conditions documented in the NHANES data.
2.5. Statistical analysis
All statistical analyses were performed using survey weights to ensure nationally representative estimates. Baseline characteristics were described with weighted mean and weighted standard deviation for continuous variables as well as sample counts and weighted percentages for categorical ones. Differences among groups were tested by 1-way analysis of variance for variables with normal distributions and the Kruskal–Wallis H test for those with non-normal distributions. Sample weights were applied for continuous variables and the Rao–Scott Chi-square tests were used for categorical variables.
Weighted univariate and multivariate logistic regression models were employed to examine the association between 4 albumin-related inflammatory biomarkers and BC. Three models were constructed: Model 1 was unadjusted, Model 2 was adjusted for age and race, and Model 3 was adjusted for age, race, education level, marital status, smoking status, alcohol consumption, BMI, diabetes, hypertension, and depression. NPAR, LYAR, LAR, and PAR were all continuous variables and further categorized into 4 groups based on their quartiles: Quartile 1 (Q1) to Quartile 4 (Q4). Tests for trends were conducted by assigning the median of each quartile group, generating a continuous variable in the models. Model fit statistics are presented in Tables S1 and S2, Supplemental Digital Content, https://links.lww.com/MD/Q866. Subsequently, our cohort was randomly split into a training set (70%, n = 9948) and a testing set (30%, n = 4263). The discrimination ability of Model 3 was assessed by calculating the area under the receiver operating characteristic curve (AUC). Additionally, we constructed restricted cubic spline models with survey weights to further examine the potential nonlinear dose-response relationship between NPAR, LYAR, and BC. Finally, sensitivity analyses were conducted after identifying patients with both BC and additional malignancies simultaneously as BC cases.
Significance was set as 2-sided P < .05. All analyses were performed using R (version 4.3.1, https://www.r-project.org/).
3. Results
3.1. Baseline characteristics
A total of 14,211 participants were included in this study, of which 357 had BC. Compared with the control group, BC patients were older, had a higher proportion of non-Hispanic Whites and ever smokers, were more likely to live alone, consume alcohol below moderate levels, and have hypertension and diabetes. Importantly, BC patients exhibited significantly higher NPAR levels (14.61 vs 14.02, P = .004) and lower LYAR (0.46 vs 0.53, P < .001) and PAR (60.14 vs 63.80, P < .001) levels than those without BC (Table 1).
Table 1.
Baseline characteristics of participants by breast cancer status.*
| Characteristics | Total | Control | Breast cancer | P values† |
|---|---|---|---|---|
| n = 14,211 | n = 13,854 | n = 357 | ||
| Age (%) | <.001 | |||
| 20–39 years | 5356 (40.5) | 5350 (41.5) | 6 (2.8) | |
| 40–59 years | 4666 (35.8) | 4586 (36.1) | 80 (25.2) | |
| ≥60 years | 4189 (23.7) | 3918 (22.4) | 271 (72.0) | |
| Race (%) | <.001 | |||
| Mexican American | 2368 (8.1) | 2332 (8.3) | 36 (3.4) | |
| Other Hispanic | 1495 (5.9) | 1466 (6.0) | 29 (2.8) | |
| Non-Hispanic White | 5773 (66.9) | 5568 (66.5) | 205 (82.1) | |
| Non-Hispanic Black | 3071 (11.8) | 3010 (11.9) | 61 (7.6) | |
| Other race | 1504 (7.2) | 1478 (7.3) | 26 (4.2) | |
| Education level (%) | .463 | |||
