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Br J Cancer. 2007 Apr 10; 96(7): 1139–1146.

Meat consumption and chance of chest cancer in the UK Women'south Cohort Written report

E F Taylor

¹Nutritional Epidemiology Group, thirty–32 Hyde Terrace, University of Leeds, Leeds, LS2 9LN, UK

V J Burley

^oneNutritional Epidemiology Group, thirty–32 Hyde Terrace, Academy of Leeds, Leeds, LS2 9LN, UK

D C Greenwood

²Biostatistics Unit of measurement, 30–32 Hyde Terrace, Academy of Leeds, Leeds, LS2 9LN, United kingdom of great britain and northern ireland

J Due east Cade

¹Nutritional Epidemiology Group, 30–32 Hyde Terrace, University of Leeds, Leeds, LS2 9LN, UK

Received 2006 Nov xvi; Revised 2007 Feb nineteen; Accustomed 2007 Feb 21.

Abstruse

We performed a survival analysis to appraise the effect of meat consumption and meat type on the adventure of breast cancer in the United kingdom of great britain and northern ireland Women's Cohort Report. Between 1995 and 1998 a cohort of 35 372 women was recruited, anile betwixt 35 and 69 years with a wide range of dietary intakes, assessed by a 217-item food frequency questionnaire. Hazard ratios (HRs) were estimated using Cox regression adapted for known confounders. High consumption of total meat compared with none was associated with premenopausal breast cancer, HR=1.20 (95% CI: 0.86–1.68), and high not-processed meat intake compared with none, HR=ane.20 (95% CI: 0.86–1.68). Larger effect sizes were found in postmenopausal women for all meat types, with significant associations with total, processed and red meat consumption. Processed meat showed the strongest Hour=1.64 (95% CI: 1.14–two.37) for high consumption compared with none. Women, both pre- and postmenopausal, who consumed the nearly meat had the highest risk of breast cancer.

Keywords: prospective studies, breast neoplasms, meat, risk factors

Although show that links meat consumption with cancers of the stomach, colorectum and pancreas is increasing (Sandhu et al, 2001; Gonzalez et al, 2006; Larsson et al, 2006a, 2006b; Lewin et al, 2006), studies of meat consumption and breast cancer take produced more conflicting results. A meta-analysis of 31 instance–control and accomplice studies published before 2003 found a 17% increase in run a risk associated with the highest category of meat intakes (Boyd et al, 2003). However, a pooled analysis of the raw information from 8 prospective cohort studies from North America, Canada and Western Europe was unable to demonstrate such an association (Missmer et al, 2002).

Certain show suggests an interaction between cooking methods and diet in breast cancer causation. Studies, however, are few and inconsistent. A case–control study of Chinese women in Shanghai institute that the positive association with red-meat intake was primarily restricted to those who used deep-frying cooking methods, particularly amongst those who deep-fried foods to well-washed (Dai et al, 2002) suggesting an effect of heterocyclic amines or other carcinogens formed at loftier temperatures. All the same, the Nurses' Wellness nested case–control report found no increase in risk with cooking method or meat intake even for consumption of charred meat more than than once a week in rapid acetylators (Gertig et al, 1999).

Some of the inconsistency in findings may be owing to differences in definitions of total meat, crimson, and candy meats and in the derivation of the meat content of meat dishes. Other inconsistencies may arise owing to biases, errors and the homogeneity of diet within individual population groups (Hankin, 1993; Kaaks and Riboli, 1997a). The UK Women'southward Cohort Study (UKWCS), which was established in 1993 to investigate diet in relation to cancer and mortality from selected causes, is well placed to examine meat consumption and breast cancer risk, the discipline of this paper.

MATERIALS AND METHODS

Study population

The UKWCS, described previously (Cade et al, 2004, 2007), was formed from 500 000 responders to a direct mail survey of the World Cancer Enquiry Fund (WCRF) later permission to carry out the baseline report was obtained from 174 local research ethics committees (Woodhouse et al, 1997). Lxx-five percent of the responders agreed to take part in a more detailed survey; those eligible for inclusion were women, aged between 35 and 69 years at the completion date of the original mail survey. The 35 372 women who returned completed questionnaires formed the UKWCS, this accomplice being specifically designed to have a wide range of dietary intakes and patterns to increment the potential power to detect statistically pregnant associations between specific diets and disease; 28% are cocky-reported vegetarians.

Baseline data were gathered between 1995 and 1998 using a 217-item postal food frequency questionnaire (FFQ), adult from that of the European Prospective Investigation into Cancer and Nutrition (EPIC) written report (Linseisen et al, 2002). This was validated in terms of nutrients, against a semi-weighed iv-solar day food diary (Spence et al, 2002).

