Kakkoura et al. BMC Medicine 2022, 20(1):134
https://doi.org/10.1186/s12916-022-02330-3

RESEARCH ARTICLE

Open Access

Dairy consumption and risks of total
and site‑specific cancers in Chinese adults:
an 11‑year prospective study of 0.5 million
peopleMaria G. Kakkoura1, Huaidong Du1,2*† , Yu Guo3, Canqing Yu4, Ling Yang1,2, Pei Pei5, Yiping Chen1,2,
Sam Sansome1, Wing Ching Chan1, Xiaoming Yang1, Lei Fan6, Jun Lv4, Junshi Chen7, Liming Li4,
Timothy J. Key8†, Zhengming Chen1,2*† and on behalf of the China Kadoorie Biobank (CKB) Collaborative
Group

Abstract
Background: Previous studies of primarily Western populations have reported contrasting associations of dairy
consumption with certain cancers, including a positive association with prostate cancer and inverse associations withcolorectal and premenopausal breast cancers. However, there are limited data from China where cancer rates andlevels of dairy consumption differ importantly from those in Western populations.
Methods: The prospective China Kadoorie Biobank study recruited ~0.5 million adults from ten diverse (five urban,
five rural) areas across China during 2004–2008. Consumption frequency of major food groups, including dairy products, was collected at baseline and subsequent resurveys, using a validated interviewer-administered laptop-based
food frequency questionnaire. To quantify the linear association of dairy intake and cancer risk and to account for
regression dilution bias, the mean usual consumption amount for each baseline group was estimated via combining the consumption level at both baseline and the second resurvey. During a mean follow-up of 10.8 (SD 2.0) years,
29,277 incident cancer cases were recorded among the 510,146 participants who were free of cancer at baseline.
Cox regression analyses for incident cancers associated with usual dairy intake were stratified by age-at-risk, sex andregion and adjusted for cancer family history, education, income, alcohol intake, smoking, physical activity, soy andfresh fruit intake, and body mass index.
Results: Overall, 20.4% of participants reported consuming dairy products (mainly milk) regularly (i.e. ≥1 day/week),with the estimated mean consumption of 80.8 g/day among regular consumers and of 37.9 g/day among all participants. There were significant positive associations of dairy consumption with risks of total and certain site-specificcancers, with adjusted HRs per 50 g/day usual consumption being 1.07 (95% CI 1.04–1.10), 1.12 (1.02–1.22), 1.19

*Correspondence: huaidong.du@ndph.ox.ac.uk; zhengming.chen@ndph.
ox.ac.uk†
Huaidong Du, Timothy J. Key and Zhengming Chen contributed equallyto this work.
1
Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU),
Nuffield Department of Population Health, University of Oxford, Oxford,
UK
Full list of author information is available at the end of the article© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Kakkoura et al. BMC Medicine 2022, 20(1):134

Page 2 of 13(1.01–1.41) and 1.17 (1.07–1.29) for total cancer, liver cancer (n = 3191), female breast cancer (n = 2582) and lymphoma (n=915), respectively. However, the association with lymphoma was not statistically significant after correcting for multiple testing. No significant associations were observed for colorectal cancer (n = 3350, 1.08 [1.00–1.17]) orother site-specific cancers.
Conclusion: Among Chinese adults who had relatively lower dairy consumption than Western populations, higher
dairy intake was associated with higher risks of liver cancer, female breast cancer and, possibly, lymphoma.
Keywords: Cancer, Dairy products, Diet, China, Prospective cohort study

Background
Cancer is one of the leading causes of adult mortality
globally [1]. In China, there are >4 million new cancer
cases and ~3 million cancer deaths annually, with lung,colorectal, stomach, liver and female breast cancers being
the most frequently diagnosed cancers [2]. Major risk
factors for cancer include smoking, chronic infection,
female reproductive factors, alcohol consumption and,potentially, certain dietary factors, including low intakesof fresh fruit, vegetables and whole grains and high intake
of processed meat [3–5].
Several large prospective studies, including meta-analyses of these studies, of primarily Western populations
have examined the associations of dairy intake with cancer risks, showing an inverse association with colorectal
cancer [6], positive association with risk of prostate cancer [7] but lack of clear associations with risks of breast[8] and many other site-specific cancers [9]. Based on
the available evidence, the World Cancer Research Fund/
American Institute for Cancer Research (WCRF/AICR)
concluded that although the overall evidence on thedairy and cancer relationship is still inconsistent, there isstrong evidence supporting the probable protective roleof dairy products in the incidence of colorectal cancer
and limited evidence suggesting that higher dairy intake
is associated with higher prostate cancer risk [5].
Few prospective studies have been conducted in nonWestern populations [5], among which cancer rates [1],the type and average amount of dairy consumption [10,11] and genetic ability to metabolize dairy products differed greatly from those in Western countries [12]. In
China, although the per capita consumption of milk
has increased during recent decades, it is still far lower
than in Europe (32.7 vs. 215.1 kg/year in 2013) [13]. In
addition, cheese is hardly consumed [14, 15] and a large
majority of the population cannot properly digest lactose[12]. To date, the few prospective studies from China on
the topic showed no significant associations linking dairy
intake with risks of female breast or colorectal cancers[16, 17].
To fill in the knowledge gap, we investigated the associations of habitual dairy consumption with total and sitespecific cancer incidence in the China Kadoorie Biobank(CKB) study, a large nationwide prospective cohort studyof Chinese adults. As a secondary objective, we also
assessed whether potential cancer-related risk factors,
such as sociodemographic and lifestyle factors and adiposity [1, 4], could modify the associations between dairy
intake and cancer incidence.

Methods
Study population

The CKB is a population-based prospective study withover 0.5 million adults recruited from ten geographically diverse regions in China, with the majority of theparticipants being Han Chinese (~97%) and all of them
speaking the Chinese language. The five urban regions
included Qingdao (in Shandong Province), Harbin (inHeilongjiang Province), Haikou (in Hainan Province),
Suzhou (in Jiangsu Province) and Liuzhou (in Guangxi
Zhuang Autonomous Region) and the five rural regions
were in Sichuan, Gansu, Henan, Zhejiang and Hunan
Provinces. The study design and methods have beenpreviously described elsewhere [18]. Briefly, these ten
regions were chosen to cover a wide range of risk exposures and disease patterns. Between June 2004 and July
2008, all residents aged 35–74 years in preselected ruralvillages or urban residential committees were invited
to participate in the study. About one in three (33% in
rural areas and 27% in urban areas) responded and wereenrolled in the study. In total, 512,726 participants were
included, including 3.3% of them (i.e. with 9817 <35 years
old and 7283 >74 years old) who were just outside thepre-specified target age range (therefore the actual baseline age ranged from 30 to 79 years), and they all provided
informed consent. The study protocol and standard operating procedures have been applied to all participants.
Ethics approvals were obtained at local, national andinternational levels prior to the beginning of recruitment.
Baseline data collection

At each local study assessment clinic, trained healthprofessionals administered a laptop-based questionnaire collecting information on sociodemographic
characteristics, medical history and lifestyle factors. Anthropometrics were measured using standard

Kakkoura et al. BMC Medicine 2022, 20(1):134

protocols and a venous blood sample was collectedfrom each participant for long-term storage. On-sitemeasurement of hepatitis B surface antigen (HBsAg)
was performed (ACON Biotech).
Assessment of dietary intake

