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Dietary patterns are associated with cardiometabolic risk factors
in a representative study population of German adults
Christin Heidemann
1
, Christa Scheidt-Nave
1
, Almut Richter
2
and Gert B. M. Mensink
1
*
1
Department of Epidemiology and Health Monitoring, Robert Koch Institute, General-Pape-Strasse 64, D-12101 Berlin,
Germany
2
Department of Marketing and Consumer Research, Technische Universita
¨tMu
¨nchen, Alte Akademie 16, D-85350 Freising,
Germany
(Received 9 September 2010 – Revised 25 January 2011 – Accepted 11 February 2011 – First published online 17 May 2011)
Abstract
Studies that investigated complex actual eating behaviours of the general population and their relation to cardiometabolic risk markers are
sparse. We aimed to identify dietary patterns within a nationally representative sample of 4025 German adults by factor analysis based on
validated dietary history interviews. Furthermore, we evaluated associations of the derived dietary patterns with abnormalities clustered
within the metabolic syndrome and related metabolic markers by logistic regression models and ANCOVA. A high adherence to the ‘pro-
cessed foods’ pattern reflected a high intake of refined grains, processed meat, red meat, high-sugar beverages, eggs, potatoes, beer, sweets
and cakes, snacks and butter, whereas a high adherence to the ‘health-conscious’ pattern represented a high intake of vegetables, vegetable
oils, legumes, fruits, fish and whole grains. For subjects in the highest compared with those in the lowest quintile of the processed foods
pattern, the occurrence of abdominal obesity was 88 (95 % CI 31, 169) % higher, hypertension was 34 (95% CI 24, 86) % higher, hyper-
triacylglycerolaemia was 59 (95 % CI 11, 128 ) % higher and the metabolic syndrome was 64 (95 % CI 10, 143) % higher when adjusted for
age, sex, energy intake, socio-economic status, sport activity and smoking. Furthermore, subjects in the highest quintile had statistically
significantly higher uric acid concentrations and lower folate concentrations (Pfor trend ,0·05). In contrast, subjects in the highest quintile
of the health-conscious pattern had a 30 (95 % CI 10, 46) % lower occurrence of hypertension, higher folate concentrations and lower
homocysteine and fibrinogen concentrations (Pfor trend ,0·05). These data strengthen the findings from non-representative studies
and emphasise the importance of healthy overall food patterns for preventing metabolic disturbances.
Key words: Dietary patterns: Germany: Dietary surveys: Metabolic syndrome
Dietary pattern analysis has been recognised as an approach
that considers the complexity of overall diet and facilitates
nutritional recommendations
(1,2)
. Accordingly, organisations
aiming for the prevention and treatment of cardiovascular
and other chronic diseases increasingly focus on healthy
overall dietary patterns instead of solely on single dietary
components in their nutritional guidelines
(3,4)
.
Dietary patterns, which are exploratively derived by factor
analysis, reflect eating habits that are characteristic for the
underlying study population
(1)
. So far, most studies that have
identified such patterns and investigated their association
with cardiometabolic factors have been based on selected sub-
populations, particularly on subjects with a specific sex
(5 – 11)
,
age range
(5,9,10,12 – 14)
, professional background
(5 – 7,10)
or
living area
(8 – 10,15 – 18)
, rather than on nationally representative
samples. Thus, generalisability of results to the population
level was limited. Furthermore, most studies on food patterns
and metabolic risk factors involved study populations from the
USA
(6,7,17 – 21)
or Asian countries
(5,8 – 10,13,22)
, leaving largely
unclear whether similar findings can be observed in popu-
lations from other parts of the world, especially from
European countries.
Therefore, the aims of the present study were, first, to ident-
ify major food patterns existing in a nationally representative
sample of German adults and, second, to evaluate their associ-
ation with metabolic risk factors of CVD.
Methods
Study population
The German Health Interview and Examination Survey 1998,
including 7124 German adults representative of the non-
institutionalised 18- to 79-year-old German population, was
*Corresponding author: Dr. G. B. M. Mensink, fax þ49 30 18754 3211, email mensinkg@rki.de
Abbreviation: DISHES 98, Dietary Interview Software for Health Examination Studies.
British Journal of Nutrition (2011), 106, 1253–1262 doi:10.1017/S0007114511001504
qThe Authors 2011
British Journal of Nutrition
conducted from October 1997 until March 1999
(23)
. For the
selection of participants, a two-stage sampling procedure
was applied. First, a representative sample of communities
with regard to community size and federal state was drawn.
Random samples of adult residents stratified by age (5-year
intervals) and sex were then drawn from local population
registries in proportion to the age and sex structure of the
German adult population. The response rate was 61·4 %. A
weighting factor that adjusts for deviations in demographic
characteristics from the official German population assured
the population representativeness of participants.
For the present analysis, we included the randomly selected
subsample of 4030 subjects, who had also participated in a
comprehensive dietary assessment of the German Nutrition
Survey
(23)
. To correct for non-response and disproportionality
compared with population structure (due to efforts to include
a large proportion of women in childbearing age), a specific
weighting factor was derived for the German Nutrition
Survey sample. After excluding subjects with an implausible
low total energy intake (,800 kcal/d (,3347 kJ/d) for men
and ,500 kcal/d (,2092) kJ/d) for women), the overall ana-
lytical sample comprised 4025 subjects (1761 men and 2264
women). To account for the issue of reverse causation, we
excluded subjects with a history of myocardial infarction,
stroke, diabetes or cancer in the analysis of cardiometabolic
factors and the metabolic syndrome. To additionally minimise
the effects of medication or supplement use, we further
excluded subjects with antihypertensive medication in the
analysis of blood pressure, with antihyperlipidaemic medi-
cation in the analysis of lipids, with antidiabetic medication
in the analysis of glucose and HbA
1c
and with regular folate
or vitamin B-complex supplement use in the analysis of
folate and homocysteine. We also excluded pregnant
women from the analysis of BMI, waist circumference and
the metabolic syndrome.
