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Vitamin D-related Nutrigenetics and Cognitive Decline in an Elderly Population

  • Charlotte Martin1,
  • Zoe Yates2,
  • Martin Veysey3,4,
  • Katrina King4,
  • Suzanne Niblett4 and
  • Mark Lucock1,*
Exploratory Research and Hypothesis in Medicine 2017;():-

DOI: 10.14218/ERHM.2017.00006



Published online:

 Author information


Background and objective

Vitamin D has been linked to brain function. To date, there have been limited studies investigating vitamin D receptor (VDR) genetic polymorphisms and cognition. The objective of this study was, therefore, to examine whether any relationships exist between VDR polymorphisms and cognitive decline in an elderly population.


Six hundred and fifty participants aged ≥ 65 years were recruited from the Central Coast, New South Wales, Australia, and were genotyped for 8 VDR polymorphisms (VDR-ApaI, VDR-BsmI, VDR-TaqI, VDR-FokI, VDR-Tru91, VDR-Cdx2, VDR-A1012G, and VDR-NIaIII). Gene variants were identified using polymerase chain reaction, followed by restriction fragment length polymorphism analysis and gel electrophoresis. Cognitive decline was measured using the mini-mental state examination (MMSE), while a self-administered food frequency questionnaire was used to estimate participants’ dietary intake of vitamin D.


Odds ratio (OR) analysis found that VDR-BsmI and VDR-TaqI polymorphic alleles were both associated with increased risk of cognitive decline (OR = 1.55 and OR = 1.49, respectively). VDR-TaqI was also found to be significantly associated with MMSE score, following adjustment for age and sex (p = 0.0005). Examination of the distribution of VDR-TaqI genotypes showed that a greater proportion of participants with the homozygous recessive tt genotype had some degree of cognitive decline (24%). As might be predicted, a significant association was also observed between age and MMSE score (p = 0.015). When examined by sex, a significant relationship was found between age and MMSE for females (p ≤ 0.0001) but no relationship was observed in males. Dietary intake of vitamin D did not influence MMSE outcomes in this cohort.


The VDR-BsmI and VDR-TaqI genetic polymorphisms are associated with cognitive decline in an elderly population.


Vitamin D, Cognition, Aging


Vitamin D receptor (VDR) is a member of the nuclear receptor superfamily and participates in a number of diverse biological actions, due to its distribution in almost all organs and tissues. This expression pattern includes the brain, where VDR expression has been detected in the hypothalamus, hippocampus, cortex and sub-cortical regions,1,2 which are essential for cognition. The active form of vitamin D, 1,25(OH)2D3, plays an important role in neuronal differentiation and maturation by controlling the synthesis of neurotrophic agents,1,3 and also by regulating the expression of numerous genes involved in neurotransmitter synthesis, predominantly in the hippocampus (Fig. 1).4,5

Interaction of calcitriol in the target cell in regulating metabolic processes such as brain health.
Fig. 1  Interaction of calcitriol in the target cell in regulating metabolic processes such as brain health.

The VDR is well known for modulating the transcription of genes encoding proteins that execute the “classic” genomic functions of vitamin D for skeletal and mineral homeostasis. However, it also regulates the expression of several genes that mediate “non-classical” actions in non-calcemic tissues. Many of these “non-classical” actions are associated with decreased risk for disorders associated with ageing, including those linked to the nervous system.6,7 The elderly are particularly at risk for a number of degenerative disorders related to low serum calcidiol (25(OH)D3) levels. This is due to a decrease in ultraviolet radiation (UVR) exposure as a consequence of changes in lifestyle, particularly a decline in outdoor activity.8

Diet can also become less varied with age, resulting in low intake of vitamin D content.9 Most significant, however, is a decrease in the ability to synthesise cutaneous vitamin D after UVR exposure–a result of atrophic changes in the skin and a reduced level of the vitamin D precursor, 7-dehydrocholesterol.10 Investigation of the impact of vitamin D deficiency on the ageing brain is critical, with evidence linking low vitamin D status with poorer outcomes on one or more cognitive function tests,11,12 or a higher frequency of dementia,13 mood disorders,14 cognitive decline and Alzheimer’s disease (AD).1517

