Association of vitamin B6, vitamin B12 and methionine with risk of breast cancer: a dose-response meta-analysis.

BACKGROUND: Epidemiological studies evaluating the association of vitamin B6, vitamin B12 and methionine with breast cancer risk have produced inconsistent results.
METHODS: Pertinent studies were identified by a search in PubMed and Web of Knowledge. Random-effect model was used. Dose-response relationship was assessed by restricted cubic spline.
RESULTS: The combined relative risk (95% confidence interval) of breast cancer for the highest vs lowest category of serum pyridoxal 5'-phosphate (PLP, active form of vitamin B6) levels and dietary methionine intake was 0.80 (0.66-0.98, P=0.03) and 0.94 (0.89-0.99, P=0.03), respectively, and the associations of breast cancer with higher serum PLP levels and dietary methionine intake were significant among post-menopausal women, but not among pre-menopausal women. The inverse association between breast cancer risk and dietary vitamin B6 intake, serum vitamin B12 levels and dietary vitamin B12 intake was not significant overall. Linear dose-response relationship was found, and the risk of breast cancer decreased by 23% (P<0.00) for every 100 pmol ml(-1) increment in PLP levels and 4% (P=0.05) for every 1 g per day increment in dietary methionine intake, respectively.
CONCLUSION: Serum PLP levels and methionine intake might be inversely associated with breast cancer risk, especially among postmenopausal women, which need to be confirmed.

Breast cancer is the most frequently diagnosed types of cancer and the leading cause of cancer death among women worldwide, accounting for 23% of the total cancer cases and 14% of the cancer deaths, and about half the breast cancer cases and 60% of the deaths are estimated to occur in economically developing countries (Jemal ). Although incidence rates of breast cancer in some western countries decreased since the beginning of 2000, the incidence rates have been rising in many African and Asian countries (Jemal ). According to the American Cancer Society Guidelines, the nutrition- and physical activity-related advice (Wu ) is necessary to reduce the risk of breast cancer (Kushi ). One-carbon metabolism nutrients, such as folate, vitamin B6, vitamin B12 and methionine, may protect against cancer through DNA synthesis and methylation, upholding DNA integrity and regulating gene expression (Ames, 2001; Selhub, 2002), and pyridoxal 5′-phosphate (PLP; the active form of vitamin B6) is involved in almost 100 enzymatic reactions (Bairoch, 2000). Although no significant effect of folate on breast cancer was found in the previous meta-analysis overall (Larsson ), high alcohol (the known antagonist for these B-vitamins) consumption was shown to be associated with increased risk of breast cancer (Suzuki ). However, results from observational studies on vitamin B6, vitamin B12 and methionine and breast cancer risk are not consistent, and no meta-analysis is available. In addition, folate, vitamin B6 and vitamin B12 act in concert to affect the pathways of one-carbon metabolism (Kim, 2007), and these nutrient interactions were also proposed (Davis and Uthus, 2004). Therefore, we conducted a meta-analysis to (1) first assess the breast cancer risk for the highest vs lowest categories of vitamin B6, vitamin B12 and methionine; (2) then evaluate the possible dose–response relationship of vitamin B6, vitamin B12 and methionine with breast cancer risk; (3) investigate the joint association between folate intake with vitamin B6, vitamin B12 and methionine intake, and the risk of breast cancer; (4) evaluate the modification of key covariates to the association of vitamin B6, vitamin B12 and methionine with breast cancer risk; (5) and assess the heterogeneity among studies and publication bias.

Materials and methods

Literature search and selection

We performed a literature search up to 18 June 2013 using the databases of Pubmed and Web of Knowledge, using the following search terms vitamin B6 or pyridoxal 5'-phosphate (PLP; the active form of vitamin B6), or vitamin B12 or methionine, and breast cancer without restrictions. Moreover, we reviewed the reference lists from retrieved articles to search for further relevant studies.

Two investigators independently reviewed all identified studies, and studies were included if they met the following criteria: (1) the exposure of interest was vitamin B6 or PLP, or vitamin B12 or methionine; (2) the outcome of interest was breast cancer; (3) relative risk (RR) or odds ratio with 95% confidence interval (CI) was provided (we presented all results with RR for simplicity); (4) for dose–response analysis, the number of cases and participants or person-years for each category of vitamin B6 or PLP, or vitamin B12 or methionine, must also be provided (or data available to calculate them). If data were duplicated in more than one study, we included the study with the largest number of cases.

Data extraction

The following data were extracted from each study by two investigators: the design type (case–control or prospective study), the first author's last name, publication year, location where the study was performed, sample size and number of cases, variables adjusted for in the analysis, RR estimates with corresponding 95% CI for the highest versus lowest categories of vitamin B6, vitamin B12 and methionine, respectively. The result for dietary intake of these nutrients was extracted if the result for both dietary intake and total intake (dietary intake plus supplement) were provided. To investigate the joint association between folate intake with vitamin B6, vitamin B12 and methionine intake, and risk of breast cancer, RR was extracted from the group with both the highest folate intake and highest vitamin B6, vitamin B12 or methionine intake group versus the group with both the lowest folate intake and lowest vitamin B6, vitamin B12 or methionine intake.

For dose–response analysis, the number of cases and participants (person-years), and RR (95% CI) for each category of vitamin B6, vitamin B12 and methionine were also extracted. The median or mean level of vitamin B6, vitamin B12 and methionine for each category was assigned to the corresponding RR for every study. If the upper boundary of the highest category was not provided, we assumed that the boundary had the same amplitude as the adjacent category (Larsson ; Hong ). We extracted the RRs that reflected the greatest degree of control for potential confounders.

Statistical analysis

Pooled measure was calculated as the inverse variance-weighted mean of the logarithm of RR with 95% CI, to assess the strength of association between vitamin B6, vitamin B12 and methionine and the risk of breast cancer. Random-effects model was used to combine study-specific RR (95%CI), which considers both within-study and between-study variation (DerSimonian and Laird, 1986). The I2 was used to assess heterogeneity, and I2 values of 0, 25, 50 and 75% represent no, low, moderate and high heterogeneity (Higgins ), respectively. Meta-regression with restricted maximum likelihood estimation was performed to assess the potentially important covariates that might exert substantial impact on between-study heterogeneity (Higgins and Thompson, 2004). A sensitivity analysis was performed with one study removed at a time to assess whether the results could have been affected markedly by a single study. Publication bias was evaluated using the Egger regression asymmetry test (Egger ).