The survey was approved by the Federal Office for the
Protection of Data, Germany. Each participant gave informed
written consent before enrolment into the survey.
Assessment of dietary intake and dietary patterns
The dietary assessment within the German Nutrition Survey
has been described in detail previously
(24)
. Briefly, trained
nutritionists interviewed participants using the Dietary Inter-
view Software for Health Examination Studies (DISHES 98;
Robert-Koch Institute, Berlin, Germany), a computerised
face-to-face dietary history instrument designed to assess the
usual dietary intake of the preceding 4 weeks. Based on the
assessment of the participant’s usual meal patterns, frequency
and amount of the food and drink items consumed during
each meal were obtained. In addition, questions on dietary
regimen, changes in dietary habits during the last 4 weeks,
use of dietary supplements and, for women, questions on
pregnancy and lactation were included in the interview. The
DISHES 98 tool codes the specified food items and dietary
supplements during the interview. For further standardisation,
the software connects item codes directly to the German Food
Code and Nutrient Data Base
(25)
version II.3 and a supplement
composition table to calculate nutrient intakes. Estimation of
portion sizes was facilitated by standardised tableware
models and food templates. The relative validity of DISHES
98 was assessed in comparison with 3 d weighted dietary
records and a 24 h dietary recall and revealed correlations
for nutrient intakes in a reasonable range (0·34– 0·69 for 3 d
weighted dietary records and 0·27–0·65 for the 24 h recall)
(24)
.
To identify dietary patterns, the 2678 different food items
assessed by DISHES 98 were first aggregated into food
groups. For that purpose, we used the 133 food categories
combined previously in the present study population
(26)
,
added fruit juice, beer, wine, liquor, coffee and tea as separate
categories for drinks, and aggregated all defined categories
according to similarities in nutrient profiles into thirty-four
food groups (compare with Table 1). Second, we applied
factor analysis (principal component analysis) with the orthog-
onal rotation procedure varimax to the predefined food
groups
(27)
. Each obtained dietary pattern (called factor)
represents a linear combination of all food groups, which
are weighted by their factor loadings. The first pattern
Table 1. Factor loadings for food groups of the two major dietary
patterns in a representative sample of German adults*
Food group
Factor 1
‘Processed
foods pattern’
Factor 2
‘Health-conscious
pattern’
Refined grains 0·72 –
Processed meat 0·66 –
Red meat 0·57 0·34
High-sugar beverages 0·50 20·16
Eggs 0·41 0·23
Potatoes 0·38 0·32
Beer 0·38 –
Sweets and cakes 0·37 –
Snacks 0·37 –
Butter 0·37 0·16
Organ meats 0·19 –
Margarine 0·19 –
Coffee 0·16 –
High-fat diary – –
Liquor – –
Mayonnaise – –
Fruit juice – –
Low-fat dairy – –
Tea 20·24 0·18
Cruciferous vegetables – 0·65
Fruity and root vegetables 20·19 0·58
Other vegetables† – 0·55
Leafy vegetables – 0·55
Vegetable oils 0·16 0·52
Legumes – 0·39
Fruits 20·32 0·39
Fish – 0·34
Whole grains 20·30 0·31
Other animal fats‡ 0·26 0·31
Poultry – 0·26
Nuts and seeds – 0·17
Olives and olive oil 0·16 0·17
Wine – 0·16
Low-sugar beverages – –
* Factor loadings are identical to Pearson’s correlation coefficients. Factor loadings
with absolute values ,0·15 are not shown for simplicity (n4025).
† Vegetables other than cruciferous, fruity and root or leafy vegetables.
‡ Animal fats other than butter.
C. Heidemann et al.1254
British Journal of Nutrition
explains as much inter-individual variation of the food groups
as possible, the next pattern explains as much of the remain-
ing variation as possible and so on. Each subject receives a
score for each dietary pattern, with a higher score indicating
a higher adherence to the respective pattern. We determined
the dietary patterns to retain based on the scree test, i.e. the
graphical presentation of eigenvalues, with eigenvalues
greater than 1 explaining a greater amount of variance than
contributed by any food group
(27)
. The scree test allowed
us to clearly identify two major patterns with the largest
eigenvalues (3·15 and 2·65, followed by eigenvalues #1·80
for the subsequent patterns). Based on the food groups that
were loading highest on each pattern, these patterns were
labelled as the ‘processed foods’ and ‘health-conscious’
patterns.
Assessment of sociodemographic and lifestyle factors
Information on age, socio-economic characteristics, smoking
and sport activity was obtained by a standardised, self-admi-
nistered questionnaire, which was checked for plausibility
and completeness of information by trained interviewers in
the presence of the participants. Socio-economic status was
defined by an index combining information on education,
household income and professional group
(28)
and assigned
to a low, middle or high category. Smoking status was defined
as never smoking, former smoking, occasional smoking or
daily smoking. Sport activity was assessed using five cat-
egories ranging from ‘no sport’ to ‘regularly more than 4 h/
week’ and was classified into no sport, less than 2 h sport/
week and 2 h or more sport/week. Furthermore, a standar-
dised, computer-assisted personal interview was conducted
by specifically trained physicians to obtain information
on medical history, including physician-diagnosed chronic
diseases and medications used within the past 12 months.
