An application of partial least squares for identifying dietary patterns in bone health
An application of partial least squares for identifying dietary patterns in bone health
Tiffany C. Yang 0 1 2
Lorna S. Aucott 0 1 2
Garry G. Duthie 0 1 2
Helen M. Macdonald 0 1 2
0 Natural Products Group, Rowett Institute of Nutrition and Health, University of Aberdeen , Aberdeen AB21 9SB , UK
1 Health Sciences Building, University of Aberdeen , Foresterhill, Aberdeen AB25 2ZD , UK
2 Department of Health Sciences, University of York , Seebohm Rowntree, York YO10 5DD , UK
3 Tiffany C. Yang
Summary In a large cohort of older women, a mechanismdriven statistical technique for assessing dietary patterns that considers a potential nutrient pathway found two dietary patterns associated with lumbar spine and femoral neck bone mineral density. A Bhealthy^ dietary pattern was observed to be beneficial for bone mineral density. Introduction Dietary patterns represent a broader, more realistic representation of how foods are consumed, compared to individual food or nutrient analyses. Partial least-squares (PLS) is a data-reduction technique for identifying dietary patterns that maximizes correlation between foods and nutrients hypothesized to be on the path to disease, is more hypothesis-driven than previous methods, and has not been applied to the study of dietary patterns in relation to bone health. Methods Women from the Aberdeen Prospective Osteoporosis Screening Study (2007-2011, n = 2129, age = 66 years (2.2)) provided dietary intake using a food frequency questionnaire; 37 food groups were created. We applied PLS to the 37 food groups and 9 chosen response variables (calcium, potassium, vitamin C,
Bone mineral density; Dietary patterns; Partial least-squares; Postmenopausal women
vitamin D, protein, alcohol, magnesium, phosphorus, zinc) to
identify dietary patterns associated with bone mineral density
(BMD) cross-sectionally. Multivariable regression was used to
assess the relationship between the retained dietary patterns and
BMD at the lumbar spine and femoral neck, adjusting for age,
body mass index, physical activity level, smoking, and national
Results Five dietary patterns were identified, explaining 25%
of the variation in food groups and 77% in the response
variables. Two dietary patterns were positively associated with
lumbar spine (per unit increase in factor 2: 0.012 g/cm2 [95%
CI: 0.006, 0.01]; factor 4: 0.007 g/cm2 [95% CI: 0.00001,
0.01]) and femoral neck (factor 2: 0.006 g/cm2 [95%
CI: 0.002, 0.01]; factor 4: 0.008 g/cm2 [95% CI: 0.003,
0.01)]) BMD. Dietary pattern 2 was characterized by high
intakes of milk, vegetables, fruit and vegetable juices, and
wine, and low intakes of processed meats, cheese, biscuits,
cakes, puddings, confectionary, sweetened fizzy drinks and
spirits while dietary pattern 4 was characterized by high
intakes of fruits, red and white meats, and wine, and low intakes
of vegetables and sweet spreads.
Conclusion Our findings using a robust statistical technique
provided important support to initiatives focusing on what
constitutes a healthy diet and its implications.
Osteoporosis is a global health issue that affects millions of
individuals around the world, with fragility and fracture
resulting from reduced bone mass or increased
microarchitectural deterioration of the bone tissue [
intake has long been considered to have an important role in
bone mineral density (BMD) and maintenance through
various mechanisms, including alterations in the endocrine system
or in bone metabolism and structure [2, 3]. Traditionally,
individual foods or nutrients were singled out, and most
research has focused on the roles of certain vitamins or minerals.
This approach with single nutrients or foods, while valuable,
is problematic due to its inherent reductionist nature and lack
of dietary context [4, 5].
Dietary patterns provide a more complete perspective to
health and disease, as these patterns would more closely
mirror what occurs in the real world where individuals eat
meals of combined foods and nutrients. The combination
of multiple nutrients utilized in a dietary patterns analysis
may therefore be more powerful in detecting effects, as the
influence of a single nutrient may be too small and the
resulting recommendations too difficult to follow .
Examining dietary patterns could have a greater public
health importance, as evaluating the overall combinations
of food intake would be more practical and easily
translatable to the public and has been advocated by the US
Dietary Guidelines Advisory Committee .