For dose–response analysis, a two-stage random-effects dose–response meta-analysis (Orsini ) was performed to compute the trend from the correlated log RR estimates across levels of vitamin B6, vitamin B12 and methionine, respectively, taking into account the between-study heterogeneity. In the first stage, a restricted cubic spline model with three knots at the 25th, 50th and 75th percentiles (Harrell ) of the levels of vitamin B6, vitamin B12 and methionine was estimated using generalised least-square regression, taking into account the correlation within each set of published RRs (Orsini and Bellocco, 2006). Then the study-specific estimates were combined using the restricted maximum likelihood method in a multivariate random-effects meta-analysis (Jackson ). A P-value for non-linearity was calculated by testing the null hypothesis that the coefficient of the second spline is equal to 0. All statistical analyses were performed with STATA version 12.0 (Stata Corporation, College Station, TX, USA). All reported probabilities (P-values) were two-sided with P<0.05 considered statistically significant.

Results

Literature search and study characteristics

The search strategy identified 552 articles from Pubmed and 1144 articles from the Web of Knowledge, and 28 articles were reviewed in full after reviewing the abstract. Two studies that assessed the MTHFR C677T (Maruti ), MTR and MTRR (Shrubsole ) polymorphisms and breast cancer by intakes of one-carbon metabolism nutrients were further excluded because of duplicate reports from the same study population. Three studies that did not report the risk estimate were also excluded (Potera ; Goodman ; Schroecksnadel ). Finally, 23 articles (Thorand ; Wu ; Levi ; Shrubsole ; Feigelson ; Zhang ; Zhu ; Lajous ; Lajous ; Cho ; Kabat ; Lin ; Xu ; Ma ; Ma ; Maruti ; Stevens ; Chou ; Shrubsole ; Zhang ; Lurie ; Bassett ; Yang ) were included in this meta-analysis. The detailed steps of our literature search are shown in Supplementary Material.

For PLP levels, data from four nested case–control articles with five studies were used, including 2509 breast cancer cases, and all studies were carried out in the United States. For vitamin B6 intake, data from 14 articles (6 prospective studies and 8 case–control studies) were used, including 14 260 breast cancer cases. Five studies were carried out in the United States, four in China, one in Japan, one in Mexico, one in Switzerland, one in Brazilia and one in Australia. For serum vitamin B12 levels, data from three nested case–control articles with four studies were used, including 1803 breast cancer cases, and all studies were carried out in the United States. For vitamin B12 intake, data from 14 articles (7 prospective studies and 7 case–control studies) were used, including 15 783 breast cancer cases. Five studies were carried out in the United States, four in China, one in Japan, one in Mexico, one in France, one in Brazilia and one in Australia. For methionine, data from 13 articles (7 prospective studies and 6 case–control studies) were used, including 17 060 breast cancer cases. Seven studies were carried out in the United States, three China, one in Canada, one in Germany and one in Australia. The detailed characteristics of the studies are shown in Tables 1, 2, 3.

Table 1

Characteristics of studies on serum PLP and vitamin B6 intake and breast cancer risk