Ascertainment of cardiometabolic factors
Physical examinations, including anthropometric measure-
ments, blood pressure measurements and blood sampling,
were performed by trained health professionals. BMI was cal-
culated as the ratio of body weight to squared height. Mean
systolic and diastolic blood pressure was calculated from the
second and the third measurement using a mercury sphygmo-
manometer (Erkameter 3000; Erka, Bad Jo
¨lz, Germany).
Venous blood samples were drawn after a fasting period of
at least 3 h using the Gel-Monovettensystem supplied by
Becton-Dickinson (Franklin Lakes, NJ, USA) and immediately
processed and separated into aliquots. Serum was frozen
and stored at 2408C until laboratory analysis.
Total serum cholesterol was assayed using the enzymatic
cholesterol oxidase–peroxidase-4-aminophenazone method
(Merck, Darmstad, Germany). Serum HDL-cholesterol was
determined with an immunoseparation-based homogeneous
assay (WAKO, Chuo-ku, Osaka, Japan). Serum TAG were
measured with the glycerophosphate oxidase–peroxidase-
4-aminophenazone method (Merck). LDL-cholesterol was
calculated from the measurements of total cholesterol,
HDL-cholesterol and TAG by means of the Friedewald
equation
(29)
. Serum lipoprotein (a) was measured on a
Cobas Mira analyser using turbidimetry with multiple-point
calibration (Roche, Mannheim, Germany). Serum glucose
was determined using the glucose oxidase – peroxidase-4-ami-
nophenazone method (Merck). HbA
1c
was analysed in whole
blood using HPLC on a Diamat HPLC analyser (Bio-Rad,
Munich, Germany) with a test-kit of Recipe (Munich). Fibrino-
gen in EDTA plasma (stored 3d below 2208C) was assayed
using the immuno-nephelometric method on a BNA analyser
(DADE-Behring, Schwalbach, Germany). Serum uric acid
was measured by the uricase–peroxidase-4-aminophenazone
method (Merck). Serum homocysteine was analysed with
a commercially available HPLC kit (Immundiagnostik, Ben-
sheim, Germany) using a Shimadzu chromatography system
(Chiyoda-ku, Tokyo, Japan) with fluorescence detection.
Serum folate was estimated using a microparticle enzyme
immunoassay on an AxSym analyser (Abbott, Chicago, IL, USA).
Definition of the metabolic syndrome
The metabolic syndrome was defined based on the National
Cholesterol Education Program’s Adult Treatment Panel III
criteria
(30)
, i.e. by the presence of at least three of the
following five abnormalities: abdominal obesity (waist cir-
cumference .102 cm in men and .88 cm in women),
hypertension (blood pressure $130/$85 mmHg), low HDL-
cholesterol (,400 mg/l in men and ,500 mg/l in women),
hypertriacylglycerolaemia (fasting TAG $1500 mg/l) and
abnormal glucose homeostasis (fasting glucose $1100 mg/l).
According to a recent study, on estimating the metabolic syn-
drome prevalence in this study population
(31)
, we additionally
used TAG values $2000 mg/l and HbA
1c
values .6·1 %
for non-fasting individuals (fasting time ,8 h) as well as
medication for diabetes or hypertension to classify participants
in terms of hypertriacylglycerolaemia, abnormal glucose
homeostasis or hypertension, respectively.
Statistical analyses
Mean values with 95 % CI of cardiometabolic risk markers
according to quintiles of dietary pattern scores were calculated
using ANCOVA. Standardised regression coefficients for the
association between cardiometabolic risk markers and the
continuous dietary pattern scores were obtained from linear
regression analysis. Prevalence OR (95 % CI) for each pattern
quintile were estimated by logistic regression analysis, using
the lowest quintile as the reference category. Means,
regression coefficients and OR were adjusted for age (years),
sex and total energy intake (continuous). In a second
model, we further adjusted for socio-economic status (low,
middle and high), sport activity (none, 0·1–1·9, $2·0 h/
week) and smoking status (never, past, occasional and
daily). Trend tests were conducted by including the median
score of each pattern quintile as a continuous variable into
the models. Furthermore, we conducted stratified analyses
to investigate whether the observed associations between diet-
ary patterns and metabolic abnormalities were modified by
Dietary patterns and cardiometabolic risk 1255
British Journal of Nutrition
sex or changed dietary habits before the dietary assessment.
Interaction tests were performed by including a product
term with the respective stratification variable and the
median score of the pattern quintile as a continuous variable
into the model.
For all analyses, a specific weighting factor that corrects for
deviations in demographic characteristics between the study
population and the German population structure as of 31
December 1997 was used. For each subject, this weighting
factor is proportional to the under- or over-representation of
the subject’s 5-year age interval, sex, community size and
federal state. For example, if in a specific age, community
size and state subgroup, men are under-represented by a
factor of 2 compared with women, then men of this specific
subgroup get a weighting factor twice as high compared
with women of the same subgroup. However, the total
weighted sample size is identical to the unweighted.
All statistical analyses were performed using the SAS statisti-
cal software package version 9.2 (SAS Institute, Cary, NC,
USA). For all tests, P,0·05 was considered significant.
Results
A high score for the processed foods pattern was characterised
by a relatively high consumption of refined grains, processed
meat, red meat, high-sugar beverages, eggs, potatoes, beer,
sweets and cakes, snacks and butter (Table 1). In contrast, a
high score for the health-conscious pattern represented a
relatively high consumption of cruciferous vegetables, fruity
vegetables, leafy vegetables, all other vegetables, vegetable
oils, legumes, fruits, fish and whole grains. A high health-
conscious pattern score also corresponded to a relatively
high consumption of red meat and potatoes, but to a lesser
degree than the processed foods pattern. When patterns
were derived separately for men and women, they each
showed a composition that was similar to that described for
the overall population.
