Coffee consumption and prostate cancer risk: further evidence for inverse relationship

<p>Abstract</p> <p>Background</p> <p>Higher consumption of coffee intake has recently been linked with reduced risk of aggressive prostate cancer (PC) incidence, although meta-analysis of other studies that examine the association between coffee consumption and overall PC risk remains inconclusive. Only one recent study investigated the association between coffee intake and grade-specific incidence of PC, further evidence is required to understand the aetiology of aggressive PCs. Therefore, we conducted a prospective study to examine the relationship between coffee intake and overall as well as grade-specific PC risk.</p> <p>Methods</p> <p>We conducted a prospective cohort study of 6017 men who were enrolled in the Collaborative cohort study in the UK between 1970 and 1973 and followed up to 31st December 2007. Cox Proportional Hazards Models were used to evaluate the association between coffee consumption and overall, as well as Gleason grade-specific, PC incidence.</p> <p>Results</p> <p>Higher coffee consumption was inversely associated with risk of high grade but not with overall risk of PC. Men consuming 3 or more cups of coffee per day experienced 55% lower risk of high Gleason grade disease compared with non-coffee drinkers in analysis adjusted for age and social class (HR 0.45, 95% CI 0.23-0.90, p value for trend 0.01). This association changed a little after additional adjustment for Body Mass Index, smoking, cholesterol level, systolic blood pressure, tea intake and alcohol consumption.</p> <p>Conclusion</p> <p>Coffee consumption reduces the risk of aggressive PC but not the overall risk.</p

Seasonal changes in patterns of gene expression in avian song control brain regions.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Photoperiod and hormonal cues drive dramatic seasonal changes in structure and function of the avian song control system. Little is known, however, about the patterns of gene expression associated with seasonal changes. Here we address this issue by altering the hormonal and photoperiodic conditions in seasonally-breeding Gambel's white-crowned sparrows and extracting RNA from the telencephalic song control nuclei HVC and RA across multiple time points that capture different stages of growth and regression. We chose HVC and RA because while both nuclei change in volume across seasons, the cellular mechanisms underlying these changes differ. We thus hypothesized that different genes would be expressed between HVC and RA. We tested this by using the extracted RNA to perform a cDNA microarray hybridization developed by the SoNG initiative. We then validated these results using qRT-PCR. We found that 363 genes varied by more than 1.5 fold (>log(2) 0.585) in expression in HVC and/or RA. Supporting our hypothesis, only 59 of these 363 genes were found to vary in both nuclei, while 132 gene expression changes were HVC specific and 172 were RA specific. We then assigned many of these genes to functional categories relevant to the different mechanisms underlying seasonal change in HVC and RA, including neurogenesis, apoptosis, cell growth, dendrite arborization and axonal growth, angiogenesis, endocrinology, growth factors, and electrophysiology. This revealed categorical differences in the kinds of genes regulated in HVC and RA. These results show that different molecular programs underlie seasonal changes in HVC and RA, and that gene expression is time specific across different reproductive conditions. Our results provide insights into the complex molecular pathways that underlie adult neural plasticity

Do I Have My Attention? Speed of Processing Advantages for the Self-Face Are Not Driven by Automatic Attention Capture

We respond more quickly to our own face than to other faces, but there is debate over whether this is connected to attention-grabbing properties of the self-face. In two experiments, we investigate whether the self-face selectively captures attention, and the attentional conditions under which this might occur. In both experiments, we examined whether different types of face (self, friend, stranger) provide differential levels of distraction when processing self, friend and stranger names. In Experiment 1, an image of a distractor face appeared centrally – inside the focus of attention – behind a target name, with the faces either upright or inverted. In Experiment 2, distractor faces appeared peripherally – outside the focus of attention – in the left or right visual field, or bilaterally. In both experiments, self-name recognition was faster than other name recognition, suggesting a self-referential processing advantage. The presence of the self-face did not cause more distraction in the naming task compared to other types of face, either when presented inside (Experiment 1) or outside (Experiment 2) the focus of attention. Distractor faces had different effects across the two experiments: when presented inside the focus of attention (Experiment 1), self and friend images facilitated self and friend naming, respectively. This was not true for stranger stimuli, suggesting that faces must be robustly represented to facilitate name recognition. When presented outside the focus of attention (Experiment 2), no facilitation occurred. Instead, we report an interesting distraction effect caused by friend faces when processing strangers’ names. We interpret this as a “social importance” effect, whereby we may be tuned to pick out and pay attention to familiar friend faces in a crowd. We conclude that any speed of processing advantages observed in the self-face processing literature are not driven by automatic attention capture

Increasing vegetable intakes: rationale and systematic review of published interventions

