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Milk, Fruit and Vegetable, and Total Antioxidant Intakes in Relation to Mortality Rates: Cohort Studies in Women and Men

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High milk consumption might shorten life span through increased oxidative stress. We aimed to determine whether higher mortality rates with high milk consumption are modified by fruit and vegetable intake or total antioxidant intake (oxygen radical absorbance capacity). We used information from food frequency questionnaires completed by 61,420 women in a Swedish cohort (22,391 deaths from the 1987–1990 baseline onward), 36,714 women from a second survey (1997) of this cohort, and 45,280 Swedish men (15,478 deaths from the 1998 baseline onward). Compared with low consumption of milk (<1 glass/day) and high consumption of fruits/vegetables (≥5 servings/day), time-updated information revealed an adjusted hazard ratio for death of 2.79 (95% confidence interval (CI): 2.42, 3.21) in women who consumed ≥3 glasses of milk/day and <1 serving/day of fruit/vegetables and a hazard ratio of 1.60 (95% CI: 1.40, 1.82) in women who consumed the same amount of milk but ≥5 servings/day of fruits/vegetables. The same comparisons in men, based on a single food frequency questionnaire, displayed hazard ratios of 1.31 (95% CI: 1.14, 1.51) and 1.07 (95% CI: 0.97, 1.18), respectively. Total antioxidant consumption showed similar patterns as fruit/vegetable intakes. Dietary antioxidant intake, especially in women, seems to modify the elevated death rate associated with high milk consumption.
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Vol. 185, No. 5
DOI: 10.1093/aje/kww124
Advance Access publication:
February 10, 2017
Original Contribution
Milk, Fruit and Vegetable, and Total Antioxidant Intakes in Relation to Mortality
Rates: Cohort Studies in Women and Men
Karl Michaëlsson*, Alicja Wolk, Håkan Melhus, and Liisa Byberg
*Correspondence to Dr. Karl Michaëlsson, Department of Surgical Sciences, Faculty of Medicine, Uppsala University,
SE-751 85 Uppsala, Sweden (e-mail: karl.michaelsson@surgsci.uu.se).
Initially submitted October 30, 2015; accepted for publication April 22, 2016.
High milk consumption might shorten life span through increased oxidative stress. We aimed to determine
whether higher mortality rates with high milk consumption are modied by fruit and vegetable intake or total anti-
oxidant intake (oxygen radical absorbance capacity). We used information from food frequency questionnaires
completed by 61,420 women in a Swedish cohort (22,391 deaths from the 19871990 baseline onward), 36,714
women from a second survey (1997) of this cohort, and 45,280 Swedish men (15,478 deaths from the 1998 base-
line onward). Compared with low consumption of milk (<1 glass/day) and high consumption of fruits/vegeta-
bles (5 servings/day), time-updated information revealed an adjusted hazard ratio for death of 2.79 (95%
condence interval (CI): 2.42, 3.21) in women who consumed 3 glasses of milk/day and <1 serving/day of fruit/
vegetables and a hazard ratio of 1.60 (95% CI: 1.40, 1.82) in women who consumed the same amount of milk but
5 servings/day of fruits/vegetables. The same comparisons in men, based on a single food frequency question-
naire, displayed hazard ratios of 1.31 (95% CI: 1.14, 1.51) and 1.07 (95% CI: 0.97, 1.18), respectively. Total anti-
oxidant consumption showed similar patterns as fruit/vegetable intakes. Dietary antioxidant intake, especially in
women, seems to modify the elevated death rate associated with high milk consumption.
antioxidants; fruit; galactose; lactose; milk; mortality; oxidative stress; vegetables
Abbreviations: CI, condence interval; FFQ, food frequency questionnaire; GALT, galactose-1-phosphate uridylyltransferase;
HR, hazard ratio; ORAC, oxygen radical absorbance capacity; SD, standard deviation; SMC, Swedish Mammography Cohort.
High milk consumption has long been promoted as
strengthening bone and reducing the likelihood of fragility
fractures. However, we recently demonstrated a higher risk of
fracture with high daily milk consumption in women (1).
Mortality rates were also increased in both women and men
with high milk consumption. We hypothesized that the under-
lying mechanism could be explained by the lactose content of
milk (1).
Milk is the main dietary source of D-galactose, one compo-
nent of the disaccharide lactose. Chronic D-galactose exposure
in animals, with a dose corresponding to 12 glasses of milk
in humans (1,2), is deleterious to health by means of oxida-
tive stress damage and chronic inammation (25). Female
animals seem to be especially vulnerable (68). The increased
oxidative stress with aging and chronic low-grade inamma-
tion is not only a pathogenic mechanism of cardiovascular
disease and cancer in humans (9,10) but also a mechanism of
age-related bone loss and sarcopenia (10,11).
Oxidative stress and inammation can be reduced by a diet
rich in antioxidants (1215), and such foods could potentially
reduce rates of death (16,17). Because of the antioxidant cap-
acity of vegetables and fruits and the high content of lactose/
galactose in milk, which may induce oxidative stress and
low-grade inammation, we hypothesized that a high intake
of fruits and vegetables or a high total antioxidant intake
(18,19) may counteract the observed associations of milk in-
take with mortality. Indeed, experimental evidence in animals
indicates that galactose-induced aging can be prevented by a
higher intake of fruits and vegetables (2024).
To our knowledge, no previous clinical study has com-
bined milk consumption with fruit and vegetable intake and
total antioxidant intake to evaluate associations with the rate
345 Am J Epidemiol. 2017;185(5):345361
of death. In Scandinavia, consumption of milk and of fruits
and vegetables displays a wide range in intake (1,25,26).
Therefore, our main objective in this Swedish cohort study
was to determine whether fruit and vegetable intake or total
antioxidant intake modies the previously observed rela-
tionship between milk consumption and death.
METHODS
We used data from 2 previously described (1) population-
based cohort studies, the Swedish Mammography Cohort
(SMC) and the Cohort of Swedish Men. The SMC started in
19871990 when 74% of all 90,303 women aged 3974
years residing in 2 Swedish counties completed a question-
naire covering diet (food frequency questionnaire (FFQ)) and
lifestyle that had been enclosed with a mailed invitation to
undergo routine mammography screening. In 1997, a subse-
quent expanded questionnaire was sent to the 56,030 women
still livingin the study area (response rate 70%). We excluded
women with implausible values for total energy intake (3
standard deviations below or above the log-transformed mean
energy intake; cutoffs were 574 kcal/day and 4,707 kcal/day)
(27) and those with missing data on all items regarding fruit
and vegetable consumption. Exclusion of outliers for energy
intake, in addition to adjustment for total energy intake in the
statistical analyses, compensates for overall under- or overre-
porting of dietary intake (28). In the present study, a rst anal-
ysis included 61,240 women without a prevalent cancer
diagnosis in the SMC with information from 19871990 and
38,331 women with updated information from 1997. In a sec-
ond analysis with baseline set at the second examination, we
included 36,714 women who were alive on January 1, 1998,
and free of any previous cancers.
The Cohort of Swedish Men was established in 1997.
All men aged 4579 years residing in 2 counties in central
Sweden were invited to participate in the study (n=100,303).
The FFQ and lifestyle questionnaire was completed by
48,850 men. Despite a response rate of only 49%, the Co-
hort of Swedish Men is representative of Swedish men in
this age range in relation to age distribution, educational level,
and prevalence of overweight (29). We excluded men with
implausible values for total energy intake (cutoffs were
861 kcal/day and 7,311 kcal/day). For the present analysis,
45,280 men who were alive on January 1, 1998, and free of
previous cancers were available.
The single FFQ administered in 1997 was used to simplify
the comparison between men and women, but in women we
also used time-updated information by means of the complete
SMC data set. The studies have been approved by the Regional
Ethical Review Board in Stockholm, Sweden.
Exposures
The participants reported, by means of a valid and reprodu-
cible FFQ, their average frequency of consumption of up to 96
foods and beverages during the past year, including milk (ei-
ther low-fat (0.5%), medium-fat (1.5%), or high-fat (3%)),
sour milk, yogurt, cheese, 5 fruits (apples, bananas, berries,
oranges/citrus fruit, and other fruit), orange juice, and 13
vegetables (carrots, beet root, broccoli, cabbage, cauliower,
lettuce, onion, garlic, peas, pea soup, peppers, spinach, toma-
toes, and other vegetables) (2931).Therewere8possiblefre-
quency categories in increasing order from zero times per
month to more than 3 times per day. In the 19871990 FFQ,
the numbers of fruit (n=4) and vegetable (n=5) categories
were fewer, but they comprised more fruit or vegetable items
in each category. The fruit and vegetable categories repre-
sented the typical consumption pattern in Sweden at the time
of each investigation, with a higher number of items over
time. In accordance with national dietary guidelines (32), only
1 glass of juice (fresh or from concentrate) was included in
the calculation of daily intake, independent of the amount in-
gested. Instructions in the FFQ stated that 1 serving of milk
corresponds to 1 glass of 200 mL. Milk intake was specied
according to fat content, and intakes were summed into a sin-
gle measure representing total milk intake on a continuous
scale. Missing values for individual dairy products were inter-
preted as no intake of that particular food (33). The small frac-
tion of missing data for single items, which were regarded as
zero consumption, is unlikely to represent a bias for the ob-
served ndings (33). In fact, 92% of those who did not report
milk consumption on the FFQ part of the questionnaire re-
ported that they did not consume milk when posed a specic
question, and 99.8% had consumption of less than 1 glass/day
according to complementary open questions regarding dairy
consumption.
Nutrient intakes were estimated by multiplying the con-
sumption frequency of each food item by the nutrient con-
tent of age-specic portion sizes and reference data obtained
from the Swedish National Food Agency database (34) and
were adjusted for total energy intake using the residual
method (28). According to validation studies of the self-
reported milk intakes, the Spearman coefcient for correl-
ation between the FFQ and four 7-day food records every
third month (a gold standard reference) was approximately
0.7 (35). Spearman coefcients for correlation between the
FFQ and the averages of these four 7-day dietary records
ranged between 0.4 and 0.7 for individual fruit and vege-
table items.
We calculated estimates of total antioxidant capacity from
diet analyzed with an oxygen radical absorbance capacity
(ORAC) assay, as described in detail previously (18). The
FFQ contained 31 items with available ORAC values. The to-
tal antioxidant capacity of the diet (µmol/day) was calculated
as the sum of the antioxidant content of the 31 food items,
calculated by multiplying the average daily consumption of
each food by its ORAC concentration (μmol Trolox equiva-
lents (TE)/100 g) (Trolox: F. Hoffmann-La Roche AG, Basel,
Switzerland). Because antioxidants in coffee and tea have
been shown to be poorly absorbed, we took into account ab-
sorption (6% for coffee and 4% for tea) when calculating the
ORAC (36). The correlation between the total antioxidant
capacities of dietary ORAC and plasma ORAC was 0.31.
