Relationship between Vitamin C Deficiency and Cognitive Impairment in Older Hospitalised Patients: A Cross-Sectional Study

Abstract

Vitamin C is a powerful antioxidant and facilitates neurotransmission. This study explored association between vitamin C deficiency and cognitive impairment in older hospitalised patients. This prospective study recruited 160 patients ≥ 75 years admitted under a Geriatric Unit in Australia. Cognitive assessment was performed by use of the Mini-Mental-State-Examination (MMSE) and patients with MMSE scores <24 were classified as cognitively-impaired. Fasting plasma vitamin C levels were determined using high-performance-liquid-chromatography. Patients were classified as vitamin C deficient if their levels were below 11 micromol/L. Logistic regression analysis was used to determine whether vitamin C deficiency was associated with cognitive impairment after adjustment for various covariates. The mean (SD) age was 84.4 (6.4) years and 60% were females. A total of 91 (56.9%) were found to have cognitive impairment, while 42 (26.3%) were found to be vitamin C deficient. The mean (SD) MMSE scores were significantly lower among patients who were vitamin C deficient (24.9 (3.3) vs. 23.6 (3.4), p-value = 0.03). Logistic regression analysis suggested that vitamin C deficiency was 2.9-fold more likely to be associated with cognitive impairment after adjustment for covariates (aOR 2.93, 95% CI 1.05–8.19, p-value = 0.031). Vitamin C deficiency is common and is associated with cognitive impairment in older hospitalised patients.

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Citation: Sharma, Y.; Popescu, A.;
Horwood, C.; Hakendorf, P.;
Thompson, C. Relationship between
Vitamin C Deficiency and Cognitive
Impairment in Older Hospitalised
Patients: A Cross-Sectional Study.
Antioxidants 2022,11, 463. https://
doi.org/10.3390/antiox11030463
Academic Editor: Adrianne
Bendich
Received: 4 February 2022
Accepted: 24 February 2022
Published: 26 February 2022
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antioxidants
Article
Relationship between Vitamin C Deficiency and Cognitive
Impairment in Older Hospitalised Patients:
A Cross-Sectional Study
Yogesh Sharma 1,2,* , Alexandra Popescu 3, Chris Horwood 4, Paul Hakendorf 4and Campbell Thompson 5
1College of Medicine & Public Health, Flinders University, Adelaide 5042, Australia
2Department of General Medicine, Division of Medicine, Cardiac & Critical Care, Flinders Medical Centre,
Adelaide 5042, Australia
3Department of Geriatrics & Rehabilitation, Flinders Medical Centre, Adelaide 5042, Australia;
alexandra.popescu@sa.gov.au
4Department of Clinical Epidemiology, Flinders Medical Centre, Adelaide 5042, Australia;
chris.horwood@sa.gov.au (C.H.); paul.hakendorf@sa.gov.au (P.H.)
5Discipline of Medicine, The University of Adelaide, Adelaide 5005, Australia;
campbell.thompson@adelaide.edu.au
*Correspondence: yogesh.sharma@flinders.edu.au; Tel.: +61-8-82046694
Abstract:
Vitamin C is a powerful antioxidant and facilitates neurotransmission. This study explored
association between vitamin C deficiency and cognitive impairment in older hospitalised patients.
This prospective study recruited 160 patients
≥
75 years admitted under a Geriatric Unit in Australia.
Cognitive assessment was performed by use of the Mini-Mental-State-Examination (MMSE) and
patients with MMSE scores <24 were classified as cognitively-impaired. Fasting plasma vitamin C
levels were determined using high-performance-liquid-chromatography. Patients were classified as
vitamin C deficient if their levels were below 11 micromol/L. Logistic regression analysis was used to
determine whether vitamin C deficiency was associated with cognitive impairment after adjustment
for various covariates. The mean (SD) age was 84.4 (6.4) years and 60% were females. A total of
91 (56.9%) were found to have cognitive impairment, while 42 (26.3%) were found to be vitamin C
deficient. The mean (SD) MMSE scores were significantly lower among patients who were vitamin C
deficient (24.9 (3.3) vs. 23.6 (3.4), p-value = 0.03). Logistic regression analysis suggested that vitamin
C deficiency was 2.9-fold more likely to be associated with cognitive impairment after adjustment
for covariates (aOR 2.93, 95% CI 1.05–8.19, p-value = 0.031). Vitamin C deficiency is common and is
associated with cognitive impairment in older hospitalised patients.
