Abstract
Background:
The peripheral white blood cell (WBC) and neutrophil-to-lymphocyte ratio (NLR) reflect levels of inflammation and adaptive immunity. They are associated with cancer prognosis, but their associations with cancer incidence are not established.
Methods:
We evaluated 443,540 cancer-free adults in the UK Biobank with data on total WBC and its subsets, follow-up starting one year after baseline. Cox regression was used to estimate hazard ratios (HRs) per quartile of WBC or NLR for incidence of 73 cancer types.
Results:
22,747 incident cancers were diagnosed during a median of 6.9 years of follow-up. WBC was associated with risk of cancer overall (HR 1.05, 95%CI 1.03-1.06), chronic lymphocytic leukemia/small lymphocytic leukemia (CLL/SLL: 2.79, 2.45-3.18), lung cancer (1.14, 1.08-1.20), and breast cancer (1.05-1.02-1.08). NLR was positively associated with cancer overall (HR 1.03, 95%CI 1.02-1.04, per quartile) and kidney cancer (1.16, 1.07-1.25), and inversely with CLL/SLL (0.38, 0.33-0.42).
Conclusion:
High WBC or NLR may reflect excessive inflammatory status, promoting development of some cancers. Conversely, low NLR indicates a relative rise in lymphocytes, which could reflect an increase in circulating premalignant cells before CLL/SLL diagnosis. Peripheral WBC and NLR, in combination with other clinical information or biomarkers, may be useful tools for cancer risk stratification.
Impact:
Elevated levels of white blood cells or an increased neutrophil-to-lymphocyte ratio may indicate an overly active inflammatory response, potentially contributing to the eventual onset of certain types of cancer.
Keywords: White blood cell counts, Neutrophil-to-Lymphocyte Ratio, Epidemiology
INTRODUCTION
Chronic inflammation is an important etiologic mechanism of carcinogenesis (1,2). A large body of epidemiological evidence supports that chronic conditions of unresolved inflammation are associated with a higher risk of malignancies, such as chronic Helicobacter pylori infection of the stomach with gastric cancer, and inflammatory bowel disease with colorectal cancer. Anti-inflammatory drugs such as aspirin are considered promising chemopreventive agents (3).
In addition, a healthy immune system may limit the growth of malignant tumors through immunosurveillance that can control or eliminate premalignant cells. This process may be most important for virus-associated cancers, as immunosuppressed individuals have an elevated incidence of these malignancies (4). Immunity is increasingly recognized to play a role in determining patient outcomes after cancer diagnosis (5–7). Advances in immunotherapy, especially with the introduction of immune checkpoint inhibitors, can allow the immune system to control cancer. We are also beginning to understand the contribution of circulating immune cells to the antitumor immune response (8).
White blood cells (WBCs) are the primary effectors of the immune system protecting the body against infection and resolving tissue injury. Among different WBC subtypes, neutrophils are the most abundant in peripheral blood and secrete inflammatory mediators that act against bacteria and fungi in acute and chronic inflammation. Lymphocytes, which are part of the adaptive immune system, regulate inflammation. B-lymphocytes function in humoral immunity by producing antibodies, whereas T-lymphocytes play a role in cell-mediated immune destruction of abnormal cells. Neutrophils and lymphocytes together comprise ~90% of peripheral WBCs. The neutrophil-to-lymphocyte ratio (NLR) measured in peripheral blood is a simple ratio representing the balance between chronic inflammation and adaptive immunity and is considered a more stable marker that is less affected by acute conditions than total WBC or counts of WBC subsets (9,10). Alterations in NLR can arise from changes in either the numerator (neutrophil count) or the denominator (lymphocyte count), or both. However, the ratio of these two components under the same total WBC levels can provide valuable insights into the immune response’s balance in terms of inflammation and adaptive immunity, while the total WBC count captures information about the total level of inflammation and immune activity.
Both total peripheral WBC and NLR have been associated with various clinical outcomes (9–11), but there are only a limited number of studies that investigate their association with the incidence of cancer. In prospective cohort studies, higher WBC counts were associated with an increased risk of cancers of the lung, breast, colorectal, endometrial, and stomach (12–16). However, most of these studies did not investigate the association with WBC subtypes that may render differential risk. NLR has been associated with cancer risk in a small number of studies with inconsistent findings (12,17–22). Two studies using data from the United Kingdom (UK) Biobank, a large prospective cohort study, found higher NLR associated with lung cancer and colorectal cancer (12,21). However, these studies did not take into account the total WBC level in their models, leaving open the possibility that measured NLR-cancer associations were driven by the absolute neutrophil and lymphocyte levels, rather than their balance. Furthermore, most studies on WBC or NLR associations with cancer risk were limited to a small number of frequent cancer types.
In the present study, we comprehensively investigated the independent associations of total peripheral WBC count and NLR with incident cancer risk. We evaluated 73 individual cancer types using data from the UK Biobank.
MATERIALS AND METHODS
Study design and participants
The UK Biobank study design has been described on the study website (https://www.ukbiobank.ac.uk/) and elsewhere (23). Briefly, adults registered in the UK National Health Service (NHS) aged 40-69 years residing within 40 km of 22 assessment centers were eligible. During 2006-2010, the study enrolled 503,317 individuals who were administered questionnaires, underwent physical examinations, and provided biological samples including whole blood (24). The UK Biobank study was approved by governmental and ethical review boards. Written consent was obtained from all participants.
Data from 502,505 participants with WBC measurements were available for our study. As shown in Supplementary Figure S1, we excluded individuals who withdrew from the study, had missing baseline assessment date, had cancer diagnosed before the assessment, or were missing differential WBC measurement or information on potential confounding factors. Furthermore, to minimize the possibility that the WBC count was influenced by undiagnosed cancer (i.e., reverse causation), we started follow-up one year after the baseline assessment. The final sample for analysis comprised 443,540 participants.
Assessment of blood cells
The complete blood count including the total and differential WBC (neutrophils, lymphocytes, monocytes, eosinophils) was measured in baseline blood samples collected in 4 ml ethylene diamine tetraacetic acid vacutainers using the Coulter LH 750 System (Beckman Coulter, Brea, CA; https://biobank.ndph.ox.ac.uk/ukb/ukb/docs/haematology.pdf). Samples were mostly analyzed within 24 hours of blood draw. Absolute neutrophil and lymphocyte counts, and NLRs, were calculated from these measurements.