Outcomes
We considered as the primary outcome all-cause mortal-
ity registered between baseline and September 30, 2015, in
the Swedish Cause of Death Registry. We used the under-
lying cause of death from the Swedish Cause of Death
Am J Epidemiol. 2017;185(5):345361
346 Michaëlsson et al.
Table 1. Characteristics of the Swedish Mammography Cohort (Women) According to Milk Consumption at the 19871990 Baseline (n=61,240)
Characteristic
Milk Intake, glasses/day
<11<22<33
Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons %
All participants 16,869 27.5 23,385 38.2 15,405 25.2 5,581 9.1
Age at entry, years 53.2 (9.5) 54.0 (9.7) 54.1 (9.9) 52.8 (9.6)
Body mass index
a
(n=58,960) 24.4 (3.9) 24.7 (3.8) 25.0 (4.0) 24.9 (4.2)
Height, m (n=59,685) 1.64 (0.06) 1.64 (0.06) 1.64 (0.06) 1.64 (0.06)
Dietary intake
Energy, kcal/day 1,414 (433) 1,537 (413) 1,709 (433) 1,967 (525)
Milk, g/day
b
17.3 (37.3) 201.6 (14.9) 400.2 (6.0) 676.8 (151.9)
Yogurt, g/day 102.7 (117.3) 97.8 (101.2) 93.4 (105.1) 87.2 (113.1)
Cheese, g/day 26.8 (21.2) 26.3 (19.6) 26.8 (19.8) 27.7 (22.0)
Fruit, g/day (n=61,031) 201.0 (143.4) 196.6 (132.5) 191.6 (136.7) 183.0 (147.1)
Vegetables, g/day (n=61,104) 92.5 (67.5) 87.6 (60.2) 85.7 (63.8) 81.7 (63.9)
Fruit and vegetables, servings/day 3.6 (2.1) 3.5 (1.9) 3.4 (2.0) 3.2 (2.1)
Red and processed meat, g/day 70.2 (42.2) 75.5 (39.8) 79.8 (40.6) 85.4 (44.8)
Protein, g/day 62.3 (9.1) 66.4 (8.0) 69.9 (8.2) 73.1 (8.8)
Total fat, g/day 44.3 (17.0) 48.9 (16.7) 55.0 (18.4) 65.0 (24.2)
Saturated fat, g/day 19.0 (8.1) 21.4 (8.0) 24.5 (9.1) 30.0 (12.6)
Calcium, mg/day 733 (159) 859 (139) 972 (144) 1,101 (174)
Vitamin D, μg/day 3.9 (1.3) 4.4 (1.2) 4.8 (1.4) 5.1 (1.7)
Phosphorus, mg/day 1,242 (184) 1,365 (165) 1,478 (174) 1,587 (204)
Retinol, mg/day 0.94 (0.70) 1.03 (0.64) 1.09 (0.61) 1.09 (0.59)
Alcohol, g/day 3.1 (4.0) 2.6 (3.5) 2.1 (3.0) 1.9 (3.0)
Physical activity, MET-hours/day
c
42.2 (4.8) 42.4 (4.8) 42.6 (4.8) 42.8 (5.0)
Leisure-time physical activity, hours/week
c
<1 3,256 19.3 4,396 18.8 2,900 18.8 1,077 19.3
1 4,254 25.2 6,094 26.1 3,954 25.7 1,429 25.6
23 5,282 31.3 7,526 32.2 4,900 31.8 1,783 31.9
45 2,423 14.4 3,286 14.1 2,301 14.9 783 14.0
>5 1,654 9.8 2,083 8.9 1,350 8.8 509 9.1
Table continues
Am J Epidemiol. 2017;185(5):345361
Milk, Antioxidant Foods, and Mortality 347
Table 1. Continued
Characteristic
Milk Intake, glasses/day
<11<22<33
Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons %
Education, years
9 13,235 78.5 18,676 79.9 12,487 81.1 4,393 78.7
1012 1,366 8.1 1,699 7.3 1,054 6.8 403 7.2
>12 897 5.3 1,146 4.9 632 4.1 266 4.8
Other
d
1,371 8.1 1,864 8.0 1,232 8.0 519 9.3
Smoking status
c
Never smoker 8,206 48.6 12,221 52.3 7,870 51.1 2,595 46.5
Former smoker 5,318 31.5 6,875 29.4 4,450 28.9 1,646 29.5
Current smoker 3,345 19.8 4,289 18.3 3,085 20.0 1,340 24.0
Living alone 3,939 23.4 5,292 22.6 3,741 24.3 1,430 25.6
Charlson comorbidity index
e
0 15,143 89.8 21,111 90.3 13,817 89.7 4,907 87.9
1 1,401 8.3 1,791 7.7 1,231 8.0 528 9.5
2 325 1.9 483 2.1 357 2.3 146 2.6
Current use of calcium-containing supplements
(regular or occasional)
c
2,914 17.3 3,840 16.4 2,159 14.0 801 14.4
Ever use of antioxidant-containing supplements
c
5,027 29.8 6,700 28.7 3,895 25.3 1,348 24.2
Ever use of estrogen replacement therapy
c
3,495 20.7 5,063 21.7 3,508 22.8 1,458 26.1
Ever use of cortisone
c
897 5.3 1,057 4.5 733 4.8 256 4.6
Abbreviations: MET, metabolic equivalent; SD, standard deviation.
a
Weight (kg)/height (m)
2
.
b
1 g/day of milk 1 mL/day.
c
Values were imputed from the 1997 questionnaire.
d
Such as vocational education.
e
International Classication of Diseases (Eighth, Ninth, and Tenth revisions) diagnosis codes were collated from the National Patient Registry to calculate weighted Charlson comorbidity
scores. The Charlson comorbidity index predicts mortality for a patient who may have a range of up to 17 comorbid conditions. Each condition is assigned a score of 16, depending on the
risk of dying associated with the condition.
Am J Epidemiol. 2017;185(5):345361
348 Michaëlsson et al.
Table 2. Characteristics of the Swedish Mammography Cohort (Women) According to Milk Consumption at the 1997 Baseline (n=36,714)
Characteristic
Milk Intake, glasses/day
<11<22<33
Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons %
All participants 26,617 72.5 7,282 19.8 2,157 5.9 658 1.8
Age at entry, years 61.2 (9.2) 63.4 (9.3) 63.2 (9.1) 63.0 (9.5)
Body mass index
a
(n=36,076) 24.9 (3.9) 25.4 (4.0) 25.6 (4.2) 25.4 (4.1)
Height, m (n=36,551) 1.65 (0.06) 1.64 (0.06) 1.65 (0.06) 1.65 (0.06)
Dietary intake
Energy, kcal/day 1,676 (504) 1,827 (521) 2,005 (561) 2,271 (651)
Milk, g/day
b
75.6 (60.6) 277.2 (34.4) 465.0 (65.1) 908.7 (307.8)
Yogurt, g/day 173.7 (194.8) 182.1 (209.4) 195.3 (250.0) 276.3 (409.6)
Cheese, g/day 49.3 (40.0) 50.4 (37.6) 55.6 (42.5) 59.6 (51.8)
Fruit, g/day (n=36,450) 227.8 (155.3) 218.7 (152.8) 209.9 (149.9) 223.9 (176.2)
Vegetables, g/day (n=36,628) 219.4 (144.8) 211.6 (141.0) 208.4 (135.2) 213.5 (148.6)
Fruit and vegetables, servings/day 5.4 (3.0) 5.1 (2.9) 5.0 (2.8) 5.2 (3.1)
Oxygen radical absorbance capacity,
µmol/day
12,841 (5,030) 13,225 (5,254) 13,374 (5,106) 13,803 (5,865)
Red and processed meat, g/day 63.8 (45.0) 67.7 (46.1) 70.8 (50.4) 68.3 (51.0)
Protein, g/day 69.5 (11.4) 72.8 (11.0) 75.3 (11.2) 81.2 (11.9)
Total fat, g/day 60.1 (10.4) 59.1 (9.8) 58.5 (10.2) 56.2 (10.8)
Saturated fat, g/day 27.3 (6.4) 27.1 (6.1) 27.1 (6.3) 26.9 (6.6)
Calcium, mg/day 977 (270) 1,157 (268) 1,297 (288) 1,618 (383)
Vitamin D, μg/day 4.3 (1.6) 4.9 (1.5) 5.4 (1.5) 6.0 (1.8)
Phosphorus, μg/day 1,356 (211) 1,488 (210) 1,581 (219) 1,800 (280)
Retinol, mg/day 0.86 (0.68) 0.95 (0.73) 0.98 (0.73) 1.00 (0.49)
Alcohol, g/day 4.5 (5.4) 3.2 (4.5) 2.9 (4.3) 3.8 (7.2)
Physical activity, MET-hours/day (n=28,321) 42.3 (4.7) 42.8 (4.8) 42.6 (5.0) 42.7 (5.4)
Leisure-time physical activity, hours/week
(n=32,799)
<1 4,687 19.6 1,285 19.8 375 19.8 118 21.0
1 5,729 24.0 1,469 22.7 428 22.6 116 20.7
23 8,045 33.7 2,220 34.3 635 33.5 170 30.3
45 2,773 11.6 797 12.3 228 12.0 71 12.7
>5 2,633 11.0 703 10.9 231 12.2 86 15.3
Table continues
Am J Epidemiol. 2017;185(5):345361
Milk, Antioxidant Foods, and Mortality 349
Table 2. Continued
Characteristic
Milk Intake, glasses/day
<11<22<33
Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons %
Education, years (n=36,683)
9 19,126 71.9 5,759 79.2 1,721 79.8 483 73.4
1012 2,133 8.0 465 6.4 115 5.3 50 7.6
>12 5,294 19.9 1,035 14.2 311 14.4 123 18.7
Other
c
40 0.2 16 0.2 10 0.5 2 0.3
Smoking status (n=36,182)
Never smoker 13,804 52.7 4,134 57.6 1,157 54.2 333 51.2
Former smoker 6,348 24.2 1,384 19.3 395 18.5 150 23.0
<20 pack-years
d
4,910 19.2 1,038 14.8 277 13.3 115 18.1
20 pack-years 1,069 4.2 255 3.6 81 3.9 26 4.1
Current smoker 6,064 23.1 1,662 23.1 583 27.3 168 25.8
<20 pack-years 3,328 13.0 886 12.6 297 14.3 74 11.7
20 pack-years 2,460 9.6 700 10.0 268 12.9 86 13.6
Living alone 5,580 21.0 1,691 23.2 504 23.4 172 26.1
Charlson comorbidity index
e
0 23,416 88.0 6,290 86.4 1,857 86.1 541 82.2
1 2,163 8.1 668 9.2 219 10.2 74 11.2
2 1,038 3.9 324 4.4 81 3.8 43 6.5
Current use of calcium-containing
supplements (regular or occasional)
6,465 24.3 1,735 23.8 529 24.5 160 24.3
Ever use of antioxidant-containing
supplements
11,764 44.2 3,174 43.6 930 43.1 288 43.8
Ever use of aspirin 11,495 43.2 3,132 43.0 960 44.5 285 43.3
Ever hypercholesterolemia
f
9,520 35.8 2,699 37.1 771 35.7 216 32.8
Ever use of estrogen replacement therapy 12,440 47.5 3,115 43.5 906 42.7 313 48.5
Ever use of cortisone 1,929 7.2 550 7.6 196 9.1 52 7.9
Abbreviations: MET, metabolic equivalent; SD, standard deviation.
a
Weight (kg)/height (m)
2
.
b
1 g/day of milk 1 mL/day.
c
Such as vocational education.
d
Information on pack-years of smoking was available for 35,298 participants.
e
International Classication of Diseases (Eighth, Ninth, and Tenth revisions) diagnosis codes were collated from the National Patient Registry to calculate weighted Charlson comorbidity
scores. The Charlson comorbidity index predicts mortality for a patient who may have a range of up to 17 comorbid conditions. Each condition is assigned a score of 16, depending on the
risk of dying associated with the condition.
f
High cholesterol or ever use of statins.