Details of women fulfilling the eligibility criteria were submitted to the Uk Part of National Statistics and subsequently flagged on the NHS central register. Incident cancers and cause of decease were coded according to the International Classification of Diseases nine and 10. The investigation conscience appointment was 31st October 2004, with median follow-up of eight years when a full of 1750 incident malignant cancer cases had been recorded, including 283 premenopausal and 395 postmenopausal breast cancers. Menopausal status was based on answers to the baseline questionnaire regarding menstrual and obstetric history and the historic period at baseline. Ability calculations suggested 283 premenopausal chest cancer cases would give approximately 80% power to detect a relative run a risk of ane.4 comparing two levels of a binary exposure with equal numbers in each group (P<0.05), or more than 90% power for a relative run a risk of one.five. In terms of postmenopausal breast cancer, 395 cases would give approximately 90% power to notice a relative gamble of 1.4 (P<0.05). Analysing the exposure as a continuous variable would provide even more ability.

Meat consumption

For the purpose of the written report, meat types and meat dishes were grouped into the following categories: cerise meat, poultry, offal and processed meat. Full meat was the sum of these four categories. Not-processed was the sum of cherry-red meat, poultry and offal. Red meat consisted of beef, pork, lamb and other crimson meats included in mixed dishes, for example, meat lasagne, moussaka, ravioli and filled pasta with sauce; poultry included roast chicken, chicken slices, breadstuff crumbed craven, chicken or turkey in a creamy sauce and craven back-scratch; meats considered as processed were salary, ham, corned beef, spam, luncheon meats, sausages, pies, pasties, sausage rolls, liver pate, salami and meat pizza; offal (organ meats) existed every bit a unmarried item on the FFQ.

Daily intakes of each of the four main meat types (red, poultry, offal and processed) were calculated by summing the daily intakes of the individual food items within each meat blazon as described above. Intakes of each item were determined past using the frequency categories to gauge the number of daily portions. These were then converted into weights by referring to standard portion sizes for each nutrient item (Food Standards Agency, 2002). Intakes of each meat type were grouped into consumption categories of 'none', 'low', 'medium' and 'high' by classing zero intakes as 'not-consumers' and dividing not-zero intakes into tertiles. Consumption of offal tended to exist more limited and naturally fell into the three categories 'none', 'low' and 'high' consumption merely, where depression consumption was defined every bit ii g or less per twenty-four hours and high as over 2 g per day.

Statistical analysis

Exposures of involvement were total meat consumption, non-processed vs processed meat consumption and consumption of different meat types. Candy meat formed a separate category to be compared against non-processed meat. Survival analyses were conducted in Stata version 9 using Cox regression weighted by the inverse of the probability of beingness sampled to take into business relationship the big proportion of vegetarians in the cohort. The fourth dimension variable used in the survival analysis was time in the written report (person years), calculated as the time from the date the questionnaire was filled in until either a report of incident breast cancer, death or the conscience date of the assay, whichever came first. Women with extremely high or depression full energy intake (more than than 6000 kcal and less than 500 kcal) were excluded, as were women with prevalent breast cancer.

2 models were developed. Model ane adjusted only for age (continuous) and energy intake by the residuals method (split into quartiles) (Willett and Stampfer, 1986; Margetts and Nelson, 2000). Model ii adjusted for age, energy intake, body mass index (BMI) (continuous), physical activeness (continuous), parity (no children, 1–2 children, 3–4 children and v+ children) and combined fruit and vegetable consumption (split into quartiles). Smoking condition, hormone replacement therapy utilize (HRT) and oral contraceptive pill use were also included and all classed every bit present, past or never. Additional confounders were included such as socioeconomic course (professional and managerial, intermediate, and routine and manual), level of educational qualifications gained (none beyond age 14, O level, A level and degree level). Partial polynomials were used to fit a smooth bend to the relationship between breast cancer and total meat intake using Model 2.

As breast cancer may represent different diseases in the ii menopause condition groups, an initial analysis combined both groups, incorporating menopausal condition as a confounder in the model. As a test for interaction between meat consumption and menopausal condition confirmed a potential modifying effect of menopausal status, we have treated pre- and postmenopausal women independently. The proportional hazards supposition was checked using graphical methods of log–log curves and Schoenfeld goodness of fit tests (Schoenfeld, 1982), which confirmed the hazards were proportional. Owing to the likelihood of differences in lifestyle characteristics between vegetarians and meat eaters in improver to the absenteeism of the meat component within their diet, sensitivity analyses were undertaken excluding vegetarians. The sensitivity of results to excluding women with whatsoever cancer incident within 1 year of entry to the study, and to the model building strategy was assessed. Further analysis of sensitivity of results to the menopausal categorisation was carried out by excluding women anile 48–55 years whose menopausal status may have been cryptic. HRT users (past and present) were besides excluded in a sensitivity analysis.