Outcome assessment
Participants were followed from one year after baseline until a first incident cancer diagnosis, death, or censoring at the end of cancer ascertainment (January 31, 2016, for England and Wales, and November 30, 2015, for Scotland), whichever was the earliest. Invasive cancer diagnoses were provided by the UK Health and Social Care Information Centre and the NHS Central Register. Cancers excluding nonmelanoma skin cancers were categorized using a modified version of the Surveillance, Epidemiology, and End Results Program Site Recode (https://seer.cancer.gov/siterecode/icdo3_dwhoheme/index.html). There were a total of 73 prespecified cancer types, 10 of which had 10 or fewer cases and were included in the overall cancer analysis but not assessed separately (see Table 2 footnote).
Table 2.
Associations of total white blood cell (WBC) count and neutrophil-to-lymphocyte ratio (NLR) with cancer incidence.
| Total WBC, Per Quartile Increase | NLR, Per Quartile Increase | |||||||
|---|---|---|---|---|---|---|---|---|
| N | % | HR | (95%CI) | p-trend | HR | (95%CI) | p-trend | |
| All Cancers | 22,747 | 100.0 | 1.05 | (1.03-1.06) | <0.0001 | 1.03 | (1.02-1.04) | <0.0001 |
| Tongue | 143 | 0.6 | 0.91 | (0.77-1.07) | 0.24 | 0.98 | (0.84-1.14) | 0.79 |
| Salivary Gland | 28 | 0.1 | 1.12 | (0.78-1.60) | 0.55 | 0.84 | (0.60-1.19) | 0.34 |
| Floor of Mouth | 23 | 0.1 | 1.17 | (0.73-1.89) | 0.51 | 1.29 | (0.86-1.93) | 0.22 |
| Gum and Other Mouth | 74 | 0.3 | 0.91 | (0.72-1.14) | 0.39 | 1.16 | (0.93-1.43) | 0.19 |
| Nasopharynx | 15 | 0.1 | 1.01 | (0.62-1.66) | 0.96 | 1.30 | (0.80-2.11) | 0.30 |
| Tonsil | 113 | 0.5 | 0.98 | (0.81-1.17) | 0.80 | 1.06 | (0.89-1.25) | 0.54 |
| Oropharynx | 18 | 0.1 | 0.60 | (0.37-0.95) | 0.03 | 1.24 | (0.80-1.92) | 0.34 |
| Hypopharynx | 21 | 0.1 | 0.75 | (0.49-1.15) | 0.19 | 1.19 | (0.79-1.79) | 0.41 |
| Esophagus | 426 | 1.9 | 1.06 | (0.96-1.16) | 0.27 | 1.11 | (1.01-1.21) | 0.02 |
| Stomach | 283 | 1.2 | 1.18 | (1.05-1.32) | 0.01 | 1.07 | (0.96-1.19) | 0.26 |
| Small Intestine | 102 | 0.5 | 1.08 | (0.89-1.31) | 0.43 | 1.13 | (0.94-1.36) | 0.19 |
| Colon excluding Rectum | 1,709 | 7.5 | 1.04 | (0.99-1.09) | 0.09 | 1.06 | (1.01-1.10) | 0.02 |
| Rectum and Rectosigmoid Junction | 888 | 3.9 | 1.07 | (1.00-1.14) | 0.05 | 1.03 | (0.97-1.09) | 0.37 |
| Anus | 76 | 0.3 | 1.13 | (0.90-1.42) | 0.28 | 0.96 | (0.78-1.18) | 0.68 |
| Liver | 146 | 0.6 | 0.76 | (0.65-0.89) | 0.0008 | 0.91 | (0.78-1.06) | 0.22 |
| Intrahepatic Bile Duct | 105 | 0.5 | 1.22 | (1.01-1.47) | 0.04 | 0.98 | (0.82-1.17) | 0.79 |
| Gallbladder | 59 | 0.3 | 1.41 | (1.09-1.84) | 0.01 | 1.01 | (0.79-1.28) | 0.96 |
| Other Biliary | 77 | 0.3 | 0.91 | (0.73-1.13) | 0.41 | 0.90 | (0.73-1.11) | 0.33 |
| Pancreas | 495 | 2.2 | 1.01 | (0.93-1.11) | 0.77 | 1.07 | (0.98-1.16) | 0.11 |
| Retroperitoneum | 12 | 0.1 | 1.19 | (0.69-2.06) | 0.53 | 1.08 | (0.64-1.83) | 0.78 |
| Peritoneum, Omentum and Mesentery | 67 | 0.3 | 0.90 | (0.71-1.14) | 0.38 | 1.01 | (0.81-1.26) | 0.94 |
| Nose, Nasal Cavity and Middle Ear | 23 | 0.1 | 1.02 | (0.68-1.53) | 0.93 | 0.82 | (0.56-1.20) | 0.31 |
| Larynx | 85 | 0.4 | 0.97 | (0.77-1.20) | 0.75 | 1.02 | (0.83-1.24) | 0.87 |
| Lung | 1,657 | 7.3 | 1.14 | (1.08-1.20) | <0.0001 | 1.04 | (1.00-1.09) | 0.06 |
| Bones and Joints | 25 | 0.1 | 1.21 | (0.82-1.79) | 0.34 | 0.98 | (0.68-1.41) | 0.89 |
| Soft Tissue including Heart | 119 | 0.5 | 1.05 | (0.88-1.25) | 0.57 | 1.29 | (1.08-1.53) | 0.005 |
| Melanoma of the Skin | 1,223 | 5.4 | 1.00 | (0.95-1.06) | 0.99 | 1.00 | (0.95-1.06) | 0.95 |
| Non-Melanoma Skin | 41 | 0.2 | 0.96 | (0.71-1.30) | 0.81 | 1.21 | (0.90-1.62) | 0.21 |
| Breast | 4,467 | 19.6 | 1.05 | (1.02-1.08) | 0.0004 | 1.02 | (0.99-1.05) | 0.17 |
| Cervix | 56 | 0.3 | 1.21 | (0.93-1.57) | 0.15 | 1.01 | (0.79-1.29) | 0.94 |
| Uterine Corpus | 694 | 3.1 | 1.07 | (1.00-1.15) | 0.07 | 1.11 | (1.04-1.19) | 0.003 |
| Uterus, Not Otherwise Specified | 13 | 0.1 | 0.91 | (0.54-1.54) | 0.73 | 1.21 | (0.72-2.02) | 0.47 |
| Ovary | 441 | 1.9 | 1.01 | (0.92-1.10) | 0.88 | 1.08 | (0.99-1.18) | 0.09 |
| Vagina | 12 | 0.1 | 1.01 | (0.59-1.74) | 0.96 | 1.37 | (0.80-2.35) | 0.25 |
| Vulva | 52 | 0.2 | 1.07 | (0.83-1.40) | 0.60 | 1.13 | (0.88-1.46) | 0.34 |
| Other Female Genital Organs | 46 | 0.2 | 0.88 | (0.66-1.16) | 0.36 | 0.99 | (0.76-1.30) | 0.96 |
| Prostate | 4,757 | 20.9 | 1.01 | (0.98-1.03) | 0.71 | 1.01 | (0.99-1.04) | 0.36 |
| Testis | 58 | 0.3 | 1.08 | (0.84-1.40) | 0.53 | 1.02 | (0.80-1.30) | 0.86 |
| Penis | 28 | 0.1 | 0.98 | (0.68-1.41) | 0.92 | 1.17 | (0.82-1.67) | 0.39 |
| Urinary Bladder | 495 | 2.2 | 1.02 | (0.93-1.11) | 0.69 | 1.08 | (1.00-1.18) | 0.06 |
| Kidney and Renal Pelvis | 640 | 2.8 | 1.07 | (0.99-1.15) | 0.09 | 1.15 | (1.07-1.24) | 0.0002 |