Am J Epidemiol. 2017;185(5):345361
350 Michaëlsson et al.
Table 3. Characteristics of the Cohort of Swedish Men (Men) According to Milk Consumption at the 1997 Baseline (n=45,280)
Characteristic
Milk Intake, glasses/day
<11<22<33
Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons %
All participants 20,144 44.5 10,803 23.9 7,459 16.5 6,874 15.2
Age at entry, years 59.5 (9.6) 61.2 (9.9) 61.1 (9.8) 60.1 (9.5)
Body mass index
a
(n=42,975) 25.6 (3.2) 25.6 (3.3) 25.9 (3.4) 26.4 (3.6)
Height, m (n=43,190) 1.77 (0.07) 1.77 (0.07) 1.77 (0.07) 1.77 (0.07)
Dietary intake
Energy, kcal/day 2,511 (789) 2,583 (772) 2,749 (798) 3,129 (914)
Milk, g/day
b
64.7 (68.3) 267.5 (59.4) 467.3 (49.3) 909.1 (372.4)
Yogurt, g/day 181.4 (237.6) 165.7 (222.1) 177.9 (245.2) 185.6 (286.8)
Cheese, g/day 72.8 (59.8) 70.6 (54.9) 75.0 (57.7) 85.8 (66.1)
Fruit, g/day (n=44,870) 182.1 (140.6) 182.5 (133.0) 173.8 (137.3) 162.1 (131.3)
Vegetables, g/day (n=45,110) 184.2 (125.0) 181.2 (123.6) 173.0 (118.3) 163.5 (118.7)
Fruit and vegetables, servings/day 4.2 (2.5) 4.2 (2.4) 3.9 (2.4) 3.7 (2.3)
Oxygen radical absorbance capacity,
µmol/day
14,218 (5,376) 14,740 (5,354) 14,834 (5,563) 15,153 (5,708)
Red and processed meat, g/day 101.9 (63.8) 102.3 (63.8) 103.3 (59.2) 108.8 (61.4)
Protein, g/day 98.3 (14.7) 102.1 (13.9) 105.4 (14.0) 111.3 (15.1)
Total fat, g/day 89.6 (15.5) 89.3 (14.6) 89.2 (14.9) 89.0 (15.6)
Saturated fat, g/day 40.3 (9.5) 40.6 (9.1) 41.2 (9.5) 42.0 (10.0)
Calcium, mg/day 1,247 (386) 1,450 (370) 1,640 (386) 1,945 (483)
Vitamin D, μg/day 6.1 (3.0) 6.7 (2.9) 7.0 (2.9) 7.7 (3.0)
Phosphorus, mg/day 1,922 (294) 2,063 (282) 2,185 (292) 2,387 (350)
Retinol, mg/day 1.19 (0.96) 1.25 (0.85) 1.28 (0.71) 1.35 (0.74)
Alcohol, g/day (n=40,493) 17.0 (22.9) 13.7 (17.6) 13.1 (18.7) 14.2 (26.8)
Physical activity, MET-hours/day (n=34,812) 41.2 (4.8) 41.5 (4.8) 41.8 (5.0) 42.3 (5.3)
Leisure-time physical activity, hours/week
(n=40,496)
<1 3,988 22.1 1,940 20.0 1,452 21.7 1,488 24.6
1 3,470 19.2 1,845 19.0 1,295 19.4 1,078 17.9
23 5,691 31.5 3,127 32.2 2,064 30.9 1,792 29.7
45 2,315 12.8 1,256 12.9 876 13.1 728 12.1
>5 2,592 14.4 1,547 15.9 999 14.9 953 15.8
Table continues
Am J Epidemiol. 2017;185(5):345361
Milk, Antioxidant Foods, and Mortality 351
Table 3. Continued
Characteristic
Milk Intake, glasses/day
<11<22<33
Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons % Mean (SD) No. of
Persons %
Education, years (n=45,129)
9 13,072 65.1 7,437 69.0 5,502 74.1 5,308 77.5
1012 3,151 15.7 1,526 14.2 884 11.9 710 10.4
>12 3,775 18.8 1,764 16.4 1,021 13.7 800 11.7
Other
c
80 0.4 46 0.4 23 0.3 30 0.4
Smoking status (n=44,858)
Never smoker 6,996 35.0 4,152 38.7 2,718 36.9 2,301 33.8
Former smoker 8,191 41.0 4,028 37.6 2,694 36.6 2,488 36.6
<20 pack-years
d
4,884 25.8 2,381 23.6 1,535 22.3 1,337 21.0
20 pack-years 2,656 14.1 1,277 12.6 895 13.0 900 14.1
Current smoker 4,786 24.0 2,539 23.7 1,954 26.5 2,011 29.6
<20 pack-years 1,763 9.3 933 9.2 712 10.3 653 10.2
20 pack-years 2,599 13.8 1,352 13.4 1,027 14.9 1,188 18.6
Living alone 3,317 16.5 1,831 16.9 1,311 17.6 1,376 20.0
Charlson comorbidity index
e
0 17,102 84.9 8,892 82.3 6,182 82.9 5,667 82.4
1 2,152 10.7 1,314 12.2 883 11.8 838 12.2
2 890 4.4 597 5.5 394 5.3 369 5.4
Current use of calcium-containing
supplements (regular or occasional)
2,912 14.5 1,526 14.1 976 13.1 837 12.2
Ever use of antioxidant-containing
supplements
6,395 31.7 3,380 31.3 2,250 30.2 1,978 28.8
Ever use of aspirin 6,500 32.3 3,585 33.2 2,397 32.1 2,255 32.8
Ever hypercholesterolemia
f
8,967 44.5 4,704 43.5 3,221 43.2 3,145 45.8
Ever use of cortisone 843 4.2 449 4.2 309 4.1 310 4.5
Abbreviations: MET, metabolic equivalent; SD, standard deviation.
a
Weight (kg)/height (m)
2
.
b
1 g/day of milk 1 mL/day.
c
Such as vocational education.
d
Information on pack-years of smoking was available for 42,259 participants.
e
International Classication of Diseases (Eighth, Ninth, and Tenth revisions) diagnosis codes were collated from the National Patient Registry to calculate weighted Charlson comorbidity
scores. The Charlson comorbidity index predicts mortality for a patient who may have a range of up to 17 comorbid conditions. Each condition is assigned a score of 16, depending on the
risk of dying associated with the condition.
f
High cholesterol or ever use of statins.
Am J Epidemiol. 2017;185(5):345361
352 Michaëlsson et al.
Registry to dene secondary outcomes: mortality from cardio-
vascular diseases (International Classication of Diseases,
Tenth Revision, codes I00I99) and cancer (International
Classication of Diseases, Tenth Revision, C-codes) through
December 31, 2014. For 19871996, we used corresponding
codes from the International Classication of Diseases, Ninth
Revision.
Statistical analysis
We calculated time at risk for each participant from study
entry until the date of each outcome, the date of emigration,
or the end of the study period, whichever came rst. We rst
evaluated trends in mortality rates according to milk intake,
fruit and vegetable intake, and ORAC using restricted cubic-
spline Cox regression with 3 knots placed at percentiles 10,
50, and 90 of the exposures (37). We calculated age-adjusted
death rates and age- and multivariable-adjusted hazard ratios
and 95% condence intervals for categories of milk intake
(<1, 1<2, 2<3, or 3 glasses/day) and categories of fruit
and vegetable intake or quartiles of ORAC. We categorized
fruit and vegetable intake as <1, 1<2, 2<3, 3<4, 4<5,
or 5 servings/day, with the latter category reecting dietary
recommendations (32). The proportional hazards assumptions
were conrmed graphically by log-log plots.
To select suitable covariates for the multivariable model,
we used present knowledge and directed acyclic graphs
(38). The model for the total effect included age, total en-
ergy intake, body mass index (weight (kg)/height (m)
2
),
height, intakes of yogurt, cheese, red and processed meat,
and alcohol (all continuous), educational level (9 years,
1012 years, >12 years, or other), living alone (yes/no),
ever use of antioxidant supplements (yes/no), physical activ-
ity (metabolic equivalent-hours/day; continuous), smoking
status (never, former with <20 pack-years, former with 20
pack-years, current with <20 pack-years, or current with
20 pack-years) and Charlsons comorbidity index (pos-
sible range of scores, 033; continuous) (39,40). To avoid
loss of efciency and limit the introduction of bias by re-
stricting the analysis to persons with complete data alone,
missing data on covariates were imputed using multiple im-
putation (41). We also imputed covariates not assessed at
the baseline of the SMC in 19871990 (e.g., smoking status
and physical activity) (1). Additional sensitivity analyses in-
cluded exclusion of the rst 2 years of follow-up, persons
with a body mass index greater than 35, and current smok-
ers. To our second model, we also added as covariates use
of calcium-containing supplements and, for baseline 1997,
use of aspirin and prevalent hypercholesterolemia. In a
fourth model, we additionally adjusted our estimates for
energy-adjusted dietary intakes of protein, total and satu-
rated fat, calcium, vitamin D, retinol, and phosphorus; ever
use of cortisone; and, among women, hormone replacement
therapy.
Measures of interaction were calculated on the basis of
adjusted hazard ratios (HRs), using persons consuming
less than 1 glass of milk and 5 or more servings of fruit
and vegetables per day as the reference category for the
following groups (annotations in parentheses): milk intake
1.0
1.5
2.0
2.5
Adjusted Hazard Ratio
0 2 4 6
Milk Intake, glasses/day
A)
0.4
0.6
0.8
1.0
Adjusted Hazard Ratio
0 5 10 15 20
Fruit and Vegetable Intake, servings/day
B)
0.5
0.6
0.7
0.8
0.9
1.0
Adjusted Hazard Ratio
0 10,000 20,000 30,000 40,000
Oxygen Radical Absorbance Capacity, µmol/day
C)
Figure 1. Sex-specic multivariable-adjusted spline curves illustrating
the relationship of milk intake (A), fruit and vegetable intake (B), and
oxygen radical absorbance capacity (ORAC; µmol/day) (C) with hazard
ratios for death from all causes by the use of time-updated information
in the whole Swedish Mammography Cohort (SMC; baseline 1987
1990) (solid line), in the SMC after administration of the second food fre-
quency questionnaire (baseline 1997) (short-dashed line), and in the
Cohort of Swedish Men (baseline 1998) (long-dashed line). The shaded
areas illustrate 95% condence intervals. One glass of milk corre-
sponds to 200 mL. Covariates were age, body mass index (weight (kg)/
height (m)
2
), height, energy intake, alcohol intake, intakes of yogurt,
cheese, and red and processed meat, education, marital status (living
alone vs. not), physical activity (metabolic equivalent-hours/day), smok-
ing habits (never, former, or current smoker and, for baseline 1997, also
pack-years of smoking), ever use of antioxidant-containing supple-
ments, and weighted Charlsons comorbidity index. Associations with
milk intake were further adjusted for intake of fruit and vegetables, and
associations with fruit and vegetable intake and ORAC were adjusted
for intake of milk.