The relationship between dietary patterns and bone health
has been previously explored through data reduction methods
such as dietary indices like the Mediterranean diet , factor
and principal component analysis (PCA) [9, 10], reduced rank
regression (RRR)  and cluster analysis . However,
though data-reduction methods such as PCA and RRR that
are more commonly used, they may not be ideal in selecting
dietary patterns in relation to disease because of how they
reduce dietary data. PCA derives a set of uncorrelated factors
(dietary patterns) characterized by the different foods through
explanation of variation in the food intake with no
consideration for the outcome measure . RRR is similar to PCA in
deriving uncorrelated factors, but has a different goal: dietary
patterns are derived to account for the variation not in food
intake, but in a set of response variables which are nutrients or
biomarkers thought to be important to the health or disease
Partial least squares (PLS) is a mix between PCA and RRR,
where extracted dietary patterns account for variation in both
dietary intake and the intermediary response variables related
to the health or disease outcome . Dietary patterns derived
by maximizing the variance in both food groups (the
Bpredictors^) and the outcome-related nutrients or biomarkers
(the Bresponse variables^) may be more suitable as it would
allow for directed data reduction of food groups through specific
nutrients or biomarkers of interest. Common in bioinformatics
and chemometrics research, PLS has not been widely utilized in
epidemiology, and not in relation with bone health. It is
hypothesized to be a more useful method of deriving dietary patterns
because it considers the potential biochemical pathways by
which the dietary patterns may influence health .
Our objective was to identify dietary patterns through the
novel use of partial least-squares analysis using nutrients
hypothesized to influence bone health as the intermediary
response variables. We then assessed and evaluated the
relationship between these derived dietary patterns and BMD in a
population of women in northeast Scotland.
Subjects are women from the Aberdeen Prospective
Osteoporosis Screening Study (APOSS) cohort, a
population-based screening program for assessing
osteoporotic fracture risk, which initially recruited 5119
women aged 45–54 years between 1990 and 1994 using
random selection from Community Health Index . No
exclusion criteria were applied, as baseline participants
were recruited for a population-based screening program
for osteoporosis fracture risk. We utilized information
from third (2007–2011; n = 2129) follow-up visit. At this
visit, subjects were given questionnaires to evaluate risk
factor assessment and dietary intake through food
frequency questionnaires (FFQ) and completed bone
Participants were weighed in kilograms (kg) on calibrated
balance scales (Seca, Hamburg, Germany) while wearing light
clothing and no shoes. Heights were measured in centimetres
(cm) using a stadiometer (Holtain Ltd., Crymych, UK). Body
mass index (BMI) was calculated as weight in kilograms
divided by height in metres squared.
Usual dietary intake over the previous 12 months was
assessed using a 98-question semi-quantitative FFQ based
on the Caerphilly FFQ and modified to include more detail
on foods commonly consumed in northeast Scotland. It has
been validated using 7-day weighed food records and
biochemical markers of antioxidant status, including ascorbic
acid, carotene, retinol, and α- and γ-tocopherol [16, 17]. Intakes
of specific nutrients were calculated using the UK’s McCance
and Widdowson’s composition of foods  based on the
weight of each food consumed multiplied by the frequency
of intake with its assigned portion size. All food and beverage
items were grouped into 37 food groups on the basis of
similarities of food and nutrient composition (Appendix Table 1).
BMD was measured using dual-energy X-ray
absorptiometry (DXA; Lunar iDXA, GE Healthcare, Madison, WI, USA)
at the femoral neck (FN) and L1–L4 lumbar spine (LS).
Encapsulated spine phantoms were measured daily. A plot
of phantom measurements showed an upward shift of 0.7%;
BMDs measured after the phantom shift (n = 193) were
adjusted for this shift.
BMI body mass index, HRT hormone-replacement therapy
a Physical activity level is defined as an individual’s total energy
expenditure over 24 h, divided by their basal metabolite rate and is unitless
b National Deprivation Category based on postcode classification where
B1^ represents most affluence/least deprived and B6^ represents least
All procedures involving human participants were
approved by the East of Scotland Research Ethics Service.