Information on consumption frequency (five categories including daily, 4–6 days/week, 1–3 days/week,
monthly or never/rarely) of 12 major food groups (i.e.
rice, wheat products, coarse grain products, red meat,
poultry, fish, eggs, total dairy products, fresh vegetables, preserved vegetables, fresh fruit and soybean
products) [3] was obtained using a validated interviewer-administered laptop-based questionnaire. This
questionnaire aimed to record the habitual consumption frequency of major food groups by the participants
over the preceding year. A separate validation study
was performed among 432 CKB participants during
2015–2016 to assess the reproducibility, and validityof the questionnaire, using 12-day 24-hour recalls as
reference (19). The results supported a good reproducibility and relative validity of total dairy consumption
frequency, with the weighted kappa statistics being 0.82
and 0.75 for reproducibility and validity, respectively.
In addition, the reproducibility and validity of all otherdietary variables (mentioned above) were good as well,with the weighted kappa values being >0.60 for all, andthe Spearman correlation coefficients for the validity ofmilk, yoghurt and other dairy products being 0.43, 0.36
and 0.31, respectively [19].
After completion of the baseline survey, two resurveys were undertaken in 5–6% of randomly selectedsurviving participants, with procedures largely identical to those at baseline. The data from these resurveys
allow us to control for regression dilution bias when
examining the prospective associations between baseline exposures and subsequent disease incidence ormortality in the whole population, by estimating longterm usual levels of exposures (i.e. dairy consumption)[20]. The second resurvey, performed from August
2013 to September 2014 among ~25,000 participants,
also collected more detailed dietary data, including
daily portions of each food group and the consumption(both frequency and amount) of three subtypes of dairy
products, i.e. cow milk, yoghurt and other dairy products (e.g. cheese and milk powder). That information
was used to estimate the mean usual amount of consumption (i.e. average level of dietary intake during thefollow-up period) for each category of food consumption variable at baseline, including total dairy consumption (Additional file 1: Methods and Additional file 1:
Table 1).

Page 3 of 13

Follow‑up for incident cancer

The vital status of participants (including causes ofdeaths) was determined based on information fromthe Disease Surveillance Points system in China [21],checked annually against local residential records andhealth insurance records and confirmed by street committees or village administrators. Furthermore, information on cancer incidence was obtained through linkages
via unique personal identification with cancer registries
and nationwide health insurance claim databases, which
provide electronic linkage to almost all hospitalizations(~99% coverage across the ten study regions) for CKB
participants. These were supplemented by annual active
follow-up of uninsured participants (~1%) to minimize
any underreporting and ensure that the false-negative
rate in cancer reporting is properly controlled [22]. In
addition, the ongoing cancer outcome adjudication in a
subset of cancer cases (n = 16,998 by 1 January 2018) via
review of medical notes showed a ~90% reporting accuracy of primary diagnosis. Fatal and non-fatal eventswere International Classification of Diseases (ICD)-10
coded by trained staff, who were blinded to baseline
information [18]. In the present study, we analysed theincidence of all cancers (ICD-10 C00-C97) and 17 common cancer sites (Additional file 1: Table 2). Only the
first incident cancer diagnosis of each type was counted.
Statistical analysis

Participants with a baseline history of cancer (n = 2578)
or those with missing values for body mass index (BMI)(n = 2) were excluded, leaving 510,146 participants foranalyses.
Multiple linear regression (for continuous outcomes)
or logistic regression (for binary outcomes) were used to
calculate the means (standard deviations (SDs)) or percentages of various baseline characteristics across threefrequency categories of dairy consumption (i.e. never/rarely, monthly and ≥1 day/week-characterized as regular), with adjustments for sex, age and region whereappropriate. Cross-sectional associations of dairy consumption with BMI, body weight, standing height, leg
length, waist circumference (WC) and body fat percentage (BF%) in each sex group were analysed using multiple linear regression, adjusting for age, region, education,
annual household income, smoking, alcohol consumption, total physical activity and fresh fruit consumption.
Analyses for WC and BF% were additionally adjusted for
BMI. A similar analysis was run, among those who participated in the second resurvey (n = 18,132), to examine
the associations of dairy consumption (three frequency
consumption categories) with changes in standing height,
leg length, body weight and BMI.

Kakkoura et al. BMC Medicine 2022, 20(1):134

Cox regression was used to calculate hazard ratios(HRs) and 95% confidence intervals (CIs) for cancer
incidence in relation to baseline frequency categories ofdairy consumption, stratified by age-at-risk (in 5-year
intervals), sex, and region and adjusted for the aforementioned potential confounders (i.e. education, annual
household income, smoking, alcohol consumption, total
physical activity and fresh fruit consumption) plus family
history of cancer, soy consumption and, for liver cancer
only, status of HBsAg. We adjusted only for two dietary
variables, fresh fruit and soybean, because fresh fruit
intake was previously shown to be associated with cancer
risk [3] and associations of dairy milk consumption withcancer risk may be confounded by soymilk consumption [23]. No other dietary variables played a confounding role in the current analysis including fresh vegetables(cooked and raw-eaten fresh vegetables but no preserved
vegetables) for which there was an extremely high consumption frequency across all study areas (with 95% ofparticipants reporting daily consumption). Participants
were classified into three aforementioned frequency categories of dairy consumption to ensure adequate numbersof cancer cases in each consumption category. The proportional hazards assumption was assessed by comparing the HRs for the first and second half of the follow-up
period, but no violation was observed. Participants who
had died (~9%) or were lost to follow-up (~1%) were censored in the prospective analyses (censoring date was 1
January 2018). The floating absolute risk method, which
provides variance of log risk for each category (including the reference group), was used to facilitate comparisons between any two exposure groups rather than justwith an arbitrarily chosen reference group [24]. The HRs(95% CIs) for each 50-g/day increment in usual dairy
consumption were calculated using Cox regression analyses (with same covariates mentioned above) to quantify
the linear association and to correct for regression dilution bias [20] (see Additional file 1: Methods and Additional file 1: Table 1 on calculation of usual amount of
dairy consumption). Stratified analyses by potential effectmodifiers, such as age-at-risk, region, smoking and adiposity, were carried out and chi-square tests were used to
examine the significance of a trend or heterogeneity test.
The Benjamini-Hochberg method was used to accountfor multiple comparisons and both unadjusted P values
and false discovery rate (FDR)-corrected P values werereported (at 5% FDR).
Sensitivity analyses were performed by excluding thefirst 2 years of follow-up, excluding participants withbaseline age below 35 and above 74 years and additionallyadjusting for other covariates, including baseline prevalent cardiovascular disease and diabetes, other dietary
variables, anthropometric factors and other potential risk

Page 4 of 13

factors for specific cancer types (e.g. baseline history ofchronic hepatitis/cirrhosis for liver cancer and female
reproductive factors for breast cancer). In addition, theanalyses for breast cancer were also re-run after excluding women who reported a prior history of lumpectomy
at baseline. The small numbers of participants with missing values for any of the variables used in the analyticalmodels were omitted from the analyses. The statisticalpackages SAS (version 9.4, SAS Institute, Cary, NC, USA)and R 3.6.3 (https://​www.R-​proje​ct.​org/) were used for
performing the analyses and creating the figures.