RESULTS

Basic characteristics and meat consumption in the accomplice

Characteristics of the 33 725 women in the study are shown in Table 1. At baseline, the mean age was 52 years and the boilerplate BMI 24.v kg m⁻². Cohort participants were relatively health conscious, with low rates of smoking (11%) and alcohol consumption more than than once per calendar week (52%). Most did not use full-fat milk (28 383, 88%), and a large proportion reported taking dietary supplements (eighteen 561, 58%). Meat eaters business relationship for a higher percentage of present HRT users than vegetarians, although it must be taken into consideration that vegetarians tend to exist younger and therefore less likely to be using HRT. In general, the cohort is well educated and middle class where 8784 (27%) had been educated to caste level and 20 879 (63%) worked in professional or managerial positions. More item regarding the cohort women has been provided previously (Cade et al, 2004).

Table i

Baseline characteristics past category of meat consumption

	Full meat consumption
	*None (0 g ) due north=8881*	Depression (<62 g ) n=8281	*Medium (62–103 thou* ) north=8282**	High (>103 g ) n=8281	Total n=33 725
Age (years), mean (s.d.)	49 (8)	53 (ix)	54 (9)	53 (9)	52 (9)
BMI (kg m⁻²), hateful (south.d.)	23.3 (iii.8)	24.0 (4.1)	24.ix (iv.3)	25.seven (v.9)	24.5 (4.4)
Free energy intake (MJ), mean (s.d.)	9.8 (three.0)	eight.9 (2.8)	9.4 (two.5)	11.ii (3.1)	9.9 (3.four)
Physical activity (min), hateful (south.d.)	17 (29)	fourteen (28)	13 (26)	14 (31)	xiv (28)
Electric current smoker (%)	x	11	10	thirteen	xi
Current HRT use (%)	xiv	20	22	23	twenty
Electric current OCP employ (%)	5	four	3	4	four
No children (%)	27	23	17	14	twenty
Professional person and managerial (%)	70	66	threescore	57	63
Low intake of fruit and vegetables (%)	xviii	27	31	25	25
Total meat (grams), mean (s.d.)	0	34 (19)	82 (12)	148 (48)	65 (62)

Table 1 shows that non-meat consumers were younger, more physically active, and had a lower mean BMI than consumers. High meat consumers were more than likely to exist smokers, had the highest total energy intake, highest mean BMI, highest proportion with no instruction beyond age 14 and everyman proportion employed in professional or managerial occupations. Medium meat consumers were most likely to exist low fruit and vegetable consumers (less than 400 g daily). The everyman free energy intake was seen in the group with depression meat consumption.

Meat consumption and breast cancer

The initial analysis combining both pre- and postmenopausal women to test for effect modification past menopausal status (Table 2) showed several meaning interactions. Indeed, when independent analyses were conducted for each menopausal status, trends were considerably different.

Table ii

Combined pre- and postmenopausal breast cancer

				Model 1^a		Model 2^b
	Consumption ( g /day)	Person years (mean (due south.d.))	Cases/not-cases	Hr	95% CI	HR	95% CI
Total meat
Categorical
None (ref)	0	seven.50 (0.68)	149/8881	one.00	—	one.00	—
Low	<62	7.25 (0.92)	162/8281	1.ten	(0.88, one.39)	i.04	(0.82, i.33)
Medium	62–103	6.79 (0.98)	182/8282	1.thirty	(1.04, one.63)	1.25	(0.98, ane.60)
High	>103	six.63 (0.94)	185/8281	i.40	(1.12, one.75)	1.34	(1.05, 1.71)

Continuous			Risk per 50 g/twenty-four hour period	i.xi	(1.05, one.17)	one.11	(1.04, 1.18)
				P (trend) <0.001		P (tendency)=0.001
Test for result modification by menopausal status				0.0269		0.0492

Non-processed meat (including red meat, poultry and offal)
Chiselled
None (ref)	0	seven.51 (0.67)	151/9135	1.00	—	ane.00	—
Low	<50	7.22 (0.93)	163/8196	1.12	(0.89, 1.41)	1.07	(0.84, one.36)
Medium	50–84	half-dozen.79 (0.98)	185/8198	1.34	(1.07, 1.68)	one.34	(1.05, ane.lxx)
Loftier	>84	half-dozen.63 (0.94)	179/8196	i.37	(1.09, 1.72)	1.33	(1.04, 1.69)