Written informed consent was obtained from all participants.
Measurement of confounding factors
Physical activity level (PAL) was obtained using the same
questionnaire as in the Scottish Heart Healthy Study .
PAL is calculated from the duration and intensity of activity
performed in a 24-h period divided by basal metabolic rate;
these were assessed for working and non-working days .
National deprivation category was assigned from postal codes
in 1997–2000; the lowest number denotes the most affluent/
least deprived and is used as a measure for socioeconomic
status . Questionnaires were used to assess social and
demographic information including age, menopause status
(pre-, peri-, post-menopausal), hormone-replacement therapy
(HRT; past user, present user) use and smoking status (yes/
All analyses were conducted using Statistical Analysis
Systems statistical analysis package ver. 9.3 (SAS Institute,
Inc.; Cary, NC, USA). Participant characteristics were
described by means and standard deviations (SD) or medians
and interquartile ranges (IQR) for continuous variables and
percentages for categorical variables. Non-normally
distributed variables were natural log-transformed prior to analysis.
The PLS method was used to extract successive,
orthogonal, linear combinations (Bdietary patterns^ or Bfactors^) of
the predictor (i.e. the 37 food groups) and response variables
(i.e. nutrients associated with bone) to maximize the
covariance between them (described in greater detail by Hoffman
et al. ). The resulting factors are uncorrelated with one
another and can be concurrently used in regression models
without risk of confounding each other.
Response variables were chosen for their relationship with
bone health and include dietary intakes of calcium, vitamin D,
vitamin C, protein, alcohol, potassium, magnesium,
phosphorus and zinc.
Food groups and nutrient response variables were adjusted
for energy intake using the residual method . We fit the 37
food groups as predictor variables and included the nine
described dietary nutrients as response variables to obtain the
dietary patterns (factors). Split-sample cross-validation with
van der Voet’s test and inspection of correlation plots between
the predictor and response scores were used to determine the
number of factors to retain .
The retained factors were entered as continuous variables
in regression models with continuous BMD as the outcome.
Unadjusted and multivariable regression models were
constructed separately for LS and FN BMD, adjusting for age,
BMI, physical activity level, smoking status (current smokers/
not) and national deprivation category (category 6 [least
affluent/most deprived] as reference). Factors that were
significant in these models were additionally categorized into
quartiles to explore potential non-linear relationships. All P values
are two-sided. We tested LS and FN BMD for trend across
quartiles of the dietary patterns.
Additional adjustment for previous HRT use (n = 1259)
and the interaction between the selected dietary patterns and
previous HRT use were initially included but were not
significant and not retained in the final model. Sensitivity
analyses excluding participants with energy intake <3.35 or
>14.65 MJ (n = 20), and participants with rheumatoid arthritis
(n = 40), osteoarthritis (n = 358), other unspecified bone
diseases (n = 18), previous bisphosphonates use n = 14) or oral
steroid use (n = 44) did not markedly alter effect estimates.
Regression models without energy-adjustment for food
groups were explored but did not result in differences in
results from regression modelling and were left adjusted.
Participant characteristics are shown in Table 1. Our study
participants, on average, were 66 years old (SD 2.2) and
overweight, consumed 10.1 MJ/day and had a LS BMD of 1.09
(0.17) and 0.93 g/cm2 (0.12) for FN BMD.
Five factors were retained from the PLS analysis. These
five factors explained 25% of the variation in the food groups
and 77% of the variation in the response variables (Table 2).
The main food group loadings for factors 2 and 4 are shown in
Fig. 1. Factor loadings, which are the correlations between the
derived factors and the food groups, were considered
important when values were ≥0.2 using absolute values [13, 23]. A
higher factor loading for a food item indicates greater
contribution in constructing the factor and is used to interpret the
composition of the factor. Factor loadings for factor 2 were
shown to be primarily characterized by high intakes of fluid
dairy, potatoes, vegetables, fruit and vegetable juices, and
wine, and low consumption of processed meats, cheese, cakes,
puddings, confectionary, fizzy/carbonated drinks, and spirits.
Factor 4 was characterized by high intakes of red and white
meats, fruits, and wine, and low intakes of vegetables, sweet
spreads, and fruit and vegetable juices.
