White blood cell count and risk of incident lung cancer in the UK Biobank

Background The contribution of measurable immunological/inflammatory parameters to lung cancer development remains unclear, particularly among never-smokers. We investigated the relationship between total and differential white blood cell (WBC) counts and incident lung cancer risk overall and among subgroups defined by smoking status and sex in the United Kingdom (UK). Methods We evaluated 424,407 adults aged 37-73 years from the UK Biobank. Questionnaires, physical measurements, and blood were administered/collected at baseline in 2006-2010. Complete blood cell counts were measured using standard methods. Lung cancer diagnoses and histological classifications were obtained from cancer registries. Multivariable Cox regression models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) of incident lung cancer in relation to quartiles (Q) of total WBC and subtype-specific counts, with Q1 as the reference. Results There were 1,493 incident cases diagnosed over an average 7-year follow-up. Overall, the highest quartile of total WBC count was significantly associated with elevated lung cancer risk (HRQ4=1.67, 95% CI:1.41-1.98). Among women, increased risks were found in current-smokers (ncases/n=244/19,464, HRQ4=2.15, 95% CI:1.46-3.16), former-smokers (ncases/n=280/69,198, HRQ4=1.75, 95% CI:1.24-2.47), and never-smokers without environmental tobacco smoke exposure (ncases/n=108/111,294, HRQ4=1.93, 95% CI:1.11-3.35). Among men, stronger associations were identified in current-smokers (ncases/n=329/22,934, HRQ4=2.95, 95% CI:2.04-4.26) and former-smokers (ncases/n= 358/71,616, HRQ4=2.38, 95% CI:1.74-3.27) but not in never-smokers. Findings were similar for lung adenocarcinoma and squamous cell carcinoma and were driven primarily by elevated neutrophil fractions. Conclusions Elevated WBCs could potentially be one of many important markers for increased lung cancer risk, especially among never-smoking women and ever-smoking men

Methods and participant characteristics in the Cancer Risk in Vegetarians Consortium: a cross-sectional analysis across 11 prospective studies

\ua9 The Author(s) 2024. Background: The associations of vegetarian diets with risks for site-specific cancers have not been estimated reliably due to the low number of vegetarians in previous studies. Therefore, the Cancer Risk in Vegetarians Consortium was established. The aim is to describe and compare the baseline characteristics between non-vegetarian and vegetarian diet groups and between the collaborating studies. Methods: We harmonised individual-level data from 11 prospective cohort studies from Western Europe, North America, South Asia and East Asia. Comparisons of food intakes, sociodemographic and lifestyle factors were made between diet groups and between cohorts using descriptive statistics. Results: 2.3 million participants were included; 66% women and 34% men, with mean ages at recruitment of 57 (SD: 7.8) and 57 (8.6) years, respectively. There were 2.1 million meat eaters, 60,903 poultry eaters, 44,780 pescatarians, 81,165 vegetarians, and 14,167 vegans. Food intake differences between the diet groups varied across the cohorts; for