Participants with higher scores for the processed foods
pattern were younger, more often men, with a lower percen-
tage having high socio-economic status and more likely to
smoke than those with lower scores for this pattern (Table
2). Furthermore, they were less likely to use vitamin or min-
eral supplements regularly and had a higher energy intake
and a more unfavourable nutrient profile, particularly in
terms of total and saturated fat, cholesterol, fibre, folate, vita-
min C, vitamin E, b-carotene and calcium. Participants with
higher scores for the health-conscious patterns were older,
with a higher percentage having high socio-economic status,
more active and less likely to smoke than those who scored
low on this pattern. The relationships of the health-conscious
pattern to vitamin and mineral supplements as well as to
dietary intakes were generally in the opposite direction, but
less pronounced, compared with the processed foods pattern.
In models adjusting for age, sex and energy intake, mean
values of the cardiometabolic risk markers, including BMI,
waist circumference, lipoprotein(a), TAG, the ratio of total:
HDL-cholesterol, glucose, HbA
1c
and uric acid, increased
across rising quintiles of the processed foods pattern, whereas
mean values of HDL-cholesterol (in women) and folate
decreased (Pfor trend ,0·05; Table 3). For the health-
conscious pattern, mean values of folate increased across
rising quintiles, whereas mean values of the systolic blood
pressure, HbA
1c
, fibrinogen and homocysteine decreased.
These results were supported by those from linear regression,
when we analysed the association between the cardiometa-
bolic risk markers and the continuous dietary pattern scores
(P,0·05; Table 4). After further accounting for socio-
economic status, sport activity and smoking status, associ-
ations remained significant for anthropometric measures,
TAG, glucose, uric acid and folate for the processed foods
pattern and systolic blood pressure, HbA
1c
, fibrinogen, homo-
cysteine and folate for the health-conscious pattern.
When specifically focusing on the cardiometabolic abnorm-
alities clustered within the metabolic syndrome, age-, sex- and
energy intake-adjusted models revealed that associations
between the patterns and each abnormality pointed into the
expected direction (Table 5). While the direct associations
with the processed foods pattern were all statistically signifi-
cant, the inverse associations with the health-conscious
pattern reached significance only for hypertension (Pfor
trend ,0·05). Adjustment for socio-economic status and
lifestyle factors attenuated the strength of associations,
although their trend remained significant (Pfor trend ,0·05)
for abdominal adiposity (OR 1·88, 95 % CI 1·31, 2·69 for the
highest v. the lowest quintile), hypertension (OR 1·34, 95 %
CI 0·96, 1·86), hypertriacylglycerolaemia (OR 1·59, 95 % CI
1·11, 2·28) and the metabolic syndrome overall (OR 1·64,
95 % CI 1·10, 2·43) with respect to the processed foods pattern
as well as for hypertension (OR 0·70, 95 % CI 0·54, 0·90) in
terms of the health-conscious pattern. Joint classification of
the two patterns revealed the following, when participants
in the lowest quintile of the processed foods pattern and the
highest quintile of the health-conscious pattern (reference)
were compared with those in the highest quintile of the
processed foods pattern and the lowest quintile of the
health-conscious pattern: OR 1·48 (95 % CI 0·79, 2·79) for
abdominal obesity, OR 2·55 (95 % CI 1·49, 4·36) for hyperten-
sion, OR 0·92 (95 % CI 0·53, 1·59) for low HDL-cholesterol, OR
2·36 (95 % CI 1·33, 4·17) for hypertriacylglycerolaemia, OR
2·60 (95 % CI 1·03, 4·56) for abnormal glucose homeostasis
and OR 2·26 (95 % CI 1·33, 4·16) for the metabolic syndrome
(data not shown). Additional analyses for the metabolic syn-
drome showed no significant interaction between the patterns
and sex or a changed diet in the 4 weeks before examination
(Pfor interaction .0·05).