Keywords:
vitamin C deficiency; cognitive impairment; geriatric patients; older hospitalised patients;
clock drawing test; mini mental state examination
1. Introduction
Vitamin C, also referred to as ascorbic acid, is a powerful antioxidant that cannot
be synthesised by humans and some other primates due to the lack of an enzyme called
gulonolactone oxidase [
1
]. Vitamin C plays an essential biological role by acting as a
co-factor for a number of enzymes, which are required for proper functioning across a
number of organs and tissue systems [
2
]. In the gastrointestinal tract, vitamin C helps in the
absorption of non-heme iron and is involved in the formation of bile acids via cholesterol
hydroxylation [
2
,
3
]. In addition, it plays a role in immune function and is involved
in the synthesis of corticosteroids, aldosterone, adrenal hormones and in the formation
of collagen [
4
,
5
]. Severe vitamin C deficiency results in development of scurvy, which
can manifest as bleeding, fatigue, bony pains, skin manifestations such as perifollicular
haemorrhages, petechiae and ecchymosis [6].
Antioxidants 2022,11, 463. https://doi.org/10.3390/antiox11030463 https://www.mdpi.com/journal/antioxidants

Antioxidants 2022,11, 463 2 of 11
Vitamin C plays a significant role in the functioning of the brain by regulating neu-
rotransmitter synthesis and release [
7
]. These functions include acting as a co-factor for
dopamine beta-hydroxylase, which converts dopamine to noradrenaline. Vitamin C is
involved in the modulation of glutamatergic, dopaminergic, cholinergic and GABAergic
neurotransmission and regulates the release of catecholamines and acetylcholine from
synaptic vesicles [
8
,
9
]. In addition, the antioxidant properties of vitamin C limit damage
caused by ischaemia-reperfusion mediated injury and protects against glutamate excitotox-
icity [9,10].
Previous studies [
11
–
13
] indicate that vitamin C deficiency may have a role in neu-
rocognitive dysfunction and may be associated with cognitive impairment, depression and
confusion. Two cross-sectional studies [
11
,
14
] have linked lower vitamin C status with
greater cognitive impairment. However, an Australian study [
12
] in otherwise healthy
volunteers found no association between cognitive dysfunction and vitamin C deficiency.
A systematic review by Travica et al. [
7
], which included 50 studies, found no correlation
between vitamin C levels and cognition, however, the majority of studies included in this
review involved community-dwelling healthy participants with a higher baseline cognitive
performance, which could have narrowed the chance of detecting cognitive effects of vita-
min C. In addition, the studies included in this meta-analysis had limitations in terms of
handling of blood samples and biochemical analyses because underestimation of vitamin C
concentrations could occur if the sample was not protected from light and not transported
on ice [7,15].
Furthermore, some of the studies till date have other methodological limitations
namely: use of small sample size and a variable definition for classification of patients as
vitamin C deficient, with one study [
12
] classifying patients with vitamin C levels below
28
µ
mol/L as deficient while another study using even higher cut-off levels [
16
]. Evidence
suggests that clinical manifestations of scurvy usually develop once vitamin C levels drop
below 11.4
µ
mol/L [
17
,
18
], thus it is possible that cognitive dysfunction may not be present
at a higher vitamin C level and manifests only in patients who have a severe vitamin C
deficiency. The present study investigated the relationship between cognitive status and
vitamin C deficiency in older hospitalised patients unit using lower vitamin C cut-off levels,
which usually result in clinical manifestations of scurvy. The hypothesis for this research
was that vitamin C deficiency is common in older hospitalised patients and that patients
with severe vitamin C deficiency will have lower cognitive scores.
2. Materials and Methods
Patients
≥
75 years who were admitted to a geriatric unit of Flinders Medical Centre,
Adelaide, South Australia, were recruited by convenience sampling in this research. Written
informed consent was obtained from the participants, and, in cases of cognitive impairment,
consent was obtained from the legal guardian. The exclusion criteria were lack of valid
consent, patients receiving end-of-life care, and those on vitamin C replacement. Ethical
approval for this study was granted by the Southern Adelaide Human Clinical Research
Ethics Committee (approval no 64.190, dated 9 August 2019) and this study was registered
with Australia and New Zealand Clinical Trial Registry.
2.1. Patient and Public Involvement
Cognitive impairment is highly prevalent in older hospitalised patients and, therefore,
the study results are likely to be high priority for patients. However, patients were not
directly involved in the study design, conduct or outcomes of this research project.