| Kidney | 593 | 2.6 | 1.09 | (1.01-1.18) | 0.03 | 1.16 | (1.07-1.25) | 0.0002 |
| Renal Pelvis | 47 | 0.2 | 0.82 | (0.62-1.08) | 0.16 | 1.08 | (0.83-1.42) | 0.57 |
| Ureter | 26 | 0.1 | 1.09 | (0.74-1.59) | 0.67 | 1.13 | (0.79-1.63) | 0.50 |
| Eye and Orbit | 52 | 0.2 | 1.06 | (0.81-1.38) | 0.67 | 0.92 | (0.71-1.19) | 0.52 |
| Brain and Other Nervous System | 374 | 1.6 | 0.96 | (0.87-1.06) | 0.40 | 1.05 | (0.95-1.15) | 0.33 |
| Thyroid | 189 | 0.8 | 0.95 | (0.83-1.09) | 0.49 | 1.01 | (0.88-1.16) | 0.88 |
| Other Endocrine including Thymus | 25 | 0.1 | 0.96 | (0.65-1.40) | 0.82 | 0.83 | (0.58-1.21) | 0.34 |
| Hodgkin Lymphoma | 60 | 0.3 | 1.24 | (0.96-1.60) | 0.10 | 0.97 | (0.77-1.23) | 0.80 |
| Non-Hodgkin Lymphoma | 1,199 | 5.3 | 1.26 | (1.20-1.34) | <0.0001 | 0.82 | (0.78-0.86) | <0.0001 |
| Diffuse Large B-cell Lymphoma | 358 | 1.6 | 0.93 | (0.84-1.03) | 0.15 | 1.06 | (0.96-1.17) | 0.24 |
| Chronic Lymphocytic Leukemia/Small Lymphocytic Leukemia | 315 | 1.4 | 2.79 | (2.45-3.18) | <0.0001 | 0.38 | (0.33-0.42) | <0.0001 |
| Follicular | 191 | 0.8 | 0.79 | (0.68-0.90) | 0.0007 | 1.21 | (1.06-1.39) | 0.01 |
| Other Non-Hodgkin Lymphoma | 335 | 1.5 | 1.18 | (1.06-1.31) | 0.002 | 0.98 | (0.89-1.08) | 0.67 |
| Myeloma | 364 | 1.6 | 0.85 | (0.77-0.95) | 0.002 | 0.85 | (0.77-0.94) | 0.001 |
| Acute Lymphocytic Leukemia | 18 | 0.1 | 1.28 | (0.81-2.03) | 0.29 | 1.00 | (0.65-1.53) | 0.99 |
| Other Lymphocytic Leukemia | 32 | 0.1 | 0.93 | (0.66-1.31) | 0.66 | 0.55 | (0.38-0.79) | 0.001 |
| Acute Myeloid Leukemia | 157 | 0.7 | 0.84 | (0.72-0.97) | 0.02 | 0.93 | (0.81-1.08) | 0.35 |
| Chronic Myeloid Leukemia | 40 | 0.2 | 1.15 | (0.84-1.56) | 0.38 | 0.98 | (0.74-1.31) | 0.89 |
| Other Myeloid/Monocytic Leukemia | 26 | 0.1 | 1.15 | (0.78-1.69) | 0.49 | 0.90 | (0.63-1.28) | 0.55 |
| Mesothelioma | 201 | 0.9 | 1.01 | (0.88-1.15) | 0.92 | 0.97 | (0.85-1.10) | 0.63 |
| Miscellaneous | 90 | 0.4 | 1.08 | (0.88-1.32) | 0.47 | 0.89 | (0.74-1.08) | 0.25 |
| Uncertain Cancer Type | 17 | 0.1 | 0.84 | (0.53-1.33) | 0.45 | 1.20 | (0.76-1.88) | 0.44 |
Multivariate models include the following variables: age as 3-knot spline, sex, race/ethnicity (White, Black, Asian, mixed or missing), smoking history (25-level variable incorporating current smoking status, smoking intensity [current and former smokers]; time since quitting [former smokers], and cigar and pipe use [current and former smokers]), alcohol drinking (never, past, current), body mass index (<25, 25-30, ≥30kg/m2), WBC count (quartile), and NLR (quartile). Results are not shown separately for the following cancers where the number of cases was less than 11 (n=65): cancers of the lip (n=9), other oral cavity and pharynx (n=9), other digestive organs (n=7), pleura (n=3), trachea/mediastinum/other respiratory organs (n=7), other urinary organs (n=9), acute monocytic leukemia (n=4), other acute leukemia (n=7), aleukemic/subleukemic/not otherwise specified leukemia (n=7), and Kaposi sarcoma (n=3).
Abbreviations: CI, confidence interval; HR, hazard ratio; NLR, neutrophil-to-lymphocyte ratio; WBC, white blood cell
Bold indicates significant p-values after Bonferroni correction
Statistical analysis
Total WBC and NLR were categorized into quartiles according to the distribution of the analytic cohort. We used multivariable Cox proportional hazard regression models with follow-up time as the time metric, to estimate hazard ratios (HRs) and 95% confidence intervals (95%CI) for cancer risk. HRs per quartile increase were modeled jointly for total WBC and NLR and were adjusted for age at baseline (3-knot spline), sex, race (White, Black, Asian, mixed or missing), smoking (25-level variable incorporating current smoking status, smoking intensity [current and former smokers], time since quitting [former smokers], and cigar and pipe use [current and former smokers]; method used in (25) and also see Supplementary Table S1), alcohol drinking (never, past, current), and body mass index (BMI: <25, 25-30, ≥30 kg/m2).
For those cancers that showed significant associations with total WBC count or NLR, we plotted the baseline values of total WBC, NLR, neutrophil count, and lymphocyte count in the cancer cases as a function of time from the start of follow-up to cancer diagnosis. A cubic spline was fitted to the values in these plots using five knots placed at the quantiles 5%, 27.5%, 50%, 72.5%, and 95%, for comparison of the cases with the overall population means. We tested the proportional hazards assumption by incorporating an interaction comparing the hazard ratios for WBC or NLR between two periods (before and after median follow-up time for cancer incidence), where the two periods were modeled as a time-dependent binary 0–1 variable. We observed no statistically significant deviations from this assumption.