example, fruit and vegetable intakes were generally higher in vegetarians than in meat eaters in all the cohorts except in China. BMI was generally lower in vegetarians, particularly vegans, except for the cohorts in India and China. In general, but with some exceptions, vegetarians were also more likely to be highly educated and physically active and less likely to smoke. In the available resurveys, stability of diet groups was high in all the cohorts except in China. Conclusions: Food intakes and lifestyle factors of both non-vegetarians and vegetarians varied markedly across the individual cohorts, which may be due to differences in both culture and socioeconomic status, as well as differences in questionnaire design. Therefore, care is needed in the interpretation of the impacts of vegetarian diets on cancer risk

Correction to: Methods and participant characteristics in the Cancer Risk in Vegetarians Consortium: a cross-sectional analysis across 11 prospective studies (BMC Public Health, (2024), 24, 1, (2095), 10.1186/s12889-024-19209-y)

\ua9 The Author(s) 2025. Following publication of the original article [1], the authors identified an error in Table 6 and Table S5. The percentage for the every used oral contraceptives value for the NIH-AARP study has been corrected to 39%. Women-specific characteristics by cohort (n = 1,546,217)1 Cohort Age at menarche ≤ 12 years Parous Age at first birth ≥ 25 years Postmenopausal Age at menopause ≥ 50 years2 Ever used oral contraceptive Ever used HRT Adventist Health Study-2 51.4 83.1 31.7 77.0 64.5 58.8 39.4 CARRS-1 12.2 94.1 - 36.9 19.7 3.5 - CARRS-2 12.9 93.8 - 41.9 23.4 6.9 EPIC-Oxford 39.8 61.0 38.2 40.2 54.8 73.7 18.2 Oxford Vegetarian Study - 47.5 29.4 26.6 - 55.3 - Tzu Chi Health Study 8.6 91.8 - 63.0 54.2 15.7 19.6 UK Women’s Cohort Study 41.6 77.2 44.8 60.9 48.0 66.9 27.2 China Kadoorie Biobank 5.5 98.7 32.5 57.2 43.1 9.8 - Million Women Study 38.8 88.4 36.4 100 56.8 61.3 53.4 NIH-AARP 48.6 83.7 23.5 100 39.6 39.0 52.9 UK Biobank 37.6 81.2 47.1 77.8 63.3 81.1 37.7 33.5 87.3 35.3 84.2 52.1 50.6 37.8 Abbreviations: CARRS, Centre for cArdiometabolic Risk Reduction in South Asia; HRT, hormone replacement therapy; EPIC, European Prospective Investigation into Cancer and Nutrition; NIH-AARP, National Institutes of Health-AARP Diet and Health Study. 1Values are % of women within cohort. 2Postmenopausal women only. “-” indicates that no information was available for this variable in the specified cohort. Baseline characteristics of women by cohort1 Total AHS-2 CARRS-1 CARRS-2 EPIC-Oxford OVS TCHS UKWCS CKB MWS NIH-AARP UK Biobank N 1,546,217 42,194 6,372 5,065 41,335 6,480 3,329 30,148 300,909 639,026 215,905 255,454 Living with partner Yes 1,145,967 (74.1) 27,503 (65.2) 5,671 (89.0) 4,332 (85.5) 27,614 (66.8) 3,497 (54.0) 2824 (84.8) 22,282 (73.9) 267,799 (89.0) 511,789 (80.1) 95,749 (44.3) 176,907 (69.3) No 336,711 (21.8) 13,937 (33.0) 701 (11.0) 733 (14.5) 13,649 (33.0) 2,934 (45.3) 504 (15.1) 7,415 (24.6) 33,110 (11.0) 118,337 (18.5) 118,140 (54.7) 27,251 (10.7) Unknown 63,539 (4.1) 754 (1.8) 0 (0.00) 0 (0.00) 72 (0.17) 49 (0.76) 1 (0.00) 451 (1.5) 0 (0.00) 8,900 (1.4) 2,016 (0.93) 51,296 (20.1) Educational status Less than secondary/high