The VDR gene is located on chromosome 12 (12q13.11) and contains several known polymorphisms. VDR-ApaI (G>T substitution) and VDR-BsmI (A>G substitution) are restriction fragment length polymorphisms (RFLPs) located at the intron between exon 8–9, and are considered to be silent single nucleotide polymorphisms (SNPs) as they do not change the amino acid sequence of the encoded VDR protein.15,16 They may, however, alter gene expression through regulation of messenger RNA.16,17 VDR-TaqI (T>C substitution) is a RFLP in exon 9, while the VDR-FokI polymorphism (T>C substitution) is situated at the start of the codon in exon 2 of the VDR gene.18 The VDR-Tru91 (G>A substitution) located within the intron 8 region has been studied to a lesser extent.16,19 Other polymorphisms in the 1A promoter region, including VDR-Cdx2, VDR-A1012G and VDR-G1520C, have also recently been reported.20

The objective of this study was to examine the relationship between eight vitamin D-related genetic polymorphisms and cognitive decline in an elderly cohort. Furthermore, dietary intake of vitamin D and other related markers were analysed to determine their influence on cognition.


Study design

A total of 650 participants (287 males and 363 females, aged 65–95 years) living independently in either a retirement village or within the community on the Central Coast, New South Wales were assessed for the prevalence of eight VDR genetic polymorphisms, dietary intake of vitamin D, and cognitive ability using the mini-mental state examination (MMSE). Following screening, all dementia participants were excluded from the study.

Informed consent was obtained from all participants prior to study participation. This cross-sectional study was approved by the University of Newcastle Human Research Ethics Committee (H-2008-0431), and the Occupational Health and Safety Committee (28.2009). The study was fully compliant with the Helsinki Declaration. The data used in the present study formed part of a larger Retirement Health and Lifestyle Study (RHLS) funded by an Australian Research Council Linkage Project Grant.

DNA analysis

VDR gene variants were examined using polymerase chain reaction (PCR) to amplify blood DNA, followed by RFLP analysis and gel electrophoresis. Initially, a QIAamp DNA blood mini-kit was used to extract DNA from whole blood using the QIAamp blood and body fluid spin protocol.21 The optimal PCR reaction mixture and temperature varied for each polymorphism examined. Table 1 provides the thermal cycling conditions for each VDR polymorphism, whilst Table 219,2226 gives the primer sequence details for VDR-ApaI, VDR-BsmI, VDR-TaqI, VDR-FokI, VDR-Tru91, VDR-Cdx2, VDR-A1012G and VDR-NIaIII. RFLP was performed following PCR amplification, as described in Table 3. VDR-Cdx2 requires a nested primer allele-specific strategy for genotype scoring, eliminating the need for enzyme digestion. Examples of each genotype for each VDR polymorphism can be seen in Figure 2.

Table 1

Summary of the PCR reagents and thermal cycling conditions for each VDR polymorphism

Gene variantGoTaq® Green Master Mix, µLPrimer in 5 pmol, µLDNA, µLH2O, µLThermal cycling conditions
VDR-A1012G1042495°C x 2 min; 35 x [95°C x 30 sec; 58°C x 30 sec; 72°C x 30 sec]; 72°C x 7 min; hold at 15°C
VDR-TaqI, VDR-ApaI, VDR-BsmI, VDR-FokI, VDR-Tru91***1042495°C x 3 min; 35 x [95°C x 30 sec; 61°C x 30 sec; 72°C x 30 sec]; 72°C x 7 min; hold at 15°C
VDR-Cdx2 G*
VDR-Cdx2 A*
95°C x 3 min; 35 x [95°C x 30 sec; 56°C x 45 sec; 72°C x 30 sec]; 72°C x 7 min; hold at 15°C
VDR-NIaIII1042495°C x 2 min; 35 x [95°C x 30 sec; 58°C x 30 sec; 72°C x 30 sec]; 72°C x 7 min; hold at 15°C
Table 2

Primer sequences for each VDR polymorphism

Gene variantForward primer, 5′→3′Reverse primer, 5′→3′Fragment length, bpRef
VDR-Cdx2 G*
VDR-Cdx2 A*
Table 3

Summary of the restriction enzyme digestion reactions and electrophoresis conditions for each VDR polymorphism