Results
Among the study participants, the mean (SD) baseline age was 52 (10.7) years, 59% were women and 44%
resided in urban areas (Table 1). Overall, 20.4% reported
consuming dairy at least once per week (defined as regular consumers hereafter), and 68.5% reported never orrare consumption (defined as non-consumers hereafter).
Regular consumers were more likely to be women, had
higher education and income levels and were less likely
to report poor self-rated health. Moreover, regular dairy
consumers were more likely to have a higher consumption of all major foods, except preserved vegetables.
There was also a slightly higher proportion of regularconsumers who self-reported prior history of cardiovascular disease or diabetes than non-consumers. The percentages of participants who had a positive HBsAg status
and the mean values of total physical activity (metabolic
equivalent-h/day) were similar across the three dairy
consumption groups. In men, regular consumers wereless likely to be smokers or alcohol drinkers (Table 1).
In women, regular consumers reported a slightly higher
proportion of oral contraceptive use and a slightly shortermean breastfeeding duration (Additional file 1: Table 3).
In both sexes, dairy consumption was higher in urban
than in rural areas. Across ten regions, the proportionof regular dairy consumers varied nearly 30-fold in both
sexes (Additional file 1: Fig. 1). The estimated usual mean
dairy consumption (37.9 g/day overall and 80.8 g/day
in regular consumers) followed a similar ranking order(Additional file 1: Fig. 2). Baseline dairy consumption
had a U- or L-shaped age pattern in both urban men andwomen, with the lowest proportion of regular consumers
at 50 to 55 years old. However, in rural areas, the consumption increased gradually with age (Fig. 1).
Standing height and leg length were both positively
associated with dairy consumption in both sexes,
with regular consumers having 0.6 cm higher standing height and 0.3 cm higher leg length than nonconsumers. Body weight and BMI were both inversely
associated with dairy consumption, with regular consumers being 0.5/0.9 kg and 0.4/0.5 kg/m2 lighter than

Kakkoura et al. BMC Medicine 2022, 20(1):134

Page 5 of 13

Table 1 Characteristics of participants by frequency of dairy intake at baseline survey (2004–2008)
Characteristic

Frequency of dairy intake

Overall(n = 510,146)

Never/rarely

Monthly

Regular(n = 349,325)(n = 56,750)(n = 104,071)

Usual dairy intake, g/daya

24.0

44.4

80.8

37.9

Mean age (SD), years

51.9 (11.1)

51.7 (10.8)

52.5 (11.8)

52.0 (10.7)

Women, %

58.3

57.5

62.1

59.0

Urban, %

30.8

54.2

83.0

44.1

Education >6 years, %

42.2

54.9

69.7

49.2

Household income >20,000 yuan/year, %

36.0

50.9

60.8

42.7

Ever regular smoking in men, %

76.0

73.7

68.6

74.3

Ever regular smoking in women, %

3.6

3.1

2.2

3.2

Ever regular alcohol drinking in men, %

38.6

34.6

32.8

37.0

Ever regular alcohol drinking in women, %

2.3

2.3

3.1

2.5

Red meat

44.9

48.6

54.3

47.2

Poultry

23.6

30.1

42.7

28.2

Fish

7.8

9.2

12.5

8.9

Eggs

20.3

26.5

37.1

24.4

Fresh fruit

21.9

28.5

48.9

28.2

Fresh vegetables

94.4

93.9

96.4

94.8

Preserved vegetables

24.5

19.4

18.0

22.6

Soy products

8.0

13.2

14.4

9.9

Coarse grain products

12.9

12.3

18.4

13.9

Rice

71.1

71.8

73.4

71.7

Wheat products

44.0

47.6

57.7

47.2

Mean total physical activity (SD), MET-h/day

21.4 (12.4)

20.8 (12.1)

20.4 (13.3)

21.1 (13.9)

Mean BMI (SD), kg/m2

23.7 (3.4)

23.6 (3.3)

23.4 (3.6)

23.7 (3.4)

HBsAg positive, %c

3.1

3.1

3.1

3.1

Prevalent cardiovascular disease, %d

4.1

4.9

5.5

4.5

Prevalent diabetes, %

5.4

6.3

7.4

5.9

Self-rated poor health, %

10.4

10.4

9.7

10.2

Frequency of food intake, %beMultiple linear regression (for continuous outcomes) or logistic regression (for binary outcomes) was used to calculate the means (SDs) or percentages of various
baseline characteristics across three frequency categories of dairy consumption (i.e. never/rarely, monthly and ≥1 day/week-characterized as regular), withadjustments for age (continuous), sex (dichotomous variable) and region (ten regions), where appropriate
BMI body mass index, HBsAg hepatitis B surface antigen, MET metabolic equivalent of task hours, SD standard deviationa

Crude mean values from the second resurvey (2013–2014) of randomly selected 24,700 participants without cancer at either baseline or second resurveybPercentage values indicate the frequency intake by the participants as ‘daily’ for fresh vegetable intake; ‘≥1 day/week’ for poultry intake and ‘≥4 days/week’ (i.e.
‘regular’) for all other food groupscValues for HBsAg status were missing for 8159 participantsdIncluding participants with self-reported prior history of either chronic heart disease, stroke or transient ischemic attackeIncluding participants with either screen-detected or self-reported prior history of physician-diagnosed diabetes

non-consumers in males and females, respectively(Fig. 2). In addition, higher dairy consumption was
related with a smaller height loss, less weight gain andlower BMI increase than in non-consumers, although
the differences were rather small (Additional file 1:
Fig. 3). No clear association was observed of dairy consumption with either WC or BF% (data not shown).

During a mean follow-up of 10.8 (SD 2.0) years andapproximately 5.4 million person-years, a total of 29,277
incident cancer cases were recorded at ages 35–79 years(incidence rate 5.47 per 1000 person-years), with lungcancer having the highest incidence rate in the total population, followed by cancers of the female breast, stomach, colorectum and liver (Additional file 1: Table 2).

Kakkoura et al. BMC Medicine 2022, 20(1):134

Page 6 of 13

Fig. 1 Percentage of men and women who reported regular dairy intake by age and study area at baseline (2004–2008)

After adjusting for the various aforementioned covariates, dairy consumption was significantly and positively
associated with the risk of total cancer, with the adjusted
HR of 1.09 (95% CI 1.06–1.12) when comparing regular with non-consumers. In addition, regular dairy consumption was also significantly associated with 18%(1.18; 1.08–1.29) and 22% (1.22; 1.12–1.32) higher risksof liver cancer and female breast cancer, respectively, incomparison with the non-consumers (Fig. 3). All theseassociations remained significant after FDR correction.
For lymphoma, although regular dairy consumption was
significantly associated with 23% (1.23; 1.04–1.46) higher
risk, the P trend value became non-significant after FDR
correction. After correction for regression dilution bias,
for each 50-g/day higher dairy consumption, the corresponding HRs were 1.07 (1.04–1.11) for total cancer, 1.12
(1.02–1.22) for liver cancer, 1.19 (1.01–1.41) for lymphoma and 1.17 (1.07–1.29) for breast cancer (Fig. 3). For
other site-specific cancers, there were no clear associations with dairy consumption (Table 2).
Associations of dairy consumption with risks of livercancer, lymphoma and female breast cancer tended to bepositive in nearly all of the subgroups, with few statistically significant heterogeneities being observed (P < 0.05,
Additional file 1: Fig. 4-7), without adjusting for multiple

testing. In particular, no significant heterogeneity was
found across the five urban areas and five rural areas for
all three types of cancers (Additional file 1: Fig. 7). Additionally, no significant heterogeneity was found between
pre- and post-menopausal women for the association
with breast cancer (Additional file 1: Fig. 6) and between
men and women for the associations with any site-specific cancer (Additional file 1: Table 2).
The associations did not materially change in thevarious sensitivity analyses with further exclusions andfurther adjustments (Additional file 1: Table 4 and Additional file 1: Fig. 8-10).

Discussion
In this large prospective study of Chinese adults with relatively low dairy consumption, higher dairy consumption
was associated with a higher risk of overall cancer, livercancer, female breast cancer and lymphoma, with each
50-g/day higher usual intake being associated with 7%,12%, 17% and 19% higher risks, respectively; however, theassociation with lymphoma became non-significant after
multiple-testing correction. The observed associations
were independent of other lifestyle factors including adiposity and were largely consistent across the various subgroups of participants.