school 514,795 (33.3) 3,052 (7.2) 1,568 (24.6) 1,271 (25.1) 5,426 (13.1) 309 (4.8) 1023 (30.7) 4,671 (15.5) 170,715 (56.7) 217,050 (34.0) 67,342 (31.2) 42,368 (16.6) Secondary/high school or equivalent 572,012 (37.0) 18,125 (43.0) 3,859 (60.6) 2,908 (57.4) 16,422 (39.7) 4,573 (70.6) 1636 (49.1) 15,539 (51.5) 116,872 (38.8) 305,150 (47.8) 22,927 (10.6) 64,001 (25.1) University degree or equivalent 430,226 (27.8) 20,431 (48.4) 945 (14.8) 884 (17.5) 16,406 (39.7) 434 (6.7) 670 (20.1) 7,372 (24.5) 13,322 (4.4) 106,496 (16.7) 118,637 (54.9) 144,629 (56.6) Unknown 29,184 (1.9) 586 (1.4) 0 (0.00) 2 (0.04) 3,081 (7.5) 1,164 (18.0) 0 (0.00) 2,566 (8.5) 0 (0.00) 10,330 (1.6) 6,999 (3.2) 4,456 (1.7) Cigarette smoking Never 974,560 (63.0) 35,304 (83.7) 6,094 (95.6) 4,985 (98.4) 25,457 (61.6) 3,595 (55.5) 3274 (98.3) 16,851 (55.9) 285,673 (94.9) 346,928 (54.3) 94,000 (43.5) 152,399 (59.7) Previous 406,326 (26.3) 6,166 (14.6) 5 (0.08) 21 (0.41) 11,253 (27.2) 1,809 (27.9) 40 (1.20) 9,148 (30.3) 2,619 (0.87) 211,953 (33.2) 84,058 (38.9) 79,254 (31.0) Current 146,379 (9.5) 410 (0.97) 58 (0.91) 59 (1.2) 4,439 (10.7) 1,049 (16.2) 15 (0.45) 3,224 (10.7) 12,617 (4.2) 71,277 (11.2) 30,343 (14.1) 22,888 (9.0) Unknown 18,952 (1.2) 314 (0.74) 215 (3.4) 0 (0.00) 186 (0.45) 27 (0.42) 0 (0.00) 925 (3.1) 0 (0.00) 8,868 (1.4) 7,504 (3.5) 913 (0.36) Physical activity Inactive 357,643 (23.1) 8,553 (20.3) 2,681 (42.1) 3,815 (75.3) 23,253 (56.3) 2,596 (40.1) 1248 (37.5) 22,033 (73.1) 99,953 (33.2)2 36,813 (5.8) 79,280 (36.7) 77,418 (30.3) Moderately active 426,785 (27.6) 13,711 (32.5) 797 (12.5) 491 (9.7) 6,931 (16.8) 1,871 (28.9) 1082 (32.5) 7,074 (23.5) 100,654 (33.4)2 70,898 (11.1) 99,164 (45.9) 124,112 (48.6) Highly active 570,911 (36.9) 17,753 (42.1) 1,494 (23.4) 40 (0.79) 4,770 (11.5) 1,831 (28.3) 999 (30.0) 1,032 (3.4) 100,302 (33.3)2 364,921 (57.1) 34,746 (16.1) 43,023 (16.8) Unknown 190,878 (12.3) 2,177 (5.2) 1,400 (22.0) 719 (14.2) 6,381 (15.4) 182 (2.8) 0 (0.00) 9 (0.03) 0 (0.00) 166,394 (26.0) 2,715 (1.3) 10,901 (4.3) History of diabetes Yes 71,748 (4.6) 3,195 (7.6) 777 (12.2) 830 (16.4) 459 (1.1) 33 (0.51) 155 (4.7) 544 (1.8) 18,426 (6.1) 21,570 (3.4) 16,204 (7.5) 9,555 (3.7) No 1,466,761 (94.9) 38,512 (91.3) 5,569 (87.4) 4,231 (83.5) 36,684 (88.7) 6,419 (99.1) 3174 (95.3) 27,293 (90.5) 282,483 (93.9) 617,456 (96.6) 199,701 (92.5) 245,239 (96.0) Unknown 7,708 (0.50) 487 (1.2) 26 (0.41) 4 (0.08) 4,192 (10.1) 28 (0.43) 0 (0.00) 2,311 (7.7) 0 (0.00) 0 (0.00) 0 (0.00) 660 (0.26) Age at menarche ≤ 10 years 60,622 (3.9) 3,658 (8.7) 16 (0.25) 18 (0.36) 1,800 (4.4) - 3 (0.09) 1,760 (5.8) 278 (0.09) 26,924 (4.2) 14,720 (6.8) 11,445 (4.5) 11–12 years 457,022 (29.6) 18,046 (42.8) 759 (11.9) 635 (12.5) 14,635 (35.4) - 282 (8.47) 10,787 (35.8) 16,130 (5.4) 220,840 (34.6) 90,177 (41.8) 84,731 (33.2) 13–14 years 624,908 (40.4) 15,807 (37.5) 3,931 (61.7) 2,817 (55.6) 18,925 (45.8) - 1482 (44.5) 13,111 (43.5) 84,506 (28.1) 285,866 (44.7) 89,002 (41.2) 109,461 (42.8) ≥ 15 years 375,784 (24.3) 4,365 (10.3) 1,664 (26.1) 1,570 (31.0) 5,399 (13.1) - 1536 (46.1) 3,877 (12.9) 199,947 (66.4) 95,559 (15.0) 19,860 (9.2) 42,007 (16.4) Unknown 27,881 (1.8) 318 (0.75) 2 (0.03) 25 (0.49) 576 (1.4) 6,480 (100) 26 (0.8) 613 (2.0) 48 (0.02) 