For kidney cancer, we further assessed whether associations with WBC and NLR were confounded due to certain prevalent conditions, so we further adjusted Cox models for baseline presence of hypertension, diabetes mellitus, and kidney disease (defined using a combination of medical history and baseline measurements, Supplementary Table S2). We also included BMI as a continuous variable in the model to account for the strong association of BMI with kidney cancer. Among women with known menopausal status (n=200,198, 83.8%), WBC and NLR associations with breast cancer were investigated separately by baseline menopausal status. Postmenopausal women were followed until breast cancer diagnosis or censoring, whereas premenopausal women were censored earlier at age 55 (i.e., presumed menopause).
Circulating high-sensitivity C-reactive protein (hs-CRP) is another biomarker of inflammation measured in the UK Biobank baseline samples. We assessed the Pearson correlation between hs-CRP levels and total WBC, WBC subsets, and NLR in those individuals with a measurement (n=423,685, 96%). In a sensitivity analysis, we adjusted associations with WBC and NLR for hs-CRP levels for selected cancer types.
All covariates included in the regression models were measured at baseline. Statistical tests were 2-sided. To account for multiple comparisons, we applied a Bonferroni correction for the trend test across quartiles of WBC and NLR requiring p<0.0007 (i.e., 0.05/73) based on analyses of 73 cancer types to be statistically significant. We used SAS (version 9.4; SAS Institute Inc.) and GraphPad Prism (version 8; GraphPad Software, Inc.) for analyses.
Data Availability Statement
The data underlying this article has been provided by the UK Biobank Resources under Application Number 52255 (RRID:SCR_012815). We do not have permission to share it directly. UK Biobank data are globally available to approved researchers through the UK Biobank research portal (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).
RESULTS
Baseline characteristics of the 443,540 subjects are presented in Table 1. There were slightly more females (53.8%) than males, with a median age of 57 years (interquartile range [IQR] 50-63), and the majority were of White race (94.4%).
Table 1.
Baseline characteristics, total white blood cell counts, and neutrophil-to-lymphocyte ratios for individuals in the UK Biobank
| Number of Subjects | Total White Blood Cell Count (x109/L) | Neutrophil-to-lymphocyte Ratio | ||||
|---|---|---|---|---|---|---|
| N | % | Median | Interquartile Range | Median | Interquartile Range | |
| Overall | 443,540 | 100.0 | 6.7 | 5.6-7.9 | 2.1 | 1.7-2.8 |
| Sex | ||||||
| Female | 238,769 | 53.8 | 6.6 | 5.6-7.8 | 2.1 | 1.6-2.6 |
| Male | 204,771 | 46.2 | 6.7 | 5.7-7.9 | 2.2 | 1.7-2.9 |
| Age group, years | ||||||
| <50 | 107,477 | 24.2 | 6.6 | 5.6-7.9 | 2.2 | 1.7-2.8 |
| 50-59 | 149,335 | 33.7 | 6.6 | 5.6-7.7 | 2.1 | 1.6-2.7 |
| ≥ 60 | 186,728 | 42.1 | 6.7 | 5.7-7.9 | 2.2 | 1.7-2.8 |
| Race | ||||||
| White | 418,717 | 94.4 | 6.7 | 5.7-7.9 | 2.2 | 1.7-2.8 |
| Mixed | 2,610 | 0.6 | 6.8 | 5.7-8.1 | 2.1 | 1.6-2.7 |
| Asian | 9,974 | 2.3 | 7.0 | 6.0-8.2 | 1.9 | 1.5-2.4 |
| Black | 6,829 | 1.5 | 5.5 | 4.6-6.7 | 1.4 | 1.1-1.9 |
| Unknown | 5,410 | 1.2 | 6.6 | 5.5-7.9 | 1.9 | 1.4-2.5 |
| Smoking status | ||||||
| Never | 244,884 | 55.2 | 6.5 | 5.5-7.6 | 2.1 | 1.7-2.7 |
| Past | 152,270 | 34.3 | 6.6 | 5.7-7.8 | 2.1 | 1.7-2.7 |
| Current | 46,386 | 10.5 | 7.9 | 6.6-9.3 | 2.2 | 1.7-2.8 |
| Alcohol drinking status | ||||||
| Never | 19,354 | 4.4 | 6.8 | 5.7-8.0 | 2.1 | 1.6-2.7 |
| Past | 15,545 | 3.5 | 6.9 | 5.8-8.3 | 2.2 | 1.7-2.8 |
| Current | 408,641 | 92.1 | 6.6 | 5.6-7.8 | 2.1 | 1.7-2.8 |
| Body mass index, kg/m2 | ||||||
| <25 | 147,150 | 33.2 | 6.3 | 5.4-7.5 | 2.1 | 1.7-2.8 |
| 25-30 | 189,047 | 42.6 | 6.6 | 5.7-7.8 | 2.1 | 1.7-2.7 |
| ≥30 | 107,343 | 24.2 | 7.2 | 6.1-8.4 | 2.1 | 1.7-2.7 |
| Hypertension | ||||||
| No | 320,995 | 72.4 | 6.5 | 5.5-7.7 | 2.1 | 1.6-2.7 |
| Yes | 122,545 | 27.6 | 7.0 | 5.9-8.3 | 2.2 | 1.7-2.9 |
| Diabetes mellitus | ||||||
| No | 418,507 | 94.4 | 6.6 | 5.6-7.8 | 2.1 | 1.7-2.8 |
| Yes | 25,033 | 5.6 | 7.5 | 6.3-8.8 | 2.2 | 1.7-2.9 |
| Kidney disease* | ||||||
| No | 406,485 | 91.7 | 6.6 | 5.6-7.8 | 2.1 | 1.7-2.7 |
| Yes | 33,418 | 7.5 | 7.1 | 6.0-8.4 | 2.3 | 1.8-3.0 |
Information on kidney disease was missing for n=3,637 (0.8%) of the study population.
At baseline, the median WBC was 6.7 x109/L ([IQR] 5.6-7.9 x109/L) and the median NLR was 2.1 (IQR 1.7-2.8). WBCs were similar between males and females (median 6.6-6.7 x109/L), across age groups (median, 6.6-6.7 x109/L), and according to alcohol use. Compared to White individuals (median 6.7, IQR 5.7-7.9 x109/L), WBC was slightly higher in Asian (7.0, 6.0-8.2) and lower in Black individuals (5.5, 4.6-6.7). Total WBCs were strongly increased in current smokers (median 7.9, IQR 6.6-9.3 x109/L), obese individuals with BMI ≥30 kg/m2 (7.2, 6.1-8.4), and individuals with hypertension (7.0, 5.9-8.3), diabetes mellitus (7.5, 6.3-8.8), or kidney disease (7.1, 6.0-8.4). NLR values were similar across most participant characteristics (median values ranging from 2.1 to 2.3), with the exception of Black individuals who had lower levels compared to White individuals (median 1.4 vs. 2.2).