Purpose While the health benefits of a high fruit and vegetable consumption are well known and considerable work has attempted to improve intakes, increasing evidence also recognises a distinction between fruit and vegetables, both in their impacts on health and in consumption patterns. Increasing work suggests health benefits from a high consumption specifically of vegetables, yet intakes remain low, and barriers to increasing intakes are prevalent making intervention difficult. A systematic review was undertaken to identify from the published literature all studies reporting an intervention to increase intakes of vegetables as a distinct food group. Methods Databases—PubMed, PsychInfo and Medline—were searched over all years of records until April 2015 using pre-specified terms. Results Our searches identified 77 studies, detailing 140 interventions, of which 133 (81 %) interventions were conducted in children. Interventions aimed to use or change hedonic factors, such as taste, liking and familiarity (n = 72), use or change environmental factors (n = 39), use or change cognitive factors (n = 19), or a combination of strategies (n = 10). Increased vegetable acceptance, selection and/or consumption were reported to some degree in 116 (83 %) interventions, but the majority of effects seem small and inconsistent. Conclusions Greater percent success is currently found from environmental, educational and multi-component interventions, but publication bias is likely, and long-term effects and cost-effectiveness are rarely considered. A focus on long-term benefits and sustained behaviour change is required. Certain population groups are also noticeably absent from the current list of tried interventions

Changing atmospheric CO2 concentration was the primary driver of early Cenozoic climate

The Early Eocene Climate Optimum (EECO, which occurred about 51 to 53 million years ago)1, was the warmest interval of the past 65 million years, with mean annual surface air temperature over ten degrees Celsius warmer than during the pre-industrial period2–4. Subsequent global cooling in the middle and late Eocene epoch, especially at high latitudes, eventually led to continental ice sheet development in Antarctica in the early Oligocene epoch (about 33.6 million years ago). However, existing estimates place atmospheric carbon dioxide (CO2) levels during the Eocene at 500–3,000 parts per million5–7, and in the absence of tighter constraints carbon–climate interactions over this interval remain uncertain. Here we use recent analytical and methodological developments8–11 to generate a new high-fidelity record of CO2 concentrations using the boron isotope (δ11Β) composition of well preserved planktonic foraminifera from the Tanzania Drilling Project, revising previous estimates6. Although species-level uncertainties make absolute values difficult to constrain, CO2 concentrations during the EECO were around 1,400 parts per million. The relative decline in CO2 concentration through the Eocene is more robustly constrained at about fifty per cent, with a further decline into the Oligocene12. Provided the latitudinal dependency of sea surface temperature change for a given climate forcing in the Eocene was similar to that of the late Quaternary period13, this CO2 decline was sufficient to drive the well documented high- and low-latitude cooling that occurred through the Eocene14. Once the change in global temperature between the pre-industrial period and the Eocene caused by the action of all known slow feedbacks (apart from those associated with the carbon cycle) is removed2–4, both the EECO and the late Eocene exhibit an equilibrium climate sensitivity relative to the pre-industrial period of 2.1 to 4.6 degrees Celsius per CO2 doubling (66 per cent confidence), which is similar to the canonical range (1.5 to 4.5 degrees Celsius15), indicating that a large fraction of the warmth of the early Eocene greenhouse was driven by increased CO2 concentrations, and that climate sensitivity was relatively constant throughout this period

Criteria for the differentiation between young and old Onchocerca volvulus filariae

Drugs exist that show long-lasting inhibition of embryogenesis and microfilaria production or macrofilaricidal activity against Onchocerca volvulus. Therefore, the patients have to be followed-up for several years. Clinical drug trials have to be performed in areas with ongoing transmission to assess the efficacy on younger worms. In addition, future vaccine trials may also require demonstrating efficacy against establishment of new worms. For the evaluation of the efficacy, it is necessary to differentiate between older worms, which were exposed to the drug, and younger worms newly acquired after drug treatment or vaccination. Here, we describe criteria for the differentiation between young and old filariae based on histological studies of worms with a known age from travellers, or from children, or patients living in areas with interrupted transmission in Burkina Faso, Ghana or Uganda. Older worms were larger and presented degenerated tissues. Gomori's iron stain showed that the worms accumulated more iron with increasing age, first in the gut and later in other organs. Using an antibody against O. volvulus lysosomal aspartic protease, the gut of young worms was stained only weakly; whereas, it was stronger labelled in older worms, accompanied by additional staining of hypodermis and epithelia. Using morphological and immunohistological criteria, it was possible to differentiate young (1–3 years old) from older females and to identify young males

A Pooled Analysis of 15 Prospective Cohort Studies on the Association Between Fruit, Vegetable, and Mature Bean Consumption and Risk of Prostate Cancer.