Kakkoura et al. BMC Medicine 2022, 20(1):134

Page 7 of 13

Fig. 2 Adjusted mean leg length, standing height, body weight and body mass index by frequency of dairy intake in men (M) and women (F)at baseline (2004–2008). Linear regression analyses were adjusted for age (continuous variable), region (ten regions), education (four categories),
annual household income (four categories), smoking (four categories), alcohol consumption (four categories), total physical activity (continuous
variable) and fresh fruit consumption (five categories). Vertical lines represent 95% CIs. Solid squares represent men and open squares represent
women

In recent decades, although dairy (mainly milk) consumption has increased substantially in China (meanintake from 14.9 g/day in 1992 to 24.7 g/day in 2012)[11], it is still much lower than in Western countries (e.g.
mean intake of ~400 g/day in the USA in 2015) [25]. As
in several previous surveys in China [14, 15], we observed
higher consumption levels in urban than in rural
areas, reflecting probably different stages of economic

development in terms of availability and affordability[10, 14]. Our findings on the taller height at baseline andlower height loss during subsequent follow-up (i.e. frombaseline to resurvey) in participants with higher dairy
intake are also consistent with previous results fromobservational studies and randomized controlled trials[26, 27], supporting the role of dairy products in slowing down bone loss. The observed inverse association

Kakkoura et al. BMC Medicine 2022, 20(1):134

Page 8 of 13

Fig. 3 Associations of usual dairy intake (g/day) with the incidence of total cancer, liver cancer, lymphoma and female breast cancer. Cox
regression analyses were performed among 510,146 participants with no prior self-reported history of cancer at baseline. Analyses were stratified
by age-at-risk (continuous), sex (dichotomous variable) and region (ten regions) and were adjusted for education (four categories), income (fourcategories), smoking (four categories), alcohol consumption (four categories), total physical activity (continuous variable), family history of cancer(dichotomous), fresh fruit consumption (five categories), soy consumption (three categories) and BMI (continuous variable). B Analysis for livercancer was additionally adjusted for HBsAg status (three categories). The y axis was plotted on a l­oge scale with the lowest intake group (never/rarely) as a reference category. The estimated crude mean values of usual dairy intake (g/day) were 24.0, 44.4 and 80.8 g/day in the lowest (never/rarely), medium (monthly) and highest (regular) intake groups, respectively. The FDR-corrected P trend values for the associations with incidenceof total cancer, liver cancer, lymphoma and female breast cancer were 0.002, 0.04, 0.17 and 0.01, respectively. The black squares represent HRs withthe size being inversely proportional to the variance of the ­loge of HR and the vertical lines represent 95% CIs. The numbers above the vertical lines
are point estimates for HRs and the numbers below the lines refer to the number of incident cancer cases. Dashed diagonal lines indicate the linear
associations between dairy intake and cancer risk

Kakkoura et al. BMC Medicine 2022, 20(1):134

Page 9 of 13

Table 2 Adjusted hazard ratios for other site-specific cancers associated with dairy i­ntakea
Types

Oral cavity

Never/rarely intake

Monthly intake

Regular intake

No. of cases HR (95% CI)

No. of cases HR (95% CI)

No. of cases HR (95% CI)

P trend FDRHR (95% CI)
corrected P per 50 g/day­trendbof usual dairy
intake573

1.00 (0.89–1.12) 98

0.97 (0.80–1.18) 156

0.98 (0.82–1.18) 0.83

0.90

1.00 (0.83–1.20)

Oesophagus 1953

1.00 (0.95–1.06) 209

0.89 (0.78–1.02) 306

1.07 (0.94–1.21) 0.73

0.90

1.03 (0.92–1.15)

2495

1.00 (0.95–1.05) 338

1.06 (0.95–1.17) 744

1.08 (1.00–1.18) 0.10

0.34

1.07 (0.99–1.16)

Colon-rectum 2066

Stomach1.00 (0.94–1.06) 385

1.10 (1.00–1.21) 899

1.09 (1.01–1.18) 0.06

0.26

1.08 (1.00–1.17)

Pancreas605

1.00 (0.89–1.12) 74

0.96 (0.76–1.20) 207

1.11 (0.95–1.31) 0.33

0.70

1.09 (0.93–1.29)

Larynx142

1.00 (0.81–1.24) 19

0.79 (0.05–1.23) 48

1.08 (0.77–1.52) 0.86

0.90

1.05 (0.74–1.47)

Lung

4185

1.00 (0.96–1.04) 667

1.02 (0.94–1.10) 1430

1.03 (0.96–1.09) 0.48

0.82

1.03 (0.96–1.09)

Kidney258

1.00 (0.85–1.18) 45

1.01 (0.76–1.35) 147

1.21 (0.99–1.48) 0.15

0.43

1.18 (0.96–1.46)

Bladder368

1.00 (0.87–1.14) 66

1.04 (0.82–1.32) 134

0.84 (0.69–1.02) 0.19

0.46

0.91 (0.75–1.11)

Leukaemia453

1.00 (0.88–1.13) 76

1.12 (0.89–1.39) 133

0.96 (0.79–1.18) 0.90

0.90

0.92 (0.75–1.13)

Prostate278

1.00 (0.85–1.17) 40

0.93 (0.69–1.27) 107

0.95 (0.76–1.19) 0.70

0.90

0.97 (0.77–1.22)

Cervix847

1.00 (0.91–1.09) 155

0.97 (0.83–1.14) 257

1.09 (0.93–1.26) 0.44

0.82

1.07 (0.92–1.25)

Endometrium 292

1.00 (0.85–1.17) 53

1.07 (0.82–1.40) 123

0.94 (0.76–1.16) 0.72

0.90

1.00 (0.80–1.25)

Ovary

1.00 (0.86–1.16) 54

0.91 (0.70–1.19) 129

0.98 (0.79–1.20) 0.79

0.90

1.03 (0.83–1.29)

324

CI confidence interval, FDR false discovery rate, HR hazard ratioa

Cox regression analyses were performed among 510,146 participants with no prior self-reported history of cancer at baseline. Analyses were stratified by age-at-risk(continuous variable), sex (dichotomous variable) and individual regions (ten regions) and were adjusted for education (four categories), income (four categories),
smoking (four categories), alcohol consumption (four categories), total physical activity (continuous variable), family history of cancer (dichotomous variable), fresh
fruit consumption (five categories), soy consumption (three categories) and body mass index (continuous variable)bSignificance was assessed at a 5% FDR

between dairy intake and body weight gain is also in linewith some, but not all, previous reports [28], with calcium in dairy products potentially playing a role [29].
Liver cancer has been historically the most prevalentcancer type in China, with over half of the worldwide
liver cancer incident cases and deaths being reported
there [4]. This was chiefly due to the high prevalence ofhepatitis B infection and aflatoxins exposure [4, 30] andthe emerging roles of smoking and alcohol. Existing evidence linking dairy products to liver cancer is rather
limited and inconsistent [31]. As in our study, the mostrecent meta-analysis of four prospective studies (1261
cases) from the USA and Europe reported a borderline
significant ~30% higher risk of liver cancer comparing highest vs. lowest intake of dairy products [32]. Our
study with 3191 cases added important new evidence
linking dairy consumption with the risk of liver cancer,
independent of hepatitis B infection.
Lymphoma is also one of the common cancers
worldwide and in China, with chronic infections (e.g.
Epstein-Barr virus) and certain occupational exposures
to pesticides being the most well-established risk factors [33]. To date, four prospective studies, with 100–
1300 cases each, have investigated the associations ofdairy intake with risk of lymphoma [34–37], with noneof them showing a significant association. However, ina meta-analysis including two of the aforementioned
prospective studies [34, 36] and five case-control studies with a total of over 4000 cases, higher dairy intake
was associated with 20% (2–42%) higher risk of lymphoma [38]. Our study provided further supportive
evidence linking dairy consumption with the risk oflymphoma, although the causality of the association
cannot be properly established.
Breast cancer is the most common type of cancer inwomen worldwide [1], accounting for 15% (~268,600
new cases) of all new cancers in China in 2015 [4].
According to the WCRF/AICR, the major risk factors
for post-menopausal breast cancer include reproductive and hormonal factors, low physical activity, alcohol consumption and obesity [39]. The overall evidence
on dairy intake in relation to both pre- and post-menopausal breast cancer, however, is limited [5, 39]. In
one recent prospective study of 53,000 North-American women [23], dairy intake was positively associated with overall breast cancer risk, with a HR of 1.22
(1.05–1.40) comparing top vs. bottom deciles of dairy
intake, broadly consistent with our study findings. This
cohort included a high proportion of vegetarians andvegans with a much lower average dairy consumption