| Less than high school | 3137 (13.9) | 3067 (14.0) | 70 (11.4) | |
| High school | 3116 (22.4) | 3038 (22.4) | 78 (22.4) | |
| College or above | 7958 (63.7) | 7749 (63.7) | 209 (66.2) | |
| Marital status (%) | <.001 | |||
| Married | 7679 (59.1) | 7488 (59.1) | 191 (58.2) | |
| Alone | 3646 (21.4) | 3498 (21.0) | 148 (37.1) | |
| Never married or living with partner | 2886 (19.5) | 2868 (19.9) | 18 (4.8) | |
| Smoking status (%) | .001 | |||
| Current | 2409 (17.9) | 2374 (18.1) | 35 (10.2) | |
| Ever | 2376 (18.8) | 2276 (18.6) | 100 (27.0) | |
| Never | 9426 (63.4) | 9204 (63.4) | 222 (62.9) | |
| Alcohol consumption (%) | <.001 | |||
| Above moderate | 5084 (41.1) | 5018 (41.6) | 66 (21.9) | |
| Moderate | 3228 (26.3) | 3110 (26.0) | 118 (39.6) | |
| Ever | 2890 (16.9) | 2802 (16.9) | 88 (18.8) | |
| Never | 3009 (15.6) | 2924 (15.5) | 85 (19.7) | |
| BMI (%) | .155 | |||
| Normal | 4430 (35.2) | 4326 (35.3) | 104 (29.3) | |
| Overweight | 3976 (27.4) | 3866 (27.3) | 110 (32.9) | |
| Obese | 5805 (37.4) | 5662 (37.4) | 143 (37.8) | |
| Diabetes (%) | 1569 (7.9) | 1499 (7.7) | 70 (15.5) | <.001 |
| Hypertension (%) | 4765 (28.8) | 4558 (28.2) | 207 (49.7) | <.001 |
| Depression (%) | 1421 (8.8) | 1384 (8.8) | 37 (9.1) | .893 |
| NPAR, mean (SD) | 14.04 (2.45) | 14.02 (2.44) | 14.61 (2.68) | .004 |
| LYAR, mean (SD) | 0.52 (0.18) | 0.53 (0.18) | 0.46 (0.17) | <.001 |
| LAR, mean (SD) | 1.75 (0.55) | 1.76 (0.55) | 1.69 (0.56) | .175 |
| PAR, mean (SD) | 63.71 (17.75) | 63.80 (17.77) | 60.14 (16.36) | <.001 |
BMI = body mass index, LAR = leukocyte-to-albumin ratio, LYAR = lymphocyte-to-albumin ratio, NPAR = neutrophil percentage-to-albumin ratio, PAR = platelet-to-albumin ratio, SD = standard deviation.
All results were survey-weighted except for counts of categorical variables.
P value obtained from Rao–Scott Chi-square tests for categorical variables and 1-way analysis of variance adjusted for sample weights for continuous variables.
3.2. Relationship between albumin-related inflammatory biomarkers and BC
The association between 4 albumin-related inflammatory biomarkers and the risk of BC was analyzed by logistic regression as continuous variables and categorical variables, respectively (Table 2). We found that NPAR was positively associated with BC (odds ratio [OR] = 1.10, 95% confidence interval [CI]: 1.03–1.17, P = .002), whereas LYAR had a negative association with BC (OR = 0.07, 95% CI: 0.02–0.24, P < .001). These results were still stable in Model 2 (NPAR: OR = 1.08, 95% CI: 1.02–1.14, P = .008; LYAR: OR = 0.20, 95% CI: 0.06–0.66, P = .009) and Model 3 (NPAR: OR = 1.08, 95% CI: 1.02–1.14, P = .008; LYAR: OR = 0.19, 95% CI: 0.06–0.65, P = .009). Using the lowest quartile (Q1) as the reference, the adjusted OR for the highest quartile (Q4) was 1.60 (Model 3, 95% CI: 1.05–2.44, P for trend = .016) for NPAR and 0.53 (Model 3, 95% CI: 0.32–0.87, P for trend = .011) for LYAR. No significant associations were found between LAR, PAR, and BC. Receiver operating characteristic curves for NPAR (Figure S1a and b, Supplemental Digital Content, https://links.lww.com/MD/Q866) and LYAR (Figure S1c and d, Supplemental Digital Content, https://links.lww.com/MD/Q866) in the fully adjusted model demonstrated excellent discriminatory ability in both the training (NPAR, AUC = 0.823; LYAR, AUC = 0.795) and testing sets (NPAR, AUC = 0.824; LYAR, AUC = 0.798).
Table 2.
Association between albumin-related inflammatory biomarkers and breast cancer.