Cognitive status was determined by use of the Mini Mental State Examination (MMSE) [
19
]
and the Clock-Drawing Test (CDT). The MMSE is scored on a 30-point scale and uses items
that assess: orientation (temporal and spatial, 10 points), memory (registration and recall,
6 points), attention and concentration, 5 points, language (verbal and written, 8 points),
and visuospatial function (1 point) [
19
]. While different cut-off points were used across
different studies [
20
], for this study MMSE scores below 24 were regarded as abnormal and

Antioxidants 2022,11, 463 3 of 11
indicative of cognitive impairment. While the MMSE was originally developed to identify
cognitive impairment among psychiatric patients [
21
], it was subsequently validated for
use as a screening tool for dementia across a wide range of patients in both outpatient and
inpatient settings [22,23].
We used the CDT in addition to the MMSE because studies indicate that the CDT is
highly sensitive and specific in the detection of mild dementia and is reasonably accurate
in separating patients with mild cognitive impairment (MCI) from healthy patients, and
the combination of the CDT with the MMSE enhances the psychometric properties of
these scales and is valid for detection of dementia [
24
,
25
]. The CDT was performed by
providing the participants with a 10 cm pre-drawn circle on a piece of paper, and they were
asked to draw an analogue clock, including all the numbers, and set the clock hands to a
specified time of 10 past 1100 h. Performance on the CDT depends upon a combination of
visuospatial ability, executive function, motor function, attention, numerical knowledge
and language comprehension [
26
]. Patients were scored on a simple subjective qualitative
interpretation of clock drawing as normal (without error) and abnormal (with error) as
suggested by Sleutjes et al. [23].
Mood can affect cognition and was assessed by using the Geriatric Depression Scale
(GDS) [
27
]. GDS is a 15-item tool that has been validated for screening depressive symptoms
in the older population including acutely hospitalised medical patients [
28
,
29
]. Frailty
also relates strongly to impaired cognition. Its assessment was performed by use of the
Edmonton Frail Scale (EFS). The EFS is a valid and reliable instrument for identification
of frailty in hospitalised patients and predicts clinical outcomes [
30
,
31
]. The EFS contains
9 components and is scored out of 17. Individual components include cognition, general
health status, self-reported health, functional independence, social support, polypharmacy,
mood, continence and functional performance. The component scores are summed, and the
following cut-off scores are used to classify the severity of frailty: not frail (0–5), apparently
vulnerable (6–7), mild frailty (8–9), moderate frailty (10–11) and severe frailty (12–17). For
this study, patients with EFS scores
≥
8 were classified as frail and those with EFS scores <8
as non-frail.
Nutritional risk was determined by the use of the Malnutrition Universal Screening
Tool (MUST) [
32
]. Fruit and vegetable consumption was determined by asking the patients
their approximate daily intake of standard portions/day in the week prior to their admis-
sion to the hospital. Patients were specifically examined for any signs suggestive of scurvy,
namely: ecchymosis, bruising, gingivitis and perifollicular hyperkeratosis [
33
]. Impairment
of mobility and gait were risk factors for dementia thus the activities of daily living (ADL)
were assessed by use of the Hospital Admission Risk Profile (HARP) score [
34
], which
predicts patients at high risk of discharge to a facility. We determined the sociodemographic
status of the participants by including the following variables: living status (whether living
alone or with a partner), education level (secondary school or a higher university degree)
and annual income (
≥
or <AUD 40,000/year). Polypharmacy was defined as being on 5 or
more medications. Medications with anticholinergic activity, which can impact cognition
(such as the use of antihistamines, antiparkinson, opiates, antimuscarinic, antipsychotic
and antiepileptic drugs) [35] were also determined.
A trained phlebotomist obtained fasting blood samples to determine vitamin C levels.