Kakkoura et al. BMC Medicine 2022, 20(1):134

compared to general Western populations [8, 40], similar to our population. This intake range is poorly investigated in previous studies, making it probably the only
study to which our results could be well-compared.
Our study did not observe any significant associationof dairy consumption with risks of other site-specificcancers, including colorectal and prostate cancer. Existing evidence tends to support an inverse association of
dairy intake with colorectal cancer risk and a positive
association with prostate cancer risk [5–7]. However,large heterogeneity by geographic region was observedin previous studies on the topic [5] and no significant
association was reported from China on either dairy consumption [17] or dairy calcium intake in relation to risksof these two types of cancers [41]. Despite the recent
increase, the incidence of colorectal and prostate cancers
is still lower in China than in the West [4, 42, 43]. Studies
involving a larger number of cases (e.g. prolonged followup of CKB) are needed to confirm (or refute) any modest
associations.
Several biological mechanisms might possibly explain
the positive findings in our study. Firstly, higher dairy
intake may lead to higher plasma levels of insulin-likegrowth factor-I (IGF-I) [44, 45], which plays a key rolein cellular proliferation and cancer development [44, 46,47]. Higher levels of IGF-I have been associated withhigher risks of several cancers including breast cancer,
and recent Mendelian randomization studies have suggested that these associations could be causal [46–48].
Secondly, there is suggestive evidence that the relatively
high content of branched-chain amino acids, lactose(which provides galactose), and IGF-I in milk could activate and enhance the mechanistic target of rapamycincomplex 1 signalling, which might subsequently promote cell proliferation, leading potentially to carcinogenesis [49]. Thirdly, saturated fatty acids (SFA) andtrans-fatty acids from dairy products have been associated with insulin resistance [50] and increased levels of
proinflammatory cytokines [51], which are possible risk
factors for the development of liver cancer [30, 31] andlymphoma [51], respectively. Fourthly, the observed association with breast cancer might relate to the fat-soluble
sex hormones, such as oestrogen [52] and progesterone,
contained in cow’s milk [53]. Finally, there is a hypothesisthat people who are lactose intolerant may have alteredingestion of dairy products [54], which might produce
different breakdown products and exert differences inrisks of diseases, such as cancer [55]. However, there is
still a large evidence gap and more studies are needed to
explore further the potential mediating role of lactose
intolerance in linking dairy intake and cancer risk.
The main strengths of our study include its prospective
design, large sample size and long follow-up. We used

Page 10 of 13

repeat measurements in the resurvey to control regression dilution bias caused by long-term variation andmeasurement errors in the self-reported dairy intake.
The rich data in CKB also allowed us to adjust for a wide
range of potential confounders (including all other dietary variables) and to investigate the potential impactsof reverse causation. Nevertheless, our study has several
limitations. Firstly, our questionnaire collected intake
data only for few major food groups rather than for individual food items. Therefore, it was not possible to adjust
for the intake of total energy or specific nutrients (e.g.
SFA and calcium) or to distinguish the associations ofindividual dairy items (e.g. milk and cheese). Nonetheless, we have adjusted our analyses for both BMI and total
physical activity, which together should provide a reasonable proxy for total energy intake [56–58], as the association between dairy consumption (mainly milk) andtotal energy intake in our study population is expected
to be modest [59, 60]. Secondly, we could not assess theassociations between different types of dairy products(i.e. milk, yoghurt and other dairy products) and cancer
risk because such information was only collected during the second resurvey (with ~3 years of follow-up) of
a subsample of participants (~24,700). Thirdly, we did
not collect individual-level dairy consumption amountat baseline and the HRs for each 50 g/day of dairy consumption were estimated based on the assumption thatthe daily portions of dairy products did not change frombaseline to the 2nd resurvey. Fourthly, although a large
number of cancer cases were recorded, the statisticalpower remained low for certain less common cancer sites(e.g. prostate cancer). Even for common cancer types, thenumber of cases might not be large enough to allow reliable results in various subgroup analyses (e.g. by oestrogen receptor status in breast cancer). Lastly, although we
adjusted analyses for a range of potential confounders,
residual confounding may still exist; thus, the causality ofthe observed associations cannot be confirmed.

Conclusions
In summary, in Chinese adults, higher dairy intake wasassociated with higher risks of liver cancer, lymphoma
and female breast cancer. Our study was the first andlargest Chinese prospective cohort study, which showed
positive associations between dairy intake and risks oftotal and several site-specific cancers in China where
the levels of dairy consumption are low but increasing.
Future studies are warranted to establish the causality and potential underlying mechanisms involved. The
findings of this study in combination with future studies
might provide important information for evidence-based
dietary recommendations on cancer prevention in China.

Kakkoura et al. BMC Medicine 2022, 20(1):134

Abbreviations
BF%: Body fat percentage; BMI: Body mass index; CI: Confidence interval; CKB:
China Kadoorie Biobank; FDR: False discovery rate; HBsAg: Hepatitis B surface
antigen; HR: Hazard ratio; ICD: International Classification of Diseases; IGF-I:
Insulin-like growth factor-I; SD: Standard deviation; SFA: Saturated fatty acids;
WC: Waist circumference; WCRF/AICR: World Cancer Research Fund/American
Institute for Cancer Research.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​022-​02330-3.