Am J Epidemiol. 2017;185(5):345361
Milk, Antioxidant Foods, and Mortality 353
Table 4. Age-Standardized Rates of Mortality in the Swedish Mammography Cohort (Women) and the Cohort of Swedish Men (Men) per 1,000 Person-Years at Risk, According to Milk
Consumption, Fruit and Vegetable Intake, and Quartile of Oxygen Radical Absorbance Capacity, 19872015
a
Milk Intake, glasses/day
<11<22<33
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
b
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
Swedish Mammography Cohort (Women), Baseline 19871990 (Time-Updated Analyses
c
)
Fruit/vegetable
intake,
servings/day
<1 22.5 20.3, 24.8 416 16,627 23.3 21.3, 25.4 509 20,458 28.6 25.8, 31.7 423 15,336 27.1 23.3, 31.5 213 8,424
1<2 17.1 16.1, 18.2 971 53,107 19.6 18.5, 20.7 1,264 62,158 19.8 18.3, 21.3 776 42,498 21.5 19.1, 24.2 310 17,065
2<5 14.1 13.7, 14.5 4,116 283,776 15.5 15.0, 15.9 4,389 282,627 17.6 16.9, 18.4 2,257 143,057 19.4 17.9, 21.0 734 47,815
5 12.1 11.7, 12.6 2,786 218,644 13.3 12.8, 13.9 2,168 149,108 15.1 14.1, 16.2 812 56,873 17.1 15.0, 19.4 247 16,597
Swedish Mammography Cohort (Women), Baseline 1997
Fruit/vegetable
intake,
servings/day
<1 27.7 23.8, 32.1 219 413 32.5 25.5, 40.7 92 160 41.8 28.0, 60.0 29 52 40.2 19.9, 72.0 12 20
1<2 21.9 20.1, 23.7 624 1,541 23.8 20.8, 27.2 240 528 21.6 16.6, 27.6 66 171 29.3 18.9, 43.6 29 51
2<5 18.2 17.6, 18.8 3,277 11,827 18.8 17.7, 20.0 1,124 3,325 21.5 19.3, 23.8 373 1,025 23.6 19.4, 28.4 113 296
5 16.0 15.4, 16.7 2,827 12,836 17.4 16.3, 18.6 926 3,269 18.8 16.7, 21.2 284 909 18.0 14.2, 22.5 79 291
Cohort of Swedish Men, Baseline 1997
Fruit/vegetable
intake,
servings/day
<1 32.8 29.6, 36.3 383 774 32.2 27.9, 37.1 227 399 32.6 27.8, 38.0 191 331 32.5 28.4, 37.1 228 457
1<2 25.7 24.1, 27.5 890 2,354 26.4 24.2, 28.7 571 1,300 28.3 25.8, 30.9 488 1,097 26.5 24.1, 29.2 438 1,133
2<5 21.4 20.7, 22.2 3,291 10,889 22.4 21.4, 23.4 2,087 5,979 23.2 22.0, 24.4 1,434 4,100 24.6 23.3, 26.0 1,312 3,773
5 20.2 19.3, 21.2 1,754 6,127 21.7 20.4, 23.0 1,030 3,125 21.4 19.7, 23.1 634 1,931 22.9 21.0, 24.9 520 1,511
Swedish Mammography Cohort (Women), Baseline 1997
d
ORAC quartile
1 20.0 19.2, 20.8 2,298 6,850 22.2 20.6, 24.0 698 1,705 25.9 22.4, 29.8 202 467 27.0 21.0, 34.1 76 157
2 17.3 16.4, 18.1 1,711 6,729 19.0 17.5, 20.6 586 1,759 21.1 18.2, 24.3 194 555 22.1 15.9, 29.8 43 135
3 16.9 16.0, 17.7 1,622 6,667 17.0 15.6, 18.5 538 1,843 19.0 16.1, 22.2 159 508 19.7 14.6, 25.9 51 161
4 15.8 15.0, 16.7 1,316 6,371 17.3 15.9, 18.8 560 1,975 18.5 16.0, 21.2 197 627 19.5 15.0, 25.0 63 205
Table continues
354 Michaëlsson et al.
Am J Epidemiol. 2017;185(5):345361
Table 4. Continued
Milk Intake, glasses/day
<11<22<33
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
b
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
Mortality
Rate 95% CI No. of
Cases
PY at
Risk
Total
No.
Cohort of Swedish Men, Baseline 1997
e
ORAC quartile
1 25.1 24.1, 26.2 2,180 5,517 26.7 25.1, 28.3 1,160 2,564 28.1 26.2, 30.1 833 1,731 29.7 27.4, 32.0 659 1,508
2 21.8 20.7, 22.9 1,617 5,154 21.6 20.3, 23.1 943 2,645 24.1 22.4, 25.9 710 1,857 25.4 23.4, 27.4 625 1,664
3 20.3 19.2, 21.4 1,361 4,906 22.2 20.8, 23.6 947 2,809 22.3 20.6, 24.2 610 1,904 23.2 21.3, 25.2 566 1,701
4 19.9 18.7, 21.1 1,160 4,567 21.6 20.2, 23.1 865 2,785 20.9 19.2, 22.7 594 1,967 23.1 21.3, 24.9 648 2,001
Abbreviations: CI, condence interval; ORAC, oxygen radical absorbance capacity; PY, person-years.
a
Baseline dates varied by cohort and analysis. Follow-up ended on September 30, 2015.
b
Total number of participants in category.
c
Because of the time-updated exposures, person-years at risk are given.
d
Median ORAC value: quartile 1 (<9,546 µmol/day), 7,914 µmol/day; quartile 2 (9,546<12,217 µmol/day), 10,892 µmol/day; quartile 3 (12,217<15,488 µmol/day), 13,653 µmol/day; quartile 4 (15,488 µmol/day),
18,331 µmol/day.
e
Median ORAC value: quartile 1 (<10,880 µmol/day), 8,967 µmol/day; quartile 2 (10,880<13,881 µmol/day), 12,418 µmol/day; quartile 3 (13,881<17,457 µmol/day), 15,477 µmol/day; quartile 4 (17,457 µmol/
day), 20,490 µmol/day.
Milk, Antioxidant Foods, and Mortality 355
Am J Epidemiol. 2017;185(5):345361
3 glasses/day, fruit and vegetable intake 5 servings/day
(HR
10
); milk intake <1 glass/day, fruit and vegetable intake
<1 serving/day (HR
01
); and milk intake 3 glasses/day,
fruit and vegetable intake <1 serving/day (HR
11
). The rela-
tive excess risk of interaction (interaction on the additive
scale) was calculated as HR
11
HR
10
HR
01
+1(42), and
95% condence intervals were obtained by means of the
bootstrap percentile method with 1,000 bootstrap samples.
The statistical analyses were performed with Stata 13.1
(StataCorp LP, College Station, Texas).
RESULTS
Characteristics of the study population by baseline date and
sex are presented in Tables 13. Approximately 9% of the
womenreportedmilkconsumptionof3 glasses/day in 1987
1990 (Table 1),whereasin1997only2%reportedsuchintake
(Table 2). Men had on average higher consumption of milk;
15% drank 3 glasses/day in 1997 (Table 3). Average re-
ported consumption of fruits and vegetables among women
was 3.5 servings/day (standard deviation (SD), 2.0) at baseline
and 5.3 servings/day (SD, 3.0) at the follow-up examination.
Men reported an average consumption of 4.1 servings/day
(SD, 2.5). Intake of fruits and vegetables did not vary by milk
intake in either women or men.
During a mean follow-up period of 23 years (maximum 29
years), 22,391 women (total time at risk =1,434,171 person-
years) died. From January 1, 1998, onward, 10,314 women
(581,785 person-years) and 15,478 men (687,688 person-years)
died during a mean follow-up period of 15 years (maximum 17
years). Death rates increased in both sexes with increasing milk
consumption (Figure 1A), and death rates decreased with high-
er consumption of fruit and vegetables (Figure 1B), as well as
with higher ORACs (Figure 1C), although a threshold seemed
to be discerned (>5 servings of fruit/vegetables per day and
15,000 µmol ORAC/day). In men, higher death rates started to
be observed after only 3 or more glasses of milk per day
(Figure 1A). In women, death rates were already increased at
12 glasses of milk per day.
In further analyses, we combined milk intake with fruit and
vegetable consumption, as well as with ORACs. In Table 4,
we present age-adjusted rates and numbers of deaths by each
combination category. The rate of mortality was highest
among persons consuming less than 1 serving of fruit and vege-
tables per day (or in the lowest quartile of ORAC) combined
with a high consumption of milk, in both men and women. In
contrast, the lowest age-adjusted mortality rates were found in
women and men who reported high consumption of fruits and
vegetables or had high ORACs combined with low intake of
milk.
Figures 2and 3depict the multivariable-adjusted hazard ra-
tios for mortality by milk and fruit/vegetable intake or ORAC,
using the group with the lowest intake of milk (<1 glass/day)
and the highest intake of fruit and vegetables (5 servings/
day) or ORAC (highest quartile) as the reference. Hazard ra-
tios for mortality tended to increase with higher milk con-
sumption in every category of fruit and vegetable intake or
ORAC, although the estimates were attenuated with increasing
1.82
(1.64, 2.02)
1.43
(1.32, 1.54)
1.18
(1.13, 1.24)
1.00
(reference)
1.94
(1.76, 2.13)
1.78
(1.67, 1.91)
1.39
(1.32, 1.46)
1.10
(1.04, 1.17)
3.03
(2.73, 3.37)
2.04
(1.88, 2.22)
1.83
(1.72, 1.94)
1.35
(1.25, 1.46)
2.79
(2.42, 3.21)
2.27
(2.01, 2.55)
2.14
(1.97, 2.33)
1.60
(1.40, 1.82)
<1
1−1.9
2−4.9
5
Fruit and Vegetable Intake, servings/day
A)
1.41
(1.22−1.63)
1.23
(1.12, 1.35)
1.10
(1.04, 1.16)
1.00
(reference)
1.76
(1.42−2.17)
1.34
(1.17, 1.53)
1.09
(1.01, 1.17)
1.03
(0.96, 1.11)
2.73
(1.89−3.94)
1.12
(0.88, 1.43)
1.28
(1.14, 1.42)
1.10
(0.97, 1.24)
1.81
(1.03−3.20)
1.61
(1.11, 2.32)
1.29
(1.07, 1.56)
1.10
(0.88, 1.38)
<1
1−1.9
2−4.9
5
Fruit and Vegetable Intake, servings/day
B)
1.35
(1.21, 1.51)
1.16
(1.07, 1.26)
1.02
(0.96, 1.08)
1.00
(reference)
1.24
(1.08, 1.43)
1.10
(1.00, 1.21)
1.06
(0.99, 1.13)
1.05
(0.97, 1.13)
1.32
(1.14, 1.54)
1.25
(1.13, 1.39)
1.06
(0.99, 1.14)
1.01
(0.92, 1.11)
1.31
(1.14, 1.51)
1.11
(0.99, 1.23)
1.14
(1.06, 1.23)
1.07
(0.97, 1.18)
<1
1−1.9
2−4.9
5
Fruit and Vegetable Intake, servings/day
<1 1−1.9 2−2.9 3
Milk Intake, glasses/day
<1 1−1.9 2−2.9 3
Milk Intake, glasses/day
<1 1−1.9 2−2.9 3
Milk Intake, glasses/day
C)
Figure 2. Adjusted hazard ratios (HRs) and 95% condence intervals
(in parentheses) for all-cause mortality according to combined intakes
of milk and fruit and vegetables, using persons with the lowest milk in-
take and the highest fruit and vegetable intake as the reference group.