Study	Country, study name	Study design	Subjects (cases)	Age (years)	Category	RR (95% CI)	Adjustment for covariates
Potera et al (1977)a	USA	Case–control	130 (94)		PLP (ng ml−1)		None
				56	Control: 7.20
				52	Early breast cancer: 5.71
				56	Local recurrence: 4.43
				53	Systemic metastases: 3.95
Wu et al (1999a), the 1974 cohort	USA (MD) serum bank	Prospective (NCC)	266 (133)	18–90	PLP		Age, menopausal status and year of blood donation
					Q1	1
					Q2	1.89 (0.88–4.17)
					Q3	1.56 (0.71–3.45)
					Q4	1.41 (0.70–2.86)
					Q5	1.09 (0.49–2.44)
Wu et al (1999b), the 1989 cohort	USA, (MD) serum bank	Prospective (NCC)	220 (110)	18–90	PLP		Age, menopausal status and year of blood donation
					Q1	1
					Q2	1.19 (0.50–2.86)
					Q3	0.82 (0.37–1.82)
					Q4	1.28 (0.53–3.03)
					Q5	1.56 (0.59–4.00)
Levi et al (2001)	Switzerland	Case–control (HCC)	731 (289)	Cases: 57	Vitamin B6		Age, education, parity, menopausal status, BMI, total energy intake and alcohol
				Control: 59	1.4 (mg per day)	1
					1.9	0.58 (0.35–0.95)
					2.6	0.54 (0.30–0.96)
Shrubsole et al (2001)	China, The Shanghai Breast Cancer Study	Case-control (PCC)	2703 (1321)	25–64	Vitamin B6		Total energy, age, education, FHBC, personal history of fibroadenoma, age at menarche, parity, age at first live birth and menopause, menopausal status, physical activity, waist:hip ratio, total fruit and vegetableintake, and total animal food intake
					Q1	1
					Q2	1.26 (0.97–1.65)
					Q3	1.22 (0.92–1.63)
					Q4	1.35 (0.99–1.84)
					Q5	1.46 (1.01–2.13)
Goodman et al (2001)a	USA	Prospective (NCC)	225 (112)	Cases: 60.4	PLP (pmol ml−1)a		None
				Control: 60.2	COMTHH
					Cases: 67.52, Controls: 38.72
					COMTHL
					Cases: 42.24, Controls: 51.29
					COMTLL
					Cases: 44.55, Controls: 40.99
Zhang et al (2003)	USA, The Nurses' Health Study	Prospective (NCC)	1424 (712)	43–69	PLP		Age at menarche, parity, age at first birth, age at menopause, FHBC in mother or a sister, HBBC, alcohol intake, BMI and duration of post-menopausal hormone use
					<28.5(pmol ml−1)	1
					28.5–40.2	0.86 (0.60–1.22)
					40.2–57.0	0.80 (0.56–1.14)
					57.1–94.1	0.79 (0.55–1.14)
					>95.3	0.70 (0.48–1.02)
Lajous et al (2006a	Mexico	Case–control (PCC)	1866 (475)	18–82	Vitamin B6		Age, socioeconomic status, FHBC, menopausal status, parity, BMI, total caloric intake, dietary fibre and carbohydrate intake, and polyunsaturated fat intake
					1.06 (mg per day)	1
					1.26	0.67 (0.49–0.92)
					1.40	0.76 (0.55–1.05)
					1.60	0.84 (0.61–1.13)
Cho et al (2007)	USA, The Nurses' Health Study II	Prospective	90 663 (1032)	26–46	Vitamin B6		Age, year of the current questionnaire cycle, smoking, height, parity and age at first birth, BMI, age at menarche, FHBC, HBBD, oral contraceptive use, alcohol, energy and animal fat
					1.6 (mg per day)	1
					1.9	1.16 (0.95–1.41)
					2.1	1.09 (0.89–1.33)
					2.3	0.96 (0.78–1.18)
					2.7	1.18 (0.96–1.44)
Lin et al (2008)	USA, The Women's Health Study	Prospective (NCC)	1696 (848)	>45	PLP:		Age, ethnicity, menopausal status, fasting status, month and year of blood return, post-menopausal hormone use and trial randomisation date, randomised treatment assignment, BMI, FHBC in a first-degree relative, HBBD, smoking, physical activity, alcohol, age at menarche, age at menopause, parity and age at first birth
					⩽38.9(pmol ml−1)	1
					38.9–52.2	1.02 (0.73–1.43)
					52.2–66.3	0.91 (0.65–1.29)
					66.3–102.2	0.92 (0.65–1.32)
					>102.2	0.91 (0.63–1.30)
					Vitamin B6:
					⩽1.7 (mg per day)	1
					1.7–1.9	1.16 (0.70–1.95)
					1.9–2.1	1.00 (0.61–1.63)
					2.1–2.3	1.02 (0.60–1.71)
					>2.3	1.02 (0.61–1.69)
Ma et al (2009a)	Brazilian	Case–control (HCC)	916 (458)	20–74	Vitamin B6		Smoking, alcohol, moderate physical activity in the preceding 5 years, and number of live births
					<0.6 (mg per day)	1
					0.6–1.0	1.04 (0.74–1.46)
					⩾1.0	1.18 (0.84–1.05)
Ma et al (2009b)	Japan	Case–control (HCC)	776 (388)	20–74	Vitamin B6		BMI, education, smoking, alcohol, age at menarche, age at first live birth in parous women, menstruation status, breast feeding, number of live births and moderate physical activity in the preceding 5 years
					<1.5 (mg per day)	1
					1.5–1.8	0.65 (0.41–1.03)
					>1.8	0.85 (0.53–1.38)
Maruti et al (2009a	USA, The VITAL cohort study	Prospective	35 023 (743)	50–76	Vitamin B6		Age, race, FHBC, mammography within 2 y preceding baseline, history of breast biopsy, age at menarche, age at first birth, age at menopause, years of combined oestrogen and progestin post-menopausal hormone use, BMI, physical activity, alcohol intake in the past year and energy intake
					0.96 (mg per day)	1
					1.39	1.04 (0.83–1.30)
					1.79	1.08 (0.84–1.38)
					2.45	0.90 (0.66–1.21)
Stevens et al (2010)	USA, CPS-II Nutrition Cohort	Prospective	70 656 (3898)	54–74	Vitamin B6		Age, alcohol, multivitamin use, race, education, FHBC, history of breast lump, HRT, parity and age at first birth, age at menarche, age at menopause, physical activity, BMI and energy.
					<1.05 (mg per day)	1
					1.05–1.36	1.01 (0.90–1.12)
					1.36–2.29	0.93 (0.82–1.06)
					2.29–3.45	0.90 (0.75–1.09)
					>3.45	0.95 (0.78–1.17)
Zhang et al (2011)	China	Case–control (HCC)	876 (438)	25–70	Vitamin B6		Age at menarche, live births and age at first live birth, months of breast feeding, BMI, HBBD, FHBC, physical activity, passive smoking and total energy intake
					<0.6 (mg per day)	1
					0.6–0.76	0.83 (0.57–1.21)
					0.76–0.93	0.42 (0.28–0.64)
					>0.93	0.46 (0.30–0.69)
Shrubsole et al (2011)	China, Shanghai Women's Health Study	Prospective	7 2816 (718)	40–70	Vitamin B6		Age at baseline, age at menarche, parity, age at first live birth, educational attainment, physical activity, use of a B vitamin supplement, height and total daily intakes of energy, vegetable and fat, and menopausal status
					1.23	1
					1.50	1.26 (0.98–1.61)
					1.67	1.01 (0.77–1.33)
					1.88	1.02 (0.76–1.36)
					2.23	1.05 (0.76–1.46)
Chou et al (2011)	China	Case-control (HCC)	782 (391)	24–72	Vitamin B6		Date of enrolment and fasting status, age at enrolment, age at menarche, age at first full-term pregnancy, parity, menopausal status, age at menopause, post-menopausal hormone use, FHBC, use of multi-vitamin supplements and total energy intake
					⩽0.58 (mg per day)	1
					0.58–0.70	0.78 (0.64–2.52)
					>0.70	0.64 (0.26–0.92)
Lurie et al (2012)	USA, The Multiethnic Cohort	Prospective (NCC)	1412 (706)		PLP		Date of birth, study site, ethnicity, date of blood draw, hours fasting before blood draw and HRT use at blood draw, education, FHBC, BMI, age at menarche, parity, age at first parity, use of contraceptive hormones, oophorectomy, hysterectomy, age at natural menopause, current smoking status, ethanol, hours of daily moderate/vigorous physical activity
					⩽41.1 (nmol l l−)	1
					41.2–66.0	0.91 (0.66–1.25)
					66.1–116.6	0.77 (0.55–1.08)
					>116.6	0.70 (0.50–0.98)
Yang et al (2013)	USA, The 4-Corners Breast Cancer Study	Case–control (PCC)	4850 (2325)	25–79	Vitamin B6		Age, centre, vitamin B2, vitamin B6, vitamin B12, folate, methionine (mutual adjustment), ethnicity, education, BMI, total MET hours per week, total energy intake per day, total daily fibre intake, cigarette status, alcohol, parity, FHBC, oral contraceptive use and menopausal status
					⩽2.29 (mg per day)	1
					2.29–3.87	1.10 (0.87–1.40)
					3.87–4.90	1.11 (0.81–1.54)
					>4.90	1.09 (0.75–1.58)
Bassett et al (2013)	Australia, The MCCS Study	Prospective	20 756 (936)	27–80	Vitamin B6		Ethnicity, menopausal status, age at menarche, parity and lactation, oral contraceptive use, HRT use, physical activity, alcohol, smoking status, education level and BMI
					1.25 (mg per day)	1
					1.56	0.81 (0.67–0.97)
					1.88	0.84 (0.70–1.01)
					2.80	1.03 (0.86–1.22)

Abbreviations: BMI=body mass index; CI=confidence interval; COMT=Catechol-Omethyltransferase; FHBC=family history of breast cancer; HBBD=history of benign breast disease; HCC=hospital-based case–control study; HRT=hormone replacement treatment; MET=metabolic equivalent; NCC=nested case–control study; PCC=population-based case–control study; PLP= pyridoxal 5′-phosphate; Q=quintile; RR=relative risk.

The two studies were excluded in the final analysis, because RR was not available; the mean concentration of PLP was presented by COMT genotype (HH/HL/LL).