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| NPAR | ||||||
| Continuous | 1.10 (1.03–1.17) | .002 | 1.08 (1.02–1.14) | .008 | 1.08 (1.02–1.14) | .008 |
| Categories | ||||||
| Q1 | ref | ref | ref | |||
| Q2 | 0.88 (0.59–1.31) | .523 | 0.93 (0.63–1.38) | .713 | 0.92 (0.62–1.37) | .69 |
| Q3 | 1.25 (0.79–1.96) | .340 | 1.22 (0.77–1.92) | .394 | 1.23 (0.77–1.94) | .384 |
| Q4 | 1.67 (1.09–2.55) | .018 | 1.58 (1.05–2.40) | .030 | 1.60 (1.05–2.44) | .028 |
| P for trend | .009 | .017 | .016 | |||
| LYAR | ||||||
| Continuous | 0.07 (0.02–0.24) | <.001 | 0.20 (0.06–0.66) | .009 | 0.19 (0.06–0.65) | .009 |
| Categories | ||||||
| Q1 | ref | ref | ref | |||
| Q2 | 0.67 (0.48–0.93) | .016 | 0.82 (0.59–1.15) | .251 | 0.81 (0.59–1.11) | .180 |
| Q3 | 0.53 (0.36–0.80) | .002 | 0.72 (0.47–1.09) | .120 | 0.71 (0.48–1.06) | .092 |
| Q4 | 0.34 (0.21–0.54) | <.001 | 0.53 (0.32–0.87) | .013 | 0.53 (0.32–0.87) | .013 |
| P for trend | <.001 | .011 | .011 | |||
| LAR | ||||||
| Continuous | 0.80 (0.57–1.13) | .203 | 0.95 (0.67–1.34) | .768 | 0.98 (0.68–1.40) | .898 |
| Categories | ||||||
| Q1 | ref | ref | ref | |||
| Q2 | 1.01 (0.69–1.47) | .969 | 1.05 (0.72–1.54) | .797 | 1.05 (0.72–1.52) | .814 |
| Q3 | 0.83 (0.56–1.22) | .333 | 0.92 (0.61–1.37) | .671 | 0.93 (0.63–1.38) | .711 |
| Q4 | 0.80 (0.49–1.30) | .370 | 1.03 (0.63–1.69) | .896 | 1.08 (0.65–1.81) | .765 |
| P for trend | .251 | .981 | .862 | |||
| PAR | ||||||
| Continuous | 0.99 (0.98–0.99) | .001 | 1.00 (0.99–1.00) | .318 | 1.00 (0.99–1.00) | .371 |
| Categories | ||||||
| Q1 | ref | ref | ref | |||
| Q2 | 0.85 (0.57–1.26) | .407 | 0.98 (0.63–1.50) | .909 | 0.99 (0.64–1.53) | .947 |
| Q3 | 0.78 (0.55–1.13) | .186 | 1.05 (0.71–1.56) | .801 | 1.07 (0.71–1.61) | .752 |
| Q4 | 0.59 (0.41–0.85) | .004 | 0.88 (0.60–1.29) | .511 | 0.90 (0.61–1.31) | .571 |
| P for trend | .004 | .605 | .681 | |||
Survey sample weights were taken into consideration in the logistic regression models accompanying the NHANES data. Model 1: unadjusted; Model 2: adjusted for age, race, and marital status; Model 3: adjusted for age, race, education level, marital status, BMI, diabetes, hypertension, and depression.
BMI = body mass index, CI = confidence interval, LAR = leukocyte-to-albumin ratio, LYAR = lymphocyte-to-albumin ratio, NHANES = National Health and Nutrition Examination Survey, NPAR = neutrophil-to-albumin ratio, OR = odds ratio, PAR = platelet-to-albumin ratio, Q1 = quartile 1, Q2 = quartile 2, Q3 = quartile 3, Q4 = quartile 4.
Besides, restricted cubic spline analysis was adopted in this study to assess the potential nonlinearity of the association of NPAR and LYAR with BC. As shown in Figure 2, a nonlinear relationship between LYAR and BC was found (P for overall < .001; P for nonlinearity < .001) and a linear relationship could be observed between NPAR and BC (P for overall < .001; P for nonlinearity = .207).
Figure 2.
Weighted restricted cubic spline curve describing the dose-response relationship between albumin-related inflammatory biomarkers and breast cancer risk. (a) NPAR and (b) LYAR. CI = confidence interval, LYAR = lymphocyte-to-albumin ratio, NPAR = neutrophil-to-albumin ratio.
3.3. Subgroup analysis
Subgroup analyses and interaction tests were conducted by age, race, education level, marital status, smoking status, alcohol consumption, BMI, diabetes, hypertension, and depression to examine the consistency of the association between NPAR, LYAR, and BC in the overall population, as well as to assess the potential influence of effect modifiers. NPAR remained significantly and positively associated with BC across most subgroups and no significant interaction between the aforementioned characteristics and NPAR was found (P for interaction > .05) (Fig. 3a). Similarly, LYAR was identified as a protective factor against BC risk in most subgroups, with no significant interaction found between LYAR and the evaluated characteristics (P for interaction > .05) (Fig. 3b).
Figure 3.
Subgroup analyses of the association between albumin-related inflammatory biomarkers and breast cancer. (a) NPAR and breast cancer; (b) LYAR and breast cancer. CI = confidence interval, LYAR = lymphocyte-to-albumin ratio, NPAR = neutrophil-to-albumin ratio, OR = odds ratio.