Discussion
In the present representative study population of German
adults, we identified two major dietary patterns, which we
labelled as the processed foods pattern and health-conscious
pattern. Greater adherence to the processed foods pattern –
reflecting a high intake of refined grains, processed meat,
red meat, high-sugar beverages, eggs, potatoes, beer, sweets
and cakes, snacks and butter – was related to a higher
prevalence of metabolic derangements, including abdominal
C. Heidemann et al.1256
British Journal of Nutrition
Table 2. Sample characteristics by quintile of dietary patterns in a representative sample of German adults
(Mean values and standard deviations or percentages, n4025)
Quintiles of the processed foods pattern Quintiles of the health-conscious pattern
1 (Lowest) 3 5 (Highest) 1 (Lowest) 3 5 (Highest)
Characteristics Mean SD Mean SD Mean SD Pfor trend Mean SD Mean SD Mean SD Pfor trend
Age (years) 50·2 16·3 48·7 15·9 37·6 14·2 41·7 16·9 47·9 16·3 48·1 16·3 ,0·0001
Sex (% male) 22·0 40·6 85·6 ,0·0001 47·6 46·0 57·4 ,0·0001
Socio-economic status (%)*
Low 22·8 18·4 22·5 27·8 20·5 17·4
Middle 50·8 55·7 60·7 56·1 55·2 55·7
High 26·3 25·9 16·8 ,0·0001 16·1 24·3 26·9 ,0·0001
Current smoking (%)* 22·1 25·9 46·4 ,0·0001 39·5 28·5 27·3 ,0·0001
Sport activity $2 h/week (%)* 22·3 20·1 22·2 0·34 17·2 18·6 26·8 ,0·0001
Vitamin or mineral supplements (%)† 26·1 23·3 15·6 ,0·0001 17·9 23·3 24·0 0·02
Alcohol consumption (g/d) 6·1 10·2 9·3 11·6 19·0 22·0 ,0·0001 9·114·010·013·413·318·5,0·0001
Energy intake (MJ/d) 7·0 2·1 8·6 2·0 13·5 3·4 ,0·0001 8·6 3·4 9·1 2·8 10·9 3·8 ,0·0001
Dietary intake
Total fat (% energy) 31·9 5·3 35·9 5·0 36·2 5·8 ,0·0001 34·6 5·8 35·4 5·1 35·2 5·8 0·14
SFA (% energy) 13·6 2·9 15·7 2·8 15·5 3·1 ,0·0001 15·2 3·2 15·4 2·8 14·6 3·1 ,0·0001
Unsaturated fatty acids (% energy) 10·7 2·3 12·6 2·1 13·1 2·4 ,0·0001 12·4 2·5 12·4 2·1 12·2 2·6 0·01
PUFA (% energy) 5·24 1·74 5·08 1·31 5·10 1·61 0·08 4·66 1·46 5·10 1·29 5·92 2·26 ,0·0001
Cholesterol (mg/d) 225 85 326 93 518 177 ,0·0001 290 130 346 132 410 195 ,0·0001
Fibre (g/MJ) 3·96 0·94 2·89 0·62 2·17 0·53 ,0·0001 2·43 0·74 2·93 0·76 3·46 1·11 ,0·0001
Folate (mg/MJ) 42·5 22·4 35·2 59·2 24·2 8·3 ,0·0001 30·3 58·2 32·2 15·5 37·7 23·5 ,0·0001
Vitamin C (mg/MJ) 29·0 20·1 20·9 27·5 11·7 7·4 ,0·0001 16·1 26·2 18·6 13·7 24·1 17·1 ,0·0001
Vitamin E (mg/MJ) 3·05 6·68 2·87 9·84 1·26 1·85 ,0·0001 2·09 7·29 2·17 6·71 2·70 6·19 0·02
b-Carotene (mg/MJ) 0·85 0·59 0·55 0·67 0·33 0·20 ,0·0001 0·36 0·67 0·52 0·34 0·79 0·53 ,0·0001
Vitamin D (mg/MJ) 0·39 0·33 0·39 0·35 0·28 0·17 ,0·0001 0·28 0·28 0·38 0·30 0·42 0·37 ,0·0001
K (g/MJ) 0·49 0·10 0·39 0·07 0·33 0·07 ,0·0001 0·34 0·08 0·40 0·08 0·45 0·10 ,0·0001
Mg (mg/MJ) 64·6 16·5 51·1 12·8 40·4 8·6 ,0·0001 47·9 16·2 51·5 15·2 54·7 14·6 ,0·0001
Ca (mg/MJ) 175 56 135 42 97 34 ,0·0001 130 55 136 53 141 51 0·0004
Fe (mg/MJ) 2·02 1·02 1·66 0·43 1·44 0·27 ,0·0001 1·50 0·46 1·71 0·99 1·85 0·48 ,0·0001
* For sporting activity: missing values, n20; for smoking: missing values, n12; for socio-economic status: missing values, n37.
† Supplement intake of vitamin B complex, vitamin C, vitamin E, folate, multivitamins or minerals $1 times/week.
Dietary patterns and cardiometabolic risk 1257
British Journal of Nutrition
Table 3. Cardiometabolic risk markers by quintile of dietary patterns in a representative sample of German adults*
(Mean values and 95 % confidence intervals)
Quintiles of the processed foods pattern Quintiles of the health-conscious pattern
1 (Lowest) 3 5 (Highest) 1 (Lowest) 3 5 (Highest)
Characteristics Mean 95 % CI Mean 95 % CI Mean 95 % CI
Pfor
trend Mean 95 % CI Mean 95 % CI Mean 95 % CI
Pfor
trend
BMI (kg/m
2
) 25·7 25·4, 26·1 26·2 25·9, 26·5 26·9 26·5, 27·2 ,0·0001 26·1 25·8, 26·4 26·2 25·9, 26·5 26·1 25·8, 26·4 0·26
Waist (cm)
Men 92·9 91·1, 94·7 94·5 93·3, 95·8 96·0 95·2, 96·9 ,0·0001 93·9 92·8, 95·0 95·2 94·1, 96·4 94·9 93·9, 95·9 0·05
Women 81·1 80·2, 82·1 82·9 81·9, 83·9 86·3 84·1, 88·5 ,0·0001 82·6 81·5, 83·7 82·2 81·1, 83·3 81·9 80·7, 83·1 0·59
Systolic blood pressure
(mmHg)
130 128, 131 130 129, 131 132 131, 134 0·06 133 131, 134 129 128, 131 129 128, 131 0·002
Diastolic blood pressure
(mmHg)
81·7 80·7, 82·6 81·6 80·8, 82·4 82·0 81·0, 82·9 0·74 81·4 80·6, 82·2 81·8 81·0, 82·6 80·9 80·1, 81·8 0·27
Total cholesterol (mmol/l) 5·84 5·75, 5·93 5·95 5·87, 6·04 5·98 5·88, 6·08 0·22 5·91 5·83, 5·99 5·99 5·91, 6·07 5·95 5·87, 6·03 0·34
HDL-cholesterol (mmol/l)