Table 2

Characteristics of studies on serum vitamin B12 (SVB12) and vitamin B12 intake and breast cancer risk

Study	Country, study name	Study design	Subjects (cases)	Age (years)	Category	RR (95% CI)	Adjustment for covariates
Wu et al (1999a); the 1974 cohort	USA (MD) serum bank	Prospective (NCC)	266 (133)	18–90	SVB12		Age, menopausal status and year of blood donation
					Q1	1
					Q2	1.04 (0.47–2.33)
					Q3	1.06 (0.45–2.56)
					Q4	0.88 (0.38–2.04)
					Q5	0.39 (0.17–0.90)
Wu et al (1999a); the 1989 cohort	USA (MD) serum bank	Prospective (NCC)	220 (110)	18–90	SVB12		Age, menopausal status and year of blood donation
					Q1	1
					Q2	0.64 (0.25–1.64)
					Q3	0.95 (0.38–2.38)
					Q4	0.65 (0.27–1.56)
					Q5	0.48 (0.20–1.15)
Goodman et al (2001)a	USA	Prospective (NCC)	225 (112)	cases:	SVB12 (pg ml−1)a		None
				60.4	COMTHH
				Control:60.2	Cases: 450.97,
					Controls: 500.56
					COMTHL
					Cases: 428.64, Controls: 438.58
					COMTLL
					Cases: 407.92, Controls: 486.07
Shrubsole et al (2001)	China, The Shanghai Breast Cancer Study	Case-control (PCC)	(1321)	25–64	Vitamin B12		Total energy, age, education, FHBC, personal history of fibroadenoma, age at menarche, parity, age at first live birth, menopausal status, age at menopause, physical activity, waist:hip ratio, total fruit and vegetable intake, and total animal food intake.
					Q1	1
					Q2	1.11 (0.85–1.44)
					Q3	1.14 (0.87–1.49)
					Q4	1.05 (0.80–1.36)
					Q5	1.01 (0.77–1.32)
Zhang et al (2003)	USA, The Nurses' Health Study	Prospective (NCC)	1424 (712)	43–69	SVB12		Age at menarche, parity, age at first birth, age at menopause, FHBC in mother or a sister, HBBC, alcohol intake, BMI and duration of post-menopausal hormone use
					<320.5 (pg ml−1)	1
					320.5–389.6	0.75 (0.51–1.08)
					389.8–469.4	0.98 (0.69–1.38)
					469.9–571.5	0.62 (0.42–0.89)
					>572.7	0.76 (0.52–1.10)
Lajous et al (2006a)	Mexico	Case–control (PCC)	1866 (475)	18–82	Vitamin B12		Age, socioeconomic status, FHBC, menopausal status, parity, BMI, total calorie intake, dietary fibre carbohydrate intake and polyunsaturated fat intake
					2.61 (μg per day)	1
					4.03	0.80 (0.59–1.10)
					5.68	0.51 (0.36–0.73)
					7.46	0.32 (0.22–0.49)
Lajous et al (2006b)	France, the E3N cohort	Prospective	62 739 (1812)	—	Vitamin B12		Unclear
					Q5 vs Q1	1.05 (0.90–1.20)
Cho et al (2007)	USA, The Nurses' Health Study II	Prospective	90 663 (1032)	26–46	Vitamin B12		Age at start of follow-up, calendar year of the current questionnaire cycle, smoking, height, parity and age at first birth, BMI, age at menarche, FHBC, HBBD, oral contraceptive use, alcohol, energy and animal fat
					4 (μg per day)	1
					5	1.09 (0.90–1.34)
					6	1.08 (0.88–1.32)
					7	1.13 (0.93–1.39)
					9	0.96 (0.78–1.19)
Lin et al (2008)	USA, The Women's Health Study	Prospective (NCC)	1696 (848)	>45	SVB12		Age, ethnicity, menopausal status, fasting status, month and year of blood return, post-menopausal hormone use and trial randomisation date, randomised treatment assignment, BMI, FHBC in a first-degree relative, HBBD, smoking, physical activity, alcohol, age at menarche, age at menopause, parity and age at first birth
					⩽337 (pg ml−1)	1
					337–414	1.12 (0.80–1.56)
					414–512	1.45 (1.03–2.04)
					512–686	1.23 (0.87–1.72)
					>686	1.29 (0.92–1.82)
					Vitamin B12
					⩽3.8 (μg per day)	1.00
					3.8–4.8	0.66 (0.39–1.10)
					4.8–5.6	0.78 (0.46–1.33)
					5.6–7.1	1.19 (0.73–1.94)
					>7.1	0.88 (0.54–1.44)
Ma et al (2009a)	Brazilian	Case–control (HCC)	916 (458)	20–74	Vitamin B12		Smoking, alcohol, moderate physical activity in the preceding 5 years, and number of live births.
					<3.9 (μg per day)	1
					3.9–7.3	0.90 (0.64–1.25)
					⩾7.3	0.90 (0.65–1.26)
Ma et al (2009b)	Japan	Case-control (HCC)	776 (388)	20–74	Vitamin B12		BMI, education, smoking, alcohol, age at menarche, age at first live birth in parous women, menstruation status, breast feeding, number of live births and moderate physical activity in the preceding 5 years
					<7.4 (μg per day)	1.00
					7.4–10.3	1.03 (0.66–1.63)
					>10.3	0.79 (0.50–1.24)
Maruti et al (2009a	USA, The VITAL cohort study	Prospective	35 023 (743)	50–76	Vitamin B12		Age, race, FHBC, mammography within 2 years preceding baseline, history of breast biopsy, age at menarche, age at first birth, age at menopause, years of combined oestrogen and progestin, post-menopausal hormone use, BMI, physical activity, alcohol intake in the past year and energy intake
					2.71 (μg per day)	1.00
					4.42	0.99 (0.80–1.23)
					6.14	0.84 (0.66–1.06)
					9.33	0.91 (0.70–1.18)
Stevens et al (2010)	USA, CPS-II Nutrition Cohort	Prospective	70 656 (3898)	54–74	Vitamin B12		Age, alcohol, multivitamin use, race, education, FHBC, history of breast lump, HRT, parity and age at first birth, age at menarche, age at menopause, physical activity, BMI and energy
					<1.94 (μg per day)	1.00
					1.94–2.80	0.94 (0.85–1.04)
					2.80–4.96	0.95 (0.86–1.06)
					4.96–9.07	0.96 (0.79–1.16)
					⩾9.07	0.98 (0.80–1.19)
Zhang et al (2011)	China	Case–control (HCC)	876 (438)	25–70	Vitamin B12		Age at menarche, live births and age at first live birth, months of breast-feeding, BMI, HBBD, FHBC, physical activity, passive smoking and total energy intake
					<0.93 (μg per day)	1.00
					0.93–1.57	0.74 (0.49–1.09)
					1.57–2.29	0.74 (0.50–1.11)
					>2.29	0.83 (0.56–1.24)
Shrubsole et al (2011)	China, Shanghai Women's Health Study	Prospective	72 816 (718)	40–70	Vitamin B12		Age at baseline, age at menarche, parity, age at first live birth, educational attainment, physical activity, use of a B vitamin supplement, height, and total daily intakes of energy, vegetables and fat, and menopausal status
					1.00 (μg per day)	1.00
					1.83	0.89 (0.69–1.14)
					2.44	0.88 (0.68–1.15)
					3.11	0.91 (0.69–1.19)
					4.50	0.83 (0.61–1.12)
Chou et al (2011)	China	Case–control (HCC)	782 (391)	24–72	Vitamin B12		Date of enrolment and fasting status, age at enrolment, age at menarche, age at first full-term pregnancy, parity, menopausal status, age at menopause, post-menopausal hormone use, , use of multi-vitamin supplements and total energy intake
					⩽5.28 (μg per day)	1.00
					5.29–8.15	0.89 (0.53–1.65)
					>8.15	0.83 (0.73–2.54)
Yang et al (2013)	USA, the 4-Corners Breast Cancer Study	Case–control (PCC)	4850 (2325)	25–79	Vitamin B12		Age, centre, vitamin B2, vitamin B6, vitamin B12, folate, methionine (mutual adjustment), ethnicity, education, BMI, total MET hours per week, total energy intake per day, total daily fibre intake, cigarette status, alcohol, parity, FHBC, oral contraceptive use and menopausal status
					⩽5.32 (μg per day)	1.00
					5.32–9.78	0.75 (0.60–0.93)
					9.78–13.98	0.83 (0.62–1.11)
					>13.98	0.73 (0.53–1.00)
Bassett et al (2013)	Australia, The MCCS study	Prospective	20 756 (936)	27–80	Vitamin B12		Ethnicity, menopausal status, age at menarche, parity and lactation, oral contraceptive use, HRT use, physical activity, alcohol, smoking status, education level and BMI
					1.66 (μg per day)	1.00
					2.33	1.06 (0.89–1.27)
					3.04	0.94 (0.78–1.14)
					4.61	1.21 (1.00–1.46)