9,837 (1.5) 2,146 (0.99) 7,810 (3.1) Parity None 187,425 (12.1) 6,542 (15.5) 329 (5.2) 295 (5.8) 15,748 (38.1) 3,244 (50.1) 255 (7.66) 3,739 (12.4) 3,956 (1.3) 72,831 (11.4) 32,816 (15.2) 47,670 (18.7) One 254,901 (16.5) 5,457 (12.9) 546 (8.6) 385 (7.6) 5,357 (13.0) 917 (14.2) 184 (5.53) 3,748 (12.4) 103,690 (34.5) 78,095 (12.2) 22,460 (10.4) 34,062 (13.3) Two 593,107 (38.4) 13,342 (31.6) 1,692 (26.6) 1,287 (25.4) 12,261 (29.7) 1,386 (21.4) 1148 (34.5) 11,668 (38.7) 95,596 (31.8) 287,141 (44.9) 55,974 (25.9) 111,612 (43.7) 3–4 430,594 (27.8) 13,045 (30.9) 2,489 (39.1) 1,983 (39.2) 7,062 (17.1) 714 (11.0) 1550 (46.6) 7,255 (24.1) 77,933 (25.9) 182,369 (28.5) 78,618 (36.4) 57,576 (22.5) 5–9 70,558 (4.6) 3,066 (7.3) 1,211 (19.0) 1,042 (20.6) 526 (1.3) 63 (0.97) 173 (5.20) 603 (2.0) 19,542 (6.5) 17,126 (2.7) 23,014 (10.7) 4,192 (1.6) ≥ 10 1,305 (0.08) 133 (0.32) 59 (0.93) 52 (1.0) 2 (0.00) 1 (0.02) 0 (0.00) 12 (0.04) 192 (0.06) 63 (0.01) 746 (0.35) 45 (0.02) Unknown 8,327 (0.54) 609 (1.4) 46 (0.72) 21 (0.41) 379 (0.92) 155 (2.4) 19 (0.6) 3,123 (10.4) 0 (0.00) 1,401 (0.22) 2,277 (1.1) 297 (0.12) Age at first birth ≤ 19 years 157,185 (10.2) 5,947 (14.1) - - 1,489 (3.6) 195 (3.0) - 1,661 (5.5) 28,021 (9.3) 62,803 (9.8) 37,301 (17.3) 19,768 (7.7) 20–24 years 619,652 (40.1) 13,002 (30.8) - - 7,934 (19.2) 967 (14.9) - 8,101 (26.9) 170,982 (56.8) 258,825 (40.5) 92,737 (43.0) 67,104 (26.3) 25–29 years 401,728 (26.0) 8,627 (20.4) - - 9,889 (23.9) 1,259 (19.4) - 8,883 (29.5) 86,929 (28.9) 173,713 (27.2) 38,202 (17.7) 74,226 (29.1) 30–34 years 108,882 (7.0) 3,479 (8.2) - - 4,444 (10.8) 490 (7.6) - 3,434 (11.4) 9,222 (3.1) 45,920 (7.2) 9,717 (4.5) 32,176 (12.6) 35–39 years 30,023 (1.9) 1,049 (2.5) - - 1,248 (3.0) 134 (2.1) - 1,025 (3.4) 1,433 (0.48) 11,223 (1.8) 2,387 (1.1) 11,524 (4.5) ≥ 40 years 5,511 (0.36) 236 (0.56) - - 193 (0.47) 23 (0.35) - 157 (0.52) 218 (0.07) 2,002 (0.31) 413 (0.19) 2,269 (0.89) No children 187,425 (12.1) 6,542 (15.5) 329 (5.2) 295 (5.8) 15,748 (38.1) 3,244 (50.1) 255 (7.7) 3,739 (12.4) 3,956 (1.3) 72,831 (11.4) 32,816 (15.2) 47,670 (18.7) Unknown 35,811 (2.3) 3,312 (7.8) 6,043 (94.8) 4,770 (94.2) 390 (0.94) 168 (2.6) 3074 (92.3) 3,148 (10.4) 148 (0.05) 11,709 (1.8) 2,332 (1.1) 717 (0.28) Menopausal status Pre-menopausal 244,562 (15.8) 9,689 (23.0) 4,018 (63.1) 2,941 (58.1) 24,718 (59.8) 4,754 (73.4) 1232 (37.0) 11,796 (39.1) 128,741 (42.8) 1 (0.00) 0 (0.00) 56,672 (22.2) Post-menopausal 1,301,655 (84.2) 32,505 (77.0) 2,354 (36.9) 2,124 (41.9) 16,617 (40.2) 1,726 (26.6) 2097 (63.0) 18,352 (60.9) 172,168 (57.2) 639,025 (100.0) 215,905 (100) 198,782 (77.8) Age at menopause (postmenopausal women only) < 40 years 77,087 (5.9) 2,594 (8.0) 406 (17.2) 292 (13.7) 0 (0.00) - 131 (6.2) 2,004 (10.9) 6,464 (3.8) 21,158 (3.3) 38,407 (17.8) 5,631 (2.8) 40–44 years 124,498 (9.6) 3,005 (9.2) 639 (27.1) 518 (24.4) 1,508 (9.1) - 251 (12.0) 2,008 (10.9) 18,184 (10.6) 52,184 (8.2) 33,393 (15.5) 12,808 (6.4) 45–49 years 296,861 (22.8) 5,383 (16.6) 757 (32.2) 653 (30.7) 3,142 (18.9) - 556 (26.5) 3,953 (21.5) 65,017 (37.8) 132,074 (20.7) 51,357 (23.8) 33,969 (17.1) 50–54 years 438,854 (33.7) 7,762 (23.9) 340 (14.4) 375 (17.7) 4,893 (29.4) - 964 (46.0) 6,294 (34.3) 60,141 (34.9) 223,470 (35.0) 