In multivariable models, two of the factors were
significantly associated with LS or FN BMD (Table 3). In adjusted
analyses, a 1-unit increase in factor 2 was associated with a
0.012 g/cm2 higher LS BMD (95% CI: 0.006, 0.01) and
0.006 g/cm2 (95% CI: 0.002, 0.01) higher FN BMD, while a
unit increase in factor 4 was associated with a 0.008 g/cm2
(95% CI: 0.003, 0.01) increase in FN BMD. When examining
the relationship between quartiles of factors 2 and 4 with
BMD, LS and FN BMD increased significantly across
quartiles of both factor 2 (LS BMD: P-trend = 0.0001; FN BMD:
P-trend = 0.02) and factor 4 (LS BMD: P-trend = 0.02; FN
BMD: P-trend = 0.006) (Fig. 2).
In this study assessing the use of a novel data-reduction
technique to derive dietary patterns in relation to bone health in
post-menopausal women, we observed two dietary patterns
that were positively associated with LS and FN BMD. These
two dietary patterns, or factors, are described by foods that are
considered components of a Bhealthy^ diet: intakes of fruits,
vegetables, milk and wine, and low intakes of processed
meats, cheese, cakes and sweets, fizzy/carbonated beverages
Similar to other studies, we found that consumption of
alcohol, in the form of wine, was positively associated with BMD,
which has been observed in other studies . Moderate
alcohol consumption may be beneficial by raising serum estradiol
levels  and stimulating secretion of calcitonin , which
has been associated with decreased rate of vertebral fractures
and increased bone mass. Wine, rather than spirits, may be
valuable to bone health because it is a source of boron, which
supports bone growth and maintenance, possibly through
enhanced angiogenesis or signal transduction [27, 28].
Explained variance in food groupsa
Explained variance in responsesb
a Foods from the food frequency questionnaire were aggregated into 37 good groups: red meat, white meat,
processed meat, white fish, oily fish, other fish, eggs, milk, yogurt and cream, cheese, potato, vegetables, fruit,
bread, pulses, rice/pasta, cereals, biscuits, cakes, puddings, tinned, dried fruit, confectionary, soups, crisps and
nuts, milk-based sauces, condiments, sweet spreads, fats and oils, coffee, tea, sugar in hot drinks, fruit and
vegetables juices, fizzy drinks, diet fizzy drinks, beer, spirits and wine
b Responses are dietary intakes of alcohol, protein, vitamin D, vitamin C, calcium, magnesium, zinc, phosphorus
Interestingly, vegetables and fruit and vegetable juices
were negatively correlated with factor 4, one of the two dietary
patterns positively associated with BMD. This perhaps
suggests that other elements of this dietary factor, such as fruit
intake, may have offset the negative loading of vegetables and
fruit and vegetable juices to result in the overall beneficial
relationship with BMD. A similar explanation was proposed
for the loading of a partially hydrogenated soybean oil onto a
Bvegetable^ dietary pattern associated with a decrease in risk
of myocardial infarction . The authors hypothesized that
the potentially atherogenic effects of partially hydrogenated
fatty acids may have been counteracted by the beneficial
elements in the vegetable dietary pattern.
Dairy intake also differed in how it loaded on factor 2,
which was characterized by high intakes of fluid dairy but
low intakes of cheese. Włodarek et al.  observed a similar
situation, with positive correlation between milk intake and
BMD, but negative correlation with rennet and cottage
cheeses. Calcium may be the major nutrient attributed to dairy
products in relation to bone health but its absorption is
promoted, and therefore influenced, by lactose . The
authors hypothesized that, as lactose content is higher in milk than
in cheeses, the increased calcium absorption led to the positive
association with BMD. But, as calcium intake in our population
was above the reference nutrient intake (RNI) in the UK of
700 mg/day for adults , we postulate that our finding may
also be a result of how our food groups were constructed.