3.4. Sensitivity analyses
Sensitivity analyses were conducted after identifying patients with both BC and additional malignancies simultaneously as BC cases. The results were approximately consistent with those in the main analysis. In the fully adjusted model, participants in Q4 of NPAR levels had a significantly higher risk of BC prevalence compared to those in Q1 (OR = 1.69; 95% CI: 1.15–2.48, P for trend = .005). Q4 of LYAR was consistently associated with a reduced risk of BC compared with Q1 (OR = 0.53, 95% CI: 0.33–0.84, P for trend = .004) (Table 3).
Table 3.
Sensitivity analysis of association between albumin-related inflammatory biomarkers and breast cancer.
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| NPAR | ||||||
| Continuous | 1.11 (1.05–1.16) | <.001 | 1.08 (1.03–1.14) | .001 | 1.09 (1.03–1.14) | .001 |
| Categories | ||||||
| Q1 | ref | ref | ref | |||
| Q2 | 0.98 (0.67–1.43) | .902 | 1.03 (0.70–1.52) | .884 | 1.02 (0.68–1.53) | .916 |
| Q3 | 1.31 (0.85–2.02) | .214 | 1.28 (0.83–1.97) | .27 | 1.29 (0.83–1.99) | .257 |
| Q4 | 1.76 (1.20–2.57) | .004 | 1.67 (1.15–2.43) | .008 | 1.69 (1.15–2.48) | .008 |
| P for trend | .002 | .005 | .005 | |||
| LYAR | ||||||
| Continuous | 0.06 (0.02–0.18) | <.001 | 0.17 (0.06–0.52) | .002 | 0.18 (0.06–0.54) | .003 |
| Categories | ||||||
| Q1 | ref | ref | ref | |||
| Q2 | 0.63 (0.47–0.86) | .003 | 0.79 (0.58–1.07) | .125 | 0.77 (0.57–1.05) | .096 |
| Q3 | 0.50 (0.35–0.72) | <.001 | 0.68 (0.47–0.99) | .045 | 0.69 (0.48–0.97) | .036 |
| Q4 | 0.33 (0.22–0.49) | <.001 | 0.52 (0.33–0.81) | .004 | 0.53 (0.33–0.84) | .007 |
| P for trend | <.001 | .003 | .004 | |||
Survey sample weights were taken into consideration in the logistic models accompanying the NHANES data. Model 1: unadjusted; Model 2: adjusted for age, race, and marital status; Model 3: adjusted for age, race, education level, marital status, BMI, diabetes, hypertension, and depression.
BMI = body mass index, CI = confidence interval, LYAR = lymphocyte-to-albumin ratio, NHANES = National Health and Nutrition Examination Survey, NPAR = neutrophil-to-albumin ratio, OR = odds ratio, Q1 = quartile 1, Q2 = quartile 2, Q3 = quartile 3, Q4 = quartile 4.
4. Discussion
In this nationally representative study of US adults, we investigated the association between 4 albumin-related inflammatory biomarkers (NPAR, LYAR, LAR, and PAR) and BC. Higher levels of NPAR were associated with an increased risk of BC, while LYAR was inversely associated with BC. These associations remained statistically significant after adjusting for multiple confounding factors. However, no significant relationship was observed between LAR, PAR, and BC. Our findings highlighted the potential role of systemic inflammation and nutritional status in BC pathogenesis and provided significant implications for primary prevention in populations with a high incidence of BC.
Neutrophils, the most abundant circulating leukocytes in humans, are the first immune cells recruited to sites of inflammation and play crucial roles in tumor initiation.[37] Samson et al[38] reported that neutrophils can release oxygen and nitrogen free radicals, which promote DNA point mutations and contribute to genetic instability. Additionally, neutrophil elastase supports tumor cell proliferation by activating the PI3K and PDGFR signaling pathways and is also involved in neutrophil-related epithelial-to-mesenchymal transition.[39,40] Elevated neutrophil levels, reflected in higher NPAR, may indicate a pro-inflammatory state that fosters a pro-tumor microenvironment. Serum albumin, the most abundant protein in plasma, usually reflects an individual’s nutritional status and level of inflammation.[41] It is also an independent prognostic factor for survival in various cancers.[42–44] Lower albumin levels, often observed in chronic inflammatory conditions, may exacerbate the inflammatory burden, further increasing BC risk.[45]
The negative association between LYAR and BC risk suggested that lymphocytes, as essential components of the immune system, may play a protective role against cancer development. Tumor-infiltrating lymphocytes mediate response to chemotherapy and improve clinical outcomes in all BC subtypes.[46] Higher LYAR levels, reflecting a robust immune response and better nutritional status, could enhance immune surveillance and tumor suppression.[47] This finding was supported by prior research, which identified LYAR as an independent prognostic factor for survival outcomes in non-small-cell lung cancer.[25] The protective effect of LYAR may also be attributed to the anti-inflammatory and immunomodulatory properties of albumin, which, when combined with higher lymphocyte counts, could create a more favorable immune environment for combating cancer.