The blood sample was wrapped in an aluminium foil and immediately placed on ice for
transport to a central laboratory. High performance liquid chromatography (HPLC) was
used to determine vitamin C levels. HPLC has been previously validated for rapid and spe-
cific measurement of vitamin C [
36
]. Plasma vitamin C levels correlate with dietary vitamin
C intake, and unlike leucocyte vitamin C levels, plasma vitamin C levels are not influenced
by changes in the white blood cell (WBC) count and thus represent an accurate measure of
vitamin C status [
36
,
37
]. According to Johnston’s criteria [
38
], vitamin C levels
≥
28
µ
mol/L
are classified as normal, 11–27
µ
mol/L as vitamin C depletion, and <11
µ
mol/L as vitamin
C deficiency. In addition, blood samples were drawn for the determination of haemoglobin,
creatinine, C-reactive protein (CRP), vitamin D and vitamin B12 levels. The haemoglobin

Antioxidants 2022,11, 463 4 of 11
and creatinine levels were determined using spectrophotometry, while C-RP, vitamin D
and vitamin B12 levels were determined by a rapid immunoassay, Roche Diagnostics
(https://www.roche-australia.com) (accessed on 1 May 2020), in a central laboratory.
2.2. Statistics
The normality of the data was assessed by visual inspection of the histograms. Contin-
uous variables were assessed by use of the student ttests or rank-sum tests and categorical
variables by Chi-squared statistics or Fisher’s exact test as appropriate. Patients with MMSE
scores
≥
24 were classified as having normal cognition, while those with MMSE scores <24
as cognitively impaired. For this study, patients with vitamin C levels <11
µ
mol/L were
defined as vitamin C deficient and were compared with the group whose vitamin C levels
were ≥11 µmol/L.
Logistic regression analysis was used to determine whether vitamin C deficiency was
associated with cognitive impairment after adjustment for the following covariates: age,
sex, Charlson index, MUST score, HARP score, depression, living status (whether alone),
education level, socioeconomic status, fruit/vegetable intake, polypharmacy, haemoglobin,
creatinine, vitamin D and vitamin B12 levels.
The use of logistic regression model with the use of small to moderate sample sizes may
sometimes lead to an introduction of an analytical bias, which may result in overestimation
of the effect size. Corrective measures were applied by the performance of sensitivity
analysis with the use of the bootstrap method as suggested by Nemes et al. [
39
] and
bootstrapped standard errors (SE) with 95% confidence intervals were generated. In
addition, a prediction graph with 95% confidence intervals was plotted to determine
the probability of vitamin C deficiency at different MMSE scores using the margins plot
command in STATA.
The sample size for this study was based on a pilot study involving 20 older hospi-
talised patients, which found that the mean (SD) MMSE scores were 27 (7.5) in patients
who were vitamin C replete compared to 22 (12.5) in vitamin C deficient patients, with an
alpha level of 0.05 and power of 80% the calculated sample size was 136 and assuming
15% missing data 156 patients were thought to be sufficient for this study. All statistical
analyses were conducted using Stata version 17.0 (StataCorp, College Station, TX, USA).
3. Results
A total of 603 patients were admitted under the geriatric unit between May-December
2020, of whom, 176 patients were approached by convenient sampling for participation
and 160 patients were recruited for this study (Figure 1). The characteristics of patients
who were not approached for participation were not significantly different from those
who were included in this study in terms of age, sex, Charlson index, living status and
length of hospital stay (LOS) (p> 0.05). The mean (SD) age was 84.4 (6.4) years range
(73–105 years) and 96 (60%) were females. All patients were residing in their own homes
and 78 (48.7%) were living with their partners. The mean Charlson index was 8.4 (2.6)
and the majority of patients were on polypharmacy (130; 81.3%) and many were admitted
with falls as the principal diagnosis (69, 43.1%). The mean (SD) MMSE score was 24.6 (3.4)
(range 19–30). A total of 69 (43.1%) patients had normal cognition (MMSE score
≥
24)
and 91 (56.9%) were found to have cognitive impairment (MMSE score < 24). Patients
with cognitive impairment were older, with a higher Charlson index and frailty scores
and were less likely to have a university degree than cognitively intact patients (p< 0.05).
However, there was no difference with regards to gender, nutrition status, marital status
and number of medications between the cognitively normal and impaired groups (p> 0.05).
The mean (SD) vitamin C levels were 26.8 (23.0)
µ
mol/L, (range 3–148). The median (IQR)
time from hospital admission to the collection of vitamin C sample was 4 (4, 4) days. A
total of 118 (73.7%) patients were not vitamin C deficient (vitamin C levels
≥
11
µ
mol/L),
while 42 (26.3%) were classified as vitamin C deficient (levels <11 µmol/L) (Figure 1).

Antioxidants 2022,11, 463 5 of 11
Antioxidants 2022, 11, x FOR PEER REVIEW 5 of 12
time from hospital admission to the collection of vitamin C sample was 4 (4, 4) days. A
total of 118 (73.7%) patients were not vitamin C deficient (vitamin C levels ≥11 μmol/L),
while 42 (26.3%) were classified as vitamin C deficient (levels <11 μmol/L) (Figure 1).