Continuous			Gamble per 50 k/day	1.11	(one.04, 1.18)	ane.ten	(one.03, 1.nineteen)
				P (trend)=0.003		P (trend)=0.007
Examination for effect modification by menopausal status				0.0454		0.0452

Processed meat
Categorical
None (ref)	0	seven.51 (0.70)	175/10306	1.00	—	i.00	—
Low	<x	7.08 (0.94)	160/7824	i.17	(0.93, 1.47)	1.19	(0.94, 1.53)
Medium	10–20	six.77 (0.99)	172/7814	1.31	(ane.04, 1.64)	i.30	(1.02, 1.66)
High	>20	half-dozen.69 (0.95)	171/7781	i.35	(1.08, 1.70)	1.39	(1.09, 1.78)

Continuous			Hazard per l thou/day	1.40	(1.18, 1.67)	1.59	(1.22, 2.06)
				P (trend) <0.001		P (tendency) <0.001
Examination for effect modification by menopausal status				0.1365		0.4523

Red meat
Categorical
None (ref)	0	vii.52 (0.68)	186/11199	1.00	—	1.00	—
Low	<32	vii.15 (0.97)	162/7512	i.28	(one.02, 1.61)	1.21	(0.95, ane.54)
Medium	32–57	6.72 (0.95)	163/7560	one.36	(1.08, ane.71)	1.40	(1.10, 1.78)
Loftier	>57	6.57 (0.93)	167/7454	1.47	(1.17, i.84)	1.41	(1.11, 1.81)

Continuous			Risk per 50 grand/day	1.12	(1.03, 1.21)	1.12	(ane.03, 1.22)
				P (trend)=0.005		P (trend)=0.007
Examination for effect modification past menopausal status				0.0325		0.0577

Poultry
Categorical
None (ref)	0	7.48 (0.70)	160/9607	ane.00	—	1.00	—
Low	<fourteen	6.96 (0.98)	160/7401	i.24	(0.97, i.57)	1.19	(0.92, ane.54)
Medium	14–23	6.81 (0.99)	191/8678	1.xxx	(1.03, 1.63)	1.25	(0.98, 1.59)
Loftier	>23	6.88 (0.97)	167/8039	1.25	(0.99, 1.58)	i.22	(0.95, one.56)

Continuous			Run a risk per fifty chiliad/day	ane.14	(0.95, 1.35)	ane.eleven	(0.92, 1.34)
				P (trend)=0.154		P (trend)=0.285
Test for result modification by menopausal status				0.7242		0.8897

Offal
Chiselled
None (ref)	0	7.23 (0.88)	366/20499	1.00	—	1.00	—
Low	⩽2	6.79 (0.99)	190/7833	1.34	(1.11, 1.61)	1.35	(i.11, 1.64)
—	—			—	—	—	—
Loftier	>2	vi.73 (0.99)	122/5393	1.22	(0.99, i.52)	1.17	(0.93, 1.48)

Continuous			Take chances per 50 g/day	1.92	(0.81, 4.53)	1.75	(0.68, 4.50)
				P (trend)=0.136		P (trend)=0.248
Exam for effect modification past menopausal condition				0.6334		0.6039

The associations betwixt meat consumption and premenopausal breast cancer are presented in Tabular array iii for both Model 1 and Model two. Utilize of the complex model showed take a chance of breast cancer to increase with consumption of total meat, Hour (hazard ratios)=i.xx (95% CI: 0.86–i.68) for loftier consumers vs non-consumers. The estimated relative chance for an increment in total meat consumption of 50 thousand day⁻ⁱ (approximately half a portion) was 1.12 (95% CI: 1.02–1.23, P _trend=0.02). Non-processed meat consumption was positively associated with take a chance, Hr=1.twenty (95% CI: 0.86–1.68) for high consumers vs non-consumers with a relative adventure per 50 g day^−one of 1.13 (95% CI: 1.01–one.26, P _trend=0.03). The association with processed meat was not statistically meaning although the risk in high consumers was similar to that observed in non-candy meat. The deadline non-significant association with scarlet meat consumption tended to evidence the largest upshot sizes of all meat types, HR=one.32 (95% CI: 0.93–1.88) for high consumption vs the reference category with relative risk per 50 g day⁻ⁱ of one.xiii (95% CI: 0.99–1.29, P _trend=0.08).