Gene variantRestriction endonucleasePCR product, µLDigestion bufferOtherIncubation length & temperatureGel conditions - Agarose %Fragment of gel, bp
VDR-A1012G20U EcoRV510x Buffer 4 (1 µL)3.8 µL H2O6 hr @ 37°C3AA: 150, 27
AG: 177, 150, 27
GG: 177
VDR-TaqI20U TaqI510x Buffer 4 (1 µL)1 µL BSA
2.8 µL H2O
3 hr 20 min @ 65°C3TT: 211
Tt: 211, 172, 39
tt: 172, 39
VDR-ApaI50U ApaI510x Buffer 4 (1 µL)1 µL BSA
2.9 µL H2O
3 hr @ 25°C3AA: 211
Aa: 211, 121, 90
aa: 121, 90
VDR-BsmI10U BsmI510x Buffer 4 (1 µL)3.5 µL H2O3 hr 20 min @ 65°C3BB: 236
Bb: 236, 197, 39
bb: 197, 39
VDR-Cdx2 G*
VDR-Cdx2 A*
Not applicable3GG: 297, 110
AG: 297, 235, 110
AA: 297, 235
VDR-FokI4U FokI510x Buffer 4 (1 µL)3 µL H2O3.5 hr @ 37°C3FF: 157
Ff: 157, 121, 36
ff: 121, 36
VDR-NIaIII10U NIaIII510x Buffer 4 (1 µL)2.5 µL H2O6 hr @ 37°C3GG: 236
GC: 236, 197, 39
CC: 197, 39
VDR-Tru914U MseI510x Buffer 4 (1 µL)3 µL H2O3.5 hr @ 37°C3UU: 177
Uu: 177, 91, 86
uu: 91, 86
Examples of each genotype (wild type, heterozygote, homozygous recessive) for the VDR polymorphisms examined in this study.
Fig. 2  Examples of each genotype (wild type, heterozygote, homozygous recessive) for the VDR polymorphisms examined in this study.

A: VDR-ApaI; B: VDR-BsmI; C: VDR-A1012G; D: VDR-FokI; E: VDR-TaqI; F: VDR-Tru91; G: VDR-Cdx2. See Table 3 for further information on banding patterns.


The MMSE is a screening tool used to assess dementia, and focuses solely on the cognitive characteristics of mental functions, excluding questions involving mood, abnormal mental experiences and the form of thinking.27 The MMSE is comprised of 11 questions, covering: orientation to time; orientation to place; registration of three words; language; and visual construction. A score of more than 25 (out of 30) is classified as normal, whilst a score of 25 or less is indicative of some degree of cognitive decline.28

Food frequency questionnaire (FFQ)

Participants completed a self-administered FFQ, enabling an estimation of daily vitamin D intake. The FFQ was comprised of 38 questions relating to diet, covering 205 food items and all food groups. Participants also provided a full list of supplements they were taking, enabling an estimation of total dietary intake of vitamin D. Each FFQ was analysed in FoodWorks™ (version 6.0.2562) nutritional analysis software program (Xyris Software, Brisbane, QLD, Australia), providing a breakdown of vitamin D intake measured as an average per day. This software package is comprised of a number of different food databases, covering the majority of foods consumed by Australians. These include: Abbott products, Ausfoods (brand food) 2007, and Aus Nut (all foods) 2006.


Statistical analysis was conducted using Microsoft Excel 2010 and JMP for Windows (version 11; SAS Institute Inc., Cary, NC, US). Age quartiles, sex and dietary vitamin D data distributions were examined, with mean, standard deviation (SD), median and interquartile range (IQR) reported as appropriate. Participants were stratified by sex and by cognitive decline based on the cut-off score of the MMSE (≤ 25 = cognitive decline; > 25 = no cognitive decline). Genotype prevalence (%), allele number (frequency) and carriage of polymorphic allele (%) were ascertained and tabulated according to cognitive decline. The degree and significance of an allele as a risk factor for a given biochemical/clinical phenotype was ascertained using an odds ratio (OR) and associated 95% confidence interval (CI).