Figure 1. Study flow diagram.
The median (IQR) time for the collection of the vitamin C sample after hospital ad-
mission was not significantly different between patients who were not vitamin C deficient
compared to those who had vitamin C deficiency (4 (4, 3) vs. 4 (4, 4) days, p-value = 0.095).
Patients with vitamin C deficiency were more likely to be current smokers with a higher
Charlson index and mean creatinine level than patients who were not vitamin C deficient
(Table1). When compared to patients who were not vitamin C deficient, the mean (SD)
MMSE scores were significantly lower among patients who were vitamin C deficient (24.9
(3.3) vs. 23.6 (3.4), p-value = 0.03). However, there was no difference in the proportion of
patients who made errors on the CDT between the two groups (Table 1).
Patients admitted in Geriatric Evaluation and Management
Unit between May-December 2020
n=603
Patients approached for participation
n=176
Patients excluded due to various reasons
n=16
Reasons for exclusion
-feeling unwell
-not interested in participation
-upcoming discharge
-engaged in other activities at the time of interview
Patients enrolled in this study
n=160
Cognitive impairment
n=91(56.9%)
Normal cognition
n=69 (43.1%)
Not vitamin C deficient
n=118 (73.7%)
Vitamin C deficient
n=42 (26.3%)
Figure 1. Study flow diagram.
The median (IQR) time for the collection of the vitamin C sample after hospital
admission was not significantly different between patients who were not vitamin C deficient
compared to those who had vitamin C deficiency (4 (4, 3) vs. 4 (4, 4) days, p-value = 0.095).
Patients with vitamin C deficiency were more likely to be current smokers with a higher
Charlson index and mean creatinine level than patients who were not vitamin C deficient
(Table 1). When compared to patients who were not vitamin C deficient, the mean (SD)
MMSE scores were significantly lower among patients who were vitamin C deficient
(24.9 (3.3) vs. 23.6 (3.4), p-value = 0.03). However, there was no difference in the proportion
of patients who made errors on the CDT between the two groups (Table 1).
Logistic regression analysis suggested that vitamin C deficiency was 2.9-fold more
likely to be associated with cognitive impairment after adjustment for age, sex, Charlson
index, MUST score, HARP score, depression, living status (whether alone), education
level, socioeconomic status, fruit/vegetable intake, polypharmacy, haemoglobin, creatinine,
vitamin D and vitamin B12 levels (aOR 2.93, 95% CI 1.05–8.19, p-value = 0.031) (Table 2).
Sensitivity analysis confirmed that vitamin C deficiency was associated with cognitive
impairment after adjustment for the above-mentioned covariates (Coefficient 1.03, Bootstrap
SE 0.50, 95% CI 0.05–2.03, p-value 0.039). The margins plot suggested that lower MMSE
scores increases the probability of being diagnosed with vitamin C deficiency (Figure 2).

Antioxidants 2022,11, 463 6 of 11
Table 1. Characteristics of patients with and without vitamin C deficiency.