Tabular array 3

Pre menopausal breast cancer

				Model 1^a		Model 2^b
	Consumption ( m /day)	Person years (hateful (s.d.))	Cases/non-cases	Hr	95% CI	HR	95% CI
Full meat
Categorical
None (ref)	0	7.50 (0.66)	98/5435	one.00	—	1.00	—
Low	<62	vii.35 (0.85)	52/3586	0.72	(0.51, i.03)	0.68	(0.47, 0.99)
Medium	62–103	6.96 (0.99)	63/3309	i.00	(0.72, one.39)	ane.08	(0.76, 1.53)
High	>103	6.83 (0.97)	70/3334	1.16	(0.85, 1.58)	1.twenty	(0.86, 1.68)
Continuous			Risk per 50 1000/day	1.10	(ane.00,1.20)	1.12	(1.02, 1.23)
				P (trend)=0.046		P (trend)=0.02

Non-processed meat (including red meat, poultry and offal)
Categorical
None (ref)	0	seven.50 (0.66)	98/5556	i.00	—	1.00	—
Low	<50	seven.32 (0.86)	51/3539	0.73	(0.51, 1.04)	0.69	(0.47, 1.01)
Medium	50–84	6.97 (1.01)	66/3271	1.09	(0.79, one.51)	i.18	(0.83, 1.66)
Loftier	>84	6.83 (0.97)	68/3298	ane.17	(0.86, ane.half-dozen)	ane.20	(0.86, 1.68)
Continuous			Risk per 50 g/day	ane.10	(0.99, ane.22)	i.13	(1.01, ane.26)
				P (trend)=0.069		P (trend)=0.03

Processed meat
Categorical
None (ref)	0	7.51 (0.68)	109/6069	1.00	—	1.00	—
Low	<10	7.22 (0.89)	55/3196	0.88	(0.62, 1.24)	0.94	(0.65, 1.36)
Medium	10–20	6.95 (1.00)	56/3223	0.94	(0.67, 1.32)	one.04	(0.72, 1.51)
High	>20	6.89 (0.97)	63/3176	1.thirteen	(0.82, 1.56)	1.twenty	(0.85, ane.7)
Continuous			Risk per 50 g/day	1.44	(0.96, 2.18)	1.45	(0.95, 2.23)
				P (tendency)=0.079		P (tendency)=0.09

Reddish meat
Categorical
None (ref)	0	7.52 (0.66)	113/6463	1.00	—	ane.00	—
Low	<32	7.24 (0.90)	fifty/3328	0.83	(0.58, 1.eighteen)	0.80	(0.55, ane.17)
Medium	32–57	half-dozen.91 (0.98)	59/3050	i.11	(0.79, ane.55)	i.19	(0.83, ane.seven)
Loftier	>57	6.78 (0.98)	61/2823	ane.28	(0.93, 1.77)	i.32	(0.93, one.88)
Continuous			Risk per fifty g/day	1.x	(0.97, 1.25)	1.13	(0.99, 1.29)
				P (trend)=0.143		P (trend)=0.08

Poultry
Categorical
None (ref)	0	7.fifty (0.67)	99/5700	one.00	—	1.00	—
Low	<fourteen	vii.17 (0.96)	53/2854	1.05	(0.74, 1.48)	1.07	(0.74, 1.54)
Medium	fourteen–23	6.99 (0.99)	64/3486	ane.06	(0.77, i.47)	1.05	(0.75, 1.49)
Loftier	>23	7.00 (0.95)	67/3624	1.10	(0.81, 1.51)	ane.15	(0.82, 1.61)
Continuous			Adventure per fifty g/day	one.23	(0.91, 1.65)	1.28	(0.93, 1.75)
				P (trend)=0.172		P (trend)=0.13

Offal
Categorical
None (ref)	0	7.32 (0.82)	183/10616	1.00	—	1.00	—
Low	⩽ii	6.97 (0.98)	69/3252	1.24	(0.93, ane.66)	i.32	(0.98, one.78)
—	—			—	—	—	—
High	>2	six.96 (1.00)	31/1796	0.99	(0.67, 1.47)	0.96	(0.63, i.45)
Continuous			Adventure per 50 g/day	1.53	(0.22, ten.36)	one.63	(0.22, xi.9)
				P (trend)=0.665		P (trend)=0.63

In postmenopausal women, slight positive trends were observed across the low, medium and high meat categories with a more marked difference between those not consuming meat and those that do. Still, splitting the meat categories into more groups by dividing the depression consumers into low and very low consumers strengthened the dose response relationship with meat consumption. There was a trend for the point estimates to be somewhat larger in postmenopausal than in premenopausal women (using Model 2), equally shown in Table four. Total meat intake was positively associated with postmenopausal breast cancer, Hr=1.63 (95% CI: 1.ten–2.xxx) for high consumption vs the reference category, and when treated as a continuous variable, resulted in a significant linear tendency and relative adventure per fifty g twenty-four hours⁻¹ of i.ten (95% CI: 1.01–ane.twenty, P _trend =0.02). Relationships betwixt both processed meat and cherry-red meat and postmenopausal breast cancer were likewise significant. Risks for the iii meat types were similar when considering HRs of the categorical analysis, however, plumbing equipment meat in the model as a continuous predictor resulted in a much stronger human relationship with processed meat, giving a relative risk per 50 g day^−one of 1.64 (95% CI: 1.09–2.27, P _trend=0.003).