A) HRs based on time-updated information on the whole Swedish Mam-
mography Cohort (SMC) (women, baseline 19871990); B) HRs based
on the SMC after administration of the second food frequency question-
naire (women, baseline 1997); C) HRs based on the Cohort of Swedish
Men (men, baseline 1997). The shading corresponds to the value of the
HR; the darker the shading, the larger the HR. One glass of milk corre-
sponds to 200 mL. Covariates were age, body mass index (weight (kg)/
height (m)
2
), height, energy intake, alcohol intake, intakes of yogurt,
cheese, and red and processed meat, education, marital status (living
alone vs. not), physical activity (metabolic equivalent-hours/day), smok-
ing habits (never, former, or current smoker and, for baseline 1997, also
pack-years of smoking), ever use of antioxidant-containing supple-
ments, and weighted Charlsons comorbidity index.
Am J Epidemiol. 2017;185(5):345361
356 Michaëlsson et al.
consumption of fruits and vegetables or ORAC. The pattern
was clearer with time-updated information as compared with a
single exposure assessment. Accordingly, in time-updated
analysisoftheSMC,ahighintakeofmilk(3glasses/day)
with a concomitant low intake of fruit and vegetables (<1
serving/day) conferred a multivariable-adjusted hazard ratio of
2.79 (95% condence interval (CI): 2.42, 3.21), and with a
combined high intake of fruit and vegetables, the hazard ratio
was 1.60 (95% CI: 1.40, 1.82). In women with a single expo-
sure assessment, the corresponding estimates were 1.81 (95%
CI: 1.03, 3.20) and 1.10 (95% CI: 0.88, 1.38), respectively.
The same comparisons in men revealed a hazard ratio of 1.31
(95% CI: 1.14, 1.51) for high consumers of milk with a low
fruit and vegetable intake and a hazard ratio of 1.07 (95% CI:
0.97, 1.18) for high consumers of milk who also consumed 5
or more servings of fruits and vegetables per day. If we used
ORAC intake instead of fruit and vegetable intake as the
effect-measure modier, the estimates remained essentially
unaltered. The relative excess risk of interaction estimate
of 0.37 (95% CI: 0.01, 1.27) in the time-updated analysis of
women indicated a modest additive interaction. No signicant
interaction was discovered among men (data not shown).
Hazard ratios were not attenuated in women after addi-
tional adjustment, including adjustment for vitamin and min-
eral nutrients common in milk, although they were somewhat
attenuated in men (see Web Table 1, available at http://aje.
oxfordjournals.org/). The total number of cardiovascular dis-
ease or cancer deaths was less than half that of the number of
deaths from any cause (Web Tables 2 and 3). Nevertheless,
for cardiovascular mortality (Web Table 4), the hazard ratios
remained similar to estimates of all-cause mortality, whereas
hazard ratios for cancer mortality were lower (Web Table 5).
Exclusion of the rst 2 years of follow-up (Web Table 6;
2%5% of all deaths were excluded, depending on the anal-
ysis), persons with a body mass index greater than 35 (Web
Table 7; 2% of all deaths were excluded), and current smok-
ers (Web Table 8; 24%29% of all deaths were excluded)
gave estimates similar to those seen in the total cohort.
DISCUSSION
In 2 independent population-based cohorts, mortality rates
were highest in persons with high consumption of milk com-
bined with low consumption of fruits and vegetables or a low
ORAC. However, the gradient of risk with increasing milk
consumption was more pronounced in women, and an additive
interaction for mortality rates between milk consumption and
fruit and vegetable consumption was found only in women.
The ndings of our observational investigation should not
be evaluated in isolation and have to be interpreted cautiously.
A recent attempt to perform a meta-analysis demonstrated sub-
stantial heterogeneity in nonfermented milk consumption
among cohort studies in relation to mortality from all causes
(43). Heterogeneity among studies was observed in most sub-
groups dened by sex, country, and study quality (43). Besides
methodological differences, a potential explanation for the in-
consistent ndings may be related to the variability in the range
of milk consumption in different populations and, as also indi-
cated by our present study, by different patterns of intake of
antioxidant-rich foods in the populations. To our knowledge,
no randomized trial has examined the association of milk in-
take with incidence of mortality, and this study design is un-
likely to ever be implemented. Another possible analytical
approach might be the use of genetic variation in lactase per-
sistence within a Mendelian randomization study design, but
this specic genetic variant is probably weak as an instrumen-
tal variable (44), with conceivable pleiotropic effects (45,46).
The present study extends our previous nding of higher
mortality rates with high milk consumption (1). Our postu-
lated mechanism is that milk consumption induces oxidative
stress by way of the galactose component of lactose,
1.22
(1.13, 1.33)
1.07
(0.99, 1.15)
1.08
(1.00, 1.16)
1.00
(reference)
1.30
(1.18, 1.43)
1.10
(1.00, 1.22)
1.04
(0.94, 1.15)
1.06
(0.96, 1.17)
1.51
(1.30, 1.75)
1.28
(1.10, 1.49)
1.08
(0.91, 1.27)
1.11
(0.96, 1.30)
1.62
(1.29, 2.05)
1.28
(0.94, 1.73)
1.02
(0.77, 1.35)
1.22
(0.95, 1.58)
1
2
3
4
ORAC Quartile
A)
1.20
(1.11, 1.30)
1.07
(0.99, 1.16)
1.06
(0.98, 1.14)
1.00
(reference)
1.19
(1.09, 1.30)
1.07
(0.98, 1.16)
1.11
(1.02, 1.22)
1.07
(0.98, 1.17)
1.28
(1.17, 1.41)
1.15
(1.05, 1.27)
1.08
(0.98, 1.19)
1.00
(0.90, 1.10)
1.28
(1.16, 1.42)
1.16
(1.05, 1.28)
1.12
(1.01, 1.24)
1.09
(0.99, 1.21)
1
2
3
4
ORAC Quartile
B)
<1 1−1.9 2−2.9 3
Milk Intake, glasses/day
<1 1−1.9 2−2.9 3
Milk Intake,
g
lasses/day
Figure 3. Adjusted hazard ratios (HRs) and 95% condence inter-
vals (in parentheses) for all-cause mortality according to combined
daily intake of milk and oxygen radical absorbance capacity (ORAC;
µmol/day), using persons with the lowest intake of milk and the high-
est quartile of ORAC as the reference group. A) HRs based on the
Swedish Mammography Cohort after administration of the second
food frequency questionnaire (women, baseline 1997); B) HRs based
on the Cohort of Swedish Men (men, baseline 1997). The shading
corresponds to the value of the HR; the darker the shading, the larger
the HR. One glass of milk corresponds to 200 mL. Covariates were
age, body mass index (weight (kg)/height (m)
2
), height, energy intake,
alcohol intake, intakes of yogurt, cheese, and red and processed
meat, education, marital status (living alone vs. not), physical activity
(metabolic equivalent-hours/day), smoking habits (never, former, or
current smoker and pack-years of smoking), ever use of antioxidant-
containing supplements, and weighted Charlsons comorbidity
index.
Am J Epidemiol. 2017;185(5):345361
Milk, Antioxidant Foods, and Mortality 357
because galactose supplementation results in premature
aging in animals through induction of oxidative stress and
inammation (25). Oxidative stress induced by galactose is
a consequence of an imbalance between prooxidant and
antioxidant defenses, which causes accumulation of ad-
vanced glycation end-products and reactive oxygen species,
especially superoxide radicals and hydrogen peroxide (25).
Indeed, we have previously noted higher concentrations of
oxidative stress and inammation markers in human urine
and serum with high consumption of milk (1).
Mortality rates were increased at more moderate levels of
milk consumption in women as compared with men; excess
mortality was seen with 12 glasses of milk per day in women,
while twice that amount was necessary for the observation of
excess mortality in men. A sex difference in sensitivity to
galactose exposure has been identied experimentally (68),
and galactose elimination capacity is also higher in men than
in women, but it declines with increasing age (4749).
Galactose is used in the endogenous production of human
breast milk. Most lactose in human breast milk is synthe-
sized from galactose taken up from the blood, and only
one-third is made from endogenous synthesis (50). Liver
glycogen in infants is formed mainly from breast milkde-
rived galactose (51), and it acts as a reservoir for subsequent
hepatic glucose release to the circulation during times of
fasting (52,53). A lower female degradation of galactose
might have been an evolutionary survival mechanism for
the child. Specically, the enzyme galactose-1-phosphate
uridylyltransferase (GALT) in the Leloir breakdown pathway
of galactose (Figure 4) has a higher activity in male animals
than in female animals (7,54,55). GALT deciency is also
the main cause of galactosemia, an inborn error of metabol-
ism resulting in early death without avoidance of galactose
intake. With lower capacity of galactose degradation to glu-
cose by the Leloir pathway, an alternative route is the polyol
pathway, where galactose is converted to galactitol by al-
dose reductase, secondarily leading to free-radical formation
(56,57). In addition, galactose reacts nonenzymatically with
amino groups in proteins and peptides, forming advanced
glycation end-products (3). Although the exact mechanisms
are not known, galactose-treated rodents, ies, and tissue
culture cells also display evidence of lower-than-expected
antioxidant enzyme activities, suggesting that the normal
defenses might be compromised (2,5,58).
By reducing oxidative stress and inammation processes,
higher fruit and vegetable intake has convincingly been
shown to promote longevity (17,59) and reduce the risk of
cardiovascular disease (13,17,60) and some cancers (61).
Intriguingly, and supporting our results in women, there is
experimental evidence that fruits and vegetables or extracts
of them can rescue animals from the premature aging pheno-
type induced by galactose supplementation (2024,62,63).
We found no clear interaction between milk intake and
fruit and vegetable intake in men. This failure to nd an
interaction could have several explanations. The association
between milk and mortality was more modest in men, and a
clear excess mortality rate was observed in men only above
3 glasses/day, which limited our possibility to detect an
Galactitol
Galactonate
Aldose
Reductase
Galactose
Dehydrogenase
Lactase
Galactose-1-phosphate
Lactose From Milk
Endogenous Production
Free Radicals
Galactose
UDP-Galactose
UDP-Glucose
Galactokinase
(GALK)
Galactose 1-Phosphate
Uridyltransferase (GALT)
Uridine Diphosphate
Galactose 4-
Epimerase (GALE)
Advanced Glycation End
Products (AGEs)
Nonenzymatic reaction with
amino groups in proteins, lipids,
and nucleic acids
Figure 4. Overview of galactose metabolism. The major pathway of galactose metabolism (the Leloir pathway) operates via the enzymes galac-
tokinase (GALK), galactose-1-phosphate uridylyltransferase (GALT), and uridine diphosphate (UDP) galactose 4-epimerase (GALE), resulting in
UDP-glucose. The conversion to galactitol by aldose reductase via the polyol pathway results in decreased availability of nicotinamide adenine di-
nucleotide phosphate (NADPH) and glutathione, with increased production of free radicals (56). By way of a nonenzymatic reaction with amino
groups in proteins, lipids, and nucleic acids, galactose is converted to advanced glycation end products (AGEs).