A total of 22,747 cancers were diagnosed at a median of 4.3 years after the start of follow-up (maximum 9.0 years) during a median of 6.9 years (maximum 9.1 years) follow-up for the study population (Table 2). Increases in the total WBC were associated with a greater risk of cancer overall (HR 1.05, 95%CI 1.03-1.06 per quartile), and specifically for cancers of the lung (1.14, 1.08-1.20) and breast (1.05, 1.02-1.08), as well as chronic lymphocytic leukemia/small lymphocytic leukemia (CLL/SLL, 2.79, 2.45-3.18). Higher NLR was associated with elevated risk for overall cancer (HR 1.03, 95%CI 1.02-1.04 per quartile) and specifically for kidney cancer (1.16, 1.07-1.25), and with strongly decreased risk for CLL/SLL (0.38, 0.33-0.42). An analysis without mutual adjustment for the NLR and WBC is shown in Supplementary Table S3, revealing minimal difference in the risk estimates (maximum 18% for CLL/SLL).
For the four cancers that were significantly associated with WBC and/or NLR, we present scatterplots of the baseline measurements in cases according to time to cancer diagnosis (Figure 1, 2 and Supplementary Figs S2 and S3). For lung cancer (Figure 1), total WBC and neutrophil count were elevated for cases compared to the population average, regardless of the time to cancer diagnosis; similar patterns were seen for cancers of the breast, and kidney, although the elevations appeared smaller (Supplementary Figs S2 and S3). In contrast, CLL/SLL showed a higher total WBC count than the population mean, which was much higher when cases were diagnosed within the first 4 years of follow-up (i.e., within 5 years of the baseline blood draw); this pattern was driven by the high lymphocyte counts, and the NLRs were correspondingly low (Figure 2).
Figure 1.
Scatter plots for lung cancer. Results are shown for total white blood cell count (upper left), neutrophil-to-lymphocyte ratio (upper right), neutrophil count (lower left), lymphocyte count (lower right) as a function of time from the start of follow-up (one year after the baseline assessment) until cancer diagnosis. Solid line indicates cubic regression trend fitted for cases and the red dotted line indicates the population mean values of each y-axis variable.
Figure 2.
Scatter plots for chronic lymphocytic leukemia/small lymphocytic leukemia. Results are shown for total white blood cell count (upper left), neutrophil-to-lymphocyte ratio (upper right), neutrophil count (lower left), lymphocyte count (lower right) a function of time from the start of follow-up (one year after the baseline assessment) until cancer diagnosis. Solid line indicates cubic regression trend fitted for cases and the red dotted line indicates the population mean values of each y-axis variable.
For kidney cancer, we performed additional analyses adjusting for BMI and the baseline presence of hypertension, diabetes, and kidney disease (Supplementary Table S4). With all factors in the same model, the adjusted association between NLR and kidney cancer incidence (HR 1.13, 95%CI 1.04-1.22 per quartile) was similar to the result in the primary analysis (1.16, 1.07-1.25).
As shown in Table 3, total WBC was associated with breast cancer among both premenopausal and postmenopausal women, whereas NLR showed a suggestive positive association only in postmenopausal women (HR 1.03, 95%CI 1.00-1.07 per quartile; Pinteraction=0.04).
Table 3.
Associations of WBC and NLR with breast cancer incidence among women, stratified by baseline menopausal status
| Total WBC, per quartile | NLR, per quartile | |||||||
|---|---|---|---|---|---|---|---|---|
| HR | (95%CI) | p-trend | Pinteraction | HR | (95%CI) | p-trend | Pinteraction | |
| All | 1.06 | (1.03-1.09) | 0.0002 | 1.02 | (0.99-1.05) | 0.24 | ||
| According to menopausal status at baseline* | ||||||||
| Premenopause (n=57,606) | 1.07 | (1.01-1.14) | 0.02 | 0.6** | 0.95 | (0.89-1.01) | 0.10 | 0.04** |
| Postmenopause (n=142,592) | 1.05 | (1.01-1.09) | 0.007 | 1.03 | (1.00-1.07) | 0.08 | ||
Models included WBC and NLR quartiles and were additionally adjusted for age as 3-knot spline, sex, race/ethnicity (White, Black, Asian, mixed or missing), smoking history (25-level variable incorporating current smoking status, smoking intensity [current and former smokers]; time since quitting [former smokers], and cigar and pipe use [current and former smokers]), alcohol drinking (never, past, current), and body mass index (<25, 25-30, ≥30kg/m2).
Abbreviations: CI, confidence interval; HR, hazard ratio; NLR, neutrophil-to-lymphocyte ratio; WBC, white blood cell
N=37,397 women with unknown menopausal status and N=1,174 women who were over age 55 years and reported being premenopausal at baseline were excluded.
Pinteraction between the breast cancer associations in premenopausal vs. postmenopausal women.
Hs-CRP was positively correlated with total WBC (Pearson rho=0.21), neutrophil count (0.24), and NLR (0.15) (Supplementary Table S5). However, additional adjustment for hs-CRP did not alter the magnitude of the associations for WBC or NLR and cancer by more than 3% (Table 4).
Table 4.