We separated dairy products into three groups: milk drinks
(including dried, condensed, soy), yogurt and cream products
(including full-fat, low-fat, skimmed), and cheese products
(including full-fat, low-fat, hard, soft) in order to differentiate
between the dairy products. There is no consensus for how to
group foods when conducting a dietary pattern analysis;
studies subjectively cluster foods based on similarities in food and
nutrient intake, culinary preference, and logic . How other
studies chose to categorize dairy products may be why
positive associations between dairy and bone health were
observed when all dairy products were grouped together ,
while inconsistent relationships were found when dairy
products were in multiple sub-groups .
LS lumbar spine, BMD bone mineral density, FN femoral neck, CI confidence interval
a Adjusted for age, BMI, physical activity level, smoking status, national deprivation category
This is the first study to apply PLS in determining dietary
patterns and their relation to bone; there are two other studies
applying a statistical method that takes nutrient or biomarker
response variables into consideration when identifying dietary
patterns, through the use of RRR [11, 34]. In a study of
Australian adolescents by van den Hooven et al. , patterns
were characterized based on whether the response variables
(protein, calcium and potassium) were high or low. Two
patterns were extracted: pattern 1 with high protein, calcium and
potassium, and pattern 2 with high protein, low calcium and
potassium. Only the first pattern was positively associated
with BMD and bone mineral content; the foods that loaded
on this pattern were comparable to a healthy dietary pattern
characterized by high intakes of low-fat dairy, whole grains,
vegetables, fish, fruit, legumes and low intakes of
confectionary, chips/crisps, sweets and processed meats. The study by
Ward et al.  utilized food diaries collected from age
36 years to 60–64 years to determine RRR dietary patterns
constructed with protein, calcium, and potassium as response
variables, and how dietary patterns may track through time.
The first dietary pattern extracted when participants were
36 years old was positively associated with dietary intakes
of fruits and vegetables and low-fat dairy (milk and yogurt),
while negatively associated with intakes of refined grain
products, processed and sugary foods, and alcohol. Adherence to
this dietary pattern at each subsequent visit was calculated for
each individual, and the trajectories showed that an adherence
to the dietary pattern was positively associated with bone
health at age 60–64 years. These two studies utilizing another
dietary data reduction technique also showed that a healthy,
nutrient-dense diet is beneficial for bone health in both young
and older participants.
Other studies deriving non-PLS dietary patterns in older
adults also found healthy or nutrient-dense dietary patterns
to be associated with decreased bone resorption ,
decreased fracture risk [8, 35] or increased BMD , and
dietary patterns with high loadings for energy-dense, processed
foods were associated with lower BMD [9, 10, 12]. While
some studies report null or contradictory relationships
between healthy dietary patterns and bone health , adherence
to dietary patterns which are nutrient-dense are beneficial for
health outcomes beyond bone, including hypertension 
and type 2 diabetes .
Strengths of our study include a large sample size and the
use of bone-related nutrient biomarkers as response variables
in the novel PLS procedure to construct dietary patterns. The
benefit of using PLS, as opposed to other data reduction
techniques, results from including knowledge about the
intermediary response variables between the food groups and the
health or disease outcome. Studies differ in what intermediary
response variables to include. We included calcium and
vitamin D, as they are necessary for calcium absorption and bone
formation; in postmenopausal women, calcium intake was
positively associated with FN BMD change and hypothesized
to reduce bone loss  and there is good evidence to suggest
that intakes of combined calcium and vitamin D are beneficial
for BMD . Vitamin C is an antioxidant that could reduce
bone loss by counteracting oxidative stress that may reduce
BMD, and dietary intakes have been associated with increased
BMD . Protein is necessary for many bone-related
activities including growth factors and hormones that impact bone
synthesis, break-down, and bone matrix structure .
Moderate alcohol intake has been positively associated with
BMD and less bone loss by promoting secretion of calcitonin
or increasing endogenous oestrogens . Phosphorus is
necessary for mineralization of the skeleton and inadequate levels
result in impaired bone integrity and can lead to osteomalacia
. Magnesium may influence bone metabolism through its
necessity as a co-factor in metabolism and enzymatic
processes and, directly, it may decrease hydroxyapatite crystal size
. Zinc is necessary for collagen synthesis and osteoblastic
activity, and a trial among postmenopausal women showed
that supplementation resulted in a small increase in BMD over
a 2-year period [43, 44]. Finally, potassium is hypothesized to
benefit bone by producing an alkaline environment, reducing
the need to recruit skeletal calcium salts to counteract the acids
generated from acid-generating foods [3, 43].