The cognitive decline phenotype was defined as nominal data, and stepwise regression was used to create the best model, for subsequent nominal logistic regression analysis. Stepwise regression was performed in a mixed direction with significant probability [0.250] for a parameter to be considered as a forward step and entered into the model or considered as a backward step and removed from the model. Mallow’s Cp criterion was used for selecting the model where Cp first approaches p variables. R2 is reported and is the proportion of the variation in the response that can be attributed to terms in the model as opposed to random error. While an initial alpha level of 0.05 was set, Bonferroni corrections for multiple comparisons were also applied. Data is reported after adjusting for age and sex in analyses where stepwise regression modelling was used.


Descriptive data for all subjects with and without cognitive decline is provided in Table 4, while the distribution of cognitive decline by age quartiles for each sex is given in Table 5. Seventy-three participants (11%) were found to exhibit some degree of cognitive decline based on their MMSE score. When examined by age quartiles, the proportion of participants with cognitive decline was highest in males in the 65–72 year-old group (15.2%) and the 73–80 year-old group (11.3%), and highest in females in the 81–88 year-old group (15.9%) and 89–95 year-old groups (28.6%) (Table 5). Total vitamin D intake (diet + supplements) was similar for participants with or without cognitive decline.

Table 4

Descriptive data for all subjects and for those participants with and without cognitive decline, based on MMSE (≤25 = cognitive decline; >25 = no cognitive decline)

All subjectsCognitive declineNo cognitive decline
Number of participants65073 (11%)575 (89%)
  Male28733 (12%)253 (88%)
  Female36340 (11%)322 (89%)
Age, x ± SD; median (IQR)77. 8 ± 7.0;
78 (72–83);
79.6 ± 7.6;
80 (74–86);
77.5 ± 6.9;
78 (72–83);
Total dietary vitamin D in µg/d,
x ± SD; median (IQR)
7.76 ± 11.54;
2.65 (1.77–7.93)
8.41 ± 10.78;
2.87 (1.9–12.24)
7.65 ± 11.61;
2.65 (1.75–7.41)
Table 5

Distribution of cognitive decline by age quartiles for each sex, based on MMSE (≤25 = cognitive decline; >25 = no cognitive decline) with percentage of participants with and without cognitive decline for each age quartile

65–72 yr73–80 yr81–88 yr89–95 yr
MalesCognitive decline
No cognitive decline
12 (15%)
67 (85%)
12 (11%)
94 (89%)
8 (9%)
77 (91%)
1 (6%)
15 (94%)
FemalesCognitive decline
No cognitive decline
3 (3%)
85 (97%)
12 (9%)
127 (91%)
17 (16%)
90 (84%)
8 (29%)
20 (71%)

Table 6 presents the genetic distribution of each vitamin D-related polymorphism for the cognitive decline phenotype, and gives the genotype prevalence (%), allele number (frequency) and carriage of polymorphic allele (%). An OR and associated 95% CI were calculated to assess the degree of significance of risk for each of the vitamin D-related gene polymorphisms in relation to cognitive decline. Table 6 also shows that the VDR-BsmI and VDR-TaqI polymorphic alleles were both associated with an increased risk of cognitive decline (OR = 1.55, 95% CI: 1.08–2.22 and OR = 1.49, 95% CI: 1.04–2.14, respectively).

Table 6

Occurrence of cognitive decline phenotype, based on MMSE (≤25 = cognitive decline; >25 = no cognitive decline), for genotype prevalence, allele number and carriage of polymorphic allele for vitamin D-related genetic polymorphisms