Association between WBC/NLR and selected cancers before and after high-sensitive C-reactive protein (hs-CRP) adjustment in a subset of individuals with hs-CRP information (n=425,417; 96% of all study participants)
| Cancer Site | Before hs-CRP Adjustment | After hs-CRP Adjustment | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total WBC, Per Quartile Increase | NLR, Per Quartile Increase | Total WBC, Per Quartile Increase | NLR, Per Quartile Increase | |||||||||
| HR | (95%CI) | p-trend | HR | (95%CI) | p-trend | HR | (95%CI) | p-trend | HR | (95%CI) | p-trend | |
| All Cancers | 1.05 | (1.03-1.06) | <0.0001 | 1.03 | (1.02-1.04) | <0.0001 | 1.04 | (1.03-1.06) | <0.0001 | 1.03 | (1.01-1.04) | <0.0001 |
| Lung | 1.14 | (1.08-1.02) | <0.0001 | 1.04 | (1.00-1.09) | 0.06 | 1.13 | (1.07-1.19) | <0.0001 | 1.03 | (0.99-1.08) | 0.19 |
| Breast | 1.05 | (1.02-1.08) | 0.0004 | 1.02 | (0.99-1.05) | 0.17 | 1.06 | (1.03-1.09) | 0.0001 | 1.02 | (0.99-1.05) | 0.12 |
| Kidney | 1.09 | (1.01-1.18) | 0.03 | 1.16 | (1.07-1.25) | 0.0002 | 1.08 | (0.99-1.17) | 0.07 | 1.13 | (1.04-1.22) | 0.003 |
| CLL/SLL | 2.79 | (2.45-3.18) | <0.0001 | 0.38 | (0.33-0.42) | <0.0001 | 2.79 | (2.44-3.19) | <0.0001 | 0.38 | (0.34-0.43) | <0.0001 |
Abbreviations: CI, confidence interval; HR, hazard ratio; NLR, neutrophil-to-lymphocyte ratio; WBC, white blood cell; CLL/SLL, Chronic lymphocytic leukemia/small lymphocytic leukemia
DISCUSSION
In this large prospective study of nearly 450,000 individuals in the UK general population, we observed a positive association of total WBC and NLR with overall cancer risk. Four out of 73 specific cancer sites investigated were significantly associated with either total peripheral WBC or NLR. Cancers associated with higher WBC or NLR included those for which an inflammatory component to etiology has been suggested, including lung cancer, although the vast majority of participants had total WBC counts within the normal range. The pattern for CLL/SLL was strikingly different, with strong associations with higher WBC and lower NLR, a pattern that likely represents manifestations in the peripheral blood indicative of asymptomatic early cancer.
As expected, higher WBC was observed in conditions associated with systemic inflammation, including obesity, hypertension, diabetes, and kidney disease. Differences across sex, age groups, or alcohol use were not observed. Black individuals had relatively lower total WBC and NLR, a well-known phenomenon, yet the exact reason for the difference is still unknown (26). The majority of the total peripheral WBC is comprised of neutrophils, and it is notable that both WBC and NLR were much higher in smokers, since the adverse effects of smoking in promoting cardiovascular diseases, stroke, and cancer may be partly mediated through chronic inflammation. Various circulating inflammation-related markers have been reported to be associated with smoking, including CRP, interleukin (IL)-1β, and IL-6, as well as total WBC (27,28). Higher NLR has also been reported to be associated with greater cumulative smoking (29). Adipose tissue is an important endocrine organ for the regulation of hormones and chemokines, and obesity is associated with increased systemic levels of pro-inflammatory markers (30–32). In our study, the correlations of total WBC and NLR with CRP were moderate, indicating that these markers capture somewhat separate components of the inflammatory process.
The association that we observed between total WBC and incidence of lung cancer plausibly reflects an etiologic role of chronic inflammation. Smoking induces strong inflammatory responses as described above. However, it may be noted that the association with lung cancer is unlikely to be due to residual confounding by smoking, given our detailed adjustment for smoking in regression models. We saw clear elevations in the total WBC and neutrophil count up to 7 years prior to lung cancer diagnosis (Figure 1), and previous studies have demonstrated similar associations between WBC or NLR with lung cancer (12,15,18,21). The etiologic role of inflammation in lung cancer is further supported by its associations with other immune-related markers such as hs-CRP, IL-6, IL-8, and additional cytokines/chemokines (33,34).
Although breast cancer has not typically been considered to be related to inflammation, our results showed a small magnitude of increased risk with higher total WBC, regardless of menopausal status. Other recent studies have suggested potential links with inflammation. For example, one study reported that women with benign breast disease or cancer had bacterial DNA present in sterile surgical tissue samples without any evidence of infection, perhaps reflecting a source of local inflammation (35). Several epidemiologic studies have used circulating inflammatory biomarkers to investigate the role of inflammation in breast carcinogenesis (36). hs-CRP has been the most consistent and frequently investigated marker, but other cytokines (e.g., IL-1β) were also explored. Our findings extend the results of earlier smaller studies that likewise showed small to moderate positive associations between higher leukocyte counts and breast cancer risk (14,37).
More than 90% of kidney cancers are renal cell carcinomas (RCCs). Evidence supporting a role for immunity in RCC includes reports of spontaneous regression associated with cytoreductive nephrectomy, the presence of immune infiltrates in some tumors and the sensitivity of RCC to immunotherapy (38). In addition, elevated levels of inflammatory markers (such as hs-CRP or erythrocyte sedimentation rate) and a dietary inflammation index are associated with kidney cancer incidence in prospective studies (39–41). Genetic variants in genes involved in oxidative stress and inflammation (including COMPT, CPX5, and NOS2A) are also associated with RCC (42). We found an increased incidence of kidney cancer among individuals with higher NLR values, even after adjusting for tobacco use, hypertension, diabetes, and kidney diseases, which are known risk factors for kidney cancer. Notably, higher NLR was also strongly associated with kidney disease mortality among participants of the US National Health and Nutrition Examination Survey (11).
The pattern for CLL/SLL was unique, in that incidence was associated positively with total WBC but inversely with NLR. These associations were driven by elevated lymphocyte counts, and many individuals who later developed CLL/SLL manifested remarkably elevated lymphocyte counts at baseline. CLL/SLL has a long asymptomatic phase before clinical diagnosis, and its precursor condition is termed “monoclonal B-cell lymphocytosis” (43). To a large extent, the significant results in our study thus reflect the presence of indolent or subclinical disease, and some individuals in our study who had strongly elevated lymphocyte counts developed CLL/SLL more than 5 years after baseline. However, only a minority of individuals who later developed CLL/SLL had clinically defined leukocytosis (>11,000 total WBC/uL; 35%) or lymphocytosis (>4,000 lymphocytes/uL; 40%) at baseline. Furthermore, even among individuals without leukocytosis or lymphocytosis, we still observed a significant positive association of CLL/SLL incidence with WBC (per quartile increase HR 1.51; 95%CI 1.30-1.75; p-trend<0.0001) and an inverse association with NLR (0.63; 0.55-0.73; p<0.0001). To our knowledge, our study is the first to report these prospective associations with CLL/SLL.
This study has several strengths, including its large sample size, which allowed us to detect modest effects and assess uncommon cancer sites, and our systematic analysis of 73 different types of cancer. We used a prospective design to establish temporality and started follow-up one year after the UK Biobank baseline to minimize potential reverse causation. Our study also used data obtained from comprehensive government cancer registries to ascertain cancer outcomes. Blood samples were typically analyzed at the UK Biobank central laboratory within 24 hours of blood draw, reducing laboratory artifacts. A limitation is that the blood counts were only assessed once at baseline, which did not allow us to assess the change of these levels in relation to cancer risk. Secondly, we made multiple statistical comparisons, which could have led to some chance associations. However, we used a stringent Bonferroni p-value cutoff to minimize this possibility. Thirdly, alternative methods for modeling neutrophils and lymphocytes, such as examining extreme values, could be explored in future studies.