Tabular array four

Postmenopausal chest cancer

				Model ane^a		Model 2^b
	Consumption ( k per day)	Person years (mean(s.d.))	Cases/non-cases	HR	95% CI	Hr	95% CI
Full meat
Chiselled
None (ref)	0	vii.51 (0.70)	51/3297	one.00		1.00	—
Low	<62	7.18 (0.96)	110/4533	i.68	(1.nineteen, 2.36)	1.52	(1.06, 2.18)
Medium	62–103	6.66 (0.95)	119/4791	one.81	(1.29, 2.56)	1.58	(1.09, ii.27)
High	>103	6.48 (0.88)	115/4762	1.87	(1.33, 2.63)	1.63	(1.13, 2.35)
Continuous			Gamble per l g/day	1.11	(1.03, one.nineteen)	ane.x	(1.01, 1.20)
				P (trend)=0.004		P (trend)=0.021

Non-processed meat (including scarlet meat, poultry and offal)
Categorical
None (ref)	0	7.51 (0.70)	53/3428	1.00		1.00	—
Low	<fifty	seven.14 (0.98)	112/4494	ane.62	(1.fourteen, two.31)	1.53	(1.06, 2.21)
Medium	50–84	six.67 (0.94)	119/4742	1.74	(1.22, 2.46)	1.63	(1.13, ii.36)
High	>84	6.49 (0.89)	111/4719	ane.72	(i.21, 2.44)	one.59	(1.1, 2.3)
Continuous			Risk per 50 g/solar day	1.11	(1.01, i.21)	ane.09	(0.99, 1.20)
				P (trend)=0.023		P (tendency)=0.088

Candy meat
Categorical
None (ref)	0	7.51 (0.72)	66/4062	1.00		1.00	—
Low	<10	half-dozen.99 (0.97)	105/4468	ane.53	(1.09, 2.15)	i.48	(ane.04, 2.12)
Medium	10–20	six.64 (0.96)	116/4419	1.76	(1.26, ii.47)	1.60	(1.12, two.29)
High	>20	6.54 (0.91)	108/4434	1.70	(1.21, 2.39)	1.64	(one.fourteen, 2.37)
Continuous			Adventure per l 1000/twenty-four hours	1.40	(ane.xvi, ane.70)	one.64	(1.19, 2.27)
				P (tendency)=0.001		P (trend)=0.003

Cherry meat
Categorical
None (ref)	0	7.52 (0.70)	73/4550	1.00		1.00	—
Low	<32	7.08 (1.01)	112/4022	1.78	(ane.28, two.47)	ane.63	(1.xv, 2.31)
Medium	32–57	6.59 (0.90)	104/4347	1.67	(1.19, 2.33)	1.64	(1.xv, ii.34)
High	>57	6.44 (0.87)	106/4464	ane.73	(1.24, 2.41)	1.56	(1.09, 2.23)
Continuous			Take a chance per 50 m/twenty-four hours	one.13	(1.02, one.25)	i.12	(i.01, one.26)
				P (tendency)=0.019		P (trend)=0.040

Poultry
Categorical
None (ref)	0	7.45 (0.76)	61/3747	1.00		1.00	—
Low	<xiv	vi.82 (0.97)	107/4387	1.43	(1, 2.05)	ane.32	(0.9, 1.93)
Medium	xiv–23	vi.70 (0.97)	127/5001	1.51	(1.07, 2.xiv)	i.39	(0.96, 2.02)
High	>23	6.78 (0.97)	100/4248	1.41	(0.99, ii.01)	one.thirty	(0.89, 1.89)
Continuous			Risk per l g/day	1.06	(0.85, one.33)	one.00	(0.78, 1.28)
				P (trend)=0.585		P (tendency)=0.985

Offal
Categorical
None (ref)	0	seven.13 (0.92)	183/9517	1.00		1.00	—
Low	⩽2	6.67 (0.97)	121/4391	1.39	(1.09, 1.76)	1.37	(ane.05, ane.77)
—	—					—	—
High	>2	6.61 (0.96)	91/3475	1.34	(1.03, 1.73)	1.26	(0.95, 1.67)
Continuous			Run a risk per 50 g/twenty-four hours	2.01	(0.79, five.13)	i.62	(0.57, iv.59)
				P (trend)=0.142		P (tendency)=0.363

Risk ratios in the highest meat consumption category for Model 1 in premenopausal women were slightly lower than for Model 2 for all meat types with the exception of offal (total meat: Model 1 HR=ane.sixteen, Model ii Hour=1.xx). Tests for trend were more significant in Model ii. The opposite is true for postmenopausal chance where HRs are lowered in the refined model and P-values become less significant with greater adjustments. Figure ane presents the fitted bend from fractional polynomials for total meat intake showing similar increasing gamble with increasing full meat intake for both pre- and postmenopausal women, apart from premenopausal women with low meat intake who appear at lower risk than vegetarians.