Variable Vitamin C Not Deficient
≥11 µmol/L
Vitamin C Deficient
<11 µmol/L p-Value
N (%) 118 (73.7) 42 (26.3)
Age 84.4 (6.3) 84.5 (6.6) 0.969
Sex male 46 (38.9) 18 (42.8) 0.660
Income < 40 k/year n(%) 70 (59.8)) 24 (57.1) 0.761
Education university degree n(%) 52 (44.1) 13 (30.9) 0.357
Married/defacto n(%) 73 (62.4) 23 (54.8) 0.386
Living status alone n(%) 66 (55.9) 16 (38.1) 0.047
Current smokers n(%) 3 (2.5) 4 (9.5) 0.041
Alcohol drinks/week mean (SD) 1.8 (2.9) 0.9 (2.6) 0.106
Fruits/vegetable intake/day mean (SD) 1.3 (0.6) 1.2 (0.5) 0.989
Charlson index mean (SD) 8.1 (2.5) 9.2 (2.7) 0.021
CDT n(%) 76 (64.4) 33 (78.6) 0.091
Medication number mean (SD) 7.2 (3.6) 8.4 (3.6) 0.078
Polypharmacy, n(%) 77 (65.3) 31(73.8) 0.309
Patients prescribed with medications with
anticholinergic activity * 62 (52.5) 18 (42.9) 0.281
Scurvy symptoms n(%) 54 (45.8) 26 (61.9) 0.072
BMI in kg/m2mean (SD) 26.2 (5.8) 26.6 (4.7) 0.700
MUST score mean (SD) 0.9 (1.2) 0.8 (1.1) 0.404
MMSE score mean (SD) 24.9 (3.4) 23.6 (3.3) 0.030
GDS scores mean (SD) 4.5 (2.8) 4.5 (2.6) 0.964
Depression n(%) 43 (36.4) 18 (42.9) 0.462
EFS score, mean (SD) 9.6 (2.2) 10.3 (1.9) 0.070
Frail n(%) 97 (82.2) 38 (90.5) 0.321
HARP mean (SD) 3.0 (1.1) 3.1 (0.9) 0.494
Haemoglobin g/L mean (SD) 118.0 (16.8) 114.8 (17.4) 0.293
Albumin g/L mean (SD) 34.1 (22.9) 30.3 (6.9) 0.295
Creatinine µmol/L µmol/L 88.3 (35.5) 110.6 (56.4) 0.003
Vitamin C µmol/L mean (SD) 34.3 (22.4) 5.6 (2.4) <0.001
Vitamin D nmol/L mean (SD) 68.9 (31.4) 66.8 (30.8) 0.705
Vitamin B12 pmol/L mean (SD) 491.8 (348.5) 436.5 (349.9) 0.355
* Medications with anticholinergic activity such as antihistamines, anti-parkinson, opiates, antimuscarinic, an-
tipsychotic and antiepileptic drugs. SD, standard deviation; CDT, clock drawing test; BMI, body mass index;
MUST, malnutrition universal screening tool; MMSE, mini mental state examination; GDS, geriatric depression
scale; EFS, Edmonton frail scale; HARP, hospital admission risk profile score.
Antioxidants 2022, 11, x FOR PEER REVIEW 7 of 12
Table 2. Logistic regression model comparing patients with vitamin C deficiency with non-vitamin
C deficient patients and cognitive impairment as an outcome variable.
Variable aOR 95% CI
p
-Value
Vitamin C deficiency 2.93 1.05–8.19 0.031
Age 1.02 0.94–1.09 0.658
Sex male 0.64 0.22–1.83 0.407
Living alone 5.30 1.81–15.19 0.002
Charlson index 1.16 0.97–1.39 0.111
MUST score 1.15 0.81–1.64 0.444
Depression 1.89 0.86–4.14 0.115
Education university degree 0.51 0.26–0.99 0.048
Income < $40,000/year 2.50 0.93–6.72 0.068
HARP score 1.92 1.19–3.09 0.008
Polypharmacy 0.53 0.23–1.19 0.123
Fruit/vegetable intake 1.02 0.91–1.14 0.707
Smokers 1.46 0.65–3.27 0.357
Haemoglobin 0.99 0.96–1.01 0.271
Vitamin D 0.99 0.99–1.01 0.745
Vitamin B12 1.00 0.99–1.00 0.889
aOR, adjusted odds ratio; CI, confidence interval; MUST, malnutrition universal screening tool;
HARP, hospital admission risk profile score.
Figure 2. Prediction probability of vitamin C deficiency according to MMSE scores.
4. Discussion
A substantial proportion (26.3%) of older hospitalised patients were vitamin C defi-
cient. Only a few clinical characteristics, namely a history of current smoking and higher
Charlson index and creatinine levels predicted vitamin C deficiency. Vitamin C deficiency
was associated with an increased risk of cognitive impairment as assessed by the MMSE
scores but not when assessed by the CDT even after adjustment for a number of covariates.
The results of our study corroborate previous evidence [3,40,41] that a high propor-
tion of older hospitalised patients have vitamin C deficiency. Interestingly, our study in-
dicates that there are only a few clinical correlates, which can predict a low vitamin C of
the home-dwelling but currently hospitalised elderly. Furthermore, according to this
study, the symptoms, which were compatible with the diagnosis of scurvy, were not
Figure 2. Prediction probability of vitamin C deficiency according to MMSE scores.

Antioxidants 2022,11, 463 7 of 11
Table 2.
Logistic regression model comparing patients with vitamin C deficiency with non-vitamin C
deficient patients and cognitive impairment as an outcome variable.