In conclusion, our study demonstrated that total WBC and NLR are predictive of certain cancer risks in the UK general population. Cancers of the lung, breast, and kidney showed association with higher WBC or NLR, suggestive of a possible etiologic or mediating contribution from inflammation. On the other hand, the associations with CLL/SLL likely reflect the presence of its precursor condition. Further studies are warranted to understand the specific role of various WBC subsets, which will allow a deeper dissection of the role of immunity and inflammation in the development of cancer. It is also possible that the total peripheral WBC and its subsets, in combination with other clinical information or biomarkers, may be useful as clinical tools for cancer risk stratification.
Supplementary Material
ACKNOWLEDGEMENTS
This research was conducted using the UK Biobank resource (application 52255). The UK Biobank was established by the Wellcome Trust, the Medical Research Council, the UK Department of Health, and the Scottish Government. The UK Biobank has also received funding from the Welsh Assembly Government, the British Heart Foundation, and the Diabetes United Kingdom. We thank Michael Stagner, BS, at Information Management Services, who provided technical support, for which he was compensated.
FUNDING/SUPPORT
This work was supported by the Intramural Research Program of the National Cancer Institute.
ABBREVATIONS
- BMI
body mass index
- CI
confidence interval
- CLL/SLL
lymphocytic leukemia/small lymphocytic leukemia
- HRs
hazard ratios
- Hs-CRP
high-sensitivity C-reactive protein
- IQR
interquartile range
- NHS
UK National Health Service
- NLR
neutrophil-to-lymphocyte ratio
- RCC
renal cell carcinomas
- WBC
white blood cells
Footnotes
Disclosure of conflicts of interest: The authors declare no potential conflicts of interest
REFERENCES
- 1.Coussens LM, Werb Z. Inflammation and cancer. Nature 2002;420(6917):860–7 doi 10.1038/nature01322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell 2010;140(6):883–99 doi 10.1016/j.cell.2010.01.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Crusz SM, Balkwill FR. Inflammation and cancer: advances and new agents. Nat Rev Clin Oncol 2015;12(10):584–96 doi 10.1038/nrclinonc.2015.105. [DOI] [PubMed] [Google Scholar]
- 4.Grulich AE, van Leeuwen MT, Falster MO, Vajdic CM. Incidence of cancers in people with HIV/AIDS compared with immunosuppressed transplant recipients: a meta-analysis. Lancet 2007;370(9581):59–67 doi 10.1016/S0140-6736(07)61050-2. [DOI] [PubMed] [Google Scholar]
- 5.Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 2002;3(11):991–8 doi 10.1038/ni1102-991. [DOI] [PubMed] [Google Scholar]
- 6.Engels EA. Epidemiologic perspectives on immunosuppressed populations and the immunosurveillance and immunocontainment of cancer. Am J Transplant 2019;19(12):3223–32 doi 10.1111/ajt.15495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wang JH, Derkach A, Pfeiffer RM, Engels EA. Immune-related conditions and cancer-specific mortality among older adults with cancer in the United States. Int J Cancer 2022;151(8):1216–27 doi 10.1002/ijc.34140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hiam-Galvez KJ, Allen BM, Spitzer MH. Systemic immunity in cancer. Nat Rev Cancer 2021;21(6):345–59 doi 10.1038/s41568-021-00347-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Asadollahi K, Beeching NJ, Gill GV. Leukocytosis as a predictor for non-infective mortality and morbidity. QJM 2010;103(5):285–92 doi 10.1093/qjmed/hcp182. [DOI] [PubMed] [Google Scholar]
- 10.Faria SS, Fernandes PC Jr., Silva MJ, Lima VC, Fontes W, Freitas-Junior R, et al. The neutrophil-to-lymphocyte ratio: a narrative review. Ecancermedicalscience 2016;10:702 doi 10.3332/ecancer.2016.702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Song M, Graubard BI, Rabkin CS, Engels EA. Neutrophil-to-lymphocyte ratio and mortality in the United States general population. Sci Rep 2021;11(1):464 doi 10.1038/s41598-020-79431-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Wong JYY, Bassig BA, Loftfield E, Hu W, Freedman ND, Ji BT, et al. White Blood Cell Count and Risk of Incident Lung Cancer in the UK Biobank. JNCI Cancer Spectr 2020;4(2):pkz102 doi 10.1093/jncics/pkz102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Phillips AN, Neaton JD, Cook DG, Grimm RH, Shaper AG. The leukocyte count and risk of lung cancer. Cancer 1992;69(3):680–4 doi . [DOI] [PubMed] [Google Scholar]
- 14.Margolis KL, Rodabough RJ, Thomson CA, Lopez AM, McTiernan A, Women’s Health Initiative Research G. Prospective study of leukocyte count as a predictor of incident breast, colorectal, endometrial, and lung cancer and mortality in postmenopausal women. Arch Intern Med 2007;167(17):1837–44 doi 10.1001/archinte.167.17.1837. [DOI] [PubMed] [Google Scholar]
- 15.Sprague BL, Trentham-Dietz A, Klein BE, Klein R, Cruickshanks KJ, Lee KE, et al. Physical activity, white blood cell count, and lung cancer risk in a prospective cohort study. Cancer Epidemiol Biomarkers Prev 2008;17(10):2714–22 doi 10.1158/1055-9965.EPI-08-0042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Iida M, Ikeda F, Ninomiya T, Yonemoto K, Doi Y, Hata J, et al. White blood cell count and risk of gastric cancer incidence in a general Japanese population: the Hisayama study. Am J Epidemiol 2012;175(6):504–10 doi 10.1093/aje/kwr345. [DOI] [PubMed] [Google Scholar]
- 17.Sanchez-Salcedo P, de-Torres JP, Martinez-Urbistondo D, Gonzalez-Gutierrez J, Berto J, Campo A, et al. The neutrophil to lymphocyte and platelet to lymphocyte ratios as biomarkers for lung cancer development. Lung Cancer 2016;97:28–34 doi 10.1016/j.lungcan.2016.04.010. [DOI] [PubMed] [Google Scholar]