Abbreviations: BMI=body mass index; CI=confidence interval; COMT=Catechol-O-methyltransferase; FHBC=family history of breast cancer; HBBD=history of benign breast disease; HCC=hospital-based case–control study; HRT=hormone replacement treatment; MET=metabolic equivalent; NCC=nested case–control study; PCC=population-based case–control study; PLP= pyridoxal 5′-phosphate; Q=quintile; RR=relative risk.

The study excluded in the final analysis, because RR was not available; the mean concentration of PLP was presented by COMT genotype (HH/HL/LL).

Table 3

Characteristics of studies on methionine intake and breast cancer risk

Study	Country, study name	Study design	Subjects (cases)	Age (years)	Category	RR (95% CI)	Adjustment for covariates
Thorand et al (1998)	German, The EURAMIC	Case-control (PCC)	149 (43)	38–80	1.73 vs 1.37 (g per day)	1.29 (0.76–2.19)	Age, BMI, exogenous hormone use, age at menarche, nulliparity, smoking status, socioeconomic status
Shrubsole et al (2001)	China, The Shanghai Breast Cancer Study	Case-control (PCC)	2703 (1321)	25–64	Q1	1.00	Total energy, age, education, FHBC, personal history of fibroadenoma, age at menarche, parity, age at first live birth, menopausal status, age at menopause, physical activity, waist:hip ratio, total fruit and vegetable intake, and total animal food intake
					Q2	0.95 (0.73–1.24)
					Q3	0.84 (0.63–1.12)
					Q4	1.00 (0.74–1.36)
					Q5	0.79 (0.54–1.16)
Feigelson et al (2003)	USA, the CPS-II Nutrition Cohort	Prospective	66 561 (1303)	50–74	<0.64 (g per day)	1.00	Age, alcohol, dietary folate, multivitamin use, race, education, first-degree FHBC, history of breast lump, mammographic history, HRT use, parity and age at first birth, age at menopause, age at menarche, physical activity, BMI, adult weight gain and energy.
					0.64–0.76	1.01 (0.85–1.20)
					0.76–0.88	1.08 (0.91–1.28)
					0.88–1.04	1.01 (0.84–1.20)
					>1.04	0.92 (0.77–1.11)
Zhu et al (2003)	USA	Case–control (PCC)	609 (304)	>20	⩽0.54 (g per day)	1.00	Age, employment status, marital status, educational level, income, number of people household, religion, smoking, use of electric blanket/mattress pad, menopausal status, use of oestrogen, use of progesterone, HBBD, FHBC, weight, height, physical activity, number of pregnancies, number of miscarriages, age at menarche, age at first birth, on a diet to lose weight, having an infertility test, intake of vitamin B2, B6, B12 and C, and total energy intake per day
					0.55–0.78	0.69 (0.40–1.18)
					0.79–1.06	0.67 (0.35–1.29)
					>1.06	0.50 (0.22–1.15)
Cho et al (2007)	USA, The Nurses' Health Study II	Prospective	90 663 (1032)	26–46	1.6 (g per day)	1.00	Age at start of follow-up, calendar year of the current questionnaire cycle, smoking, height, parity and age at first birth, BMI, age at menarche, FHBC, HBBD, oral contraceptive use, alcohol, energy and animal fat
					1.8	0.92 (0.76–1.13)
					2.0	1.03 (0.85–1.25)
					2.2	0.86 (0.70–1.06)
					2.5	1.10 (0.89–1.36)
Kabat et al (2008)	Canada, CNBSS	Prospective	89 835 (2491)	40–59	<1.78 (g per day)	1.00	Age, BMI, years of education, menopausal status, FHBC, history of breast biopsy, age at menarche, parity, oral contraceptive use, HRT, intake of calories and alcohol
					1.78–2.00	1.04 (0.92–1.19)
					2.00–2.20	1.08 (0.95–1.22)
					2.20–2.48	0.98 (0.86–1.12)
					>2.48	1.00 (0.88–1.14)
Xu et al (2008)	USA	Case–control (PCC)	3064 (1508)	—	<0.65 (g per day)	1.00	Age and daily energy intake
					0.65–0.86	0.97 (0.77–1.23)
					0.86–1.06	0.98 (0.77–1.26)
					1.06–1.34	0.94 (0.69–1.30)
Maruti et al (2009a	USA, The VITAL cohort study	Prospective	35 023 (743)	50–76	0.82 (g per day)	1.00	Age, race, FHBC, mammography within 2 years preceding baseline, history of breast biopsy, age at menarche, age at first birth, age at menopause, years of combined oestrogen and progestin post-menopausal hormone use, BMI, physical activity, alcohol intake in the past year and energy intake
					1.22	0.98 (0.78–1.22)
					1.59	0.84 (0.65–1.08)
					2.17	0.78 (0.56–1.09)
Stevens et al (2010)	USA, CPS-II Nutrition Cohort	Prospective	70 656 (3898)	54–74	<0.64 (g per day)	1.00	Age, alcohol, multi-vitamin use, race, education, FHBC, history of breast lump, HRT, parity and age at first birth, age at menarche, age at menopause, physical activity, BMI and energy
					0.64–0.76	0.92 (0.83–1.01)
					0.76–0.88	0.98 (0.88–1.08)
					0.88–1.04	0.93 (0.84–1.03)
					⩾1.04	0.88 (0.79–0.98)
Zhang et al (2011)	China	Case–control (HCC)	876 (438)	25–70	<0.84 (g per day)	1.00	Age at menarche, live births and age at first live birth, months of breast-feeding, BMI, HBBD, FHBC, physical activity, passive smoking and total energy intake
					0.84–1.10	0.83 (0.56–1.24)
					1.10–1.38	0.79 (0.53–1.18)
					>1.38	0.79 (0.53–1.17)
Shrubsole et al (2011)	China, Shanghai Women's Health Study	Prospective	72 816 (718)	40–70	1.13 (g per day)	1.00	Age at baseline, age at menarche, parity, age at first live birth, educational attainment, physical activity, use of a B vitamin supplement, height, and total daily intakes of energy, vegetable and fat, and menopausal status
					1.34	0.98 (0.76–1.26)
					1.48	0.94 (0.71–1.23)
					1.65	0.98 (0.73–1.32)
					1.97	0.92 (0.66–1.28)
Yang et al (2013)	USA, the 4-Corners Breast Cancer Study	Case-control (PCC)	4850 (2325)	25–79	⩽1.56 (g per day)	1.00	Age, centre, vitamin B2, vitamin B6, vitamin B12, folate, methionine (mutual adjustment), ethnicity, education, BMI, total MET hours per week, total energy intake per day, total daily fibre intake, cigarette status, alcohol, parity, FHBC, oral contraceptive use and menopausal status.
					1.56–1.82	1.05 (0.88–1.24)
					1.82–2.10	1.00 (0.84–1.18)
					>2.10	0.98 (0.82–1.17)
Bassett et al (2013)	Australia, The MCCS study	Prospective	20 756 (936)	27–80	1.154 (g per day)	1.00	Ethnicity, menopausal status, age at menarche, parity and lactation, oral contraceptive use, HRT use, physical activity, alcohol, smoking status, education level and BMI
					1.398	1.04 (0.88–1.25)
					1.616	0.90 (0.75–1.08)
					1.969	0.97 (0.80–1.17)