Am J Epidemiol. 2017;185(5):345361
358 Michaëlsson et al.
interaction pattern with fruit and vegetables. Furthermore,
the gene expression and activity of antioxidant enzymes
(such as mitochondrial glutathione peroxidase and super-
oxide dismutase) in animals seem to be higher in females
than in males, giving females an enhanced capacity to pro-
vide mitigation of oxidative damage through an increased
intake of antioxidants (64).
The main strengths of this study were the use of data
from 2 large population-based cohorts and the comprehen-
sive FFQ administered in a setting with a wide range of
milk intakes and intakes of antioxidant foods. We found
consistency in the results irrespective of whether fruit and
vegetable consumption or ORAC was used as the effect
measure modier. Loss to follow-up was negligible, be-
cause we used the individual personal identication number
for linkage to the death registry. Through the use of time-
updated information and with a larger number of outcomes
in the SMC, we observed stronger estimates compared with
use of a single assessment of exposure. Questions regarding
fruit and vegetable intake were more diversied in the sec-
ond FFQ, administered in 1997, but still the mortality rate
patterns with a threshold at 5 servings/day were similar
(Figure 1B). Our results might not apply to people of other
ethnic origins, such as those with a high prevalence of lac-
tose intolerance, or to children and adolescents.
Our observational results in this population of Swedish
adults question the value of recommending high consump-
tion of milk, especially in women not meeting the recom-
mended requirements for fruit and vegetable intake (5
servings/day).
ACKNOWLEDGMENTS
Author afliations: Unit of Orthopedics, Department of
Surgical Sciences, Faculty of Medicine, Uppsala University,
Uppsala, Sweden (Karl Michaëlsson, Liisa Byberg); Unit of
Clinical Pharmacology, Department of Medical Sciences,
Faculty of Medicine, Uppsala University, Uppsala, Sweden
(Håkan Melhus); and Unit of Nutritional Epidemiology,
Institute of Environmental Medicine, Karolinska Institutet,
Stockholm, Sweden (Alicja Wolk).
This work was supported by grants from the Swedish
Research Council.
The funding organization played no role in the design or
conduct of the study.
Conict of interest: none declared.
REFERENCES
1. Michaëlsson K, Wolk A, Langenskiold S, et al. Milk intake
and risk of mortality and fractures in women and men: cohort
studies. BMJ. 2014;349:g6015.
2. Cui X, Zuo P, Zhang Q, et al. Chronic systemic D-galactose
exposure induces memory loss, neurodegeneration, and
oxidative damage in mice: protective effects of R-alpha-lipoic
acid. J Neurosci Res. 2006;83(8):15841590.
3. Song X, Bao M, Li D, et al. Advanced glycation in
D-galactose induced mouse aging model. Mech Ageing Dev.
1999;108(3):239251.
4. Hao L, Huang H, Gao J, et al. The inuence of gender, age
and treatment time on brain oxidative stress and memory
impairment induced by D-galactose in mice. Neurosci Lett.
2014;571C:4549.
5. Cui X, Wang L, Zuo P, et al. D-galactose-caused life
shortening in Drosophila melanogaster and Musca domestica
is associated with oxidative stress. Biogerontology. 2004;5(5):
317325.
6. Lin YN, Radin NS. Sexual differences in galactose
metabolism: galactosyl ceramide galactosidase and other
galactosidases in mouse kidney. Biochem J. 1973;136(4):
11251127.
7. Parkhurst GW, Mayes JS. Galactose toxicity and activities of
the galactose-metabolizing enzymes during development of
the chick. Arch Biochem Biophys. 1972;150(2):742745.
8. Nordin JH, Wilken DR, Bretthauer RK, et al. A consideration
of galactose toxicity in male and female chicks. Poultry Sci.
1960;39(4):802812.
9. Reuter S, Gupta SC, Chaturvedi MM, et al. Oxidative stress,
inammation, and cancer: how are they linked? Free Radic
Biol Med. 2010;49(11):16031616.
10. Manolagas SC, Partt AM. What old means to bone. Trends
Endocrinol Metab. 2010;21(6):369374.
11. Michaëlsson K, Wolk A, Byberg L, et al. Intake and serum
concentrations of alpha-tocopherol in relation to fractures in
elderly women and men: 2 cohort studies. Am J Clin Nutr.
2014;99(1):107114.
12. Harasym J, Oledzki R. Effect of fruit and vegetable
antioxidants on total antioxidant capacity of blood plasma.
Nutrition. 2014;30(5):511517.
13. Estruch R, Ros E, Salas-Salvado J, et al. Primary prevention
of cardiovascular disease with a Mediterranean diet. N Engl J
Med. 2013;368(14):12791290.
14. Fito M, Guxens M, Corella D, et al. Effect of a traditional
Mediterranean diet on lipoprotein oxidation: a randomized
controlled trial. Arch Intern Med. 2007;167(11):11951203.
15. Kris-Etherton PM, Hu FB, Ros E, et al. The role of tree nuts
and peanuts in the prevention of coronary heart disease:
multiple potential mechanisms. J Nutr. 2008;138(9):
1746S1751S.
16. Bellavia A, Larsson SC, Bottai M, et al. Fruit and vegetable
consumption and all-cause mortality: a dose-response analysis.
Am J Clin Nutr. 2013;98(2):454459.
17. Wang X, Ouyang Y, Liu J, et al. Fruit and vegetable consumption
and mortality from all causes, cardiovascular disease, and
cancer: systematic review and dose-response meta-analysis of
prospective cohort studies. BMJ. 2014;349:g4490.
18. Rautiainen S, Serani M, Morgenstern R, et al. The validity
and reproducibility of food-frequency questionnaire-based
total antioxidant capacity estimates in Swedish women. Am J
Clin Nutr. 2008;87(5):12471253.
19. Rautiainen S, Levitan EB, Orsini N, et al. Total antioxidant
capacity from diet and risk of myocardial infarction: a
prospective cohort of women. Am J Med. 2012;125(10):
974980.
20. Coban J, Dogan-Ekici I, Aydin AF, et al. Blueberry treatment
decreased D-galactose-induced oxidative stress and brain
damage in rats. Metab Brain Dis. 2015;30(3):793802.
21. Coban J, Betul-Kalaz E, Kucukgergin C, et al. Blueberry
treatment attenuates D-galactose-induced oxidative stress and
tissue damage in rat liver. Geriatr Gerontol Int. 2014;14(2):
490497.
Am J Epidemiol. 2017;185(5):345361
Milk, Antioxidant Foods, and Mortality 359
22. Stefek M. Natural avonoids as potential multifunctional
agents in prevention of diabetic cataract. Interdiscip Toxicol.
2011;4(2):6977.
23. Ghanbari S, Yonessi M, Mohammadirad A, et al. Effects of
IMODand Angiparson mouse D-galactose-induced
model of aging. Daru. 2012;20(1):68.
24. Mohammadirad A, Aghamohammadali-Sarraf F, Badiei S,
et al. Anti-aging effects of some selected Iranian folk
medicinal herbsbiochemical evidences. Iran J Basic Med
Sci. 2013;16(11):11701180.
25. European Food Information Council. Fruit and Vegetable
Consumption in EuropeDo Europeans Get Enough?
Brussels, Belgium: European Food Information Council;
2012.
26. Boffetta PL, Couto E, Wichmann J, et al. Fruit and vegetable
intake and overall cancer risk in the European Prospective
Investigation into Cancer and Nutrition (EPIC). J Natl Cancer
Inst. 2010;102(8):529537.
27. Willett W. Nutritional Epidemiology. 3rd ed. New York, NY:
Oxford University Press; 2013.
28. Willett WC, Howe GR, Kushi LH. Adjustment for total
energy intake in epidemiologic studies. Am J Clin Nutr. 1997;
65(4 suppl):1220S1228S.
29. Thomas LD, Michaëlsson K, Julin B, et al. Dietary cadmium
exposure and fracture incidence among men: a
population-based prospective cohort study. J Bone Miner Res.
2011;26(7):16011608.
30. Warensjö E, Byberg L, Melhus H, et al. Dietary calcium
intake and risk of fracture and osteoporosis: prospective
longitudinal cohort study. BMJ. 2011;342:d1473.
31. Larsson SC, Bergkvist L, Wolk A. Long-term dietary calcium
intake and breast cancer risk in a prospective cohort of
women. Am J Clin Nutr. 2009;89(1):277282.
32. Brugård Konde Å, Bjerselius R, Haglund L, et al. Swedish
Dietary GuidelinesRisk and Benet Management Report.
(Livsmedelsverkets rapportserie no. 5/2015). Uppsala,
Sweden: Livsmedelsverket (National Food Agency); 2015.
http://www.livsmedelsverket.se/globalassets/rapporter/2015/
rapp-hanteringsrapport-engelska-omslaginlagabilagor-eng-
version.pdf. Accessed April 21, 2016.
33. Hansson LM, Galanti MR. Diet-associated risks of disease and
self-reported food consumption: how shall we treat partial
nonresponse in a food frequency questionnaire? Nutr Cancer.
2000;36(1):16.
34. Bergstrom L, Kylberg E, Hagman U, et al. The food
composition database KOST: the National Administrations
information system for nutritive values of food [in Swedish].
Vår Föda. 1991;43:439447.
35. Larsson SC, Andersson SO, Johansson JE, et al. Cultured
milk, yoghurt, and dairy intake in relation to bladder cancer
risk in a prospective study of Swedish women and men. Am J
Clin Nutr. 2008;88(4):10831087.
36. Natella F, Nardini M, Giannetti I, et al. Coffee drinking
inuences plasma antioxidant capacity in humans. J Agric
Food Chem. 2002;50(21):62116216.
37. StataCorp LP. Stata Reference Manual, Release 11. College
Station, TX: Stata Press; 2009.
38. VanderWeele TJ, Hernan MA, Robins JM. Causal directed
acyclic graphs and the direction of unmeasured confounding
bias. Epidemiology. 2008;19(5):720728.
39. Charlson ME, Pompei P, Ales KL, et al. A new method of
classifying prognostic comorbidity in longitudinal studies:
development and validation. J Chronic Dis. 1987;40(5):
373383.
40. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms
for dening comorbities in ICD-9-CM and ICD-10
administrative data. Med Care. 2005;43(11):11301139.
41. Horton NJ, Kleinman KP. Much ado about nothing: a
comparison of missing data methods and software to t
incomplete data regression models. Am Stat. 2007;61(1):
7990.
42. Knol MJ, VanderWeele TJ. Recommendations for presenting
analyses of effect modication and interaction. Int J
Epidemiol. 2012;41(2):514520.
43. Larsson SC, Crippa A, Orsini N, et al. Non-fermented milk
consumption and mortality from all causes, cardiovascular
disease, and cancer: a systematic review and meta-analysis.