66,050 (30.6) 68,565 (34.5) ≥ 55 years 104,681 (8.0) 12,232 (37.6) 103 (4.4) 73 (3.4) 739 (4.4) - 173 (8.2) 1,051 (5.7) 7,779 (4.5) 46,200 (7.2) 14,617 (6.8) 21,714 (10.9) Unknown 259,674 (19.9) 1,529 (4.7) 109 (4.6) 213 (10.0) 6,335 (38.1) 1,726 (100) 22 (1.0) 3,042 (16.6) 14,583 (8.5) 163,939 (25.7) 12,081 (5.6) 56,095 (28.2) Ever used oral contraceptives Yes 791,983 (51.3) 24,802 (58.8) 223 (3.5) 349 (6.9) 30,475 (73.7) 3,582 (55.3) 523 (15.7) 20,167 (66.9) 29,611 (9.8) 391,488 (61.3) 84,234 (39.0) 207,052 (81.1) No 742,243 (48.1) 17,392 (41.2) 6,148 (96.5) 4,688 (92.6) 10,503 (25.4) 2,848 (44.0) 2778 (83.4) 9,521 (31.6) 271,252 (90.1) 243,805 (38.2) 128,537 (59.5) 47,549 (18.6) Unknown 8,662 (0.6) - 1 (0.02) 28 (0.55) 357 (0.86) 50 (0.77) 28 (0.01) 460 (1.5) 46 (0.02) 3,733 (0.58) 3,134 (1.45) 853 (0.33) Ever used hormone replacement therapy Yes 584,677 (37.8) 16,619 (39.4) - - 7,506 (18.2) - 654 (19.6) 8,198 (27.2) - 341,137 (53.4) 114,186 (52.9) 96,377 (37.7) No 628,851 (40.7) 24,294 (57.6) - - 33,239 (80.4) - 1432 (43.0) 20,997 (69.6) - 289,096 (45.2) 101,719 (47.1) 158,074 (61.9) Unknown 332,689 (21.5) 1,281 (3.0) 6,372 (100) 5,065 (100) 590 (1.4) 6,480 (100) 1243 (37.3) 953 (3.2) 300,909 (100) 8,793 (1.4) 0 (0.00) 1,003 (0.39) Abbreviations: AHS-2, Adventist Health Study-2; CARRS, Centre for cArdiometabolic Risk Reduction in South Asia; CKB, China Kadoorie Biobank; EPIC, European Prospective Investigation into Cancer and Nutrition; MWS, Million Women Study; NIH-AARP, National Institutes of Health-AARP Diet and Health Study; OVS, Oxford Vegetarian Study; TCHS, Tzu Chi Health Study; UKWCS, UK Women’s Cohort Study. 1All values are N (%). 2Sex-specific tertiles of metabolic equivalents. c indicates that no information was available for this variable in the specified cohort. The original article [1] has been corrected

Genetic predisposition to mosaic Y chromosome loss in blood.

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism1-5, yet our knowledge of the causes and consequences of this is limited. Here, using a computational approach, we estimate that 20% of the male population represented in the UK Biobank study (n = 205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes that are involved in cell-cycle regulation and cancer susceptibility, as well as somatic drivers of tumour growth and targets of cancer therapy. We demonstrate that genetic susceptibility to LOY is associated with non-haematological effects on health in both men and women, which supports the hypothesis that clonal haematopoiesis is a biomarker of genomic instability in other tissues. Single-cell RNA sequencing identifies dysregulated expression of autosomal genes in leukocytes with LOY and provides insights into why clonal expansion of these cells may occur. Collectively, these data highlight the value of studying clonal mosaicism to uncover fundamental mechanisms that underlie cancer and other ageing-related diseases.This research has been conducted using the UK Biobank Resource under application 9905 and 19808. This work was supported by the Medical Research Council [Unit Programme number MC_UU_12015/2]. Full study-specific and individual acknowledgements can be found in the supplementary information