The PLS approach in including response variables results
in dietary factors that are underpinned by the underlying
nutrients of interest, so the choice of the intermediary response
variables will influence how dietary factors are constructed.
While RRR is similar to PLS, dietary patterns identified by
RRR are limited to those nutrients included as the
intermediary variables and may miss out on dietary patterns that specify
nutrient pathways not included as intermediary response
variables. Therefore, the dietary patterns produced by RRR are
more tailored to the health outcome in question, whereas the
PLS procedure is a better tool for informing how to
modify intake of dietary intake to elicit a nutrient response and
impact health outcomes. While Hoffman et al. 
concluded that RRR was better at identifying dietary patterns
in relation to diabetes risk, DiBello et al.  found that
PCA and PLS produced more dietary patterns associated
with their outcome of myocardial infarction risk. Thus, our
objective was methodologically driven by the use of this
novel technique to assess how dietary patterns may be
derived. The dietary patterns we identified support
previous findings using other dietary reduction techniques, and
this present analysis using a different data reduction
technique confirms and adds to the evidence of the benefits of
a healthy dietary pattern.
This study also had several limitations. Like other
datareduction methods, the assessment of dietary intake is subject
to measurement error and residual confounding, even though
our FFQ had been previously validated using weighed food
records and biochemical markers of antioxidant status . It is
also possible that the dietary patterns observed were based on
nutrients that were chosen as our response variable nutrients,
where different nutrient response variables may elicit
formation of different dietary patterns. Additionally, dietary patterns
extracted in one population with data-reduction methods will
not reproduce the same dietary factors with the same food
loadings in another data set, and thus, our results are limited
to postmenopausal women from northeast Scotland. Another
limitation is the cross-sectional nature of our study, where
causality cannot be assumed.
Nevertheless, multiple studies have reported general
Bhealthy^ dietary patterns that have similar food group
loadings. The persistent relationships between similar dietary
patterns with various outcome measures have led government
advisory groups such as the US Dietary Guidelines
Advisory Committee to recommend that the population
achieve optimal health through a healthy diet, rather than
focusing on specific foods or nutrients . Earlier initiatives had
also attempted to move from nutrient population goals to one
that is foods-based .
In summary, dietary pattern analysis using the PLS
method extracted dietary patterns rooted in a biological link
between dietary intakes and the health outcome; this is
important because foods are the modifiable aspect in this
relationship. In this study, we observed that adherence to
dietary patterns with higher intakes of nutrient-rich foods and
lower intakes of energy-dense foods was associated with an
increase in bone mineral density in a population of
postmenopausal women. Our results show that PLS is an
appropriate method to determine which dietary patterns are
associated with BMD and supports previous findings using
other data-reduction techniques on the relationships between
diet and health.
Authorship T.C.Y., H.M.M. and G.G.D. contributed to the
conception and design of the research, T.C.Y. analysed and
wrote the manuscript, H.M.M. and G.G.D. obtained funding
for the research and supervised research direction, L.S.A. and
H.M.M. supervised analysis and interpretation. All authors
reviewed the manuscript and approved the final version.
Compliance with ethical standards
Financial support This work was supported by the Foods Standards
Agency and the UK Department of Health (grant number N05086) and
the Scottish Funding Council. We are grateful for funding from the
Scottish Government’s Rural and Environmental Science and
Analytical Services (RESAS) Food, Land and People Programme. Any
views expressed are the authors’ own; none of the funders had a role in
design, analysis or writing of the present study.
Conflicts of interest None.
