A comparison of principal component analysis, partial least-squares and reduced-rank regressions in the identification of dietary patterns associated with bone mass in ageing Australians
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The relative advantages of dietary analysis methods, particularly in identifying dietary patterns associated with bone mass, have not been investigated. We evaluated principal component analysis (PCA), partial least-squares (PLS) and reduced-rank regressions (RRR) in determining dietary patterns associated with bone mass.
Data from 1182 study participants (45.9% males; aged 50 years and above) from the North West Adelaide Health Study (NWAHS) were used. Dietary data were collected using a food frequency questionnaire (FFQ). Dietary patterns were constructed using PCA, PLS and RRR and compared based on the performance to identify plausible patterns associated with bone mineral density (BMD) and content (BMC).
PCA, PLS and RRR identified two, four and four dietary patterns, respectively. All methods identified similar patterns for the first two factors (factor 1, “prudent” and factor 2, “western” patterns). Three, one and none of the patterns derived by RRR, PLS and PCA were significantly associated with bone mass, respectively. The “prudent” and dairy (factor 3) patterns determined by RRR were positively and significantly associated with BMD and BMC. Vegetables and fruit pattern (factor 4) of PLS and RRR was negatively and significantly associated with BMD and BMC, respectively.
RRR was found to be more appropriate in identifying more (plausible) dietary patterns that are associated with bone mass than PCA and PLS. Nevertheless, the advantage of RRR over the other two methods (PCA and PLS) should be confirmed in future studies.
KeywordsDietary analysis methods Principal component analysis Partial least-squares regression Reduced-rank regression Bone mass Ageing population
Akaike's information criterion
Bone mineral content
Bone mineral density
Body mass index
Computer assisted telephone interview
Dietary Questionnaire for Epidemiological Studies
Dual energy X-ray absorptiometry
Explanatory factor analyses
Food frequency questionnaire
National Health Survey
North West Adelaide Health Study
Physical activity level
Principal component analysis
Short test of functional health literacy in adults
World Health Organization
We are thankful to NWAHS participants for their participation in the study. We are grateful for the support provided by Australian Government Research Training Program Scholarship. The NWAHS was funded by The University of Adelaide, the South Australian Department of Health and The Queen Elizabeth Hospital for which the authors are grateful.
YAM, TKG, RA and ZS conceived the study. YAM conducted all analyses and wrote all drafts of the paper. ZS assisted with analysis and reviewed and provided comment on all drafts. TKG, AWT and RA reviewed and commented on all drafts. All authors read and approved the final manuscript.
Compliance with ethical standards
Conflict of interest
The authors have no financial or personal conflicts of interest to declare.
- 5.Cattell RB (1973) Factor analysis. Greenwood, WestportGoogle Scholar
- 13.Hodge A, Patterson AJ, Brown WJ, Ireland P, Giles G (2000) The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle-aged women in a study of iron supplementation. Aust N Z J Public Health 24:576–583CrossRefPubMedGoogle Scholar
- 15.National Heart Foundation, Australian Institute of Health and Welfare (1989) Risk factor prevalence study, Survey no 3. NHF, CanberraGoogle Scholar
- 16.World Health Organization (1995). Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. WHO Technical Report Series 854, GenevaGoogle Scholar
- 17.Australian Bureau of Statistics (2012/13) National nutrition and physical activity survey questionnaire. http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/4363.0.55.0012011-13?OpenDocument. Accessed 29 Jan 2016
- 22.World Health Organization (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group. WHO Tech Rep Ser 843:1–129Google Scholar
- 26.Zhou W, Langsetmo L, Berger C, Poliquin S, Kreiger N et al (2013) Longitudinal changes in calcium and vitamin D intakes and relationship to bone mineral density in a prospective population-based study: the Canadian Multicentre Osteoporosis Study (CaMos). J Musculoskelet Neuronal Interact 13:470–479PubMedPubMedCentralGoogle Scholar
- 31.de Jonge EA, Kiefte-de Jong JC, Hofman A, Uitterlinden AG, Kieboom BC et al (2016) Dietary patterns explaining differences in bone mineral density and hip structure in the elderly: the Rotterdam Study. Am J Clin Nutr 102(5):1035–1043Google Scholar
- 37.Willett W (2013) Nutritional epidemiology. Oxford University, New WorkGoogle Scholar
- 43.National Cancer Institute. Dietary assessment primer, Effects of measurement error. https://dietassessmentprimer.cancer.gov/concepts/error/error-effects.html. Accessed 1 Feb 2016