Nutrients. 2015;7(9):77497763.
44. Timpson NJ, Brennan P, Gaborieau V, et al. Can lactase
persistence genotype be used to reassess the relationship
between renal cell carcinoma and milk drinking? Potentials
and problems in the application of Mendelian randomization.
Cancer Epidemiol Biomarkers Prev. 2010;19(5):13411348.
45. Corella D, Arregui M, Coltell O, et al. Association of the
LCT-13910C>T polymorphism with obesity and its
modulation by dairy products in a Mediterranean population.
Obesity. 2011;19(8):17071714.
46. Wagh K, Bhatia A, Alexe G, et al. Lactase persistence and lipid
pathway selection in the Maasai. PLoS One. 2012;7(9):e44751.
47. Tygstrup N. The galactose elimination capacity in control
subjects and in patients with cirrhosis of the liver. Acta Med
Scand. 1964;175:281289.
48. Schnegg M, Lauterburg BH. Quantitative liver function in the
elderly assessed by galactose elimination capacity,
aminopyrine demethylation and caffeine clearance. J Hepatol.
1986;3(2):164171.
49. Marchesini G, Bua V, Brunori A, et al. Galactose elimination
capacity and liver volume in aging man. Hepatology. 1988;
8(5):10791083.
50. Sunehag A, Tigas S, Haymond MW. Contribution of plasma
galactose and glucose to milk lactose synthesis during
galactose ingestion. J Clin Endocrinol Metab. 2003;88(1):
225229.
51. Kunst C, Kliegman R, Trindade C. The glucose-galactose
paradox in neonatal murine hepatic glycogen synthesis. Am J
Physiol. 1989;257(5):E697E703.
52. Brown LD, Cavalli C, Harwood JE, et al. Plasma
concentrations of carbohydrates and sugar alcohols in term
newborns after milk feeding. Pediatr Res. 2008;64(2):
189193.
53. Spedale SB, Battaglia FC, Sparks JW. Hepatic metabolism of
glucose, galactose, and lactate after milk feeding in newborn
lambs. Am J Physiol. 1992;262(1):E46E51.
54. Mayes JS, Miller LR, Myers FK. The relationship of
galactose-1-phosphate accumulation and uridyl transferase
activity to the differential galactose toxicity in male and
female chicks. Biochem Biophys Res Commun. 1970;39(4):
661665.
55. McCluer RH, Gross SK. Biosynthesis of neutral
glycosphingolipids in kidney slices from male and female
mice. J Lipid Res. 1985;26(5):593599.
56. Kubo E, Miyoshi N, Fukuda M, et al. Cataract formation
through the polyol pathway is associated with free radical
production. Exp Eye Res. 1999;68(4):457464.
57. Lai K, Elsas LJ, Wierenga KJ. Galactose toxicity in animals.
IUBMB Life. 2009;61(11):10631074.
58. Jumbo-Lucioni PP, Hopson ML, Hang D, et al. Oxidative
stress contributes to outcome severity in a Drosophila
Am J Epidemiol. 2017;185(5):345361
360 Michaëlsson et al.
melanogaster model of classic galactosemia. Dis Model Mech.
2013;6(1):8494.
59. Zhang X, Shu XO, Xiang YB, et al. Cruciferous vegetable
consumption is associated with a reduced risk of total and
cardiovascular disease mortality. Am J Clin Nutr. 2011;94(1):
240246.
60. He FJ, Nowson CA, MacGregor GA. Fruit and vegetable
consumption and stroke: meta-analysis of cohort studies.
Lancet. 2006;367(9507):320326.
61. Giacosa A, Barale R, Bavaresco L, et al. Cancer prevention in
Europe: the Mediterranean diet as a protective choice. Eur J
Cancer Prev. 2013;22(1):9095.
62. Chang L, Liu X, Liu J, et al. D-galactose induces a
mitochondrial complex I deciency in mouse skeletal muscle:
potential benets of nutrient combination in ameliorating
muscle impairment. J Med Food. 2014;17(3):357364.
63. Lu J, Wu DM, Zheng YL, et al. Ursolic acid attenuates
D-galactose-induced inammatory response in mouse
prefrontal cortex through inhibiting AGEs/RAGE/NF-kappaB
pathway activation. Cereb Cortex. 2010;20(11):25402548.
64. Borras C, Sastre J, Garcia-Sala D, et al. Mitochondria from
females exhibit higher antioxidant gene expression and lower
oxidative damage than males. Free Radic Biol Med. 2003;
34(5):546552.
Am J Epidemiol. 2017;185(5):345361
Milk, Antioxidant Foods, and Mortality 361

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Diet has long been the focus of attention as a leading risk factor for non-communicable diseases. As such, a better understanding of it is crucial to establish priorities for dietary guidelines and to inform, design, and implement strategies for preventing, helping manage, and stopping the progression of sleep and mental health-related symptoms/disorders. The aim of the current study is to conduct the largest investigation of diet, sleep, and mental health to date by utilizing the UK Biobank (UKB) dataset to identify the associations between diet and (i) sleep quality/health, and (ii) mental health symptomatology. This cross-sectional population-based study involved 502,494 middle-aged adults. UKB food frequency, sleep, and psychological factors and mental health questionnaires at baseline were used. Scores were also calculated for healthy diet, healthy sleep, mental health symptomatology, partial fibre intake, and milk intake. We observed positive associations with healthy diet and sleep and mental health, especially benefits of high intakes of vegetable, fruit, fish, water, and fibre. However, processed meat and milk intake were adversely associated with sleep and mental health. These findings make clear that there are health and wellbeing benefits and drawbacks of different diets, but do not, at this stage, demonstrate the clear causal relationships, which would support dietary interventions that might play a role in the treatment and also self-management of sleep and mental health disorders/symptoms. Further research is required to understand mechanisms of actions of which diet acts on to modulate sleep and mental health, while taking comorbidity of sleep and mental health disorders/symptoms into consideration.
... On the other hand, high milk intake and worse general cognitive ability association we observed, supporting previous findings that showed associations between (i) milk intake greater than 1 glass/day and greater cognitive decline 34 , (ii) wholefat dairy intake and greater cognitive decline 33 , and (iii) saturated fat intakes from milk products and poorer global cognitive function and prospective memory 35 . Dietary saturated fatty acids 36 and d-galactose 37,38 in milk are known to cause systemic inflammation in humans and animals, respectively. Given the role of inflammation and the gut brain axis in modulating cognition 31,32,39 , these findings raise the possibility that higher milk intake, hence d-galactose and saturated fat intake, may cause inflammation and exacerbate cognition. ...
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Accumulating evidence suggests that dietary interventions might have potential to be used as a strategy to protect against age-related cognitive decline and neurodegeneration, as there are associations between some nutrients, food groups, dietary patterns, and some domains of cognition. In this study, we aimed to conduct the largest investigation of diet and cognition to date, through systematically examining the UK Biobank (UKB) data to find out whether dietary quality and food groups play a role on general cognitive ability. This cross-sectional population-based study involved 48,749 participants. UKB data on food frequency questionnaire and cognitive function were used. Also, healthy diet, partial fibre intake, and milk intake scores were calculated. Adjusted models included age, sex, and BMI. We observed associations between better general cognitive ability and higher intakes of fish, and unprocessed red meat; and moderate intakes of fibre, and milk. Surprisingly, we found that diet quality, vegetable intake, high and low fibre and milk intake were inversely associated with general cognitive ability. Our results suggest that fish and unprocessed red meat and/or nutrients that are found in fish and unprocessed red meat might be beneficial for general cognitive ability. However, results should be interpreted in caution as the same food groups may affect other domains of cognition or mental health differently. These discrepancies in the current state of evidence invites further research to examine domain-specific effects of dietary patterns/food groups on a wide range of cognitive and affective outcomes with a special focus on potential covariates that may have an impact on diet and cognition relationship.
... In einer großen Metaanalyse [22] [28]. Für Männer lagen die entsprechenden HR bei 1,31 (95% CI: 1,14-1,51) und 1,07 (95% CI: 0,97-1,18). ...
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Background: Recent epidemiological studies associate the consumption of non-fermented cow's milk, but not fermented milk products, with an increased risk of diseases of civilization. Objectives: Presentation of epidemiological and pathophysiological data on health risks associated with milk consumption. Method: Selective PubMed surveys between 2005-2020 considering epidemiological studies which clearly differentiate between non-fermented versus fermented milk and its potential health risks. Results: Epidemiological studies confirm a correlation between milk consumption and birthweight, linear growth during puberty, acne vulgaris, type 2 diabetes mellitus, prostate cancer, breast cancer, hepatocellular carcinoma, non-Hodgkin lymphoma, Parkinson's disease and over-all mortality. In comparison to milk consumption, the intake of fermented milk/milk products exhibits neutral to beneficial health effects, which are explained by attenuated mTORC1 signaling due to bacterial fermentation of milk. Conclusions: Long-term persistent consumption of non-fermented milk, but not fermented milk/milk products, might increase the risk of diseases of civilization. The avoidance of milk, especially pasteurized fresh milk, may enhance the prevention and reduce the recurrence of common Western diseases of civilization.
... Four epidemiological studies from Sweden, a country with high per capita milk consumption of pasteurized fresh milk, underline an increased dose-dependent risk of all-cause mortality with the consumption of milk [527][528][529][530][531], but not fermented milk/milk products [528,531,532]. ...
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The consumption of cow’s milk is a part of the basic nutritional habits of Western industrialized countries. Recent epidemiological studies associate the intake of cow’s milk with an increased risk of diseases, which are associated with overactivated mechanistic target of rapamycin complex 1 (mTORC1) signaling. This review presents current epidemiological and translational evidence linking milk consumption to the regulation of mTORC1, the master-switch for eukaryotic cell growth. Epidemiological studies confirm a correlation between cow’s milk consumption and birthweight, body mass index, onset of menarche, linear growth during childhood, acne vulgaris, type 2 diabetes mellitus, prostate cancer, breast cancer, hepatocellular carcinoma, diffuse large B-cell lymphoma, neurodegenerative diseases, and all-cause mortality. Thus, long-term persistent consumption of cow’s milk increases the risk of mTORC1-driven diseases of civilization. Milk is a highly conserved, lactation genome-controlled signaling system that functions as a maternal-neonatal relay for optimized species-specific activation of mTORC1, the nexus for regulation of eukaryotic cell growth, and control of autophagy. A deeper understanding of milk´s impact on mTORC1 signaling is of critical importance for the prevention of common diseases of civilization.
... High dairy consumption appears to increase overall mortality by inducing oxidative stress and low-grade inflammation. 3,4 Additionally, significantly higher rates were observed for cardiovascular disease and cancer for each glass of milk in affected women. 4 High dairy intake was also shown to be a significant predictor of insulin resistance in middle-aged women. ...
... Fermented milk intake was divided into the same categories as milk intake but because of lower fermented milk intake, the two highest categories were combined. Missing information on milk and fermented milk was treated as no intake in the primary analysis [19]. Potential nonlinear trends between milk and fermented milk intake and incident PD were assessed by restricted cubic spline Cox regression with three knots placed at the 10th, 50th, and 90th percentiles of the distribution of the exposures [20]. ...