Additional file 1: Methods. Calculation of the usual amount of dairy
intake and regression dilution bias correction. Table 1. Baseline and usual
amount (g/day) for each baseline category of dairy consumption basedon 24,700* participants who attended the second resurvey in 2013-2014.
Table 2. Incidence rates of cancers (ICD-10 coded) at 30-79 years andadjusted HRs (95% CIs) for cancers per 50 g/day of usual dairy intake inmen and women from CKB, without prior self-reported history of cancer
at baseline. Table 3. Female reproductive characteristics by frequency ofdairy intake at baseline (2004-2008). Table 4. Adjusted HRs for cancer risk
associated with dairy intake in sensitivity analyses. Figure 1. Percentage of
regular dairy consumers by sex and survey region at baseline (2004-2008).
Figure 2. Estimated mean dairy intake (g/day) by sex and survey region atbaseline (2004-2008). Figure 3. Sex-specific changes in standing height,
body weight and body mass index between baseline (2004-2008) andsecond resurvey (2013-2014) by frequency of baseline dairy intake (n =18,132). Figure 4. Adjusted HRs (95% CIs) for liver cancer per 50 g/day ofusual dairy intake by baseline characteristics. Figure 5. Adjusted HRs (95%
CIs) for lymphoma per 50 g/day of usual dairy intake by baseline characteristics. Figure 6. Adjusted HRs (95% CIs) for female breast cancer per 50
g/day of usual dairy intake by baseline characteristics. Figure 7. Adjusted
HRs (95% CIs) for liver cancer, lymphoma and female breast cancer per 50
g/day of usual dairy intake by region. Figure 8. Adjusted HRs (95% CIs) forliver cancer per 50 g/day of usual dairy intake, with step-wise adjustment.
Figure 9. Adjusted HRs (95% CIs) for lymphoma per 50 g/day of usual
dairy intake, with step-wise adjustment. Figure 10. Adjusted HRs (95% CIs)
for female breast cancer per 50 g/day of usual dairy intake, with step-wiseadjustment.
Acknowledgements
The chief acknowledgment is to the participants, the project staff and the
China National Centre for Disease Control and Prevention (CDC) and its
regional offices for assisting with the fieldwork. We thank Judith Mackay inHong Kong; Yu Wang, Gonghuan Yang, Zhengfu Qiang, Lin Feng, Maigeng
Zhou, Wenhua Zhao, Yan Zhang and Zheng Bian in China CDC; Lingzhi Kong,
Xiucheng Yu and Kun Li in the Chinese Ministry of Health; and Garry Lancaster,
Sarah Clark, Martin Radley, Mike Hill, Hongchao Pan and Jill Boreham in theCTSU, Oxford, for assisting with the design, planning, organization and conduct of the study.
Consortium
On behalf of the China Kadoorie Biobank (CKB) collaborative group. Group
members include the following:
International Steering Committee: Junshi Chen, Zhengming Chen (PI), Robert
Clarke, Rory Collins, Yu Guo, Liming Li (PI), Chen Wang, Jun Lv, Richard Peto,
Robin Walters;
International Coordinating Centre, Oxford, England: Daniel Avery, Derrick Bennett, Ruth Boxall, Ka Hung Chan, Yumei Chang, Yiping Chen, Zhengming Chen,
Johnathan Clarke; Robert Clarke, Huaidong Du, Zammy Fairhurst-Hunter,
Hannah Fry, Simon Gilbert, Alex Hacker, Mike Hill, Michael Holmes, Pek Kei
Im, Andri Iona, Maria Kakkoura, Christiana Kartsonaki, Rene Kerosi, Kuang
Lin, Mohsen Mazidi, Iona Millwood, Qunhua Nie, Alfred Pozarickij, Paul Ryder,
Saredo Said, Sam Sansome, Dan Schmidt, Paul Sherliker, Rajani Sohoni, Becky
Stevens, Iain Turnbull, Robin Walters, Lin Wang, Neil Wright, Ling Yang, Xiaoming Yang, Pang Yao;
National Coordinating Centre, Beijing, China: Yu Guo, Xiao Han, Can Hou, Qingmei Xia, Chao Liu, Jun Lv, Pei Pei, Canqing Yu;

Page 11 of 13

10 Regional Coordinating Centres:
Guangxi Provincial CDC: Naying Chen, Duo Liu, Zhenzhu Tang. Liuzhou CDC:
Ningyu Chen, Qilian Jiang, Jian Lan, Mingqiang Li, Yun Liu, Fanwen Meng,
Jinhuai Meng, Rong Pan, Yulu Qin, Ping Wang, Sisi Wang, Liuping Wei, Liyuan
Zhou;
Gansu Provincial CDC: Caixia Dong, Pengfei Ge, Xiaolan Ren. Maiji CDC:
Zhongxiao Li, Enke Mao, Tao Wang, Hui Zhang, Xi Zhang;
Hainan Provincial CDC: Jinyan Chen, Ximin Hu, Xiaohuan Wang. Meilan CDC:
Zhendong Guo, Huimei Li, Yilei Li, Min Weng, Shukuan Wu;
Heilongjiang Provincial CDC: Shichun Yan, Mingyuan Zou, Xue Zhou. Nangang
CDC: Ziyan Guo, Quan Kang, Yanjie Li, Bo Yu, Qinai Xu;
Henan Provincial CDC: Liang Chang, Lei Fan, Shixian Feng, Ding Zhang, Gang
Zhou. Huixian CDC: Yulian Gao, Tianyou He, Pan He, Chen Hu, Huarong Sun,
Xukui Zhang;
Hunan Provincial CDC: Biyun Chen, Zhongxi Fu, Yuelong Huang, Huilin Liu,
Qiaohua Xu, Li Yin. Liuyang CDC: Huajun Long, Xin Xu, Hao Zhang, Libo Zhang;
Jiangsu Provincial CDC: Jian Su, Ran Tao, Ming Wu, Jie Yang, Jinyi Zhou, Yonglin
Zhou. Suzhou CDC: Yihe Hu, Yujie Hua, Jianrong Jin Fang Liu, Jingchao Liu, Yan
Lu, Liangcai Ma, Aiyu Tang, Jun Zhang;
Qingdao Qingdao CDC: Liang Cheng, Ranran Du, Ruqin Gao, Feifei Li, Shanpeng Li, Yongmei Liu, Feng Ning, Zengchang Pang, Xiaohui Sun, Xiaocao
Tian, Shaojie Wang, Yaoming Zhai, Hua Zhang, Licang CDC: Wei Hou, Silu Lv,
Junzheng Wang;
Sichuan Provincial CDC: Xiaofang Chen, Xianping Wu, Ningmei Zhang, Weiwei
Zhou. Pengzhou CDC: Xiaofang Chen, Jianguo Li, Jiaqiu Liu, Guojin Luo, Qiang
Sun, Xunfu Zhong;Zhejiang Provincial CDC: Weiwei Gong, Ruying Hu, Hao Wang, Meng Wan, Min
Yu. Tongxiang CDC: Lingli Chen, Qijun Gu, Dongxia Pan, Chunmei Wang, Kaixu
Xie, Xiaoyi Zhang.
Authors’ contributions
MGK analysed the data and drafted the manuscript. HD, TJK and ZC provided
supervision and contributed to the conception of this paper, interpretation of
the results and the revision of the manuscript. HD, YG, CY, LY, PP, YC, SS, WCC,
XY, LF and JL were involved in data collection/management and developmentof the analytical method. ZC, LL and JC designed the CKB study and wereinvolved in funding and data acquisition. All authors critically reviewed themanuscript and approved the final submission.
Funding
This research was funded by the Wellcome Trust, Our Planet Our Health
Programme (Livestock, Environment and People - LEAP) [grant number
205212/Z/16/Z]. The CKB baseline survey and the first resurvey were supported by the Kadoorie Charitable Foundation in Hong Kong. The long-term
follow-up has been supported by Wellcome grants to Oxford University(212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z) andgrants from the National Key Research and Development Program of China(2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904)
and from the National Natural Science Foundation of China (91843302). The
UK Medical Research Council (MC_UU_00017/1, MC_UU_12026/2, MC_
U137686851), Cancer Research UK (C16077/A29186; C500/A16896) and theBritish Heart Foundation (CH/1996001/9454) provide core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University forthe project. TJK is supported by Cancer Research UK grant 8221/A29017. The
funding organizations had no role in the study design; data collection, analysis
and interpretation; writing of the manuscript; and decision to submit themanuscript for publication. The first and corresponding authors had full accessto the data and responsibility for the final decision to submit for publication.
Availability of data and materials
The China Kadoorie Biobank (CKB) is a global resource for the investigation oflifestyle, environmental, blood biochemical and genetic factors as determinants of common diseases. The CKB study group is committed to making
the cohort data available to the scientific community in China, the UK andworldwide to advance knowledge about the causes, prevention and treatment of disease. For detailed information on what data is currently available
to open access users and how to apply for it, visit http://​www.​ckbio​bank.​org/​site/​Data+​Access.
Researchers who are interested in obtaining the raw data from the China
Kadoorie Biobank study that underlines this paper should contact ckbac​
cess@​ndph.​ox.​ac.​uk. A research proposal will be requested to ensure that any

Kakkoura et al. BMC Medicine 2022, 20(1):134

analysis is performed by bona fide researchers and — where data is not currently available to open access researchers — is restricted to the topic coveredin this paper.