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1. NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis and T ( 2001 ) Osteoporosis prevention, diagnosis, and therapy . NIH Consenus Statement 17 ( 1 ): 1 - 45 2. Cashman KD ( 2007 ) Diet, nutrition, and bone health . J Nutr 137 : 2507S - 2512S 3. Prentice A ( 2004 ) Diet, nutrition and the prevention of osteoporosis . Public Health Nutr 7 ( 1A ): 227 - 243 4. Hu FB ( 2002 ) Dietary pattern analysis: a new direction in nutritional epidemiology . Curr Opin Lipidol 13 ( 1 ): 3 - 9 5. Willett WC ( 2012 ) Nutritional epidemiology, 3rd edn . Oxford University Press, New York 6. Katan MB , Brouwer IA , Clarke R et al ( 2010 ) Saturated fat and heart disease . Am J Clin Nutr 92 ( 2 ): 459 - 60 -1 7. Dietary Guidelines Advisory Committee ( 2015 ) Scientific Report of the 2015 Dietary Guidelines Advisory Committee 8 . Benetou V , Orfanos P , Pettersson-Kymmer U et al ( 2013 ) Mediterranean diet and incidence of hip fractures in a European cohort . Osteoporos Int 24 ( 5 ): 1587 - 1598 9. Langsetmo L , Poliquin S , Hanley DA et al ( 2010 ) Dietary patterns in Canadian men and women ages 25 and older: relationship to demographics, body mass index, and bone mineral density . BMC Musculoskelet Disord 11 : 20 25.
Hardcastle AC , Aucott L , Fraser WD et al ( 2011 ) Dietary patterns, bone resorption and bone mineral density in early post-menopausal Scottish women . Eur J Clin Nutr 65 ( 3 ): 378 -385 van den Hooven EH , Ambrosini GL , Huang R et al ( 2015 ) Identification of a dietary pattern prospectively associated with bone mass in Australian young adults . Am J Clin Nutr 102 ( 5 ): 1035 -1043 Tucker KL , Chen H , Hannan MT et al ( 2002 ) Bone mineral density and dietary patterns in older adults: the Framingham Osteoporosis Study . Am J Clin Nutr 76 ( 1 ): 245 -252 Hoffmann K , Schulze MB , Schienkiewitz A et al ( 2004 ) Application of a new statistical method to derive dietary patterns in nutritional epidemiology . Am J Epidemiol 159 ( 10 ): 935 -944 Ocké MC ( 2013 ) Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis .
Proc Nutr Soc 72 ( 2 ): 191 -199 Garton MJ , Torgerson DJ , Donaldson C et al ( 1992 ) Recruitment methods for screening programmes: trial of a new method within a regional osteoporosis study . BMJ 305 ( 6845 ): 82 -84 Bolton-Smith C , Casey CE , Gey KF et al ( 1991 ) Antioxidant vitamin intakes assessed using a food-frequency questionnaire: correlation with biochemical status in smokers and non-smokers . Br J Nutr 65 ( 3 ): 337 -346 New S ( 1995 ) An epidemiological investigation into the influence of mutritional factors on bone mineral density and bone metabolism . PhD Thesis . University of Aberdeen: Scotland, UK Holland B, Welch AA , Unwin ID et al (eds) ( 1991 ) McCance and Widdowson's the composition of foods . HMSO , Cambridge Tunstall-Pedoe H , Smith WC , Crombie IK et al ( 1989 ) Coronary risk factor and lifestyle variation across Scotland: results from the Scottish heart health study . Scott Med J 34 : 556 - 560 Department of Health ( 1991 ) Dietary reference values for food energy and nutrients for the United Kingdom . Report of the Panel on Dietary Reference Values of the Committee on Medical Aspects of Food Policy. Rep Health Soc Subj 41 : 1 - 210 McLoone P. Carstairs scores for Scottish postcode sectors from the 1991 census . Glasgow, United Kingdom: University of Glasgow: 1994 .
Tobias RD ( 2004 ) The PLS Procedure . SAS/STAT 9.2 User's Guide.
4759-4808 Kröger J , Ferrari P , Jenab M , et al. Specific food group combinations explaining the variation in intakes of nutrients and other important food components in the European Prospective Investigation into Cancer and Nutrition: an application of the reduced rank regression method . Eur. J. Clin. Nutr . 2009 ; 63 Suppl 4 : S263 - S2674 .