BACKGROUND: Relationships between fruit, vegetable, and mature bean consumption and prostate cancer risk are unclear. METHODS: We examined associations between fruit and vegetable groups, specific fruits and vegetables, and mature bean consumption and prostate cancer risk overall, by stage and grade, and for prostate cancer mortality in a pooled analysis of 15 prospective cohorts, including 52,680 total cases and 3,205 prostate cancer deaths among 842,149 men. Diet was measured by a food frequency questionnaire or similar instrument at baseline. We calculated study-specific relative risks using Cox proportional hazards regression, and then pooled these estimates using a random effects model. RESULTS: We did not observe any statistically significant associations for advanced prostate cancer or prostate cancer mortality with any food group (including total fruits and vegetables, total fruits, total vegetables, fruit and vegetable juice, cruciferous vegetables, and tomato products), nor specific fruit and vegetables. Additionally, we observed few statistically significant results for other prostate cancer outcomes. Pooled multivariable relative risks comparing the highest versus lowest quantiles across all fruit and vegetable exposures and prostate cancer outcomes ranged from 0.89 to 1.09. There was no evidence of effect modification for any association by age or body mass index. CONCLUSIONS AND IMPACT: Results from this large, international, pooled analysis do not support a strong role of fruits, vegetables (including cruciferous vegetables and tomato products, although few studies assessed tomato sources of more bioavailable lycopene, the potential cancer preventive agent in tomatoes), or mature beans in prostate cancer

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) &lt; 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

Adult weight change and premenopausal breast cancer risk: A prospective pooled analysis of data from 628,463 women.

Early-adulthood body size is strongly inversely associated with risk of premenopausal breast cancer. It is unclear whether subsequent changes in weight affect risk. We pooled individual-level data from 17 prospective studies to investigate the association of weight change with premenopausal breast cancer risk, considering strata of initial weight, timing of weight change, other breast cancer risk factors and breast cancer subtype. Hazard ratios (HR) and 95% confidence intervals (CI) were obtained using Cox regression. Among 628,463 women, 10,886 were diagnosed with breast cancer before menopause. Models adjusted for initial weight at ages 18-24 years and other breast cancer risk factors showed that weight gain from ages 18-24 to 35-44 or to 45-54 years was inversely associated with breast cancer overall (e.g., HR per 5 kg to ages 45-54: 0.96, 95% CI: 0.95-0.98) and with oestrogen-receptor(ER)-positive breast cancer (HR per 5 kg to ages 45-54: 0.96, 95% CI: 0.94-0.98). Weight gain from ages 25-34 was inversely associated with ER-positive breast cancer only and weight gain from ages 35-44 was not associated with risk. None of these weight gains were associated with ER-negative breast cancer. Weight loss was not consistently associated with overall or ER-specific risk after adjusting for initial weight. Weight increase from early-adulthood to ages 45-54 years is associated with a reduced premenopausal breast cancer risk independently of early-adulthood weight. Biological explanations are needed to account for these two separate factors

Association of body mass index and age with subsequent breast cancer risk in premenopausal women

Importance: The association between increasing body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and risk of breast cancer is unique in cancer epidemiology in that a crossover effect exists, with risk reduction before and risk increase after menopause. The inverse association with premenopausal breast cancer risk is poorly characterized but might be important in the understanding of breast cancer causation.Objective: To investigate the association of BMI with premenopausal breast cancer risk, in particular by age at BMI, attained age, risk factors for breast cancer, and tumor characteristics.Design, Setting, and Participants: This multicenter analysis used pooled individual-level data from 758 592 premenopausal women from 19 prospective cohorts to estimate hazard ratios (HRs) of premenopausal breast cancer in association with BMI from ages 18 through 54 years using Cox proportional hazards regression analysis. Median follow-up was 9.3 years (interquartile range, 4.9-13.5 years) per participant, with 13 082 incident cases of breast cancer. Participants were recruited from January 1, 1963, through December 31, 2013, and data were analyzed from September 1, 2013, through December 31, 2017.Exposures: Body mass index at ages 18 to 24, 25 to 34, 35 to 44, and 45 to 54 years.Main Outcomes and Measures: Invasive or in situ premenopausal breast cancer.Results: Among the 758 592 premenopausal women (median age, 40.6 years; interquartile range, 35.2-45.5 years) included in the analysis, inverse linear associations of BMI with breast cancer risk were found that were stronger for BMI at ages 18 to 24 years (HR per 5 kg/m2 [5.0-U] difference, 0.77; 95% CI, 0.73-0.80) than for BMI at ages 45 to 54 years (HR per 5.0-U difference, 0.88; 95% CI, 0.86-0.91). The inverse associations were observed even among nonoverweight women. There was a 4.2-fold risk gradient between the highest and lowest BMI categories (BMI≥35.0 vs <17.0) at ages 18 to 24 years (HR, 0.24; 95% CI, 0.14-0.40). Hazard ratios did not appreciably vary by attained age or between strata of other breast cancer risk factors. Associations were stronger for estrogen receptor-positive and/or progesterone receptor-positive than for hormone receptor-negative breast cancer for BMI at every age group (eg, for BMI at age 18 to 24 years: HR per 5.0-U difference for estrogen receptor-positive and progesterone receptor-positive tumors, 0.76 [95% CI, 0.70-0.81] vs hormone receptor-negative tumors, 0.85 [95% CI: 0.76-0.95]); BMI at ages 25 to 54 years was not consistently associated with triple-negative or hormone receptor-negative breast cancer overall.Conclusions and Relevance: The results of this study suggest that increased adiposity is associated with a reduced risk of premenopausal breast cancer at a greater magnitude than previously shown and across the entire distribution of BMI. The strongest associations of risk were observed for BMI in early adulthood. Understanding the biological mechanisms underlying these associations could have important preventive potential