The integration of inflammatory and nutritional indicators, exemplified by NPAR and LYAR, provides a more comprehensive approach to assessing BC risk. This combination captured the interplay between systemic inflammation, immune function, and nutritional status, all of which are critical factors in cancer development and progression. However, we found no significant associations between LAR, PAR, and BC risk, which may be attributed to biological redundancy resulting from the inherent complexity of leukocytes and platelets. LAR involves total leukocyte count, which is a broad measure encompassing various subpopulations such as neutrophils and lymphocytes that play complex and sometimes opposing roles in inflammation and tumor microenvironments.[8] Platelets have multifaceted roles in tumorigenesis, including promoting tumor growth and metastasis as well as modulating immune responses.[48,49] Such complexity may reduce the sensitivity and specificity of PAR as independent indicators of BC risk.
Our study had several strengths. First, to the best of our knowledge, this is the first nationally representative study to examine the relationship between albumin-related inflammatory biomarkers and BC using data from NHANES. Furthermore, the large sample size and the incorporation of sample weights in all analyses ensured that our estimates were both representative and statistically reliable. Finally, sensitivity analyses demonstrated that the majority of our findings remained robust. However, several limitations should be noted. Firstly, due to the cross-sectional study design of NHANES data, causality or the direction of association cannot be determined. Additionally, as an observational study, residual confounding cannot be entirely excluded, although we adjusted for a number of covariates selected based on prior literature regarding BC risk to mitigate this issue. Lastly, there may be recall bias from self-reported BC status and other covariates in the NHANES database. Further prospective studies are needed to establish the temporal relationship between albumin-related inflammatory biomarkers and BC development.
5. Conclusion
In conclusion, our study demonstrated that NPAR and LYAR were significantly associated with BC risk, with NPAR serving as a potential risk factor and LYAR as a protective factor. These findings underscored the significance of systemic inflammation and nutritional status in BC pathogenesis. Incorporating these cost-effective biomarkers into screening protocols or public health strategies may aid in the early identification of high-risk individuals and facilitate timely preventive interventions. Further research is warranted to explore the clinical applications of these biomarkers and their role in enhancing BC prevention and management.
Acknowledgments
The efforts and contributions of all NHANES participants and the staff of this study are gratefully acknowledged.
Author contributions
Conceptualization: Yuhang Shang.
Data curation: Jiangwei Liu.
Formal analysis: Yunqiang Duan, Jiangwei Liu.
Funding acquisition: Baoliang Guo.
Investigation: Runze Guo.
Methodology: Runze Guo, Zhengbo Fang.
Project administration: Fei Ma, Baoliang Guo.
Resources: Yuhang Shang.
Software: Zhengbo Fang.
Supervision: Fei Ma, Baoliang Guo.
Validation: Yunqiang Duan, Zhengbo Fang.
Visualization: Yunqiang Duan, Zhengbo Fang.
Writing – original draft: Yuhang Shang.
Writing – review & editing: Yuhang Shang, Yunqiang Duan, Baoliang Guo.
Supplementary Material
Abbreviations:
- AUC
- area under the receiver operating characteristic curve
- BC
- breast cancer
- BMI
- body mass index
- CI
- confidence interval
- LAR
- leukocyte-to-albumin ratio
- LYAR
- lymphocyte-to-albumin ratio
- NHANES
- National Health and Nutrition Examination Survey
- NPAR
- neutrophil percentage-to-albumin ratio
- OR
- odds ratio
- PAR
- platelet-to-albumin ratio
This study was funded by grants from the National Natural Science Foundation of China (81872135).
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available for this article.
How to cite this article: Shang Y, Duan Y, Liu J, Guo R, Fang Z, Ma F, Guo B. Association between albumin-related inflammatory biomarkers and breast cancer risk: A secondary analysis of NHANES 1998–2018. Medicine 2025;104:50(e46482).
YS and YD contributed to this article equally.
Contributor Information
Yuhang Shang, Email: shangyuhang0427@163.com.
Yunqiang Duan, Email: 1355042961@qq.com.
Jiangwei Liu, Email: 18845138849@163.com.
Runze Guo, Email: baoliangguo2020@hrbmu.edu.cn.
Zhengbo Fang, Email: 13871540009@163.com.
Fei Ma, Email: wafsfd@sina.com.
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