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Association between total meat intake and breast cancer for pre- and postmenopausal women.

In the sensitivity results excluding vegetarians, estimates were broadly similar and conclusions unchanged, emphasising a dose response across the consumption categories of meat in both pre- and postmenopausal women. Sensitivity analyses for ambiguous menopausal condition and women with cancer within ane year of entry did not substantially alter HRs or overall trends. The links between meat consumption, cooking methods (grilling, frying and casseroling of meat) and risk were investigated by considering interactions within Model 2; there was no show of changes in gamble. Excluding HRT users from the assay of postmenopausal women appeared to strengthen the human relationship with breast cancer.

DISCUSSION

The UKWCS is ane of the largest cohorts investigating diet and cancer in women in the Great britain. It was designed to include participants with a wide range of dietary exposures to optimise comparisons between different levels of meat intake, equally proposed previously (Kaaks and Riboli, 1997b; Schatzkin et al, 2001). In our analysis, significant increased risks of incident premenopausal chest cancer in relation to increased consumption of total meat and non-candy meat were observed. Borderline non-significant associations with blood-red meat were also seen. We establish positive associations between postmenopausal breast cancer and total meat, processed meat and red meat consumption.

Relationships betwixt both pre- and postmenopausal breast cancer and full and red meat consumption confirm findings of a case control report amongst Chinese women in Shanghai where positive associations were observed in pre- and postmenopausal breast cancers combined (Dai et al, 2002). Although pre- and postmenopausal women were also considered separately, the full data were not shown. Positive associations among those who normally deep-fried red meat until well washed, were establish in both groups, although statistically pregnant only in premenopausal women.

The association with red meat intake and both pre- and postmenopausal breast cancer may be due to a combination of nutritionally related factors, such as content of fat, protein and iron, and/or meat preparation (eg cooking or preserving methods) (Sinha, 2002). A comparison of high consumer HRs for all meat types investigated showed that high consumers of blood-red meat are most at risk of premenopausal breast cancer when compared with non-consumers (HR=1.32, 95% CI: 0.93–1.88). The clan found betwixt non-processed meat (red meat, poultry and offal) could also exist acquired past the red meat component within the non-processed meat category.

Results of a large case–control study (10 149 cases and 7990 controls) in northern Italy betwixt 1983 and 1996 besides plant significant positive associations of breast cancer (combined assay of pre- and post menopausal women) with ruby-red meat consumption (Tavani et al, 2000). In addition, a meta-analysis of 12 case–control and five cohort studies published between 1966 and 1993 constitute increased risks of breast cancer (combined pre- and postmenopausal) in high consumers, the clan with reddish meat (RR=i.54, 95% CI: ane.31–one.82) being stronger than that observed for total meat (Boyd et al, 2003). However, a pooled analysis of 8 previous cohort studies has shown no pregnant association between consumption of total meat, red meat or white meat and risk of breast cancer (Missmer et al, 2002) in both combined and divide analyses of pre- and postmenopausal women. The pooled assay was not able to correct for measurement error and there were considerable differences in questionnaire design between studies limiting the power of specific food analyses. Also, meat-cooking practices could non be taken into account.

Previous studies have tended to find changed relationships with consumption of poultry (Delfino et al, 2000; Ronco et al, 2003) and take generally been statistically non-significant. Our findings exercise not provide strong evidence of an association with poultry intake and breast cancer in either pre- or postmenopausal women. However, another study showed statistically significant inverse trends between consumption of poultry and postmenopausal breast cancer (Ambrosone et al, 1998). One study found that risks were increased when chicken was consumed with skin suggesting that fat rather than muscle meat may be the cause (Ronco et al, 2003). Other studies have suggested a link betwixt fat and breast cancer (Howe et al, 1991; Willett et al, 1992; Hunter et al, 1996; Smith-Warner et al, 2001; Boyd et al, 2003; Cho et al, 2003).