PolymorphismPhenotypeGenotype prevalence (%)a
Allele number (frequency)
Carriage of polymorphic, allele %Odds ratioCI (Yates corrected p value)
VDR-A1012GCognitive decline
No cognitive decline
22 (31)
185 (33)
40 (56)
272 (48)
10 (13)
108 (19)
84 (0.58)
642 (0.57)
60 (0.42)
488 (0.43)
VDR-ApaICognitive decline
No cognitive decline
14 (19)
120 (21)
30 (41)
298 (53)
29 (40)
148 (26)
58 (0.40)
538 (0.48)
88 (0.60)
594 (0.52)
VDR-BsmICognitive decline
No cognitive decline
22 (31)
207 (37)
29 (40)
277 (49)
21 (29)
79 (14)
73 (0.51)
691 (0.61)
71 (0.49)
435 (0.39)
1.551.075–2.220 (0.0180)
VDR-Cdx2Cognitive decline
No cognitive decline
12 (50)
87 (50)
11 (46)
71 (41)
1 (4)
15 (9)
35 (0.73)
245 (0.71)
13 (0.27)
101 (0.29)
VDR-FokICognitive decline
No cognitive decline
3 (12)
29 (17)
10 (40)
85 (49)
12 (48)
61 (34)
16 (0.32)
143 (0.41)
34 (0.68)
207 (0.59)
VDR-NlaIIICognitive decline
No cognitive decline
10 (40)
56 (32)
13 (52)
87 (50)
2 (8)
32 (18)
33 (0.66)
199 (0.57)
17 (0.34)
151 (0.43)
VDR-TaqICognitive decline
No cognitive decline
25 (34)
203 (37)
27 (37)
280 (51)
21 (29)
66 (12)
77 (0.53)
686 (0.62)
69 (0.47)
412 (0.38)
1.491.039–2.142 (0.0290)
VDR-Tru91Cognitive decline
No cognitive decline
59 (81)
414 (73)
13 (18)
138 (24)
1 (1)
16 (3)
131 (0.90)
966 (0.85)
15 (0.10)
170 (0.15)

Multivariate analyses of all vitamin D-related genetic polymorphisms using stepwise regression showed a significant relationship between VDR-TaqI and MMSE (p = 0.0014; R2 = 0.0292). When adjusted for age and sex, the relationship between VDR-TaqI and MMSE remained significant (p = 0.0004; R2 = 0.0473). Both results upheld their significance following a Bonferroni correction. To further examine the significant relationship between VDR-TaqI and MMSE, the percentage of participants with cognitive decline for each genotype was calculated. Figure 3 shows that the highest percentage of participants with some degree of cognitive decline was amongst those with the homozygous recessive tt genotype (24%).

Percentage of participants with each VDR-<italic>Taq</italic>I gene variant (wild type TT, heterozygote Tt, homozygous recessive tt) and cognitive decline, based on an MMSE score of ≤ 25.
Fig. 3  Percentage of participants with each VDR-TaqI gene variant (wild type TT, heterozygote Tt, homozygous recessive tt) and cognitive decline, based on an MMSE score of ≤ 25.

Nominal logistic regression analysis was used to examine the relationships between other variables and MMSE score, independent of genetic influence. Results showed a significant inverse association between age and MMSE score (p = 0.0145; R2 = 0.0131; slope estimate = −0.0180), and when examined by sex, a significant relationship was shown between age and MMSE for females (p ≤ 0.0001; R2 = 0.0865; slope estimate = −0.1160) but not males.


The present study examined the relationship between VDR polymorphisms and cognition in 650 elderly participants using the MMSE to assess their degree of cognitive decline. An OR analysis showed that both the VDR-BsmI and VDR-TaqI polymorphic alleles increased the risk of cognitive decline and that the VDR-TaqI genetic polymorphism was also significantly associated with MMSE score. The percentage of participants with some degree of cognitive decline was highest amongst those with the VDR-TaqI homozygous recessive tt genotype (24%).

Vitamin D is important for brain and other physiological functions, and plays an important role in the biosynthesis of neurotransmitters,4 neuroprotection,5 immunomodulation and detoxification.4 Consequently, some of these biological effects suggest that vitamin D may influence cognitive function and mood disorders. Evidence surrounding vitamin D and the human brain have revealed individuals are increasingly vulnerable to mood disorders during the winter months,29,30 and a vitamin D deficiency may contribute to seasonal affective disorder. Additionally, evidence has shown a link between low vitamin D status and mood disorders, accompanied with poor cognitive function.31 Balion et al.32 conducted a systematic review and meta-analysis to examine the association between vitamin D, cognitive function and MMSE score; overall, it was found that lower vitamin D concentrations were associated with poorer cognitive scores and higher AD risk. In the present study, no associations were found between dietary vitamin D intake and MMSE score.