Declarations
Ethics approval and consent to participate
All participants provided informed consent. Ethics approvals were obtained at
local, national and international levels prior to the beginning of recruitment.
Ethics approval was particularly obtained from the Ethical Review Committee of the Chinese Centre for Disease Control and Prevention (Beijing, China,
005/2004) and the Oxford Tropical Research Ethics Committee, University of
Oxford (UK, 025-04).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield
Department of Population Health, University of Oxford, Oxford, UK. 2 Medical
Research Council Population Health Research Unit (MRC PHRU), Nuffield
Department of Population Health, University of Oxford, Oxford, UK. 3 Fuwai
Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing, China. 4 Department of Epidemiology and Biostatistics,
School of Public Health, Peking University, Beijing, China. 5 Chinese Academyof Medical Sciences, Beijing, China. 6 NCDs Prevention and Control Department, Henan CDC, Zhengzhou, China. 7 China National Center for Food Safety
Risk Assessment, Beijing, China. 8 Cancer Epidemiology Unit (CEU), Nuffield
Department of Population Health, University of Oxford, Oxford, UK.
Received: 24 September 2021 Accepted: 10 March 2022
Published: 6 May 2022

References
1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al.
Global Cancer Statistics 2020: GLOBOCAN estimates of incidence andmortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin.
2021;71(3):209–49.
2. Global Cancer Observatory: Cancer today [Internet]. International Agency
for Research on Cancer. 2020. Available from: https://​gco.​iarc.​fr/​today.
[Accessed on 22 February 2022]
3. Du H, Li L, Bennett D, Yang L, Guo Y, Key TJ, et al. Fresh fruit consumption and all-cause and cause-specific mortality: findings from the China
Kadoorie Biobank. Int J Epidemiol. 2017;46(5):1444–55.
4. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.
5. World Cancer Research Fund/American Institute for Cancer Research.
Meat, fish and dairy products and the risk of cancer. Continuous Update
Project Expert Report 2018.
6. Barrubés L, Babio N, Becerra-Tomás N, Rosique-Esteban N, Salas-Salvadó
J. Association between dairy product consumption and colorectal cancer
risk in adults: a systematic review and meta-analysis of epidemiologic
studies. Adv Nutr. 2019;10(suppl_2):S190–s211.
7. Aune D, Navarro Rosenblatt DA, Chan DS, Vieira AR, Vieira R, Greenwood DC, et al. Dairy products, calcium, and prostate cancer risk: asystematic review and meta-analysis of cohort studies. Am J Clin Nutr.
2015;101(1):87–117.
8. Wu Y, Huang R, Wang M, Bernstein L, Bethea TN, Chen C, et al. Dairy foods,
calcium, and risk of breast cancer overall and for subtypes defined by
estrogen receptor status: a pooled analysis of 21 cohort studies. Am J Clin
Nutr. 2021;114(2):450–61.
9. Jeyaraman MM, Abou-Setta AM, Grant L, Farshidfar F, Copstein L, Lys J,
et al. Dairy product consumption and development of cancer: an overview of reviews. BMJ Open. 2019;9(1):e023625.

Page 12 of 13

10. He Y, Yang X, Xia J, Zhao L, Yang Y. Consumption of meat and dairy products in China: a review. Proc Nutr Soc. 2016;75(3):385–91.
11. Huang L, Wang Z, Wang H, Zhao L, Jiang H, Zhang B, et al. Nutritiontransition and related health challenges over decades in China. Eur J
Clin Nutr. 2021;75(2):247–52.
12. Storhaug CL, Fosse SK, Fadnes LT. Country, regional, and global
estimates for lactose malabsorption in adults: a systematic review andmeta-analysis. Lancet Gastroenterol Hepatol. 2017;2(10):738–46.
13. Hannah Ritchie and Max Roser. Meat and dairy production [Internet].
OurWo​rldIn​Data.​org. 2017. Available from: https://​ourwo​rldin​data.​org/​meat-​produ​ction. [Accessed on 22 February 2022]
14. Liu Z, Pang S, Li Y, Man Q, Li L, Zhang J. Consumption status of dairy
products in Chinese aged 60 and above in 2010-2012. Wei Sheng Yan
Jiu. 2016;45(5):708–13.
15. Huang F, Wang H, Wang Z, Zhang J, Su C, Du W, et al. Knowledge,
behavior and consumption types of milk and dairy products among
the Chinese aged 60 and above in 15 provinces (autonomous regions
and municipalities) in 2015. Wei Sheng Yan Jiu. 2019;48(1):9–15.
16. Li J, Koh WP, Jin AZ, Yuan JM, Yu MC, Butler LM. Calcium intake is not
related to breast cancer risk among Singapore Chinese women. Int J
Cancer. 2013;133(3):680–6.
17. Lee SA, Shu XO, Yang G, Li H, Gao YT, Zheng W. Animal origin foods
and colorectal cancer risk: a report from the Shanghai Women’s HealthStudy. Nutr Cancer. 2009;61(2):194–205.
18. Chen Z, Chen J, Collins R, Guo Y, Peto R, Wu F, et al. China Kadoorie
Biobank of 0.5 million people: survey methods, baseline characteristics
and long-term follow-up. Int J Epidemiol. 2011;40(6):1652–66.
19. Qin C, Guo Y, Pei P, Du H, Yang L, Chen Y, et al. The relative validity andreproducibility of food frequency questionnaires in the China Kadoorie
Biobank Study. Nutrients. 2022;14(4):794.
20. Clarke R, Shipley M, Lewington S, Youngman L, Collins R, Marmot M,
et al. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am J Epidemiol.
1999;150(4):341–53.
21. Yang GH, Stroup DF, Thacker SB. National public health surveillance in
China: implications for public health in China and the United States.
Biomed Environ Sci. 1997;10(1):1–13.
22. Pan R, Zhu M, Yu C, Lv J, Guo Y, Bian Z, et al. Cancer incidence
and mortality: a cohort study in China, 2008–2013. Int J Cancer.
2017;141(7):1315–23.
23. Fraser GE, Jaceldo-Siegl K, Orlich M, Mashchak A, Sirirat R, Knutsen
S. Dairy, soy, and risk of breast cancer: those confounded milks. Int J
Epidemiol. 2020;49(5):1526–37.
24. Plummer M. Improved estimates of floating absolute risk. Stat Med.
2004;23(1):93–104.
25. Department of Agriculture, Department of Health and Human Services. Scientific report of the Dietary Guidelines Advisory Committee.
Washington, DC: Government Printing Office; 2015.
26. de Beer H. Dairy products and physical stature: a systematic review andmeta-analysis of controlled trials. Econ Hum Biol. 2012;10(3):299–309.
27. Sahni S, Mangano KM, Kiel DP, Tucker KL, Hannan MT. Dairy intake isprotective against bone loss in older vitamin D supplement users: theFramingham study. J Nutr. 2017;147(4):645–52.
28. Schwingshackl L, Hoffmann G, Schwedhelm C, Kalle-Uhlmann T,
Missbach B, Knüppel S, et al. Consumption of dairy products in relation to changes in anthropometric variables in adult populations:a systematic review and meta-analysis of cohort studies. PLoS One.
2016;11(6):e0157461.
29. Li P, Fan C, Lu Y, Qi K. Effects of calcium supplementation on bodyweight: a meta-analysis. Am J Clin Nutr. 2016;104(5):1263–73.
30. Pang Y, Kartsonaki C, Turnbull I, Guo Y, Clarke R, Chen Y, et al. Diabetes, plasma glucose, and incidence of fatty liver, cirrhosis, andliver cancer: a prospective study of 0.5 million people. Hepatology.
2018;68(4):1308–18.
31. Yang WS, Zeng XF, Liu ZN, Zhao QH, Tan YT, Gao J, et al. Diet and livercancer risk: a narrative review of epidemiological evidence. Br J Nutr.
2020;124(3):330–40.
32. Zhao Q, He Y, Wang K, Wang C, Wu H, Gao L, et al. Dairy consumption andliver cancer risk: a systematic review and dose-response meta-analysis ofobservational studies. Nutr Cancer. 2021;73(11-12):2821–31.