Variable aOR 95% CI p-Value
Vitamin C deficiency 2.93 1.05–8.19 0.031
Age 1.02 0.94–1.09 0.658
Sex male 0.64 0.22–1.83 0.407
Living alone 5.30 1.81–15.19 0.002
Charlson index 1.16 0.97–1.39 0.111
MUST score 1.15 0.81–1.64 0.444
Depression 1.89 0.86–4.14 0.115
Education university degree 0.51 0.26–0.99 0.048
Income < $40,000/year 2.50 0.93–6.72 0.068
HARP score 1.92 1.19–3.09 0.008
Polypharmacy 0.53 0.23–1.19 0.123
Fruit/vegetable intake 1.02 0.91–1.14 0.707
Smokers 1.46 0.65–3.27 0.357
Haemoglobin 0.99 0.96–1.01 0.271
Vitamin D 0.99 0.99–1.01 0.745
Vitamin B12 1.00 0.99–1.00 0.889
aOR, adjusted odds ratio; CI, confidence interval; MUST, malnutrition universal screening tool; HARP, hospital
admission risk profile score.
4. Discussion
A substantial proportion (26.3%) of older hospitalised patients were vitamin C defi-
cient. Only a few clinical characteristics, namely a history of current smoking and higher
Charlson index and creatinine levels predicted vitamin C deficiency. Vitamin C deficiency
was associated with an increased risk of cognitive impairment as assessed by the MMSE
scores but not when assessed by the CDT even after adjustment for a number of covariates.
The results of our study corroborate previous evidence [
3
,
40
,
41
] that a high proportion
of older hospitalised patients have vitamin C deficiency. Interestingly, our study indicates
that there are only a few clinical correlates, which can predict a low vitamin C of the
home-dwelling but currently hospitalised elderly. Furthermore, according to this study,
the symptoms, which were compatible with the diagnosis of scurvy, were not significantly
different among patients with or without vitamin C deficiency. Scurvy is characterised by
prominent skin manifestations, including perifollicular hyperkeratosis, cork-screw hairs,
gingival bleeding, petechiae and ecchymosis [
17
,
42
] Bruising and bleeding, which charac-
terise scurvy, can be seen in older hospitalised patients because of a number of reasons such
as falls [
43
], senile purpura (which occurs because of increased skin fragility associated
with ageing) [
44
] and the adverse effects of commonly administered medications such
as antiplatelet agents, anti-coagulants and glucocorticoids [
45
]. Moreover, perifollicular
hyperkeratosis, which is regarded as a hallmark of scurvy, may be difficult to differentiate
from leukocytoclastic vasculitis [
46
]. It may, therefore, be difficult to diagnose vitamin
C deficiency solely on clinical grounds in older hospitalised patients. Given the high
prevalence of vitamin C deficiency in hospitalised patients, there is a need for heightened
vigilance, and biochemical confirmation of vitamin C status is required in suspected cases.
This study indicates that vitamin C deficiency was associated with cognitive impair-
ment as reflected by lower MMSE scores in vitamin C deficient patients when compared
to those who were not vitamin C deficient. This association remained significant after
adjustment for not only age but also various factors, which can be associated with cognitive
impairment such as a higher number of comorbidities as determined by the Charlson index,
education level, depression, socioeconomic status, polypharmacy, haemoglobin, creatinine,
vitamin D and B12 levels [
47
–
52
]. Our study results are in line with a study by Gale et al.
from the UK [
14
], which involved 921 community-dwelling older people
≥
65 years. Their
study found that patients with moderate to severe vitamin C deficiency were 1.6 fold (OR
1.6, 95% CI 1.1–2.3) more likely to be diagnosed with cognitive impairment assessed by
use of the Hodgkinson abbreviated mental test [
53
]. Similarly, another recent study [
11
]

Antioxidants 2022,11, 463 8 of 11
from New Zealand, which included a cohort of 404 people aged 49–51 years, found that
the odds of mild cognitive impairment, as determined by Montreal Cognitive Assessment
(MOCA) [
54
], were twice as high for individuals whose vitamin C levels were below
23
µ
mol/L (OR 2.1, 95% CI 1.2–3.7, p= 0.01). However, our study results are contrary to an
Australian study, which included healthy adults aged 24–96 years and assessed cognitive
function by use of a battery of cognitive screening tools: the Modified Mini Mental State Ex-
amination (3MS) [
55
], the Revised Hopkins Verbal Learning Test (HVLT-R) [
56
], the Symbol
Digits Modalities Test (SDMIT) [
57
] and the Swinburne University Computerised Cognitive
Assessment Battery (SUCCAB) [
12
]. That study found that there was no difference with
respect to the diagnosis of major cognitive impairment with 3MS test among patients with
adequate or inadequate vitamin C status. However, patients who were in the adequate
vitamin C group had significantly higher scores on measures of recognition, immediate
and delayed recall assessed by the HVLT-R and on SDMT screening when compared to
vitamin C inadequate group. Finally, using the SUCCAB, that study found that, although
the accuracy to reaction time was significantly higher in the adequate vitamin C group
for certain tasks, there was no difference with respect to measures of episodic memory or
general alertness and motor speed when compared to the vitamin C inadequate group.