Abbreviations: BMI=body mass index; CI=confidence interval; FHBC=family history of breast cancer; HBBD=history of benign breast disease; HCC=hospital-based case–control study; HRT=hormone replacement treatment; MET=metabolic equivalent; NCC=nested case–control study; PCC=population-based case–control study; PLP= pyridoxal 5′-phosphate; Q=quintile; RR=relative risk.

Quantitative synthesis

The main results are summarised in Table 4 (the result by adjustment of selected covariates is not included).

Table 4

Summary risk estimates of the association between vitamin B6, vitamin B12 and methionine and breast cancer risk

				Risk estimate (95% CI)		Heterogeneity test
Nutrients	Study	No. (cases)	No. (studies)	REM	P-value	I2 (%)	P- value
Vitamin B6	PLPa	2509	5	0.80 (0.66–0.98)	0.03	0.30	0.40
Menopausal status
	Pre-menopausal	622	4	1.16 (0.67–2.02)	0.59	16.0	0.31
	Post-menopausal	1871	5	0.71 (0.57–0.88)	0.00	0.00	0.49
ER and PR status (positive: +, negative: −)
	ER+	1577	3	0.69 (0.47–1.02)	0.06	61.3	0.08
	ER−	348	3	0.90 (0.50–1.62)	0.73	0.00	0.89
	PR+	1414	3	0.70 (0.48–1.01)	0.06	53.6	0.12
	PR−	482	3	0.87 (0.55–1.39)	0.56	0.00	0.89
	Dietary intake	14 260	14	0.95 (0.83–1.08)	0.45	56.2	0.01
Study design
	Prospective	8175	6	1.03 (0.94–1.14)	0.55	0.00	0.54
	Case–control	6085	8	0.85 (0.65–1.12)	0.25	71.1	0.00
Menopausal status
	Pre-menopausal	1885	5	0.94 (0.66–1.33)	0.73	64.9	0.02
	Post-menopausal	5661	6	0.90 (0.78–1.04)	0.16	0.80	0.41
Geographic region where the study was conducted
	America	9779	7	1.02 (0.92–1.14)	0.69	0.00	0.49
	Asia	3256	5	0.84 (0.56–1.27)	0.41	78.3	0.00
ER and PR status (positive: +, negative: −)
	ER+	2684	6	1.02 (0.88–1.19)	0.80	0.00	0.48
	ER−	1089	6	0.91 (0.71–1.18)	0.49	25.2	0.25
	PR+	1493	4	1.05 (0.89–1.23)	0.56	0.00	0.76
	PR−	579	5	0.83 (0.64–1.08)	0.16	2.30	0.39
Vitamin B12	Seruma	1803	4	0.73(0.44–1.22)	0.23	72.5	0.01
Menopausal status
	Pre-menopausal	622	4	0.78 (0.34–1.81)	0.57	60.2	0.06
	Post-menopausal	1165	4	0.79 (0.47–1.31)	0.35	59.0	0.06
	Dietary intake	15 783	14	0.88 (0.77–1.00)	0.05	68.9	0.00
Study design
	Prospective	9987	7	1.01 (0.93–1.10)	0.83	10.3	0.35
	Case–control	5796	7	0.74 (0.56–0.98)	0.04	74.5	0.00
Menopausal status
	Pre-menopausal	1567	3	0.87 (0.64–1.19)	0.38	37.1	0.20
	Post-menopausal	7111	5	0.74 (0.53–1.03)	0.07	87.3	0.00
Geographic region where the study was conducted
	America	9779	7	0.79 (0.63–1.00)	0.05	78.4	0.00
	Asia	3256	5	0.89 (0.75–1.04)	0.14	0.00	0.83
ER and PR status (positive: +, negative: −)
	ER+	2416	5	0.83 (0.62–1.12)	0.22	64.0	0.03
	ER−	986	5	1.02 (0.81–1.29)	0.85	0.00	0.54
	PR+	1198	3	0.96 (0.70–1.33)	0.25	58.2	0.09
	PR−	503	4	0.95 (0.71–1.26)	0.38	4.50	0.37
Methionine	Dietary intake	17 060	13	0.94 (0.89–0.99)	0.03	0.00	0.52
Study design
	Prospective	11 121	7	0.94 (0.88–1.00)	0.06	0.00	0.45
	Case–control	5939	6	0.92 (0.80–1.06)	0.26	6.00	0.38
Menopausal status
	Pre-menopausal	1377	2	1.06 (0.87–1.30)	0.56	0.00	0.34
	Post-menopausal	6060	5	0.89 (0.82–0.97)	0.01	0.00	0.62
Geographic region where the study was conducted
	America	13 604	8	0.94 (0.88–1.01)	0.10	14.0	0.32
	Asia	2477	3	0.84 (0.68–1.04)	0.11	0.00	0.79
ER and PR) status (positive: +, negative: −)
	ER−	900	4	1.12 (0.90–1.40)	0.32	0.00	0.56
	ER+	2150	3	0.88 (0.76–1.02)	0.10	0.00	0.74
	PR+	1000	2	0.93 (0.76–1.13)	0.45	0.00	0.76
	PR−	349	2	1.13 (0.79–1.62)	0.50	0.00	0.78
Folate+Vitamin B6		5461	4	0.91 (0.79–1.04)	0.17	0.00	0.41
Folate+Vitamin B12		5275	3	0.99 (0.77–1.29)	0.97	54.2	0.11
Folate+Methionine		5318	4	0.81 (0.62–1.05)	0.11	49.2	0.12

Abbreviations: CI=confidence interval; ER=oestrogen receptor; PLP= pyridoxal 5′-phosphate; PR=progesterone receptor; REM=random effect model.