Men 1·31 1·24, 1·38 1·31 1·26, 1·36 1·29 1·26, 1·33 0·39 1·28 1·23, 1·32 1·29 1·25, 1·34 1·34 1·30, 1·38 0·16
Women 1·73 1·69, 1·77 1·68 1·64, 1·72 1·56 1·47, 1·65 0·01 1·66 1·62, 1·71 1·70 1·65, 1·74 1·72 1·67, 1·77 0·24
Total:HDL-cholesterol
Men 4·64 4·33, 4·95 4·83 4·62, 5·05 5·00 4·85, 5·15 0·02 5·00 4·81, 5·20 4·90 4·71, 5·09 4·83 4·65, 5·00 0·74
Women 3·61 3·51, 3·72 3·73 3·62, 3·83 4·05 3·82, 4·28 0·002 3·76 3·65, 3·88 3·73 3·62, 3·84 3·67 3·54, 3·79 0·56
LDL-cholesterol (mmol/l) 3·64 3·56, 3·72 3·77 3·69, 3·84 3·73 3·64, 3·82 0·15 3·71 3·64, 3·78 3·81 3·73, 3·88 3·70 3·62, 3·77 0·15
Lipoprotein(a) (mg/l)† 283 253, 313 280 253, 308 342 311, 373 0·0007 303 277, 330 257 231, 284 294 267, 320 0·10
TAG (mmol/l) 1·49 1·40, 1·58 1·55 1·47, 1·64 1·81 1·72, 1·91 ,0·0001 1·64 1·56, 1·73 1·58 1·50, 1·66 1·60 1·52, 1·69 0·17
Glucose (mmol/l) 5·18 5·11, 5·25 5·32 5·26, 5·39 5·32 5·25, 5·40 0·01 5·28 5·22, 5·35 5·30 5·23, 5·36 5·26 5·20, 5·33 0·12
HbA
1c
(%) 5·35 5·30, 5·39 5·41 5·37, 5·45 5·43 5·39, 5·48 0·04 5·47 5·44, 5·51 5·39 5·35, 5·42 5·35 5·31, 5·39 ,0·0001
Fibrinogen (g/l)† 2·87 2·82, 2·93 2·93 2·88, 2·98 2·93 2·87, 2·99 0·47 2·99 2·94, 3·04 2·92 2·87, 2·97 2·86 2·81, 2·91 0·003
Uric acid (mmol/l) 295 288, 303 298 291, 304 317 310, 325 0·0008 301 295, 308 303 296, 309 301 295, 308 0·65
Homocysteine (mmol/l) 9·8 9·5, 10·1 9·8 9·5, 10·1 10·5 10·1, 10·8 0·05 10·7 10·4, 11·0 10·0 9·7, 10·2 9·5 9·3, 9·8 ,0·0001
Folate (mg/l)‡ 8·41 8·05, 8·76 7·69 7·34, 8·04 6·66 6·05, 7·27 ,0·0001 7·46 7·12, 7·81 8·14 7·76, 8·51 8·35 7·89, 8·80 0·003
* Adjusted for age (years), sex (where applicable) and total energy intake (continuous). Number of subjects (n4025) for the specific characteristics can vary due to missing values and specific exclusion criteria (compare description
of the study population).
† Conversion factor for lipoprotein (a): 1 mg/l ¼0·00357 mmol/l and for fibrinogen: 1 g/l ¼2·94 mmol/l.
‡ Folate was measured in women aged 18 – 40 years only.
C. Heidemann et al.1258
British Journal of Nutrition
obesity, hypertension, hypertriacylglycerolaemia and the
metabolic syndrome. In addition, greater adherence to this
pattern was associated with more unfavourable concentrations
of markers that are discussed to be related to the complex of
the metabolic syndrome, particularly with higher concen-
trations of uric acid and lower concentrations of folate. In
contrast, greater adherence to the health-conscious pattern –
characterised by a high intake of vegetables, vegetable oils,
legumes, fruits, fish and whole grains – was linked to a
lower prevalence of hypertension as well as to higher concen-
trations of folate and lower concentrations of homocysteine
and fibrinogen.
The identification of dietary patterns representative of the
general population and their relation to metabolic risk mar-
kers has been rarely examined. Similar to the present study,
two major dietary patterns, a ‘Western’ pattern (characterised
by frequent intakes of processed meat, red meat, eggs and
high-fat diary products) and an ‘American-healthy’ pattern
(characterised by frequent intakes of vegetables, salad dres-
sings and tea), were identified in a representative sample of
the adult US population
(21)
. A high adherence to the Western
pattern was adversely related to levels of folate and markers of
glucose metabolism, but not to other risk factors such as
systolic blood pressure or TAG. For the American-healthy
pattern, no significant associations were observed.
Furthermore, previous studies have investigated the rela-
tionship between dietary patterns of various subpopulations
and metabolic factors. Overall, it can be summarised from
these studies that, despite the expected deviations in the
dietary patterns’ composition, which may be partly explained
by specific characteristics of the study populations and
culturally defined differences in eating habits, similarities
with the present study are obvious. In most of these studies,
two or three patterns were extracted. Consistently, one of
the patterns was a rather unhealthy pattern, e.g. called
‘Western’, ‘pasta and meat’, ‘fats and processed meats’ or
‘refined foods’, with processed and red meats, refined
grains, eggs and sweets as predominant food groups. Gener-
ally, a distinct, rather healthful pattern was also identified,
e.g. referred to as ‘prudent’, ‘vegetable’, ‘whole grains and
fruits’, or ‘healthy balanced’, with vegetables, fruits, whole
grains and fish as determining food groups
(5 – 8,12 – 20,32 – 35)
.