Macdonald HM , New SA , Golden MHN et al ( 2004 ) Nutritional associations with bone loss during the menopausal transition: evidence of a beneficial effect of calcium, alcohol, and fruit and vegetable nutrients and of a detrimental effect of fatty acids . Am J Clin Nutr 79 ( 1 ): 155 -165 Laitinen K , Välimäki M ( 1991 ) Alcohol and bone . Calcif Tissue Int 49 ( Suppl ): S70 - S73 . doi: 10 .1007/BF02555094 Rico H ( 1993 ) Alcohol and bone mineral density . BMJ 307 ( 6909 ):939 Nielsen FH ( 2014 ) Update on human health effects of boron . J trace Elem Med Biol 28 ( 4 ): 383 - 387 28.
Bi L , Jung S , Day D , et al. Evaluation of bone regeneration, angiogenesis, and hydroxyapatite conversion in critical-sized rat calvarial defects implanted with bioactive glass scaffolds . J Biomed Mater Res - Part A 2012 ; 100 A : 3267 - 3275 .
DiBello JR , Kraft P , McGarvey ST et al ( 2008 ) Comparison of 3 methods for identifying dietary patterns associated with risk of disease . Am J Epidemiol 168 ( 12 ): 1433 -1443 Włodarek D , Głąbska D , Kołota A et al ( 2014 ) Calcium intake and osteoporosis: the influence of calcium intake from dairy products on hip bone mineral density and fracture incidence-a populationbased study in women over 55 years of age . Public Health Nutr 17 ( 2 ): 383 -389 Areco V , Rivoira MA , Rodriguez V et al ( 2015 ) Dietary and pharmacological compounds altering intestinal calcium absorption in humans and animals . Nutr Res Rev 28 ( 2 ): 83 -99 Martinez ME , Marshall JR , Sechrest L ( 1998 ) Invited commentary: factor analysis and the search for objectivity . Am J Epidemiol 148 ( 1 ): 17 -19 Shin S , Joung H ( 2013 ) A dairy and fruit dietary pattern is associated with a reduced likelihood of osteoporosis in Korean postmenopausal women . Br J Nutr 110 : 1926 -1933 Ward KA , Prentice A , Kuh DL et al ( 2016 ) Life course dietary patterns and bone health in later life in a British birth cohort study .
J bone Miner Res 31 ( 6 ): 1167 - 1176 Dai Z, Butler LM , van Dam RM et al ( 2014 ) Adherence to a vegetable-fruit-soy dietary pattern or the alternative healthy eating index is associated with lower hip fracture risk among Singapore Chinese . J Nutr 144 ( 4 ): 511 -518 Feart C , Lorrain S , Ginder Coupez V et al ( 2013 ) Adherence to a Mediterranean diet and risk of fractures in French older persons .
Osteoporos Int 24 ( 12 ): 3031 -3041 Ndanuko RN , Tapsell LC , Charlton KE et al ( 2016 ) Dietary patterns and blood pressure in adults: a systematic review and meta-analysis of randomized controlled trials . Adv Nutr 7 ( 1 ): 76 - 89 Xi P, Liu RH ( 2016 ) Whole food approach for type 2 diabetes prevention . Mol Nutr Food Res 60 ( 8 ): 1819 -1836 Chung M , Balk EM , Brendel M et al ( 2009 ) Vitamin D and calcium: a systematic review of health outcomes . Evid Rep Technol Assess 183 : 1 -420 Finck H , Hart AR , Jennings A et al ( 2014 ) Is there a role for vitamin C in preventing osteoporosis and fractures? A review of the potential underlying mechanisms and current epidemiological evidence .
Nutr Res Rev : 1 -16 Bonjour J-P ( 2011 ) Protein intake and bone health . Int J Vitam Nutr Res I 81 ( 2-3 ): 134 -142 Penido MGMG , Alon US ( 2012 ) Phosphate homeostasis and its role in bone health . Pediatr Nephrol 27 ( 11 ): 2039 -2048 Palacios C ( 2006 ) The role of nutrients in bone health, from A to Z .
Crit Rev Food Sci Nutr 46 ( 8 ): 621 - 628 Strause L, Saltman P , Smith KT et al ( 1994 ) Spinal bone loss in postmenopausal women supplemented with calcium and trace minerals . J Nutr 124 ( 7 ): 1060 - 1064 Department of Health ( 1994 ) Eat Well! An action plan from the nutrition task force to achieve the health of the nation targets on diet and nutrition . London, United Kingdom