Kakkoura et al. BMC Medicine 2022, 20(1):134

33. Liu W, Liu J, Song Y, Zeng X, Wang X, Mi L, et al. Burden of lymphoma in
China, 2006-2016: an analysis of the Global Burden of Disease Study 2016.
J Hematol Oncol. 2019;12(1):115.
34. Park Y, Leitzmann MF, Subar AF, Hollenbeck A, Schatzkin A. Dairy food,calcium, and risk of cancer in the NIH-AARP Diet and Health Study. Arch
Intern Med. 2009;169(4):391–401.
35. Rohrmann S, Linseisen J, Jakobsen MU, Overvad K, Raaschou-Nielsen O,
Tjonneland A, et al. Consumption of meat and dairy and lymphoma riskin the European Prospective Investigation into Cancer and Nutrition. Int J
Cancer. 2011;128(3):623–34.
36. Chiu BC, Cerhan JR, Folsom AR, Sellers TA, Kushi LH, Wallace RB, et al.
Diet and risk of non-Hodgkin lymphoma in older women. Jama.
1996;275(17):1315–21.
37. Iso H, Kubota Y. Nutrition and disease in the Japan Collaborative
Cohort Study for Evaluation of Cancer (JACC). Asian Pac J Cancer Prev.
2007;8(Suppl):35–80.
38. Wang J, Li X, Zhang D. Dairy product consumption and risk of non-Hodgkin lymphoma: a meta-analysis. Nutrients. 2016;8(3):120.
39. World Cancer Research Fund/American Institute for Cancer Research.
Diet, nutrition, physical activity and breast cancer. Continuous Update
Project Expert 2018.
40. Key TJ, Balkwill A, Bradbury KE, Reeves GK, Kuan AS, Simpson RF, et al.
Foods, macronutrients and breast cancer risk in postmenopausal women:a large UK cohort. Int J Epidemiol. 2019;48(2):489–500.
41. Butler LM, Wong AS, Koh WP, Wang R, Yuan JM, Yu MC. Calcium intake
increases risk of prostate cancer among Singapore Chinese. Cancer Res.
2010;70(12):4941–8.
42. Kimura T. East meets West: ethnic differences in prostate cancer
epidemiology between East Asians and Caucasians. Chin J Cancer.
2012;31(9):421–9.
43. Flood DM, Weiss NS, Cook LS, Emerson JC, Schwartz SM, Potter JD. Colorectal cancer incidence in Asian migrants to the United States and theirdescendants. Cancer Causes Control. 2000;11(5):403–11.
44. Harrison S, Lennon R, Holly J, Higgins JPT, Gardner M, Perks C, et al. Does
milk intake promote prostate cancer initiation or progression via effects
on insulin-like growth factors (IGFs)? A systematic review and metaanalysis. Cancer Causes Control. 2017;28(6):497–528.
45. Qin LQ, He K, Xu JY. Milk consumption and circulating insulin-likegrowth factor-I level: a systematic literature review. Int J Food Sci Nutr.
2009;60(Suppl 7):330–40.
46. Murphy N, Knuppel A, Papadimitriou N, Martin RM, Tsilidis KK, SmithByrne K, et al. Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, and breast cancer risk: observational and Mendelian randomization analyses with ∼430 000 women. Ann Oncol. 2020;31(5):641–9.
47. Watts EL, Fensom GK, Smith Byrne K, Perez-Cornago A, Allen NE, Knuppel
A, et al. Circulating insulin-like growth factor-I, total and free testosterone
concentrations and prostate cancer risk in 200 000 men in UK Biobank. Int
J Cancer. 2021;148(9):2274–88.
48. Murphy N, Carreras-Torres R, Song M, Chan AT, Martin RM, Papadimitriou
N, et al. Circulating levels of insulin-like growth factor 1 and insulin-likegrowth factor binding protein 3 associate with risk of colorectal cancer
based on serologic and Mendelian randomization analyses. Gastroenterology. 2020;158(5):1300–12.e20.
49. Melnik BC, Schmitz G. Pasteurized non-fermented cow’s milk but not
fermented milk is a promoter of mTORC1-driven aging and increased
mortality. Ageing Res Rev. 2021;67:101270.
50. Risérus U, Willett WC, Hu FB. Dietary fats and prevention of type 2 diabetes. Prog Lipid Res. 2009;48(1):44–51.
51. Charbonneau B, O’Connor HM, Wang AH, Liebow M, Thompson CA, Fredericksen ZS, et al. Trans fatty acid intake is associated with increased risk
and n3 fatty acid intake with reduced risk of non-Hodgkin lymphoma. J
Nutr. 2013;143(5):672–81.
52. Travis RC, Key TJ. Oestrogen exposure and breast cancer risk. Breast
Cancer Res. 2003;5(5):239–47.
53. Zhou H, Qin L-Q, Wang Y, Wang P-Y. The measurement of estrogen andprogesterone in commercial and traditional cow milk. Zhonghua Yu Fang
Yi Xue Za Zhi. 2009;43(6):509–12.
54. He T, Venema K, Priebe MG, Welling GW, Brummer RJ, Vonk RJ. The
role of colonic metabolism in lactose intolerance. Eur J Clin Invest.
2008;38(8):541–7.

Page 13 of 13

55. Shrier I, Szilagyi A, Correa JA. Impact of lactose containing foods and thegenetics of lactase on diseases: an analytical review of population data.
Nutr Cancer. 2008;60(3):292–300.
56. Jakes RW, Day NE, Luben R, Welch A, Bingham S, Mitchell J, et al. Adjusting for energy intake--what measure to use in nutritional epidemiologicalstudies? Int J Epidemiol. 2004;33(6):1382–6.
57. Du H, Li L, Bennett D, Guo Y, Key TJ, Bian Z, et al. Fresh fruit consumption and major cardiovascular disease in China. N Engl J Med.
2016;374(14):1332–43.
58. Du H, Li L, Bennett D, Guo Y, Turnbull I, Yang L, et al. Fresh fruit consumption in relation to incident diabetes and diabetic vascular complications: a 7-y prospective study of 0.5 million Chinese adults. PLoS Med.
2017;14(4):e1002279.
59. Talaei M, Pan A, Yuan JM, Koh WP. Dairy intake and risk of type 2 diabetes.
Clin Nutr. 2018;37(2):712–8.
60. Zong G, Sun Q, Yu D, Zhu J, Sun L, Ye X, et al. Dairy consumption, type2 diabetes, and changes in cardiometabolic traits: a prospective cohort
study of middle-aged and older Chinese in Beijing and Shanghai. Diabetes Care. 2014;37(1):56–63.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ready to submit your research ? Choose BMC and benefit from:• fast, convenient online submission• thorough peer review by experienced researchers in your field• rapid publication on acceptance• support for research data, including large and complex data types• gold Open Access which fosters wider collaboration and increased citations• maximum visibility for your research: over 100M website views per year
At BMC, research is always in progress.
Learn more biomedcentral.com/submissions