All studies are prospective design.

Serum PLP levels and risk of breast cancer

High serum PLP levels vs low levels were significantly associated with the risk of breast cancer (0.80 (0.66–0.98), P=0.03, I2=0.30%, Figure 1). The association was significant for post-menopausal women (0.71 (0.57–0.88), P<0.00, I2=0.00%) but not for pre-menopausal women; however, the difference between the two groups was not significant (P=0.26). No significant association was found by oestrogen receptor (ER) and progesterone receptor (PR) status. Among the five nested case–control studies, two studies in one article (Wu ) only adjusted for age, menopausal status and year of blood donation, and a positive but not significant association of serum PLP levels with the risk of breast cancer was found. Three studies (Zhang ; Lin ; Lurie ) adjusted for the most known risk factors of breast cancer, and a significantly combined effect (Zhang ; Lin ; Lurie ) was found (0.76 (0.62–0.94), P=0.01, I2=0.00%).

Figure 1

The multivariate-adjusted risk of breast cancer for the highest The size of the grey box is positively proportional to the weight assigned to each study, which is inversely proportional to the s.e. of the RR, and horizontal lines represent the 95% CIs.

For dose–response analysis, data from three studies (Zhang ; Lin ; Lurie ) were used, including 2266 breast cancer cases. We found no evidence of statistically significant departure from linearity (P=0.85). A 100 pmol ml−1 increment in serum PLP level conferred an RR of 0.77 (95% CI=0.69–0.86, P<0.00, Figure 2).

Figure 2

The dose–response analysis between serum PLP and breast cancer risk, with restricted cubic splines in a multivariate random-effects dose–response model. The solid line and the long dash line represent the estimated RR and its 95% CI. Short, dash line represents the linear relationship.

Vitamin B6 intake and risk of breast cancer

High vitamin B6 intake vs low intake was not significantly associated with the risk of breast cancer (0.95 (0.83–1.08), P=0.45, I2=56.2%, Figure 1). And no significant association was found in subgroup analysis by study design (prospective and case–control), menopausal status (pre-menopausal and post-menopausal), geographic region where the study was conducted (America and Asia), ER status and PR status. No significant association was found in the subgroup analysis by adjustment (yes or no) of the known risk factors of breast cancer, including alcohol, smoking, BMI, FHBC, reproductive factors (⩾3), physical activity, energy intake and use of exogenous hormones.

For dose–response analysis, data from eight studies (Lajous ; Lin ; Ma ; Ma ; Stevens ; Zhang ; Bassett ; Yang ) were used, including 9429 breast cancer cases. We found no evidence of statistically significant departure from linearity (P=0.08). A 1 mg per day increment in vitamin B6 intake conferred an RR of 0.96 (0.89–1.02, P=0.19).

Serum vitamin B12 levels and risk of breast cancer

High serum vitamin B12 levels vs low levels were not significantly associated with the risk of breast cancer (0.73 (0.44–1.22), P=0.23, I2=72.5%, Figure 3), and no significant association was found in the subgroup analysis by menopausal status. Only one study (Lin ) provided the association of serum vitamin B12 levels with the risk of breast cancer by ER status and PR status; thus, the subgroup analysis by ER status and PR status was not conducted. Among the four studies included, one study (Lin ) adjusted for the most known risk factors of breast cancer, and women in the highest quintile relative to those in the lowest quintile had multivariate RR of 1.29 (0.92–1.82). The result from the other three studies indicated an obvious protection of serum vitamin B12 levels on risk of breast cancer (0.61 (0.41–0.92), P=0.02, I2=22.3%).

Figure 3

The multivariate-adjusted risk of breast cancer for the highest The size of the grey box is positively proportional to the weight assigned to each study, which is inversely proportional to the s.e. of the RR, and horizontal lines represent the 95% CIs.

For the dose–response analysis, data from two studies (Zhang ; Lin ) were used, including 1560 breast cancer cases. We found no evidence of statistically significant departure from linearity (P=0.51). A 100 pmol ml−1 increment in serum vitamin B12 levels conferred an RR of 0.99 (0.92–1.08, P=0.88).

Vitamin B12 intake and risk of breast cancer

High vitamin B12 intake vs low intake was not significantly associated with the risk of breast cancer (0.88 (0.77–1.00), P=0.05, I2=68.9%, Figure 3). However, significant association was found in case–control studies (0.74 (0.56–0.98), P=0.04, I2=74.5%) but not in prospective studies. No significant association was found in other subgroup analysis.

For dose–response analysis, data from nine studies (Lajous ; Lin ; Ma ; Ma ; Stevens ; Chou ; Zhang ; Bassett ; Yang ) were used, including 9832 breast cancer cases. We found no evidence of statistically significant departure from linearity (P=0.14). A 1-μg per day increment in vitamin B12 intake conferred an RR of 0.98 (0.95–1.00, P=0.12).

Methionine intake and risk of breast cancer

High methionine intake vs low intake was significantly associated with the risk of breast cancer (0.94 (0.89–0.99), P=0.03, I2=0.00%, Figure 4). Marginally significant association was found in prospective studies (0.94 (0.87–1.00), P=0.06, I2=0.00%) but not in case–control studies (Figure 4). Significant association was found for post-menopausal women (0.89 (0.82–0.97), P=0.01, I2=0.01%) but not for pre-menopausal women; however, the difference between the two groups was not significant (P=0.24). No significant association was found by ER status and PR status. Marginally significant association was found for studies that adjusted for alcohol and use of exogenous hormones, and significant association was also found for studies that adjusted for physical activity, energy intake, BMI, FHBC and reproductive factors (⩾3).

Figure 4

The multivariate-adjusted risk of breast cancer for the highest The size of the grey box is positively proportional to the weight assigned to each study, which is inversely proportional to the s.e. of the RR, and horizontal lines represent the 95% CIs.

For dose–response analysis, data from six studies (Feigelson ; Xu ; Stevens ; Zhang ; Bassett ; Yang ) were used, including 10316 breast cancer cases. We found no evidence of statistically significant departure from linearity (P=0.82). A 1-g per day increment in methionine intake conferred an RR of 0.96 (0.92–1.00, P=0.05).