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Milk and fermented milk consumption has been linked to health and mortality but the association with Parkinson's disease (PD) is uncertain. We conducted a study to investigate whether milk and fermented milk intakes are associated with incident PD. This cohort study included 81,915 Swedish adults (with a mean age of 62 years) who completed a questionnaire, including questions about milk and fermented milk (soured milk and yogurt) intake, in 1997. PD cases were identified through linkage with the Swedish National Patient and Cause of Death Registers. Multivariable-adjusted hazard ratios were obtained from Cox proportional hazards regression models. During a mean follow-up of 14.9 years, 1251 PD cases were identified in the cohort. Compared with no or low milk consumption (<40 mL/day), the hazard ratios of PD across quintiles of milk intake were 1.29 (95% CI 1.07, 1.56) for 40-159 mL/day, 1.19 (95% CI 0.99, 1.42) for 160-200 mL/day, 1.29 (95% CI 1.08, 1.53) for 201-400 mL/day, and 1.14 (95% CI 0.93, 1.40) for >400 mL/day. Fermented milk intake was not associated with PD. We found a weak association between milk intake and increased risk of PD but no dose-response relationship. Fermented milk intake was not associated with increased risk of PD.
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Objective Many studies have showed an inverse association between higher total antioxidant capacity of the diet (DTAC) and chronic non communicable diseases, including cancer. Thus, the main objective of this study was to evaluate the association of the dietary total antioxidant capacity with anthropometric and biochemical indicators and clinical outcomes in hospitalized cancer patients. Methods A cross-sectional study was carried out with 196 hospitalized patients diagnosed with câncer. The DTAC, determined by the Ferric Reducing Antioxidant Power method, was calculated using a validated standard spreadsheet. Multivariate linear regression was used to assess the association, identifying anthropometric indicators that had association of DTAC with the variables of interest. A level of statistical significance of p <0.05 was considered. Results The individuals included in the last tertile of DTAC presented lower occurrences of death (p=0,032), constipation (p=0,010), dysphagia (p=0,010), painful swallowing and chewing (p=0,019), and dehydration (p=0,032) when compared to individuals in the first tertile. The CRP values were significantly lower (p=0,010) and handgrip strength values were higher (p=0,037) in individuals in the third tertile, when compared to the other participants. Conclusions DTAC was associated with a better prognosis of hospitalized cancer patients, considering signs and symptoms of nutritional impact, as well as the inflammatory state of the patient. All these factors may influence the length of hospital stay and mortality. The findings of this research provide important information for a preventive and nutritional management perspective in this population.
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Results from epidemiological studies of milk consumption and mortality are inconsistent. We conducted a systematic review and meta-analysis of prospective studies assessing the association of non-fermented and fermented milk consumption with mortality from all causes, cardiovascular disease, and cancer. PubMed was searched until August 2015. A two-stage, random-effects, dose-response meta-analysis was used to combine study-specific results. Heterogeneity among studies was assessed with the I² statistic. During follow-up periods ranging from 4.1 to 25 years, 70,743 deaths occurred among 367,505 participants. The range of non-fermented and fermented milk consumption and the shape of the associations between milk consumption and mortality differed considerably between studies. There was substantial heterogeneity among studies of non-fermented milk consumption in relation to mortality from all causes (12 studies; I² = 94%), cardiovascular disease (five studies; I² = 93%), and cancer (four studies; I² = 75%) as well as among studies of fermented milk consumption and all-cause mortality (seven studies; I² = 88%). Thus, estimating pooled hazard ratios was not appropriate. Heterogeneity among studies was observed in most subgroups defined by sex, country, and study quality. In conclusion, we observed no consistent association between milk consumption and all-cause or cause-specific mortality.
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A CONSIDERATION of galactose toxicity and metabolism in the chick is significant in that milk products are often fed to poultry. As problems arise in some animals and humans when galactose is ingested, it is important to understand the basis of galactose toxicity. The chick is particularly sensitive to ingested galactose and exhibits some striking and characteristic symptoms. In so far as results with he chicks may be generalized, such studies are also of some theoretical significance. Although Dam (1944) reported that a diet containing 55 percent galactose caused convulsions and death of young chicks, it was subsequently found (Rutter et al., 1953) that galactose is not tolerated by chicks when fed at levels exceeding 10 percent of the diet. A typical quivering syndrome develops after several days of feeding galactose and in more severe cases epileptiform seizures and sometimes death result. Traces of lactose and high concentrations of galactose …
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Objective To examine whether high milk consumption is associated with mortality and fractures in women and men. Design Cohort studies. Setting Three counties in central Sweden. Participants Two large Swedish cohorts, one with 61 433 women (39-74 years at baseline 1987-90) and one with 45 339 men (45-79 years at baseline 1997), were administered food frequency questionnaires. The women responded to a second food frequency questionnaire in 1997. Main outcome measure Multivariable survival models were applied to determine the association between milk consumption and time to mortality or fracture. Results During a mean follow-up of 20.1 years, 15 541 women died and 17 252 had a fracture, of whom 4259 had a hip fracture. In the male cohort with a mean follow-up of 11.2 years, 10 112 men died and 5066 had a fracture, with 1166 hip fracture cases. In women the adjusted mortality hazard ratio for three or more glasses of milk a day compared with less than one glass a day was 1.93 (95% confidence interval 1.80 to 2.06). For every glass of milk, the adjusted hazard ratio of all cause mortality was 1.15 (1.13 to 1.17) in women and 1.03 (1.01 to 1.04) in men. For every glass of milk in women no reduction was observed in fracture risk with higher milk consumption for any fracture (1.02, 1.00 to 1.04) or for hip fracture (1.09, 1.05 to 1.13). The corresponding adjusted hazard ratios in men were 1.01 (0.99 to 1.03) and 1.03 (0.99 to 1.07). In subsamples of two additional cohorts, one in males and one in females, a positive association was seen between milk intake and both urine 8-iso-PGF2α (a biomarker of oxidative stress) and serum interleukin 6 (a main inflammatory biomarker). Conclusions High milk intake was associated with higher mortality in one cohort of women and in another cohort of men, and with higher fracture incidence in women. Given the observational study designs with the inherent possibility of residual confounding and reverse causation phenomena, a cautious interpretation of the results is recommended.
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Objective To examine and quantify the potential dose-response relation between fruit and vegetable consumption and risk of all cause, cardiovascular, and cancer mortality. Data sources Medline, Embase, and the Cochrane library searched up to 30 August 2013 without language restrictions. Reference lists of retrieved articles. Study selection Prospective cohort studies that reported risk estimates for all cause, cardiovascular, and cancer mortality by levels of fruit and vegetable consumption. Data synthesis Random effects models were used to calculate pooled hazard ratios and 95% confidence intervals and to incorporate variation between studies. The linear and non-linear dose-response relations were evaluated with data from categories of fruit and vegetable consumption in each study. Results Sixteen prospective cohort studies were eligible in this meta-analysis. During follow-up periods ranging from 4.6 to 26 years there were 56 423 deaths (11 512 from cardiovascular disease and 16 817 from cancer) among 833 234 participants. Higher consumption of fruit and vegetables was significantly associated with a lower risk of all cause mortality. Pooled hazard ratios of all cause mortality were 0.95 (95% confidence interval 0.92 to 0.98) for an increment of one serving a day of fruit and vegetables (P=0.001), 0.94 (0.90 to 0.98) for fruit (P=0.002), and 0.95 (0.92 to 0.99) for vegetables (P=0.006). There was a threshold around five servings of fruit and vegetables a day, after which the risk of all cause mortality did not reduce further. A significant inverse association was observed for cardiovascular mortality (hazard ratio for each additional serving a day of fruit and vegetables 0.96, 95% confidence interval 0.92 to 0.99), while higher consumption of fruit and vegetables was not appreciably associated with risk of cancer mortality. Conclusions This meta-analysis provides further evidence that a higher consumption of fruit and vegetables is associated with a lower risk of all cause mortality, particularly cardiovascular mortality.
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In adults glucose incorporation to glycogen is indirect after recycling from lactate. In neonates galactose entry to glycogen exceeds that for glucose, but the pathway is unknown. The pathway of hexose incorporation to glycogen was studied in 5-7-day-old rats and 6-h-old rats injected intraperitoneally (IP) with either double-labeled [6-3H]glucose (nonrecycling), [U-14C]glucose (recycling), or [6-3H]glucose and [U-14C]galactose in saline. In another group of pups, 1 g/kg of glucose or galactose was administered in addition to tracers to determine glycemia and net glycogen synthesis between 15 and 180 min after injection. Blood glucose increased from 3.4 +/- 0.4 to 8.5 +/- 1.5 mM in 5-7-day-old pups in response to IP glucose; there was no glycemic response to galactose, although galactose levels increased from 0.5 to 6.3 mM at 15 min. Hepatic glycogen increased after IP glucose from 14 +/- 2 at 15 min to 30 +/- 3 at 120 min (P less than 0.01), whereas after IP galactose glycogen was 44 +/- 6 mumol/g at 120 min (P less than 0.05). After IP glucose, 3H and 14C disintegration per minute in glycogen increased slowly with 14C exceeding 3H at 120 and 180 min. In contrast IP [14C]galactose resulted in a much greater peak of 14C incorporation into glycogen. The ratio of 3H to 14C in glycogen relative to the injectate after IP glucose decreased from 0.69 +/- 0.12 to 0.36 +/- 0.03 (P less than 0.01) between 15 to 180 min, whereas the ratio after galactose was 0.20 +/- 0.007 to 0.15 +/- 0.02 at these times. The 6-h-old pups also demonstrated augmented incorporation of [14C]galactose in glycogen relative to [3H-14C]glucose. In contrast to 5-7-day-old pups there was no evidence of glucose recycling in 6-h-old pups. In conclusion galactose entry into glycogen exceeds that for glucose and is not dependent on recycling. Direct incorporation of galactose exceeds that for direct incorporation from [3H]glucose, suggesting a preferential utilization of galactose for neonatal glycogen synthesis.
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The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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D-galactose (GAL) causes aging-related changes and oxidative stress in the organism. We investigated the effect of whole fresh blueberry (BB) (Vaccinium corymbosum L.) treatment on oxidative stress in age-related brain damage model. Rats received GAL (300 mg/kg; s.c.; 5 days per week) alone or together with 5 % (BB1) and 10 % (BB2) BB containing chow for two months. Malondialdehyde (MDA),protein carbonyl (PC) and glutathione (GSH) levels, and Cu Zn-superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and glutathione transferase (GST) activities as well as acetylcholinesterase (AChE) activities were determined. Expressions of B cell lymphoma-2 (Bcl-2), Bax and caspase-3 were also evaluated in the brain by immunohistochemistry. MDA and PC levels and AChE activity increased, but GSH levels, SOD and GSH-Px activities decreased together with histopathological structural damage in the brain of GAL-treated rats. BB treatments, especially BB2 reduced MDA and PC levels and AChE activity and elevated GSH levels and GSH-Px activity. BB1 and BB2 treatments diminished apoptosis and ameliorated histopathological findings in the brain of GAL-treated rats. These results indicate that BB partially prevented the shift towards an imbalanced prooxidative status and apoptosis together with histopathological amelioration by acting as an antioxidant (radical scavenger) itself in GAL-treated rats.