The discrepancy in the results of this study compared to our study, in terms of cognition,
could be related to their inclusion of much younger patients and likely healthier patients
with a wide age range from the community compared to older hospitalised patients in
our study. In addition, that study used higher vitamin C cut off levels (<28
µ
mol/L vs.
<11
µ
mol/L) for diagnosing vitamin C deficiency compared to our study. It is possible that
cognitive dysfunction is apparent only with severely low vitamin C status (i.e., vitamin C
levels < 11
µ
mol/L) and may not manifest with less severe degrees of hypovitaminosis C
(11–28
µ
mol/L). In support of this conjecture, a re-analysis of our own data using these
authors’ looser definition of vitamin C deficiency showed no significant alteration of cog-
nition in the deficient subjects (data not shown). Subtle changes in cognition might be
detectable with a less severe deficiency of vitamin C [
12
]. Animal studies [
58
,
59
] have
indicated that higher supplementation of vitamin C reduced amyloid plaque burden in
the cortex and hippocampus in mice with resultant amelioration of blood–brain barrier
disruption and mitochondrial alteration. However, evidence in relation to the benefits of
vitamin C supplementation on cognition is limited. A recent meta-analysis [
60
], which
included randomised or quasi-randomised placebo-controlled trials of vitamin and mineral
supplementation for preventing dementia or delaying cognitive decline among patients
with mild cognitive impairment, found only one trial, which included combined vitamin E
and C supplementation in 256 patients and found no conclusive data for supplementation,
reducing the risk of progression to dementia due to very low-quality evidence. Due to this
research gap, it will be difficult to determine whether the routine determination of vitamin
C status in patients with cognitive impairment, let alone its supplementation, is a useful
and cost-effective strategy.
Limitations
The results of this study should be interpreted with caution because it included only
older inpatients receiving rehabilitation, and our findings may not be applicable to a
relatively healthy community-dwelling older population. The cross-sectional design of this
study does not point towards causality. In addition, we used MMSE to assess cognition,
a tool that is regarded as less sensitive for the detection of mild cognitive impairment
compared to other tools such as MOCA [
54
]. We used the total MMSE score to assess
cognition. The impact of vitamin C deficiency on specific areas of cognition involved
in the performance of the MMSE such as orientation, attention, concentration and short-
term memory, was not determined. However, despite the use of a less sensitive tool, our
study found an association between vitamin C deficiency and cognitive impairment in a
vulnerable cohort of older hospitalised patients.

Antioxidants 2022,11, 463 9 of 11
5. Conclusions
Vitamin C deficiency is common, and there are few clinical correlates that can usefully
lead to the identification of this condition in older hospitalised patients. Vitamin C defi-
ciency is associated with cognitive impairment, and further studies are needed to confirm
and characterise this association in greater detail.
Author Contributions:
Conceptualization, Y.S. and C.T.; Methodology, Y.S.; Software, Y.S., C.H. and
P.H.; Validation, Y.S. and A.P.; Formal Analysis, Y.S.; Investigation, Y.S.; Resources, Y.S.; Data Curation,
Y.S., A.P. and C.H.; Writing—Original Draft Preparation, Y.S.; Writing—Review and Editing, Y.S. and
C.T.; Visualization, Y.S.; Supervision, Y.S. and C.T.; Project Administration, Y.S. All authors have read
and agreed to the published version of the manuscript.
Funding:
This research received funding from the Flinders Health and Medical Reserch Institute
(FHMRI), Flinders University, South Australia.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Southern Adelaide Human Clinical Research Ethics
Committee (SAHREC) no. 64.190 on August 2019.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to ethical reasons.
Conflicts of Interest: The authors declare no conflict of interest.
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