Research surrounding VDR polymorphisms and cognition have mostly focused on AD,33,34 and to a lesser extent cognitive decline.35 In one study, a meta-analysis found associations between VDR polymorphisms and AD and Parkinson’s disease (PD) susceptibility.36 Other findings include a significant association between the VDR-TaqI “T” allele and AD susceptibility (OR = 0.735, 95% CI: 0.596–0.907),36 while in a further study the VDR-ApaI A allele and AA genotype increased the risk of mild cognitive impairment (MCI) (OR = 1.62, 95%CI: 1.13–2.31 and OR = 3.49, 95% CI: 1.570–7.740, respectively).24 The results also showed that the variant B allele of VDR-BsmI increased the risk of MCI (OR = 1.94, 95% CI: 1.240–3.050).37

Najmi Varzaneh et al.38 found the VDR-FokI was significantly associated with cognitive function following assessment using the MMSE. Examination of cognitive function amongst FokI genotypes showed “FF” participants had a higher cognitive score compared with “ff” participants.38 Similar findings were shown for FokI in a recent longitudinal study by Gatto et al.39 This study examined VDR polymorphisms with PD using the MMSE, and found that for each additional copy of the FokI-A allele (also referred to as the “f” allele) an associated decrease in the total MMSE score occurred. Furthermore, their results indicated that participants with the AA genotype (also referred to as “ff” genotype) had a faster decline in cognitive function than participants with other VDR-FokI genotypes. No significant association was found between any other VDR polymorphism and PD in this study based on a level of significance of p < 0.05.39

Another study involving elderly subjects found significant associations between VDR-BsmI, VDR-TaqI and a low composite cognitive score (calculated by averaging the scores of other cognitive function tests) but not with a low MMSE score.40 Additionally, a significant association was observed between VDR-ApaI and less depressive symptoms based on the Geriatric Depression Scale.40

Since vitamin D was the nutrient of particular interest, blood 25(OH)D3 measurements would have provided further details surrounding the vitamin D status of participants. This is therefore a limitation in the present study.

Future research directions

Further research is necessary to either confirm or refute the presently observed associations of VDR gene variants with cognitive decline as measured using the MMSE scale. By obtaining a more thorough understanding of VDR variants and their influence on risk of cognitive decline, new insights into the underlying pathophysiology of cognitive decline and development of possible intervention and treatment strategies will emerge. These may include the screening of particular VDR polymorphisms as part of a routine health check and the use of supplemental vitamin D at a younger age. Based on the preliminary results of this study, we hypothesize that the use of vitamin D as a potential preventative agent in cognitive decline will reduce the impact this degenerative disorder currently has on our health system.


Our results show that the VDR-BsmI and VDR-TaqI polymorphisms are associated with cognitive decline in an elderly population. Since the elderly are particularly at risk for a number of degenerative disorders related to low serum 25(OH)D3 levels, including cognitive decline, further exploration of the influence of VDR gene variants is necessary to reveal the full extent of potential interactions.



Alzheimer’s disease


confidence interval


food frequency questionnaire


mild cognitive impairment


mini mental state examination


odds ratio


Parkinson’s disease


polymerase chain reaction


restriction fragment length polymorphism


Retirement Health and Lifestyle Study


single nucleotide polymorphism


ultraviolet radiation


vitamin D receptor



Part of the research on which this paper is based was conducted as part of the Retirement Health and Lifestyle Study, The University of Newcastle. We are grateful to the Australian Research Council, Central Coast Local Health District Public Health Unit, UnitingCare Ageing NSW/ACT, Urbis Pty Ltd, Valhalla Village Pty Ltd, and Hunter Valley Research Foundation for funding the initial study and to the men and women of the Central Coast region who provided the information recorded. The authors would also like to thank the researchers and RHLS clinic staff based at Gosford Teaching Unit, including Jenny Marriott, Marie Mazaroli, Elizabeth Death, Jodi Humphreys, and Louise Lambeth. This research was supported by an Australian Research Council linkage grant (G0188386) awarded to Martin Veysey (lead CI).

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Study design (CM, ML), performance of experiments (CM), analysis and interpretation of data (CM, ML), manuscript writing (CM, ZY, MV), critical revision of the manuscript (CM, ZY, KK, SN, ML), statistical analysis (CM), providing critical funding (MV).


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