Although HRs for pre menopausal breast cancer indicate a positive association with meat intake, low consumers are at less gamble than vegetarians. Low meat consumers also had the everyman energy and fat intakes, only including the percent of free energy from fatty as a confounder and too calculated using the residuals method (Willett and Stampfer, 1986) did not significantly modify the risk estimates. Vegetarians possess other characteristics other than not consuming meat and these may influence the association with hazard in some manner. Although we adjusted for characteristics known to be represented differently in meat eaters and vegetarians (Davey et al, 2003; Cade et al, 2004) and performed various sensitivity analyses with the exclusion of the vegetarian group, some residual misreckoning may remain.

Genetic factors only business relationship for a small proportion of breast cancers (approximately 5–10%). The UKWCS are expected to accept a higher proportion than this equally family history of breast cancer may have encouraged them to become WCRF supporters. In add-on, some may have taken up a vegetarian diet in the conventionalities that it is protective against chest cancer. However, if these women are also genetically predisposed to breast cancer, then the chances of developing breast cancer are increased. This is more likely amid the premenopausal women considering genetic causes tend to lead to early onset of chest cancer. This could explicate why, in the premenopausal women, vegetarians have a higher risk than others.

Risks for pre- and postmenopausal women were examined separately, based on variability in some risk factors and because breast cancer may correspond different diseases in these groups (Ambrosone et al, 1998). Besides, hateful intakes of certain meats were establish to differ significantly betwixt the two menopausal groups. In addition, after the menopause, increased degradation of adipose tissue, the major site for oestrogen synthesis, volition tend to elevate the level of endogenous oestrogens (Siiteri, 1987). The association between intake of carcinogens from foods cooked at loftier temperature and breast cancer risk may exist modified past oestrogens and oestrogen-related factors. Other analysis has found a divergence in impact of dietary fibre on risk of breast cancer betwixt pre- and postmenopausal women (Cade et al, 2007).

There are several mechanisms whereby meat intake may contribute to breast cancer risk. Meat and in item processed meats can exist a rich source of saturated fat. Although outcome on mammary carcinogenesis has been shown in animals, its homo relevance is controversial (Ip, 1993). A review of prospective studies has shown that dietary fatty reduction can lower serum oestradiol levels (Wu et al, 1999). Many established risk factors are linked to oestrogens such as early menarche, late menopause and obesity in postmenopausal women (Primal and Verkasalo, 1999). Other mechanisms related to the formation of heterocyclic amines during cooking or nitroso compounds found in processed meat (Willett, 2005) may exist altered by inherited polymorphisms such as the rapid variant of Due north-acetyltransferase two (Williamson et al, 2005). Red meat also contains high biological-value protein and important micronutrients, all of which are essential for proficient health throughout life.

In postmenopausal women, the largest effects were with processed meat and this was statistically significant, HR=1.64 (95% CI: ane.fourteen–2.37) for loftier vs non-consumers with relative risk per 50 g 24-hour interval⁻¹ of ane.64 (95% CI: 1.xix–2.27, P _trend=0.003). Risks were increased by almost 50% for even depression consumers of processed meat. A case–command study in a subcohort of the Nurses' Wellness Study (466 cases) supports this, breast cancer (combined pre- and postmenopausal) being twoscore% more likely in women consuming more than 0.07 portions of bacon daily in comparison with non-consumers (Gertig et al, 1999). Although trends were statistically non-significant, not-candy meat and poultry were both positively associated with postmenopausal chest cancer. Differences in consequence trends for pre- and postmenopausal women may be attributable to the fact that oestrogen metabolism pathways differ according to menopausal status (Muti et al, 2000). If meat influences chest cancer by affecting oestrogen metabolism, the effect may exist relatively more important among women with lower levels of circulating oestrogens.

The force of this written report was the wide range of meat intake within the cohort which reduces measurement error (White et al, 1994; Kaaks and Riboli, 1997b; Schatzkin et al, 2001). Previous studies have been limited in terms of the FFQs used which may not accept been designed to capture specific food groups in sufficient detail (Missmer et al, 2002). An analysis of Ballsy-Norfolk data concluded that dietary measurement error through the utilize of their FFQ may explain the absence of a significant association with dietary fat and breast cancer risk as well as some of the previously reported inconsistencies on meat (Bingham et al, 2003).

In conclusion, women generally consuming most full meat, red and processed meat were at the highest increased risk compared with non-meat consumers, though red and processed meat were only statistically significant postmenopausally. Effect sizes were smaller in not-processed meat and only statistically significant in premenopausal women. There were no statistically significant linear associations with consumption of poultry or offal in either pre- or postmenopausal women. This study indicates relationships with sure meats and breast cancer in both pre- and postmenopausal women and merits farther investigation in a larger report.

Acknowledgments

Nosotros thank the UK Women'south Accomplice Study steering grouping, and the women themselves who participated in the written report. We also thank the WCRF for their previous funding and support. An earlier analysis of this study was funded by the Meat and Livestock Commission.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2360120/