- 18.Grieshober L, Graw S, Barnett MJ, Thornquist MD, Goodman GE, Chen C, et al. Methylation-derived Neutrophil-to-Lymphocyte Ratio and Lung Cancer Risk in Heavy Smokers. Cancer Prev Res (Phila) 2018;11(11):727–34 doi 10.1158/1940-6207.CAPR-18-0111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Fang Q, Tong YW, Wang G, Zhang N, Chen WG, Li YF, et al. Neutrophil-to-lymphocyte ratio, obesity, and breast cancer risk in Chinese population. Medicine 2018;97(30):e11692 doi 10.1097/MD.0000000000011692. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kresovich JK, O’Brien KM, Xu Z, Weinberg CR, Sandler DP, Taylor JA. Prediagnostic Immune Cell Profiles and Breast Cancer. JAMA Netw Open 2020;3(1):e1919536 doi 10.1001/jamanetworkopen.2019.19536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Nost TH, Alcala K, Urbarova I, Byrne KS, Guida F, Sandanger TM, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol 2021;36(8):841–8 doi 10.1007/s10654-021-00752-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zhao N, Ruan M, Koestler DC, Lu J, Salas LA, Kelsey KT, et al. Methylation-derived inflammatory measures and lung cancer risk and survival. Clin Epigenetics 2021;13(1):222 doi 10.1186/s13148-021-01214-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12(3):e1001779 doi 10.1371/journal.pmed.1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol 2017;186(9):1026–34 doi 10.1093/aje/kwx246. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Loftfield E, Cornelis MC, Caporaso N, Yu K, Sinha R, Freedman N. Association of Coffee Drinking With Mortality by Genetic Variation in Caffeine Metabolism: Findings From the UK Biobank. JAMA Intern Med 2018;178(8):1086–97 doi 10.1001/jamainternmed.2018.2425. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Reich D, Nalls MA, Kao WH, Akylbekova EL, Tandon A, Patterson N, et al. Reduced neutrophil count in people of African descent is due to a regulatory variant in the Duffy antigen receptor for chemokines gene. PLoS Genet 2009;5(1):e1000360 doi 10.1371/journal.pgen.1000360. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Yanbaeva DG, Dentener MA, Creutzberg EC, Wesseling G, Wouters EF. Systemic effects of smoking. Chest 2007;131(5):1557–66 doi 10.1378/chest.06-2179. [DOI] [PubMed] [Google Scholar]
- 28.Elisia I, Lam V, Cho B, Hay M, Li MY, Yeung M, et al. The effect of smoking on chronic inflammation, immune function and blood cell composition. Sci Rep 2020;10(1):19480 doi 10.1038/s41598-020-76556-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Tulgar YK, Cakar S, Tulgar S, Dalkilic O, Cakiroglu B, Uyanik BS. The effect of smoking on neutrophil/lymphocyte and platelet/lymphocyte ratio and platelet indices: a retrospective study. Eur Rev Med Pharmacol Sci 2016;20(14):3112–8. [PubMed] [Google Scholar]
- 30.Deng T, Lyon CJ, Bergin S, Caligiuri MA, Hsueh WA. Obesity, Inflammation, and Cancer. Annu Rev Pathol 2016;11:421–49 doi 10.1146/annurev-pathol-012615-044359. [DOI] [PubMed] [Google Scholar]
- 31.Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. JAMA 1999;282(22):2131–5 doi 10.1001/jama.282.22.2131. [DOI] [PubMed] [Google Scholar]
- 32.Van Alsten SC, Rabkin CS, Sawada N, Shimazu T, Charvat H, Yamaji T, et al. Metabolic Syndrome, Physical Activity, and Inflammation: A Cross-Sectional Analysis of 110 Circulating Biomarkers in Japanese Adults. Cancer Epidemiol Biomarkers Prev 2020;29(8):1639–46 doi 10.1158/1055-9965.EPI-19-1513. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Shiels MS, Pfeiffer RM, Hildesheim A, Engels EA, Kemp TJ, Park JH, et al. Circulating inflammation markers and prospective risk for lung cancer. J Natl Cancer Inst 2013;105(24):1871–80 doi 10.1093/jnci/djt309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Shiels MS, Katki HA, Hildesheim A, Pfeiffer RM, Engels EA, Williams M, et al. Circulating Inflammation Markers, Risk of Lung Cancer, and Utility for Risk Stratification. J Natl Cancer Inst 2015;107(10) doi 10.1093/jnci/djv199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Hieken TJ, Chen J, Hoskin TL, Walther-Antonio M, Johnson S, Ramaker S, et al. The Microbiome of Aseptically Collected Human Breast Tissue in Benign and Malignant Disease. Sci Rep 2016;6:30751 doi 10.1038/srep30751. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Kehm RD, McDonald JA, Fenton SE, Kavanaugh-Lynch M, Leung KA, McKenzie KE, et al. Inflammatory Biomarkers and Breast Cancer Risk: A Systematic Review of the Evidence and Future Potential for Intervention Research. Int J Environ Res Public Health 2020;17(15) doi 10.3390/ijerph17155445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Allin KH, Bojesen SE, Nordestgaard BG. Inflammatory biomarkers and risk of cancer in 84,000 individuals from the general population. Int J Cancer 2016;139(7):1493–500 doi 10.1002/ijc.30194. [DOI] [PubMed] [Google Scholar]
- 38.de Vivar Chevez AR, Finke J, Bukowski R. The Role of Inflammation in Kidney Cancer. In: Aggarwal BB, Sung B, Gupta SC, editors. Inflammation and Cancer. Basel: Springer Basel; 2014. p 197–234. [DOI] [PubMed] [Google Scholar]
- 39.Watson J, Salisbury C, Banks J, Whiting P, Hamilton W. Predictive value of inflammatory markers for cancer diagnosis in primary care: a prospective cohort study using electronic health records. Br J Cancer 2019;120(11):1045–51 doi 10.1038/s41416-019-0458-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Trichopoulos D, Psaltopoulou T, Orfanos P, Trichopoulou A, Boffetta P. Plasma C-reactive protein and risk of cancer: a prospective study from Greece. Cancer Epidemiol Biomarkers Prev 2006;15(2):381–4 doi 10.1158/1055-9965.EPI-05-0626. [DOI] [PubMed] [Google Scholar]
- 41.Shivappa N, Blair CK, Prizment AE, Jacobs DR Jr., Hebert JR. Dietary inflammatory index and risk of renal cancer in the Iowa Women’s Health Study. Eur J Nutr 2018;57(3):1207–13 doi 10.1007/s00394-017-1403-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Chow WH, Dong LM, Devesa SS. Epidemiology and risk factors for kidney cancer. Nat Rev Urol 2010;7(5):245–57 doi 10.1038/nrurol.2010.46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Rawstron AC, Bennett FL, O’Connor SJ, Kwok M, Fenton JA, Plummer M, et al. Monoclonal B-cell lymphocytosis and chronic lymphocytic leukemia. N Engl J Med 2008;359(6):575–83 doi 10.1056/NEJMoa075290. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data underlying this article has been provided by the UK Biobank Resources under Application Number 52255 (RRID:SCR_012815). We do not have permission to share it directly. UK Biobank data are globally available to approved researchers through the UK Biobank research portal (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).