Effect of combining folate with vitamin B6, vitamin B12 and methionine on risk of breast cancer

For the joint association between breast cancer risk and folate intake with vitamin B6 intake, data from four studies (Maruti ; Stevens ; Chou ; Shrubsole ) were used, and the risk of breast cancer for the subjects with both highest intake of folate and vitamin B6 was 0.91 (0.79–1.04), P=0.17, I2=0.00%. For the joint association between breast cancer risk and folate intake with vitamin B12 intake, data from three studies (Maruti ; Stevens ; Shrubsole ) were used, and the risk of breast cancer for the subjects with both highest intake of folate and vitamin B12 was 0.99 (0.77–1.29), P=0.97, I2=54.2%. For the joint association between breast cancer risk and folate intake with methionine intake, data from four studies (Thorand ; Maruti ; Stevens ; Shrubsole ) were used, and the risk of breast cancer for the subjects with both highest intake of folate and methionine was 0.81(0.62–1.05), P=0.11, I2=49.2%.

Sources of heterogeneity and meta-regression

In order to explore the moderate to high between-study heterogeneity found in several analysis, univariate meta-regression with the covariates of publication year, location where the study was conducted, study design (case–control or prospective), number of cases and degree of adjustments of covariates was performed. Degree of adjustments of covariates ranged from 0 to 9 based on adjustment (yes: 1, no: 0) of the following covariates: alcohol, smoking, BMI, FHBC, reproductive factors (⩾3), physical activity, energy intake, dietary factors (⩾2) and use of exogenous hormones. For the analysis between breast cancer risk and dietary vitamin B12 intake, study design was found contributing significantly to the between-study heterogeneity overall (P=0.02) and among post-menopausal women (P=0.02). No significant findings were found in the other analysis.

Sensitivity analysis and publication bias

Sensitivity analysis showed that no individual study had excessive influence on the above mentioned pooled effect. Egger test showed no evidence of significant publication bias for the analysis between breast cancer risk and serum PLP levels (P=0.10), vitamin B6 intake (P=0.14), serum vitamin B12 levels (P=0.18), vitamin B12 intake (P=0.12) and methionine (P=0.49). The funnel plots were provided in the Supplementary Material.

Discussion

The findings from this meta-analysis indicated that increased serum PLP levels and dietary methionine intake might be significantly associated with reduced risk of breast cancer, especially for post-menopausal women. No significant association was found between dietary vitamin B6 intake, serum vitamin B12 and dietary vitamin B12 intake and risk of breast cancer.

Several biological mechanisms for the inverse relationship of vitamin B6, vitamin B12 and methionine with the development of breast cancer have been proposed. First, vitamin B6, B12 and methionine participate in one-carbon metabolism, which is essential for DNA synthesis, repair and methylation (Ames, 2001), and vitamins B6 and B12 deficiencies also cause high uracil and chromosome breaks (Blount ). Thus, deficiency in these nutrients may interfere with DNA methylation and synthesis, leading to aberrant gene expression and DNA instability, and eventually the development of cancer (Davis and Uthus, 2004). In addition, low B-group vitamin concentrations are associated with inflammation and higher oxidative stress (Shen ), and antioxidants supplementation with B-group vitamins could enhance antioxidant capacity. Although the mechanism for the stronger association found for post-menopausal women remains unclear, previous meta-analysis also suggested that post-menopausal women are more susceptible to a wide range of dietary factors, including dietary fibre, vitamin A, β-carotene, retinol, vitamin C, calories, fat (Howe ; Wu ), folate acid (Larsson ), fatty acids (Saadatian-Elahi ), and coffee and caffeine (Jiang ), regarding their association with breast cancer risk. In addition, although these nutrient interactions were also proposed, no apparent joint association with high folate intake was found in this meta-analysis, which is consistent with the previous study (Zhang ).

As a meta-analysis of published observational studies, our findings have several limitations. First, disparate results were found on dietary vitamin B6 intake and PLP with the risk of breast cancer. The multivariable Pearson's correlation coefficients between plasma levels of vitamin B6 and vitamin B12, and the average intakes of vitamin B6 and vitamin B12 from food was 0.25 and 0.08, respectively (Zhang ). In this respect, the serum biomarker of vitamin B6 (PLP) and vitamin B12 was able to examine these associations with higher precision. Disparate results between serum biomarkers of vitamins and dietary intake of vitamins and breast cancer risk were also found in other meta-analysis, such as vitamin B6 (Larsson ) and vitamin D (Chen ). Second, a meta-analysis of observational studies is susceptible to potential bias inherent in the original studies, especially for case–control studies. Stronger association was found in the combined results from case–control studies in this meta-analysis. Overstated association could be expected from the case–control studies because of recall or selection bias, and early symptoms in patients may have resulted in a change in dietary habits. Thus, the results from prospective studies might provide a more robust estimation of the associations.

Third, although we extracted the RRs that reflected the greatest degree of control for potential confounders, the extent to which they were adjusted and the possibility that the observed association was due to unmeasured or residual confounding should be considered. Furthermore, vitamin B6 intake tends to be associated with healthy behaviours that may be protective against breast cancer (Larsson ). However, significant association was also found (0.76 (0.62–0.94)) on serum PLP levels and breast cancer risk for the three studies that adjusted for the most known risk factors of breast cancer. In addition, no significant interactions were found between MTHFR and MTR polymorphisms and B vitamins (Ma ; Ma ), and result from the study by Lin suggested a much stronger and significant association of serum PLP and vitamin B12 levels with breast cancer risk for never user of post-menopausal hormones. However, the limited data in the reported articles precluded a more robust assessment of the association by the above-mentioned risk factors.

Fourth, although significant association of PLP levels and methionine intake with risk of breast cancer was found among post-menopausal women, while not among pre-menopausal women, the difference between the two groups was not significant, and the apparent differences could simply be by chance, considering relatively small number of studies included, especially for pre-menopausal women. And subgroup analysis by tumour stage (in situ or invasive) were not conducted because of limited data availability (Zhang ; Chou ). Fifth, between-study heterogeneity was found in some analysis in this meta-analysis, but the between-study heterogeneity was not successfully explained by the subgroup analysis and meta-regression. However, other genetic and environment variables, as well as their possible interaction, non-comparable measurement of nutrients and variation of the covariates and so on, may well be potential contributors to this disease–effect unconformity (Higgins ). In this respect, the lack of relevant study-level covariates in the reported articles precluded a more robust assessment of sources of this heterogeneity. Finally, although no significant publication bias was detected in this meta-analysis, validity of publication bias test should be questioned because of small number of studies included (Sterne ), especially for PLP and serum vitamin B12.

In summary, results from this meta-analysis suggested that serum PLP levels and dietary methionine intake might be significantly associated with reduced risk of breast cancer, especially for post-menopausal women. The finding needs to be confirmed further by a well-conducted randomised trial.