For the patterns characterised by animal and refined
foods, adverse associations with parameters of abdominal
obesity
(5,9,16,18,20)
, blood pressure
(5,10,18,33)
and markers of
lipid and glucose metabolism
(5,7,10,13,15 – 17,20,33,34)
could be
observed, whereas associations of the patterns characterised
by plant foods and fish often pointed into the opposite
direction
(5,7 – 10,12 – 17,20,33,34)
. So far, few studies have investi-
gated the association between patterns and the prevalence
or incidence of the metabolic syndrome. These studies
have found either a direct association for a ‘Western’ and
‘sweets’ pattern
(5,16,18,19)
or an inverse association for a
‘healthy’ pattern
(5,16)
. In addition, some studies have indicated
Table 4. Standardised linear regression coefficients for the association between cardiometabolic risk markers and dietary patterns in a representative
sample of German adults*
(
b
-Coefficients and Pvalues)
‘Processed foods’ pattern ‘Health-conscious’ pattern
Model 1† Model 2‡ Model 1 Model 2
Characteristics
b
P
b
P
b
P
b
P
BMI (kg/m
2
) 0·10 ,0·0001 0·075 0·0005 20·0003 0·99 0·03 0·10
Waist circumference (cm)
Men 0·11 ,0·0001 0·087 0·002 0·023 0·33 0·053 0·03
Women 0·12 ,0·0001 0·077 0·002 20·018 0·39 0·009 0·67
Systolic blood pressure (mmHg) 0·041 0·04 0·034 0·12 20·054 0·001 20·042 0·01
Diastolic blood pressure (mmHg) 0·013 0·54 0·016 0·48 20·006 0·73 20·007 0·68
Total cholesterol 0·045 0·03 0·032 0·13 0·011 0·49 0·021 0·20
HDL-cholesterol
Men 20·042 0·17 0·006 0·85 0·066 0·02 0·037 0·18
Women 20·093 0·0007 20·046 0·10 0·051 0·03 0·014 0·56
Total:HDL-cholesterol
Men 0·091 0·002 0·028 0·35 20·037 0·17 0·006 0·83
Women 0·010 0·0002 0·050 0·06 20·035 0·13 0·002 0·94
LDL-cholesterol 0·038 0·07 0·021 0·32 20·003 0·85 0·011 0·49
Lipoprotein(a) 0·053 0·02 0·041 0·08 20·015 0·41 20·004 0·83
TAG 0·094 ,0·0001 0·051 0·02 20·026 0·13 20·003 0·88
Glucose 0·062 0·004 0·049 0·03 20·026 0·13 20·014 0·42
HbA
1c
0·055 0·008 0·009 0·68 20·077 ,0·0001 20·052 0·002
Fibrinogen 0·030 0·15 20·012 0·56 20·065 ,0·0001 20·034 0·04
Uric acid 0·065 0·0005 0·050 0·01 20·009 0·54 0·003 0·84
Homocysteine 0·048 0·03 0·026 0·26 20·093 ,0·0001 20·078 ,0·0001
Folate§ 20·21 ,0·0001 20·19 ,0·0001 0·13 0·0002 0·11 0·002
* Number of subjects for the specific characteristics (n4025) can vary due to missing values and specific exclusion criteria (compare description of the study population).
† Regression coefficients are adjusted for age (years), sex and total energy intake (continuous).
‡ Regression coefficients are additionally adjusted for socio-economic status (low, middle and high), sport activity (none, 0·1 – 1·9, $2 h/week) and smoking status (never, past,
occasional and daily).
§ Folate was measured in women aged 18 – 40 years only.
Dietary patterns and cardiometabolic risk 1259
British Journal of Nutrition
Table 5. Cardiometabolic abnormalities clustered within the metabolic syndrome by quintile of dietary patterns in a representative sample of German adults*
(Odds ratios and 95 % confidence intervals)
Quintiles of the processed foods pattern Quintiles of the health-conscious pattern
1 (Lowest) 3 5 (Highest) 1 (Lowest) 3 5 (Highest)
Metabolic abnormality OR 95 % CI OR 95 % CI Pfor trend OR 95% CI OR 95 % CI OR 95 % CI Pfor trend
Abdominal obesity
Model 1† 1·00 1·33 1·02, 1·72 2·46 1·74, 3·48 ,0·0001 1·00 0·92 0·71, 1·20 0·93 0·71, 1·22 0·52
Model 2‡ 1·00 1·21 0·92, 1·58 1·88 1·31, 2·69 ,0·0001 1·00 1·01 0·77, 1·32 1·12 0·85, 1·48 0·46
Hypertension
Model 1 1·00 1·18 0·91, 1·52 1·35 0·98, 1·86 0·03 1·00 0·83 0·65, 1·06 0·66 0·51, 0·85 0·002
Model 2 1·00 1·19 0·92, 1·54 1·34 0·96, 1·86 0·04 1·00 0·77 0·60, 0·99 0·70 0·54, 0·90 0·01
Low HDL-cholesterol
Model 1 1·00 0·95 0·72, 1·25 1·37 0·99, 1·91 0·01 1·00 0·80 0·62, 1·03 0·82 0·63, 1·07 0·15
Model 2 1·00 0·86 0·65, 1·14 0·95 0·67, 1·35 0·90 1·00 0·92 0·70, 1·19 1·01 0·77, 1·32 0·94
Hypertriacylglycerolaemia
Model 1 1·00 1·15 0·86, 1·53 2·06 1·46, 2·91 ,0·0001 1·00 0·87 0·67, 1·14 0·91 0·69, 1·19 0·36
Model 2 1·00 1·09 0·81, 1·46 1·59 1·11, 2·28 0·0006 1·00 0·96 0·73, 1·26 1·05 0·79, 1·38 0·86
Abnormal glucose homeostasis
Model 1 1·00 1·34 0·88, 2·03 2·30 1·34, 3·95 0·001 1·00 1·00 0·67, 1·49 0·70 0·46, 1·08 0·09
Model 2 1·00 1·17 0·76, 1·80 1·50 0·85, 2·67 0·11 1·00 1·02 0·68, 1·53 0·81 0·52, 1·25 0·26
Metabolic syndrome
Model 1 1·00 1·23 0·91, 1·65 2·49 1·70, 3·64 ,0·0001 1·00 0·96 0·72, 1·28 0·80 0·60, 1·07 0·06
Model 2 1·00 1·07 0·79, 1·45 1·64 1·10, 2·43 0·001 1·00 1·07 0·79, 1·44 0·98 0·72, 1·34 0·67
* Abdominal obesity: waist circumference .102 cm in men, .88 cm in women; hypertension: blood pressure $130/85 mmHg or use of antihypertensive medication; low HDL-cholesterol (,400 mg/l in men and ,500 mg/l in
women); hypertriacylglycerolaemia: TAG $1500 mg/l fasting or $2000 mg/l non-fasting; abnormal glucose homeostasis: glucose $1100 mg/l fasting or HbA
1c
$6·1 or use of antidiabetic medication; metabolic syndrome:
presence of at least three of the above components. Number of subjects within the specific models (n4025) can vary due to missing values and specific exclusion criteria (compare description of the study population).
† Adjusted for age (years), sex and total energy intake (continuous).
‡ Additionally adjusted for socio-economic status (low, middle and high), sport activity (none, 0·1 –1·9, $2 h/week) and smoking status (never, past, occasional and daily).
C. Heidemann et al.1260
British Journal of Nutrition
associations between the patterns and markers that are linked
to the cardiometabolic complex such as markers of systemic
inflammation
(6,15,20,32,35)
or folate metabolism
(7,20,35)
. In gen-
eral, the results of the present and previous studies underline
the suggested association between the present trend towards
a Western-style diet high in refined and animal products at
the expense of a healthier plant-based diet and the increasing
trend of obesity and related metabolic diseases in developing
countries
(36)
.
The observation of divergent associations between the two
identified dietary patterns and metabolic disturbances in the
present study is supported by distinctive nutrient compositions
of the patterns. For example, the intake of fibre and folate,
which are known protective factors for CVD
(37)
, was inversely
associated with the processed foods pattern; in contrast, the
intake of saturated fat and cholesterol, which are considered
to increase cardiovascular risk
(37)
, was directly related to this
pattern. An opposite trend was evident for most of the nutrients
for the health-conscious pattern, although associations were
generally less pronounced. The latter observation might also
have contributed to the overall weaker association of the
health-conscious pattern with the cardiometabolic profile
compared with the processed food pattern.
The carefully conducted representative design and the
co-existence of detailed information on dietary habits and life-
style factors as well as of standardised physical and biomarker
measurements are among the strengths of the present study.
However, dietary pattern identification by factor analysis is
generally exposed to the limitation of subjectivity, particularly
when grouping the food items, selecting the method of factor
rotation or defining the number of patterns to be retained
(38)
,
which potentially has an impact on the patterns’ composition
and their relation to metabolic factors. To minimise subjectiv-
ity, we defined the food groups to approximate those used in
previous studies and derived the patterns based on commonly
applied procedures. Furthermore, factor analysis – by its
nature being purely data-driven – provides no insight into
the mechanisms responsible for the observed dietary
pattern–risk factor associations. The identified dietary patterns
may indicate a lifestyle in general
(38)
. In the present study,
participants with different degrees of adherence to the
patterns differed, e.g. according to their socio-economic
status and smoking habit. Even though we adjusted for
these and other potential confounder variables and also
excluded subjects with a history of major diseases (that
could have led to changed dietary habits) from the analysis
of metabolic factors, the issues of residual confounding and
reverse causation cannot be excluded due to the cross-
sectional design of the present study.
In summary, in this general adult population, a higher
adherence to a pattern predominantly characterised by pro-
cessed foods was related to a higher prevalence of abdominal
obesity, hypertension, hypertriacylglycerolaemia and the meta-
bolic syndrome as well as to disadvantageous levels of uric acid
and folate, whereas a higher adherence to a pattern largely
characterised by vegetables, fruits and whole grains was related
to a lower prevalence of hypertension and favourable levels of
folate, homocysteine and fibrinogen. These results corroborate
previous findings from non-representative studies and further
emphasise the importance of healthy overall food patterns
to protect from metabolic disturbances known to predispose
to cardiovascular and other chronic diseases.
Acknowledgements
The original survey was supported by the German Federal
Ministry of Health. The present study was conducted at the
Department of Epidemiology and Health Monitoring, Berlin,
Germany. The authors declare that they have no conflicts of
interest. C. H., C. S.-N. and G. B. M. M. designed the study;
G. B. M. M. was responsible for the German Nutrition
Survey. C. H. performed the statistical analysis. C. H. and
G. B. M. M. wrote the manuscript and had the primary respon-
sibility for the final content; all authors critically revised
the manuscript. All authors read and approved the final
manuscript. We thank Larissa Drescher for sharing the food
grouping syntax.
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